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Adaptive filtering noisy transcranial Doppler signal by using artificial bee colony algorithm

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2013年第2期26卷 677-684页

作者： Karaboga, Nurhan Latifoglu, Fatma Erciyes Univ Fac Engn Elect & Elect Eng Dept Kayseri Turkey Erciyes Univ Biomed Eng Dept Fac Engn Kayseri Turkey

Computerized processes are supportive in the new age of medical treatment. Biomedical signals which are collected from the human body supply or important useful data that are related with the biological actions of human body organs. However, these signals may also contain some noise. Heart waves are commonly classified as biomedical signals and are non-stationary due to their statistical specifications. The probability distributions of the noise are very different, and for this reason there is no common method to remove the noise. In this study, adaptive filters are used for noise elimination and the transcranial Doppler signal is analyzed. The artificial bee colony algorithm was employed to design the adaptive IIR filters for noise elimination on the transcranial Doppler signal and the results were compared to those obtained by the methods based on popular and recently introduced evolutionary algorithms and conventional methods. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Artificial bee colony algorithm pso algorithm DE algorithm Adaptive IIR filter Noise elimination Transcranial Doppler signal

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Multi-user selection based on particle swarm optimization and power control scheme in the uplink CoMP-MU-MIMO system

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The Journal of China Universities of Posts and Telecommunications 2013年第5期20卷 79-84页

作者： LIU Jian LI Xi JI Hong ZHANG Xiao-liang School of Telecommunication Engineering Beijing University of Posts and Telecommunications

Orthogonal frequency division multiplexing （OFDM） which has been adopted in the long-term evolution （LTE） system can improve the system capacity obviously. However, it also brings about severe inter-cell interference （ICI） for cell-edge users （CEUs）. To tackle this problem, multi-user selection and power control （MuS-PC） is proposed as an efficient scheme in uplink coordinated multi-point multi-user multi-input multi-output （CoMP-MU-MIMO） transmission/reception. This paper jointly considers user＇s signal to interference plus noise ratio （S1NR） and proportional fairness （PF） to maximize the total channel capacity in multi-user selection by formulating a penalty function. To simplify the penalty function＇s computation, particle swarm optimization （pso） algorithm is introduced. In addition, power control is adopted to maximize overall energy efficiency. Simulation results demonstrate that the MuS-PC scheme can not only obtain the optimal total channel capacity while guarantee each user＇s quality of service （QoS） and PF, but also largely reduce computational complexity and improve energy efficiency. As a result, the poor communication quality of CEUs can be enhanced.

关键词： CoMP-MU-MIMO system pso algorithm penalty ftmction multi-user selection power control zero forcing （ZF） equalization

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Energy-Efficient Power Allocation for WDM/OCDM Networks With Particle Swarm Optimization

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JOURNAL OF OPTICAL COMMUNICATIONS AND NETWORKING 2013年第5期5卷 512-523页

作者： Durand, Fabio Renan Abrao, Taufik Univ Estadual Londrina Dept Elect Engn DEEL BR-86051970 Londrina Brazil

In this paper, an optical network optimization based on particle swarm intelligence is described. The particle swarm optimization (pso) algorithm is evocated in order to solve the problem of resource allocation (RA) based on the signal-to-noise plus interference ratio optimization in a hybrid wavelength division multiplexing/optical code division multiplexing network with quality-of-service restrictions. The RA optimization based on the pso strategy allows the regulation of the transmitted power in order to maximize the energy efficiency. For the network optimization context, a system model is described, figures of merit are presented, and a suitable model for the heuristic optimization approach is developed, with emphasis on the optimization of input parameters and network performance. Afterward, extensive numerical results for both optimization problems are discussed, taking into account realistic network operation scenarios.

关键词： Energy efficiency Heuristic search Optical networks Particle swarm intelligence pso algorithm Resource allocation

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Optimal capacity allocation of standalone wind/solar/battery hybrid power system based on improved particle swarm optimisation algorithm

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IET RENEWABLE POWER GENERATION 2013年第5期7卷 443-448页

作者： Wang, Jidong Yang, Fan Tianjin Univ Minist Educ Key Lab Smart Grid Tianjin 300072 Peoples R China Tianjin Univ Sch Elect Engn & Automat Tianjin 300072 Peoples R China

A standalone wind/solar/battery hybrid power system, making full use of the nature complementarity between wind and solar energy, has an extensive application prospect among various newly developed energy technologies. The capacity of the hybrid power system needs to be optimised in order to make a tradeoff between power reliability and cost. In this study, each part of the wind/solar/battery hybrid power system is analysed in detail and an objective function combining total owning cost and loss of power supply probability is built. To solve the problems with non-linearity, complexity and huge computation, an improved particle swarm optimisation (pso) algorithm is developed, which integrates the taboo list to broaden the search range and introduces 'restart' and 'disturbance' operation to enhance the global searching capability. The simulation results indicate that the proposed algorithm is more stable and provides better results in solving the optimal allocation of the capacity of the standalone wind/solar/battery hybrid power system compared with the standard pso algorithm.

关键词： battery storage plants hybrid power systems particle swarm optimisation power generation economics power generation reliability solar power stations wind power plants optimal capacity allocation standalone wind-solar-battery hybrid power system particle swarm optimisation algorithm wind energy solar energy power reliability power cost power supply probability pso algorithm

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Genetic algorithm and particle swarm optimization algorithm for speed error reduction in railway signaling systems

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INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING 2013年第6期27卷 478-487页

作者： Sandidzadeh, Mohammad Ali Heydari, Amir Khodadadi, Ali Iran Univ Sci & Technol Sch Railway Engn Tehran Iran

The paper deals with common concepts of modern methods of train speed determination with minimal errors. Balise locations depend on a variety of parameters. With genetic algorithm and particle swarm optimization, as two new intelligent algorithms, and Kalman filtering concept used, the best locations are determined to reduce tachometer errors. Copyright (c) 2012 John Wiley & Sons, Ltd.

关键词： balise placement pso algorithm GA applications in railway railway signaling system

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The Research of pso algorithm in Web Information Filtering

The Research of PSO Algorithm in Web Information Filtering

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4th International Conference on Computer Science and Education

作者： Cheng, Xiaorong Wang, Dong Xie, Kun N China Elect Power Univ Sch Comp Sci & Technol Baoding Peoples R China

ISBN: (纸本)9781424435197

With the development of information technology and the popularization of the Internet, the amount of information based on the network has raised rapidly, and the emergence and development of network information filtering provide a better choice for people to access to the information quickly, accurately and completely. In this paper, the introduction of pso uses its simple and efficient to establish accurate user model. It takes the access and update of profile as a process of learning, adjusted, learning again. Model is updated under considering inertia weight, parameters and other factors,. Then doing some experiments test it.

关键词： icnformation filtering pso algorithm inertia weight

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CHINESE JOURNAL OF ELECTRONICS 2013年第3期22卷 461-465页

作者： Chen Donghui Liu Zhijing Wang Zonghu Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China

Traditional fuzzy clustering algorithms based on objective function is unable to determine the optimum number of clusters, sensitive to the initial cluster centers, and easily sunk into the issue of local optimum. A Fuzzy similarity-based clustering (FSBC) algorithm is proposed in this paper. This method consists three phases: first, the objective function is modified by integrating Fuzzy C-means (FCM) and Possibilistic C-means (PCM) method;second, using the density function from data for similarity-based clustering to automatically generate initial prototype without requesting users to specify;finally, the iteration process optimized by Particle swarm optimization (pso) to obtain appropriate adjustment parameters that can provide better results, which avoids the local minimum problems of traditional methods. The experimental results on the synthetic data and UCI standard data sets show that the proposed algorithm has greater searching capability, less computational complexity, higher clustering precision.

关键词： Fuzzy clustering FCM algorithm PCM algorithm Mountain function Density function pso algorithm

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Visual sensor network lifetime maximization by prioritized scheduling of nodes

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JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 2013年第1期36卷 409-419页

作者： Hooshmand, M. Soroushmehr, S. M. R. Khadivi, P. Samavi, S. Shirani, S. Isfahan Univ Technol Dept Elect & Comp Engn Esfahan Iran McMaster Univ Dept Elect & Comp Engn Hamilton ON Canada

Visual surveillance of a designated air space can be achieved by a randomly distributed camera sensor network spread over a large area. The location and field of view of each battery operated sensor, after a calibration phase, will be known to a central processing node. To increase the lifetime of the network, the density of distributed sensors could be such that a subset of sensors can cover the required air space. As a sensor dies another sensor should be selected to compensate for the dead one and reestablish the complete coverage. This process should be continued until complete coverage is not achievable by the existing sensors. Thereafter, a graceful degradation of the coverage is desirable. The goal is to elongate the lifetime of the network while maintaining a maximum possible coverage of the designated air space. Since the selection of a subset of sensors for complete coverage of the target area is an NP-complete problem, we present a number of heuristics for this case. The proposed methods are categorized in two groups. In one category, the sensors are prioritized based on their visual and communicative properties and the selection is performed according to the prioritizing function. In the other group, we propose traditional evolutionary and swarm intelligence algorithms. The performance of the proposed methods is evaluated through extensive simulations. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Visual sensor network Directional sensors Visual coverage Priority based algorithms Genetic algorithm pso algorithm

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MISSILE GUIDANCE algorithm DESIGN USING PARTICLE SWARM OPTIMIZATION

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TRANSACTIONS OF THE CANADIAN SOCIETY FOR MECHANICAL ENGINEERING 2013年第3期37卷 971-979页

作者： Kung, Chien-Chun Chen, Kuei-Yi Natl Def Univ Chung Cheng Inst Technol Dept Mechatron Energy & Aerosp Engn Tao Yuan Taoyuan County Taiwan Natl Def Univ Chung Cheng Inst Technol Sch Def Sci Tao Yuan Taoyuan County Taiwan

This paper presents a pso guidance (psoG) algorithm design for the pursuit-evasion optimization problem. The initialized particles are randomly et within the guidance command solution space and the relative distance is taken as the objective function. As the psoG algorithm proceeds, the iteration will execute until the global optimum is reached. Two pursuit-evasion scenarios show that the psoG algorithm has satisfied performance in execution time, terminal miss distance, time of interception, final stage turning rate and robust pursuit capability.

关键词： missile guidance law pso algorithm pursuit-evasion

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An optimal fuzzy PI controller to capture the maximum power for variable-speed wind turbines

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NEURAL COMPUTING & APPLICATIONS 2013年第5期23卷 1359-1368页

作者： Sheikhan, Mansour Shahnazi, Reza Yousefi, Ali Nooshad Islamic Azad Univ Dept Elect Engn Fac Engn South Tehran Branch Tehran Iran

Wind energy conversion systems can work by fixed and variable speed using the power electronic converters. The variable-speed type is more desirable because of its ability to achieve maximum efficiency at all wind speeds. The main operational region for wind turbines according to wind speed is divided into partial load and full load. In the partial-load region, the main goal is to maximize the power captured from the wind. This goal can be achieved by controlling the generator torque such that the optimal tip speed ratio is tracked. Since the wind turbine systems are nonlinear in nature and due to modeling uncertainties, this goal is difficult to be achieved in practice. The proportional-integral (PI) controller, due to its robustness and simplicity, is very often used in practical applications, but finding its optimal gains is a challenging task. In this paper, to cope with nonlinearities and at the same time modeling uncertainties of wind turbines, a PI torque controller is proposed such that its optimal gains are derived via a novel scheme based on particle swarm optimization algorithm and fuzzy logic theory. The proposed method is applied to a 5-MW wind turbine model. The simulation results show the effectiveness of the proposed method in capturing maximum power in the partial-load region while coping well with nonlinearities and uncertainties.

关键词： pso algorithm Fuzzy PI controller Variable-speed wind turbine MPPT controller ATF controller

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