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10th World Congress on Intelligent Control and Automation (WCICA)

作者： Qin, Ling Wuhan Polytech Univ Sch Elect & Elect Engn Wuhan Peoples R China

ISBN: (纸本)9781467313988

Considering the characteristics of the nonlinear, complexity and relativity of the sintering burden system, the prediction model of sintering burden is established by BP neural network. In addition, a new optimization method of the sintering experiment is proposed, based on information entropy and chaotic improved particle swarm algorithm. The initial particle colony is produced by information entropy to increase the variety of the initial colony. The strategy of dynamic nonlinear adjustment is used for the inertia weight in this paper according to the iteration times, so as to improve the algorithm's searching capability. And the traversal characteristic of chaos optimization is introduced to overcome effectively the local convergence of standard particle swarm algorithm. The simulating results show that the improved particle swarm algorithm has faster converge, fewer iteration times and stronger global optimization ability.

关键词： sintering burden pso algorithm information entropy inertia weight chaotic mutation

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Design of sixth order Butterworth Gm-C Filter using Particle Swarm Optimization program for Biomedical Application 29

Design of sixth order Butterworth Gm-C Filter using Particle...

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29th International Conference on Microelectronics (ICM)

作者： Laouej, D. Daoud, H. Loulou, M. Natl Engn Sch Sfax Informat Technol & Elect Lab Sfax Tunisia

ISBN: (纸本)9781538640494

A fully differential Telescopic operational transconductance amplifier (OTA) and a sixth order Butterworth Gm-C low pass filter are presented in this paper. Both circuits are designed for medical applications in 2.4 GHz ISM band for IEEE 802.15.4 / ZigBee standard. The filter consists of three biquads. The design of the Telescopic OTA is carried out using two optimization methods, G(m)/I-D and Particle Swarm Optimization (pso) algorithm. Both circuits are implemented with TSMC 0.18 mu m CMOS technology. Based on optimized OTA, the Gm-C filter has a cutoff frequency of 2.3 MHz and consumes only 1.55mW.

关键词： Telescopic OTA Butterworth Gm-C filter 2.4GHz band ISM IEEE 802.15.4/ZigBee standard G(m)/I-D method pso algorithm

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Evaluating gridded BIOME-BGC for simulating LAI at Kasilian watershed-Iran

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Geology, Ecology, and Landscapes 2017年第4期1卷 225-231页

作者： Ramezani, Mohammad Reza Massah Bavani, Ali Reza Jafari, Mostafa Binesh, Ali Department of Irrigation and Drainage Engineering College of Abouraihan University of Tehran Tehran Iran Research Institute of Forests and Rangelands Agricultural Research Education and Extension Organization (AREEO) Tehran Iran Faculty of Civil Engineering and Built Environment University of Newcastle Newcastle Australia

Leaf Area Index is a vegetation characteristic which affects water cycle in watersheds by intercepting water. Hence, we decided to simulate LAI with high spatial resolution for different land covers in Kasilian watershed located in Iran from 2004 to 2013. For this purpose, the gridded BIOME-BGC was developed with 500 m spatial resolution which includes 319 grids within Kasilian watershed domain. In this study, The particle swarm optimization (pso) algorithm was applied to find a combination of 3 parameters including maximum stomatal conductance (Gs_max), new fine root C: new leaf C (FRC: LC), and canopy average specific leaf area which contribute to the best fit between BIOME-BGC LAI and MODIS LAI in each pixels. According to the pso results, this algorithm indicated an appropriate performance during the calibration and the root mean square error (RMSE) between BIOME-BGC LAI and MODIS LAI converged to an optimum point during the primary iterations in all pixels. Moreover, the computed per cent error (PE) values in all pixels were less than approximately 30% demonstrating that LAI was simulated by BIOME-BGC with a reasonable accuracy for three-dominant land covers Deciduous Broadleaf Forest, Shrub, and C3 grasses in the case study. © 2017 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： BIOME-BGC MODIS products pso algorithm

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A Model of Vertebral Motion and Key Point Recognition of Drilling with Force in Robot-Assisted Spinal Surgery

A Model of Vertebral Motion and Key Point Recognition of Dri...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Jiang, Zhongliang Sun, Yu Zhao, Shijia Hu, Ying Zhang, Jianwei Harbin Inst Technol Shenzhen Grad Sch Harbin Heilongjiang Peoples R China Shenzhen Inst Adv Technol CAS Key Lab Human Machine Intelligence Synergy Sy Shenzhen Peoples R China Univ Hamburg Hamburg Germany

ISBN: (纸本)9781538626825

Pedicle drilling is a crucial and high-risk process in spinal surgery. Due to the respiration and cardiac cycle, the position of spine would fluctuate during operations, which result in an increase of the difficulty in state recognition of pedicle drilling. To guarantee the safety and validity, a model-based compensation method is proposed in this paper. To build the empirical model of vertebral motion, vertebral displacement and tidal volume (Tv) signals are collected from volunteers. To rule out disturbances in original signal, FFT and wavelet transform (DWT) are used to process the experimental signals. In order to select the apt basis for different signals, the root mean square error decision-making method (RMSE-DMM) is introduced. When the filtered vertebral displacement signal is obtained, the particle swarm optimization (pso) algorithm is used to figure out the empirical model. The robot assisted systems (RAS) can easily compensate vertebral fluctuation based on the empirical model. Due to the goal of pedicle drilling is to drill a hole from surface of first cortical layer to the interior of second cortical layer, a new key point recognition algorithm, based on force, proposed in this paper. To verify the effectiveness of compensation and the recognition algorithm, 3 sets of comparison experiments are carried out. And the results of experiment show the compensation method and new key point recognition algorithm perform effectively.

关键词： vertebral motion wavelet transform robot-assisted surgery state recognition pso algorithm

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A Novel Architecture with Dynamic Queues Based on Fuzzy Logic and Particle Swarm Optimization algorithm for Task Scheduling in Cloud Computing

A Novel Architecture with Dynamic Queues Based on Fuzzy Logi...

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International Symposium on Ubiquitous Networking (UNet)

作者： Ben Alla, Hicham Ben Alla, Said Ezzati, Abdellah Mouhsen, Ahmed LAVETE Lab Sci & Tech Fac Math & Comp Sci Dept Hassan 1 Univ Settat 26000 Morocco

ISBN: (纸本)9789811016271;9789811016264

Cloud computing is an emerging high performance computing paradigm for managing and delivering services using a large collection of heterogeneous autonomous systems with flexible computational architecture. Task scheduling is one of the most challenging aspects to improve the overall performance of the cloud computing such as response time, cost, makespan, throughput etc. Task scheduling is also essential to reduce power consumption, processing time and improve the profit of service providers by decreasing operating costs and improving the system reliability. This paper focuses on Task Scheduling using a novel architecture with Dynamic Queues based on hybrid algorithm using Fuzzy Logic and Particle Swarm Optimization algorithm (TSDQ-FLpso) to optimize makespan and waiting time. The experimental result based on an open source simulator (CloudSim) show that the proposed TSDQ-FLpso provides an optimal balance results, minimizing the waiting time, reducing the makespan and improving the resource utilization compared to existing scheduling algorithms.

关键词： Task scheduling Cloud computing TSDQ-FLpso Fuzzy logic pso algorithm CloudSim

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User Requirements Based Service Identification for Big Data 24

User Requirements Based Service Identification for Big Data

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24th IEEE International Conference on Web Services (ICWS)

作者： Wang, Haiyan Zhang, Han Nanjing Univ Posts & Telecommun Jiangsu Key Lab Big Data Secur & Intelligent Proc Sch Comp Sci Nanjing Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Jiangsu High Technol Res Key Lab Wireless Sensor Sch Comp Sci Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781538607527

Service identification meets with new challenges with overwhelming rise of categories and numbers of services in big data scenarios. Most of the current service identification approaches have paid little attention to the granularity of indicator for service identification, neither do they provide with any trustworthy monitoring mechanism during the process of service identification. To address the problems above, we propose a user requirements based service identification approach for big data (URBSI-BD). In the proposed URBSI-BD, we firstly cluster massive services with BIRCH clustering algorithm to obtain a number of service sets. We then employ pso algorithm with MapReduce mechanism to achieve a fine-grained evaluation of indicator for service identification. Based on the integration, candidate services which can better meet with user requirements will be selected. Finally, we use Beth trust model on the quality of experience of users and set up a monitoring mechanism to better obtain required services. Simulation results and analysis demonstrate that the proposed approach has better performance in service identification compared with other current approaches in big data scenarios.

关键词： Service Identification User Requirements pso algorithm Quality of Experience (QoE)

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Small Image Recognition Classification based on random Dropout and pso-BP

Small Image Recognition Classification based on random Dropo...

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IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference

作者： Chen Ying-Xi Liao Xiao-Dong Wang Jian-Hua Tao Zhuang Sui Li-Yue College of Photonic and Electronic Engineering Fujian Normal University Fuzhou Fujian China Key Laboratory of Optoelectronic Science and Technology for Medicine Fujian Normal University Fuzhou Fujian China

With the development of information technology more and more rapidly, the development of image recognition classification technology is also increasingly improved. This paper proposes a method which combines random Dropout algorithm with pso-BP to realize the recognition and classification of small images. On the basis of the error back propagation adjustment weight of the traditional BP algorithm, the weight correction of pso algorithm is introduced, and the pso-BP network model is established. Then the hidden layer unit of pso-BP network is improved by using stochastic Dropout algorithm, so the fast operation speed is obtained. The results show that compared with the traditional BP and the pso-BP algorithm, the network error rate is obviously reduced after the algorithm is improved in this paper, and the real-time performance of the algorithm is improved to a certain extent.

关键词： Image identification Random dropout pso algorithm BP neural network

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A pso-based algorithm for optimal multiple chiller systems operation

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APPLIED THERMAL ENGINEERING 2012年第1期32卷 31-40页

作者： Beghi, Alessandro Cecchinato, Luca Cosi, Giovanni Rampazzo, Mirco Univ Padua Dipartimento Ingn Informaz I-35131 Padua Italy Univ Padua Dipartimento Fis Tecn I-35131 Padua Italy

In Heating, Ventilation and Air Conditioning (I-IVAC) plants of medium-high cooling capacity, multiple chiller systems are often employed. System performance is evaluated in terms of user comfort, energy use, and financial costs. The problem of efficiently managing multiple chiller systems is complex in many respects. In particular, the electrical energy consumption markedly increases if the machines are not properly managed. Therefore significant energy savings can be achieved by optimizing chiller operation. In this paper an unified method for efficient management of multiple chiller systems, by means of a Particle Swarm Optimization (pso) based algorithm, is presented. In particular, the results show that it is possible to achieve substantial energy savings while granting good load profile tracking with respect to standard approaches. The performance of the algorithm is evaluated by resorting to a simulation environment, where the plant dynamics are accurately described. The results show that pso satisfactorily deals with such kind of nonlinear constrained optimization problem, while offering advantages, with respect to other optimization techniques, in terms of ease of implementation. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Multiple chiller Optimal chiller loading Optimal chiller sequencing pso algorithm

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An ECM-pso based optimal charging current pattern searching for Li-ion batteries

An ECM-PSO based optimal charging current pattern searching ...

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2017 International Conference on Material Science,Energy and Environmental Engineering(MSEEE 2017)

作者： Haitao Min Weiyi Sun Zhiru Liu Yuanbin Yu Dongni Guo State Key Laboratory of Automotive Simulation and Control Jilin University

ISBN: (纸本)9781510847002

EV battery charging is growing in importance as it has a direct influence on the EV *** Constant Current(MCC) charging strategy is an effective rapid charging *** paper proposes an ECM-pso algorithm to search for the optimal current pattern of the MCC charging *** MCC charging model is built according to the Li-ion battery equivalent circuit model, and the object function for the ECM-pso optimization was formulated and the optimal current pattern of the MCC charging method was obtained through the ECM-pso *** experimental result shows that the obtained current pattern is capable of charging the batteries to 87.7% of the rated capacity within 36.5 *** with the conventional CC-CV charging method, the proposed charging strategy has a performance improvement of 60.3% in charging time reduction, and the average temperature rise during the proposed MCC charging process is 0.5 ℃ lower than the CC-CV charging method.

关键词： Li-ion batteries ECM pso algorithm Multistage constant current charging

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Design and Optimization of Hybrid Micro-Grid System

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Energy Procedia 2017年 117卷 95-103页

作者： M Jayachandran G Ravi

Hybrid microgrid systems (HMGS) comprise of several parallel connected distributed resources with electronically controlled strategies, which are capable to operate in both islanded and grid connected mode. HMGS based on renewable energy sources (RES) is the cost-effective option for solving the power supply problem in remote areas, which are located far from grids. In this paper, the wind and solar meteorological data for Sundarban (India) station are used to design Islanded HMGS for providing necessary electricity. Cost effectiveness and system reliability are major factors considered for designing HMGS to achieve better power management scheme. Particle Swarm Optimization (pso) scheme is applied to identify the sizing of wind turbines (WT), photovoltaic (PV) module, battery energy storage system (BESS) and diesel generator, and find the optimal configuration of HMGS system. The design and optimal operation of HMGS system has been developed and validated through MATLAB software.

关键词： HMGS pso algorithm Power Management Scheme

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