International Journal of Intelligent Engineering Informatics (27 papers in press)
A Twofold Self-Healing Approach for MANET Survivability Reinforcement
by Leila Mechtri, Fatiha Djemili Tolba, Salim Ghanemi, Damien Magoni
Abstract: Distributed systems are by nature fault-prone systems. The situation becomes more complex in the presence of intrusions that continue to grow in both number and severity, especially in open environments like MANET. In this paper, we present a twofold self-healing approach to reinforce MANET survivability. First, a fault-tolerant IDS is designed by replication of individual agents within MASID to ensure continuous supervision of the network. However, since not all intrusions are predictable, there might have been some serious effects on the network before being detected and completely removed. For that, even if the implications of intrusions could be minimized by the intrusion detection system MASID, still the need for the recovery of altered or deleted data is a vital step to ensure the correct functioning of the network. For that, a recovery-oriented approach for a self-healing MANET is also presented. It is based on the ability of MASID-R to assess the damage caused by the detected intrusions and aimed at enabling the supervised network to heal itself of those faults and damages. Simulations using ns-2 have been performed to study the feasibility and prove the optimality of the proposed approach.
Keywords: Survivability; Fault-tolerance; Intrusion Detection; Self-healing; Replication; Recovery-oriented Approach; MANET.
Increasing the Hiding Capacity in Image Steganography using Braille Code.
by Mona A. S. Ali, Essam H. Houssein, Noha Eldemerdash, Aboul Ella Hassanien
Abstract: Least Significant Bit insertion (LSB) steganography is a one of the
most widely used methods for implementing covert data channels in image file
exchanges. This popularity comes from its simplicity in implementation and low
computational complexity of the algorithm moreover the primary reason being
low image distortion. Many researchers try to increase the embedding capacity of
LSB algorithm by increasing the layers of the image by keeping the image with
minimal distortion effects. This paper, introduces a new approach for embedding
the data within the images using Braille code and bit-slicing technique. It will
be shown that this unique steganography method has minimal visual distortion
affects while also hiding the message with a secure and small code.
Keywords: Steganography; bit-slicing technique; Braille method.
ECG Signals Classification: A review
by Essam Houssein, Moataz Kilany, Aboul Ella Hassanien
Abstract: Electrocardiogram (ECG), non-stationary signals, is extensively used
to evaluate the rate and tuning of heartbeats. Comparison of overall ECG
waveform pattern and shape qualifies doctors to diagnose possible diseases. This
paper presents various applications of feature extraction and Machine Learning
(ML) used in ECG classification. The main purpose of this paper is to provide
an overview of utilizing ML and swarm optimization algorithms in ECG signals
to obtain the best performance accuracy in order to recognize the abnormal
Cardiovascular Diseases (CVDs). Furthermore, ECG feature extraction is the
main stage in ECG signal classification to find a set of relevant features of ECG
data that can attain the best classification accuracy performance and there are
several diverse of the classifier. Swarm optimization algorithm is combined with
classifiers for the purpose of searching the best value of classification parameters
that best fits its discriminant purpose, and by looking for the best subset of
features that produce the highest classification performance. Finally, this paper
introduces an ECG heartbeat classification approach based on the Water Wave
Optimization (WWO) and SVM. Published literature presented in this paper
indicates the potential of ANN and SVM as a useful tool for ECG classification.
Author strongly believes that this review will be quite useful to the researchers,
scientific engineers working in this area to find out the relevant references and
the current state of the field.
Keywords: Electrocardiogram (ECG); Feature extraction; Feature optimization;
Classification; Artificial Neural Networks (ANNs); Support Vector Machines
Assessing visual control activities in ceramic tile surface defect detection: an eye-tracking study
by Berna Ulutas, N. Firat Ozkan
Abstract: Digital cameras and image processing algorithms may be helpful in inspection and classification of ceramic tiles in a production line. However, workers decision making capability and ability to tolerate some type of defects are the main reasons for several firms to still rely on human visual inspection. Further, it is believed that the investment and maintenance costs of the automated systems may be higher than labor costs. This study considers a ceramic tile line where workers are assigned to identify tile surface defects. Main aim is to attract attention to differences between novice and expert workers in terms of visual scanning performance and mental workload indicators that result from high concentration during visual inspection. A mobile type eye-tracker is used to record the data for duration of fixation and number of fixations to determine fatigue that arises over a period of working time. Data are analyzed and it is concluded that the eye tracking systems have a potential to identify human related problems during visual inspection.
Keywords: eye-tracking; ceramic tile manufacturing; visual inspection; surface inspection; analyzing human work.
An Intelligent Undersampling Technique based upon Intuitionistic Fuzzy sets to alleviate Class Imbalance Problem of Classification with Noisy Environment
by Prabhjot Kaur, Anjana Gosain
Abstract: Traditional classification algorithms (TCA) does not work with the unequal class sizes. There are applications wherein the requirement is to discover the exceptional/rare cases such as frauds in credit card database or fraudulent mobile calls etc. TCA, when applied in such cases, are failed to detect rare cases. This is stated as the problem of imbalance classes. The problem is more serious when TCA are applied on the data distribution having other impurities like noise, overlapping classes and imbalance within classes. This paper presented an intelligent undersampling and ensemble based classification method to resolve the problem of imbalanced classes in noisy situation. A synthetic data-sets with different extent of noise is used to assess the classification performance of the proposed techniques. The results indicate that the presented undersampling and ensemble based classifier techniques has better classification performance in noisy situation when we compare them with RUS and SMOTE having classifiers like C4.5, RIPPLE, KNN, SVM, MLP, Naivebayes and with the ensemble techniques like Boosting, Bagging and RandomForest
Keywords: Class Imbalance; Intuitionistic Fuzzy Set; Undersampling; Class imbalance Learning; skewed distribution; Noisy environment; data level methods; ensemble approaches; Bagging; Boosting; Randomforest; Noise detection.
Special Issue on: ACECS-2016 Intelligent Control Design and Applications
Discrete and continuous emotion recognition using sequence kernels
by imen trabelsi, Med Salim Bouhlel
Abstract: The field of automatic speech emotion recognition is a highly active and
multi-diverse research area. The current state-of-the-art approaches in machine analysis
of human emotion has focused on recognition of discrete emotional states, such as the
six basic emotion categories. However, emotion is deemed complex and is characterized
in terms of latent dimensions. Accordingly, this paper aims at recognizing discrete and
continuous emotional states by adapting the emotional recognition system to the advanced
Kernel-based machine learning algorithms from the field of speaker recognition, we argue
that it is more efficient in terms of recognition performance. The focus in this paper is
to build a range of sequence kernel to recognize discrete and continuous emotions from
the well-established real-life speech dataset (IEMOCAP) and the acted Berlin emotional
speech dataset (Emo-Db).
Keywords: Speech emotion recognition, arousal, valence, GUMI kernel, GLDS, kullback kernel
New CSMA/CA prioritization based on fuzzy control mechanism
by Imen Bouazzi, Jamila Bhar, Mohamed Atri
Abstract: Wireless sensor networks (WSN) consist of several small nodes that use different types of sensors to gather different types of data and to communicate with each other through several protocols. The IEEE 802.15.4 presents a soft medium access control (MAC) protocol that provides good efficiency for data transmission by adapting its parameters according to characteristics of different applications. The main idea of this paper is to apply a dynamic allocation of priority level for nodes that compete for the access to the medium through tuning parameters in the carrier sense multiple access/collision avoidance (CSMA/CA) algorithm. A priority scheduling is achieved using a fuzzy logic mechanism exploiting queue level and traffic rate of each node in order to ensure adequate dynamic allocation. This paper demonstrates various advantages of priority-based CSMA/CA for quality of service (QoS). Results show significant improvements achieved by our approach among the IEEE 802.15.4 MAC standards.
Keywords: Fuzzy logic;CSMA/CA;priority;queue length;traffic rate;energy consumption.
Synthetic aperture radar image compression based on Multiscale geometric transforms
by Amel Bouchemha, Mohamed Cherif Nait-Hamoud, Noureddine Doghmane
Abstract: Image representation in separable orthogonal basis cannot take advantage of geometrical regularity contained in basic images. When, explored efficiently geometrical regularity improves image compression. In this paper, we propose to experiment and compare an adaptive multiscale geometric decomposition for Synthetic Aperture Radar (SAR) image compression, called multiscale Bandelet transform, and a non adaptive multiscale geometric representation called Ridgelet transform. The second generation of Bandelet transform adopted in this work, is constructed in discrete domain with bandeletization of warped wavelet transform along the optimal direction of geometric flow that minimizes the Lagrangian. We discuss the criteria and results to assess SAR image compression performances using wavelet, Bandelet, and Ridgelet transforms. Our experiments revealed that during the compression phase, the speckle noise is removed from the SAR images inducing further improvements of the coding efficiency. In order, to evaluate the robustness of Bandelet transform, we have proposed a progressive compression scheme based on the second generation of Bandelet transform combined to SPIHT encoder, which is generally integrated with the wavelet transform.
Keywords: SAR image compression; bandelet transform; geometrical flow; ridgelet transform; wavelet; SPIHT encoder
Power Quality Improvement Using Fuzzy Logic Controlled Voltage Source PWM Rectifiers
by Aziz Boukadoum, Tahar Bahi, Abla Bouguerne
Abstract: In the last years many papers about research on PWM rectifiers have been published, these converters are widely used in in power electrical networks, wind and solar power AC-DC interfacing, and in many other industry applications. In this paper, the direct current control based on hysteresis fuzzy logic controlled three-phase voltage source PWM rectifier for eliminating harmonics and compensating reactive power simultaneously in a power system is presented. A detailed mathematical model of system is described and analyzed. The proposed strategies is used to obtained nearly sinusoidal input current, low harmonics distortion (THD), improve the power factor at the unity, regulation of DC-link voltage at the required level with a better responses and excellent performance. Simulation results show the effectiveness of the proposed strategy.
Keywords: PWM Rectifier; fuzzy logic; harmonic; power quality; Simulation.
Vehicle recognition system based on customized HOG for Automotive Driver Assistance System
by haythem ameur, Abdelhamid Helali, Hassen Maaref
Abstract: In the last decade, Advanced Driver Assistance Systems (ADAS) made enormous progress. However, obstacle recognition tasks remain a challenge. In this paper, an optimization vehicle detection system based on a customized Histogram of Oriented Gradients (HOG) was presented and investigated to achieve an accurate vehicle recognition system. Our contribution in this work can be summarized in two fundamental points. First, a re-optimization of the standard HOG parameters was made to get the best results for the car detection. Secondly, an amplification factor was distributed for each bin weight according to its contribution in the extracted car-features. Our studies using a Linear Support Vector Machine (SVM) classifier in MATLAB and heterogeneous databases of vehicle and non- vehicle images were made to achieve an excellent recognition rate that outperforms other similar approaches.
Keywords: ADAS; HOG features; SVM, vehicle detection;
Diagnosis and classification using ANFIS approach of stator and rotor faults in induction machine
by MERABET Hichem, Bahi Tahar, Drici Djalel
Abstract: Three-phase squirrel cage induction motors are one of the important elements of the industrial production system, and are mostly used because of their robustness, reliability, relatively simple construction and their low cost. Nevertheless, during their function in different process, this machine types are submitted to external and internal stresses which can lead to several electrical or mechanical failures. In this paper we are proposed a reliable approach for diagnosis and detection of stator short-circuit windings and rotor broken bars faults in induction motor under varying load condition based on relative energy for each level of stator current signal using wavelet packet decomposition which will be useful as data input of adaptive Neuro-Fuzzy inference system (ANFIS). The adaptive Neuro-Fuzzy inference system is able to identify the induction motor and it is proven to be capable of detecting broken bars and stator short-circuit fault e with high precision. The diagnostic ANFIS algorithm is applicable to a variety of industrial process based on the induction machine for detection and classified the any faults types. This approach is applied under the MATLAB software
Keywords: Induction machine; diagnosis; detection; Neuro-Fuzzy inference system; modeling; simulation.
Control of a photovoltaic system by fuzzy logic, comparative studies with conventional controls: results, improvements and perspectives
by Wassila Issaadi
Abstract: This study presents the principle of the MPPT command. The command techniques most used in MPPT control are reviewed and analysed such as: observation and perturbation (O&P) and conductance incrementing (CI). The aim of this study is the association of the fuzzy logic command with the MPPT command, then analysing and comparing its behaviour with other techniques (O&P and CI) used in the control of photovoltaic systems.
Keywords: commands; power; MPPT; engineering; techniques; performances; algorithms; improvements; yield; conversion; electrical energy; artificial intelligence; observation and disturbance; incrementing conductance; fuzzy logic; photovoltaic generator.
Fuzzy regional inequality measurement: A new stochastic dominance approach with application to Tunisia
by Amal Jmaii, Besma Belhadj
Abstract: : Since economic reforms in 1986, Tunisia's economic growth and urbanization has occurred a dramatic increases in regional inequality, and a corollary threat to sustainable development and social cohesion. This research clearly demonstrates that the spatial distribution of ethnic minorities reflects not only their spatial segregation, but also the degree of their socio-economic exclusion from the majority. This paper propose a fuzzy version of dynamic stochastic approach to analyze between-region inequalities. We propose a class of fuzzy index and include them in a stochastic dominance framework to present a dominance criterion able to class regional distribution. This study contribute to the literatures of inequalities as it take into consideration the intrinsic nature of poverty.
Keywords: Fuzzy approach; stochastic dominance approach; regional inequalities; Tunisia.
Special Issue on: CODIT'2016 New Trends in Intelligent Systems Modelling and Control
Tardiness minimization heuristic for job shop scheduling under uncertainties using group sequences
by Zakaria YAHOUNI, Nasser MEBARKI, Zaki SARI
Abstract: In an industrial environment, manufacturing systems may be subject to considerable uncertainties which could lead to numerous schedule disturbances. These disturbances prevent the execution of a manufacturing schedule as it was planned. The "groups of permutable operations" method copes with this drawback by proposing a family of schedules instead of a unique one. However, the selection of the appropriate schedule that accounts for real-time disturbances, represents a combinatorial optimization challenge. In this paper, we propose a new decision-aid criterion for selecting the schedule that fits best the real state of the shop. This criterion is measured using a greedy heuristic that anticipates the maximum tardiness in a job shop scheduling environment. Simulation tests performed on benchmark problems show the usefulness of the proposed criterion compared to another frequently used criterion. The final results emphasize the usefulness of this criterion in a bi-criteria decision-aid system.
Keywords: Scheduling; Decision-aid system; Job shop; Maximum tardiness; Optimization.
SKEWNESS MAP: ESTIMATING OBJECT ORIENTATION FOR HIGH SPEED 3D OBJECT RETRIEVAL SYSTEM
by Vicky Sintunata, Kurumi Kaminishi, Terumasa Aoki
Abstract: 3D object retrieval system is a system where a similar or the same object in the database should be retrieved given a 2D query image (sketches or photographs). Unfortunately, as the appearance of 3D object might vary depending on the viewing directions, a vast amount of 2D rendered images must be processed (matched) to solve this problem. In this paper, we present a novel method called Skewness Map to relieve this problem. Skewness Map can estimate the orientation of the object and select a few representative images accurately from the database; therefore matching every image in the database can be avoided. Experimental results show the retrieval system becomes much faster (14 times faster in matching time) and accurate in estimating the object orientation (less than one degree error in average).
Keywords: Skewness; Object Orientation; 3D Object Retrieval System.
Enhanced Approach to Cascade Reconfiguration Control Design
by Dusan Krokavec, Anna Filasova
Abstract: Following the concept of fault tolerant control systems, the paper is concerned with the problem of reconfiguration to retain fault tolerance in control of linear continuous-time systems with system dynamics faults. The main idea is to use a reference model output to be followed when a fault occurs, while the nominal control loop structure is kept untouched and the controllers with nominal parameters remains a part of the reconfigured control loop scheme. The full state control principle is applied for nominal control strategy and the static output control principle is proposed for the compensation control law specification. Exploiting the D-stability circle region precept, new conditions for control laws parameter design are introduced and proven as well as stability of the cascadelike reconfiguration structure is analyzed in the paper. To illustrate oncoming properties, the proposed feasible procedure is compared with that which was obtained using Bounded real lemma principle. The results, offering the sufficient and necessary design conditions, are illustrated with a numerical example to note the effectiveness of the proposed approach and its applicability.
Keywords: fault tolerant control; state control; asymptotic stability; static output control; cascade structures; Lyapunov inequality; linear matrix inequalities;.
Discovering dependencies between domains of redox potential and plant defence through triplet extraction and copulas
by Dragana Miljkovic, Nada Lavrač, Marko Bohanec, Biljana Mileva Boshkoska
Abstract: Knowledge discovery, especially in the field of literature mining, is often involved in searching for some interconnecting concepts between two different literature domains, which might bring new understanding of both domains. This paper presents a new approach to discovering dependencies between different biological domains based on copula analysis of literature mining results. More specifically, we have explored dependencies between literature from the domains of plant defence response and redox potential. Copula analysis of triplets, extracted by Bio3graph tool, shows that dependencies exist between these two domains indicating a potential for cross-domain literature exploration. Bio3graph is a rule-based natural language processing tool which extracts relations in the form (subject, predicate, object) triplets. It is publicly available at http://ropot.ijs.si/bio3graph/software/. Copula analysis was performed by using Clayton and Frank fully nested copulas and the software is publicly available at: http://source.ijs.si/bmileva/copulasfordexapps.git.
Keywords: triplets; relation extraction; modelling the domain dependence; copula functions.
Application of Multi-Verse Optimizer Based Fuzzy-PID Controller to Improve Power System Frequency Regulation in Presence of HVDC Link
by Nour E.L. Yakine KOUBA, Mohamed Menaa, Mourad Hasni, Mohamed Boudour
Abstract: This paper presents the design of a novel optimal fuzzy-PID controller based Multi-Verse Optimizer (MVO) for Load Frequency Control (LFC) of a two-area power system interconnected via High Voltage Direct Current (HVDC) transmission link. The MVO algorithm was adopted to estimate the unknown parameters of the test system and model the HVDC link for the LFC analysis, and then, was used to optimize the fuzzy-PID controller parameters including the scaling factors of fuzzy logic and the PID controller gains. To demonstrate the effectiveness of the proposed control strategy, a two-area power system with HVDC link connection was investigated for the simulation. The estimated unknown parameters of the simulated model were compared with other existing results obtained by simulating the same model with the Nonlinear Least-Squares Data-Fitting Algorithm (LSDFA) in MATLAB Optimization Toolbox available in literature. The system dynamic responses are obtained considering single, multi and dynamic load disturbance in both areas. A comparative study of performance of proposed controller, fuzzy logic and conventional PID controller was carried out. Furthermore, the robustness analysis of the proposed control strategy was also performed by varying the system parameters over the wide range from the nominal system values. The obtained results satisfy the LFC requirements and reveal that the optimized fuzzy-PID controller based MVO algorithm enhances power system frequency regulation in presence of HVDC link.
Keywords: Multi-Verse Optimizer (MVO); PID Controller; Fuzzy Logic Controller (FLC); Load Frequency Control (LFC); HVDC Link.
EGSA: a New Enhanced Gravitational Search Algorithm to Resolve Multiple Sequence Alignment Problem
by Elamine ZEMALI, Abdelmadjid Boukra
Abstract: Multiple sequence alignment is a very important and usefulrntool for genomic analysis in many tasks in bioinformatics. However, findingrnan accurate alignment of DNA or protein sequences is very difficultrnsince the computational effort required grows exponentially with the sequencesrnnumber. In this paper, we propose a new sequence alignmentrnalgorithm based on gravitational search algorithm (GSA). The gravitationalrnsearch algorithm (GSA) is a recent metaheuristic inspired fromrnNewtons laws of universal gravitation and motion. Moreover, to avoidrnthe convergence toward local optima, we enhance GSA behavior by introducingrna new mechanism based on simulated annealing concept. Suchrnconcept offers a good balance between the exploration and exploitationrnin GSA and can lead to good alignment quality for the MSA problem.rnThe accuracy and efficiency of the proposed algorithm are comparedrnwith recent and well-known alignment methods using BAliBASE benchmarkrndatabase. The analysis of the experimental results shows that thernalgorithm can achieve competitive solutions quality
Keywords: Multiple sequence alignment; gravitational search algorithm;rnMetaheuristics; Bioinformatics; simulated annealing.
Ant colony optimization combined with variable neighborhood search for scheduling preventive railway maintenance activities
by SAFA KHALOULI, RACHID BENMANSOUR, SAID HANAFI
Abstract: Railway infrastructure maintenance is of fundamental importance in order to
ensure a good service in terms of punctuality, safety and efficiently operation of trains on railway track and also for passenger comfort. Track maintenance covers a large amount of different activities such as inspections, repairs, replacement of failed components or modules and renewals. In this paper, we address the problem of scheduling the preventive railway maintenance activities. The goal is to prevent track failure probability and breakdowns to guarantee a stable and safe service in specified conditions. These activities ensure the increasing of the system reliability and its availability but require considerable resources and large costs, which can be minimized by scheduling the maintenance operations. This problem is proven to be NP-hard, and consequently the development of heuristic and meta-heuristic approaches to solve it is well justified. Thus, we propose two meta-heuristics, a variable neighborhood search (VNS) and an ant colony optimization (ACO), based on opportunities to deal with this problem. Then, we develop a hybrid approach combining ACO with VNS. The performance of our proposed algorithms is tested
by numerical experiments on a large number of randomly generated instances. A comparison with optimal solutions are presented. The results show the effectiveness of our proposed methods.
Keywords: Preventive maintenance; Scheduling; Variable neighborhood search;
Ant colony optimization; Local search; Rail transportation.
Special Issue on: Applications of Soft Computing and Intelligent Control
Analysis of Enhanced Complex SVR Interpolation and SCG-based Neural Networks for LTE Downlink System
by Anis CHARRADA
Abstract: In this article, we operate and evaluate the performance of Radial Basis Function(RBF)-based Support Vector Machine Regression (SVR) and Scaled Conjugate gradient Backpropagation (SCG)-based Artificial Neural Network (ANN), to estimate the channel deviations in frequency domain using the standardized pilot symbols structure for LTE Downlink system. We apply complex SVR and ANN to estimate the real vehicular A channel environment well-defined by the International Telecommunications Union (ITU).rnThe suggested procedures use data obtained from the received pilot symbols to estimate the overall frequency response of the frequency selective multipath fading channel in two stages. In the first stage, each technique learns to adjust to the channel fluctuations, then, in the second stage, it predicts all the channel frequency responses. Lastly, in order to assess the abilities of the considered channel estimators, we deliver performance of complex SVR and ANN, which are compared to traditional Least Squares (LS) and Decision Feedback (DF) methods. Computer simulation results demonstrate that the complex RBF-based SVR approach has a better precision than other estimation methods.
Keywords: SVR; SCG; RBF; ANN; OFDM; LTE.
Software Fault Prediction using Firefly Algorithm
by Ishani Arora, Anju Saha
Abstract: Software fault prediction models have enriched the quality analysts with prior information in hand indicating the fault prone modules detected during the early software development lifecycle. This has enabled the software organisations to focus the resources on the vulnerable modules and hence, deliver a low cost, maintainable and quality product to its customers. The software fault prediction literature has shown an immense growth of the research studies involving the artificial neural network based fault prediction models. However, the default gradient descent back propagation learning algorithm used in artificial neural networks show a high risk of getting stuck in the local minima of the search space. A class of nature inspired computing methods, being stochastic and non-gradient descent based, overcome this disadvantage of the back propagation optimisation method. This feature of the nature inspired techniques have helped artificial neural networks to evolve into a class of adaptive and optimised neural network. In this work, we propose a hybrid software fault prediction model built using firefly algorithm (FA) and artificial neural network (ANN). It also performs an empirical comparison of the classification performance of the developed model with the genetic algorithm (GA) and the particle swarm optimisation (PSO) based evolutionary methods in optimising the connection weights of a neural network. Seven different datasets from the PROMISE repository were involved in the experiments and mean square error (MSE) and the confusion matrix parameters were used for performance evaluation. The results have shown that FA-ANN model has performed better than the genetic and particle swarm optimised ANN fault prediction models.
Keywords: artificial neural network; firefly algorithm; genetic algorithm; metaheuristic techniques; optimisation; particle swarm; software fault; software fault prediction; software quality; software testing.
A very low speech model based on frequency selection-GA approach
by Lahcène MITICHE, Amel Baha Houda ADAMOU-MITICHE
Abstract: Using a new model order reduction based on frequency selection and optimal genetic algorithm, a second order speech model is calculated. In our algorithm, the modeling process starts with a full-order classical all poles model obtained by Burg method. The full order model AR is reduced using the proposed approach based on the genetic algorithms and the original speech production system dominant frequencies. The model reduction yields to a ARMA second order model which interestingly preserves the key properties of the original fullorder model in the time and frequency domains. To illustrate the performance and the effectiveness of the proposed approach, some computer simulations are conducted on some practical speech segments. To show the novelty of our approach, a comparative study with an approximant given by the robust SV D −Schur technique is presented.
Keywords: AR model; ARMA model; genetic algorithm; model order reduction; SVD−Schur technique; speech model; pole selection; ISE criterion.
An Improved Quantum Particle Swarm Optimization and Its Application on Hand Kinematics Tracking
by Zheng Zhao, Naigong Yu
Abstract: The evolutional motivated particle swarm optimization (PSO) has been widely employed in various scientific areas, and there has been plenty of contribution on the modification and improvement of PSO. Recently, a quantum behaviour inspired optimization algorithm (QPSO) was developed by modelling a Delta potential well in quantum space, which shows better performance in global search ability and convergence precision compared with the original PSO algorithm. In this paper, based on the principle of QPSO, we proposed a dynamic search strategy fused with chaos map to strengthen the ability of escaping from local optima, and replaced the attractor with beta distribution for faster convergence speed. We first compared this improved algorithm (DCQPSO) with PSO and QPSO on general optimization benchmark functions. Then, from the point view of application, we also achieved a simplicity-oriented human hand kinematics tracking system by utilizing DCQPSO, which can be further served in human computer interaction (HCI). Indicated by the experiments result, DCQPSO outperforms either traditional PSO or QPSO algorithm, and it can be well qualified with optimization task in hand kinematics tracking,
Keywords: PSO; QPSO; Chaos; Optimization; Hand Kinematics Tracking; HCI.
General study for energy recovery from used batteries using Fuzzy logic and PI controllers
by Jabrane Chakroun
Abstract: In this paper, we propose a special design for energy recovery from used batteries into a renewable power source. The proposed technique consists in designing and implementing Proportional-Integral (PI) and Fuzzy-Logic (FL) controllers to ensure a high ability of conversion. The suggested controllers are designed specially to adapt the dynamic aspect of the batteries charge and discharge. To obtain the optimum residual energy, both methods are compared using MATLAB- SIMULINK. In our case, it turned out that the Fuzzy-Logic controller method improves the performance and the rapidity of the system.
Keywords: PI Controller; Battery; Fuzzy Logic Controller; Buck converter; Residual Power; Simulink.
Application of firefly algorithm for congestion management problem in the deregulated electricity market
by A. AHAMED JEELANI BASHA, M. ANITHA, E.B. ELANCHEZHIAN
Abstract: In the deregulation of electricity market, the transmission Congestion Management (CM) has become extremely important in order to ensure security and reliability of the system. This paper proposes a method to manage congestion by optimal rescheduling of the active powers of generators based on Firefly Algorithm (FA). However, all generators in the system need not take part in CM. Thus, in this article, generators are selected based on the magnitude of generator sensitivities to the congested line. In this paper, the proposed FA is tested on standard IEEE 30 bus, 118 bus systems and a practical Indian utility 62 bus system for the solution of CM problem. The results of these test systems provide minimum rescheduling cost and are compared with that of CPSO, PSO-TVIW, PSO-TVAC, VEPSO and PSO-ITVAC methods. Results prove that FA is indeed capable of getting a high quality solution for the CM problem.
Keywords: Firefly algorithm; Generator sensitivity factor; Congestion management; Deregulated power market.
Multi Agent model based on combination of Chemical Reaction Optimization metaheuristic with Tabu Search for Flexible Job shop Scheduling Problem
by Bilel Marzouki, Olfa Belkahla Driss, Khaled Ghédira
Abstract: Scheduling in production systems consists in assigning operations on a set of available resources in order to achieve defined objectives. The Flexible Job shop Scheduling Problem (FJSP) is one of the scheduling problems and also an extension of classical job shop scheduling problem such that each operation can be processed on different machine and its processing time depends on the used machine. This paper proposes a multi-agent model based on combination of chemical reaction optimization metaheuristic with tabu search to solve the FJSP in order to minimize the maximum completion time (makespan). To evaluate the performance of our model, experiments are performed on well known benchmark instances proposed in the literature and comparison are made with others approaches of literature.
Keywords: Manufacturing; Production System; Industrial Engineering; Scheduling; Optimization; Flexible job Shop Problem; Artificial Intelligence; Multi Agent System; Chemical Reaction Optimization metaheuristic; Tabu search; Decision Making; Metaheuristic; Hybridization.