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Highly sensitive terahertz non-destructive testing technology for stone relics deterioration prediction using SVM-based machine learning models

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HERITAGE SCIENCE 2021年第1期9卷 1-9页

作者： Meng, Tianhua Huang, Rong Lu, Yuhe Liu, Hongmei Ren, Jianguang Zhao, Guozhong Hu, Weidong Shanxi Datong Univ Inst Solid State Phys Xingyun St Datong 037009 Shanxi Peoples R China Capital Normal Univ Dept Phys Beijing 100048 Peoples R China Res Inst Yungang Grottoes Datong 037007 Shanxi Peoples R China Datong Inst Terahertz Technol Datong 037305 Shanxi Peoples R China Beijing Key Lab Millimeter Wave & Terahertz Techn Beijing 100081 Peoples R China

The hollowing deterioration of stone relics required effective non-destructive testing (NDT) methods for their timely restoration and maintenance. To this end, a new NDT method based on terahertz (THz) technology by using support vector machine (SVM)-based machine learning models was developed to assess and diagnose the hollowing deterioration of the Yungang Grottoes. According to experiment design, a series of hollowing deterioration samples with various thicknesses of hollowing deterioration were prepared and then measured by using THz time-domain spectroscopy (THz-TDS). Based on the THz-TDS results of 30 randomly selected samples, a SVM-based hollowing deterioration prediction model (SVM-HDPM) was established by analyzing the relationship between the hollowing samples and the THz spectral information. The reliability and accuracy of the model was further proved by verified and compared with using the THz spectral data of the remaining 10 samples. The experimental results with the linear kernel function greatly demonstrated that the SVM-HDPM can have superior prediction accuracy, implying that the model is feasible for the prediction the hollowing deterioration of the stone relics. Moreover, one data preprocess was introduced into SVM-HDPM to meet the needs of field-based test. The predicted results of five different hollowing deterioration with different flaked stone thickness revealed good performance with very low mean square error (MSE) value. Therefore, it is believed that the proposed method can be regarded as an effective NDT technique with practical applications in analyzing cultural relics and have promising future prospects in inspection stone relics-like ancient heritage for hidden flaws.

关键词： Stone cultural relics Yungang Grottoes Terahertz spectral LS-SVM linear kernel function

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Feature based approach for Efficient Crowd Density Estimation

Feature based approach for Efficient Crowd Density Estimatio...

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Innovative Trends and Advances in Engineering and Technology (ICITAET), International Conference on

作者： Neeta Anil Nemade V. V. Gohokar Department of Electronics & Telecomm. Engg. SSGB College of Engg. & Tech. Bhusawal India Department of Electronics and Comm. Engineering MIT College of Engineering Pune India

ISBN: (数字)9781728119014

ISBN: (纸本)9781728119021

Presently there is major interest in visual surveillance systems for crowd analysis. For preventing the problems caused by large crowd, proper control and management is necessary. In this paper a novel individual feature is introduced and used in combination with the holistic features, to describe crowd density. Various databases are analyzed to check the validity of the feature extraction process. In order to cluster the crowd frames according to congestion degree Support Vector Machine and Artificial Neural Network are used as classifiers. Results obtained shows that the proposed feature performs well in classifying real-world crowd scenes.

关键词： pixel based foreground area edge count isolated nodes convolution classifier linear kernel function RBF kernel function MLP kernel function Classifiers kernel functionS Convolution Feature based Extraction processes Artificial neural networks Visual surveillance

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A New Compound kernel function for SVM 12

A New Compound Kernel Function for SVM

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12th IEEE Int Conf Ubiquitous Intelligence & Comp/12th IEEE Int Conf Autonom & Trusted Comp/15th IEEE Int Conf Scalable Comp & Commun & Associated Workshops/IEEE Int Conf Cloud & Big Data Comp/IEEE Int Conf Internet People

作者： Mao, Yonghua He, Xingshi Gui, Xiaolin Guo, Ying Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian Shaanxi Peoples R China Xian Polytech Univ Sch Sci Xian Shaanxi Peoples R China

ISBN: (纸本)9781467372114

Support Vector Machines (SVM) is one of most important algorithm in machine learning area. The choice of kernel function can have great influence on classification and approximation ability. Choosing appropriate kernel function and weight parameters is one of the keys to utilize SVM. Single kernel function always has its limitation in the application. We propose a new kernel function based on the analysis about the constitute conditions of the kernel function and the characteristics of different kinds of kernel function-linear compound kernel function, this function not only can reduce the amount of parameters of the kernel function, but also has good learning ability and generalizing ability. And we have tested the effectiveness of the kernel function through simulation.

关键词： support vector machine condition positive kernel function linear kernel function compound kernel function

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Lymph diseases diagnosis approach based on support vector machines with different kernel functions 9

Lymph diseases diagnosis approach based on support vector ma...

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9th International Conference on Computer Engineering & Systems (ICCES)

作者： Elshazly, Hanaa Ismail Elkorany, Abeer Mohamed Hassanien, Aboul Ella Cairo Univ Fac Comp & Informat Cairo Egypt Sci Res Grp Egypt Giza Egypt

ISBN: (纸本)9781479965946

In this paper, a Genetic algorithm (GA) hased supporting vector machine classifier (GA-SVM) is proposed for lymph diseases diagnosis. In the first stage, dimension of lymph diseases dataset that has 18 features is reduced to six features using GA. In the second stage, a support vector machine with different kernel functions including linear, Quadratic and Gaussian was utilized as a classifier. The Lymphography database was obtained from the University Medical Center, Institute of Oncology, Ljubljana, Yugoslavia. The obtained classification accuracy was very promising with regard to the other classification applications in the literature for this problem. The performance of SVM classifier with each kernel function was evaluated by using performance indices such as accuracy, sensitivity, specificity, area under curve (AUC) or (ROC), Matthews Correlation Coefficient (MCC) and F-Measure. linear kernel function obtained highest results which verifies the efficiency of GA-linear stategy.

关键词： diseases genetic algorithms medical diagnostic computing patient diagnosis pattern classification support vector machines GA based supporting vector machine classifier GA-SVM GA-linear stategy Gaussian kernel function Institute of Oncology Ljubljana Lymphography database SVM classifier performance University Medical Center Yugoslavia classification accuracy classification applications genetic algorithm linear kernel function lymph disease dataset lymph disease diagnosis approach performance indices quadratic kernel function Algorithm design and analysis Classification algorithms Extraterrestrial measurements Indexes kernel Prediction algorithms Support vector machines Support Vector Network kernel functionS Genetic Algorithm (GA) medical diagnostic computing Extraterrestrial measurements Algorithm design and analysis Academic Medical Centers performance indicators patient diagnosis Prediction algorithms Sorting algorithm Pattern recognition Disease diagnosis kernel maladjustment

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Adjusted Sequence kernel Association Test for Rare Variants Controlling for Cryptic and Family Relatedness

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GENETIC EPIDEMIOLOGY 2013年第4期37卷 366-376页

作者： Oualkacha, Karim Dastani, Zari Li, Rui Cingolani, Pablo E. Spector, Timothy D. Hammond, Christopher J. Richards, J. Brent Ciampi, Antonio Greenwood, Celia M. T. McGill Univ Jewish Gen Hosp Lady Davis Inst Med Res Montreal PQ H3T 1E2 Canada McGill Univ Dept Epidemiol Biostat & Occupat Hlth Montreal PQ Canada Univ Quebec Montreal Dept Math Montreal PQ Canada McGill Univ Dept Comp Sci Montreal PQ Canada McGill Univ Montreal PQ Canada Genome Quebec Innovat Ctr Montreal PQ Canada Kings Coll London Dept Twin Res & Genet Epidemiol London WC2R 2LS England McGill Univ Jewish Gen Hosp Dept Med Montreal PQ H3T 1E2 Canada McGill Univ Dept Human Genet Montreal PQ Canada McGill Univ Dept Oncol Montreal PQ Canada

Recent progress in sequencing technologies makes it possible to identify rare and unique variants that may be associated with complex traits. However, the results of such efforts depend crucially on the use of efficient statistical methods and study designs. Although family-based designs might enrich a data set for familial rare disease variants, most existing rare variant association approaches assume independence of all individuals. We introduce here a framework for association testing of rare variants in family-based designs. This framework is an adaptation of the sequence kernel association test (SKAT) which allows us to control for family structure. Our adjusted SKAT (ASKAT) combines the SKAT approach and the factored spectrally transformed linear mixed models (FaST-LMMs) algorithm to capture family effects based on a LMM incorporating the realized proportion of the genome that is identical by descent between pairs of individuals, and using restricted maximum likelihood methods for estimation. In simulation studies, we evaluated type I error and power of this proposed method and we showed that regardless of the level of the trait heritability, our approach has good control of type I error and good power. Since our approach uses FaST-LMM to calculate variance components for the proposed mixed model, ASKAT is reasonably fast and can analyze hundreds of thousands of markers. Data from the UK twins consortium are presented to illustrate the ASKAT methodology.

关键词： gene-based test rare variant methods resequencing mixed models family-based design score statistics linear kernel function

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An Empirical Comparison Study on kernel Based Support Vector Machine for Classification of Gene Expression Data Set

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Procedia Engineering 2012年 38卷 1340-1345页

作者： Smruti Rekha Das Kaberi Das Debahuti Mishra Kailash Shaw Sashikala Mishra Institute of Technical Education and Research Siksha O Anusandhan Deemed to be University Bhubaneswar Odisha India Gandhi Engineering College

Classification is a vital tool for understanding the relationships of living things using which similar things can be grouped together. Classification of elements into groups makes the study relatively easy. Therefore, classification is necessary to know salient features and characteristics of living organisms as well as their inter relationship among different group of organisms, as the correct classification of a person's disease is important for proper treatment. Support vector machine (SVM) was the first proposed kernel-based method, which uses a kernel function to transfer data from input space into high dimensional feature space; it searches for a separating hyper-plane. SVM is based on simple ideas which originated in statistical learning theory; hence the aim is to solve only the problem of interest without solving a more difficult problem as an intermediate step. SVM apply a simple linear method to the data but in a high-dimensional feature space non-linearly related to the input space. Even though we can think of SVM as a linear algorithm in high dimensional space, but in practice it does not involve any computations in that high-dimensional space. As dimensionality is curse to gene expression data set, in this paper Principal Component Analysis (PCA) is used for feature reduction to breast cancer, lung cancer and cardiotography data sets, and SVM is trained by linear, polynomial and radial basis function (RBF) kernels applied on each of these data sets and the comparison among them shows that RBF is better for the three data sets.

关键词： Principal Component Analysis Support Vector Machine linear kernel function Polynomial kernel function Radial Basis function

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New predictive control algorithms based on Least Squares Support Vector Machines

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2005年第5期6卷 440-446页

作者：刘斌苏宏业褚健 National Laboratory of Industrial Control Technology Institute of Advanced Process Control Zhejiang UniversityHangzhou 310027 China School of Information Science and Engineering Wuhan University of Science and Technology Wuhan 430081 China School of Information Science and Engineering Wuhan University of Science and Technology Wuhan 430081 China School of Information Science and Engineering Wuhan University of Science and Technology Wuhan 430081 China

Used for industrial process with different degree of nonlinearity, the two predictive control algorithms presented in this paper are based on Least Squares Support Vector Machines (LS-SVM) model. For the weakly nonlinear system, the system model is built by using LS-SVM with linear kernel function, and then the obtained linear LS-SVM model is transformed into linear input-output relation of the controlled system. However, for the strongly nonlinear system, the off-line model of the controlled system is built by using LS-SVM with Radial Basis function (RBF) kernel. The obtained nonlinear LS-SVM model is linearized at each sampling instant of system running, after which the on-line linear input-output model of the system is built. Based on the obtained linear input-output model, the Generalized Predictive Control (GPC) algorithm is employed to implement predictive control for the controlled plant in both algorithms. The simulation results after the presented algorithms were implemented in two different industrial processes model; respectively revealed the effectiveness and merit of both algorithms.

关键词： Least Squares Support Vector Machines linear kernel function RBF kernel function Generalized predictive control

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