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particle swarm optimization Least Square Support Machine Based Missing Data Imputation algorithm in Wireless Sensor Network for Nuclear Power Plant's Environmental Radiation Monitor

Particle Swarm Optimization Least Square Support Machine Bas...

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2nd International Conference on Materials and Products Manufacturing Technology (ICMPMT 2012)

作者： Gao, Song Tang, Yaogeng Qu, Xing South Univ China Inst Elect Heng Yang 421001 Peoples R China

ISBN: (纸本)9783037855447

In wireless sensor network for nuclear power plant's peripheral environmental radiation monitoring the gamma dose rate data may be missed affected by various factors, which will influence the validity of environmental radiation monitoring. To solve the problem, a missing data imputation algorithm is proposed based on particle swarm optimized least squares support vector machine. This algorithm imputes missing data utilizing node's previous monitoring data and neighbor node's current monitoring data jointly. Experimental results using the real radiation monitoring data around a nuclear power plant show that the proposed algorithm can impute the missing gamma dose rate data accurately.

关键词： environmental radiation monitoring wireless sensor network missing data imputation least squares support vector machine particle swarm optimization algorithm

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Bio Inspired Approach for Load Balancing to Reduce Energy Consumption in Cloud Data Center

Bio Inspired Approach for Load Balancing to Reduce Energy Co...

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2015 Communication Control and Intelligent Systems (CCIS)

作者： Goyal, Akhil Chahal, Navdeep S. C DAC Software Technol Div Mohali India

ISBN: (纸本)9781467375412

Cloud computing has made it feasible to access various IT resources through a high speed network from anywhere in the world. Constant increasing demand of cloud computing is equally popular in consumers as well as providers. But along with advancement every technology is also associated with some ill effects. On same path, cloud computing also accompanies a serious issue with it and that issue is energy consumption. In this paper Firefly algorithm has been selected as a proposed bio-inspired approach to perform load balancing to reduce energy consumption in cloud data center. Further, the results are compared with particle swarm optimization algorithm (PSO). The energy consumed in case of Firefly algorithm is less than energy consumed in PSO algorithm.

关键词： Cloud Computing Firefly algorithm particle swarm optimization algorithm Energy Consumption Load Balancing

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Performance evaluation of proposed Differential Evolution and particle swarm optimization algorithms for scheduling m-machine flow shops with lot streaming

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JOURNAL OF INTELLIGENT MANUFACTURING 2013年第1期24卷 175-191页

作者： Chakaravarthy, G. Vijay Marimuthu, S. Sait, A. Naveen Fathima Michael Coll Engn & Technol Dept Mech Engn Madurai 625020 Tamil Nadu India Latha Mathavan Engn Coll Dept Mech Engn Madurai 625301 Tamil Nadu India Chendhuran Coll Engn & Technol Dept Mech Engn Pudukkottai 622507 India

We consider n-job, m-machine lot streaming problem in a flow shop with equal size sub lots where the objective is to minimize the makespan and total flow time. Lot streaming (Lot sizing) is a technique that splits a production lot consisting of identical items into sub lots to improve the performance of a multi stage production system by over lapping the sub lots on successive machines. There is a scope for efficient algorithms for scheduling problems in m-machine flow shop with lot streaming. In recent years, much attention is given to heuristics and search techniques. To solve this problem, we propose a Differential Evolution algorithm (DEA) and particle swarm optimization (PSO) to evolve best sequence for makespan/total flow time criterion for m-machine flow shop involved with lot streaming and set up time. In this research, we propose the DEA and PSO algorithms for discrete lot streaming with equal sub lots. The proposed methods are tested and the performances were evaluated. The computational results show that the proposed algorithms are very competitive for the lot streaming flow shop scheduling problem.

关键词： Flow shop Lot streaming Scheduling Differential evolution algorithm particle swarm optimization algorithm

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Identifying New Prognostic Features for Remaining Useful Life Prediction Using particle Filtering and Neuro-Fuzzy System Predictor 15

Identifying New Prognostic Features for Remaining Useful Lif...

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IEEE 15th International Conference on Environment and Electrical Engineering (EEEIC)

作者： Boukra, Tahar Univ 20 Aout 1955 Lab Electrotech Skikda LES Dept Genie Elect Skikda Algeria

ISBN: (纸本)9781479979929

An accurate prediction of the remaining useful life (RUL) from a prognosis system relies on a good selection of prognosis features. The latter should well capture the trend of the fault progression. In situation where the development of degradation model is difficult, we must be addressed to the identification of new features having an obvious trending quality. in this context, This paper present a new selection method based upon a particle swarm optimization algorithm to identify the advanced prognosis feature and a particle filtering for the prediction of the remaining useful life. The fault growth model is integrated to the particle filter using a Neuro-Fuzzy System with its process noise. This method was validated on a set of experimental data collected from bearings run-to-failure tests.

关键词： Prognostics Remaining Useful Life particle swarm optimization algorithm Feature selection particle Filter Neuro-Fuzzy System

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Optimal Design of Power System stabilizer in a Multi-machine System Using Evolutionary Computing, A Comparative Study

Optimal Design of Power System stabilizer in a Multi-machine...

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IEEE Power, Communication and Information Technology Conference (PCITC)

作者： Nayak, Niranjan Pradhan, Soumya Ray, Sangram Keshari Rout Siksha O Anusandhan Univ Inst Tech Educ & Res Dept EICE Bhubaneswar Odisha India Siksha O Anusandhan Univ Inst Tech Educ & Res Dept EE Bhubaneswar Odisha India Siksha O Anusandhan Univ Inst Tech Educ & Res Dept EEE Bhubaneswar Odisha India

ISBN: (纸本)9781479974559

When the power system subjected to disturbances, stability is a challenging task. The Power System Stabilizer plays an important role to reduce the low frequency oscillation (LFO) and enhance the performance. This is because, the PSS is low cost, flexible and easy to implement. In this work the problem is designed as a single objective optimization technique to tune the PSS parameters with Eigenvalue analysis. Here the Invasive Weed optimization algorithm which is found suitable for these types of problems is selected as a tool to find optimal solutions. Simulations are executed on a 4-machine power system for different operating conditions such as heavy load, light load and capacitive load. The results are obtained under different fault conditions with Invasive Weed optimization technique and compared the same with PSO. At last it is observed that the IWO technique performs better in damping overshoot and settling time.

关键词： particle swarm optimization algorithm Invasive weed optimization technique Low frequency oscillation Stability of Power system

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Calibration of a Dual-PTZ-Camera System for Stereo Vision Based on Parallel particle swarm optimization Method

Calibration of a Dual-PTZ-Camera System for Stereo Vision Ba...

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Conference on Image Processing - algorithms and Systems XII

作者： Chang, Yau-Zen Wang, Huai-Ming Lee, Shih-Tseng Wu, Chieh-Tsai Hsu, Ming-Hsi Chang Gung Univ Dept Mech Engn Taoyuan 333 Taiwan Chang Gung Mem Hosp Dept Neurosurg Taoyuan 333 Taiwan

ISBN: (纸本)9780819499363

This work investigates the calibration of a stereo vision system based on two PTZ (Pan-Tilt-Zoom) cameras. As the accuracy of the system depends not only on intrinsic parameters, but also on the geometric relationships between rotation axes of the cameras, the major concern is the development of an effective and systematic way to obtain these relationships. We derived a complete geometric model of the dual-PTZ-camera system and proposed a calibration procedure for the intrinsic and external parameters of the model. The calibration method is based on Zhang's approach using an augmented checkerboard composed of eight small checkerboards, and is formulated as an optimization problem to be solved by an improved particle swarm optimization (PSO) method. Two Sony EVI-D70 PTZ cameras were used for the experiments. The root-mean-square errors (RMSE) of corner distances in the horizontal and vertical direction are 0.192 mm and 0.115 mm, respectively. The RMSE of overlapped points between the small checkerboards is 1.3958 mm.

关键词： PTZ cameras Stereo vision Active camera calibration particle swarm optimization algorithm

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Estimation of Photovoltaic Cells Model Parameters using particle swarm optimization 17th

Estimation of Photovoltaic Cells Model Parameters using Part...

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17th International Workshop on the Physics of Semiconductor Devices (IWPSD)

作者： Khanna, Vandana Das, B. K. Bisht, Dinesh Vandana Singh, P. K. ITM Univ Gurgaon India JIIT Univ Noida India Natl Phys Lab New Delhi India

ISBN: (纸本)9783319030029;9783319030012

swarm intelligence based technique has been used in this work for the estimation of parameters of photovoltaic cells using the two-diode model of the photovoltaic cell. particle swarm optimization algorithm was used to fit the calculated current-voltage characteristics of the photovoltaic cells to the experimental one. The estimated parameters were the generated photocurrent, saturation currents, series resistance, shunt resistance and ideality factors. The proposed approach was validated using industrial photovoltaic cells.

关键词： Photovoltaic Cell particle swarm optimization algorithm Parameter Estimation

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Multiple Groups of Gradient particle swarm optimization and Its Application in Optimal Operation of Reservoir 10

Multiple Groups of Gradient Particle Swarm Optimization and ...

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10th International Conference on Natural Computation (ICNC)

作者： Jia, Yangyang Wang, Jianqun Xiao, Qingyuan Hohai Univ Coll Hydrol & Water Resources Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781479951512

In this paper, the particle swarm optimization algorithm (PSO) for reservoir optimal operation is studied. A new algorithm which is suitable for reservoir optimal operation called multiple groups of gradient particle swarm optimization algorithm (MGPSO) is proposed to avoid the shortcomings of PSO including premature convergence, poor search accuracy and easily falling into local optimal solution. The gradient searching strategy is introduced to improve the search accuracy of local optima. Grouping and randomly updating strategy are used to improve the searching ability of global optima. Simulation experiments and the example of reservoir optimal operation show that the new algorithm MGPSO obviously outperforms the standard PSO and shuffled frog leaping particle swarm optimization (SFLPSO), and is effective in solving the optimal operation of hydropower station reservoir.

关键词： particle swarm optimization algorithm shuffled frog leaping algorithm global optima hydropower station optimal operation

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Optimal Coded Sequence Design for Radar using Fractional Oppositional particle swarm optimization

Optimal Coded Sequence Design for Radar using Fractional Opp...

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IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE)

作者： Pandu, J. Balaji, N. Naidu, C. D. Dept ECE Sreyas IET Hyderabad Andhra Pradesh India INTUK Dept ECE Vizianagaram India VNRVIIET Dept ECE Hyderabad Andhra Pradesh India

ISBN: (纸本)9781479940400

Polyphase coded radar signals is one of the most important and usually used in MIMO Radar systems. Polyphase coded radar signals with good orthogonal properties having low autocorrelation and a low crosscorrelation property is a nonlinear multivariable optimization problem. The proposed technique yield polyphase waveforms with orthogonal properties which effectively removes the interference issues faced by radars and also help in attaining high radar resolution. The oppositional concept is included in the initial population selection in the PSO algorithm and fractional calculus is used for modifying the velocity updation equation in PSO. Peak Sidelobe Ratio (PSLR) and Integrated Energy Sidelobe ratio (ISLR) of the polyphase waveforms are incorporated in the fitness function defined for the PSO. The experimental analysis based on different phases, different sequence lengths, different code set and different cost function are carried out. In all cases, the proposed technique has achieved good results.

关键词： particle swarm optimization algorithm Fractional Calculu Polyphase Coding Waveform MIMO Oppositional

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An Improved particle swarm optimization and Its Application for Micro-grid Economic Operation optimization

An Improved Particle Swarm Optimization and Its Application ...

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9th International Conference on Bio-Inspired Computing - Theories and Applications (BIC-TA)

作者： Liu, Tao Wang, Jun Sun, Zhang Luo, Juan He, Tingting Chen, Ke Xihua Univ Sch Elect & Informat Engn Chengdu 610039 Sichuan Peoples R China

ISBN: (纸本)9783662450482

A particle swarm optimization algorithm based on adaptive mutation and P systems is proposed to overcome trapping in local optimum solution and low optimization precision in this paper. At the same time, the proposed algorithm is investigated in experiments which are based on the function optimization of micro-grid's economic operation. Furthermore, the feasibility and effectiveness of the proposed algorithm are showed in the experimental results.

关键词： particle swarm optimization algorithm P system Function optimization Economic operation

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