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Detection of electrocardiogram signals using an efficient method

APPLIED SOFT COMPUTING 2014年 22卷 108-117页

作者： Ebrahimzadeh, A. Shakiba, B. Khazaee, A. Babol Univ Technol Fac Elect & Comp Engn Babol Sar Iran

Automatic detection of electrocardiogram (ECG) signals is very important for clinical diagnosis of heart disease. This paper investigates the design of a three-step system for recognition of the five types of ECG beat. In the first step, stationary wavelet transform (SWT) is used for noise reduction of the electrocardiogram (ECG) signals. Feature extraction module extracts higher order statistics of ECG signals in combination with three timing interval features. Then hybrid bees algorithm-radial basis function (RBF_BA) technique is used to classify the five types of electrocardiogram (ECG) beat. The suggested method can accurately classify and discriminate normal (Normal) and abnormal heartbeats. Abnormal heartbeats include left bundle branch block (LBBB), right bundle branch block (RBBB), atrial premature contractions (APC) and premature ventricular contractions (PVC). Finally, the classification capability of five different classes of ECG signals is attained over eight files from the MIT/BIH arrhythmia database. Simulation results show that classification accuracy of 95.79% for the first dataset (4000 beats) and an overall accuracy of detection of 95.18% are achieved over eight files from the MIT/BIH arrhythmia database. (C) 2014 Elsevier B.V. All rights reserved.

关键词： ECG beat classification Higher order statistics Radial basis function neural network bees algorithm

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A Global Multilevel Thresholding Using Differential Evolution Approach

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MATHEMATICAL PROBLEMS IN ENGINEERING 2014年第1期2014卷 1-23页

作者： Charansiriphaisan, Kanjana Chiewchanwattana, Sirapat Sunat, Khamron Khon Kaen Univ Dept Comp Sci Fac Sci Khon Kaen 40002 Thailand

Otsu's function measures the properness of threshold values in multilevel image thresholding. Optimal threshold values are necessary for some applications and a global search algorithm is required. Differential evolution (DE) is an algorithm that has been used successfully for solving this problem. Because the difficulty of a problem grows exponentially when the number of thresholds increases, the ordinary DE fails when the number of thresholds is greater than 12. An improved DE, using a new mutation strategy, is proposed to overcome this problem. Experiments were conducted on 20 real images and the number of thresholds varied from 2 to 16. Existing global optimization algorithms were compared with the proposed algorithms, that is, DE, rank-DE, artificial bee colony (ABC), particle swarm optimization (PSO), DPSO, and FODPSO. The experimental results show that the proposed algorithm not only achieves a more successful rate but also yields a lower threshold value distortion than its competitors in the search for optimal threshold values, especially when the number of thresholds is large.

关键词： DIFFERENTIAL evolution (Computer science) PROBLEM solving bees algorithm GLOBAL optimization SEARCH algorithms

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Robust Fault-Tolerant Control for Satellite Attitude Stabilization Based on Active Disturbance Rejection Approach with Artificial Bee Colony algorithm

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MATHEMATICAL PROBLEMS IN ENGINEERING 2014年第1期2014卷 1-17页

作者： Song, Fei Qin, Shiyin Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China

This paper proposed a robust fault-tolerant control algorithm for satellite stabilization based on active disturbance rejection approach with artificial bee colony algorithm. The actuating mechanism of attitude control system consists of three working reaction flywheels and one spare reaction flywheel. The speed measurement of reaction flywheel is adopted for fault detection. If any reaction flywheel fault is detected, the corresponding fault flywheel is isolated and the spare reaction flywheel is activated to counteract the fault effect and ensure that the satellite is working safely and reliably. The active disturbance rejection approach is employed to design the controller, which handles input information with tracking differentiator, estimates system uncertainties with extended state observer, and generates control variables by state feedback and compensation. The designed active disturbance rejection controller is robust to both internal dynamics and external disturbances. The bandwidth parameter of extended state observer is optimized by the artificial bee colony algorithm so as to improve the performance of attitude control system. A series of simulation experiment results demonstrate the performance superiorities of the proposed robust fault-tolerant control algorithm.

关键词： bees algorithm FAULT tolerance (Engineering) ROBUST control NATURAL satellites BANDWIDTHS PARAMETERS (Statistics)

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Benchmarking and comparison of nature-inspired population-based continuous optimisation algorithms

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SOFT COMPUTING 2014年第5期18卷 871-903页

作者： Pham, D. T. Castellani, M. Univ Birmingham Sch Mech Engn Birmingham B15 2TT W Midlands England Univ Bergen Dept Biol N-5020 Bergen Norway

This paper describes an experimental investigation into four nature-inspired population-based continuous optimisation methods: the bees algorithm, Evolutionary algorithms, Particle Swarm Optimisation, and the Artificial Bee Colony algorithm. The aim of the proposed study is to understand and compare the specific capabilities of each optimisation algorithm. For each algorithm, thirty-two configurations covering different combinations of operators and learning parameters were examined. In order to evaluate the optimisation procedures, twenty-five function minimisation benchmarks were designed by the authors. The proposed set of benchmarks includes many diverse fitness landscapes, and constitutes a contribution to the systematic study of optimisation techniques and operators. The experimental results highlight the strengths and weaknesses of the algorithms and configurations tested. The existence and extent of origin and alignment search biases related to the use of different recombination operators are highlighted. The analysis of the results reveals interesting regularities that help to identify some of the crucial issues in the choice and configuration of the search algorithms.

关键词： Optimisation bees algorithm Swarm intelligence Evolutionary algorithms Optimisation benchmarks

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Optimized fuzzy controller for a power-torque distribution in a hybrid vehicle with a parallel configuration

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2014年第14期228卷 1654-1674页

作者： Derakhshan, Mohsen Shirazi, Kourosh Heidari Shahid Chamran Univ Ahvaz Dept Mech Engn Ahvaz Iran

In this study, an optimized fuzzy controller was designed for a power-torque distribution in a hybrid passenger vehicle using the bees algorithm. For this purpose, a dynamic model for a parallel hybrid vehicle, including an internal-combustion engine, an electric motor and battery, a torque converter, a continuously variable transmission, torque-speed coupling and wheels (forward dynamic), is built. To simulate the driver's skill and performance when facing different conditions, a fuzzy controller (which is called the first-layer controller) is used. On the one hand, decisions made by the first-layer controller are a function of the desired acceleration, the desired speed and the speed error and, on the other hand, they depend on the vehicle's performance mode. The vehicle's performance mode is determined with regard to the charged condition (the state of charge) of the batteries, the vehicle's required power and the driver commands by another fuzzy controller which is called the second-layer controller. The necessary commands for the subsystems such as the throttle angle, the controller command for the electric motor and the appropriate speed ratio in the continuously variable transmission are determined by another controller, called the third-layer controller. Both the first layer and the second layer are optimized using the bees algorithm. According to the results, the optimized controller indicates an appropriate performance based on the defined rules in different situations. It also improves the vehicle's performance parameter and decreases the fuel consumption and the pollutions produced by the engine.

关键词： Optimized fuzzy controller torque distribution strategy hybrid electric vehicle parallel configuration bees algorithm

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A support vector machine approach for classification of welding defects from ultrasonic signals

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NONDESTRUCTIVE TESTING AND EVALUATION 2014年第3期29卷 243-254页

作者： Chen, Yuan Ma, Hong-Wei Zhang, Guang-Ming Xian Univ Sci & Technol Sch Sci Xian 710054 Peoples R China Xian Univ Sci & Technol Sch Mech Engn Xian 710054 Peoples R China Liverpool John Moores Univ Gen Engn Res Inst Liverpool L3 3AF Merseyside England

Defect classification is an important issue in ultrasonic non-destructive evaluation. A layered multi-class support vector machine (LMSVM) classification system, which combines multiple SVM classifiers through a layered architecture, is proposed in this paper. The proposed LMSVM classification system is applied to the classification of welding defects from ultrasonic test signals. The measured ultrasonic defect echo signals are first decomposed into wavelet coefficients by the wavelet packet transform. The energy of the wavelet coefficients at different frequency channels are used to construct the feature vectors. The bees algorithm (BA) is then used for feature selection and SVM parameter optimisation for the LMSVM classification system. The BA-based feature selection optimises the energy feature vectors. The optimised feature vectors are input to the LMSVM classification system for training and testing. Experimental results of classifying welding defects demonstrate that the proposed technique is highly robust, precise and reliable for ultrasonic defect classification.

关键词： support vector machine bees algorithm wavelet packet transform defect classification ultrasonic non-destructive evaluation

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A Modified Artificial Bee Colony algorithm Based on Search Space Division and Disruptive Selection Strategy

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MATHEMATICAL PROBLEMS IN ENGINEERING 2014年第1期2014卷 1-14页

作者： He, Zhen-an Ma, Caiwen Wang, Xianhong Li, Lei Wang, Ying Zhao, Yuan Guo, Huinan Chinese Acad Sci Xian Inst Opt & Precis Mech Xian 710019 Shaanxi Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Peoples Liberat Army Unit 96165 Shangrao 334000 Jiangxi Peoples R China North Optoelect Stock Ltd Xian 710032 Shaanxi Peoples R China Chinese Armed Police Force Engn Univ Xian 710086 Shaanxi Peoples R China

Artificial bee colony (ABC) algorithm has attracted much attention and has been applied to many scientific and engineering applications in recent years. However, there are still some insufficiencies in ABC algorithm such as poor quality of initial solution, slow convergence, premature, and low precision, which hamper the further development and application of ABC. In order to further improve the performance of ABC, we first proposed a novel initialization method called search space division (SSD), which provided high quality of initial solutions. And then, a disruptive selection strategy was used to improve population diversity. Moreover, in order to accelerate convergence rate, we changed the definition of the scout bee phase. In addition, we designed two types of experiments to testify our proposed algorithm. On the one hand, we conducted experiments to make sure how much each modification makes contribution to improving the performance of ABC. On the other hand, comprehensive experiments were performed to prove the superiority of our proposed algorithm. The experimental results indicate that SDABC significantly outperforms other ABCs, contributing to higher solution accuracy, faster convergence speed, and stronger algorithm stability.

关键词： bees algorithm SEARCH algorithms SPACE division multiple access STABILITY theory PERFORMANCE evaluation

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Fuzzy Multiobjective Optimal Power Flow Based on Modified Artificial Bee Colony algorithm

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MATHEMATICAL PROBLEMS IN ENGINEERING 2014年第1期2014卷 1-12页

作者： He, Xuanhu Wang, Wei Beijing Jiaotong Univ Natl Act Distribut Network Technol Res Ctr Beijing 100044 Peoples R China

This paper presents a modified artificial bee colony (MABC) algorithm to solve optimal power flow (OPF) problem. In the proposed MABC algorithm, the searching operation for new food source of artificial bee colony (ABC) algorithm is replaced with mutation and crossover operation of differential evolution (DE) algorithm to improve exploitation capacity. The OPF objective functions involve minimization of total fuel cost of generating units, minimization of emission of atmospheric pollutants, minimization of active power losses, and minimization of voltage deviations. The fuzzy satisfaction-maximizing method is utilized to convert the multiobjectives problem into single objective problem. The proposed approach is applied to the OPF problem on IEEE 30-bus test system. And the results are compared with those obtained by other heuristic algorithms, which demonstrate that the MABC algorithm not only has a better exploration capacity but also possesses stronger exploitation capacity and can effectively solve the OPF problem.

关键词： bees algorithm FUZZY systems ARTIFICIAL intelligence PROBLEM solving ELECTRIC power distribution BUSES (Computers)

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INACT-INDECT Advanced Image Cataloguing Tool

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MULTIMEDIA TOOLS AND APPLICATIONS 2014年第1期68卷 95-110页

作者： Grega, Michal Bryk, Damian Napora, Maciej AGH Univ Sci & Technol PL-30059 Krakow Poland

Child pornography possession and distribution are crimes which are prosecuted in most countries around the world. In some cases the law is so strict that even police forces are not allowed to gather and catalogue evidence for future reference. This paper presents an innovative solution to this problem. The authors present tools for cataloguing high- and low-level metadata of the evidence material. Furthermore, a tool for fast and accurate search for such evidence in suspects' file systems is proposed.

关键词： Live forensics Query by example bees algorithm Child pornography

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Blind Source Separation Based on Covariance Ratio and Artificial Bee Colony algorithm

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MATHEMATICAL PROBLEMS IN ENGINEERING 2014年第1期2014卷 1-12页

作者： Chen, Lei Zhang, Liyi Guo, Yanju Huang, Yong Liang, Jingyi Tianjin Univ Commerce Sch Informat Engn Tianjin 300134 Peoples R China Tianjin Univ Sch Elect Informat Engn Tianjin 300072 Peoples R China Hebei Univ Technol Sch Informat Engn Tianjin 300401 Peoples R China

The computation amount in blind source separation based on bioinspired intelligence optimization is high. In order to solve this problem, we propose an effective blind source separation algorithm based on the artificial bee colony algorithm. In the proposed algorithm, the covariance ratio of the signals is utilized as the objective function and the artificial bee colony algorithm is used to solve it. The source signal component which is separated out, is then wiped off from mixtures using the deflation method. All the source signals can be recovered successfully by repeating the separation process. Simulation experiments demonstrate that significant improvement of the computation amount and the quality of signal separation is achieved by the proposed algorithm when compared to previous algorithms.

关键词： BLIND source separation COVARIANCE matrices bees algorithm SIMULATION methods & models MIXTURES

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