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2011 International Symposium on Water Resource and Environmental Protection(ISWREP 2011)

作者： Lili HOU, Yunfeng LI, Nianjun YE, Yiping ZHU Department of Hydrogeology, Nanjing Institute of Geology and Mineral Resources, Nanjing, China Yuqiao LONG Department of Hydrology and Water Resources, Nanjing Hydraulic Research Institute, Nanjing, China

Based on the meteorological data from 6 stations in Taiyuan, variation characteristics of precipitation, evaporation and temperature in recent 60 years were analyzed, and variation trends of the three meteorological elements in Taiyuan were detected with linear regression method and Mann-Kendall rank correlation method. The results indicate that annual precipitation in Taiyuan during 1951～2006 presented weak decreasing trend which has been obviously since 1990's with average decreasing rate of 13.03mm/10a;among four seasons, decreasing in autumn is the most significant for the rate of 6.13 mm/10a. Annual evaporation in Taiyuan showed obvious decreasing trend from 1970's, and the average rate is 16.28mm/10a, as for seasons, decreasing trend in summer to be the most significant as decreasing rate is 8.42 mm/10a. Annual average temperature presented evident increasing trend from 1980's with the warming rate of 0.38/10a, temperature increasing trend in winter to be more significant than in other seasons, and average rate is 0.61/10a.

关键词： precipitation evaporation temperature Mann-Kendall rank correlation method trend detection linear regression method

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Greek long-term energy consumption prediction using artificial neural networks

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ENERGY 2010年第2期35卷 512-517页

作者： Ekonomou, L. ASPETE Sch Pedag & Technol Educ Dept Elect Engn Educators Athens 14121 Greece

In this paper artificial neural networks (ANN) are addressed in order the Greek long-term energy consumption to be predicted. The multilayer perceptron model (MLP) has been used for this purpose by testing several possible architectures in order to be selected the one with the best generalizing ability. Actual recorded input and output data that influence long-term energy consumption were used in the training, validation and testing process. The developed ANN model is used for the prediction of 2005-2008, 2010, 2012 and 2015 Greek energy consumption. The produced ANN results for years 2005-2008 were compared with the results produced by a linear regression method, a support vector machine method and with real energy consumption records showing a great accuracy. The proposed approach can be useful in the effective implementation of energy policies, since accurate predictions of energy consumption affect the capital investment, the environmental quality, the revenue analysis, the market research management, while conserve at the same time the supply security. Furthermore it constitutes an accurate tool for the Greek long-term energy consumption prediction problem, which up today has not been faced effectively. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Artificial neural networks Energy consumption Gross domestic product Installed capacity Multilayer perceptron Prediction linear regression method Support vector machine method

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vFitness: a web-based computing tool for improving estimation of in vitro HIV-1 fitness experiments

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BMC BIOINFORMATICS 2010年第8期11卷 1-6页

作者： Ma, Jingming Dykes, Carrie Wu, Tao Huang, Yangxin Demeter, Lisa Wu, Hulin Univ Rochester Sch Med & Dent Dept Biostat & Computat Biol Rochester NY 14642 USA Univ Rochester Sch Med & Dent Dept Med Rochester NY 14642 USA Univ S Florida Coll Publ Hlth Dept Epidemiol & Biostat Tampa FL USA

Background: The replication rate (or fitness) between viral variants has been investigated in vivo and in vitro for human immunodeficiency virus (HIV). HIV fitness plays an important role in the development and persistence of drug resistance. The accurate estimation of viral fitness relies on complicated computations based on statistical methods. This calls for tools that are easy to access and intuitive to use for various experiments of viral fitness. Results: Based on a mathematical model and several statistical methods (least-squares approach and measurement error models), a Web-based computing tool has been developed for improving estimation of virus fitness in growth competition assays of human immunodeficiency virus type 1 (HIV-1). Conclusions: Unlike the two-point calculation used in previous studies, the estimation here uses linear regression methods with all observed data in the competition experiment to more accurately estimate relative viral fitness parameters. The dilution factor is introduced for making the computational tool more flexible to accommodate various experimental conditions. This Web-based tool is implemented in C# language with Microsoft ***, and is publicly available on the Web at http://***/vFitness/.

关键词： Computing Tool Dilution Factor Mutant Virus Relative Fitness linear regression method

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GROUP-MEMBERSHIP RECOGNITION methodS BASED ON CONFIDENCE BOUNDARIES AND THEIR TESTING IN CLINICAL ONCOLOGY

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CYBERNETICS AND SYSTEMS ANALYSIS 2009年第4期45卷 517-527页

作者： Petunin, Yu I. Klyushin, D. A. Golubeva, E. N. Naleskina, L. A. Kunskaya, L. N. Chekhun, V. F. Natl Taras Shevchenko Univ Kiev Ukraine Natl Acad Sci Ukraine RE Kavetsky Inst Expt Pathol Oncol & Radiobiol Kiev Ukraine

Confidence intervals are constructed for an unknown correlation coefficient and mathematical expectation using 3s(1) and 2s(1) rules. The intervals make it possible to solve the problem of statistical analysis of the dependence between different random variables. The results obtained are used to analyze the correlation between the content of homocysteine and various clinical indices.

关键词： linear regression method confidence interval for mathematical expectation confidence interval for a correlation coefficient homocysteine breast cancer

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Application of Group method of Data Handling to Stream-Way Transition

Application of Group Method of Data Handling to Stream-Way T...

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1st IITA International Joint Conference on Artificial Intelligence

作者： Ma, Shih-Wei Kou, Chang-Huan Chen, Li Wang, An-Pei Chung Hua Univ Dept Civil Engn & Engn Informat Hsinchu 30012 Taiwan Chung Yuan Christian Univ Dept Civil Engn Chungli 32023 Taiwan

ISBN: (纸本)9780769536156

The main purpose of this paper is to predict streamway transition with group method of data handling (GMDH). Therefore, the downstream stream-way transition according to the upstream conditions is forecasted by group method of data handling. Five main factors may affect the stream-way transition include inflow position, inflow angle, slope, flow discharge, and sand content of suspended sediment. We selected some cross sections of Ta-Chia River in Taiwan as a case study. The results show that GMDH has better performance than the traditional linear regression method.

关键词： Stream-way transition group method of data handling linear regression method

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Comparison of some linear regression methods – available in R – for a QSPR problem

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BMC Chemistry 2009年第1期3卷 1-1页

作者： K Varmuza P Filzmoser Vienna University of Technology Institute of Chemical Engineering Getreidemarkt 9/166 A-1060 Vienna Austria Vienna University of Technology Institute of Statistics and Probability Theory Wiedner Hauptstrasse 8-10 A-1040 Vienna Austria

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Application of Data Mining in Psychological Evaluation

Application of Data Mining in Psychological Evaluation

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International Symposium on Computer Science and Computational Technology

作者： Tong, Hengqing Guo, Li Wuhan Univ Technol Dept Math Wuhan 430070 Peoples R China

ISBN: (纸本)9780769534985

This paper studies the realizing progress of data mining in psychological evaluation. Based on the matrix of evaluation data and the actual evaluation work, we propose a kind of generalized linear regression model with convex constraint, named as Evaluation Model, in which the dependent variable and regression coefficients all are unknown. We analyze the algorithm of the model with linear regression method. Then the least square estimate of parameters using the iterative projection algorithm is presented. At last, we use this least square iterative method to work out the parameters of the evaluation model. Our work could be a useful guide for the further research of data mining in psychological evaluation.

关键词： Matrix of Evaluation Data Evaluation Model linear regression method Iterative Projection Algorithm

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Interpolating support information granules

Interpolating support information granules

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Neurocomputing

作者： Appolloni, Bruno Bassis, Simone Malchiodi, Dario Pedrycz, Witold Department of Computer Science University of Milan Via Comelico 39/41 20135 Milano Italy Department of Electrical and Computer Engineering University of Alberta ECERF 9107 - 116 Street Edmonton AL T6G 2V4 Canada

We introduce a regression method that fully exploits both global and local information about a set of points in search of a suitable function explaining their mutual relationships. The points are assumed to form a repository of information granules. At a global level, statistical methods discriminate between regular points and outliers. Then the local component of the information embedded in the former is used to draw an optimal regression curve. We address the challenge of using a variety of standard machine learning tools such as support vector machine (SVM) or slight variants of them within the unifying hat of Granular Computing realm to obtain a definitely new featured nonlinear regression method. The performance of the proposed approach is illustrated with the aid of three well-known benchmarks and ad hoc featured datasets. © 2008 Elsevier B.V. All rights reserved.

关键词： Information granules Benchmarking Granular computing Granulation regression analysis Support vector machines Algorithmic Inference Global and local informations Kernel methods linear regression confidence region Modified SVM Non linear regression method regression method Standard machines

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Improving case mix for description and funding in rehabilitation in France: additive model is better than tree-classification

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BMC Health Serv Res 2008年第SUPPL 1期8卷 A2-A2页

作者： Pierre Métral N Ducret A Patris P Steunou ATIH agence technique de l'information sur l'Hospitalisation 117 boulevard Vivier Merle 69392 Lyon cedex 03 France

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Radiometric cross-calibration of CBERS-02 IRMSS thermal channel

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JOURNAL OF INFRARED AND MILLIMETER WAVES 2006年第4期25卷 261-266页

作者： Zhang Yong Gu Xing-Fa Yu Tao Zhang Yu-Xiang Qi Rui-Li Li Xiao-Wen China Meteorol Adm Natl Satellite Meteorol Ctr Beijing 100081 Peoples R China CAS Inst Remote Sensing Applicat State Key Lab Remote Sensing Sci Beijing 100101 Peoples R China Natl Space Adm Demonstrat Ctr Spaceborne Remote Sensing Beijing 100101 Peoples R China INRA Climat Sol Environm F-84914 Avignon France Xian Technol Univ Coll Opt Elect Engn Xian 710032 Peoples R China

TERRA MODIS band 31 was selected as the criterion for doing the radiometric cross-calibration of CBERS-02 IRMSS thermal band. From August to December, 2004, seven times day and night synchronous images of two sensors passing through the Lake Qinghai and Lake Taihu were selected to obtain the cross-calibration data. TERRA MODIS band 31 data were used to conduct out the pupil radiance of CBERS-02 IRMSS band 9 based on the two sensors' spectrum matching, and then the DN values were picked-up from the IRMSS data in the same area. A new model to calculate the radiometric calibration coefficients was carried out in this study. The model is multi-points linear regression method with 7 times day and night synchronous images at different dates and locations. The radiometric cross-calibration coefficients are 8.0567 (gain, unit: DN/(W/m(2)/sr(1)/mu m(1))) and 47.892 (offset, unit: DN). Accuracy estimate of calibration coefficients was carried out. The results show that the calibration coefficients obtained from the linear regression method have a similar precision as TERRA MODIS band 31 ' s. The calibration coefficients can satisfy the quantitative applications of CBERS-02 IRMSS thermal data.

关键词： thermal channel spectrum matching radiometric cross-calibration linear regression method

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