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A novel multimode process monitoring method integrating LDRSKM with Bayesian inference

Shi-jin REN,Yin LIANG,Xiang-jun ZHAO,Mao-yun YANG

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 8,   Pages 617-633 doi: 10.1631/FITEE.1400263

Abstract: regularized soft -means algorithm by exploiting the local and non-local geometric information of the dataand generalized linear discriminant analysis to provide a better and more meaningful data partition.LDRSKM can perform clustering and subspace selection simultaneously, enhancing the separability of dataWith the data partition obtained, kernel support vector data description (KSVDD) is used to establish

Keywords： Multimode process monitoring     Local discriminant regularized soft k-means clustering     Kernel supportvector data description     Bayesian inference     Tennessee Eastman process    

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Image quality assessmentmethod based on nonlinear feature extraction in kernel space Article

Yong DING,Nan LI,Yang ZHAO,Kai HUANG

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 10,   Pages 1008-1017 doi: 10.1631/FITEE.1500439

Abstract: Furthermore, by introducing kernel methods to transform the linear problem into a nonlinear one, a full-reference

Keywords： Image quality assessment     Full-reference method     Feature extraction     Kernel space     Support vector regression    

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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 susceptibilitypotential of SVM model in prediction of liquefaction using actual field cone penetration test (CPT) dataFor Chi-Chi earthquake, the model predicts with accuracy of 100%, and in the case of global data, SVMThe effect of capacity factor ( ) on number of support vector and model accuracy has also been investigatedthat SVM can be used as a practical tool for prediction of liquefaction potential, based on field CPT data

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

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Unconfined compressive strength prediction of soils stabilized using artificial neural networks and supportvector machines

Alireza TABARSA, Nima LATIFI, Abdolreza OSOULI, Younes BAGHERI

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 2,   Pages 520-536 doi: 10.1007/s11709-021-0689-9

Abstract: Two artificial-intelligence-based models including artificial neural networks and support vector machinesThis study demonstrates the better performance of support vector machines in predicting the strengthThe type of kernel function used in support vector machine models contributed positively to the performance

Keywords： unconfined compressive strength     artificial neural network     support vector machine     predictive models     regression    

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Modeling of bentonite/sepiolite plastic concrete compressive strength using artificial neural network and supportvector machine

Ali Reza GHANIZADEH, Hakime ABBASLOU, Amir Tavana AMLASHI, Pourya ALIDOUST

Frontiers of Structural and Civil Engineering 2019, Volume 13, Issue 1,   Pages 215-239 doi: 10.1007/s11709-018-0489-z

Abstract: paper aims to explore two machine learning algorithms including artificial neural network (ANN) and supportvector machine (SVM) to predict the compressive strength of bentonite/sepiolite plastic concretes.For this purpose, two unique sets of 72 data for compressive strength of bentonite and sepiolite plasticconcrete samples (totally 144 data) were prepared by conducting an experimental study.

Keywords： bentonite/sepiolite plastic concrete     compressive strength     artificial neural network     support vector machine    

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Diagnosis of sewer pipe defects on image recognition of multi-features and support vector machine in

Xiangyang Ye, Jian’e Zuo, Ruohan Li, Yajiao Wang, Lili Gan, Zhonghan Yu, Xiaoqing Hu

Frontiers of Environmental Science & Engineering 2019, Volume 13, Issue 2, doi: 10.1007/s11783-019-1102-y

Abstract:

An image-recognition-based diagnosis system of pipe defect types was established.

1043 practical pipe images were gathered by CCTV robot in a southern Chinese city.

The overall accuracy of the system is 84% and the highest accuracy is 99.3%.

The accuracy shows positive correlation to the number of training samples.

Keywords： Sewer pipe defects     Defect diagnosing     Image recognition     Multi-features extraction     Support vector machine    

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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:

According to the theory that the present data contains more future informationthan historical data in time-series,the paper extends the prediction method of least square supportvector machine and obtains a more general prediction model of least square support vector machine,andextended model is more effective.Therefore it improves the value of the prediction method of least square supportvector machine.

Keywords： least square support vector machine     generalization     time series     forecasting    

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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: Rapid growth in information technology and computer networks has resulted in the universal use of dataHowever, the major challenge faced by digital data owners is protection of data against unauthorizedaudio water-marking scheme using a synergistic combination of singular value decomposition (SVD) and supportvector machine (SVM).the extraction process, an intelligent detector using SVM is suggested for extracting the watermark data

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

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Big Data to support sustainable urban energy planning: The EvoEnergy project

Moulay Larbi CHALAL, Benachir MEDJDOUB, Nacer BEZAI, Raid SHRAHILY

Frontiers of Engineering Management 2020, Volume 7, Issue 2,   Pages 287-300 doi: 10.1007/s42524-019-0081-9

Abstract: consumption where we developed a 3D urban energy prediction system (EvoEnergy) using the old UK panel datasurvey, namely, the British household panel data survey (BHPS).aim, the household transition and energy prediction modules of EvoEnergy have been tested under both dataand had a good prediction accuracy (MAPE  5%) when compared to actual energy performance certificate data, to consider merging the BHPS and UKHLS data sets.

Keywords： urban energy planning     sustainable planning     Big Data     household transition     energy prediction    

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Research on the data processing methods of airborne vector gravimetry using SINS/GNSS

Ning Jinsheng

Strategic Study of CAE 2014, Volume 16, Issue 3,   Pages 4-13

Abstract:

Airborne vector gravimetry is an advanced and efficient technology to determine high frequency informationThe principle of airborne vector gravimetry using SINS（strapdown inertial navigation system）/GNSS（globalnavigation satellite systems）is introduced in the paper，and then the data preprocessing，data reductiontopographical effect and downward continuation are discussed，as well as the geoid determination from airborne vectorIt provides the support for the development of airborne vector gravimetry in our country.

Keywords： airborne vector gravimetry     SINS/GNSS data fusion     earth gravity field     geoid    

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An assessment of surrogate fuel using Bayesian multiple kernel learning model in sight of sooting tendency

Frontiers in Energy 2022, Volume 16, Issue 2,   Pages 277-291 doi: 10.1007/s11708-021-0731-6

Abstract: surrogate fuels was proposed with the application of a machine learning method, named the Bayesian multiple kernel

Keywords： sooting tendency     yield sooting index     Bayesian multiple kernel learning     surrogate assessment     surrogate    

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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: be further improved by hybridization of the two models or by performing pre-processing on the input data

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

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artificial intelligence based method for evaluating power grid node importance using network embedding and supportvector regression Research Papers

Hui-fang WANG, Chen-yu ZHANG, Dong-yang LIN, Ben-teng HE

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 6,   Pages 816-828 doi: 10.1631/FITEE.1800146

Abstract: Finally, a support vector regression model is trained based on the optimized sample set for the later

Keywords： Power grid     Artificial intelligence     Node importance     Text-associated DeepWalk     Network embedding     Supportvector regression    

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LSSVM-based approach for refining soil failure criteria and calculating safety factor of slopes

Shiguo XIAO; Shaohong LI

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 7,   Pages 871-881 doi: 10.1007/s11709-022-0863-8

Abstract: In the work reported here, an analysis method based on the least square support vector machine (LSSVMto establish a complex nonlinear failure criterion via iteration computation based on strength test data

Keywords： slope stability     safety factor     failure criterion     least square support vector machine    

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Assessing compressive strengths of mortar and concrete from digital images by machine learning techniques

Amit SHIULY; Debabrata DUTTA; Achintya MONDAL

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 3,   Pages 347-358 doi: 10.1007/s11709-022-0819-z

Abstract: These include support-vector machine model and various deep convolutional neural network models, namelyto train machine learning models to allow them to predict compressive strength based upon the image data

Keywords： support vector machine     deep convolutional neural network     microscope     digital image     curing period    

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Title Author Date Type Operation

A novel multimode process monitoring method integrating LDRSKM with Bayesian inference

Shi-jin REN,Yin LIANG,Xiang-jun ZHAO,Mao-yun YANG

Journal Article

Image quality assessmentmethod based on nonlinear feature extraction in kernel space

Yong DING,Nan LI,Yang ZHAO,Kai HUANG

Journal Article

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

Pijush SAMUI

Journal Article

Unconfined compressive strength prediction of soils stabilized using artificial neural networks and supportvector machines

Alireza TABARSA, Nima LATIFI, Abdolreza OSOULI, Younes BAGHERI

Journal Article

Modeling of bentonite/sepiolite plastic concrete compressive strength using artificial neural network and supportvector machine

Ali Reza GHANIZADEH, Hakime ABBASLOU, Amir Tavana AMLASHI, Pourya ALIDOUST

Journal Article

Diagnosis of sewer pipe defects on image recognition of multi-features and support vector machine in

Xiangyang Ye, Jian’e Zuo, Ruohan Li, Yajiao Wang, Lili Gan, Zhonghan Yu, Xiaoqing Hu

Journal Article

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

Xiang Xiaodong

Journal Article

A robust intelligent audio watermarking scheme using support vector machine

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

Journal Article

Big Data to support sustainable urban energy planning: The EvoEnergy project

Moulay Larbi CHALAL, Benachir MEDJDOUB, Nacer BEZAI, Raid SHRAHILY

Journal Article

Research on the data processing methods of airborne vector gravimetry using SINS/GNSS

Ning Jinsheng

Journal Article

An assessment of surrogate fuel using Bayesian multiple kernel learning model in sight of sooting tendency

Journal Article

A comprehensive review and analysis of solar forecasting techniques

Pardeep SINGLA, Manoj DUHAN, Sumit SAROHA

Journal Article

artificial intelligence based method for evaluating power grid node importance using network embedding and supportvector regression

Hui-fang WANG, Chen-yu ZHANG, Dong-yang LIN, Ben-teng HE

Journal Article

LSSVM-based approach for refining soil failure criteria and calculating safety factor of slopes

Shiguo XIAO; Shaohong LI

Journal Article

Assessing compressive strengths of mortar and concrete from digital images by machine learning techniques

Amit SHIULY; Debabrata DUTTA; Achintya MONDAL

Journal Article