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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.The model input variables consisted of pipeline diameter (d), the distance between the pipe inletData-driven simulation results indicated that the HGSO algorithm accurately trains the GMDH methods better

Keywords： sedimentation     water resources     dam engineering     machine learning     heuristic    

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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    

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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-drivenmodel     Piecewise linear regression    

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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    

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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    

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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 model is comprised of self-organizing-map and the neural network parts.The self-organizing-map part maps the input data into multiple two-dimensional planes and sends themDevelopment of the MISR model of hydrocracking unit also benefits from the utilization of prior knowledgeMoreover, the MISR model has smoother error convergence than the previous model.Numerical experiments show that the MISR model is suitable for modeling nonlinear conversion units which

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

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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 study uses the hardening soil (HS) model to develop a 200-sample finite element analysis (FEA) databaseThe 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    

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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    

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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 improved

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

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A hybrid Wavelet-CNN-LSTM deep learning model for short-term urban water demand forecasting

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 2, doi: 10.1007/s11783-023-1622-3

Abstract:

● A novel deep learning framework for short-term water demand forecasting.

Keywords： Long-short term memory neural network     Convolutional Neural Network     Wavelet multi-resolution analysis     Data-driven    

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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, leveragingthe advantages of both and model-based algorithms.The time-varying constant velocity model is integrated into the (GP) of to improve the performanceThrough the simulations, it has been demonstrated that the hybrid-driven approach exhibits significantperformance improvements in comparison with widely used algorithms such as the interactive multi-model

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

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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

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Evaluation of the k-nearest neighbor method for forecasting the influent characteristics of wastewater treatment plant

Minsoo KIM,Yejin KIM,Hyosoo KIM,Wenhua PIAO,Changwon KIM

Frontiers of Environmental Science & Engineering 2016, Volume 10, Issue 2,   Pages 299-310 doi: 10.1007/s11783-015-0825-7

Abstract: The optimum search range for considering data size was one year.

Keywords： influent wastewater     prediction     data-driven model     k-nearest neighbor method (k-NN)    

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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: Thus, in this study, a new hybrid model based on deep neural networks (DNNs), particle swarm optimizationequation, and the PSO and GA can optimize the selected DNN and improve the performance of the prediction model

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

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Data driven models for compressive strength prediction of concrete at high temperatures

Mahmood AKBARI, Vahid JAFARI DELIGANI

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 2,   Pages 311-321 doi: 10.1007/s11709-019-0593-8

Abstract: The use of data driven models has been shown to be useful for simulating complex engineering processesIn this study, four data-driven models, namely multiple linear regression, artificial neural network,In addition for each model, two different sets of input variables are examined: a complete set and adriven models to predict the compressive strength at high temperature.driven models to make satisfactory results.

Keywords： data driven model     compressive strength     concrete     high temperature    

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Title Author Date Type Operation

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

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

Optimal Antibody Purification Strategies Using Data-Driven Models

Songsong Liu, Lazaros G. Papageorgiou

Journal Article

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

Teng Zhou, Rafiqul Gani, Kai Sundmacher

Journal Article

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

Journal Article

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

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

A hybrid Wavelet-CNN-LSTM deep learning model for short-term urban water demand forecasting

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

On the Data-Driven Materials Innovation Infrastructure

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

Journal Article

Evaluation of the k-nearest neighbor method for forecasting the influent characteristics of wastewater treatment plant

Minsoo KIM,Yejin KIM,Hyosoo KIM,Wenhua PIAO,Changwon KIM

Journal Article

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

Journal Article

Data driven models for compressive strength prediction of concrete at high temperatures

Mahmood AKBARI, Vahid JAFARI DELIGANI

Journal Article