Resource Type

Journal Article 554

Conference Videos 41

Conference Information 1

Year

2024 1

2023 62

2022 86

2021 66

2020 53

2019 39

2018 29

2017 39

2016 25

2015 32

2014 16

2013 16

2012 18

2011 10

2010 15

2009 6

2008 12

2007 15

2006 9

2005 5

open ︾

Keywords

big data 20

data mining 12

Big data 9

Machine learning 8

Artificial intelligence 4

Blockchain 4

Deep learning 4

optimization 4

Data mining 3

Industry 4.0 3

digital twin 3

hydrogen production 3

ANN 2

Additive manufacturing 2

Anomaly detection 2

Big Data 2

Big data analytics 2

CFD 2

China 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 handling) hybrid models with particle swarm optimization (PSO) and Henry gas solubility optimization (HGSO) methods, and simple equations methods were applied to simulate the maximum hydro-suction dredging depth (Also, 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    

HTML PDF Collect

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 distributedtested on different distribution networks and the simulation results are compared with those of other methods

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

HTML PDF Collect

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 datamodeling method is adapted due to its simplicity and better prediction accuracy in comparison with other methodsto 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    

HTML PDF Collect

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    

HTML PDF Collect

Concrete corrosion in wastewater systems: Prediction and sensitivity analysis using advanced extreme learning machine

Mohammad ZOUNEMAT-KERMANI, Meysam ALIZAMIR, Zaher Mundher YASEEN, Reinhard HINKELMANN

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 2,   Pages 444-460 doi: 10.1007/s11709-021-0697-9

Abstract: The implementation of novel machine learning models can contribute remarkably to simulating the degradation of concrete due to environmental factors. This study considers the sulfuric acid corrosive factor in wastewater systems to simulate concrete mass loss using five machine learning models. The models include three different types of extreme learning machines, including the standard, online sequential, and kernel extreme learning machines, in addition to the artificial neural network, classification and regression tree model, and statistical multiple linear regression model. The reported values of concrete mass loss for six different types of concrete are the target values of the machine learning models. The input variability was assessed based on two scenarios prior to the application of the predictive models. For the first assessment, the machine learning models were developed using all the available cement and concrete mixture input variables; the second assessment was conducted based on the gamma test approach, which is a sensitivity analysis technique. Subsequently, the sensitivity analysis of the most effective parameters for concrete corrosion was tested using three different approaches. The adopted methodology attained optimistic and reliable modeling results. The online sequential extreme learning machine model demonstrated superior performance over the other investigated models in predicting the concrete mass loss of different types of concrete.

Keywords： sewer systems     environmental engineering     data-driven methods     sensitivity analysis    

HTML PDF Collect

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: As theoretical methods and associated tools are being continuously improved and computer power has reacheda high level, it is now efficient and popular to use computational methods to guide material selectionmodeling, 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    

HTML PDF Collect

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    

HTML PDF Collect

Big Data Creates New Opportunities for Materials Research: A Review on Methods and Applications of Machine Review

Teng Zhou, Zhen Song, Kai Sundmacher

Engineering 2019, Volume 5, Issue 6,   Pages 1017-1026 doi: 10.1016/j.eng.2019.02.011

Abstract:

Materials development has historically been driven by human needs and desires, and this is likelyAs big data generated by modern experimental and computational techniques is becoming more readily available, data-driven or machine learning (ML) methods have opened new paradigms for the discovery and rationalIn this review article, we provide a brief introduction on various ML methods and related software or

Keywords： Big data     Data-driven     Machine learning     Materials screening     Materials design    

HTML PDF Collect

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    

HTML PDF Collect

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: Traditional first-principle model-based methods are becoming insufficient when faced with the ever-growingThe burgeoning era of machine learning (ML) and data-driven control (DDC) techniques promises an improvedalternative to these outdated methods.optimization, and fault detection of power generation systems, with a particular focus on uncovering how these methods

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

HTML PDF Collect

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

HTML PDF Collect

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    

HTML PDF Collect

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    

HTML PDF Collect

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    

HTML PDF Collect

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    

HTML PDF Collect

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

Concrete corrosion in wastewater systems: Prediction and sensitivity analysis using advanced extreme learning machine

Mohammad ZOUNEMAT-KERMANI, Meysam ALIZAMIR, Zaher Mundher YASEEN, Reinhard HINKELMANN

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

Big Data Creates New Opportunities for Materials Research: A Review on Methods and Applications of Machine

Teng Zhou, Zhen Song, Kai Sundmacher

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