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Liquefaction prediction using support vector machine model based on cone penetration data

Pijush SAMUI

Frontiers of Structural and Civil Engineering 2013, Volume 7, Issue 1,   Pages 72-82 doi: 10.1007/s11709-013-0185-y

Abstract: A support vector machine (SVM) model has been developed for the prediction of liquefaction susceptibilityThis paper examines the potential of SVM model in prediction of liquefaction using actual field coneThe SVM, a novel learning machine based on statistical theory, uses structural risk minimization (SRMFurther, developed SVM model has been applied to different case histories available globally and theresults obtained confirm the capability of SVM model.

Keywords： earthquake     cone penetration test     liquefaction     support vector machine (SVM)     prediction    

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A comprehensive review and analysis of solar forecasting techniques

Pardeep SINGLA, Manoj DUHAN, Sumit SAROHA

Frontiers in Energy 2022, Volume 16, Issue 2,   Pages 187-223 doi: 10.1007/s11708-021-0722-7

Abstract: In the last two decades, renewable energy has been paid immeasurable attention to toward the attainment of electricity requirements for domestic, industrial, and agriculture sectors. Solar forecasting plays a vital role in smooth operation, scheduling, and balancing of electricity production by standalone PV plants as well as grid interconnected solar PV plants. Numerous models and techniques have been developed in short, mid and long-term solar forecasting. This paper analyzes some of the potential solar forecasting models based on various methodologies discussed in literature, by mainly focusing on investigating the influence of meteorological variables, time horizon, climatic zone, pre-processing techniques, air pollution, and sample size on the complexity and accuracy of the model. To make the paper reader-friendly, it presents all-important parameters and findings of the models revealed from different studies in a tabular mode having the year of publication, time resolution, input parameters, forecasted parameters, error metrics, and performance. The literature studied showed that ANN-based models outperform the others due to their nonlinear complex problem-solving capabilities. Their accuracy can be further improved by hybridization of the two models or by performing pre-processing on the input data. Besides, it also discusses the diverse key constituents that affect the accuracy of a model. It has been observed that the proper selection of training and testing period along with the correlated dependent variables also enhances the accuracy of the model.

Keywords： forecasting techniques     hybrid models     neural network     solar forecasting     error metric     support vector machine(SVM)    

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A robust intelligent audio watermarking scheme using support vector machine Article

Mohammad MOSLEH,Hadi LATIFPOUR,Mohammad KHEYRANDISH,Mahdi MOSLEH,Najmeh HOSSEINPOUR

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 12,   Pages 1320-1330 doi: 10.1631/FITEE.1500297

Abstract: water-marking scheme using a synergistic combination of singular value decomposition (SVD) and support vector machine(SVM).In the extraction process, an intelligent detector using SVM is suggested for extracting the watermark

Keywords： Audio watermarking     Copyright protection     Singular value decomposition (SVD)     Machine learning     Supportvector machine (SVM)    

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Performance analysis of new word weighting procedures for opinion mining Article

G. R. BRINDHA,P. SWAMINATHAN,B. SANTHI

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 11,   Pages 1186-1198 doi: 10.1631/FITEE.1500283

Abstract: The proliferation of forums and blogs leads to challenges and opportunities for processing large amounts of information. The information shared on various topics often contains opinionated words which are qualitative in nature. These qualitative words need statistical computations to convert them into useful quantitative data. This data should be processed properly since it expresses opinions. Each of these opinion bearing words differs based on the significant meaning it conveys. To process the linguistic meaning of words into data and to enhance opinion mining analysis, we propose a novel weighting scheme, referred to as inferred word weighting (IWW). IWW is computed based on the significance of the word in the document (SWD) and the significance of the word in the expression (SWE) to enhance their performance. The proposed weighting methods give an analytic view and provide appropriate weights to the words compared to existing methods. In addition to the new weighting methods, another type of checking is done on the performance of text classification by including stop-words. Generally, stop-words are removed in text processing. When this new concept of including stop-words is applied to the proposed and existing weighting methods, two facts are observed: (1) Classification performance is enhanced; (2) The outcome difference between inclusion and exclusion of stop-words is smaller in the proposed methods, and larger in existing methods. The inferences provided by these observations are discussed. Experimental results of the benchmark data sets show the potential enhancement in terms of classification accuracy.

Keywords： Inferred word weight     Opinion mining     Supervised classification     Support vector machine (SVM)     Machine    

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Robust SVM-direct torque control of induction motor based on sliding mode controller and sliding mode

Abdelkarim AMMAR,Amor BOUREK,Abdelhamid BENAKCHA

Frontiers in Energy 2020, Volume 14, Issue 4,   Pages 836-849 doi: 10.1007/s11708-017-0444-z

Abstract: A robust electromagnetic torque and flux controllers are designed to overcome the conventional SVM-DTCstationary frame and give them to the controlled motor after modulation by a space vector modulation (SVM

Keywords： induction motor     direct torque control (DTC)     space vector modulation (SVM)     sliding mode control (SMC)    

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Identification of thermal error in a feed system based on multi-class LS-SVM

Chao JIN, Bo WU, Youmin HU, Yao CHENG

Frontiers of Mechanical Engineering 2012, Volume 7, Issue 1,   Pages 47-54 doi: 10.1007/s11465-012-0307-6

Abstract: error of a ball-screw is one of the most important objects to consider for high-accuracy and high-speed machineUsing multi-class least squares support vector machines (LS-SVM), the thermal positioning error of the

Keywords： least squares support vector machine (LS-SVM)     feed system     thermal error     precision machining    

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UsingKinect for real-time emotion recognition via facial expressions

Qi-rong MAO,Xin-yu PAN,Yong-zhao ZHAN,Xiang-jun SHEN

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 4,   Pages 272-282 doi: 10.1631/FITEE.1400209

Abstract: Emotion recognition via facial expressions (ERFE) has attracted a great deal of interest with recent advances in artificial intelligence and pattern recognition. Most studies are based on 2D images, and their performance is usually computationally expensive. In this paper, we propose a real-time emotion recognition approach based on both 2D and 3D facial expression features captured by Kinect sensors. To capture the deformation of the 3D mesh during facial expression, we combine the features of animation units (AUs) and feature point positions (FPPs) tracked by Kinect. A fusion algorithm based on improved emotional profiles (IEPs) and maximum confidence is proposed to recognize emotions with these real-time facial expression features. Experiments on both an emotion dataset and a real-time video show the superior performance of our method.

Keywords： Emotion recognition     Facial expression     Real-time classification     Fusion algorithm     Support vector machine(SVM)    

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Using hybrid models to predict blood pressure reactivity to unsupported back based on anthropometric characteristics

Gurmanik KAUR,Ajat Shatru ARORA,Vijender Kumar JAIN

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 6,   Pages 474-485 doi: 10.1631/FITEE.1400295

Abstract: network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and the least squares support vector machine(LS-SVM) model for the prediction of BP reactivity to an unsupported back in normotensive and hypertensiveof the constructed models, using appropriate statistical indices, shows clearly that a PCA-based LS-SVM(PCA-LS-SVM) model is a promising approach for the prediction of BP reactivity in comparison to others

Keywords： Artificial neural network (ANN)     Adaptive neuro-fuzzy inference system (ANFIS)     Least squares support vector machine(LS-SVM)    

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Prediction of vibration response of powerhouse structures based on LS-SVM optimized by PSO

Lian Jijian,He Longjun,Wang Haijun

Strategic Study of CAE 2011, Volume 13, Issue 12,   Pages 45-50

Abstract: powerhouse,and then the vibration response forecasting model of the powerhouse is built based on LS-SVM

Keywords： powerhouse     coupled vibration     particle swarm optimization algorithm     least squares support vector machines     response prediction    

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Anefficient parallel and distributed solution to nonconvex penalized linear SVMs Personal View

Lei GUAN, Tao SUN, Lin-bo QIAO, Zhi-hui YANG, Dong-sheng LI, Ke-shi GE, Xi-cheng LU

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 4,   Pages 587-603 doi: 10.1631/FITEE.1800566

Abstract: Support vector machines (SVMs) have been recognized as a powerful tool to perform linear classification. When combined with the sparsity-inducing nonconvex penalty, SVMs can perform classification and variable selection simultaneously. However, the nonconvex penalized SVMs in general cannot be solved globally and efficiently due to their nondifferentiability, nonconvexity, and nonsmoothness. Existing solutions to the nonconvex penalized SVMs typically solve this problem in a serial fashion, which are unable to fully use the parallel computing power of modern multi-core machines. On the other hand, the fact that many real-world data are stored in a distributed manner urgently calls for a parallel and distributed solution to the nonconvex penalized SVMs. To circumvent this challenge, we propose an efficient alternating direction method of multipliers (ADMM) based algorithm that solves the nonconvex penalized SVMs in a parallel and distributed way. We design many useful techniques to decrease the computation and synchronization cost of the proposed parallel algorithm. The time complexity analysis demonstrates the low time complexity of the proposed parallel algorithm. Moreover, the convergence of the parallel algorithm is guaranteed. Experimental evaluations on four LIBSVM benchmark datasets demonstrate the efficiency of the proposed parallel algorithm.

Keywords： Linear classification     Support vector machine (SVM)     Nonconvex penalty     Alternating direction method of    

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Challenges of human–machine collaboration in risky decision-making

Frontiers of Engineering Management 2022, Volume 9, Issue 1,   Pages 89-103 doi: 10.1007/s42524-021-0182-0

Abstract: The purpose of this paper is to delineate the research challenges of human–machine collaboration in riskyTechnological advances in machine intelligence have enabled a growing number of applications in human–machineTherefore, it is desirable to achieve superior performance by fully leveraging human and machine capabilitiesAfterward, we review the literature on human–machine collaboration in a general decision context, fromthe perspectives of human–machine organization, relationship, and collaboration.

Keywords： human–machine collaboration     risky decision-making     human–machine team and interaction     task allocation     human–machine relationship    

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Spatial prediction of soil contamination based on machine learning: a review

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 8, doi: 10.1007/s11783-023-1693-1

Abstract:

● A review of machine learning (ML) for spatial prediction of soil

Keywords： Soil contamination     Machine learning     Prediction     Spatial distribution    

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Predicting the elemental compositions of solid waste using ATR-FTIR and machine learning

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 10, doi: 10.1007/s11783-023-1721-1

Abstract:

● A method based on ATR-FTIR and ML was developed to predict CHNS contents in waste.

Keywords： Elemental composition     Infrared spectroscopy     Machine learning     Moisture interference     Solid waste     Spectral    

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State-of-the-art applications of machine learning in the life cycle of solid waste management

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 4, doi: 10.1007/s11783-023-1644-x

Abstract:

● State-of-the-art applications of machine learning (ML) in solid waste

Keywords： Machine learning (ML)     Solid waste (SW)     Bibliometrics     SW management     Energy utilization     Life cycle    

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Research and application of visual location technology for solder paste printing based on machine vision

Luosi WEI, Zongxia JIAO

Frontiers of Mechanical Engineering 2009, Volume 4, Issue 2,   Pages 184-191 doi: 10.1007/s11465-009-0034-9

Abstract: Using machine vision technology to complete the location mission is new and very efficient.This paper presents an integrated visual location system for solder paste printing based on machine vision

Keywords： machine vision     visual location     solder paste printing     VisionPro    

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Title Author Date Type Operation

Liquefaction prediction using support vector machine model based on cone penetration data

Pijush SAMUI

Journal Article

A comprehensive review and analysis of solar forecasting techniques

Pardeep SINGLA, Manoj DUHAN, Sumit SAROHA

Journal Article

A robust intelligent audio watermarking scheme using support vector machine

Mohammad MOSLEH,Hadi LATIFPOUR,Mohammad KHEYRANDISH,Mahdi MOSLEH,Najmeh HOSSEINPOUR

Journal Article

Performance analysis of new word weighting procedures for opinion mining

G. R. BRINDHA,P. SWAMINATHAN,B. SANTHI

Journal Article

Robust SVM-direct torque control of induction motor based on sliding mode controller and sliding mode

Abdelkarim AMMAR,Amor BOUREK,Abdelhamid BENAKCHA

Journal Article

Identification of thermal error in a feed system based on multi-class LS-SVM

Chao JIN, Bo WU, Youmin HU, Yao CHENG

Journal Article

UsingKinect for real-time emotion recognition via facial expressions

Qi-rong MAO,Xin-yu PAN,Yong-zhao ZHAN,Xiang-jun SHEN

Journal Article

Using hybrid models to predict blood pressure reactivity to unsupported back based on anthropometric characteristics

Gurmanik KAUR,Ajat Shatru ARORA,Vijender Kumar JAIN

Journal Article

Prediction of vibration response of powerhouse structures based on LS-SVM optimized by PSO

Lian Jijian,He Longjun,Wang Haijun

Journal Article

Anefficient parallel and distributed solution to nonconvex penalized linear SVMs

Lei GUAN, Tao SUN, Lin-bo QIAO, Zhi-hui YANG, Dong-sheng LI, Ke-shi GE, Xi-cheng LU

Journal Article

Challenges of human–machine collaboration in risky decision-making

Journal Article

Spatial prediction of soil contamination based on machine learning: a review

Journal Article

Predicting the elemental compositions of solid waste using ATR-FTIR and machine learning

Journal Article

State-of-the-art applications of machine learning in the life cycle of solid waste management

Journal Article

Research and application of visual location technology for solder paste printing based on machine vision

Luosi WEI, Zongxia JIAO

Journal Article