Forthcoming articles

International Journal of Computer Applications in Technology

International Journal of Computer Applications in Technology (IJCAT)

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International Journal of Computer Applications in Technology (68 papers in press)

Regular Issues

  • 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 
    Keywords: .

  • 3D Scanning Machine and Additive Manufacturing: Concurrent Product and Process Development
    by Ismet P. Ilyas 
    Keywords: .

  • Simulation and visualisation approach for accidents in chemical plants   Order a copy of this article
    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.

  • Detecting occluded faces in unconstrained crowd digital pictures   Order a copy of this article
    by Chandana Withana, S. Janahiram, Abeer Alsadoon, A.M.S. Rahma 
    Abstract: Face detection and recognition mechanisms, a concept known as face detection, are widely used in various multimedia and security devices. There are significant numbers of studies into face recognition, particularly for image processing and computer vision. However, there remain significant challenges in existing systems owing to limitations behind algorithms. Viola Jones and Cascade Classifier are considered the best algorithms from existing systems. They can detect faces in an unconstrained crowd scene with half and full face detection methods. However, limitations of these systems are affecting accuracy and processing time. This project proposes a solution called Viola Jones and Cascade (VJaC), based on the study of current systems, features and limitations. This system considered three main factors: processing time, accuracy and training. These factors are tested on different sample images, and compared with current systems.
    Keywords: face detection; unconstrained crowd digital pictures; face recognition.

  • Combining structural and semantic cohesion measures to identify extract class refactoring   Order a copy of this article
    by Mustafa Hammad, Mohammad Alnabhan, Sarah Al-Sarairah 
    Abstract: Class cohesion is a major design factor that affects the quality of classes. Classes that have related methods are easy to comprehend and maintain. Classes with many responsibilities are refactored by extracting some methods to new classes. This paper investigates class metrics to identify extract class refactoring opportunities to increase the degree of cohesion. An approach is presented that combines both the structural and the semantic metrics of classes to determine methods that need to be extracted in new classes. A case study is presented to evaluate the proposed approach. The aim of the study is to compare results obtained from applying semantic metrics, structural metrics, and combined metrics together. Results revealed that the proposed approach can provide a valuable set of extract class refactoring suggestions to improve class cohesion.
    Keywords: class cohesion; extract class refactoring; SCOM metric; Cosine distance; LOCM metric; Levenshtein distance.

  • A multi-states continuous time Markov chain model for secondary spectrum access in dynamic spectrum access networks   Order a copy of this article
    by Hui Sun, Chuang Yang, Rui Wang, Sabir Ghauri 
    Abstract: Dynamic Spectrum Access (DSA) networks are vulnerable to hackers who normally pretend themselves to be the primary users; this is called the Primary User Emulation Attack (PUEA). Research communities have already reported a vast use of PUEA in the existing research. Other potential attackers, such as greedy users, should not be ignored when investigating the dynamic spectrum access networks. In this paper, we propose a multi-states Continuous Time Markov Chain (CTMC) model to describe the behaviour of DSA, analysis of the channel states and discussion on the impacts of normal, normal greedy and greedy malicious users in DSA network. The CTMC model is simulated and the simulation results are discussed and validated by comparing with the existing models. Finally, it is proved that CTMC model is an improved method to analyse the performance of the DSA networks when PUEA occurs.
    Keywords: dynamic spectrum access; continuous time Markov chain; primary user emulation attack; malicious user; greedy user.

  • Research on grid-connected photovoltaic inverter based on quasi-Pr controller adjusting by dynamic diagonal recurrent neural network   Order a copy of this article
    by Zhenxiong Zhou, Bingshen Liu, Wenbao Wang, Hongxi Wang 
    Abstract: The single-phase grid-connected photovoltaic inverter system is studied in this paper. In view of the nonlinear and time-varying characteristics of this system, the three-closed-loop control strategy consisting of DC voltage outer loop, grid-connected current inner loop and capacitive current inner loop based on quasi-PR control is proposed. Since the quasi-PR controller of fixed parameters is unable to adapt to changes of parameters in the power network, a quasi-PR control method of dynamic self-tuning based on a dynamic diagonal recurrent neural network (DRNN) is presented. DRNN is based on the recursive prediction error (RPE) algorithm with second-order gradient, which has faster convergence than the BP algorithm. The simulation and experiment results prove that the grid-connected photovoltaic inverter the above control algorithm have a good quality of the output current and fast performance in dynamic response.
    Keywords: photovoltaic inverter; Quasi-PR; DRNN; RPE; grid-connected inverter.

  • A reliable route repairing scheme for internet of vehicles   Order a copy of this article
    by Mustafa Banikhalaf, Ahmad M. Manasrah, Ahmed F. AlEroud, Nabhan Hamadneh, Ahmad Qawasmeh, Ahmed Y. Al-Dubai 
    Abstract: Recently, the Internet of Vehicles (IoV) has been recognised as a key solution for vehicular communications. Connected vehicles, personal smart devices, and infrastructures roadside units have been shaping the underlying architecture of IoVs technology, where the conventional routing protocols cannot facilitate reliable and efficient communication for dynamic IoV topologies. Hence, this technology is highly susceptible to frequent network fragmentations, thus exposing communication channels to regular failure problems. Reliable communication between vehicles requires adopting the existing routing strategies along with the current requirements. This paper, thus, introduces a novel routing repair strategy, referred as Reliable Route Repairing Strategy (RRRS) to tackle routing failure problems. Repairing the operation of channel communications between the source and destination pairs is prioritised according to stability degree of the connected vehicles. The RRRS defines three routing priority zones classified based on the angular values between source, vehicles and destination, and privileges repairing a broken link to the high-active zone only. The RRRS features are combined with the traditional AOMDV protocol, and a comparison study has been conducted to compare the AOMDV, the RRRS-AOMDV and the HM-AOMDV protocols. The simulation results demonstrate that the RRRS-AOMDV achieves better performance, about 30% to 45% in terms of packet transmission overhead, packet repairing overhead and average data packets latency.
    Keywords: IoVs; Internet of Things; AOMDV; broken links.

  • Adaptive neural-fuzzy and backstepping controller for port-Hamiltonian systems   Order a copy of this article
    by Ahmad Taher Azar, Fernando E. Serrano, Marco A. Flores, Sundarapandian Vaidyanathan, Quanmin Zhu 
    Abstract: In this article, a novel control strategy is shown for the stabilisation of dynamic systems in the form of port-Hamiltonian systems. This hybrid approach composed by a neural-fuzzy and backstepping controller is implemented to stabilise the port-Hamiltonian system by dividing it into two blocks in order to separate the variables and yielding an efficient control strategy. Many kinds of dynamical system, such as power, electrical, mechanical and fluid systems, can be represented in port-Hamiltonian form. Thus, it is important to develop new control strategies to stabilise port-Hamiltonian systems considering that this is not a simple task, especially to increase the robustness, to deal with the uncertainties and to improve the system performance. The proposed control strategy consists in an hybrid approach formed by a neural-fuzzy and backstepping controller. A four-layer neural-fuzzy controller is implemented to stabilise the port-Hamiltonian system, where fuzzification, fuzzy rules inference system and defuzzification layers are considered. The neural-fuzzy controller consists of two steps: an offline training implementing a gradient descent algorithm and an online training by a Lyapunov stability approach. The backstepping controller is designed by a recursive method considering the port-Hamiltonian system properties and implementing a Lyapunov stability approach. Along with the proposed control strategy, a neural-fuzzy observer is implemented to estimate the port-Hamiltonian system states considering the properties of the system representation. Finally, a cart-pendulum example is shown to verify the effectiveness of the proposed observer and controller along with a comparative analysis
    Keywords: neural-fuzzy system; backstepping control; observer design; port-Hamiltonian system.

  • A simulation model of pedestrian wayfinding behaviour in familiar environments   Order a copy of this article
    by Amina Bouguetitiche, Foudil Cherif, Fabrice Lamarche 
    Abstract: In this paper, we present a novel approach to simulate wayfinding behaviour of pedestrians familiar with their environment. This approach is inspired from spatial cognition and space syntax domains in order to achieve naturally crowd navigation. Therefore, the proposed wayfinding process is incremental; route choice decisions are made at every street junction, taking into account spatial configuration and individual knowledge of the environment as well as individual preferences. An adequate environment description is provided; it is a graph automatically generated, informed with pre-calculated data, that is used by agents to quantify the benefit cost of a route choice. The environment description is also used to endow agents with mental maps that contain the regions supposed to be experienced by them without going through a learning phase. Obtained results demonstrate that, under our model, agents calculate paths that have the same characteristics as those chosen by pedestrians familiar with their surroundings.
    Keywords: environment description; wayfinding; mental map; space syntax; cognitive science; urban environment.

  • Real-time high speed 5-D hyperchaotic Lorenz system on FPGA   Order a copy of this article
    by Ismail Koyuncu, Murat Alcin, Murat Tuna, Ihsan Pehlivan, Metin Varan, Sundarapandian Vaidyanathan 
    Abstract: Chaotic systems have several engineering applications, such as cryptology, random number generators, image processing and secure communication. A basic structure used in these studies is a chaotic oscillator design that produces a chaotic signal. In this paper, the 5-D hyperchaotic Lorenz system (Hu, 2009) has been implemented on FPGA using Heun algorithm to improve the chaos-based embedded engineering applications. The 32-bit IEEE-754-1985 floating point format has been used in the Heun-based design. The design has been coded in VHDL. The maximum operating frequency of FPGA-based 5-D hyperchaotic Lorenz system reaches 430.146 MHz. In addition, a real circuit realisation of the 5-D hyperchaotic Lorenz system has been performed using analogue circuit elements. The results of the new FPGA-based 5-D hyperchaotic Lorenz system have been compared with the results of computer-based numerical simulation and then the error analyses (MSE and RMSE) have been carried out.
    Keywords: hyperchaos; FPGA; VHDL; Heun algorithm.

  • DANP-based method for determining the adoption of hospital information system   Order a copy of this article
    by Khuram Shahzad, Zeng Jianqiu, Asma Zubedi, Xin Wen, Lei Wang, Muhammad Hashim 
    Abstract: Hospital information system (HIS) is an integrated electronic system that provides comprehensive information regarding every aspect of the hospital and patients whenever it is required. In Pakistan, the diffusion of HIS is in the early stages and the rate of adoption is very slow. The primary purpose of this study is to identify the essential factors that are significantly driving or hindering the decision to adopt HIS. For better understanding, this study proposed the initial theoretical model that integrates Technology Organization Environment (TOE) framework, Human Organization Technology (HOT) fit model and institutional theory. Hence, the initial model consists of four main dimensions and 13 variables, which are the most frequently used in prior literature and are essential for the investigation of HIS adoption. The data were collected from healthcare experts who have full knowledge of HIS. Accordingly, the recently developed DANP (Decision Making Trial and Evaluation Laboratory (DEMATEL) based Analytic Network Processes (ANP)) method is employed for assessing interdependency and give weights to dimensions and criteria. According to the experts' knowledge and experience, the results indicate that perceived technical competence, compatibility, top management support, and vendor support are found to be the most essential variables for the successful adoption of HIS concerning people, technology organisation and environment, respectively. Hence, the finding of this study has contributed theoretically, and the practical implementation of this integrated model will give deep insight to healthcare providers for the successful implementation of HIS.
    Keywords: hospital information system; public hospitals; TOE framework; HOT-fit model; institutional theory; DANP method; public hospitals; Pakistan.

  • Experimental and modelling study of flow characteristics on large-scale roughness bed   Order a copy of this article
    by Zhang Jianmei, Han Zhengguo, Che Quan, Zhu Feng 
    Abstract: Large-scale roughness bed flow is a special flow condition that is commonly seen in mountainous rivers. In this paper, a gradient flume experiment is carried out with plum-blossom distributed cubic obstruction blocks with 50 mm edge, and large-scale roughness bed flow characteristics including free surface morphology, flow velocity distribution, turbulence propagation and friction head loss coefficient are studied by high precision measuring and statistical analysis. In addition, a CFD-based numerical model is established to verify the experimental results and provide a prediction method to expand the experiment. After that, a modified empirical formula of friction head loss coefficient considering more important influence factors is proposed, based on the experimental results and the numerical simulation results, which provide a practical calculation method of friction head loss coefficient for engineering reference.
    Keywords: large-scale roughness; plum-blossom distribution; flow characteristics; numerical simulation; empirical formula.

  • VoiCon: A Matlab GUI-based tool for voice conversion applications   Order a copy of this article
    by Sanghamitra Nath, Nabadip Borah, Aparajita Gohain, Utpal Sharma 
    Abstract: Voice conversion finds applications in a wide variety of areas, such as customisation of text to speech systems, voice editing and dubbing, voice restoration systems, in addition to its initial applications of speaker conversion and conversion of speaking styles. The basic steps required for the implementation of voice conversion however remain the same. The alignment of features using various techniques, such as dynamic time warping and time sequence matching, the development of the mapping function, and finally the conversion of features, are computation intensive and require the researcher to have in-depth knowledge of the various techniques used. In this work, in an effort to reduce the tasks of a researcher interested in using voice conversion for his application, a Matlab GUI-based tool has been designed, implemented and tested for carrying out spectral feature conversion for three applications, conversion of female to male speech, whispered to normal speech and synthetic to natural speech. The tool not only provides an easy to use interface by carrying out feature conversions using various mapping functions but also enables the user to view, save and compare his results graphically.
    Keywords: voice conversion; mel cepstral coefficients; fundamental frequency; Gaussian mixture model; vector quantisation; artificial neural networks; root mean square error; mel cepstral distortion.

  • Hand-drawn electronic component recognition using deep learning algorithm   Order a copy of this article
    by Haiyan Wang, Tianhong Pan, Mian Khuram Ahsan 
    Abstract: Hand-drawn circuit recognition plays an increasingly important role in circuit design work and electrical knowledge teaching. Hand-drawn electronic component recognition is an indispensable part of hand-drawn circuit recognition. Accurate electronic component recognition ensures accurate circuit recognition. In this paper, a hand-drawn electronic component recognition method using a convolutional neural network (CNN) and a softmax classifier is proposed. The CNN is composed of a convolutional layer, an activation layer and an average-pooling layer and is designed to extract features of a hand-drawn electronic component image. The kernel function for the CNN is obtained by a sparse autoencoder method. A softmax classifier is trained for classification based on the features extracted by the CNN. The recognition method can identify rotating electronic components because of the added rotated image and achieve 95% recognition accuracy.
    Keywords: electronic component recognition; convolutional neural network; sparse autoencoder.

  • FCM: a component-based platform with explicit support of crosscutting and dynamic features   Order a copy of this article
    by Abdelhakim Hannousse 
    Abstract: Dealing with crosscutting and dynamic features in component software is a longstanding problem, primarily owing to the nature of used components: components may be available only as black box software units and their implementations may be protected against alteration. Aspect-orientation provides a valuable means to deal with crosscutting features in different paradigms; however, existing endeavours to use aspects in component software have several limitations, such as the lack of suitable design of aspects and the absence of proper aspect runtime weaving mechanisms. In this paper, we contribute by proposing a new aspect component model to solve such problems. In the proposed model, components and aspects are first-class entities that remain separated from design to implementation; and aspects can be added and removed at runtime. We also developed a tool support for the model in Java. We demonstrate the viability of the model through the implementation of a running example.
    Keywords: component-based software engineering; aspect-oriented programming; crosscutting and dynamic features.

  • Correlation-based search for time series data   Order a copy of this article
    by Ibrahim A. Ibrahim, Abdullah Albarrak 
    Abstract: Exploration of time series data based on correlation is a key ingredient of various analysis tasks. However, such exploration entails massive CPU and I/O costs owing to the quadratic nature of the exploration space. Searching for a time sub-interval in which all time series pairs are correlated within certain values is one aspect of time series exploration and has various applications in many domains. Consequently, in this paper, we formulate the targeted correlation matrix search problem where the goal is to find an optimal sub-interval with a correlation matrix that maximises the closeness and similarity to targeted pairwise correlation values. We show the computational hardness of this problem, and propose the RELATE scheme to address the associated challenges by using the incremental property of correlation. Further, we propose two-level pruning techniques for the RELATE scheme to minimise the associated computational and I/O costs. These techniques enable RELATE to avoid exhaustively traversing the search space by pruning unqualified candidate queries, and avoid computing pairwise correlation of every time series pair wherever possible.We demonstrate by experiments the performance gains of RELATE against state-of-the-art algorithm with real and synthetic datasets.
    Keywords: correlation; time series; search.

  • Comparison among different tools for tolerance analysis of rigid assemblies   Order a copy of this article
    by Wilma Polini, Andrea Corrado 
    Abstract: Tolerance analysis is an important task to design and to manufacture high precision mechanical assemblies; it has received considerable attention in the literature. Actually, there are some different tools used or proposed in the literature to make the tolerance analysis of an assembly, but none of them is completely and univocally accepted. A comparison between a Computer Aided Tolerancing (CAT) tool for geometry assurance and some methods proposed in the literature is discussed in this work. Therefore, the aim of this work is to solve, by a CAT software, five case studies that were already solved by different methods in the literature. The potentialities and the limits in using a CAT technique for geometry assurance are highlighted.
    Keywords: geometry assurance; tolerance analysis; computer aided tolerancing.

  • Cloud-based electricity consumption analysis using neural network   Order a copy of this article
    by Nand Kumar, Vilas Gaidhane, Ravi Kant Mittal 
    Abstract: In recent years, optimisation of resource usage is very much required to analyse and understand energy consumption patterns. This analysis has previously been carried out using algorithms, which needs many assumptions, and meeting all the assumptions in practice is a very difficult task. However, there are other methods available to analyse and understand energy consumption. In this paper, an efficient approach for energy consumption pattern analysis is proposed. It is based on the Levenberg-Marquardt algorithm-based neural network (LMNN) and clustering technique. The energy consumption data is collected from the educational institute building using a smart system. The various experimentations are carried out on the collected real time database. The experimental results illustrate that the proposed approach is effective and computationally efficient for consumption pattern classification. The performance of the presented approach is found superior to existing clustering approaches.
    Keywords: educational institute building; Levenberg-Marquardt algorithm; neural network; classification; confusion matrix; ROC curve.

  • A novel ANN-based four-dimensional two-disk hyperchaotic dynamical system, bifurcation analysis, circuit realisation and FPGA-based TRNG implementation   Order a copy of this article
    by Sundarapandian Vaidyanathan, Ihsan Pehlivan, Leutcho Gervais Dolvis, Kengne Jacques, Murat Alcin, Murat Tuna, Ismail Koyuncu 
    Abstract: This paper describes the modelling, bifurcation analysis, circuit realisation and FPGA implementation of a novel ANN-based four-dimensional two-disk dynamical system exhibiting hyperchaos and hidden attractor. This paper provides a detailed analysis of the multistability, coexisting attractors and bifurcation properties of the novel system. The system does not possess any rest point pinpointing the presence of hidden hyperchaotic attractor. We realise the dynamic equations of the two-disk hyperchaotic dynamical system with a real circuit. Next, we build, design and implement the ANN-based two-disk hyperchaotic dynamical system on FPGA. Finally, using the FPGA-based implementation, we design and implement a novel high speed True Random Number Generator (TRNG).
    Keywords: circuit design; hyperchaos; hyperchaotic systems; artificial neural network; FPGA implementation; TRNG.

  • Prediction modelling of exhaust characteristics of a marine engine for SCR urea dosing calibration   Order a copy of this article
    by Zhuo Zhang, Mingwei Shi, Zibin Yin, Defeng Wu, Leyang Dai 
    Abstract: The International Maritime Organization (IMO) issued Annex VI of the MARPOL Convention to control the serious exhaust pollution of marine diesel, specifying the NOx emission limitation requirements. In this paper, the exhaust characteristics of a marine diesel engine were tested, and the exhaust characteristic model was established by a BP neural network, which has been verified via learning ability and generalisation ability. The relative errors of the exhaust flow, NOx concentration and exhaust temperature prediction are within 6%, which can be used to predict the exhaust performance of a marine diesel engine in steady state. The calibration for urea dosing of an SCR system was based on an ammonia-nitrogen ratio of 1:1, whose data are predicted by the exhaust characteristic model.
    Keywords: marine engine; exhaust characteristics; BP neural network; SCR system.

  • A comparative study of meta-heuristic optimisation techniques for prioritisation of risks in agile software development   Order a copy of this article
    by B. Prakash, Viswanathan Viswanathan 
    Abstract: Risks are in general termed as threats or uncertainties that influence the project performance and its outcomes to the greater extent. To ensure software quality and project success, every organisation should enforce a proper mechanism to efficiently manage the risks irrespective of the development model they follow. Risk prioritisation is a most critical step in risk management process that helps the organisation to resolve the risks in a shorter time. In this paper, a comparative study about different meta-heuristic optimisation techniques for prioritising the risks in agile environments is presented. The five most effective meta-heuristic optimisation algorithms, namely Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Grey Wolf Optimization (GWO) and Analytical Hierarchy Process (AHP), are considered and the results are evaluated based on four key criteria, namely error rate, accuracy, reliability, and running time. The result proves that GWO outperforms the other four meta-heuristic optimisation techniques for the prioritisation of risks in an agile environment.
    Keywords: risk management; risk prioritisation; agile software development; meta-heuristic optimisation; project management.

  • Reasoned bargaining protocol in construction contracts using a novel Bayesian game   Order a copy of this article
    by Vu Hong Son Pham 
    Abstract: The objective of this paper is to provide new insights on some dimensions of the bargaining process asymmetries and uncertainties in particular- by using a Novel Expert Bayesian Game (NEBG). We develop decision support system for determining the price in construction supply contract. Our results confirm that uncertainty affects negotiators behavior and modify the likelihood of a self-enforcing agreement to emerge. The validation analysis revealed that a novel approach to BN construction by combining domain knowledge from experts possessing incomplete observation data substantially improved the estimation ability of negotiators. Exhibiting a high success rate and profit as well as low negotiation time, the proposed model is superior to those reported in previous research.
    Keywords: decision support system; expert system; Bayesian game.

  • Manifold multi-view learning for cartoon alignment   Order a copy of this article
    by Wei Li, Huosheng Hu, Chao Tang, Yuping Song 
    Abstract: Cartoon alignment is a key to retrieve cartoon characters and synthesise new cartoon clips. To successfully achieve the tasks, it is necessary to extract visual features that comprehensively denote cartoon characters and to align the feature points accurately between cartoon characters. In this paper, Speed Up Robust Feature (SURF) and Shape Context (SC) are introduced to characterise the cartoon character from multi-view. The two features are complementary to each other, and each feature set is thought as a single view. To increase accuracy rate of cartoon character alignment, traditional methods, such as semi-supervised alignment and Procrustes alignment, require predetermining the correspondence. However, this is a tedious task. To overcome the flaw, we propose a manifold multi-view learning (MML) to align cartoon characters. MML learns a projection that maps data instance (from cartoon characters with different dimensionality) to a lower dimensional space, which simultaneously matches the local geometry and preserves the neighbourhood relationship within each cartoon character. The matching relationship can be obtained from local geometry structure. Experimental results show the good performance, such as a matching accuracy rate of more than 90% and processing time of average 100 milliseconds that is only 30% of the traditional algorithm in the certain dataset. Hence, MML can also be potentially applied in mobile devices.
    Keywords: cartoon alignment; manifold; multi-view; speed-up robust feature; shape context.

  • Relevant harmonics selection based on mutual information for electrical appliances identification   Order a copy of this article
    by Abdenour Hacine-Gharbi, Philippe Ravier, Mohamed Nait-Meziane 
    Abstract: Recently, research on electrical appliances identification for non-intrusive load monitoring has become attractive, particularly for smart grid applications. Many appliance identification systems use harmonics of current signals as features. However, the choice of the order and number of relevant harmonics for this task has never been demonstrated. Here, we propose to tackle this issue using relevant feature selection algorithms. Indeed, investigating harmonics in the whole spectral band leads to high dimensional feature vectors for high sampling frequency. This makes the selection intractable if the identification accuracy is used as a relevance criterion. Hence, we propose to analyse the relevance and redundancy of harmonics for appliance identification using feature selection algorithms based on the mutual information criterion. Six heuristic strategies were implemented and their selection results compared. For the choice of a minimal subset of relevant harmonics, we propose a stopping criterion in the selection procedure. In order to validate the selected subset of harmonics, a hidden Markov model based classier was used and evaluated on PLAID dataset. Results highlight odd order harmonics relevance. Furthermore, the feature subset {1, 2, 3, 4, 5, 7, 9} was selected by 3 strategies as strongly relevant since this minimal subset is sufficient for essentially explaining appliances signatures. Mainly, this study shows that the harmonic order 2 is a strongly relevant feature among the first ones, which was never demonstrated in the state-of-the-art studies.
    Keywords: electrical appliances identification; harmonics relevance; mutual information; filters feature selection; feature extraction; hidden Markov models.

  • Towards optimal thread pool configuration for run-time systems of integration platforms   Order a copy of this article
    by Daniela L. Freire, Rafael Z. Frantz, Fabricia Roos-Frantz 
    Abstract: Companies seek technological alternatives to increase competitiveness. One example is the integration platforms, which develop integration processes in order to connect functionalities and data from applications that compose software ecosystems. Threads are computational resources of the platforms, responsible for integration processes execution. Thus, the configuration of threads has a direct influence on the performance of platforms. However, this is a challenge faced by software engineers, who do this configuration empirically. Our scientific and technical literature review did not identify a systematic approach to find the ideal configuration, which depends on factors such as workload, hardware and integration process. Thus, it is appropriate to seek alternatives for a configuration that provides a positive impact on the performance of the run-time system, increases productivity, and reduces costs. Inspired by the particle swarm optimisation meta-heuristic, this article proposes an algorithm that finds the ideal configuration for local thread pool, minimising the total average processing time to improve the execution of integration platforms. The algorithm was implemented and tested using a real-life integration process, and its performance measures show the feasibility and efficiency of our proposal, supported by a rigorous statistical analysis of results.
    Keywords: enterprise application integration; optimisation; particle swarm optimisation; meta-heuristics; multi-thread; makespan; workflow; integration patterns.

  • Ensuring the Correctness of Adaptive Business Processes: A Systematic Literature Review
    by Fairouz Fakhfakh 
    Abstract: Adaptability in process management systems is gaining an increasing attentionrnto satisfy the variable enterprise requirements. This concept has been recognized by the process community for a long time and various approaches in this area have been developed so far. In this context, one of the most difficult challenge is to ensure that change operations are applied correctly and do not cause any inconsistencies. This paper presents a survey that examines the existing studies ensuring the correctness of process changes.rnOur survey follows the guidelines of systematic literature reviews (SLR). It provides a comparison of the existing approaches based on some criteria such as verified properties, modeling languages and verification tools. Finally, we highlight some recommendations and possible future researches which need further investigations. So, throughout this present paper, we provide information for researchers and developers to understand the contributions and challenges of the current studies to pave the way for improving theirrnsolution.
    Keywords: Process; adaptability; correctness; systematic literature review; challenges.

  • Developing a Biotech Scheme using fuzzy logic model to predict occurrence of diseases using persons functional state
    by Riad Taha Al-kasasbeh, Nikolay Korenevskiy, Altyn Amanzholovna Aikeyeva, Sofia Nikolaevna Rodionova, Ashraf Shaqadan, Maksim Ilyash 
    Abstract: The work deals with the issues of the synthesis of combined fuzzy decision rules for classification and evaluation of the level of functional states on two blocks of heterogeneous characteristics: the subjective test questionnaires and indicators describing the human attention.
    Keywords: - level of functional reserve, psycho emotional pressure, intellectual exhaustion, physical exhaustion, heterogeneous fuzzy models.

  • Improved XGBoost model Based on Genetic Algorithm
    by Feng Zhao, Jinxiang Chen, Yanguang Sun, Yilan Yin 
    Abstract: An optimized XGBoost model based on genetic algorithm to search for optimal parameter combinations be proposed in this paper. It was proved that the improved algorithm has better classification effect through the liver disease data set Liver Disorders Data Set in the UCI Machine Learning Repository. In recent years, there have been many excellent intelligent algorithms in the field of machine learning and XGBoost is one of them. However, when using the XGBoost algorithm, it usually involves the adjustment of various parameters in the XGBoost model, and the selection of different parameter combinations has a greater impact on the classification performance of the model. In this paper, after encoding the XGBoost model parameters optimized by genetic algorithm, the global approximate optimal solution is obtained through operations such as selection, crossover and mutation, which greatly improves the performance of the model.
    Keywords: XGBoost; Parameter Optimization; Genetic Algorithm

Special Issue on: Computational Intelligence and Applications

  • Intelligent game-based learning: an effective learning model approach   Order a copy of this article
    by Tanzila Saba 
    Abstract: Game-Based Learning (GBL) broadly refers to the use of video games applications to support teaching and learning processes. This research focuses on the concept of GBL in the context of stimulating interest in the field of computer science education specifically. In contrast to theoretical learning, GBL is a practical learning approach that is meant to teach and be enjoyed at the same time. Additionally, a GBL model with visual features has been proposed and tested. Promising feedback has received from learners through the post conducted surveys. The research findings exhibit that GBL is an effective methodology in transferring knowledge, enhancing learning, and making the learning a more enjoyable process in computer science studies than just the theoretical approach.
    Keywords: binary games; game-based learning; logical games; theoretical learning.

Special Issue on: Emerging Trends in Computer Applications in Technology

  • Cooperative evaluation mechanism based on the optimal decision of DE-CA-CR   Order a copy of this article
    by Cui Guotao, Zhang Ying 
    Abstract: University-enterprise deep cooperation is the important measure to overcome the vocational education development bottleneck. However, owing to the restrictions of factors such as market environment, school-enterprise cooperation system and mechanism, the school-enterprise cooperation is just at a junior level and forms no benign interaction between them. This paper establishes a comprehensive evaluation index system of university-enterprise through the stakeholder analysis method, and builds a university-enterprise deep cooperation evaluation model combined with DEA-CCR optimal decision. Besides, based on previous research and survey, the paper tries to analyse the problems in school-enterprise cooperation and influencing factors from the perspective of enterprise, and thus to establish the school-enterprise cooperation performance evaluation model and conduct verification through living examples.
    Keywords: industry-education integration; school-enterprise cooperation; confidence mechanism; data envelopment analysis.

  • Improved data envelopment analysis model based on geometric mean model   Order a copy of this article
    by Feng Yanhong, Wang Zhongfu 
    Abstract: According to the characteristics of the circular economy conception of underdeveloped regions under new economy situation, the improved data envelopment analysis model based on geometric mean model is reconstructed in this paper through increasing unexpected input (waste recycling quantity) in the input and increasing unexpected output (waste discharge quantity) in the output. Then, the improved data envelopment analysis model was adopted to evaluate the efficiency of the circular economy of the underdeveloped regions in 31 provinces and cities in China during 2002-2012. The research result shows that the efficiency of the circular economy of underdeveloped regions in China is generally improved, and the scale efficiency gradually tends to reach the optimum efficiency, but the obvious insufficiency of pure technical efficiency has influenced the development level of the circular economy of underdeveloped regions in China.
    Keywords: underdevelopment; circular economy of underdeveloped region; efficiency evaluation; DEA model.

  • Improved face recognition with accelerated robust features improved by means of mean shift K-means clustering   Order a copy of this article
    by Jiao Ding, Minfeng Zhang, Tianfei Zhang, Haiyan Long, Meiyu Liang 
    Abstract: To improve the precision of the heterogeneous face recognition model, a heterogeneous face recognition model method based on binary multilayer Gabor extreme learning machine (GELM) is proposed in this paper. Firstly, a random weighted Gabor feature extraction scheme is proposed based on pixel weight. It propagates the locally geometric input image sub-block to the hidden node, and embeds the extracted Gabor feature into the hidden layer. Moreover, it conducts random weighting and sum using a group of Gabor kernels so as to realise a convolution operation of the nonlinear activation function of the propagated pixel. Then, it estimates the output layer by means of linear weighting that is similar to extreme learning machine (ELM). At last, the performance of heterogeneous face recognition method of the proposed algorithm is verified through BERC VIS-TIR database and CASIA NIR-VIS 2.0 database.
    Keywords: mean shift; K-means clustering; face recognition; precision.

  • Analysis of system implementation effect based on Bayes analysis of imbalanced measures   Order a copy of this article
    by Huang Liumei, Lian Huijie 
    Abstract: In order to enhance the analysis effectiveness of the compulsory education policy implementation situation of regional economic development, this paper puts forward an analysis method based on inconsistent-measurement Bayes of the compulsory education policy implementation situation. Firstly, it researches the evaluation index model of compulsory education resource allocation, and establishes the evaluation model based on the relevant indexes, such as education infrastructure, teacher resources and appropriation for education; secondly, it puts forward a Bayes filtering algorithm based on forward-backward compression to effectively handle the noising and uncertainty problems that exist in the compulsory education policy implementation, realise the effective estimation on the equilibrium model of compulsory education policy implementation, and deeply analyse the influence of compulsory education policy in domestic compulsory education policy implementation trend. The research result verifies the effectiveness of the method.
    Keywords: regional economy; compulsory education; policy implementation; Bayes filtering.

  • Effect of cognitive need and purchase involvement on information processing in the online shopping decision-making   Order a copy of this article
    by Liu Chuanlei, Chen Baishu, Huang Dijian 
    Abstract: This research uses the information board technology to simulate the network shopping decision task. Through the experiment, it discusses the influence of cognitive needs and purchase involvement on information processing in network shopping decision-making. The experimental results show that (1) cognitive needs have a significant influence on information processing in shopping decision; (2) purchase involvement has a significant impact on information processing in the decision-making process of network shopping; (3) cognitive needs and purchase involvement have a significant impact on information processing in the network shopping decision making process.
    Keywords: cognitive need; purchase involvement; online shopping; information processing.

  • Analysis and prediction of autistic children's game characteristics   Order a copy of this article
    by Liu Chuanlei, Han Yuanfei, Li Jiao 
    Abstract: Children with autism showed great defects in the playing of games, and the study of autistic children games is unseen. This research, designated "CePingBiao of autistic childrens game ability", undertook a study on the game features of 130 children diagnosed with autism, and gave 69 children rehabilitation training game ability for 4 months. The results showed: (1) the autistic children's ability of game on the game type has high and low points; the best body level of game development, followed by the structure of the game, again is a symbol of games, social games, and the lowest development level is the rules of the game; (2) age significantly influences the ability of children with autism; with the increase of age, autism game levels increase, and 5 to 6 years old is the rapid development period; after this period, the game development level of children with autism begins to fall; the development curve is inverted U type; (3) the training time influences the structure of the autistic children's game development level; the structure game level of children with autism increased with the increase of training time, but the training period needs to be at least 9 months; (4) language is the important factor affecting the development of autistic children's game-playing ability; the language level obviously promotes the development of this ability. The paper concludes that there are differences in game type, age, training duration and language level of children with autism, and their game levels can predict their overall development level.
    Keywords: autistic children; play ability; assessment.

  • Development mode of circular economy industrial cluster based on game theory   Order a copy of this article
    by Feng Yanhong, Wang Zhongfu 
    Abstract: Policy decision is an external driving factor for cyclic economy, under the perspective of global value chain, the subjects of the interested parties are the cyclic economy enterprises and the government responsible for supervision. In the development process of enterprises, the cyclic economy industry cluster development mode is the important link for development, which has an important position in the environmentally friendly and resource conservation development goal of the Chinese economy, and is an urgent problem needing to be solved. This paper, based on the game analysis method, establishes a model for the relationship between enterprise and government under the cyclic economy industry cluster development mode, and conducts game analysis on the cyclic economy innovation process under the driving of the government, thus to research the relationship between the cyclic economy enterprise and the government responsible for supervision under the cyclic economy development mode from the perspective of theory. Finally, based on the model establishment and analysis results, the paper puts forward reasonable suggestions on strengthening the cyclic economy industry cluster development.
    Keywords: cyclic economy industry cluster; industry cluster; innovation game; external factor.

  • Community discovery method based on complex network of data fusion based on super-network perspective   Order a copy of this article
    by Li Pei 
    Abstract: To enhance the computational efficiency and precision of community discovery, a community discovery algorithm with the mixed label based on the minimum description length (MDL) of information compression is proposed in this paper. Firstly, the community detection is converted into an information compression problem of seeking an effective network structure, and the quality evaluation function is constructed based on MDL criterion. Secondly, the community discovery algorithm with heuristic mixed label movement is constructed based on the label node movement algorithm and Louvain community addition algorithm so as to reduce the quality evaluation function. At last, the simulation experiment in the standard test set and API capture Sina microblog dataset shows that the proposed algorithm is superior to the selected comparison algorithm in computational efficiency and precision.
    Keywords: super-network perspective; label movement; complex network; heuristic; community discovery.

  • Relay protection method based on decentralised control logic based on two-sided active excitation detection   Order a copy of this article
    by Wei Bin, Xu Chong, Wu Xiaokang, Gao Chao, Xu Jinxing 
    Abstract: To lower the wireless charge coil losses and leakage level of magnetic field for electromobiles, a relay method with decentralised control logic based on active excitation detection by a secondary side is proposed. It is guaranteed to only excite the primary coil below electromobile that raises charging requirement, and to realise precise localisation and local power supply. The method fully multiplexes the primary/secondary power coil and needs no additional sensing unit or centralised signal line. The active excitation detection circuit is configured as a series connection compensating network and uses its characteristic of frequency splitting to enhance the intensity of the detecting signal and avoid the overflow of the secondary side's active excitation. A reasonable relay control flow is designed to reduce the power needed for detection and avoid the conflict of simultaneous excitation of primary and secondary sides. Lastly, a simulative experiment was conducted to verify the feasibility of relay method proposed.
    Keywords: segmental; electromobile; wireless charging; offset compensation.

  • A dynamic modelling method for dynamic wireless charging system of electric vehicles based on dual LCL non-resonant compensation   Order a copy of this article
    by Huang Xiaohua, Wei Bin, Gao Chao, Wu Xiaokang, Xu Jinxing 
    Abstract: This paper starts with the mutual inductance coupling model and takes the compensation structure of "series connection-series connection" as an example to respectively deduce the mathematical expressions for system output power and efficiency when traditional resonant compensation strategy and non-resonant compensation strategy are adopted, so as to compare the advantages and disadvantages of the two strategies from the aspect of the influence of coupling coefficient change on the two strategies output power and efficiency. Then, to cope with the drawbacks of the non-resonant compensation strategy, a dynamic modelling method for electromobile's dynamic wireless charging system based on dual-LCL non-resonant compensation is proposed to analyse the structure's function of improving the system's power transmission capacity and determination method of different component parameters from the perspectives of circuit equivalence and impedance conversion. Models are built for the rectifier and filter circuit, the DC-DC circuit and the loading battery pack for the receiving terminal, and the characteristics of each link are expressed by mathematical expressions.
    Keywords: LCL non-resonant compensation; electromobile; wireless charging; dynamic modelling; rectifying and filtering.

  • An image segmentation algorithm based on combination of slope width reduction and cross-cortical model   Order a copy of this article
    by Zhang Zhen 
    Abstract: An image segmentation algorithm based on the ramp width reduction combined with an intersecting cortical model (ICM) is proposed to resolve the problem that ICM in the segmentation of images with weak edges produces geometric distortion. By virtue of prewitt boundary operator and edge ramp model, the algorithm defines the objective edge point, adjusts the gray level of edge pixel, and reduces the width of image edge. On this basis, the paper uses 2D histogram to expand the cross entropy to 2D space so as to obtain the optical segmentation threshold of ICM. The experiment indicates that the algorithm not only overcomes the impact of edge blur and segments the image with weak edge accurately, but also improves the processing speed greatly.
    Keywords: automatic local ratio; Chan-Vese model; image segmentation; boundary operator; intersecting cortical model; cross entropy.

  • A comprehensive evaluation model based on fuzzy meta-association rules   Order a copy of this article
    by Qian Hao-yun 
    Abstract: At present, the attention to the competitiveness of the provincial-level administrative region in China has become a hot topic. Based on principles of scientificity, systematicness, comparability and feasibility, this paper establishes a set of index systems for comprehensively evaluating the comprehensive competitiveness of provincial-level administrative regions, and proposes a kind of fuzzy meta-association rule method based on hierarchy theory. It carries out binary fusion extraction of the meta-rules for urban development evaluation element knowledge by making use of the similar structure of the data stored by the development branch in each city, with no need to process the entire dataset. It is able to obtain results/modes from a single database to reduce the time required for rule mining. Finally, the comprehensive competitiveness level of the provincial-level administrative regions all over the country is analysed and evaluated from the microcosmic level and macroscopic level through factor analysis and clustering analysis.
    Keywords: hierarchical analysis; association rules; competitiveness; comprehensive evaluation.

  • A risk preference model for teaching resource allocation based on functional link fuzzy neural network algorithm classifier   Order a copy of this article
    by Wei Tongpeng, Chen Li 
    Abstract: The volleyball teaching resource allocation model based on the functional link fuzzy neural network algorithm is proposed to improve the effectiveness of volleyball teaching resource allocation in the course arrangement process. Firstly, the risk preference allocation model for volleyball teaching resource allocation is designed based on the functional link fuzzy neural network algorithm classifier, and the risk is divided into existing and non-existing risk preferences and the functional link fuzzy neural network algorithm classifier is used to achieve the training and data optimisation of the volleyball teaching resource allocation dataset. Secondly, considering that the functional link fuzzy neural network algorithm classifier may break down in the volleyball teaching resource allocation prediction process, the functional link fuzzy neural network algorithm is used to achieve optimisation of the volleyball teaching resource allocation process. Finally, the stimulation research on the volleyball teaching resource allocation model example shows that a more reasonable volleyball teaching resource allocation model can be obtained by the proposed algorithm, reflecting the effectiveness of the algorithm.
    Keywords: volleyball teaching resource; neural network; functional link; resource allocation.

  • Fusion algorithm for information interaction control of multi-UAVs based on intelligent algorithm   Order a copy of this article
    by Chen Guangming 
    Abstract: This paper is devoted to designing a kind of UAV robust information interaction detection and tracking control system suitable for external interference suppression. Firstly, it models the UAV rotor as a linear parameter-varying system (LPV), takes it as an objective to make system design, and considers information interaction an detection and isolation scheme through an observer library, to detect and isolate sensor information interaction. Then, the paper improves a kind of existing adaptive variable space algorithm, introduces the algorithm thought into improvement of particle swarm optimisation, and when the evolutional generation of population reaches an integer multiple of a preset period, automatically expands or shrinks the size of the search space according to the improved adaptive variable space algorithm, which automatically searches for proper search space, improves convergence rate and accuracy, and effectively prevents premature convergence of particle swarm optimisation. Finally, the effectiveness of the algorithm is verified through experiment in a simulation model.
    Keywords: information interaction; control fusion; particle swarm optimisation.

  • Computer-based outdoor sport sustainable development using wavelet neural network   Order a copy of this article
    by Chen Shan 
    Abstract: In order to enhance the effectiveness of research on sustainable square dancing under the background of national fitness, this paper puts forward a research method based on a wavelet neural network, applies the time series prediction theory of the wavelet neural network into the prediction on sustainable square dancing, obtains the LF approximate part and HF approximate part in the sustainable square dancing data through wavelet decomposition and restructuring, and then, based on analysing the good and bad models, selects the most effective model or model combination to establish the prediction model for researching sustainable square dancing. Finally, it conducts model simulation by aid of the actual sustainable square dancing data, and the result shows that the model can effectively enhance the prediction precision of sustainable square dancing.
    Keywords: square dancing; wavelet analysis; neural network.

  • Design of extensible multi-source signal acquisition device based on DSP and STM32   Order a copy of this article
    by Li Bo, Xiong Di, Guohua Chen 
    Abstract: As large numbers of multi-type sensor signals are required to be collected in numerical control machine tool tests, a general acquisition system could only collect certain types of sensor signal, which did not have extended function, so that its application scope in machine tool test was limited, leading to excessive acquisition equipment categories and insufficient compatibility with each other. Aiming at above-mentioned problems, a kind of extensible multisource acquisition system based on DSP and STM32 was designed, which took a microcontroller as DSP and STM32. DSP microcontroller was mainly used to complete data acquisition and output functions, while STM32 microcontroller was mainly used to complete data storage and communication functions. The acquisition system had a multisource signal interface, which could automatically identify analogue signal type and support parallel acquisition for temperature, vibration, voltage, current, pressure, displacement, and other types of signal. Extended functions could be realised so as to increase the multichannel analogue input interface through a removable extended module so as to satisfy acquisition requirements for different machine tool tests.
    Keywords: numerical control machine tool; extensible; multisource acquisition.

  • Design and implementation of LTE physical layer on FPGA   Order a copy of this article
    by V. Venkataramanan, S. Lakshmi, A. Vineet Kanetkar 
    Abstract: Changing trends in the communication industry pertain to the configuration of devices and their processing for maximised result. Each device needs a processing unit comprising a microcontroller or a Field Programmable Gate Array (FPGA). This paper deals with the use of FPGAs and how they can be configured as hardware in loop (HIL) for validation along with Simulink and Xilinx System Generator (XSG). Further, their compatibility is mentioned for long term use and durability in communication. The comparison of related work in the field of communication is done with the FPGA implementation of a Long Term Evolution (LTE) physical layer with different modulation schemes, different antenna configurations and different signal-to-noise ratio systems implemented on Virtex and Spartan FPGA boards. On the other hand, the simulation is carried out with Xilinx Vivado Design suite to analyse the power, resource use, timing summary, and memory use.
    Keywords: field programmable gate array; hardware co-simulation; LTE; MIMO; OFDM; 3GPP.

  • Innovation mechanism of cluster industry based on weighted time-varying multi criteria and similarity evaluation method   Order a copy of this article
    by Feng Yanhong, Wang Zhongfu 
    Abstract: In order to enhance the effectiveness of the cooperation mechanism of cluster industry innovation of booming megalopolises, this paper puts forward a research method based on weight time-varying multi-criteria and similarity evaluation method for the cooperation mechanism of cluster industry innovation of booming megalopolises. Firstly, under the background of internet+, it researches the indicator system establishment of the cluster industry innovation platform of booming megalopolises, establishes the comprehensive evaluation system with five first-level indicators in scientific research innovation capability, intelligent production and service support capability, information transmission capability, infrastructure and environment support capability and platform system establishment capability. Secondly, it puts forward the weight time-varying multi-criteria and similarity evaluation method to enhance the industry innovation cooperation mechanism recommendation precision, combined with the wight time-varying process, deeply considers the criteria weight of different times periods to enhance the decision scientificity of the industry innovation cooperation mechanism; finally, it verifies the effectiveness of algorithm through empirical analysis.
    Keywords: megalopolises; industry innovation; cooperation mechanism; weight time-varying; similarity evaluation.

  • Collaborative sparse unmixing using variable splitting and augmented Lagrangian with total variation   Order a copy of this article
    by Nareshkumar Patel, Himanshukumar Soni 
    Abstract: Linear Spectral Unmixing (LSU) is a widely used technique in the field of Remote Sensing (RS) for the estimation of fractional abundances of endmembers and their spectral signatures. Large data size, poor spatial resolution, non-availability of pure endmember signatures in dataset, mixing of materials at various scales and variability in spectral signature make LSU a challenging and inverse-ill posed task. Broadly there are three basic approaches to manage the LSU problem: geometrical, statistical and sparse regression. The first and second approaches are types of blind source separation (BSS). The third approach assumes the availability of some standard publicly available spectral libraries, which contain signatures of many materials measured on the Earth's surface using advance spectroradiometry. In the sparse re- gression approach, the problem of LSU is simplified to finding the optimal subset of spectral signatures from the library known in advance. In this paper, the con- cept of collaborative sparse regression is incorporated to improve the performance of the existing SUnSAL-TV algorithm. SUnSAL-TV is a recently proposed Total Variation(TV) spatial regularisation based approach. Our simulation results conducted for standard and publicly available synthetic fractal dataset show 10 to 15%performance improvement in signal to reconstruction error for different data cubes. Simulation is also performed for a subset of real cuprite data cube and compared with the outcome of recent algorithms.
    Keywords: linear spectral unmixing; sparse regression; augmented Lagrangian; ADMM; hyperspectral unmixing; total variation; collaborative.

Special Issue on: ISMIC 2018 Information Processing and Control Technologies

  • Automatic selection of lexical features for detecting Alzheimer's disease using bag-of-words model and genetic algorithm   Order a copy of this article
    by Gang Lyu, Aimei Dong 
    Abstract: Early detection of Alzheimer's disease is the key to treatment. Neuropsychological testing has the advantages of being non-invasive and low-cost, but the need for manual selection of features and expert diagnosis is not conducive to the popularity of this method. This paper proposes an approach for automatically extracting and selecting features from texts. First, it uses the bag-of-words model of natural language processing technology to extract all the vocabulary features in the texts. Secondly, unlike the manual selection of features by t-test, it uses the genetic algorithm to select lexical features automatically. We tested the new approach with the DementiaBank database. Its classification accuracy for Alzheimer's disease is 79%, close to the best value of the hand-crafted-feature-based method. The new approach also has the ability to process data quickly and automatically, which can greatly help clinicians improve their work.
    Keywords: bag-of-words model; genetic algorithm; hyperparameter; machine learning; naïve Bayes algorithm; Alzheimer's disease.

  • A new topology and power control of grid-connected photovoltaic array   Order a copy of this article
    by Li-ping Zhong 
    Abstract: Under the partial shading, the series photovoltaic modules will generate additional power loss and present a multi-peak power-voltage curve that causes difficulties for the maximum power point tracking. By using a novel grid-connected topology and power control method presented in this paper, the photovoltaic array can be connected through a full parallel structure and thus the shortcomings mentioned above can be overcome. A higher voltage, required for grid connection, also can be obtained through the topology without the need of any step-up transformer or boost circuit. Furthermore, the output power of the photovoltaic array can be adjusted by controlling the phase of the modulated wave. As a result, the maximum power point tracking can be achieved with a simple method. The simulation and experiment results verified the validity of the proposed topology and control method.
    Keywords: partial shading; multi-peak power-voltage curve; CLC immittance converter; phase control; maximum power point tracking.

  • Low frequency structure-borne noise refinement based on rigid-flexible coupling model of powertrain mounting system   Order a copy of this article
    by Rang-Lin Fan, Zhen-Nan Fei, Cheng-Cheng Feng, Fang Yin, Wei-Cun Zhang 
    Abstract: The refinement of low frequency structure-borne noise generated by automotive powertrain mounting system usually adopts transfer path analysis (TPA) and vehicle body plate optimisation, which requires a lot of simulation or experiment work based on the vehicle body. This paper proposes a simple method to provide an optimal mount stiffness target for the refinement of structure-borne noise. The method is based on rigid-flexible coupling model of powertrain mounting system, and the model requires complete and accurate parameters such as mass, inertia, position and stiffness. The excitation forces of engine which are used as input of the rigid-flexible coupling model are identified by an indirect semi-experiment method. Based on this model, the direction of mount stiffness with maximum sensitivity to the dynamic characteristics of the powertrain mounting system is identified. Then the low frequency structure-borne noise is refined by changing the mount stiffness in this direction.
    Keywords: structure-borne noise; rigid-flexible coupling model; rigid modal; powertrain mounting system; automotive.

  • An adaptive multi-threshold segmentation algorithm for complex images under unstable   Order a copy of this article
    by Wei Ding, Yanfang Zhao, Reilei Zhang 
    Abstract: Images acquired from the actual manufacturing
    Keywords: threshold segmentation; peak distribution; gray level probability density; prior knowledge.

  • Multiple cell tracking by generalised labelled multi-Bernoulli filter   Order a copy of this article
    by Jian Shi, Mingli Lu 
    Abstract: Cell detection and tracking in microscopy images are of great importance to medical research and related fields. In this paper, a generalised labelled multi-Bernoulli (GLMB) track-before-detect (TBD) filter is proposed for the tracking of multiple cells. In this filter, GLMB based on random finite set (RFS) theory is used to jointly estimate the positions and the numbers of cells in images, and TBD is adopted to track cells without an explicit detection step. The experimental results indicate that the proposed method can accurately discover cells and maintain their tracks in low contrast image sequences.
    Keywords: cell tracking; random finite set; track–before–detect; generalised labelled multi-Bernoulli.

  • Broad learning system for human activity recognition using sensor data   Order a copy of this article
    by Aiqiang Yang, Xinghong Yu, Tingli Su, Xuebo Jin, Jianlei Kong 
    Abstract: In a multi-sensor environment, it is efficient to record and reflect peoples information of activities, using the large amount of data. However, the data cannot directly display the form of activity itself so that it is necessary to do the further job of exploration and processing. Deep Learning (DL) methods have attracted more attention and have shown some superior performance, while they have the problem of structural complexity. Therefore, this paper creatively used Broad Learning System (BLS) method for human activity recognition. We use sliding window to get the data segmented. The weights involved in are fine-tuned by pseudo-inverse and ridge regression algorithms, and we achieve an accurate classification of activities. The method is verified by using OPPORTUNITY dataset. The results show that this method can greatly shorten the learning time and improve the accuracy, as well as the performance in comparison with traditional method.
    Keywords: broad learning system; human activity recognition; sensor data; sliding window processingrn.

  • Crowd counting via scale-adaptive convolutional neural network in extremely dense crowd images   Order a copy of this article
    by Ran Yan, Shengrong Gong, Shan Zhong 
    Abstract: Crowd counting, a high accuracy and high-speed technology, has been applied in new retail, shopping mall, underground, rail station and vehicle surveillance systems. However, owing to the inconsistent sizes of human heads, there are a lot of counting errors and instability of the crowd density estimation in extremely dense crowd images. Therefore, a scale-adaptive CNN (Convolutional Neural Network) architecture is proposed by introducing residual network on the basis of multi-column CNN. In the process of model training, joint learning is proposed in this paper. Through alternating training for residual network and multi-column CNN, network parameters with the best accuracy are selected after iteration. Joint learning helps to enhance the modelling ability for massive scale transformation and the scale self-adaptability of the network. The proposed method is tested on public dense crowd datasets. Experimental results prove that scale-adaptive CNN shows better counting capability than the current state-of-the-art method.
    Keywords: crowd counting; density estimation; convolutional neural network; scale-adaptive; joint learning.

  • Design of a new type of float flowmeter and remote monitoring system based on ARM microcontroller   Order a copy of this article
    by Wu Qian, Shen Bingbing, Jiang Ling, Zhao Fengsheng, Hua Liang 
    Abstract: A new type of float flowmeter remote monitoring system developed in this paper has been optimised in terms of system integration, software and hardware. The system uses a cost-effective 32-bit ARM microcontroller as the control processing unit to achieve accurate measurement of liquid fluid flow. The host computer monitoring system and the client computer of flowmeter controllers use the prescribed communication protocol for data interaction. Each port number of the IP address of the host computer server can be connected to 255 devices. The system makes full use of the advantages of the IOT (Internet of Things) wireless communication technology, ARM technology and communication protocol, which realises the complete separation between the monitoring terminal of the host computer and the field device of the actual industrial site conditions. The user can remotely monitor the float flowmeter at the job site on any PC. The instrument has features of high precision, efficiency, explicit structure, and better maintainability and it has broad prospects in application.
    Keywords: remote monitoring; ARM microcontroller; wireless communication; communication protocol.

  • An operation sequence based temporal multilayer networks model for production process in flexible manufacturing systems   Order a copy of this article
    by M.E.I. Dai, Zhicheng Ji, Yan Wang 
    Abstract: This paper focuses on flexible manufacturing systems, which are typical discrete event systems. Unlike traditional approaches for the system, this paper provides a multidimensional, ordinal, temporal topology structure to depict the dynamic production process from the perspective of complex networks. Firstly, the factors of a real manufacturing environment are mapped to the multilayer networks and the framework of the model is given. The proposed temporal multilayer networks model includes intra-layers and inter-layers, involving two aspects of task and resource nodes and time-dependent graphs, respectively. Secondly, the generation rules to construct the networks are presented. A novel approach for evaluating bottleneck resources in time blocks is presented to develop the networks model. Finally, the temporal multilayer networks model is generated based on the theory of constraints. The proposed model based on operation sequence presents superiority in measurement of time-dependent performance of the system. Moreover, an application case for energy consumption evaluation confirms that the proposed model can support exploratory analysis of the time-related performance criterion in discrete manufacturing.
    Keywords: multilayer networks; temporal networks; flexible manufacturing system; generation rule; bottleneck identification.

  • Analysis of an approach to reducing drops of secondary user on primary user emulation attack   Order a copy of this article
    by Hui Sun, Chuang Yang, Rui Wang, Sabir Ghauri 
    Abstract: In this paper, we propose a method for a Cognitive Radio Network (CRN) to reduce the drop probability of a secondary user (SU) due to the primary user emulation attack (PUEA) by a malicious user (MU). Instead of abandoning the current channel, a novel method called a pause approach is used if the primary user (PU) or an MU accesses the same channel. This method helps the SU to find the attack behaviour of the MU and increases the network throughput performance. Also, we analyse the channel states with a Continuous Time Markov Chain (CTMC). The simulation results validate the proposed method based on Matlab.
    Keywords: cognitive radio; PUEA; Markov chain; drop probability.

  • A model for target acquisition and edge detection under complex scenes   Order a copy of this article
    by Fei Wang, Jihong Zhu 
    Abstract: For the needs of target acquisition and edge extract under complex scenes, a method based on layer by layer segmentation is proposed. First of all, morphological reconstruction technology is combined with an ant colony edge detection method to execute pre-segmentation to get the foreground that targets locate. Secondly, a line scanning technique is used to divide the foreground into several parts for further segmentation. And then, mean difference characteristic is adopted to determine the seed point, with which a region-growing algorithm is applied to find true targets. Finally, an active contour method is used to find the edges of targets. The experimental results show that our method is effective in finding targets and extracting edges under complex scenes.
    Keywords: morphology reconstruction; ant algorithm; active contour.

  • Multi-threading parallel reinforcement learning   Order a copy of this article
    by Qiming Fu, Yiyi Kang 
    Abstract: With respect to the problem of the slow convergence of the traditional reinforcement learning algorithm in practical applications, we proposed a novel multi-threading parallel reinforcement learning (MPRL) algorithm. MPRL is mainly composed of two parts. One is the FCM-based reinforcement learning multi-threading partitioning method, which transforms the multi-threading partitioning problem into a clustering partition problem to obtain the optimal multi-threading partitioning solution. Another is the parallel reinforcement learning framework, which makes the parallel execution between the policy evaluation and the interaction with the environment. In the learning process, the experience replay is adopted to update the value function, which can also solve the problem of the non-convergence in the off-policy evaluation. Experimentally, the MPRL algorithm is applied to the windy grid world problem and the cart pole problem, and compared with Q-Learning, Sarsa and KCACL. The experimental results show that MPRL has a faster convergence rate and better convergence performance.
    Keywords: reinforcement learning; multi-threading technology; thread partitioning; parallel reinforcement learning; experience replay.

  • Rapid freshness prediction of crab based on a portable electronic nose system   Order a copy of this article
    by Peiyi Zhu, Yulin Zhang, Lu Ding 
    Abstract: In this paper, an automatic freshness prediction system for the living Chinese mitten crab was explored, which was formed from an electronic nose based on seven metal oxide semiconductor sensors. The prediction system acquired test data from the characteristic compounds in the headspace of the crab, and then was dealt with four different dimension reduction algorithms including PCA, LDA, KPCA and LE to reduce dimensions and extract effective features of sensor scores. Experimental results illustrated that the prediction system sensitively responded to crabs. PCA and LDA results failed to differentiate the response data of the living crabs. LE and KPCA were able to identify the different response data of crab samples. Back propagation neural network was used as a prediction model after dimension reduction. The model based on LE-BPNN reached a high identification rate of 90.6%. The simulation and experiment results was clarified the prediction system can estimate the freshness of the living crab.
    Keywords: Chinese mitten crab; electronic nose; Freshness; Laplacian eigenmaps; back propagation neural network.

  • Research on robot location based on improved method of map feature matching   Order a copy of this article
    by Mao Limin, P.U. Yuhuan, Wang Liangyu 
    Abstract: With respect to robot self-positioning, this study reports that the map feature extraction algorithm based on Euclidean distance is improved, the processing of outliers and class division points in line segment landmark fitting is added, and the slope and intercept of the line are added. The aggregation step reduces the influence of class over-segmentation of the map feature extraction. According to RANSAC feature matching, a map matching method based on corner points and line segment landmarks is proposed.
    Keywords: straight line fitting; map feature matching; data point classification; feature extraction; line landmark.

  • Development of shipbuilding safety information monitoring and management system   Order a copy of this article
    by Qing Zhang, Liang Hua, Xiaojie Tian, Zijun Tang, Lubing Nian 
    Abstract: Production safety accidents in the shipbuilding industry in China frequently occur nowadays. Based on the key technology of the internet of things as the communication basis, this paper designs a new type of shipbuilding safety monitoring system based on wireless communication. The wireless heterogeneous network is organically formed to solve the special problem of shielding a ship's airtight steel structure, as well as the problem of networking in bad working conditions. It adopts multi-sensor coordination and pattern recognition technology to achieve reliable collection and intelligent processing of environmental information and human body information. The system adopts network management to realise the connection between all items and networks, based on multi-sensor real-time monitoring and intelligent analysis of various physiological conditions and surrounding environmental conditions, combined with RFID technology and ZigBee technology for identity security identification. Finally, the collected information is transmitted to the host computer and Android client through WiFi.
    Keywords: shipbuilding; safety monitoring system; internet of things system; multi-sensor; host computer management system.

Special Issue on: Advances in Computer Graphics and Imaging

  • Research on the Design of Visual Interface in Information Visualization
    by Guangtao Ma, Tao Liu, Yang Zhou, Jun Li 

Special Issue on: Machine Vision and Computational Intelligence in Recent Industrial Practice

  • Robust Skin Segmentation using Color Space Switching
    by Ankit Chaudhary, Ankur Gupta 

Special Issue on: Xxxx

  • A Query Driven Method of Mapping from Global Ontology to Local Ontology in Ontology-based Data Integration
    by Haifei Zhang