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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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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: and 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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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: regression (SWR), artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and the leastsquares support vector machine (LS-SVM) model for the prediction of BP reactivity to an unsupportedperformance of 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： stepwise regression     Artificial neural network (ANN)     Adaptive neuro-fuzzy inference system (ANFIS)     Leastsquares support vector machine (LS-SVM)    

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Timing decision-making method of engine blades for predecisional remanufacturing based on reliability analysis

Le CHEN, Xianlin WANG, Hua ZHANG, Xugang ZHANG, Binbin DAN

Frontiers of Mechanical Engineering 2019, Volume 14, Issue 4,   Pages 412-421 doi: 10.1007/s11465-019-0551-0

Abstract: predicted by using the characteristic values of performance degradation of products combined with the leastsquares support vector regression algorithm.

Keywords： predecisional remanufacturing     reliability     least squares support vector regression (LS-SVR)     game theory    

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electroencephalogram signals using spatial constraint independent component analysis based recursive leastsquares in brain-computer interface

Bang-hua YANG,Liang-fei HE,Lin LIN,Qian WANG

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 6,   Pages 486-496 doi: 10.1631/FITEE.1400299

Abstract: interfaces (BCIs), a method named spatial constraint independent component analysis based recursive leastsquares (SCICA-RLS) is proposed.

Keywords： Brain-computer interface (BCI)     Spatial constraint independent component analysis based recursive leastsquares (SCICA-RLS)    

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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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Structural total least squares algorithm for locating multiple disjoint sources based on AOA/TOA/FOA None

Xin CHEN, Ding WANG, Rui-rui LIU, Jie-xin YIN, Ying WU

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 7,   Pages 917-936 doi: 10.1631/FITEE.1700735

Abstract: Based on this, a structural total least squares (STLS) optimization model is developed and the inversethat the theoretical performance of the STLS method is consistent with that of the constrained total leastsquares method under first-order error analysis, both of which can achieve the Cramér-Rao lower bound

Keywords： Single-station     Structural total least squares     Inverse iteration     Angle-of-arrival (AOA)     Time-of-arrival    

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Comparison of modeling methods for wind power prediction: a critical study

Rashmi P. SHETTY, A. SATHYABHAMA, P. Srinivasa PAI

Frontiers in Energy 2020, Volume 14, Issue 2,   Pages 347-358 doi: 10.1007/s11708-018-0553-3

Abstract: Prediction of power generation of a wind turbine is crucial, which calls for accurate and reliable models. In this work, six different models have been developed based on wind power equation, concept of power curve, response surface methodology (RSM) and artificial neural network (ANN), and the results have been compared. To develop the models based on the concept of power curve, the manufacturer’s power curve, and to develop RSM as well as ANN models, the data collected from supervisory control and data acquisition (SCADA) of a 1.5 MW turbine have been used. In addition to wind speed, the air density, blade pitch angle, rotor speed and wind direction have been considered as input variables for RSM and ANN models. Proper selection of input variables and capability of ANN to map input-output relationships have resulted in an accurate model for wind power prediction in comparison to other methods.

Keywords： power curve     method of least squares     cubic spline interpolation     response surface methodology     artificial    

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Generalization and application in time series forecasting of the least square support vector machine

Xiang Xiaodong

Strategic Study of CAE 2008, Volume 10, Issue 11,   Pages 89-92

Abstract: more future information than historical data in time-series,the paper extends the prediction method of leastsquare support vector machine and obtains a more general prediction model of least square support vectormachine,and develops algorithm of the extended prediction model.Prediction examples of two time-seriesthat the extended model is more effective.Therefore it improves the value of the prediction method of leastsquare support vector machine.

Keywords： least square support vector machine     generalization     time series     forecasting    

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New decentralized control technique based on substructure and LQG approaches

Ying LEI, Ying LIN,

Frontiers of Mechanical Engineering 2009, Volume 4, Issue 4,   Pages 386-392 doi: 10.1007/s11465-009-0041-x

Abstract: An algorithm of recursive least squares estimation for the unknown excitation is proposed.

Keywords： substructures     decentralized control     linear quadratic Gaussian (LQG)     Kalman filter     unknown input     least-squares    

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Impacts of building information modeling (BIM) implementation on design and construction performance: a resource dependence theory perspective

Dongping CAO, Heng LI, Guangbin WANG

Frontiers of Engineering Management 2017, Volume 4, Issue 1,   Pages 20-34 doi: 10.15302/J-FEM-2017010

Abstract: designers and general contractors in BIM-based construction projects in China, the results from partial leastsquares analysis and bootstrapping mediation test provide clear evidence that BIM-enabled capabilities

Keywords： interorganizational collaboration     construction project performance     resource dependence theory     partial leastsquares modeling    

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Optical plasma boundary reconstruction based on least squares for EASTTokamak None

Hao LUO, Zheng-ping LUO, Chao XU, Wei JIANG

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 9,   Pages 1124-1134 doi: 10.1631/FITEE.1700041

Abstract: image plane to the Tokamak poloidal plane by a mathematical model, which is optimally resolved by using leastsquares to minimize the error between the optically reconstructed result and the EFIT result.

Keywords： Optical boundary reconstruction     Boundary detection     Global contrast     Least square     EAST Tokamak    

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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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Modified Performance Index Method for Parameter Estimation for Industrial Process with Time-varying

Zhang Chenghui

Strategic Study of CAE 2001, Volume 3, Issue 11,   Pages 54-59

Abstract: based on modified performance index is proposed for time-varying process to avoid the ill-condition of leastsquares method.Based on the new method,a series of new recursive algorithms such as the new recursive least squares

Keywords： parameter estimation     method of calculation     least square method     time-varying system     industrial production    

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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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Title Author Date Type Operation

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

Chao JIN, Bo WU, Youmin HU, Yao CHENG

Journal Article

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

Lian Jijian,He Longjun,Wang Haijun

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

Timing decision-making method of engine blades for predecisional remanufacturing based on reliability analysis

Le CHEN, Xianlin WANG, Hua ZHANG, Xugang ZHANG, Binbin DAN

Journal Article

electroencephalogram signals using spatial constraint independent component analysis based recursive leastsquares in brain-computer interface

Bang-hua YANG,Liang-fei HE,Lin LIN,Qian WANG

Journal Article

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

Pijush SAMUI

Journal Article

Structural total least squares algorithm for locating multiple disjoint sources based on AOA/TOA/FOA

Xin CHEN, Ding WANG, Rui-rui LIU, Jie-xin YIN, Ying WU

Journal Article

Comparison of modeling methods for wind power prediction: a critical study

Rashmi P. SHETTY, A. SATHYABHAMA, P. Srinivasa PAI

Journal Article

Generalization and application in time series forecasting of the least square support vector machine

Xiang Xiaodong

Journal Article

New decentralized control technique based on substructure and LQG approaches

Ying LEI, Ying LIN,

Journal Article

Impacts of building information modeling (BIM) implementation on design and construction performance: a resource dependence theory perspective

Dongping CAO, Heng LI, Guangbin WANG

Journal Article

Optical plasma boundary reconstruction based on least squares for EASTTokamak

Hao LUO, Zheng-ping LUO, Chao XU, Wei JIANG

Journal Article

A comprehensive review and analysis of solar forecasting techniques

Pardeep SINGLA, Manoj DUHAN, Sumit SAROHA

Journal Article

Modified Performance Index Method for Parameter Estimation for Industrial Process with Time-varying

Zhang Chenghui

Journal Article

A robust intelligent audio watermarking scheme using support vector machine

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

Journal Article