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检索条件"主题词=Particle swarm optimization Algorithm"

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particle swarm algorithm Based Task Scheduling for Many-Core Systems 12

Particle Swarm Algorithm Based Task Scheduling for Many-Core...

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12th IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Lu Junliang Hu Wei Shen Huan Li Yaxin Liu Jing Wuhan Univ Sci & Technol Coll Comp Sci & Technol Wuhan Hubei Peoples R China Hubei Prov Key Lab Intelligent Informat Proc & Re Wuhan 430065 Hubei Peoples R China

ISBN: (纸本)9781509061617

Task scheduling is one of the key factors that determine the performance of the computer systems. When there are more cores on a single chip, how to schedule the multiple tasks to these cores is still important and also a challenge. In order to optimize the efficiency of task scheduling of many-core computer systems, this paper proposes a new task scheduling algorithm based on particle swarm ant colony algorithm. In this algorithm, the fitness function is chosen according to the system model with many cores and multiple tasks. This algorithm uses B-level scheduling method in the initial stage to generate the initial pheromone distribution. In the iteration process, the improved way is used to update pheromone to adjust operator. At the same time, the crossover and mutation strategy of genetic algorithm is used for adaptive crossover and mutation of local optimum and global optimum particles, which makes the location of particles be able to change and update according to the fitness value of particles. The experimental results show that the improved algorithm is superior to the genetic algorithm in the performance of task scheduling.

关键词： task scheduling particle swarm optimization algorithm many-core

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Handwritten Numeral Recognition with a Quantum Neural Network Model 2

Handwritten Numeral Recognition with a Quantum Neural Networ...

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2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC 2017)

作者： Mao Yaxuan Kazuyuki Aihara Department of Electrical Engineering and Information Systems Graduate School of Engineering The University of Tokyo Department of Mathematical Informatics Graduate School of Information Science and Technology The University of Tokyo

ISBN: (纸本)9781509064151;9781509064144

Handwritten numeral recognition has generated significant interest in last several years owing to its sundry application potentials in the fields of image processing and computer vision. Recently, quantum neural networks(QNN) have been found to be efficient for information processing, such as image classification, recognition and optimization, owing to its distinct features. Therefore, in this study, a QNN based handwritten character recognition system is studied. Experiments are conducted with the data from the MNIST database. Backpropagation(BP) algorithm and particle swarm optimization(PSO) algorithm are used during the training process to improve the performance of the QNN. The resulting recognition rate of this proposed system is up to 99.16%. The results of the experiments clearly demonstrate the superiority of the proposed QNN in terms of its convergence speed and recognition rate as compared to other classical recognition methods, and simultaneously show the superiority of the QNN in solving character recognition problems.

关键词： Handwritten numeral recognition Quantum neural network Back-propagation algorithm particle swarm optimization algorithm

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Application of Improved Wavelet Neural Network in MBR Flux Prediction 16

Application of Improved Wavelet Neural Network in MBR Flux P...

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16th IEEE/ACIS International Conference on Computer and Information Science (ICIS)

作者： Cai, Guoshuai Li, Chunqing Tianjin Polytech Univ Coll Comp Sci & Software 399 Bin Shui Xi Rd Tianjin 300387 Peoples R China

ISBN: (纸本)9781509055074

Membrane Bio-Reactor(MBR) technology plays an important role in modern sewage treatment, but the performance of the MBR technology is seriously affected by the membrane fouling. In general, the result of membrane fouling is decline of MBR membrane flux, and the effect of MBR sewage treatment is directly affected by the decrease of membrane flux. In order to predict MBR membrane flux accurately and rapidly, the forecasting model of MBR membrane flux based on particle swarm improving wavelet neural network algorithm (PSO_WNN) was established. In view of the complexity of the MBR membrane fouling factor, in the beginning, the main components of the factors affecting the flux of MBR membrane were analyzed. The important factor is extracted as the input of the PSO_WNN prediction model, and the membrane flux is used as the output. Then, the PSO_WNN simulation model is established, and the prediction results are obtained by using the model. By comparing the predicted data and experimental data, the predictive accuracy of this algorithm is high on the membrane flux, and compared with the BP neural network model, the comparative results show that the PSO_WNN forecasting model has higher predicted accuracy.

关键词： MBR Membrane flux particle swarm optimization algorithm Wavelet neural network BP neural network

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Application of PSO algorithm with Dynamic Inertia Weight in Medical Image Thresholding Segmentation 19

Application of PSO algorithm with Dynamic Inertia Weight in ...

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19th Annual IEEE International Conference on e-Health Networking, Applications and Services (Healthcom)

作者： LiuNa JiangYan LiShu Hubei Univ Med Ctr Hlth Adm & Dev Studies Shiyan Peoples R China

ISBN: (纸本)9781509067046

Medical image segmentation is an important application in medical image processing. In order to improve the efficiency of Medical image segmentation, a method to select optimal threshold by PSO algorithm with Dynamic Inertia Weight(DW-PSO) is proposed. It makes the proportion of the local and global searching ability can be effectively controlled in the whole process of optimal searching. Compared with PSO algorithm, DW-PSO gets better effects of image segmentation. The data of experiment shows that DW-PSO has obvious advantage in computing efficiency, the precision and stability of searching optimal threshold, as well as the effect of image segmentation.

关键词： particle swarm optimization algorithm Inertia weight Optimal threshold Medical image segmentation

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Intelligent Sign Language Recognition Using Enhanced Fourier Descriptor: A Case of Hausa Sign Language 2

Intelligent Sign Language Recognition Using Enhanced Fourier...

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2nd IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)

作者： Hassan, S. T. Abolarinwa, J. A. Alenoghena, C. O. Bala, S. A. David, M. Farzaminia, Ali Fed Univ Technol Minna Dept Telecommun Engn Minna Nigeria Univ Malaysia Sabah Fac Engn Kota Kinabalu Malaysia

ISBN: (纸本)9781538608463

Hausa sign language (HSL) is the main communication medium among deaf-mute Hausas in northern Nigeria. HSL is so unique that a deaf-mute individual from other part of the country can rarely understand it. HSL includes static and dynamic hand gesture recognitions. In this paper we present an intelligent recognition of static, manual and non-manual HSL using an enhanced Fourier descriptor. A Red Green Blue (RGB) digital camera was used for image acquisition and Fourier descriptor was used for features extraction. The features extracted chosen manually and fed into artificial neural network (ANN) which was used for classification. Thereafter particle swarm optimization algorithm (PSO) was used to optimize the features based on their fitness in order to obtain high recognition accuracy. The optimized features selected gave a higher recognition accuracy of 90.5% compared to the manually selected features that gave 74.8% accuracy. High average recognition accuracy was achieved;hence, intelligent recognition of HSL was successful.

关键词： Hausa Sign Language Fourier Descriptor particle swarm optimization algorithm Artificial Neural Network

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Dimensional Synthesis of a Four-bar Linkage Mechanism via a PSO-based Cooperative Neural Network Approach 25

Dimensional Synthesis of a Four-bar Linkage Mechanism via a ...

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25th Iranian Conference on Electrical Engineering (ICEE)

作者： Asaeikheybari, Golnoush Lafmejanit, Amir Salimi Kalhorj, Ahmad Masouleh, Mehdi Tale Shahid Beheshti Univ Dept Elect Engn Tehran Iran Univ Tehran Fac New Sci & Technol Human & Robot Interact Lab Tehran Iran Univ Tehran Sch Elect & Comp Engn Control & Intelligent Proc Ctr Excellence Tehran Iran Univ Tehran Human & Robot Interact Lab Sch Elect & Comp Engn Tehran Iran

ISBN: (纸本)9781509059638

The dimensional synthesis problem is one of the challenging problems in robotics which has initiated several mathematical challenges. In this paper, a novel algorithm is proposed based on combination of particle swarm optimization (PSO) and Cooperative Neural Network (CNN) for solving synthesis problem of a four-bar linkage which leads to an optimization problem. The cooperative network, so-called PS-CNN, consists of memory-retaining particles which collaborate together based on PSO algorithm in a cooperative interaction converge to the optimal dimensional synthesis solution. In the complete-connected network, each neuron provides a solution. Thereby, solutions are updated according to the neurons' memory, their interaction with other neurons and the global best solution of the neurons in order to provide a proper solution to the optimization problem. The objective of the optimization problem is to minimize the distance of the robot's end-effector from the 5 prescribed points by the user when traversing them. Simulation results reveal the desirable performance of the PS-CNN for robot synthesis with higher complexities. Furthermore, the proposed approach opens an avenue to extend it.

关键词： Interactive Neural Network Four-bar Linkage particle swarm optimization algorithm Robot Synthesis

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Binary Cat swarm optimization for Cryptanalysis 11

Binary Cat Swarm Optimization for Cryptanalysis

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IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS)

作者： Amic, Seeven Soyjaudah, K. M. Sunjiv Ramsawock, Gianeshwar Univ Mascareignes Dept Software Engn Pamplemousses Mauritius Univ Mauritius Dept Elect & Elect Engn Reduit Mauritius

ISBN: (纸本)9781538623473

This research paper investigates the relevance of the Cat swarm optimization (CSO) algorithm to cryptanalysis. It is a relatively new evolutionary metaheuristic technique to solve those problems which belong to NP-hard class based on the swarm intelligence of felids, commonly known as cats. Cryptanalysis is an active field of experimental research as it can consolidate or depreciate the viability of modern ciphers. To the extent of our knowledge, there is no evidence that the CSO has ever been applied to the cryptanalysis problem. Experimental outcomes demonstrate that the Binary Cat swarm optimization (BCSO) algorithm is efficient for the cryptanalysis of the Data Encryption Standard (DES) using chosen plaintext attack. Further, it produces optimal keys with higher fitness as compared to particle swarm optimization. This paper shows that BCSO can be successfully utilized for the cryptanalysis of block ciphers with promising outcomes.

关键词： Cryptanalysis DES swarm intelligence Binary Cat swarm optimization algorithm particle swarm optimization algorithm Fitness Function

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Biomechanical study on the nonlinear stiffness behavior of the calf muscle and ground reaction force estimation during running 5

Biomechanical study on the nonlinear stiffness behavior of t...

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5th RSI International Conference on Robotics and Mechatronics (IcRoM)

作者： Jalali, Peyman Varahram, Sina Hassannejad, Reza Ettefagh, Mir Mohammad Univ Tabriz Dept Mech Engn Tabriz Iran

ISBN: (纸本)9781538657034

Understanding the biomechanical behavior of the human body in different conditions of locomotion can be very advantageous for many purposes, such as developing the humanoid robots. With this approach, the aim of this study is investigating the nonlinear stiffness behavior of the calf muscle and the exerted ground reaction force (GRF) during running. For this purpose, a new active model of human body is proposed, which the distinctive features of this model are as follows: 1) The simulation model is considered to be five degrees of freedom (5-DOF) by using the standard solid model to simulate the calf muscle;2) The optimum values of the considered mechanical parameters are obtained using the particle swarm optimization (PSO) algorithm;3) The stiffness of the calf muscle is considered to be nonlinear and active, which is appropriately formulated with regard to the viscoelasticity nature of the calf muscle. This model is tested for two types of hard and soft shoes, and validated by the results of previous studies. Then the effects of different conditions of body mass distribution, touchdown velocities, and different types of shoes on the GRF are investigated and discussed. The results of this study can be used in producing and development of humanoid robots, artificial limbs, etc.

关键词： Calf muscle Ground Reaction Force Active Mass-Spring-Damper model particle swarm optimization algorithm

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SVM Classification: optimization with the SMOTE algorithm for the Class Imbalance Problem 6

SVM Classification: Optimization with the SMOTE Algorithm fo...

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6th Mediterranean Conference on Embedded Computing (MECO)

作者： Demidova, Liliya Klyueva, Irina Ryazan State Radio Engn Univ Ryazan Russia

ISBN: (纸本)9781509067428

In this paper a new approach to selection of the optimal parameters values for the SMOTE (Synthetic Minority Over-sampling Technique) algorithm in the problem of the SVM (Support Vector Machine) classification of imbalanced datasets has been suggested. This approach allows reducing the time expenditures for the search of the optimum parameters values of the SMOTE algorithm. The experimental results show that the offered approach allows increasing the classification quality of the SVM classifier.

关键词： sampling imbalanced dataset SVM classifier particle swarm optimization algorithm hybridization

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Optimal Placement of Fault Indicators in Distribution System Using PSO algorithm 43

Optimal Placement of Fault Indicators in Distribution System...

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43rd Annual Conference of the IEEE-Industrial-Electronics-Society (IECON)

作者： Li, Weijian Chen, Wei Guo, Chuangxin Jin, Yu Gong, Xiao Zhejiang Univ Coll Elect Engn Hangzhou Zhejiang Peoples R China China Southern Power Grid Guizhou Power Grid Guiyang Power Supply Bur Guiyang Guizhou Peoples R China

ISBN: (纸本)9781538611272

Recently, fault indicators with communication function have been increasingly used in fault diagnosis of distribution networks. In this paper, an objective function which considers the reliability index of the distribution network and economic factors synthetically has been proposed for optimal placement of fault indicators in distribution networks, and binary particle swarm optimization algorithm is used for solving the optimization problem. The validity of the proposed model is verified by testing on the IEEE-33 node distribution system. The test results show that there is no need to install too many fault indicators in distribution networks and reasonable choice of the number and location of the fault indicator can achieve the comprehensive optimization of distribution network reliability and economy. At the same time, the results and processes of optimization are compared with those obtained by immune algorithm, the result shows that binary particle swarm optimization algorithm is better because of its fast convergence speed and less iteration times, which is an advantage for solving this optimal allocation problem.

关键词： Fault Indicators Optimal Placement particle swarm optimization algorithm

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