International Journal of Computer Applications in Technology (94 papers in press)
Non-linear modified equation modeling in dynamical systems (Case Study research on Long Jump patterns)
by Farzad Sharifat
Improving Arabic Text Categorization using FA Words with K-Nearest Neighbor and Centroid-Based classification algorithms
by El-Sayed Atlam, M.E. Abd El-Monsef, O. El-Barbary
3D Scanning Machine and Additive Manufacturing: Concurrent Product and Process Development
by Ismet P. Ilyas
Simulation and visualisation approach for accidents in chemical plants
by Feng Ting-Fan, Tan Jing, Liu Jin, Deng Wensheng
Abstract: A new general approach to lay the foundation for building a more effective and real-time evacuation system for accidents in chemical plants is presented. In this work, we build the mathematical models and realise automatic grid generating based on the physical models stored in advance with several algorithms in jMonkeyEngine environment. Meanwhile, the results of the simulation data through finite difference method (FDM) are visualised coupling with the physical models. Taking fire as an example, including fire with single and multiple ignition sources, shows the feasibility of the presented approach. Furthermore, a coarse alarm and evacuation system from fire have been developed with a multiple SceneNode and roam system, which also includes the making and importing of the physical models. However, to improve the accuracy of the mathematical models, adaptability and refinement of the grids and universality of the evacuation system is the direction of efforts.
Keywords: simulation; chemical accidents; alarm and evacuation system; jMonkeyEngine.
Global asymptotically stability of neural networks with uncertain parameters and time-varying delay
by Yang Li, Jianhua Zhang, Xueli Wu
Abstract: This paper is concerned with the global asymptotically stability problem for a class of neural networks with uncertain parameters and time-varying delay. Using a new proposed inequality called free-matrix-based integral inequality, a new robust criterion is proposed, which is expressed by linear matrix inequalities, by means of linear matrix inequality technique to deal with the unknown parameters in the neutral systems. Examples are simulated to demonstrate the effectiveness and efficiency of the obtained criterion.
Keywords: neural networks stability; time-varying delay; uncertain parameters; Lyapunov-Krasovskii functional.
A novel multi-criteria self-organising migrating algorithm for engineering problems
by Bilel Najlawi, Nejlaoui Mohamed
Abstract: Solving engineering design and resources optimisation via multi-objective evolutionary algorithms has attracted much attention in the last few years. In this study, an improved Self-Organizing Migrating Algorithm (MOSOMA) is developed and investigated to solve multi-objective engineering design problems. The proposed MOSOMA algorithm uses a migration approach for the search of optima. In order to obtain a uniform distribution of Pareto optimal solutions, the crowding distance method is introduced. Pareto dominance is incorporated into the algorithm in order to allow this heuristic to handle problems with several objective functions. The performance of the MOSOMA algorithm is assessed by applying it on a set of multi- objective standard test functions and constrained engineering design problems. The results show that the proposed approach is competitive and effective compared with other algorithms contemplated in this work and it can also find the result with greater precision.
Keywords: multi-objective optimization; self-organising migrating algorithm; test problem; Pareto front.
Monitoring data-based automatic fault diagnosis for the brake pipe of high speed train
by Guo Xie, Minying Ye, Xinhong Hei, Fucai Qian
Abstract: As a key part of the railway system, the brake pipe is essential for the brake system, and its fault may cause serious consequences, and even threaten lives. The generally employed approach for fault diagnosis of the brake pipe is manual inspection during the parking time, which is time-consuming, laborious, and dependent on the experience of the inspectors. In view of these problems, an automatic fault diagnosis analysis for the brake pipe of high speed trains based on monitoring data is proposed in this paper. Based on the concept of big data, the characteristics of monitoring data are analysed, and the fault features are extracted, then the fault diagnosis rules are established. Specifically, the main steps are as follows. The first step is the data preprocessing, including correcting the singular zero points and populating the missing points. The second step aims to eliminate the noise and measurement errors. The third step includes the establishment of the fault diagnosis rules, data analysis and fault diagnosis. Lastly, the data from an actual train line is analysed, and the analysis results demonstrate the effectiveness and feasibility of the proposed method.
Keywords: high speed train; fault diagnosis; brake pipe pressure; data-based.
A recursive algorithm for open information extraction from Persian texts
by Mahmoud Rahat, Alireza Talebpour, Seyedamin Monemian
Abstract: Owing to the proliferation of textual data available in non-English languages, one of the current challenges of open information extraction (Open IE) is to adapt existing techniques to those languages. While several Open IE systems have been introduced for English, many other languages, such as Persian, lack such tools. Fundamental differences between English and Persian in syntax and the corresponding dependency representations make the process of adapting an Open IE system for Persian very challenging. To the best of our knowledge, this article is the first published paper about open information extraction for Persian. Many traditional Open IE systems apply a large set of lexical patterns, which is inefficient in out-of-domain text. We replace this large pattern set with a few syntactic rules upon dependency parse of a sentence that are specifically designed for Persian. Moreover, we investigated Open IE at document level, which helped our system to enhance the results. Our analysis on a parallel corpus reveals that the proposed system covers 85% of the area under the precision-yield curve of the best available systems for English. We also addressed some Persian-specific phenomena to enhance the results. The rules are designed to cover both inter-clause and intra-clause relations. Besides, the recursive nature of our algorithm enabled us to handle nested sentences.
Keywords: open information extraction; Persian text processing; dependency parsers; natural language processing.
Research on target recognition and path planning for EOD robot
by Wei Deng, Hui Zhang, Yibin Li, Feng Gao
Abstract: In order to improve the autonomy of the explosive ordnance disposal (EOD) robot, the paper makes a research on the target recognition and path planning algorithm based on laser range finder and Time of Flight (TOF) camera. The target position of the explosive is selected by the operator from the gray-scale image, then EOD robots use Kernelized Correlation Filters (KCF) tracking algorithm and distance constraint to realise the real-time explosives identification. In the case of the A* algorithm, it does not take the heading information of the robot into consideration, which may lead to the problem of heading shakes in the vicinity of the obstacle. In this paper, the heading constraint is introduced to improve the A* algorithm so as to eliminate the problem of heading shakes and improve the stability of the robot. The simulation and experiment results demonstrate the feasibility of the algorithm proposed in the paper.
Keywords: EOD robot; KCF algorithm; path planning; TOF camera.
Quran search engines: challenges and design requirements
by Yasser Alginahi
Abstract: The increased research work in the area of Islamic studies has introduced many new tracks of research. Thus, search engines/tools for the Holy Quran are very essential for researchers in order to locate particular verses of the Quran and their related concepts, meaning, location, etc. This study intends to gather as much information on available Quran search engines/tools in order to set up guidelines for the minimum requirements needed in designing Quran search engines. To achieve the goal of this study, the pros and cons of available engines are discussed. Owing to the difficulties encountered in collecting information on these Quran search tools and the unavailability of technical background for Quran-related search engines; in some cases the results may not be comparable. Finally, from the Quran search engines surveyed the challenges are identified in order to aid in the design of efficient Quran-related search tools.
Keywords: search engines; information retrieval; Quran.
Design of a switched hyperchaotic system and its application
by Rui Wang, Qisheng Xie, Yongtao Huang, Hui Sun, Yigang Sun
Abstract: This paper proposes an automatically switched hyperchaotic system between two hyperchaotic systems. Especially, one of the systems is designed by changing three equations of another system. Two linear-term parameters and one nonlinear term are changed for respective equations. The corresponding characteristics have been analysed in detail. There are two main contributions of this paper. One is that the new system automatically switches. Another is that the other hyperchaotic system is constructed by changing more parameters compared with the existing switched chaotic systems. In addition, the dynamic characteristics of the attractor of the new switched hyperchaotic system are analysed in detail by Matlab. Correspondingly, Multisim simulation results are also conducted and compared with circuit implementation results. They are consistent with each other to verify the existence of the attractor of the new switched hyperchaotic system. The system is also used for a secure communication implementation based on a chaotic masking method. The simulation for secure communication application is conducted and shows the success of this application.
Keywords: hyperchaotic systems; secure communication; switched systems; circuit implementations.
Implementing weighted entropy-distance based approach for the selection of software reliability growth models
by Aakash Gupta, Neeraj Gupta, Ramesh Kumar
Abstract: A computational quantitative model based on Weighted Euclidean Distance Based Approximation (WEBDA) has been developed to evaluate, select and rank the Software Reliability Growth Models (SRGMs) in ascending or descending order, based on their Euclidean distance value from the optimal SRGM. The SRGM with Rank 1 is considered the optimal selection for the software developers on the particular dataset under consideration. The main problem of SRGMs selection and ranking is deployed as a multi-criteria decision making (MCDM) problem, in which numerous inter-related attributes collectively termed as ranking criteria are identified to evaluate the available alternatives. In the present research, a dataset from Tandem Computer Software Failure has been used to show the utility of the developed model. Furthermore, the concept of methodology validation strengthens the present research by making a comparison of obtained results with those of existing MCDM approaches, such as analytical hierarchy processing (AHP) and technique for order of preference by similarity to ideal solution (TOPSIS).
Keywords: software reliability; ranking criteria; WEDBA; SRGM.
Earth pressure prediction in the chamber for earth pressure balanced shield machines
by Yi An, Xiaoli Zhou, Zhuohan Li, Cheng Shao
Abstract: Earth pressure balanced (EPB) shield machines are large and complex mechanical systems and have been widely applied to tunnel projects. The earth pressure in the chamber is very important for EPB shield machines to avoid ground settlement and guarantee safe construction during the tunnelling process. In order to improve the prediction accuracy of the earth in the chamber, we propose a new earth pressure prediction method in this paper. First, we analyse the main factors influencing the earth pressure in the chamber and determine the inputs and output of the prediction model. Then, we use the least squares support vector machine to establish the prediction model whose parameters are optimised by the particle swarm optimisation. The experimental results demonstrate that the proposed method has high prediction accuracy and is an effective way to predict the earth pressure in the chamber for EPB shield machines.
Keywords: earth pressure; shield machine; prediction model; support vector machine; particle swarm optimisation.
Model selection using historical pattern: case study of forecasting Indonesian research areas
by Indra Budi, Agus Widodo
Abstract: To guide the research directions among research institutes in Indonesia, a National Research Agenda is periodically formulated. The list of prospective research areas in this document is usually determined by the judgement of experts whose opinions could be subjective. Meanwhile, a more objective approach has been studied by several researchers to find the emerging research areas by tracking the frequency of scientific publications. This paper investigates the use of this bibliometric approach to identify and forecast the emerging research areas listed in the Indonesian National Research Agenda. Diverse forecast methods are employed and selected based on their performance on previous dataset. In this way, there is no need for training on the current dataset, which may reduce time to select the best method. The dataset is compiled from the Scopus search engine by querying the research area in question. To construct the historical database, we use the dataset from M1 competition and the testing dataset from NN3 competition as well as time series constructed from query on Scopus. Our experimental results indicate that our model selection may perform well compared with the individual predictor. In addition, the most emerging research topics based on the forecast results can be quantitatively identified.
Keywords: emerging research areas; forecast combination; time series characteristics; bibliometric; Indonesia.
Integrating IoT and cloud in a smart city context: the #SmartMe case study
by Dario Bruneo, Francesco Longo, Giovanni Merlino, Antonio Puliafito, Nidhi Kushwaha
Abstract: #SmartME is a crowdfounding project that aims at exploring the possible synergies between the cloud computing and the Internet of Things paradigms. The project's main aim is to morph Messina into a smart city. In this direction, the Stack4Things framework has been implemented, extending OpenStack towards the management of Internet of Things resources. This paper summarises the first two years of the #SmartME project and presents technical details about Stack4Things, with specific reference to the underlying technologies and to the web portals that are used for administration and semantically enabled data retrieval.
Keywords: smart city; Internet of Things; IaaS cloud; semantic web; machine learning.
Low power low voltage CMOS full adder cells based on energy efficient architecture
by Pankaj Kumar, Rajender Sharma
Abstract: This paper presents two low power full adder cells based on energy efficient internal logic approach and pass transistor logic. These two new designs successfully operate at low voltage with tremendous signal integrity and driving capability. These designs are tested on a common environment using 90-nm CMOS process technology at many supply voltages. The adder cells are compared with eight of the popularly known full adders based on power consumption, speed and power-delay-product (PDP) and area efficiency. Intensive simulation runs on cadence environment and spectra show that the proposed full adder cells outperform their counterparts, exhibiting 59.48% and 55.41% improvement in their PDP metrics.
Keywords: low power; high speed; low voltage; hybrid design; energy efficient; full adder.
LexiPal: Kinect-based application for dyslexia using multisensory approach and natural user interface
by Muhamad Risqi Utama Saputra, Syukron Abu Ishaq Alfarozi, Kuntoro Adi Nugroho
Abstract: Considered as an effective learning strategy for dyslexia, multisensory approach demands visual, auditory, and kinesthetic activity. While development of software application implementing multisensory approach has shown promising result, previous applications did not accurately resemble multisensory strategy because there is no kinesthetic implementation. This research proposes a Kinect-based application, termed LexiPal, which incorporates kinesthetic activity in multisensory implementation by using Natural User Interface (NUI). NUI provides more intuitive ways of interacting with the application by using body movement and gesture. LexiPal implements NUI by fusing several technologies, including Augmented/Mixed Reality (AR/MR), non-contact human-computer interaction, skeletal tracking, hand tracking, and gesture recognition. For evaluation purpose, LexiPal was tested to 40 dyslexic children and they considered LexiPal user interaction as easy to use and enjoyable, which attracted them to play the learning content again in the near future.
Keywords: Kinect; dyslexia; multisensory approach; natural user interface; body movement; gesture interaction; skeletal tracking; hand tracking; kinesthetic interaction; augmented reality; educational application.
Development of aircraft path planning scheme through automatic dependent surveillance broadcast
by Jianhua Zhang
Abstract: The intelligent flight task algorithm for aircraft is investigated to effectively determine and search the best flight routes amid complicated conditions, including the complicated topography and threats from other flights in the route airspace. The proposed method is composed of safety distance and path planning by a pulse-coupled neural network. The safety distance is regarded as a pre-processing procedure for searching for the safe path, and the pulse-coupled neural network is adopted to provide a smooth flight route for the aircraft. The global optimal flight route is acquired through wave propagation by the pulse-coupled neural network. The results of the simulation demonstrate the abilities of the proposed method to provide the best flight routes efficiently for aircraft.
Keywords: path planning; pulse-coupled neural networks; wave propagation.
An anomaly-based approach for detection of DDoS attacks in cloud environment
by Adnan Rawashdeh, Mouhammd Al-kasassbeh, Muna Al-hawawreh
Abstract: Cloud computing is one of the most important developments in information technology and the business environment in recent years and is likely to remain so for the foreseeable future. It draws significant attention from researchers owing to its widespread application and substantial benefits. Owing to its distributed nature, using virtualisation, multi-tenant and reliance on the internet to provide services, security poses a major obstacle for cloud computing. Insider Distributed Denial of Service (DDoS) attack is the biggest challenge for any cloud environment where the unavailability of services and connectivity issues in cloud can deactivate the service totally, which could result in immense business and financial losses for consumers. Hence, to protect the cloud environment, and in particular the virtual environment, from DDoS activities, we need more than the traditional defence mechanisms such as firewalls, which sniff the network packets at the boundary of the network to detect and prevent attacks from entering the network, but they cannot detect insider attacks. Intrusion detection system is an important key in the cloud infrastructure security. This work proposes an anomaly intrusion detection approach in the hypervisor layer to discourage the DDoS activities between virtual machines. The proposed approach is implemented by the evolutionary neural network, which integrates the particle swarm optimisation with neural network for detection and classification of the traffic that exchanges between virtual machines. The performance analysis and results showed that the proposed hypervisor intrusion detection approach based on the artificial neural network with particle swarm optimisation is well designed to detect and classify the DDoS attacks in cloud environment with minimum false alarms and high detection accuracy.
Keywords: distributed denial of service; DDoS; cloud computing; intrusion detection system; hypervisor; virtual machine monitor; attacks detection; neural networks.
FCM-LSSVM modelling for ethylene loss rate of distillation column with respect to operation conditions
by Shao Cheng, Dong Xiaoyun, Zhu Li
Abstract: Ethylene loss rate is an important evaluation index in ethylene distillation operation, which directly relates to the comprehensive energy efficiency level of ethylene production. Therefore, the accurate prediction of ethylene loss rate is helpful in optimising energy consumption of ethylene production in distillation column. It is observed that some working conditions, such as the composition of cracking gas, unit load and the refrigerant temperature, have a great influence on the ethylene loss rate. In this paper, the prediction of ethylene loss rate is thus concerned and a new method with FCM-LSSVM is proposed for modelling the ethylene loss rate of distillation column with respect to the operating conditions. The clustering method is employed to classify optimally the ethylene distillation operation database, and then the LSSVM is suggested to establish the ethylene loss rate prediction model with different working conditions. Finally, simulations and comparative analyses for the proposed method are carried out by using real distillation data, which demonstrates that the model set up under multi-conditions would be more effective owing to better predictive precision and generalisation ability.
Keywords: ethylene distillation column; operating condition; fuzzy C-means clustering; loss rate; LSSVM.
A survey of resource allocation in the mobile cloud computing environment
by Li Liu, Qi Fan, Dongmei Fu
Abstract: Mobile cloud computing (MCC) is the integration of mobile computing and cloud computing. It provides an opportunity for users to obtain cloud resources over the internet. Resource allocation is a complex problem owing to the presence of heterogeneous application workloads in MCC. In recent years, few studies have clearly analysed the research problem and surveyed resource allocation in the MCC environment. Hence, it is very important to provide a comprehensive review of resource allocation to complement existing literature for the MCC environment. In this paper, a survey is made of resource allocation in MCC, and the state-of-the-art of the resource allocation strategies is presented. The issues and the current approaches to solve this problem are discussed in detail. Opportunities and challenges in this field are also examined, and future research directions are presented.
Keywords: mobile cloud computing; resource allocation.
Towards development of cyber-physical systems based on integration of heterogeneous technologies
by SangSu Choi, Gyhun Kang
Abstract: Cyber-Physical System is (CPS) a concept expanded from embedded computing and is applied in various industries, such as aerospace, energy, transportation, and medical industry. Manufacturing industry is also improving its competitiveness through building CPS and is establishing environments for smart manufacturing. CPS in the manufacturing industry plays the role of controlling the entire process of product design, procurement, production, and logistics, as well as managing the comprehensive flow of information. CPS is a complex system that mandates flexible integration of various technologies. This paper introduces a CPS architecture based on heterogeneous systems and technologies, such as manufacturing execution system, product lifecycle management, process mining, discrete event simulation, and virtual reality and its applications. Future researches such as development of CPS based on cloud computing for SME users and development of standards for interoperability within CPS are also discussed.
Keywords: smart manufacturing system; system integration; interoperability; factory improvement; manufacturing system.
An improved AHP and BP neural network method for service quality evaluation of a city bus
by Ying Liu
Abstract: Aimed at the realistic problem that service quality of city buses is low in big cities, an improved AHP-BP neural network method is established based on quality survey data of real passengers, in order to evaluate the analysis of quality factors． Firstly, the weight of each expert is worked out based on the perspectives of interests related to improving the AHP, then the comprehensive weight of each index is determined by calculating the weighted average of the obtained index weight of each expert and the corresponding evaluation weight of expert. Secondly, the weight of the BP neural network is used to train and test the model based on the results of improved AHP， getting BP evaluation results with an acceptable error in order to promote the classifier system of service quality factors. Finally, an empirical research is carried for the example of service quality evaluation of a city bus in Shenyang city of China. The results show that the method fully reflects the views of the experts while avoiding the conflicts of interest among experts, and reduces the arbitrariness of subjective evaluation and the learning ability of BP neural network model, making results more accurate and reliable． This illustrates the high application value of the improved AHP-BP neural network method in the future evaluation of service quality of city buses.
Keywords: AHP improved; BP neural network; analysis of influencing factors; bus passengers' perceptions.
Ensemble effort estimation using selection and genetic algorithms
by Pichai Jodpimai, Peraphon Sophatsathit, Chidchanok Lursinsap
Abstract: Software effort estimation plays a vital role in software project management. Several
single estimation methods have been introduced vying to be the best estimation method. However, it is difficult to decide which method offers the best estimation result. This study proposes an ensemble effort estimation from several single estimation methods that yield different estimations but comparably high accuracy. The proposed technique employs a correlation-based feature selection algorithm to choose methods out of twelve methods derived from combinations of four transformation and three learning techniques. The ensemble then uses a genetic algorithm to build a mathematical function to compute one combined estimation from those selected method estimations. Experiments are set up based on six benchmark datasets to evaluate estimation performance. The resulting measure of error metrics shows that the proposed ensemble technique, deploying only necessary estimation methods, can yield more accurate estimation than the best method from the twelve methods.
Keywords: software project management; software effort estimation; estimation method; ensemble method; transformation technique; learning technique; correlation; feature selection; genetic algorithm; error metrics.
Study on seals of subsea production gate valves
by Xingping Xu, Sen Li, Ling Gong, Hai Wang, Yanzhe Wang
Abstract: A subsea oil production gate valve and its actuator are introduced. Seals for the valve seat and element are studied, and the seal principle is given. The seal of the valve cover is designed, which is composed of a Parker B3 seal ring, two Parker BD seal rings and a copper retainer ring superposed together. The seal of the balance bar is designed, which is similar to the seal of the valve cover, but without a retainer ring. The piston seal, consisting of a Parker B7 ring and a FR guide ring, end seals are designed. To validate all the seals designed, the finite element models are set up, and the stress, deformation and contacting pressure of the seals are analysed. Results show that the strength of the seal is enough and the seal is reliable.
Keywords: subsea production; gate valve; seal.
An agent-based inter-vehicle cooperative robust car-following model for longitudinal control under uncertainty
by Oussama Messaoudi, Ammar Lahlouhi
Abstract: Uncertainty is a common challenge facing system modelling and simulation in uncertain environments, where the outcome of control actions is unknown. In this paper, an agent-based robust car-following model is proposed to handle uncertainty in the longitudinal control system based on inter-vehicle cooperation, where an organisation of heterogeneous agents communicate and cooperate in a vehicular ad hoc network to provide reliable and collision-free control policies. We used agent-based modelling to model the inter-vehicle cooperative longitudinal control system while integrating human drivers in the multi-agent system to assume specific roles and cooperate with other agents to accomplish the task at hand. For behavioural analysis and comparison with other car-following models, microscopic agent-based simulations conducted using JADE and SUMO showed that the proposed model offers collision-free control of vehicle speed under different conditions including uncertainty. Also, we used the proposed model to investigate the effect of uncertainty on both vehicle's speed and its control actions.
Keywords: agent-based modelling; agent-based simulation; car-following model; longitudinal control; microscopic simulation; vehicular ad hoc network.
A consumer-based smart home and health monitoring system
by Kailas Patil, Meena Laad, Aakash Kamble, Shivani Laad
Abstract: The past few decades have seen a sharp increase in air pollution and its harmful effects on human health. Indoor air quality is a major determining factor of personal exposure to pollutants in todays scenario. A majority time is spent in indoor environments by a majority of people. Therefore monitoring indoor air quality has become an important issue. There are various reasons for poor quality of indoor air in which the oxygen level is below the healthy level, such as influx of polluted outdoor air, geological materials around the residential properties, household activities such as cooking, cleaning, and smoking, materials used in construction or painting the house, etc. With increasing pollution, respiratory diseases are on the rise and therefore there is a strong need to develop a smart home system which is capable to monitor environmental conditions for its users. This research study proposes a smart system that uses a renewable source of energy, monitors and controls the overall power consumption of the home, keeps a check on the environmental conditions by measuring the level of oxygen, and provides a provision for an alarm when the oxygen level falls below the safe limit. The proposed model is compact, cost-effective, energy-efficient and provides a smart home health system to the consumer and is especially useful for the old and physically challenged.
Keywords: health and safety; home automation; SCADA systems; smart homes; sensors; industrial automation.
Towards recent developments in the field of digital image forgery detection
by Tarun Kumar, Gourav Kumar
Abstract: Proliferation of powerful computers, innovative photo-editing software packages, and high resolution capturing devices leads to effortlessness in generating digital image falsification. Indeed, the security apprehension of digital images has ascended since a long period and diverse techniques for authenticating the integrity of digital images have been established. Conversely, significant exploration in this realm would not only be verifying the integrity of images but also correspondingly sensing the hints of tampering or falsification without necessitating additional prior information of the image content or any embedded watermarks. This paper reviews the recent developments in the field of digital image forgery detection. With the concept and methods, various applications of forgery detection are presented especially focusing on the use of soft computing for developing the techniques for forgery detection. The future of development and application of soft computing in digital image forgery detection is discussed.
Keywords: forgery detection; digital image processing; image watermarking; copy move forgery; image splicing; soft computing.
A new chaotic jerk system with two quadratic nonlinearities and its applications to electronic circuit implementation and image encryption
by Sundarapandian Vaidyanathan, Akif Akgul, Sezgin Kacar
Abstract: This work presents a new chaotic jerk system having two quadratic nonlinearities. Dynamical properties of the new jerk system are discovered through dissipativity, equilibrium point analysis, eigenvalues of the Jacobian matrices, bifurcation diagram, and Lyapunov exponents. The results also show that the new jerk system has interesting complex dynamics characteristics. Furthermore, random number generator (RNG), its NIST-800-22 tests, an image encryption application and its security analyses are realised with the new jerk system. The new chaotic jerk system can be useful in scientific fields such as physics, control, true and pseudo random number generator (TRNG,PRNG), artificial neural network, genetic algorithm, etc.
Keywords: chaos; jerk systems; chaotic systems; random number generator; chaos basedrnencryption; circuit implementation.
A new suitable feature selection and regression procedure for lithium ion battery prognostics
by Jaouher Ben Ali, Lofi Saidi
Abstract: The accurate prediction of lithium ion battery Remaining Useful Life (RUL) is indispensable for safe and lifetime-optimised operation. Thereby, the monitoring of this vital component is very necessary for planning repair work and minimising unexpected electricity outage. However, the study and the investigation of internal battery parameters show several value changes within the battery lifetime, and it is highly influenced by environmental and load conditions. Consequently, this paper presents a new prognostic method for online battery monitoring based on isometric feature mapping technique (ISOMAP) and incremental support vector regression (ISVR). ISOMAP is used to reduce some features extracted from lithium ion batteries, with different health states, in both modes of charge and discharge, and ISVR is used to regress online the selected feature. Experimental results show that the proposed methodology provides a new suitable trend parameter for battery prognostics.
Keywords: ISOMAP; ISVR; lithium ion battery; PHM; RUL.
API library-based identification and documentation of usage patterns
by Hamzeh Eyal Salman
Abstract: Application programming interfaces (APIs) are one of the most important sources for supporting source code reuse as each API provides a large set of pre-implemented functionalities that support programmers to achieve their daily work in different contexts. However, APIs provide a huge number of classes and methods that hinder programmers to understand and use APIs. Recently, numerous client-based approaches have been proposed for facilitating APIs usage through identifying frequent usage patterns. Although they represent significant efforts for helping to understand APIs, the client programs are not available for either newly released APIs libraries or APIs that are not widely used. In this article, a non-client-based approach for identification of frequent usage patterns and documentation is proposed. An experimental evaluation is conducted using four widely used APIs. For all the studied APIs, the obtained results show that the proposed approach is comparable with client-based approaches in terms of frequent usage patterns cohesion.
Keywords: reuse; frequent usage pattern; API; object-oriented; understanding; documentation.
A new hybrid strategy for data clustering using cuckoo search based on Mantegna Levy distribution, PSO and k-means
by Omid Tarkhaneh, Ayaz Isazadeh, Hossein Jabbari Khamenie
Abstract: Data clustering is one of the data mining techniques that is widely used in some applications, such as pattern recognition, machine learning, image processing, etc. Swarm intelligence-based algorithms are extensively used in data clustering in recent years. Cuckoo search (CS) is one of the recently proposed algorithm in the category of swarm intelligence-based techniques. In this paper, a new hybrid algorithm that uses CS, Particle Swarm Optimisation (PSO) and k-means is proposed (HCSPSO). The proposed algorithm employs PSO and k-means to produce new nests in standard CS to obtain better results. It also benefits the Mantegna Levy distribution to obtain higher convergence speed as well as local search. To eliminate the problem of the high number of functional evaluations in standard CS, a fraction of nests is assigned to every section of the algorithm. The proposed algorithm performance is evaluated by ten standard benchmark datasets. Evaluation results show that the proposed algorithm is an efficient method for data clustering and produces more optimised results in comparison with standard CS, PSO, elephant search algorithm, enhanced bat algorithm, bird flock gravitational search algorithm, improved cuckoo search and k-means.
Keywords: data mining; cuckoo search; Mantegna Levy distribution; swarm intelligence; particle swarm optimisation.
Reliability analysis of software with three types of error and imperfect debugging using Markov model
by Gireesh Kumar, Manju Kaushik, Rajesh Purohit
Abstract: In this analysis, we consider a Markovian software reliability model (SRM) with imperfect debugging (ID) wherein software may fail owing to three types of error called error generation (EG). For developing the governing differential equations of SRM with three types of error, an irreducible Markov process is used. Further, we have applied the Runge-Kutta (R-K) method under transient condition for obtaining reliability of concerned system in different configurations. Moreover, we suggest various software reliability indices such as reliability, failure frequency, mean time to failure (MTTF) and many more. By taking a numerical illustration, the sensitivity analysis is done to demonstrate the validity of analytical outcomes and to explore the effects of various parameters. Moreover, the analytical results are also compared with adaptive neuro-fuzzy inference system. Finally, conclusions are given.
Keywords: software reliability model; Markov model; imperfect debugging; errors; R-K method.
Analyses of parasitic capacitance effects and flicker noise of the DAC capacitor array for high resolution SAR ADCs
by Xicai Yue, Janice Kiely, Chris McLeod
Abstract: With the increasing number of bits, parasitic effects and flicker noise of switching transistors in the DAC capacitor array of the SAR ADC are getting relatively bigger when compared with the exponentially decreasing error budget. This paper analyses the effects of parasitic capacitances related to the top-plate and bottom-plate of unit capacitors on the accuracy and the noise performance of the DAC capacitor array, showing that thermal noise of the whole capacitor array decreases when parasitic capacitances are considered while in the meantime an unexpected gain error is introduced. Although the parasitic-capacitance-induced gain error is almost independent of the number of bits, parasitic effects should be minimised for high resolution SAR ADCs since the dynamic range of the high resolution ADC is severely reduced owing to the gain error. The post-layout parasitic capacitance extraction of a 10-bit poly-poly DAC capacitor array shows that the value difference between the top-plate and bottom-plate related parasitic capacitances is large, so that the parasitic-capacitance-induced gain error can be decreased by 152 times when top-plates of unit capacitors are connected together as the output node of the capacitor array. The switching transistors flicker noise calculation for a 10-bit SAR ADC shows that flicker noise can be safely ignored for 10-bit 1MSPS SAR, while the calculation for an 18-bit 1MSPS SAR ADC shows that flicker noise should be considered for the higher resolution SAR ADCs.
Keywords: successive approximation register ADC; DAC capacitor array; parasitic capacitance; thermal noise; flicker noise.
Hardware-in-the-loop simulation for the testing of smart control in grid-connected solar power generation systems
by Mohammad Hassan Khooban, Moslem Dehghani
Abstract: Those systems that convert sunlight to electricity directly are photovoltaic (PV) systems. PV(s) are solar energy supply systems. The power of a solar photovoltaic panel is strongly related to climate conditions. Because of that, a grid-connected system needs a good response for variations in the solar radiation and temperature. A smart technique which has a fine rejection of disturbances is the time-varying control. This work shows the application of the control based general type-II fuzzy for the grid-connected solar power generation system. In this study, general type-2 fuzzy logic sets (GT2FLS) and the Modified Backtracking Search Algorithm (MBSA) techniques for adaptive tuning of the most popular existing proportional-integral (PI) controller is integrated in order to tackle these uncertainties. The fast and robust response of the control allows a better use of the photovoltaic array energy to increase the efficiency of the system and guarantee stability for all the operational range. The achieved results are compared with a conventional PI controller and an Optimal Fuzzy PI (OFPI) controller, which are the most recent researches to evaluate the proficiency of the proposed controller. Finally, the extensive studies and hardware-in-the-loop simulations are presented to make obvious the successfulness and effectiveness of proposed controller.
Keywords: adaptive PI control; general type II fuzzy logic; grid-connected solar power generation system; modified backtracking search algorithm.
Novel approach for shape-based similarity search enabled by 3D PDF
by Frank Neumann, Michel Atten
Abstract: During product development, shape-based similarity search helps the engineering teams to discover parts and assemblies already existing in their companys component databases that match the ones required for the new product. Therefore, similarity search constitutes an important means to keep the overall number of components designed, managed and maintained by a company at the lowest possible level. This helps to reduce the efforts for engineering design, manufacturing planning, managing the master data, and consequently lowers the product costs. In this paper, we present a new approach for shape-based similarity search using 3D PDF as the unique neutral format for determining various types of shape descriptor. We find that this method significantly reduces the implementation efforts for analysing 3D geometries originating from various CAD systems. In addition, it permits to tackle novel use cases for shape-based similarity search owing to the versatile characteristics of the 3D PDF format.
Keywords: shape-based search; shape-based similarity search; 3D shape matching; 3D shape similarity; 3D shape retrieval; 3D shape descriptors; product data management; PDM; PLM; 3D PDF; PRC.
A framework for visualising road design prior to construction using driving simulation technologies
by Dahai Guo, Casey Baer, Xuedong Yan
Abstract: This paper proposes a framework where road designs can be visualised in a virtual reality environment prior to any physical construction. A software algorithm for generating computer graphics models automatically, enables this framework to be effective. Key road design parameters such as the number of lanes, shoulder length, speed limits, turn angles, etc., must first be identified and then a computer program is developed to build computer graphics models based on these parameters. The generated computer graphics will include triangle meshes, textures, and height maps which can be rendered within a virtual reality environment. When used in conjunction with driving simulation technologies, the program also enables road designers to drive on their road being designed. Any implementation of this framework will be the most effective if it is incorporated with an existing road design process. Additionally, a case study is presented in order to demonstrate an implementation of the proposed framework.
Keywords: visualisation; graphics modelling; driving simulation.
A metamodel-driven definition and implementation of ONDAR: a home automation ontology
by Achraf Lyazidi, Salma Mouline
Abstract: Smart homes are houses equipped by computational technology to assist inhabitants in several situations. To design these complex systems, we need to take into account the inhabitant's experiences and needs. A modelling language supporting the semantic aspect allows modelling close to end-user needs. Usually, such a language is based on the concept of ontology. However, research including ontologies to design smart homes is still limited. Also, in the ontology engineering, a field which studies the methods and methodologies for building ontologies, no standard methodology exists to design and implement ontologies. In this paper, we study several approaches for home automation design, then we present our ontology for home automation after we define a smart home and a home automation system functionalities and behaviour. We also conduct a brief state of the art about approaches for implementing ontologies, then we present our implementation process.
Keywords: ontology; home automation; smart home; ontology implementation; ontology engineering.
A deep learning approach to software evolution
by Shang Zheng, Hongji Yang
Abstract: Software evolution techniques should be made as important as software development techniques. One possible way to help with the situation is to learn from software development, and also to learn from software evolution techniques. The breakout of Machine Learning and Deep Learning (ML&DL) is becoming the big buzzwords in technology and should be studied for being made available for servicing software evolution. Open source projects provide an open defect repository to which users and developers can report bugs. It is a challenge to document bug reports to the appropriate developers. In this paper, we apply deep learning approaches and a topic model to learn the features of defect reports and then make recommendations. Compared with the traditional machine learning approaches, the proposed approach based on deep learning can perform better in accuracy and assign defect reports to developers more effectively and correctly along with the dataset increasing.
Keywords: software evolution; deep learning; machine learning.
Development and optimisation of image segmentation algorithm on an embedded DSP platform
by Boucif Beddad, Kaddour Hachemi, Sundarapandian Vaidyanathan
Abstract: In this work, a very efficient segmentation algorithm based on the analysis of vertical and horizontal histogram using edge detection method is developed in order to detect the licence plate from a colour vehicle image. VM3224K2 Daughter kit is used to capture the input image that is to be converted into YUV format and feed it to C6713 development kit. The licence plate localisation algorithm is implemented using MATLAB software and verified for its functionality. First, the in-built functions of MATLAB are replaced by user-defined functions in Simulink Model then translated into CCS project written in C using an integrated development environment provided by Texas Instruments; also, it is compiled and debugged using Code Composer Studio (CCS), downloaded onto TMS320C6713 DSP-Platform. Finally, VM3224 Daughter kit is embedded with DSK6713 kit to display the output image. The proposed algorithm has achieved an accuracy of around 100% and works for both front and rear digital images.
Keywords: image processing; VM3224K2 Daughter kit; segmentation; Code Composer Studio; TMS320C6713 DSK; edge detection.
Collaborative and ubiquitous mobile learning system prototype
by Mohammad Alnabhan, Ahmad Abu-Al-Aish, Sultan Al-Masaeed
Abstract: Mobile learning applications use the advantages of mobile technologies to increase learning opportunities, mainly on an anytime anywhere basis. The advancement of mobile technology has facilitated the development of numerous applications to improve students learning experience and performance. Successful implementation of m-learning is highly dependent on learning context and environment awareness. This work presents a multiphase exploration of early phases responsible for defining and validating the m-learning context, and later phases based on context validation results achieved from the previous phases, involving the development and evaluation of a new m-learning context prototype. This new prototype proved to provide context-aware and ubiquitous learning services, fulfilling several diverse user interaction levels and requirements.
Keywords: mobile learning; m-learning; m-learning system; e-learning; context factors; ubiquity learning.
Multi-objective evaluation of wind power acceptable capacity based on statistical characterisation
by Shuangxin Wang, Yinan Zhao, Meng Li
Abstract: The contribution of wind power to the availability of generating capacity becomes more important with increasing wind penetration. This paper presents a multi-objective evaluation of the acceptable capacity of wind power. A partitioning method that can adaptively divide the forecast power range into various regions according to its historical data is firstly proposed. Then, the multi-objective optimisation model of the power system integrated with wind is constructed, with the objectives of minimum generating cost, minimum pollutant emission and maximum wind power acceptable capacity. By amending the NDX (normal distribution crossover) operator into the binary crossover operator of NSGA2, the improved NSGA2 (named INSGA2) is tested by a benchmark for typical high-dimensional functions, and the simulation results confirmed the feasibility of the developed algorithm. Finally, a case study of IEEE 118 bus system is carried out to verify that the approach is beneficial to deal with the problems of evaluation on stochastic wind acceptance.
Keywords: wind power forecasting; multi-objective optimization; Pareto solutions; wind power acceptable capacity; statistical characterisation.
A framework for combining software patterns with the semantic web for unstructured data analysis
by Hossam Hakeem
Abstract: Unstructured data is heterogeneous and variable in nature and comes in many formats, including text document (Word documents, e.g., can be converted to text), image, audio and video. Unstructured data is growing faster than structured data. It will account for 90% of all data created in the near future. Unstructured data analytics can reveal important interrelationships that were previously difficult or impossible to determine and is currently seeking to gain richer, deeper, and more accurate insights into the business and social life for gaining competitive advantage and serving society better. To realise the full potential of unstructured data analysis, new approaches need to be developed. This paper proposes an approach to combining software patterns with the semantic web for constructing a data analysis framework for unstructured data.
Keywords: patterns; unstructured data; semantic web; data analysis.
Modelling and simulation of the ship rapids-ascending ability in Lancang River
by Zhizhou Zhao, Sichen Tong, Feng Zhu, Guangxiang Xu
Abstract: Ship sailing resistance is an important indicator to judge the navigation capacity of a waterway. In this paper, the empirical formula of Zwaan Kopf is modified according to the ship model test results in the Lancang River channel, and tests show that the navigation capacity of the channel can be represented by a critical compositive flow energy indicator ΔE=1.20. In order to validate the reliability of experimental results, this paper established a two-dimensional numerical model to simulate the water flow and ship sailing process in the channel. The relationship of flow energy indicator and the sailing velocity is studied by simulating a typical 500 tonne ship sailing in the channel of Lancang River, and the results show that the value of the critical flow energy indicator ΔE in the channel by simulation fits well with the tests, so the simulation method proposed in this paper is reasonable and feasible, and the critical flow energy indicator simulated by this method has important reference value in engineering
Keywords: waterway regulation; rapids-ascending ability; sailing resistance; numerical simulation.
Study on creep characteristics of geogrid based on modelling and experimental method
by Xia Li, Dinglong Shan, Feng Zhu, Ying Geng
Abstract: The creep characteristics of geogrid have significant influence on the analysis of the retaining wall's mechanical behaviour and deformation characteristics. This paper used digital speckle pattern interferometry to observe the model test of a retaining wall with embedded geogrid, and analysed the stress and strain distribution of soil behind the retaining wall according to the observation data. The mechanical model of stress distribution of geogrid is established in this paper, and the creep model of geogrid is also established to simulate the time-related behaviour of geogrid. In order to prove the rationality and reliability of the model proposed in this paper, the PFC2D software platform is employed to verify the numerical model of the reinforced retaining wall, and the creep model of geogrid is included in the PFC2D software. The calculation results show that the displacement of the retaining wall can be significantly reduced because of the anchoring effect of the geogrid. The time-dependent displacement of the retaining wall can approximately meet the logarithmic relationship. Therefore, the creep model of geogrid proposed in this paper is effective and reliable, and the method proposed in this paper can provide valuable reference information for similar engineering practice.
Keywords: geogrid; creep characteristics; digital speckle measurement system; particle element; numerical simulation.
EEG signals classification based on autoregressive and inherently quantum recurrent neural network
by Saleem Taha, Zahraa Taha
Abstract: This paper shows a novel hybrid approach in view of autoregressive (AR) model and Quantum Recurrent Neural Network (QRNN) for classification of two classes of electroencephalography (EEG) signals. The QRNN-AR has been shown to be capable to capture and quantify the uncertainty inherent in EEG signals, because it uses fuzzy decision boundaries to partition the feature space. Two diverse elements extraction techniques were used to extract the features from EEG signals; AR coefficients are processed with Levenion Durbin Algorithm and mean square error. AR provides a better frequency resolution and good spectrum of short EEG segments. The QRNN trained by the back-propagation algorithm is compared with Quantum Neural Network (QNN) and Quantum Wavelet Neural Network (QWNN). The average accuracy of the proposed QRNN model is 88.28452% at 6 seconds. The accuracy to time ratio value is 14.714086, which shows the superiority of the proposed model. Experimental results demonstrate that the QRNN-AR gives the highest overall accuracy and short processing time. In addition, the structure of the proposed method is more reliable.
Keywords: biomedical signal processing; electroencephalography; feature extraction; quantum computing; recurrent neural networks.
A real world online signature verification system based on correlation algorithm
by Charles Chen, Kuo-Kun Tseng, Xiaofeng Zhang
Abstract: Handwriting signature is a means of biometric identification and authentication, which is used to verify or authenticate a 'signer' and the document signed. An US law (the electronic signatures (E-Sign) in Global and National Commerce Act) and the United Nations Commission on International Trade Law Model Law on Electronic Commerce already grant electronic signatures legal validity equivalent to traditional hand-written counterparts. The intention of those laws is to cut costs while providing more stringent security, especially in the emerging e-commerce arena. In this paper, we (i) describe fundamentals and the current status of electronic signatures in detail; (ii) propose an online handwriting signature verification algorithm by using four online elements as the verification features: they are X axle, Y axle, pressure and velocity (time). A correlation algorithm is used to test and verify signatures; (iii) develop a real world application system with the online electronic signature.
Keywords: digital signature; online signature; signature verification.
Novel security issues and mitigation measures in cloud computing: Indian perspective
by Sudhakar Godi, Kurra Rajasekhar
Abstract: In the recent times, the demand and need for cloud computing is increasing owing to its wide range of applications, especially applications such as email, social networking apps, map storages, data sharing apps, etc., beside many useful applications. It has its own limitations such as cloud migration, storage space, efficient data mining, security and more. Data security is one of the major constraints of the cloud, especially in private and hybrid clouds. In this paper, a survey on the recent usage of the cloud computing technology in India is presented. Related security issues regarding the public, private and community clouds concerned are also depicted. This survey has been been conducted in both online and offline modes. It comprises opinion from 108 respondents out of 153 requests. A majority of participants reveal that they use cloud technology in their daily life applications, and approximately 73% are concerned about the security issues, especially private cloud storage and biometric security. Mitigation measures for all variants of the cloud technology have also been presented. A novel statistical survey report for 2016 is presented regarding security needs and improvements by considering various parameters. In addition, a list of cloud security attacks is also discussed.
Keywords: cloud computing; security; private cloud; real-time survey; security attacks; biometric.
Job-shop schedule modelling and parents-crossover evolutionary optimisation for integration of production schedules
by Jianxin Zhang, Jinxiang Chen, Haoyu Zhang
Abstract: An improved parents crossover evolutionary algorithm（IPCEA）is presented to deal with a class of job-shop scheduling optimisation problems. The considered integrated production process is firstly described as a job-shop model. Based on the model, the active schedules encoding and decoding approaches for production scheduling processes are proposed respectively. In order to avoid illegal chromosomes and preserve the good characteristics of the parent generation, an IPCEA is provided. Compared with the existing results, the proposed method can obtain better convergences and optimum solutions. The simulation results are given to show the effectiveness of our approaches.
Keywords: integrated production; SM-CC-HR; job-shop; IPPX-EA; ASD decode.
Design and implementation of a new cooperative approach to brain tumour identification from MRI images
by Boucif Beddad, Kaddour Hachemi, Sundarapandian Vaidyanathan
Abstract: Magnetic resonance imaging (MRI) has become a vital component of a large number of biomedical applications and also plays a major role in medical diagnostics. In this research work, the main purpose is to carry out a new cooperative approach to brain tumour detection and identification from MRI images with good segmentation accuracy. The proposed system applies a K-means algorithm to optimise the initial centroids of the improved fuzzy C-means, which incorporates the spatial information, and also to get a better estimation of the final cluster centres. Then the obtained results are considered as an initialisation of the active contour for the level sets technique. The proposed segmentation algorithm and its improvement were well implemented practically in real-time using a floating-point TMS320C6713 DSP of Texas Instruments. Performance improvement is measured by including various optimisation techniques, and all profiling and debugging results are shown using C6713 graphical user interface.
Keywords: code composer studio; fuzzy C-means; MRI image processing; level sets algorithm; segmentation.
Mobile robot navigation based on tangent circle algorithm
by Faten Cherni, Chokri Rekik, Nabil Derbel
Abstract: A difficult issue in mobile robot navigation or path planning in a static or dynamic environment is to find the path from the starting point to the target avoiding collisions with obstacles. This paper presents a new approach for solving the problem of mobile robot navigation in a dynamic environment based on tangent circle algorithm.
To plan the trajectory of the robot, the algorithm relies on the creation of different types of path between the robot and the target. In fact, during the motion path, the robot switches between a straight line and a circular trajectory in order to avoid obstacles and reach the goal. The proposed approach is implemented on different simulation scenarios involving static or dynamic obstacles in the environment. Results show that the novel method is efficient and that the mobile robot succeeds in reaching its target in a collision-free path in different cases.
Keywords: navigation; obstacle avoidance; mobile robot; dynamic environment.
Towards a contextual quality of service evaluation approach
by Jokha Al-Kalbani, Yassine Jamoussi, Naoufel Kraiem, Zuhoor Al-Khanjari
Abstract: The Quality of Service (QoS) evaluation techniques are required because web services having the same functional properties is not a guarantee of the same quality. This paper deals with a new QoS evaluation method for web services. The objective of the proposed approach is to offer the use of the existing QoS evaluation/assessment methods in constructing a new contextual QoS evaluation approach. The proposed approach is a multi-layer technique specified with the MAP formalism. The principle of the proposed contextual-oriented modelling approach is to guide the evaluator during all web services evaluation steps using different methodologies at varied levels of abstractions. A better understanding of the major web services quality factors that affect QoS will help QoS evaluators to judge the most appropriate QoS evaluation approach corresponding to their user and business requirements, with more flexibility in personalising the QoS evaluation process.
Keywords: web service; QoS evaluation techniques; MAP formalism; decision strategy; QoS prediction; contextual factors.
Mining traces between source code and textual documents
by Amir Hossein Rasekh, Seyed Mostafa Fakhrahmad, Mohammad Hadi Sadreddini
Abstract: Currently, researchers in computer science are dealing with a major challenge to link the source codes with the software documents. Writing informal documents by using natural and unstructured language causes this problem. In this paper, we present a model for recovery of traceable links between the source code and requirement documents. The proposed method is executed in four interconnected sections. The first section goes through extracting the features from the documents, which is followed by extracting the features from the source code. During the third section, abbreviations will be completed, using a similarity measure as a feature. Finally, data mining algorithms will be implemented to find the hidden links between the source code and the software documentation. The most outstanding advantage of using this method is to be independent from the language. Also, the preliminary results show that the proposed method has a good performance.
Keywords: traceability; data mining; artefacts; documentation; source code; similarity.
A national framework for E-health data collection in Jordan with current practices
by Anas Alsoboh, Ahmad Klaib, Ahlam AlYahya
Abstract: This paper proposes a health data collection and integration framework for the Jordanian health sector. It aims to strengthen the quality of Jordanian health systems (JHS) and ensure the provision of sustainable high quality e-health services for populations. The framework is developed to prepare the strategy involving current practices for health data collection based on DLL and V-Model. We studied critical features associated with collection and integration processes through concentrating on analysing a set of actual data (Cancer and Diabetes in Jordan). The result of the analysis demonstrates the needs and optimum data collection process for e-health data and directs all Jordanian health organisations to work within a common mechanism, which can reduce data inconsistency, enhance the JHSs capacity and sustainability, leverage the quality of the delivered health services, and reduce the cost of healthcare.
Keywords: data modelling; health data collection; health data analysis; health data integration; healthcare organisation; health information systems; electronic health record; Jordan.
Curvilinear tracing approach for recognition of Kannada sign language
by Ramesh Kagalkar, Shyamrao Gumaste
Abstract: Sign languages are used as a main mode of communication, however the diversity in sign symbol representation limits it usage to the given region. There is a huge diversity in sign symbol representation from one country to other, one state to other. In India, there are different sign languages observed for each state region. It is hence very difficult for an individual from one region to communicate to others using a sign symbol. This paper proposes a curvilinear tracing approach for shape representation of Kannada sign language recognition. To develop this approach, a dataset is created with all Swaragalu, Vyanjanagalu, Matras and Numbers in Kannada language. The dataset is formed by defining a vocabulary dataset for different sign symbols used in common interfacing. In the representation of sign language for recognition, edge features of hand regions are considered to be an optimal feature representation of sign language. In the processing of sign language, representative features play an important role in classification performance. In the representation of feature values, shape features represent the bounding hand regions, which in turn represent the sign language representation by one hand or two hands. However, the retrieval accuracy of sign language transformation depends on the feature used for representation and the selectivity of feature value. A wrong shape feature result is misinterpretation and also introduces a large overhead to the storage in the processing system. Hence, an attempt is to make to achieve faster processing with lower feature representation in sign language transformation.
Keywords: curvilinear feature; leap forward tracing; support vector machine.
The temporal dimension of business processes: requirements and difficulties
by José Pereira, João Varajão
Abstract: The execution of business processes has often a temporal dimension, as there is usually a set of time constraints that processes have to comply with. These time constraints may arise from legal requirements that organisational processes have to fulfil or are simply the result of the need to optimise their execution in terms of time, and include, among other aspects, the duration of processes, deadlines associated to their activities, etc. Unfortunately, both in terms of process modelling languages and systems used to manage business processes, the support for the time dimension is still very poor. In this paper, we describe the main temporal aspects that need to be considered in the management of business processes. By doing this, we identify the temporal requirements that process modelling languages need to incorporate, in order to be able to specify those aspects, as well as the difficulties to translate those aspects to business processes supported by current BPMS.
Keywords: business processes; business process modelling languages; time constraints; temporal dimension.
Smart grid resources optimisation using service oriented middleware
by Abderezak Touzene, Sultan Al Yahyai, Amar Oukil
Abstract: In this paper, we propose a new Service Oriented Architecture (SOA) for Smart Grid Resource Optimization Middleware (SGROM), which allows Smart Grid Constituencies (SGC) such as Power Generators (PG), Power Transporters (PT), Power Distributors (PD), and Power Consumers (PC) to optimise their pay-offs using the smart grid. The proposed resource optimisation management middleware aims to support power consumers with sustainable energy from power distributors, transparently at the best price on a real time basis (variable pricing). It will also decide automatically for the power distributors the best power generators and connecting transporter lines based on the current power generation and transportation costs, the demand of the distributor, and the maximum power supply from the generator. The SGROM smart engine is modelled as a Mixed-Integer Linear Program (MILP), which aims at optimising the profits and resource utilization over the whole smart grid. The experimental results show that substantial cost improvement can be achieved when using SGROM, compared with the traditional grid system.
Keywords: smart grid; SOA middleware; resource management; optimisation; linear programming.
Fundus photography quality assessment based on topological extinction values
by Alexandre Silva, Fabio Moreira, Marina Fouto, Rangel Arthur, Angelica Arthur, Yuzo Iano, Jacqueline De Faria
Abstract: The quality of retina images is important for the diagnosis and treatment of eye diseases. Blur, distortion, and low contrast, among other artifacts, inhibit the viewing of regions of interest. This work proposes a global descriptor based on two extinction values (number of descendants and topological height) for direct analysis of fundus photographs and automatic classification as "good" or "poor" quality. The method does not require any special filtering, image segmentation or internal retina structure location. It is not substantially affected by rotating, resizing or dataset type, and is computed in quadratic time. The algorithm achieved an area under ROC curve of 96.13% for UNICAMP, 88.59% for UNIFESP DR2, 99.27% for DRIMDB, and 92.07% for HRF datasets.
Keywords: topological extinction values; ophthalmic imaging; fundus quality assessment; image descriptor.
Multi-objective optimisation of ship microgrid research based on priority selective control strategy of diesel generator and energy storage
by Weiqiang Liao, Defeng Wu, Wanneng Yu
Abstract: This paper's research objective is to investigate independent ship with wind-solar-diesel-battery hybrid energy using real time variations of energy source components in energy storage systems. Firstly this paper investigates the critical output point of energy storage systems under a dynamic load, to determine the priority of energy storage control strategy at first. Secondly,this paper investigates the issue of power supply optimisation. A three-objective optimisation design model is proposed; This paper also uses the NSGA-II algorithm, which is used to investigate the numerous aspects of independent ship microgrid optimisation, while influencing different power combinations and energy control strategies. The results show that the method is accurate and effective.
Keywords: ship microgrid; priority selective strategy of diesel generator and energy storage;critical output point; power optimisation; NSGA-II.
Detecting occluded faces in unconstrained crowd digital pictures
by Chandana Withana, S. Janahiram, Abeer Alsadoon, A.M.S. Rahma
Abstract: Face detection and recognition mechanisms, a concept known as face detection, are widely used in various multimedia and security devices. There are significant numbers of studies into face recognition, particularly for image processing and computer vision. However, there remain significant challenges in existing systems owing to limitations behind algorithms. Viola Jones and Cascade Classifier are considered the best algorithms from existing systems. They can detect faces in an unconstrained crowd scene with half and full face detection methods. However, limitations of these systems are affecting accuracy and processing time. This project proposes a solution called Viola Jones and Cascade (VJaC), based on the study of current systems, features and limitations. This system considered three main factors: processing time, accuracy and training. These factors are tested on different sample images, and compared with current systems.
Keywords: face detection; unconstrained crowd digital pictures; face recognition.
Non-vocalised Arabic word classifications based on mining affixes features
by Sari Awwad, Mustafa Hammad, Safaa Al-Haj Saleh
Abstract: Arabic word classification is a challenging problem owing to the cursive nature of the language and modulation marks. The existing approaches are based on databases and dictionaries to classify Arabic words, which makes classification process operation slow. Therefore, this paper investigates Arabic word classification in the non-vocalised Arabic text by solely using affixes features, and it also explores the extent to which we can rely on these features to determine Arabic word class without the need for dictionaries or word lists. The proposed approach is mainly based on affixes features and Support Vector Machine (SVM). A Fisher encoding is also applied to remove any redundancy and to preserve important information. Our approach is tested on a dataset of two main classes (noun and verb) and six different noun sub-classes. The results indicate that this approach is helpful in achieving a success rate approaching 64% of the total words in the articles used in this study. The unsuccessful classification rate appears because there are no affixes in the target Arabic word or some original characters are considered as affixes.
Keywords: affixes features; word classification; SVM; Fisher encoding; Arabic language.
Super element method applied to MIC to reduce simulation time of compliant assemblies
by Wilma Polini, Andrea Corrado, Gillo Giuliano
Abstract: During the last twenty years, researchers have proposed several methodologies, mainly based on Finite Element Method (FEM), to solve tolerance analysis problems of compliant part assemblies. The entire literature was focused on the solving of assembly problems considering linear and non-linear phenomena due to thermal effects, residual stresses during assembly and so on. The computational efficiency to simulate compliant assemblies is an important aspect in the tolerance analysis problems. This work presents a method to reduce the simulation time involved in the tolerance analysis without reducing the number of elements used to discretise the geometry of the considered part. To verify the effectiveness of the new proposed method, in comparison with the methods of the literature, three assemblies were taken into account, the first constituted by two sheet metal parts, the second and the third constituted by two and three composite laminates, respectively. The new method halves the simulation time compared with the literatures approaches.
Keywords: tolerance analysis; compliant assemblies; simulation; time; sheet metal parts; finite element analysis; assembly; MIC; geometric tolerance; geometry.
VP-Hotspot: a tool for visualising and predicting hotspot occurrences
by Sunsika Chaikul, Santi Phithakkitnukoon
Abstract: Hotspot data can be used to identify a heat source, which can represent vegetation fires, such as forest, grass, cropland, or logging debris. This article presents a development of a tool namely VP-Hotspot, which is a visualization tool that allows the user to observe and analyse hotspot occurrence patterns of any selected geographical areas. The tool provides two modes of operation: regression and similarity search. Regression mode provides fitted regression models to the selected area data as well as its forecast. Similarity search mode allows the user to search for areas with a similar hotspot occurrence pattern. Two case studies are discussed to demonstrate the use of the tool. A user experience study was conducted to evaluate the tool with real users (130 subjects) from which the tool was well received for its usefulness and being easy to start using. We believe that the tool is useful for analysing hotspot data, and beneficial to regional and city planning and more specifically, agricultural planning and fire control, for instance.
Keywords: urban informatics; big data; hotspot; visualisation tool; vegetation fire; wildfire.
Software inspections: comparing a formal-based methodology with a classical reading methodology
by Luciana Santos, Valdivino Santiago Jr, Lucas Povoa, Albino Freitas, Cleyton Mario
Abstract: The ambitious goal of software inspection is to deliver a software product with minimum of defects taking into account all the artifacts (specifications, design documents, source code, tests, etc) created during the product development. Classical reading techniques, such as Perspective-Based Reading (PBR) and Object-Oriented Reading Techniques (OORTs), are interesting but other approaches are supported by mathematical (formal) methods. In this paper, we present a rigorous comparison of two inspection techniques: our formal method-based approach, SOLIMVA 3.0, with a set of OORT vertical reading techniques. We evaluated efficiency (required time to analyse a scenario) and effectiveness (ability to find defects within UML diagrams). Results show that the classical OORT methodology is more efficient than SOLIMVA 3.0. However, we found a strong positive correlation between the required time by using OORT and SOLIMVA 3.0. For effectiveness, in general, both methodologies presented the same performance. However, SOLIMVA 3.0 detected more inconsistency (incorrectness and extra information), ambiguity, and partial incompleteness defects whereas OORT identified more total incompleteness. Our overall conclusion is that a classical reading methodology (OORT) and a formal-based one (SOLIMVA 3.0) can be adopted in a complementary way within a software inspection process. In other words, while OORT is less costly to apply, SOLIMVA 3.0 can be used to address some specific type of defects in order to maximise the number of detected defects.
Keywords: software inspection; SOLIMVA 3.0; formal methods; object-oriented reading techniques; quasi-experiment.
Extending the WEDBA to the fuzzy multi criteria decision making environment
by Tarek Al-Hawari, Ahmed Naji, Hussam Alshraideh, Omar Bataineh
Abstract: This paper extends the Weighted Euclidean Distance Based Approach (WEDBA) to the fuzzy environment in MCDM. Fuzzy concepts are introduced, in which alternative ratings and criteria weights are described by linguistic terms. WEDBA features the usage of integrated criteria weights, objective and subjective, which enhances its performance and overcomes the drawbacks of methods that only use one type of weight such as AHP and TOPSIS. Two examples are provided to highlight the procedure of the proposed method named Fuzzy WEDBA. The first example is a fuzzy MCDM problem, which aims to evaluate five suppliers of an automotive production company. The second is a real life case study to select a contractor for a construction project. A comparative analysis is conducted based on several factors. The proposed method has greater time complexity, owing to using integrated criteria weights, but performs similarly to F-TOPSIS in most of the evaluation factors, and both perform better than F-AHP with regard to the remaining factors.
Keywords: WEDBA; fuzzy; TOPSIS; AHP; supplier; contractor; selection; MCDM.
A survey of Arabic text classification approaches
by Mostafa Sayed
Abstract: Categorisation of text is a significant trend that ultimately appears owing to the internet revolution, resulting in enormous amounts of data that depend on various languages. The Arabic language is one of the most commonly used languages all over the world; it is considered the fifth most spoken one. Various challenges occur through processing and classifying of Arabic text since it has more sophisticated techniques than the English language. These challenges are clear owing to the Arabic language variation in shape, structure and component; besides, there is a lack of adequate studies discussing Arabic text classification. This research seeks to form a general point of view by categorising different techniques of Arabic text classification through this current research for helping new researches concerning this domain. Also, it shows some prior information and innovative designs about Arabic text classification, and mentions various works that have discussed classifying Arabic text, with regard to datasets, categories and preprocessing steps, classification mechanism and assessment procedure for those techniques. These discussions aim to conclude a comprehensive overview through forming a general framework for all researchers about this domain via defects examination of the prior studies, and then the possibility of presenting more advanced directions.
Keywords: Arabic text classification; text classification techniques; Arabic text mining.
A multiple feature based offline handwritten signature verification system
by Akriti Nigam, Prateek Singh, Vivek Singh, R.C. Tripathi
Abstract: This paper proposes an efficient technique to develop an automated offline signature verification system that could help in crime prevention and biometric authentication systems. The technique proposed makes use of direction based methods to compute a set of features that are taken together as a combination. The features include the geometric details of the different strokes that compose a signature and contours of the signature. It includes the two-step/three-step features, radical points, directions and transitions of strokes and contours, energy density and angles of strokes, to name a few. A grid-based approach is applied to extract some of the features. Classification is done by using SVM. Experiments are performed on a standard CEDAR database and a self-prepared database. The results speak for the efficiency of the proposed system that achieves an accuracy much better than many of the published works.
Keywords: signature verification; SVM; chain codes; signature forgery.
The study of hydraulic automatic pressure regulating technology in water injection well
by Zhenfu Ma
Abstract: According to the energy waste, bigger burden and the different requirements of different injection pressure in the process of injecting pressure, this paper has done a conducted in-depth study and puts forward a series of new and improved technologies based on the original technologies, and the new techniques are validated, modelled and simulated, and ultimately a set of hydraulic and automatic pressure regulating energy-saving water injection technologies for ground water injection are formatted, which can transfer surplus energy from low pressure wells to high pressure well without adding new equipment energy consumption, complete the redistribution of the water injection pressure between high and low pressure wells, and realise the reasonable energy conversion between wells. All in all, it is of great significance to further improve the qualified rate of interval, saving energy and reducing consumption of water resources.
Keywords: water injection well; pressurized water injection; double action; stable pressure; early warning.
Research on universal coupling technology of downhole pressure regulating system
by Zhenfu Ma
Abstract: In the light of the way of water flooding development in Shengli Oilfield, and the problems of differences of water injection pressure in different reservoirs or different injection wells caused by reservoir physical property itself, and late blockage and damage, the hydraulic automatic pressure regulating water injection device between wells is studied. The downhole pressure regulating device is mainly composed of a screw motor and a screw pump, and also includes the key part of universal coupling. The universal coupling technology is studied in this paper, which solves the problem of the universal joint checking caused by the axial force pointing to the direction of the low pressure, and the selection and checking of the positive bearing of the rotor caused by the centrifugal inertia force of the rotor, the lateral vibration of the rotor, the dynamic load of the tubing and so on. Only through the axial and radial support of the force can the normal and efficient operation of the system be ensured.
Keywords: universal coupling; centrifugal force; vibration; dynamic load.
The impact of digital storytelling rubrics on social media engagements
by Rami Malkawi, Malek Alzaqebah, A.L.I. Al-Yousef, Bilal Abul-Huda
Abstract: Storytelling has been passed down orally from generation to generation until it reached the current digital era, which modified storytelling to be digital storytelling. Digital storytelling plays a vital role in passing information by interactive and interesting ways using digital media. Therefore, within this digital age, it is worthwhile to introduce this type of technology to the current and future generations. There are several methods that help in assessing the quality of the digital stories and classifying them into successful or failed stories. Rubrics, which depend on important aspects, can be helpful for assessing these digital stories. This paper introduces an assessment method for numbers of digital stories that have been collected from the social media site YouTube, which is concerned with presenting videos easily and instantly. Therefore, many people have uploaded their digital stories in YouTube; some of these stories are interesting, while others are boring. One of the most important aspects that helps in assessing these digital stories is number of viewers on YouTube. In this study, the aim is to analyse the effect of some aspects, such as the story seven elements and the gender of the story narrator, and the number of the story viewers on YouTube. As a result of this research study and data analysis, if three main elements have been found in a digital story, then it will be considered as attractive for viewers and it will gain a high viewer rating.
Keywords: digital storytelling; digital stories; classification; evaluation methods; rubrics; social media; data analysis.
A Flight Conflict Detection Model for UAV Based On Four Dimensional Coordinates
by jianhua zhang, shuo yang, yang li
Abstract: In view of the detection of UAV flight conflict, a flight conflict detection model of UAV based on four-dimensional coordinate is presented.The purpose of the model is to find the possible flight conflicts around the UAV and to provide sufficient time for the autonomous avoidance of UAV. The model divides the area around UAV into three regions, including the detection layer (SAR), the adjacent layer (NAR) and the collision layer (CAR). First, the detection targets are preliminarily selected, then the conflict analysis of UAVs from four-dimension is carried out to determine whether there is any possibility of flight conflict. A large number of MATLAB simulation experiments show that this model can accurately and quickly detect the risk of UAV collision, and has a positive effect on the flight avoidance problem of UAVs.
Keywords: Flight conflict; Four-dimensional coordinate; Collision risk; Risk avoiding
A discussion on robust multivariable I/O fractional transfer function of types DC or FBLFD in motion control
by Najah Yousfi-Allagui, Nabil Derbel, Pierre Melchior
Abstract: Motion control and robust path tracking design are the objective of this paper. The MIMO-QFT (Multi Input Multi Output Quantitative Feedback Theory) method is considered. This methodology permits to divide the MIMO system into sub-MISO (Multi Input Single Output) equivalent structures. After obtaining the sub-structures, the objective is the design of the controller and prefilter in order to achieve some specifications. A multivariable robust path tracking design is already studied based on fractional approaches. These developed approaches are based on two fractional prefilters of types DC (Davidson Cole) and FBLFD (Frequency Band Limited Fractional Differentiator). A new methodology that combines I/O (Input Output) fractional transfer function of types DC and FBLFD with a fractional PID controller tuning is studied in this paper. This approach is based on considering the I/O transfer function matrix as a fractional prefilter of types DC or FBLFD. Using this approach a reduced settling time of the I/O transfer function can be obtained. The multivariable fractional PID controller is tuned based on a multiobjective optimisation using genetic algorithm. The obtained method is tested with a SCARA robot model to demonstrate the efficacy and robustness of our approach.
Keywords: DC prefilter; FBLFD prefilter; fractional PID controller; multivariable systems; path tracking.
Proposal on EPC system enhancement to realise Supply Chain Process Analysis
by Tatsuya Inaba
Abstract: Although IT services for business process analysis become popular, they do not cover business process of the supply chain management, since information in the real space is difficult to capture by existing IT systems. Therefore, process analysis for the item moves in the supply chain also is not commonly used. Assuming the use of EPC (Electronic Product Code) system, an implementation of networked RFID systems, this study proposes two supply chain process analysis methods, a performance analysis method and an anomaly analysis method. In addition, this study proposes a way to enhance EPC system with Hadoop system to meet the needs for massive transaction data processing. The proposal is evaluated using an agent-based simulation tool, and the feasibility of the business process analysis methods as well as system enhancement are confirmed.
Keywords: RFID; EPC system; Hadoop; supply chain management;
A Fast and Scalable Similarity Search in High Dimensional Image Datasets
by Youssef Hanyf, Hassan Silkan
Abstract: Due to the development of image data production and use, the quantity of image datasets is exponentially increased in the last decade. Consequently, the similarity searching cost in image datasets becomes a severe problem which affects the efficiency of similarity searching engines in this data type. In this paper, we address the problem of reducing the similarity search cost in large, high-dimensional and scalable image datasets; we propose an improvement of the D-index method to reduce the searching cost and to deal efficiently with scalable datasets. The proposed improvement is based on two propositions; first, we proposed criteria and algorithms to choose effective separation values which can reduce the searching cost. Second, we proposed an algorithm for updating the structure in case of scalable datasets to resist the impact of objects’ insertion on the searching cost. The experiments show that the proposed D-index version has proved a good searching performance in comparison with the classical D-index and a significant resistance to the dataset scalability against the original D-index.
Keywords: Similarity search; High-dimensional images datasets; D-index; Image datasets indexing; Scalable datasets; Content-based retrieval; Metric spaces.
Special Issue on: BDCA'17 Computer Science and Information Technology
Dynamic VM Allocation and Traffic Control to Manage QoS and Energy Consumption in Cloud Computing Environment
by Mohamed Hanini, Said El Kafhali, Khaled Salah
Abstract: In the last few years, cloud computing technology has revolutionized the IT industry and its popularity has increased, this is due to its economic benefits even for the cloud providers and for the users. Despite the benefits that this new paradigm offers, it poses major challenges for providers. Among these challenges the guarantee of the desired Quality of Service (QoS) for the users defined in the Service Level Agreement (SLA) document. Moreover, power consumption control can improve significantly benefit for providers. In this paper, we propose a mechanism combining a scheme for Virtual Machine (VM) utilization in a given Physical Machine (PM) with a mechanism to control the access for incoming requests to the Virtual Machine Monitor (VMM). The number of activated VMs in the PM is defined according to the workload, and the control access is based on the number of requests in the system. The studied mechanism is described by a mathematical model, and the performance parameters expressions are derived. In addition, a power consumption model is described and evaluated. Numerical examples evaluating these parameters are given. In particular, in terms of QoS, we analyze the behavior of loss probability, mean number of requests, throughput and mean requests delay while varying the incoming request arrival rate. Moreover the impact of the proposed mechanism on the behavior of energy consumption is evaluated. Analysis of the obtained results shows the positive impact of the proposed mechanism on the QoS parameters and on power consumption.
Keywords: Queueing theory; Cloud Data Center; Virtual Machines; Performance Analysis; Quality of Service; Energy Consumption.
A comparison of Text Classification methods using different Stemming Techniques
by Mariem Bounabi, Karim El moutaouakil, Khalid Satori
Abstract: In the retrieval information, two factors have an important impact on the systems rnperformance: the extract features and the matching process. In this work, we compare three rnwell-known stemming Techniques: Lovins stemmer iterated Lovins and snowball Stemmer. Concerning the classification phase, we compare, experimentally, six methods: BNET, NBMU, CNB, RF, SLogicF, and SVM. Basing on this comparison, we propose a new retrieval system by calling the voting method, as a matching tool, to improve the performance of the classical systems. In this paper, we use the TF-IDF algorithm to extract features. The envisaged systems are testing on two databases: BBC NEWS and BBC SPORT. The systems based on Lovins stemmers and on the voting technique give the best results. In fact, for the first databases, the best accuracy observed is for the system Lovins +Vote with a recognition rate of 97%. Concerning the second database, the system snowball +Vote that gives us 99% as recognition rate.
Keywords: NBMU; SVM; RF; NB; SLogiF; CNB; voting technique; Classification; Stemmer;weighting term
Performance Prediction of Pharmaceutical Suppliers: A comparative study between DEA-ANFIS-PSO and DEA-ANFIS-GA
by Rohaifa Khaldi, Abdellatif El Afia, Raddouane Chiheb
Abstract: Pharmaceutical supplier’s selection is a critical task within hospital. Because dealing with the wrong supplier, may plague the overall healthcare supply chain, especially patient’s life. Thereby, this study investigates the feasibility of using DEA in conjunction with ANFIS-PSO and ANFIS-GA, to evaluate and predict supplier performance. This investigation is a comparative study between ANFIS-PSO and ANFIS-GA. For our best knowledge, it fills the gap in literature by assessing the benchmarking capabilities of the two proposed models. DEA-BCC was applied to evaluate the efficiency scores of the selected suppliers. ANFIS-PSO and ANFIS-GA were applied to learn DEA patterns and to predict the performance of unseen suppliers. To determine the accuracy of those models, a statistical analysis was performed. Besides, the results were compared with ANFIS-Hybrid model. According to RMSE and R, the results revealed that ANFIS-PSO model yields the best prediction abilities. Thus, this model can be considered as a promising decision support system at the operational and strategic level.
Keywords: Adaptive Neuro-Fuzzy Inference System; Genetic Algorithm; Particle Swarm Optimization; Data Envelopment Analysis; Benchmarking; Prediction; Performance; Pharmaceutical Suppliers; Healthcare Supply Chain.
Estimate of Stochastic Model Parameter of Exchange Rate Using
Machine Learning techniques
by Mostafa EL HACHLOUFI, Hamza FARIS, Mohammed EL HADDAD
Abstract: In this paper we present a new approach for estimating the stochastic model parameter of exchange rate using genetic algorithms and neural networks.This approach takes in consideration the minimization of exchange rate risk that is measured by the conditional value at risk CVaR in the estimation procedure of this parameter.The objective of this approach is to provide a tool of decision for the exchange market managers.
Keywords: Exchange rates; Estimation; Risk; CVaR; Stochastic model; Genetic algorithms; Neural networks.
Special Issue on: ICMIC2016 Computer Applications in Technology
Modelling, Simulation and Control of a class of Hybrid dynamic systems using hybrid Automaton, APROS and Mixed Integer Quadratic Optimization Algorithm
by Mohamed Fouzi Belazreg, Khaled Halbaoui, Djamel Boukhetala, Mohamed El-Hadi Boulheouchat
Abstract: This paper presents modelling, simulation and control of a class of hybrid dynamic systems. The Hybrid Automaton is used for modelling a transition system with continuous dynamics. The framework consists of a finite set of state and transition for modelling a discrete dynamics who called control mode. Each control mode describes continuous dynamics. Using APROS tools, it permits to simulate the behavior of the hybrid system approaching in the experiment case: actuators, pumps and valves. The Mixed Logic, dynamics formalism allow to describe the both dynamics defined by logic rules, continuous dynamics and constraints. These are described by linear dynamic equations subject to linear inequalities involving continuous, discrete and auxiliary variables. This model is used to synthesize of a predictive control law under constraints. The controller requires on-line mixed-integer quadratic programming solution to an optimization problem. Simulation was performed to illustrate performances and efficiently of these methods and tools.
Keywords: Hybrid systems; hybrid Automaton; nonlinear systems; Nodalization APROS; Mixed Logical and Dynamical; Stateflow; model predictive control; Mixed-integer quadratic programming; OPC Interface
Intelligent power system controller design
by Saoudi Kamel, Bouchama Ziyad, Ayad Mouloud, Benziane Mourad, Harmas Mohamed Naguib
Abstract: In this paper, a type-2 fuzzy based adaptive sliding mode power system controller is proposed for damping low frequency oscillations with the aim to enhance power system stability despite modele uncertainties introduced by variations of system parameters and external disturbances. Addressing these latter, Type-2 fuzzy systems approximating properties are used to approximate unknown power system nonlinear dynamics. Furthermore, to achieve more robustness, the proposed controller design is combined with sliding mode approach. The latter and Lyapunov synthesis approach are incorporated in an adaptive fuzzy control scheme such that the derived controller is robust, closely tracking any changes in power system operating conditions and guaranteeing stability while a PI control term is added to mitigate chattering. Proposed stabilizer robustness has been tested on a single machine infinite bus system and a multi-machine power system. Nonlinear simulation studies show good performance of the proposed stabilizer and confirm its superiority over conventional PSS and some other types of power stabilizers.
Keywords: power system, sliding mode control; type-2 fuzzy system; adaptive control; Lyapunov;
Towards Compact swarm intelligence: A New Compact Firefly Optimization Technique
by Lyes TIGHZERT, Cyril Fonlupt, Boubekeur MENDIL
Abstract: Firefly algorithms (FA) is a recent and promising swarm intelligence algorithm. It is inspired by the modeling of brightness and attractiveness manifested by fireflies. Like other population-based algorithms, it presents the drawbacks of high computational cost and memory storage. This paper deals with this problem and introduces a compact firefly optimization technique with minimal computational and memory requirements. So, we present four new variants of compact firefly algorithms that require only a minimal computational cost. The swarm is compacted and represented by a probability of density function (PDF). This idea is inspired from compact evolutionary algorithms (cEAs). Two solutions of memory storage of the population are presented and analyzed. The first is based on normal PDF and the second on uniform PDF. Furthermore, two versions of compact Lévy-flight firefly algorithm (cLFA) are also introduced. This paper takes a step towards new compact swarm intelligence algorithms. The proposed algorithms are compared to the state-of-art of cEAs and two original variants of FA using IEEE CEC2014 functions. In addition, the proposed algorithms are used to realize an optimal swing-up movement of a humanoid robot hanging on a bar.
Keywords: compact firefly algorithms; compact swarm intelligence; Lévy-flight; Uniform; Optimization; Gymnastics; Humanoid.
Special Issue on: Computational Intelligence and Applications
Gas outburst prediction based on the intelligent D-S evidence theory
by Caixia Gao, Fuzhong Wang, Zhan Zhang
Abstract: Accurately predicting gas outburst in coalmine extraction is an effective method to prevent gas outburst disaster. Because there are the features of suddenness, unevenness, uncertainty and dynamic in gas outburst, the existing prevention method should be improved in accuracy and effectiveness. In this paper, a predictive model of gas outburst is built by combining fuzzy neural network and D-S evidence theory, and the model specifically introduces the overall structure design of gas outburst predicted model, the selection of gas outburst evaluation indicators, the design of fuzzy neural network unit and the design of D-S evidence theory unit. The eight key factors, including the thickness of coal layer, the geological structure types of coal and the gas pressure of coal layer, are selected as the evaluation indicators of gas outburst, and the preliminary judgement of the gas outburst state in at the local point of the mining working face, is made by fuzzy neural network, and then global judgement of gas outburst state in the mining working face is made based on D-S evidence theory. The simulated result shows that this method can make accurate judgements of gas outburst state grade, and regarding the judgements of the three kinds of gas outburst state, the accuracy error is less than 0.0048% and the uncertainty value approximates to zero.
Keywords: gas outburst prediction; fuzzy neural network; D-S evidence theory; algorithm design.
Improved triple generative adversarial nets
by Yaqiu Liu, Qinghua Zhao, Kun Wang
Abstract: Generative Adversarial Nets (GANs) have shown excellent performance in image generation and semi-supervised learning (SSL). However, existing GANs have three problems: (1) the generator G and discriminator D tend to be optimal out of sync, and are not good at processing labelled data; (2) the generator G can easily generate chaotic semantics; and (3) the GANs can not learn the inverse mapping (projecting data back into the latent space, which is of benefit to feature representation of related semantics). The problems caused by the limitation of the two-player mode, where D can share only incompatible roles of identifying fake samples and predicting labels and distinguishes the data without labels. To solve these problems, we propose an Improved Triple Generative Adversarial Net (ITGAN), which consists of four parts: a generator G, a classifier C, an encoder EN and a discriminator D. The G and the C characterise the conditional distributions between images and labels, the discriminator distinguishes whether a pair of data comes from the true distribution, and the EN maps data x to latent representations z. The experimental results show that the ITGAN achieves the state-of-the-art classification results among deep generative models, which demonstrate that the additional encoder can enhance the classification accuracy effectively.
Keywords: GANs; Triple-GAN; encoder; deep convolutional networks.
A survey on computation offloading in the mobile cloud computing environment
by Li Liu, Yuanyuan Du, Fan Qi, Weicun Zhang
Abstract: Computing intensive tasks could be offloaded from the mobile devices to the remote cloud servers in the mobile cloud computing (MCC) environment. Computing offloading is a complicated problem owing to considering the partitioning methods and the migration strategies to achieve the optimal solutions. In recent years, more research works have been done to optimise the problem of computing offloading. However, there are few works that comprehensive review the computing offloading in the MCC environment in terms of its models, algorithms and so on. The purpose of this paper is to establish a taxonomy for models and algorithms of the computation offloading in the MCC environment. A survey is presented to make a classification from four different issues for computation offloading in MCC, and the different approaches taken to tackle these issues are also discussed in detail, further presenting several research challenges in this area.
Keywords: mobile cloud computing; computation offloading; computation offloading algorithm.
Indirect adaptive fuzzy control of non-linear systems using fuzzy supervisory term
by Donia Ben Halima Abid, Mohamed Chtourou
Abstract: This paper focuses on the indirect adaptive fuzzy control of single input single output (SISO) nonlinear systems with unknown linearities. The proposed adaptive fuzzy controller is based on feedback linearisation. Its parameters are updated online according to some adaptive laws, such as tracking error-based method, composite tracking and modelling error-based approach, as well as filtered composite tracking and modelling error-based approach. However, the approximation error introduced into the feedback loop increases the difficulty to guarantee the stability of the closed loop control system. To solve this problem a supervisory term should be added to the control law. In this paper, a fuzzy supervisory control is proposed to overcome the problem of chattering phenomena introduced by the classical supervisory term. Theoretical and simulation results prove the effectiveness of the proposed approaches. Faster and improved tracking are obtained using the second and third aforementioned adaptive laws combined with fuzzy supervisory control.
Keywords: adaptive fuzzy control; feedback linearization; tracking error; composite adaptive law; filtered composite adaptive law; estimation error; supervisory controller.
A Hybridized Feature Selection Approach in Molecular Classification using CSO and GA
by Ahmed Elsawy, Mazen Selim, Mahmoud Sobhy
Abstract: Feature selection in molecular classification is a basic area of research in the chemoinformatics field. This paper introduces a hybrid approach that investigates the performances of chicken swarm optimisation (CSO) algorithm with genetic algorithm (GA) for feature selection and support vector machine (SVM) for classification. The purpose of this paper is to test the effect of elimination of the inconsequential and redundant features in chemical datasets to realise the success of the classification. The proposed algorithm was applied to four chemical datasets and proved superior in achieving minimum classification error rate in comparison with different feature selection algorithms for molecular classification
Keywords: molecular classification; chicken swarm optimization; genetic algorithms; support vector machines; feature selection.
Robustness of adaptive inverse control in solving internal and external disturbance uncertainties for a class of nonlinear systems
by Shuo Zhan, Jing Bai, Chaochao Li, Lin Yue
Abstract: In this paper, adaptive inverse control is developed to deal with the problem of the control performance of nonlinear systems, which are affected by the uncertainty of the parameters and external disturbance. An adaptive inverse controller, which is used as a feedforward controller, is designed to realise real-time tracking of the system output and to enhance the robustness of the system under parameter perturbation. A noise and disturbance canceller is used to eliminate the influence of external disturbance and measurement noise for the open loop system output. The results of a simulation conducted on the variable frequency speed control system of an induction motor verify the feasibility and effectiveness of the proposed method.
Keywords: robustness; adaptive inverse control; nonlinear systems.
A Pareto optimal multi-objective optimisation for parallel dynamic programming algorithm applied in cognitive radio ad hoc networks
by Badr Benmammar, Youcef Benmouna, Francine Krief
Abstract: In this paper, we present a Pareto optimal multi-objective optimisation for parallel dynamic programming algorithm applied in cognitive radio ad hoc networks. To measure the performance of our contribution, we have used a multi-core architecture. The parallel version of the dynamic programming is implemented with the concept of Pareto. To select the most promising solution from the Pareto front, the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) was used. We have also implemented a meta-heuristic (cuckoo search) with the Pareto principle in order to validate our proposal. Our simulations achieve the desired results, showing significant gain in terms of execution time. The main objective is to allow a cognitive engine to use an exact method and to have better results compared with the use of meta-heuristics, while satisfying QoS parameters.
Keywords: Pareto optimal; multi-objective optimization; QoS; parallel computing; dynamic programming; cuckoo search.
Intelligent game-based learning: an effective learning model approach
by Tanzila Saba
Abstract: Game-Based Learning (GBL) broadly refers to the use of video games applications to support teaching and learning processes. This research focuses on the concept of GBL in the context of stimulating interest in the field of computer science education specifically. In contrast to theoretical learning, GBL is a practical learning approach that is meant to teach and be enjoyed at the same time. Additionally, a GBL model with visual features has been proposed and tested. Promising feedback has received from learners through the post conducted surveys. The research findings exhibit that GBL is an effective methodology in transferring knowledge, enhancing learning, and making the learning a more enjoyable process in computer science studies than just the theoretical approach.
Keywords: binary games; game-based learning; logical games; theoretical learning.
An improved distributed storage model of remote sensing images based on the HDFS and pyramid structure
by Linhui Li, Weipeng Jing, Nihong Wang
Abstract: With the rapidly growing amount of remote sensing data in recent years, data management needs to adopt a new architecture. Some improvements have been made in some areas. For example, the Hadoop distributed file system (HDFS) can be used for large files. However, many small files will be produced when storing a remote sensing image in the pyramid-based structure of the HDFS. In this paper, we propose a method based on the Hadoop system with MapFile. The method is an improved storage model for efficient storage and allows for access of the small files on the HDFS. The proposed method combines small files into the MapFile serialized container, and it reduces the number of small files in an effective way. To improve the search speed of the stored files, the metadata sensor information is stored in the index. Our results demonstrate the efficiency of remote sensing image processing using the pyramid model and the reduced execution time compared with the HDFS, HDWebGIS and Hadoop archives.
Keywords: remote sensing image; pyramid structure; MapReduce; MapFile.
A novel control for MDF continuous hot-pressing accurate tracking: Adaptive fuzzy approach
by Liangkuan Zhu, Yugang Zhou, Yaqiu Liu
Abstract: In order to guarantee slab thickness precision of medium density fibreboard (MDF), an adaptive fuzzy dynamic surface control (DSC) was investigated. Firstly, a mathematical model for the hydraulic servo system of the continuous hot-pressing machine with input saturation is proposed, and the auxiliary system is introduced to compensate the effect of input saturation. Owing to the uncertain function in the model, a fuzzy logic system is introduced to approximate it. Subsequently, a slab thickness tracking controller with less complexity is developed with the help of the DSC approach. Simultaneously, an online adaptive law was applied to estimate the adjustable parameter vector in the fuzzy system. Finally, experimental results validated that the proposed method can ensure the MDF slab thickness precision in the hot-pressing process.
Keywords: medium density fibreboard; continuous hot pressing process; slab thickness precision; dynamic surface control; adaptive fuzzy logic system; input saturation.
Special Issue on: Advances in Computer Graphics and Imaging
Research on the Design of Visual Interface in Information Visualization
by Guangtao Ma, Tao Liu, Yang Zhou, Jun Li
Special Issue on: Machine Vision and Computational Intelligence in Recent Industrial Practice
Robust Skin Segmentation using Color Space Switching
by Ankit Chaudhary, Ankur Gupta
Special Issue on: Xxxx
A Query Driven Method of Mapping from Global Ontology to Local Ontology in Ontology-based Data Integration
by Haifei Zhang