检索结果-内蒙古大学图书馆

Remote sensing study of wetlands in the Pearl River Delta during 1995-2015 with the support vector machine method

Frontiers of Earth Science 2018年第3期12卷 521-531页

作者： Xiaosong HAN Jiayi PAN Adam T. DEVLIN Institute of Space and Earth Information Science The Chinese University of Hong Kong Hong Kong China

In recent years, experienced rapid economic the Pearl River Delta has growth which may create a substantial burden to its ecology. In this study, the wetlands of the Pearl River Delta are investigated. Through the use of remote sensing methods, we analyze spatial and temporal variations of wetlands in this area over the past twenty years. The support vector machine （SVM） method is proven to be an effective approach for classifying the wetlands of the Pearl River Delta, and the total classifica- tion resolution reaches 94.94% with a Kappa coefficient exceeding 0.94, higher than other comparable analysis methods. Our results show that wetland areas were reduced by 36.9% during the past twenty years. The change detection analysis method shows that there was a 95.58% intertidal zone change to other land-use types, most of which （57.12%） was converted to construction land. In addition, farmland was reduced by 54.89% during the past twenty years, 47.19% of which was changed to construction land use. The inland water area increased 19.02%, but most of this growth （18.77%） was converted from the intertidal zone.

关键词： wetland Pearl River Delta support vector machine method Landsat images

来源：评论

学校读者我要写书评

暂无评论

Estimation and predicting of soil water characteristic curve using the support vector machine method

引用

EARTH SCIENCE INFORMATICS 2023年第1期16卷 1061-1072页

作者： Qin, Wenjing Fan, Guisheng Hongxing-Li Taiyuan Univ Technol Coll Econ & Management Taiyuan Shanxi Peoples R China Jinzhong Water Conservancy Dev Ctr Jinzhong Peoples R China

In this paper, we measured, in low suction region, the water-retention curves of undisturbed loess soil samples covering different soil textures and obtained the parameters of classical van Genuchten soil-water equation by using the HYPROP instrument. Meanwhile, the basic physical and chemical properties (soil particle size distribution, bulk density, organic matter content) of tested soil samples were measured by using corresponding experimental methods. Based on this database, we established a model for predicting the parameters of vG water-retention curve in the low water potential range through an improved support vector machine (SVM) method by the Particle Swarm Optimization (PSO) algorithms. The results show that the values predicted by using the established SVM model here (with input variables: clay, silt, bulk density, organic matter content, electricity conductivity and pH) are highly consistent with the experimental data according to the statistical analysis. Specifically, the obtained absolute, relative and root-mean-square errors between predicted and experimental values are less than 0.0153, 0.1071, and 0.0633, respectively. The research results provide a new method for accurately obtaining soil moisture characteristic curves and provide basic data for hydrological and water quality model.

关键词： Undisturbed loess-soil Soil water characteristic curve support vector machine method

来源：评论

学校读者我要写书评

暂无评论

Classification of dental lesions fluorescence spectra using support vector machine method

Classification of dental lesions fluorescence spectra using ...

引用

13th International School on Quantum Electronics

作者： Atanasov, IS Borisova, EG Yordanov, OI Uzunov, TT Avramov, LA Bulgarian Acad Sci Inst Elect BU-1784 Sofia Bulgaria

ISBN: (纸本)0819458228

We use the support vector machine method to distinguish fluorescence spectra obtained from demineralized teeth from those obtained from healthy teeth. We find intervals of values for the various parameters of the algorithm, such as dimension, degree of smoothness, etc, as to achieve at least 90% diagnostic accuracy. We argue that the success of the methodology is determined by its multidimensional nature.

关键词： laser-induced fluorescence tooth demineralization support vector machine method

来源：评论

学校读者我要写书评

暂无评论

Process conditions of gas production in the electrochemical treatment of fracturing flowback fluid process based on support vector machine method

引用

ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2024年第1期46卷 11122-11132页

作者： Wei, Shuxia Liu, Yang Chen, Wu Zhang, Xiaofei Zhu, Mijia Yangtze Univ Sch Chem & Environm Engn Jingzhou 434023 Hubei Peoples R China Yangtze Univ State Key Lab Petr Pollut Control Jingzhou Hubei Peoples R China

Gas production during the electrochemical treatment of waste liquids has potential safety risks. Herein, hydrogen (H-2) production in such treatment was taken as the control target. Electrode combinations with relatively low H(2)production were selected for electrochemical treatment during a fracturing flowback fluid experiment, in which mesh-like titanium-based ordinary ruthenium-iridium-palladium-coated electrode was the anode and plate-like titanium-based ordinary ruthenium-iridium-palladium-coated electrode was the cathode. The effects of electrolysis time (t), electrolytic current (I), electrode spacing (D), and other factors on H(2)production in the electrolytic process were investigated. On the basis of the experimental data, a model was established using the support vector machine (SVM) method. Firstly, the three parameters of the radial basis function kernel of the model were simultaneously optimized using the quantum-particle swarm optimization algorithm as follows: penalty parameter = 256, kernel parameter = 0.0097039, and loss parameter = 0.014928. Then, an SVM regression model was established according to the three optimal parameters. The correlation coefficient was 0.98291 (r= 0.9914), and the mean-square error was 1.1883. The regression model was used to predict the technological conditions (t, I, andD) for the maximum/minimum H(2)production in the electrochemical treatment of fracturing flowback fluid. The values weret= 50 min,I= 1.5 A, andD= 2.0 cm for the maximum H(2)production, andt= 30 min,I= 0.5 A, andD= 6.0 cm for the minimum H(2)production. Under these process conditions, the predicted maximum/minimum H(2)production volumes were 145.04 and 20.47 mL, respectively.

关键词： Fracturing flowback fluid electrochemical method support vector machine method H(2)production process conditions

来源：评论

学校读者我要写书评

暂无评论

Casing life prediction using Borda and support vector machine methods

引用

Petroleum Science 2010年第3期7卷 416-421页

作者： Xu Zhiqian Yan Xiangzhen Yang Xiujuan College of Mechanical and Electronic Engineering China University of Petroleum Dongying Shandong 257061 China College of Storage ＆ Transportation and Architectural Engineering China University of Petroleum Dongying Shandong 257061 China

Eight casing failure modes and 32 risk factors in oil and gas wells are given in this paper. According to the quantitative analysis of the influence degree and occurrence probability of risk factors, the Borda counts for failure modes are obtained with the Borda method. The risk indexes of failure modes are derived from the Borda matrix. Based on the support vector machine （SVM）, a casing life prediction model is established. In the prediction model, eight risk indexes are defined as input vectors and casing life is defined as the output vector. The ideal model parameters are determined with the training set from 19 wells with casing failure. The casing life prediction software is developed with the SVM model as a predictor. The residual life of 60 wells with casing failure is predicted with the software, and then compared with the actual casing life. The comparison results show that the casing life prediction software with the SVM model has high accuracy.

关键词： support vector machine method Borda method life prediction model failure modes riskfactors

来源：评论

学校读者我要写书评

暂无评论

Automatic categorization of diverse experimental information in the bioscience literature

引用

BMC BIOINFORMATICS 2012年第1期13卷 1-12页

作者： Fang, Ruihua Schindelman, Gary Van Auken, Kimberly Fernandes, Jolene Chen, Wen Wang, Xiaodong Davis, Paul Tuli, Mary Ann Marygold, Steven J. Millburn, Gillian Matthews, Beverley Zhang, Haiyan Brown, Nick Gelbart, William M. Sternberg, Paul W. CALTECH Howard Hughes Med Inst Pasadena CA 91125 USA CALTECH Div Biol Pasadena CA 91125 USA Wellcome Trust Sanger Inst Cambridge CB10 1SA England Univ Cambridge Dept Genet Cambridge CB2 3EH England Harvard Univ Dept Mol & Cellular Biol Cambridge MA 02138 USA Univ Cambridge Gurdon Inst Cambridge CB2 1QN England Univ Cambridge Dept Physiol Dev & Neurosci Cambridge CB2 1QN England

Background: Curation of information from bioscience literature into biological knowledge databases is a crucial way of capturing experimental information in a computable form. During the biocuration process, a critical first step is to identify from all published literature the papers that contain results for a specific data type the curator is interested in annotating. This step normally requires curators to manually examine many papers to ascertain which few contain information of interest and thus, is usually time consuming. We developed an automatic method for identifying papers containing these curation data types among a large pool of published scientific papers based on the machine learning method support vector machine (SVM). This classification system is completely automatic and can be readily applied to diverse experimental data types. It has been in use in production for automatic categorization of 10 different experimental datatypes in the biocuration process at WormBase for the past two years and it is in the process of being adopted in the biocuration process at FlyBase and the Saccharomyces Genome Database (SGD). We anticipate that this method can be readily adopted by various databases in the biocuration community and thereby greatly reducing time spent on an otherwise laborious and demanding task. We also developed a simple, readily automated procedure to utilize training papers of similar data types from different bodies of literature such as C. elegans and D. melanogaster to identify papers with any of these data types for a single database. This approach has great significance because for some data types, especially those of low occurrence, a single corpus often does not have enough training papers to achieve satisfactory performance. Results: We successfully tested the method on ten data types from WormBase, fifteen data types from FlyBase and three data types from Mouse Genomics Informatics (MGI). It is being used in the curation work flow at W

关键词： support vector machine Data Type support vector machine Algorithm support vector machine method Mouse Genomic Informatics

来源：评论

学校读者我要写书评

暂无评论

A structural SVM approach for reference parsing

引用

BMC BIOINFORMATICS 2011年第Sup3期12卷 S7-S7页

作者： Zhang, Xiaoli Zou, Jie Le, Daniel X. Thoma, George R. Natl Lib Med Lister Hill Natl Ctr Biomed Commun Bethesda MD 20894 USA

Background: Automated extraction of bibliographic data, such as article titles, author names, abstracts, and references is essential to the affordable creation of large citation databases. References, typically appearing at the end of journal articles, can also provide valuable information for extracting other bibliographic data. Therefore, parsing individual reference to extract author, title, journal, year, etc. is sometimes a necessary preprocessing step in building citation-indexing systems. The regular structure in references enables us to consider reference parsing a sequence learning problem and to study structural support vector machine (structural SVM), a newly developed structured learning algorithm on parsing references. Results: In this study, we implemented structural SVM and used two types of contextual features to compare structural SVM with conventional SVM. Both methods achieve above 98% token classification accuracy and above 95% overall chunk-level accuracy for reference parsing. We also compared SVM and structural SVM to Conditional Random Field (CRF). The experimental results show that structural SVM and CRF achieve similar accuracies at token- and chunk-levels. Conclusions: When only basic observation features are used for each token, structural SVM achieves higher performance compared to SVM since it utilizes the contextual label features. However, when the contextual observation features from neighboring tokens are combined, SVM performance improves greatly, and is close to that of structural SVM after adding the second order contextual observation features. The comparison of these two methods with CRF using the same set of binary features show that both structural SVM and CRF perform better than SVM, indicating their stronger sequence learning ability in reference parsing.

关键词： support vector machine Sequence Learning Conditional Random Field support vector machine method Binary Feature

来源：评论

学校读者我要写书评

暂无评论

Numerical parametric study on the uniaxial and biaxial compressive behavior of H-shaped steel reinforced concrete composite beam-columns

引用

ADVANCES IN STRUCTURAL ENGINEERING 2022年第13期25卷 2641-2661页

作者： Lai, Bing-Lin Tan, Wei-Kai Feng, Qi-Tong Venkateshwaran, Akshay Southeast Univ Sch Civil Engn Key Lab Concrete & Prestressed Concrete Struct Minist Educ Jiulonghu Campus Nanjing 211189 Peoples R China Sichuan Univ Key Lab Sichuan Prov Failure Mech & Engn Disaster Prevent Chengdu Peoples R China Natl Univ Singapore Dept Civil & Environm Engn Singapore Singapore Birla Inst Technol & Sci Pilani Dept Civil Engn Dubai Campus Dubai U Arab Emirates

As a follow-up work of previous experimental efforts, this paper aims to further explore the compressive behavior of Steel Reinforced Concrete (SRC) composite beam-columns with assistance of nonlinear Finite Element Analysis. The FE model was developed in ABAQUS software and it delivered desirable accuracy after calibrating against the dataset collected from published literatures. The validated FE model was then employed to evaluate the fundamental performance of SRC beam-columns under uniaxial eccentric compression and biaxial eccentric compression. On top of that, the fiber section analysis was adopted to build the three-dimensional axial force-bending moment (N-M) interaction diagrams, followed by a parametric study to investigate the effect of material grade, steel ratio, cross-section geometry, load eccentricity and load inclination on the load-carrying capacity of SRC beam-columns, and disclose the complex biaxial interaction mechanism that is not well-considered in current design codes. Based on the numerical data generated from parametric study, the support vector machine method is utilized to intelligently estimate the biaxial moment contours under varying load levels, which exhibits improved accuracy and consistency than the code-specified method and conventional empirical formulae.

关键词： steel reinforced concrete beam-column finite element analysis fiber section analysis biaxial eccentric compression three-dimensional N-M diagram support vector machine method

来源：评论

学校读者我要写书评

暂无评论

Greek long-term energy consumption prediction using artificial neural networks

引用

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

来源：评论

学校读者我要写书评

暂无评论

Knowledge-based analysis of microarrays for the discovery of transcriptional regulation relationships

引用

BMC BIOINFORMATICS 2010年第1期11卷 1-8页

作者： Seok, Junhee Kaushal, Amit Davis, Ronald W. Xiao, Wenzhong Stanford Genome Technol Ctr Palo Alto CA 94305 USA Stanford Univ Dept Elect Engn Stanford CA 94305 USA Harvard Univ Sch Med Massachusetts Gen Hosp Boston MA 02114 USA

Background: The large amount of high-throughput genomic data has facilitated the discovery of the regulatory relationships between transcription factors and their target genes. While early methods for discovery of transcriptional regulation relationships from microarray data often focused on the high-throughput experimental data alone, more recent approaches have explored the integration of external knowledge bases of gene interactions. Results: In this work, we develop an algorithm that provides improved performance in the prediction of transcriptional regulatory relationships by supplementing the analysis of microarray data with a new method of integrating information from an existing knowledge base. Using a well-known dataset of yeast microarrays and the Yeast Proteome Database, a comprehensive collection of known information of yeast genes, we show that knowledge-based predictions demonstrate better sensitivity and specificity in inferring new transcriptional interactions than predictions from microarray data alone. We also show that comprehensive, direct and high-quality knowledge bases provide better prediction performance. Comparison of our results with ChIP-chip data and growth fitness data suggests that our predicted genome-wide regulatory pairs in yeast are reasonable candidates for follow-up biological verification. Conclusion: High quality, comprehensive, and direct knowledge bases, when combined with appropriate bioinformatic algorithms, can significantly improve the discovery of gene regulatory relationships from high throughput gene expression data.

关键词： support vector machine True Positive Rate Regulatory Relationship Regulatory Relation support vector machine method

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：