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• 地理科学与资源研究所 [2]
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• 长春光学精密机械与物... [2]
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• OAI收割 [13]

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• 期刊论文 [10]
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• 2022 [2]
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浏览/检索结果: 共13条，第1-10条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择题名升序题名降序提交时间升序提交时间降序作者升序作者降序发表日期升序发表日期降序 Inner Dynamic Detection and Prediction of Water Quality Based on CEEMDAN and GA-SVM Models 期刊论文  OAI收割 REMOTE SENSING, 2022, 卷号: 14, 期号: 7, 页码: 17 作者:  Yang, Zhizhou;  Zou, Lei;  Xia, Jun;  Qiao, Yunfeng;  Cai, Diwen   |  收藏  |  浏览/下载：31/0  |  提交时间：2022/09/21 CEEMDAN method  GA-SVM model  decomposition  prediction  water quality   Inner Dynamic Detection and Prediction of Water Quality Based on CEEMDAN and GA-SVM Models 期刊论文  OAI收割 REMOTE SENSING, 2022, 卷号: 14, 期号: 7, 页码: 17 作者:  Yang, Zhizhou;  Zou, Lei;  Xia, Jun;  Qiao, Yunfeng;  Cai, Diwen   |  收藏  |  浏览/下载：23/0  |  提交时间：2022/09/21 CEEMDAN method  GA-SVM model  decomposition  prediction  water quality   Electrochemical system for anaerobic oxidation of methane by DAMO microbes with nitrite as an electron acceptor 期刊论文  OAI收割 SCIENCE OF THE TOTAL ENVIRONMENT, 2021, 卷号: 225, 页码: - 作者:  Chai, Fengguang;  Li, Lin;  Xue, Song;  Xie, Fei;  Liu, Junxin   |  收藏  |  浏览/下载：20/0  |  提交时间：2022/01/04 PM2.5  Machine learning  GA-SVM  Land use regression  Method improvement  Spatial clustering   Automatic recognition system of welding seam type based on SVM method 期刊论文  OAI收割 INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2017, 卷号: 92, 期号: 1-4, 页码: 989-999 作者:  Fan, Junfeng;  Jing, Fengshui;  Fang, Zaojun;  Tan, Min   |  收藏  |  浏览/下载：31/0  |  提交时间：2018/01/05 Welding Seam Type Recognition  Structured-light Vision  Svm Method  Feature Extraction   Automatic recognition system of welding seam type based on SVM method 期刊论文  OAI收割 INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2017, 卷号: 92, 期号: 1-4, 页码: 989-999 作者:  Fan JF(范俊峰);  Jing FS(景奉水);  Fang ZJ(方灶军);  Tan M(谭民)   |  收藏  |  浏览/下载：21/0  |  提交时间：2019/04/27 Welding Seam Type Recognition  Structured-light Vision  Svm Method  Feature Extraction   Prediction of viscosity of imidazolium-based ionic liquids using MLR and SVM algorithms 期刊论文  OAI收割 COMPUTERS & CHEMICAL ENGINEERING, 2016, 卷号: 92, 期号: SEP, 页码: 37-42 作者:  Zhao, Yongsheng;  Zhang, Xiangping;  Deng, Liyuan;  Zhang, Suojiang 收藏  |  浏览/下载：24/0  |  提交时间：2016/11/21 Ionic liquids  Viscosity  Support vector machine (SVM)  Multiple linear regression (MLR)  Fragment contribution-corresponding  states (FC-CS) method   Probabilistic back analysis for geotechnical engineering based on Bayesian and support vector machine 期刊论文  OAI收割 JOURNAL OF CENTRAL SOUTH UNIVERSITY, 2015, 卷号: 22, 期号: 12, 页码: 4778-4786 作者:  Chen Bing-rui;  Zhao Hong-bo;  Ru Zhong-liang;  Li Xian   |  收藏  |  浏览/下载：11/0  |  提交时间：2018/06/05 geotechnical engineering  back analysis  uncertainty  Bayesian theory  least square method  support vector machine (SVM)   sEMG Based Movement Quantitative Estimation of Joins Using SVM Method 会议论文  OAI收割 19th World Congress of the International Federation of Automatic Control, Cape Town, South Africa, August 24-29, 2014 作者:  Liu, Dongsheng;  Zhao XG(赵新刚);  Ye D(叶丹);  Zhao YW(赵忆文);  Wu ZW(吴镇炜) 收藏  |  浏览/下载：49/0  |  提交时间：2014/12/29 sEMG  movement estimation  quantitative estimation  SVM method  rehabilitation robot   Classification of hyperspectral image based on SVM optimized by a new particle swarm optimization (EI CONFERENCE) 会议论文  OAI收割 2012 2nd International Conference on Remote Sensing, Environment and Transportation Engineering, RSETE 2012, June 1, 2012 - June 3, 2012, Nanjing, China 作者:  Gao X.;  Yu P.;  Yu P. 收藏  |  浏览/下载：21/0  |  提交时间：2013/03/25 Support Vector Machine (SVM) is used to classify hyperspectral remote sensing image in this paper. Radial Basis Function (RBF)  which is most widely used  is chosen as the kernel function of SVM. Selection of kernel function parameter is a pivotal factor which influences the performance of SVM. For this reason  Particle Swarm Optimization (PSO) is provided to get a better result. In order to improve the optimization efficiency of kernel function parameter  firstly larger steps of grid search method is used to find the appropriate rang of parameter. Since the PSO tends to be trapped into local optimal solutions  a weight and mutation particle swam optimization algorithm was proposed  in which the weight dynamically changes with a liner rule and the global best particle mutates per iteration to optimize the parameters of RBF-SVM. At last  a 220-bands hyperspectral remote sensing image of AVIRIS is taken as an experiment  which demonstrates that the method this paper proposed is an effective way to search the SVM parameters and is available in improving the performance of SVM classifiers. 2012 IEEE.   Support Vector Machines with L-1 penalty for detecting gene-gene interactions 期刊论文  OAI收割 INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS, 2012, 卷号: 6, 期号: 5, 页码: 463-470 作者:  Shen, Yuanyuan;  Liu, Zhe;  Ott, Jurg 收藏  |  浏览/下载：28/0  |  提交时间：2015/09/09 genome-wide association study  human complex disease  gene-gene interaction  SVM  support vector machine  model selection  L-1 penalty  two-stage method  data mining  bioinformatics