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Fault classification and reconfiguration of distribution systems using equivalent capacity margin method

K. Sathish KUMAR, T. JAYABARATHI

Frontiers in Energy 2012, Volume 6, Issue 4,   Pages 394-402 doi: 10.1007/s11708-012-0211-0

Abstract: This paper investigates the capability of support vector machines (SVM) for prediction of fault classificationThe SVM, as a novel type of machine learning based on statistical learning theory, achieves good generalizationHere, the SVM has been used as a classification.The inputs of the SVM model are power and voltage values.equation has been developed for the prediction of the fault in the power system based on the developed SVM

Keywords： support vector machines (SVM)     structural risk minimization (SRM)     equivalent capacity margin (ECM)     restoration    

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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    

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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 (SRMresults obtained confirm the capability of SVM model.The effect of capacity factor ( ) on number of support vector and model accuracy has also been investigated

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

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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-DTCreference voltages in stationary frame and give them to the controlled motor after modulation by a space vectormodulation (SVM) inverter.

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

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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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Direct field oriented control scheme for space vector modulated AC/DC/AC converter fed induction motor

F. BENCHABANE, A. TITAOUINE, O. BENNIS, K. YAHIA, D. TAIBI

Frontiers in Energy 2012, Volume 6, Issue 2,   Pages 129-137 doi: 10.1007/s11708-012-0183-0

Abstract: This paper investigates a Luenberger flux observer with speed adaptation for a direct field oriented control of an induction motor. An improved method of speed estimation that operates on the principle of speed adaptive flux and current observer has been proposed. An observer is basically an estimator that uses a plant model and a feedback loop with measured stator voltage and current. Simulation results show that the proposed direct field oriented control with the proposed observer provides good performance dynamic characteristics. The induction motor is fed by an indirect power electronics converter. This indirect converter is controlled by a sliding mode technique that enables minimization of harmonics introduced by the line converter, as well as the control of the power factor and DC-link voltage. The robustness of the overall system is studied using simulation for different operating modes and varied parameters.

Keywords： induction motor     direct filed oriented control     Luenberger observer     estimation     space vector modulation(SVM)     sliding mode control     boost-rectifier    

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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 vectormachine (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     Support vectormachine (SVM)    

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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: Using multi-class least squares support vector machines (LS-SVM), the thermal positioning error of thevariance and mean square value of the temperatures of supporting bearings and screw-nut as feature vector

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： Kinect     Emotion recognition     Facial expression     Real-time classification     Fusion algorithm     Support vectormachine (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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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: neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and the least squares support vectormachine (LS-SVM) model for the prediction of BP reactivity to an unsupported back in normotensive andof 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 vectormachine (LS-SVM)    

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Two-stage scheduling on batch and single machines with limited waiting time constraint

Zhongshun SHI, Zewen HUANG, Leyuan SHI

Frontiers of Engineering Management 2017, Volume 4, Issue 3,   Pages 368-374 doi: 10.15302/J-FEM-2017034

Abstract: This study addresses the problem of two-stage scheduling on batch and single machines with limited waiting

Keywords： batch machine     flow shop     makespan     limited waiting time    

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Modular design of typical rigid links in parallel kinematic machines: Classification and topology optimization

Xinjun LIU, Xiang CHEN, Zhidong LI

Frontiers of Mechanical Engineering 2012, Volume 7, Issue 2,   Pages 199-209 doi: 10.1007/s11465-012-0315-6

Abstract:

Due to the demand of reconfigurable system in parallel kinematic machines (PKMs), modular design technology

Keywords： parallel kinematic machines (PKMs)     modular design     classification     topology optimization and improved Guide-Weight    

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An energy consumption prediction approach of die casting machines driven by product parameters

Frontiers of Mechanical Engineering 2021, Volume 16, Issue 4,   Pages 868-886 doi: 10.1007/s11465-021-0656-0

Abstract: Die casting machines, which are the core equipment of the machinery manufacturing industry, consume greatThe energy consumption prediction of die casting machines can support energy consumption quota, processNevertheless, due to the uncertainty and complexity of the energy consumption in die casting machinesTo fill this gap, this paper proposes an energy consumption prediction approach for die casting machinesConsequently, a systematic energy consumption prediction approach for die casting machines, involving

Keywords： die casting machine     energy consumption prediction     product parameters    

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Comments from young scholars: Can machines completely replace humans in manufacturing processes?

Shanlin YANG

Frontiers of Engineering Management 2018, Volume 5, Issue 4,   Pages 541-547 doi: 10.15302/J-FEM-2018207

Abstract:
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Title Author Date Type Operation

Fault classification and reconfiguration of distribution systems using equivalent capacity margin method

K. Sathish KUMAR, T. JAYABARATHI

Journal Article

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

Lian Jijian,He Longjun,Wang Haijun

Journal Article

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

Pijush SAMUI

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

A comprehensive review and analysis of solar forecasting techniques

Pardeep SINGLA, Manoj DUHAN, Sumit SAROHA

Journal Article

Direct field oriented control scheme for space vector modulated AC/DC/AC converter fed induction motor

F. BENCHABANE, A. TITAOUINE, O. BENNIS, K. YAHIA, D. TAIBI

Journal Article

A robust intelligent audio watermarking scheme using support vector machine

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

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

Performance analysis of new word weighting procedures for opinion mining

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

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

Two-stage scheduling on batch and single machines with limited waiting time constraint

Zhongshun SHI, Zewen HUANG, Leyuan SHI

Journal Article

Modular design of typical rigid links in parallel kinematic machines: Classification and topology optimization

Xinjun LIU, Xiang CHEN, Zhidong LI

Journal Article

An energy consumption prediction approach of die casting machines driven by product parameters

Journal Article

Comments from young scholars: Can machines completely replace humans in manufacturing processes?

Shanlin YANG

Journal Article