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2nd IEEE International Conference on Cloud Computing and Intelligent Systems (CCIS)

作者： Wang, Xiaoni Gao, Xuedong Univ Sci & Technol Beijing Dongling Sch Econ & Management Beijing 100083 Peoples R China Beijing Informat Sci & Technol Univ Sch Appl Sci Beijing 100192 Peoples R China

ISBN: (纸本)9781467318556;9781467318570

Web hosting has a lot of problems, such as low security, waste of bandwidth resource or sudden lack of resource. A self-adaptive cloud computing architecture--RA-Cloud architecture (Resource-aware Cloud Computing Architecture) based on web security in resource-aware situation is proposed. This architecture uses cloud computing technology and self-adaptive linear regression model to estimate the dynamic migration threshold and to search the most suitable neighbor node for data migration. This platform and algorithm distributes the huge amount of computing resource to many server "end" according to the resource constrained self-adaptive policy. The new method increases the user access speed, enhances the anti-attack ability, blocks the attacks effectively and improves the web site security.

关键词： Cloud computing architecture Web security Resource-aware Self-adaptive linear regression model

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A Semantic Integration System for Heterogeneous Bioinformatics Data

A Semantic Integration System for Heterogeneous Bioinformati...

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2nd International Conference on Computer Science and Network Technology (ICCSNT)

作者： Dai, Weidi Cheng, Jianlai Wang, Qiuwen Tianjin Univ Dept Comp Sci & Technol Tianjin Peoples R China

ISBN: (纸本)9781467329644;9781467329637

A data integrating system, designed to provide unified access to multiple, heterogeneous biological and medical data sources, is proposed in this paper. Compared with other analogous systems, a linear regression model is used to improve the efficient of ontology alignment by results of string-based ontology alignment algorithms. We build domain ontology from object-oriented perspective to integrate bioinformatics information stored in relational databases, and describe the detailed process of heterogeneous data integration.

关键词： Semantic Web ontology alignment linear regression model data integration

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Determination of in situ moisture content in soils from a measure of dielectric constant

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INTERNATIONAL JOURNAL OF GEOTECHNICAL ENGINEERING 2012年第2期6卷 251-259页

作者： Bryson, L. Sebastian Jean-Louis, Melinda Gabriel, Chrispin Univ Kentucky Dept Civil Engn 161 Raymond Bldg Lexington KY 40506 USA

There are currently several electromagnetic sensors commercially available for determining in situ moisture content. All of these sensors work in a similar manner. A known electrical signal is sent into the soil. The signal measured back from the soil is expressed in the form of a complex dielectric constant that is directly related to the amount of water present in the soil. As the real component of the dielectric constant of water is four to eight times greater than most soils, changes in water content directly affect the sensor output. Soil calibration models specific to the various soil types are developed by plotting measurements of the real component versus gravimetric moisture contents. The subsequent linear regression line then becomes the soil-specific model. The focus of this study was to compare linear calibration models developed for various soil samples, with various geotechnical properties. The intent was to develop a generalized calibration model for the electromagnetic sensor. The generalized model will allow moisture content to be determined in situ, regardless of the soil type. The results of this study was the development of a generalized soil model that predicts the in situ moisture content for fine grain soils as well as the soil-specific models. In general, this study provides a framework for developing methodology to predict other in situ geotechnical parameters from measurements of the complex dielectric constant.

关键词： moisture content clay fraction complex dielectric constant electrical permittivity electromagnetic sensor linear regression model

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Computational Identification of Proteins Sub-network in Parkinson's Disease Study

Computational Identification of Proteins Sub-network in Park...

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6th International Conference on Anti-Counterfeiting, Security and Identification (ASID)

作者： Huang, Yue Zhang, Jun Huang, Yunying Xiamen Univ Dept Commun Engn Xiamen Peoples R China Xiamen Univ Tan Kah Kee Coll Dept Elect Engn Xiamen Peoples R China

ISBN: (纸本)9781467321433

Parkinson's disease (PD) is a typical case of neurodegenerative disorder, which often impairs the sufferer's motor skills, speech, and other functions. Combination of protein-protein interaction (PPI) network analysis and gene expression studies provides a better insight of Parkinson's disease. In our work a computational approach was developed to identify protein signal network in PD study. First, a network-constrain regularization analysis is employed to the linear regression model for gene expression data from transgenic mouse models in normal and with Parkinson's disease. Proteins sub-network was then detected based on an integer linear programming model by integrating microarray data and PPI database.

关键词： Parkinson's disease microarray data linear regression model integer linear programming protein network detection

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'Suddenly I get into the zone': Examining discontinuities and nonlinear changes in flow experiences at work

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HUMAN RELATIONS 2012年第9期65卷 1101-1127页

作者： Ceja, Lucia Navarro, Jose Univ Navarra Family Owned Business Chair IESE Business Sch Barcelona 08034 Spain Univ Barcelona E-08007 Barcelona Spain

Work-related flow is defined as a sudden and enjoyable merging of action and awareness that represents a peak experience in the daily lives of workers. Employees' perceptions of challenge and skill and their subjective experiences in terms of enjoyment, interest and absorption were measured using the experience sampling method, yielding a total of 6981 observations from a sample of 60 employees. linear and nonlinear approaches were applied in order to model both continuous and sudden changes. According to the R-2, AICc and BIC indexes, the nonlinear dynamical systems model (i.e. cusp catastrophe model) fit the data better than the linear and logistic regression models. Likewise, the cusp catastrophe model appears to be especially powerful for modelling those cases of high levels of flow. Overall, flow represents a nonequilibrium condition that combines continuous and abrupt changes across time. Research and intervention efforts concerned with this process should focus on the variable of challenge, which, according to our study, appears to play a key role in the abrupt changes observed in work-related flow.

关键词： work-related flow nonlinear dynamics cusp model linear regression model employee well-being

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Personal optimization method to estimate mood using heart rate variability in daily life Mood estimation using heart rate variability

Personal optimization method to estimate mood using heart ra...

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6th International Conference on Soft Computing and Intelligent Systems (SCIS) / 13th International Symposium on Advanced Intelligence Systems (ISIS)

作者： Yoshino, Kohzoh Matsuoka, Katsunori Natl Inst Adv Ind Sci & Technol Hlth Res Inst Ikeda Osaka Japan Natl Inst Adv Ind Sci & Technol Hokkaido Ctr Sapporo Hokkaido Japan

ISBN: (纸本)9781467327435;9781467327428

The aim of this study is to present a method of assessing eight types of mood that is optimized to every individual on the basis of the heart rate variability (HRV) data which, to eliminate the influence of the inter-individual variability, are measured in a long time period during daily life. Eight types of mood are happiness, tension, fatigue, anxiety, depression, anger, vigor, and confusion. HRV and body accelerations were recorded from nine normal subjects for two months of normal daily life. Fourteen HRV indices were calculated with the HRV data at 512 seconds prior to the time of every mood level report. Data to be analyzed were limited to those with body accelerations of 30 mG (0.294 m/s(2)) and lower. Further, the differences from the reference values in the same time zone were calculated with both the mood score (Delta mood) and HRV index values (Delta HRVI). The multiple linear regression model that estimates Delta mood from the scores for principal components of Delta HRVI were then constructed for each individual. The data were divided into training data set and test data set in accordance with the 2-fold cross validation method. Multiple linear regression coefficients were determined using the training data set, and with the optimized model its generalization capability was checked using the test data set. The model was most effective on estimating tension compared with other seven types of mood. The subjects' mean Pearson correlation coefficient was 0.52 with the training data set and 0.40 with the test data set. We proposed a method of assessing mood that is optimized to every individual based on HRV data measured over a long period of daily life.

关键词： mood heart rate variability personal adaptation linear regression model daily life psychophysiology

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Prediction of the Amount of Wood Using Neural Networks

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Journal of Mathematical modelling and Algorithms 2012年第3期11卷 295-307页

作者： Blanco, Ana Martínez Sotto, Arcadio Castellanos, Angel Department of Basic Sciences Applied To Forestry Engineering School of Forestry Engineering Technical University of Madrid (UPM) 28040 Madrid Ciudad Universitaria s/n Spain Department of Chemical and Environmental Technology ESCET Universidad Rey Juan Carlos 28933 Móstoles Madrid Tulipán s/n Spain

Coniferous trees such as eucalyptus used to be preferred for papermaking because the cellulose fiber in the pulp of these species are longer, therefore making for stronger paper. In this study, the proposed neural network method solves in an efficient way, how to build prediction models in engineering. The system has been applied to predict amount of wood for production of paper, in which the coefficients can explain the variable with more influence over the variable to forecast. Obtaining a good prediction and as simple as possible, i. e. with the least number of forecast variables. © 2012 Springer Science+Business Media B.V.

关键词： Analysis Artificial neural networks linear regression model Predictive model Sensitivity Wood volume

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Application of combination forecast model in the medium and long term power load forecast

Application of combination forecast model in the medium and ...

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2nd International Conference on Mechatronics and Intelligent Materials (MIM 2012)

作者： Zhao, Ke Gan, Lin Wang, Zhong Xiong, Yan Nanchang Hangkong Univ Coll Informat Engn Nanchang Jiangxi Peoples R China

ISBN: (纸本)9783037853849

For seasonal and long-term power load forecasting problem, this paper presents an optimal combination forecasting method, which can optimize the combination of multiple predictive models. Optimize the combination of the two model predictions with two models as an example, which are the gray GM(1,1) model and linear regression model, and finally compare the predicted values of combination with the real values. The results show that: the combination forecasting method has a high prediction accuracy, and the error is very small.

关键词： power load combination forecasts moving average gray model linear regression model

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Relationship between the solar radiation and surface temperature in Perlis

Relationship between the solar radiation and surface tempera...

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1st International Conference on Energy and Environmental Protection (ICEEP 2012)

作者： Daut, Ismail Yusoff, Mohd Irwan Ibrahim, Safwati Irwanto, Muhamad Nsurface, Gomesh Univ Malaysia Perlis Sch Elect Syst Engn Kangar Malaysia Univ Malaya Inst Math Sci Fac Sci Kuala Lumpur 50603 Malaysia

ISBN: (纸本)9783037854143

Statistical models for predicting the solar radiation have been developed. In any prediction of the solar radiation, an understanding of its characteristics is of fundamental importance. This study presents an investigation of a relationship between solar radiation and surface temperature in Perlis, Northern Malaysia for the year of 2006. To achieve this, the data are presented in daily averaged maximum and minimum surface temperature, and daily averaged solar radiation. Since the scatter plots represent the straight line, the linear regression model was selected to estimate the solar radiation. It was found that the linear correlation coefficient value is 0.7473 shows that a strong linear relationship between solar radiation and surface temperature. The analysis of variance R-2 is 0.5585 that is;about 56 percent of the variability in temperature is accounted for by the straight-line fit to solar radiation. Based on the results, the fitted model is adequate to represent the estimation of solar radiation.

关键词： Solar radiation Temperature linear regression model Statistical analysis

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Robust linear regression

Robust linear regression

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作者： Bai, Xue Kansas State University

学位级别：master

In practice, when applying a statistical method it often occurs that some observations deviate from the usual model assumptions. Least-squares (LS) estimators are very sensitive to outliers. Even one single atypical value may have a large effect on the regression parameter estimates. The goal of robust regression is to develop methods that are resistant to the possibility that one or several unknown outliers may occur anywhere in the data. In this paper, we review various robust regression methods including: M-estimate, LMS estimate, LTS estimate, S-estimate, [tau]-estimate, MM-estimate, GM-estimate, and REWLS estimate. Finally, we compare these robust estimates based on their robustness and efficiency through a simulation study. A real data set application is also provided to compare the robust estimates with traditional least squares estimator

关键词： linear regression model Robust regression

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