Resource Type

Journal Article 421

Conference Videos 36

Year

2024 1

2023 52

2022 72

2021 55

2020 41

2019 33

2018 20

2017 28

2016 19

2015 24

2014 12

2013 9

2012 10

2011 9

2010 11

2009 4

2008 9

2007 7

2006 8

2005 2

open ︾

Keywords

big data 20

data mining 12

Big data 9

Machine learning 8

Blockchain 5

Artificial intelligence 4

Deep learning 4

Data mining 3

Industry 4.0 3

digital twin 3

hydrogen production 3

Additive manufacturing 2

Anomaly detection 2

Big Data 2

Big data analytics 2

China 2

Data privacy 2

Data-driven 2

Data-driven method 2

open ︾

Search scope:

排序: Display mode:

Prediction of hydro-suction dredging depth using data-driven methods

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 3,   Pages 652-664 doi: 10.1007/s11709-021-0719-7

Abstract: In this study, data-driven methods (DDMs) including different kinds of group method of data handlingAlso, 33 data samples from three previous studies were used.Data-driven simulation results indicated that the HGSO algorithm accurately trains the GMDH methods better

Keywords: sedimentation     water resources     dam engineering     machine learning     heuristic    

Data-driven distribution network topology identification considering correlated generation power of distributed

Frontiers in Energy 2022, Volume 16, Issue 1,   Pages 121-129 doi: 10.1007/s11708-021-0780-x

Abstract: This paper proposes a data-driven topology identification method for distribution systems with distributed

Keywords: power distribution network     data-driven     topology identification     distributed energy resource     maximal    

Optimal Antibody Purification Strategies Using Data-Driven Models Article

Songsong Liu, Lazaros G. Papageorgiou

Engineering 2019, Volume 5, Issue 6,   Pages 1077-1092 doi: 10.1016/j.eng.2019.10.011

Abstract: Data-driven models of chromatography throughput are developed considering loaded mass, flow velocity,height as the inputs, using manufacturing-scale simulated datasets based on microscale experimental datato minimize the total cost of goods per gram of the antibody purification process, incorporating the data-driven

Keywords: Antibody purification     Multiscale optimization     Antigen-binding fragment     Mixed-integer programming     Data-driven    

An adaptive data-driven method for accurate prediction of remaining useful life of rolling bearings

Yanfeng PENG, Junsheng CHENG, Yanfei LIU, Xuejun LI, Zhihua PENG

Frontiers of Mechanical Engineering 2018, Volume 13, Issue 2,   Pages 301-310 doi: 10.1007/s11465-017-0449-7

Abstract:

A novel data-driven method based on Gaussian mixture model (GMM) and distance evaluation techniqueThe data sets are clustered by GMM to divide all data sets into several health states adaptively andThus, either the health state of the data sets or the number of the states is obtained automatically.training data sets.sets into several health states and remove the abnormal data sets.

Keywords: Gaussian mixture model     distance evaluation technique     health state     remaining useful life     rolling bearing    

Hybrid Data-Driven and Mechanistic Modeling Approaches for Multiscale Material and Process Design Perspective

Teng Zhou, Rafiqul Gani, Kai Sundmacher

Engineering 2021, Volume 7, Issue 9,   Pages 1231-1238 doi: 10.1016/j.eng.2020.12.022

Abstract: modeling, the material properties, which are computationally expensive to obtain, are described by data-driven

Keywords: Data-driven     Surrogate model     Machine learning     Hybrid modeling     Material design     Process optimization    

A hierarchical system to predict behavior of soil and cantilever sheet wall by data-driven models

Nang Duc BUI; Hieu Chi PHAN; Tiep Duc PHAM; Ashutosh Sutra DHAR

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 6,   Pages 667-684 doi: 10.1007/s11709-022-0822-4

Abstract: The uncertainty of this data-driven system is partially investigated by developing 1000 RFC models, basedon the application of random sampling technique in the data splitting process.Investigation on the distribution of the evaluation metrics reveals negative skewed data toward the 1.0000

Keywords: finite element analysis     cantilever sheet wall     machine learning     artificial neural network     random forest    

Data-Driven Anomaly Diagnosis for Machining Processes Article

Y.C. Liang, S. Wang, W.D. Li, X. Lu

Engineering 2019, Volume 5, Issue 4,   Pages 646-652 doi: 10.1016/j.eng.2019.03.012

Abstract: To address this issue, this paper presents a novel data-driven diagnosis system for anomalies.In this system, power data for condition monitoring are continuously collected during dynamic machininganalysis, preprocessing mechanisms have been designed to denoise, normalize, and align the monitored dataImportant features are extracted from the monitored data and thresholds are defined to identify anomaliesBased on historical data, the values of thresholds are optimized using a fruit fly optimization (FFO)

Keywords: Computer numerical control machining     Anomaly detection     Fruit fly optimization algorithm     Data-driven    

Machine Learning and Data-Driven Techniques for the Control of Smart Power Generation Systems: An Uncertainty Review

Li Sun, Fengqi You

Engineering 2021, Volume 7, Issue 9,   Pages 1239-1247 doi: 10.1016/j.eng.2021.04.020

Abstract: The burgeoning era of machine learning (ML) and data-driven control (DDC) techniques promises an improved

Keywords: Smart power generation     Machine learning     Data-driven control     Systems engineering    

On the Data-Driven Materials Innovation Infrastructure

Hong Wang, X.-D. Xiang, Lanting Zhang

Engineering 2020, Volume 6, Issue 6,   Pages 609-611 doi: 10.1016/j.eng.2020.04.004

Data-driven approach to solve vertical drain under time-dependent loading

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 3,   Pages 696-711 doi: 10.1007/s11709-021-0727-7

Abstract: Currently, the vertical drain consolidation problem is solved by numerous analytical solutions, such as time-dependent solutions and linear or parabolic radial drainage in the smear zone, and no artificial intelligence (AI) approach has been applied. Thus, in this study, a new hybrid model based on deep neural networks (DNNs), particle swarm optimization (PSO), and genetic algorithms (GAs) is proposed to solve this problem. The DNN can effectively simulate any sophisticated equation, and the PSO and GA can optimize the selected DNN and improve the performance of the prediction model. In the present study, analytical solutions to vertical drains in the literature are incorporated into the DNN–PSO and DNN–GA prediction models with three different radial drainage patterns in the smear zone under time-dependent loading. The verification performed with analytical solutions and measurements from three full-scale embankment tests revealed promising applications of the proposed approach.

Keywords: vertical drain     artificial neural network     time-dependent loading     deep learning network     genetic algorithm     particle swarm optimization    

Data-driven soft sensors in blast furnace ironmaking: a survey Review Article

Yueyang LUO, Xinmin ZHANG, Manabu KANO, Long DENG, Chunjie YANG, Zhihuan SONG

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 3,   Pages 327-354 doi: 10.1631/FITEE.2200366

Abstract: With the advancement of the Internet of Things, big data, and artificial intelligence, data-driven inThis review covers the state-of-the-art studies of data-driven technologies in the .Specifically, we first conduct a comprehensive overview of various data-driven soft sensor modeling methodsSecond, the important applications of data-driven in ironmaking (silicon content, molten iron temperatureFinally, the potential challenges and future development trends of data-driven in ironmaking applications

Keywords: Soft sensors     Data-driven modeling     Machine learning     Deep learning     Blast furnace     Ironmaking process    

Multi-agent differential game based cooperative synchronization control using a data-driven method Research Article

Yu SHI, Yongzhao HUA, Jianglong YU, Xiwang DONG, Zhang REN

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 7,   Pages 1043-1056 doi: 10.1631/FITEE.2200001

Abstract: An off-policy RL algorithm using neighboring interactive data is constructed to update the controller

Keywords: Multi-agent system     Differential game     Synchronization control     Data-driven     Reinforcement learning    

Multiple input self-organizing-map ResNet model for optimization of petroleum refinery conversion units

Frontiers of Chemical Science and Engineering 2023, Volume 17, Issue 6,   Pages 759-771 doi: 10.1007/s11705-022-2269-5

Abstract: The self-organizing-map part maps the input data into multiple two-dimensional planes and sends them

Keywords: hydrocracking     convolutional neural networks     self-organizing map     deep learning     data-driven optimization    

Hybrid-driven Gaussian process online learning for highly maneuvering multi-target tracking Research Article

Qiang GUO, Long TENG, Tianxiang YIN, Yunfei GUO, Xinliang WU, Wenming SONG

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 11,   Pages 1647-1656 doi: 10.1631/FITEE.2300348

Abstract: This paper proposes a hybrid-driven approach for tracking multiple highly maneuvering targets, leveragingThrough the simulations, it has been demonstrated that the hybrid-driven approach exhibits significant

Keywords: Target tracking     Gaussian process     Data-driven     Online learning     Model-driven     Probabilistic data association    

Data-Driven Discovery of Stochastic Differential Equations Article

Yasen Wang, Huazhen Fang, Junyang Jin, Guijun Ma, Xin He, Xing Dai, Zuogong Yue, Cheng Cheng, Hai-Tao Zhang, Donglin Pu, Dongrui Wu, Ye Yuan, Jorge Gonçalves, Jürgen Kurths, Han Ding

Engineering 2022, Volume 17, Issue 10,   Pages 244-252 doi: 10.1016/j.eng.2022.02.007

Abstract: identification of SDEs governing a system is often a challenge because of the inherent strong stochasticity of datapractical utility of existing parametric approaches for identifying SDEs is usually limited by insufficient datathe linear regression problem for the discovery of SDEs that requires considerably less time-series dataThe effectiveness of the proposed framework is demonstrated using real data on stock and oil prices,bearing variation, and wind speed, as well as simulated data on well-known stochastic dynamical systems

Keywords: Data-driven method     System identification     Sparse Bayesian learning     Stochastic differential equations    

Title Author Date Type Operation

Prediction of hydro-suction dredging depth using data-driven methods

Journal Article

Data-driven distribution network topology identification considering correlated generation power of distributed

Journal Article

Optimal Antibody Purification Strategies Using Data-Driven Models

Songsong Liu, Lazaros G. Papageorgiou

Journal Article

An adaptive data-driven method for accurate prediction of remaining useful life of rolling bearings

Yanfeng PENG, Junsheng CHENG, Yanfei LIU, Xuejun LI, Zhihua PENG

Journal Article

Hybrid Data-Driven and Mechanistic Modeling Approaches for Multiscale Material and Process Design

Teng Zhou, Rafiqul Gani, Kai Sundmacher

Journal Article

A hierarchical system to predict behavior of soil and cantilever sheet wall by data-driven models

Nang Duc BUI; Hieu Chi PHAN; Tiep Duc PHAM; Ashutosh Sutra DHAR

Journal Article

Data-Driven Anomaly Diagnosis for Machining Processes

Y.C. Liang, S. Wang, W.D. Li, X. Lu

Journal Article

Machine Learning and Data-Driven Techniques for the Control of Smart Power Generation Systems: An Uncertainty

Li Sun, Fengqi You

Journal Article

On the Data-Driven Materials Innovation Infrastructure

Hong Wang, X.-D. Xiang, Lanting Zhang

Journal Article

Data-driven approach to solve vertical drain under time-dependent loading

Journal Article

Data-driven soft sensors in blast furnace ironmaking: a survey

Yueyang LUO, Xinmin ZHANG, Manabu KANO, Long DENG, Chunjie YANG, Zhihuan SONG

Journal Article

Multi-agent differential game based cooperative synchronization control using a data-driven method

Yu SHI, Yongzhao HUA, Jianglong YU, Xiwang DONG, Zhang REN

Journal Article

Multiple input self-organizing-map ResNet model for optimization of petroleum refinery conversion units

Journal Article

Hybrid-driven Gaussian process online learning for highly maneuvering multi-target tracking

Qiang GUO, Long TENG, Tianxiang YIN, Yunfei GUO, Xinliang WU, Wenming SONG

Journal Article

Data-Driven Discovery of Stochastic Differential Equations

Yasen Wang, Huazhen Fang, Junyang Jin, Guijun Ma, Xin He, Xing Dai, Zuogong Yue, Cheng Cheng, Hai-Tao Zhang, Donglin Pu, Dongrui Wu, Ye Yuan, Jorge Gonçalves, Jürgen Kurths, Han Ding

Journal Article