International Journal of Computer Applications in Technology (76 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, 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
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.
Special Issue on: ICMIC2015 Modelling, Computing, and Information Fusion
Analysis on sustainable development of manufacturing industry in Hebei Province based on synergetic degree
by Huizhen Kong, Zhi Gao, Shuyang Gao
Abstract: Manufacturing industry plays an important role in an economy and has become the chief wealth-producing sector of an economy. The sustainable development of manufacturing industry requires achieving synergy between the growth of manufacturing industry and environmental protection through innovation of science and technology. The paper tries to use synergetics theory to analyse the issue related to sustainable development of manufacturing industry. It firstly makes a brief introduction about synergetics and synergetic degree model. Next, it determines the subsystems of a sustainable development manufacturing industry system and selects the order parameters of each subsystem. Then, it makes an empirical analysis based on the data obtained from the websites of Hebei Provincial Statistic Bureau and National Bureau of Statistics of People Republic of China. In the end, it analyses the order degree of each subsystem and the synergetic degree of the sustainable development manufacturing industry system, and puts forwards relative suggestions for improving the sustainable development of manufacturing industry based on the analysis. The findings not only provide us the measures for improving the synergetic degree within a sustainable development manufacturing industry system, but also point out the ways for promoting the sustainable development of manufacturing industry through increasing the efficiency of economy, enhancing the utilisation efficiency of natural resources, and reinforcing innovation of science and technology.
Keywords: sustainable development; manufacturing industry; synergetic degree; order degree; synergetics.
Research of intelligent professional search engine based on agent
by Guoxia Yang, Xia Yang, Wei Hao
Abstract: Owing to the increasing growth of network information, users cannot quickly query. Especially for professional information, the query time needed is longer. In order to improve the precision of the professional search engine, after analysing the meta search engine technology, combining the rtf algorithm and the PageRank algorithm, an improved fusion algorithm of search results based on PageRank is proposed, which can query according to the synonym and the relevant word. The algorithm was applied to the search engine for coal geology, and the experimental results showed it can fuse the independent search engine results together in some degree, and increase the precision and the recall ratio of professional engine.
Keywords: meta-search; professional search; results fusion; improved PageRank algorithm; coal geology.
Analysis on seismic dynamic response and liquefaction area of tailings dam
by Chundi Si, Baolin Xiong, Wei Wang
Abstract: Taking a tailings dam in Hebei as the research object, the dynamic response of the dam under action of flood and earthquake is simulated by using a finite element software, and the seismic dynamic responses of the dam with respect to acceleration, stress, displacement, liquefaction area and stability are analysed. The results show that the vertical acceleration increases with the height of the dam, and the horizontal acceleration gradually increases from outside to inside. A larger horizontal and vertical displacement occurs at the 3/4 height of tailings dam. After considering the relative density of the tailings dam and drainage condition, all three kinds of seismic wave cause a small range of liquefaction area. The stability of the tailings dam is calculated with the Sweden Arc Method, showing that the dynamic stability safety factor is 1.257 when considering drainage, which is larger than code value, indicating the tailings dam is stable in the case of earthquake.
Keywords: tailings dam; seismic dynamic response; finite element method; liquefaction area; dynamic stability.
Mechanical acoustic fault diagnosis based on improved semi-blind extraction method
by Nan Pan, Yajun Sun
Abstract: According to statistics, about 30% of mechanical faults are caused by rolling bearing. Two or more combined failures may exist in the rolling bearing when the equipment is running. Many acoustic analyses just show an underdetermined situation because the number of microphones is less than the number of fault sources. In order to deal with these kinds of monitoring problem, a mechanical failure diagnosis method based on reference signal frequency domain semi-blind extraction is proposed. In this method, a dynamic particle swarm algorithm is used to construct improved multi-scale morphological filters, which are applicable to mechanical failure, in order to weaken the background noises; thus a reference signal unit semi-blind extraction algorithm is applied to do complex components blind separation band by band, coupled with J-divergence of complex independent components employed as distance measure to resolve the permutation. Finally, the estimated signal could be extracted and analysed by an envelope spectrum method. Compared with the time-domain blind deconvolution algorithm based on fuzzy clustering, it has several advantages such as being more effective and more accurate. Results from rolling bearing acoustic diagnosis experiment validate the feasibility and effectiveness of proposed method.
Keywords: frequency-domain blind deconvolution; mechanical acoustical diagnosis; J-divergence; reference signal; semi-blind extraction.
Application of EEMD and neural network in stress prediction of anchor bolt
by Hui Xing, Xiaoyun Sun, MingMing Wang, Haiqing Zheng, Jianpeng Bian
Abstract: An estimation method for free bolt stress is described. Acoustic stress wave signals of free bolts were collected under different tensile forces and analysed in the time and frequency domains after mutual correlation. The variations of wave propagation time, fundamental and secondary frequency of signals spectrum are studied. The signals are decomposed into intrinsic mode functions (IMFs) by Ensemble Empirical Mode Decomposition (EEMD). The normalised ratios of energy and correlation coefficients of IMFs are also discussed. Propagation time, fundamental and secondary frequency of signals spectrum, energy ratios and correlation coefficients of IMFs are influenced by applied tensile force. Thus they are selected as the components of eigenvector for inputs of neural network. Back-propagation neural network (BPNN) and genetic algorithm (GA) optimised BPNN are used for tensile force prediction. Eleven sets of data were used to test the stress prediction effect of BPNN after training. The results indicate that the BPNN optimised by GA can achieve small errors for stress prediction.
Keywords: back-propagation neural network; genetic algorithm; acoustic stress wave; ensemble empirical mode decomposition; stress prediction; bearing capacity detection.
Non-destructive test method of rock bolt based on D-S evidence and spectral kurtosis
by Xiaoyun Sun, Haiqing Zheng, Zhiyuan Wang, Jianpeng Bian, Hui Xing, Mingming Wang
Abstract: The length of the rock bolt is an important factor to evaluate the quality of an anchor. According to the fact that the calculated value of anchor length is far different from the actual situation owing to a lot of noise, a de-noising method based on Empirical Mode Decomposition (EMD) and Spectral Kurtosis (SK) is proposed in this paper, to filter noise and to improve the accuracy of calculating anchor length. The fundamental idea is that calculating SK for each component after EMD, the larger SK values are used to reconstruct the signal to improve the signal to noise ratio. The analysis results demonstrate that the method can improve the SK of the reconstructed signal and decrease the error of anchor length. In addition, D-S evidence is introduced to realise high precision computation for anchor length by data fusion of wavelet threshold de-noising and spectral kurtosis filter.
Keywords: spectral kurtosis; anchor; EMD decomposition; wavelet de-noising; D-S data fusion.
Application of control quality evaluation technology in complex industrial process
by Yongwei Li, Yuman Li, Hongfei Wang, Mingxing Chen
Abstract: In a class of time-varying, large delay, non-Gaussian and nonlinear characteristics of complex industrial processes, when the control loop after operation for a period of time, its control quality indicators often gradually deviate from the original design value or even show serious deterioration. The causes of this phenomenon include the changes in operating conditions, disturbances and equipment and instrument failure and other factors. In view of the above problems, it is very important to control the quality of the control loop in the complex industry process and find out why the control quality become poor. The synthetic ammonia decarbonisation is a complex industrial process that contains the characteristics of time varying large delay nonlinearity and non-Gaussian. The crystal size in the complex industrial process of the synthetic ammonia decarbonisation is chosen as the research object, and the control loop is evaluated by the method of control quality evaluation based on the minimum variance. The reasons for the variation of control quality are estimated and predicted by the method of particle filter. The simulation results show that the control quality evaluation method based on the minimum variance and the particle filter method are effective and feasible in order to estimate and predict the quality becoming poor of the control system. The control quality evaluation technique provides a feasible way for the effective control of a kind of complex industrial process.
Keywords: synthetic ammonia decarbonisation; quality evaluation; minimum variance; particle filter.
Study on licence plate location algorithm in complex weather
by Yan Wang
Abstract: An adaptive licence plate location algorithm is proposed to improve the locating accuracy of licence plates in the case of images collected in conditions of complex weather or insufficient light. The algorithm judges the different weather images by colour features and sharpness. The wavelet coefficients are used to adjust the contrast of images. At last, we fuse the vertical protection and template matching algorithms to locate the licence plate. Experiments show the algorithm can locate licence plates in varies weather conditions, such as sunny, rainy, foggy and evening. The average rate of licence plate location can reach 93.4%.
Keywords: colour feature; wavelet coefficients; vertical protection; template matching.
An improved image denoising method based on Contourlet transform and Neighshrink algorithm
by Liu Jian, Li Tong, Wei Song Bo, Xu Ke, Chang Ling
Abstract: Denoising is important in the use of images for non-destructive detection, in order to effectively remove image noise and preserve the good image detail. In this paper, a new improved image denoising method is proposed based on Contourlet and Neighshrink. Firstly, the image of the Contourlet coefficients is obtained by the Contourlet transform. Then, the Contourlet coefficients are contracted by the neighborhood shrinkage method. In order to prevent the threshold from changing over parameters of the decomposition scale, the shrinkage factor is improved. The Contourlet coefficients are processed by the improved threshold and the shrinkage factor. Lastly, the image is denoised by the inverse Contourlet transform. The universal threshold is improved by means of the new method, which combined with neighborhood window coefficient of local parameter and the new convergence factor, is constructed. It means more coefficients have been corrected instead of missing or 'killed in the process of denoising'. Taking elevator fault detection as a practical application example in the simulation study, the images of the elevator machine and the wire rope are denoised after graying. The detection images of the traction motor and wire rope are analysed and compared with other denoising methods, such as the median filter, the wavelet transform and the Contourlet transform denoising algorithms. The simulation experiments show that the improved algorithm can better protect the image details of the elevator machine and the wire rope, avoiding the Gibbs phenomenon.
Keywords: image denoising; Contourlet; NeighShrink; neighbouring coefficients; traction machine; steel wire rope.
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: ICMIC2015 Modelling, Computing, and Information Fusion
Research on face recognition based on DRNLGBP
by Zhenzhou Wang, Xing Xing Xu, Li Li, Wei Liu
Abstract: For facial recognition of those wearing glasses, this paper first proposes a DRNLGBP algorithm based on DIMPCA and non-uniform local Gabor binary pattern method combined with random sampling. The algorithm firstly extracts and supplements the contour by the DIMPCA, and then it adopts the local Gabor binary pattern to represent the facial image, by which characteristic information of multi-scale and multi-direction can be extracted and can make the recognition less sensitive to illumination and robust to noise. The paper also brings up the non-uniform region division strategy, which can ensure sufficient image spatial information acquisition while treating the content information differently at the same time with weakening interference to adjustment from glasses. The method DRNLGBP makes full use of characteristics of random subspace method and binary pattern method, complementing each others deficiencies while optimising each advantages to maximise the effect of recognition. The experimental result shows that this method is more effective for facial recognition rate improvement for those wearing glasses than traditional methods, significantly making the influence of surrounding light degree to a less sensitive extent.
Keywords: non-uniform local binary; DRNLGBP algorithmic; stochastic subspace identification memory controller.
Research on the image segmentation of icing line based on Nsct and 2-D Ostu
by Lin Qi, Jing Wang, Can Dong Li, Wei Liu
Abstract: In allusion to increased noise and blurring of icing line images, this paper proposes a segmentation method based on NSCT and 2-D OSTU, using NSCT to implement de-noising processing of image, introduces combined threshold methods of improved genetic algorithm and 2-D algorithm OSTU for image segmentation, and describes the specific algorithm. The results show that this method can effectively realise the de-noising processing of icing images, making accurate extraction of the target, and reducing the search time, which is convenient for real time applications.
Keywords: NSCT; 2-D OSTU; image segmentation; icing line; improved genetic algorithm.
Construction of logistics level evaluation system and application on Wuhan city circle
by Yanling Xu, Miao Zhang, Jingli Zhang
Abstract: With the development of the global economy, the logistics industry is increasingly playing a greater role in economic development. In this paper, based on entropy method and factor analysis, an evaluation system is built from the aspects of politics, economy, society, transportation and resident consumption level to estimate the logistics levels of the cities in Wuhan city circle. The conclusion illustrates the nine cities in Wuhan city circle have very different logistics levels and can be ranked from the highest to lowest as follows in terms of their logistics level: Wuhan, Huanggang, Xiaogan, Huangshi, Xianning, Ezhou, Tianmen, Xiantao, Qianjiang.
Keywords: index system; logistics level; entropy weight; factor analysis; evaluation.
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