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Domain knowledge enhanced deep learning for electrocardiogram arrhythmia classification Research Article

Jie SUN,sunjie@nbut.edu.cn

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 1,   Pages 59-72 doi: 10.1631/FITEE.2100519

Abstract: Deep learning provides an effective way for automatic classification of s, but in , pure data-drivenA promising solution is combining with deep learning.neural network to capture the statistical pattern between input data and the ground-truth label, and a knowledge

Keywords： Domain knowledge     Cardiac arrhythmia     Electrocardiogram (ECG)     Clinical decision-making    

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Entity and relation extraction with rule-guided dictionary as domain knowledge

Frontiers of Engineering Management   Pages 610-622 doi: 10.1007/s42524-022-0226-0

Abstract: Entity and relation extraction is an indispensable part of domain knowledge graph construction, whichcan serve relevant knowledge needs in a specific domain, such as providing support for product research, sales, risk control, and domain hotspot analysis.However, the performance of these methods degrades when they face domain-specific datasets.To address the problems above, this paper first introduced prior knowledge composed of domain dictionaries

Keywords： entity extraction     relation extraction     prior knowledge     domain rule    

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A step forward towards a comprehensive framework for assessing liquefaction land damage vulnerability: Exploration from historical data

Mahmood AHMAD, Xiao-Wei TANG, Jiang-Nan QIU, Feezan AHMAD, Wen-Jing GU

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 6,   Pages 1476-1491 doi: 10.1007/s11709-020-0670-z

Abstract: integrating multi-related factors of seismic soil liquefaction and its induced hazards using a machine learning(ML) algorithm-K2 and domain knowledge (DK) data fusion methodology.

Keywords： Bayesian belief network     liquefaction-induced damage potential     cone penetration test     soil liquefaction     structurallearning and domain knowledge    

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Thermal reffusivity: uncovering phonon behavior, structural defects, and domain size

Yangsu XIE, Bowen ZHU, Jing LIU, Zaoli XU, Xinwei WANG

Frontiers in Energy 2018, Volume 12, Issue 1,   Pages 143-157 doi: 10.1007/s11708-018-0520-z

Abstract: Like electrical resistivity which has been historically used as a theory for analyzing structural domaindefect levels of metals, the thermal reffusivity can also uncover phonon behavior, structure defects and domainFrom the residual thermal reffusivity at the 0 K limit, the structural thermal domain (STD) size of crystallineof thermal reffusivity against decreasing temperature profiles, we conclude that they reflected the structural

Keywords： thermal reffusivity theory     phonon behavior     structure defects     structural thermal domain (STD) size     2D    

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Digital image correlation-based structural state detection through deep learning

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 1,   Pages 45-56 doi: 10.1007/s11709-021-0777-x

Abstract: This paper presents a new approach for automatical classification of structural state through deep learningdesigned to fuse both the feature extraction and classification blocks into an intelligent and compact learningsystem and detect the structural state of a steel frame; the input was a series of vibration signals, and the output was a structural state.It is demonstrated that: 1) the CNN can extract the structural state information from the vibration signals

Keywords： structural state detection     deep learning     digital image correlation     vibration signal     steel frame    

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Bridging finite element and deep learning: High-resolution stress distribution prediction in structural

Frontiers of Structural and Civil Engineering   Pages 1365-1377 doi: 10.1007/s11709-022-0882-5

Abstract: Instead, Deep Learning (DL) techniques can generate results significantly faster than conventional run-timeThis can prove extremely valuable in real-time structural assessment applications.

Keywords： Deep Learning     finite element analysis     stress contours     structural components    

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Machine learning in building energy management: A critical review and future directions

Frontiers of Engineering Management 2022, Volume 9, Issue 2,   Pages 239-256 doi: 10.1007/s42524-021-0181-1

Abstract: Over the past two decades, machine learning (ML) has elicited increasing attention in building energy

Keywords： building energy management     machine learning     integrated framework     knowledge evolution    

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Estimation of optimum design of structural systems via machine learning

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 6,   Pages 1441-1452 doi: 10.1007/s11709-021-0774-0

Abstract: Three different structural engineering designs were investigated to determine optimum design variablesTo explore the estimation success of ANN models, different test cases were proposed for the three structural

Keywords： optimization     metaheuristic algorithms     harmony search     structural designs     machine learning     artificial    

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Forget less, count better: a domain-incremental self-distillation learning benchmark for lifelong crowd Research Article

Jiaqi GAO, Jingqi LI, Hongming SHAN, Yanyun QU, James Z. WANG, Fei-Yue WANG, Junping ZHANG

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 2,   Pages 187-202 doi: 10.1631/FITEE.2200380

Abstract: A robust and practical system has to be capable of continuously learning with the newly incoming domaindata in real-world scenarios instead of fitting one domain only.various domains, which is called catastrophic forgetting; (2) the well-trained model in a specific domainSpecifically, we propose a self-distillation learning framework as a benchmark (forget less, count better, or FLCB) for lifelong , which helps the model leverage previous meaningful knowledge in a sustainable

Keywords： Crowd counting     Knowledge distillation     Lifelong learning    

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A super-element approach for structural identification in time domain

LI Jie, ZHAO Xin

Frontiers of Mechanical Engineering 2006, Volume 1, Issue 2,   Pages 215-221 doi: 10.1007/s11465-006-0004-4

Abstract: For most time-domain identification methods, a complete measurement for unique identification resultsis required for structural responses.However, the number of transducers is commonly far less than the number of structural degrees of freedomstructure in the time domain.The super-element model used for time domain identification is first discussed in this study.

Keywords： numerical     effective decomposing     parameterization procedure     divide-and-conquer ability     time-domain identification    

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Layer-wise domain correction for unsupervised domain adaptation Article

Shuang LI, Shi-ji SONG, Cheng WU

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 1,   Pages 91-103 doi: 10.1631/FITEE.1700774

Abstract: Deep neural networks have been successfully applied to numerous machine learning tasks because of theirTo address the domain shift or data bias problems, we introduce layer-wise domain correction (LDC), anew unsupervised domain adaptation algorithm which adapts an existing deep network through additiveThe corrections that are trained via maximum mean discrepancy, adapt to the target domain while increasing

Keywords： Unsupervised domain adaptation     Maximum mean discrepancy     Residual network     Deep learning    

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A hybrid deep learning model for robust prediction of the dimensional accuracy in precision milling ofthin-walled structural components

Frontiers of Mechanical Engineering 2022, Volume 17, Issue 3, doi: 10.1007/s11465-022-0688-0

Abstract: study, we develop a predictive model of the dimensional accuracy for precision milling of thin-walled structuralThe aim is to classify three typical features of a structural component—squares, slots, and holes—intoThe classification accuracy of the popular machine learning methods has been evaluated in comparisonwith the proposed deep learning model.than the best machine learning algorithm considered in this paper.

Keywords： precision milling     dimensional accuracy     cutting force     convolutional neural networks     coherent noise    

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Estimation of flexible pavement structural capacity using machine learning techniques

Nader KARBALLAEEZADEH, Hosein GHASEMZADEH TEHRANI, Danial MOHAMMADZADEH SHADMEHRI, Shahaboddin SHAMSHIRBAND

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 5,   Pages 1083-1096 doi: 10.1007/s11709-020-0654-z

Abstract: The most common index for representing structural condition of the pavement is the structural number.The current procedure for determining structural numbers involves utilizing falling weight deflectometerIn this study, three machine learning methods entitled Gaussian process regression, M5P model tree, andrandom forest used for the prediction of structural numbers in flexible pavements.Using machine learning methods instead of back-calculation improves the calculation process quality and

Keywords： transportation infrastructure     flexible pavement     structural number prediction     Gaussian process regression    

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Structural performance assessment of GFRP elastic gridshells by machine learning interpretability methods

Frontiers of Structural and Civil Engineering   Pages 1249-1266 doi: 10.1007/s11709-022-0858-5

Abstract: The prediction of structural performance plays a significant role in damage assessment of glass fiberMachine learning (ML) approaches are implemented in this study, to predict maximum stress and displacementOutput features of structural performance considered in this study are the maximum stress as fSHAP is employed to describe the importance of each variable to structural performance both locally and

Keywords： machine learning     gridshell structure     regression     sensitivity analysis     interpretability methods    

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Machine learning for detecting mesial temporal lobe epilepsy by structural and functional neuroimaging

Baiwan Zhou, Dongmei An, Fenglai Xiao, Running Niu, Wenbin Li, Wei Li, Xin Tong, Graham J Kemp, Dong Zhou, Qiyong Gong, Du Lei

Frontiers of Medicine 2020, Volume 14, Issue 5,   Pages 630-641 doi: 10.1007/s11684-019-0718-4

Abstract: temporal lobe epilepsy (mTLE), the most common type of focal epilepsy, is associated with functional and structuralMachine learning (ML) techniques have been successfully used in discriminating mTLE from healthy controlsHowever, either functional or structural neuroimaging data are mostly used separately as input, and theWe conducted a multimodal ML study based on functional and structural neuroimaging measures.We achieved an accuracy of 78% by integrating functional data and 79% by integrating structural data

Keywords： mesial temporal lobe epilepsy     functional magnetic resonance imaging     structural magnetic resonance imaging     machine learning     support vector machine    

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Title Author Date Type Operation

Domain knowledge enhanced deep learning for electrocardiogram arrhythmia classification

Jie SUN,sunjie@nbut.edu.cn

Journal Article

Entity and relation extraction with rule-guided dictionary as domain knowledge

Journal Article

A step forward towards a comprehensive framework for assessing liquefaction land damage vulnerability: Exploration from historical data

Mahmood AHMAD, Xiao-Wei TANG, Jiang-Nan QIU, Feezan AHMAD, Wen-Jing GU

Journal Article

Thermal reffusivity: uncovering phonon behavior, structural defects, and domain size

Yangsu XIE, Bowen ZHU, Jing LIU, Zaoli XU, Xinwei WANG

Journal Article

Digital image correlation-based structural state detection through deep learning

Journal Article

Bridging finite element and deep learning: High-resolution stress distribution prediction in structural

Journal Article

Machine learning in building energy management: A critical review and future directions

Journal Article

Estimation of optimum design of structural systems via machine learning

Journal Article

Forget less, count better: a domain-incremental self-distillation learning benchmark for lifelong crowd

Jiaqi GAO, Jingqi LI, Hongming SHAN, Yanyun QU, James Z. WANG, Fei-Yue WANG, Junping ZHANG

Journal Article

A super-element approach for structural identification in time domain

LI Jie, ZHAO Xin

Journal Article

Layer-wise domain correction for unsupervised domain adaptation

Shuang LI, Shi-ji SONG, Cheng WU

Journal Article

A hybrid deep learning model for robust prediction of the dimensional accuracy in precision milling ofthin-walled structural components

Journal Article

Estimation of flexible pavement structural capacity using machine learning techniques

Nader KARBALLAEEZADEH, Hosein GHASEMZADEH TEHRANI, Danial MOHAMMADZADEH SHADMEHRI, Shahaboddin SHAMSHIRBAND

Journal Article

Structural performance assessment of GFRP elastic gridshells by machine learning interpretability methods

Journal Article

Machine learning for detecting mesial temporal lobe epilepsy by structural and functional neuroimaging

Baiwan Zhou, Dongmei An, Fenglai Xiao, Running Niu, Wenbin Li, Wei Li, Xin Tong, Graham J Kemp, Dong Zhou, Qiyong Gong, Du Lei

Journal Article