2010 Third International Joint Conference on Computational Science and Optimization最新文献_第10页

Single-Machine Scheduling Problems with Past-Sequence-Dependent Setup Times and Deterioration Jobs 具有过去序列相关的设置时间和劣化作业的单机调度问题

2010 Third International Joint Conference on Computational Science and Optimization Pub Date : 2010-05-28 DOI: 10.1109/CSO.2010.120

Yunqiang Yin, Z. Ruan, Hai Sun

引用次数: 2

Interpolation and Numerical Integration of the Septuple Supported Spline Wavelet 七支样条小波的插值与数值积分

2010 Third International Joint Conference on Computational Science and Optimization Pub Date : 2010-05-28 DOI: 10.1109/CSO.2010.64

Zhongshe Gao, Yonghong Shen

引用次数: 0

Robust Sensor Placement Problem in Municipal Water Networks 城市供水网络中的鲁棒传感器配置问题

2010 Third International Joint Conference on Computational Science and Optimization Pub Date : 2010-05-28 DOI: 10.1109/CSO.2010.166

Xin Ma, Yuantao Song, Jun Huang, Jun Wu

引用次数: 10

A Nonlinear Ensemble Prediction Model Based on Genetic Algorithm for Calculation Wind of the Wind Assessment 基于遗传算法的风评估计算风非线性集成预测模型

2010 Third International Joint Conference on Computational Science and Optimization Pub Date : 2010-05-28 DOI: 10.1109/CSO.2010.13

Kaiping Lin, Xiaoyan Huang, Weiliang Liang, Binglian Chen

引用次数: 2

Compression of Inverted Index for Comprehensive Performance Evaluation in Lucene Lucene中综合性能评价倒排指标的压缩

2010 Third International Joint Conference on Computational Science and Optimization Pub Date : 2010-05-28 DOI: 10.1109/CSO.2010.126

Xianghua Xu, Shengyi Pan, Jian Wan

引用次数: 6

Massive Spatial Data Processing Model Based on Cloud Computing Model 基于云计算模型的海量空间数据处理模型

2010 Third International Joint Conference on Computational Science and Optimization Pub Date : 2010-05-28 DOI: 10.1109/CSO.2010.168

Dong Cui, Yunlong Wu, Qiang Zhang

{"title":"Massive Spatial Data Processing Model Based on Cloud Computing Model","authors":"Dong Cui, Yunlong Wu, Qiang Zhang","doi":"10.1109/CSO.2010.168","DOIUrl":"https://doi.org/10.1109/CSO.2010.168","url":null,"abstract":"Cloud computing model can take advantage of the network resources, creating a powerful computing capacity to meet the real-time processing a large amount of spatial data. So this paper showed a cloud computing model based on a large amount of spatial data processing model. It used not only controller to implement the distribution of spatial data processing tasks but cloud computing power to achieve the corresponding supervised classification and unsupervised classification as well as the surface features information extraction, the extraction of NDVI (Normal Differential Vegetation Index), and made it possible to monitor the application and rapid flooding and the realization of real-time monitoring for dust storms. Forest fires can be used in monitoring, timely understanding of the spread of fire, as well as control of key areas. At the same time, in the aspect of environmental protection, the system can be applied to the ozone layer and the monitoring of glacier flow and determine the direction of study. While the ocean remote sensing can provide real-time data processing of marine red tide monitoring data, and propose feasible solutions.","PeriodicalId":427481,"journal":{"name":"2010 Third International Joint Conference on Computational Science and Optimization","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114658288","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: 28

Development of Repetitively Enhanced Neural Networks (RENN) for Efficient Missile Design and Optimization 用于导弹高效设计与优化的重复增强神经网络(RENN)

2010 Third International Joint Conference on Computational Science and Optimization Pub Date : 2010-05-28 DOI: 10.1109/CSO.2010.150

Nhu-Van Nguyen, Kwon-Su Jeon, Jae-Woo Lee, Y. Byun

引用次数: 2

Framework for Simulation of Quantized Virtual Society Based on Semantic P2P Network 基于语义P2P网络的量化虚拟社会仿真框架

2010 Third International Joint Conference on Computational Science and Optimization Pub Date : 2010-05-28 DOI: 10.1109/CSO.2010.57

Lican Huang

引用次数: 2

A Simulation-Based Study on Spurious Timeouts and Fast Retransmits of TCP in Wireless Networks 无线网络中TCP伪超时与快速重传的仿真研究

2010 Third International Joint Conference on Computational Science and Optimization Pub Date : 2010-05-28 DOI: 10.1109/CSO.2010.186

Mi-Young Park, Sang-Hwa Chung

引用次数: 3

A Novel Bayesian Additive Regression Trees Ensemble Model Based on Linear Regression and Nonlinear Regression for Torrential Rain Forecasting 基于线性回归和非线性回归的暴雨预报贝叶斯加性回归树集成模型

2010 Third International Joint Conference on Computational Science and Optimization Pub Date : 2010-05-28 DOI: 10.1109/CSO.2010.15

Jiansheng Wu, Liangyong Huang, Xiaoming Pan

{"title":"A Novel Bayesian Additive Regression Trees Ensemble Model Based on Linear Regression and Nonlinear Regression for Torrential Rain Forecasting","authors":"Jiansheng Wu, Liangyong Huang, Xiaoming Pan","doi":"10.1109/CSO.2010.15","DOIUrl":"https://doi.org/10.1109/CSO.2010.15","url":null,"abstract":"In order to improve the accuracy of precipitation forecasting with the linear regression of traditional statistical model and the nonlinear regression of Neural Network (NN) model, especially in torrential rain, a novel Bayesian Additive Regression Trees (BART) ensemble model is proposed in this paper. Firstly, three different linear regression model are used to extract the linear characteristic of rainfall system with the Partial Squares Least Regression, the Quantile Regression and the M-regression. Secondly, three different NNs model are used to extract the nonlinear characteristics of rainfall system with the General Regression Neural Network (GR--NN), the Radial Basis Function Neural Network (RBF--NN) and the Levenberg-Marquardt Algorithm Neural Network (LMA--NN). Finally, the BART is used for ensemble model based on linear and nonlinear regression. For illustration, a summer daily rainfall example is utilized to show the feasibility of the BART ensemble model in improving the accuracy of torrential rainfall with linear regression and nonlinear regression model. Empirical results obtained reveal that the torrential rainfall prediction by using the BART ensemble model is generally better than those obtained using other models presented in this paper in terms of the same evaluation measurements. Our findings reveal that the BRAT ensemble model proposed here can be used as an alternative forecasting tool for a Severe Weather application in achieving greater forecasting accuracy and improving prediction quality further.","PeriodicalId":427481,"journal":{"name":"2010 Third International Joint Conference on Computational Science and Optimization","volume":"235 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134069094","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: 17