2018 International Computers, Signals and Systems Conference (ICOMSSC)最新文献_第3页

Design and Optimization of Lorentz Actuator with Good Linearity, Large Stroke, and Low Heat Dissipation for Micro-vibration Isolation 线性度好、行程大、散热低的微振动隔离洛伦兹驱动器的设计与优化

2018 International Computers, Signals and Systems Conference (ICOMSSC) Pub Date : 2018-09-01 DOI: 10.1109/icomssc45026.2018.8941990

Qianqian Wu, Ning Cui, Xiaohong Xiao, Sifang Zhao

引用次数: 0

Research on DOA Estimation of Frequency Hopping Signal 跳频信号DOA估计方法研究

2018 International Computers, Signals and Systems Conference (ICOMSSC) Pub Date : 2018-09-01 DOI: 10.1109/icomssc45026.2018.8942035

Hanjing Li, Yuwen Wang, Qiang Shu, Yongwei Li, Yingzhu Chen

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Large-eddy Simulation of Cabin Fire Spreading under Ventilation Conditions 通风条件下舱内火势蔓延的大涡模拟

2018 International Computers, Signals and Systems Conference (ICOMSSC) Pub Date : 2018-09-01 DOI: 10.1109/icomssc45026.2018.8941788

Y. Jing, Jun Wan

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A Wideband Spectrum Sensing Based on ESPRIT Algorithm 基于ESPRIT算法的宽带频谱感知

2018 International Computers, Signals and Systems Conference (ICOMSSC) Pub Date : 2018-09-01 DOI: 10.1109/icomssc45026.2018.8941793

Yanni Shen, Q. Wan, Changxiong Xia, Y. Wan

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Path-constrained Viterbi Algorithm: an Alternative to State-transition Feature for Conditional Random Fields 路径约束Viterbi算法:条件随机场状态转换特征的替代方案

2018 International Computers, Signals and Systems Conference (ICOMSSC) Pub Date : 2018-09-01 DOI: 10.1109/icomssc45026.2018.8941699

Yulin Ren, Dehua Li

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Flexible Refocusing Method for Camera Arrays Based on Reprojection 基于重投影的相机阵列柔性重聚焦方法

2018 International Computers, Signals and Systems Conference (ICOMSSC) Pub Date : 2018-09-01 DOI: 10.1109/icomssc45026.2018.8942001

Chao Xiao, Jungang Yang, Yingqian Wang, W. An

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ICOMSSC 2018 Organizing Committees ICOMSSC 2018组委会

2018 International Computers, Signals and Systems Conference (ICOMSSC) Pub Date : 2018-09-01 DOI: 10.1109/icomssc45026.2018.8941687

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Energy Efficiency Prediction of Screw Chillers on BP Neural Network Optimized by Improved Genetic Algorithm 基于改进遗传算法优化BP神经网络的螺杆式冷水机组能效预测

2018 International Computers, Signals and Systems Conference (ICOMSSC) Pub Date : 2018-09-01 DOI: 10.1109/icomssc45026.2018.8941846

Nianfeng Mei, Fengfeng Qian, Liangwen Yan, Wei Li

{"title":"Energy Efficiency Prediction of Screw Chillers on BP Neural Network Optimized by Improved Genetic Algorithm","authors":"Nianfeng Mei, Fengfeng Qian, Liangwen Yan, Wei Li","doi":"10.1109/icomssc45026.2018.8941846","DOIUrl":"https://doi.org/10.1109/icomssc45026.2018.8941846","url":null,"abstract":"To improve the accuracy of energy efficiency prediction of screw chillers, the optimized back propagation (BP) neural network through improved genetic algorithm(GA) was proposed. In this study, a modified genetic algorithm (MGA) based on a novel selection strategy was presented to optimize back propagation (BP) neural network (MGA-BP). Each individual in selection process was offered with a fitness value. The individuals whose fitness values are greater than the average fitness of people are retained to next generation. Additionally, an improved genetic algorithm (iGA) based on an optimal selection strategy is put forward to optimize back propagation (BP) neural network (iGA-BP). Each generation population is sorted by small to large according to the fitness degree, and individuals of the even bits are reserved to the next generation. Individuals are uniformly divided into two sections, namely, the front and tail sections. The larger part of the whole was retained. 300 sets of the acquired historical data from a screw chillers were used to train the network and 50 groups were selected to verify the model. The comparison of MGA-BP and iGA-BP with GA-BP whose selection operator is roulette- wheel selection operator, the results showed that the convergence rate of MGA-BP and iGA-BP increased by 27.03% and 43.24% respectively. Furthermore, the maximum relative errors predicted by MGA-BP and iGA-BP were 5.99%, 3.45% respectively, and the average relative error was within 2.0%. Therefore, the proposed prediction method can improve availably the accuracy of the energy efficiency prediction model of screw chillers.","PeriodicalId":332213,"journal":{"name":"2018 International Computers, Signals and Systems Conference (ICOMSSC)","volume":"342 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134107440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Novel Method for Analysis of Power Quality Transient Disturbances 一种新的电能质量暂态扰动分析方法

2018 International Computers, Signals and Systems Conference (ICOMSSC) Pub Date : 2018-09-01 DOI: 10.1109/icomssc45026.2018.8941670

Fida Hussain, Hui Liu, Yue Shen

{"title":"A Novel Method for Analysis of Power Quality Transient Disturbances","authors":"Fida Hussain, Hui Liu, Yue Shen","doi":"10.1109/icomssc45026.2018.8941670","DOIUrl":"https://doi.org/10.1109/icomssc45026.2018.8941670","url":null,"abstract":"Several methods have been revealed in the reference for detection and localization of power quality (PQ) transient disturbances utilizing S-transform, Fourier transforms, Gabor-Wigner, Gabor transform, Hilbert transform and families of the wavelet transform. However, this paper presents an alternative approach to detect and locate the power quality (PQ) transient disturbances based on singular spectrum analysis (SSA). SSA is a non-parametric technique, which does not require any supposition to generate the observed signal, and affords an effective way to recognize weak transient PQ signal. It has the capability to decompose the PQ transient disturbance signals into a sum of a small number of detectable oscillatory components, removing noise and reconstructing the original signal. Based on the proposed method, transient signals are decomposed into approximate and detail signal. The experiment is performed using PQ long-and-short duration transient disturbances such as high and low-frequency oscillations, impulsive transient and voltage sag. The results of the experiment are compared with multi-resolution db8 wavelet transform. As shown in the simulation experiment results, the proposed SSA technique can be used effectively to detect and locate the transient disturbance. The proposed technique is efficient and alternative technique comparatively wavelet transform and SSA works well even in noisy environment.","PeriodicalId":332213,"journal":{"name":"2018 International Computers, Signals and Systems Conference (ICOMSSC)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115101652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Domain Decomposition Method for Propagation Prediction of Large-range Evaporation Duct 大范围蒸发管道传播预测的区域分解方法

2018 International Computers, Signals and Systems Conference (ICOMSSC) Pub Date : 2018-09-01 DOI: 10.1109/icomssc45026.2018.8941824

Gao Ying, Yan Bin-zhou, Shao Qun, Guo Shuxia

{"title":"Domain Decomposition Method for Propagation Prediction of Large-range Evaporation Duct","authors":"Gao Ying, Yan Bin-zhou, Shao Qun, Guo Shuxia","doi":"10.1109/icomssc45026.2018.8941824","DOIUrl":"https://doi.org/10.1109/icomssc45026.2018.8941824","url":null,"abstract":"In order to solve the problems of long time, low efficiency, low precision, high performance of computer and failing to meet the needs of engineering in the study of the refractive index profile and propagation loss of large range atmospheric duct, a kind of non-overlapping domain decomposition method (DDM) combined with the least square support vector machine (LSSVM) is proposed. The training database is first produced by the forward propagation model, and the large area is decomposed into multiple sub-domains by non-overlapping DDM to make a large number of data effectively divided into small sample data in the sub-domain to train the least squares support vector machine and optimize the parameters of the least squares support vector machine. A fast prediction of propagation loss and refractive index profile of large range evaporation duct is achieved. The propagation loss of the parabolic equation and the true value of the refractive index profile are compared with the predicted data. The results show that the method improves the efficiency and accuracy of the refractive index profile and propagation loss of the large range evaporation duct, reduces the computer performance requirements, which is of universal significance for the prediction of the propagation loss and refractive index profile of large range atmospheric duct, and of great value to the elimination of the influence of duct on radar and communication equipment.","PeriodicalId":332213,"journal":{"name":"2018 International Computers, Signals and Systems Conference (ICOMSSC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114375304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0