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Data-driven rolling eco-speed optimization for autonomous vehicles

Frontiers of Engineering Management doi: 10.1007/s42524-023-0284-y

Abstract: This paper introduces a data-driven algorithm for rolling eco-speed optimization in AVs aimed at enhancingFuel consumption data from the Argonne National Laboratory in the United States serves as the basis forA spatiotemporal network is subsequently developed to achieve eco-speed optimization for AVs within the

Keywords： data-driven learning     speed optimization     autonomous vehicles     energy saving    

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Development of gradient boosting-assisted machine learning data-driven model for free chlorine residual

Frontiers of Environmental Science & Engineering 2024, Volume 18, Issue 2, doi: 10.1007/s11783-024-1777-6

Abstract:

● A machine learning approach was applied to predict free chlorine residuals.

Keywords： Machine learning     Data-driven modeling     Drinking water treatment     Disinfection     Chlorination    

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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:

This work addresses the multiscale optimization of the purification processes of antibody fragmentsData-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 model     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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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(GMDH) hybrid models with particle swarm optimization (PSO) and Henry gas solubility optimization (HGSOAlso, 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    

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Predicting torsional capacity of reinforced concrete members by data-driven machine learning models

Frontiers of Structural and Civil Engineering 2024, Volume 18, Issue 3,   Pages 444-460 doi: 10.1007/s11709-024-1050-x

Abstract: Due to the complicated three-dimensional behaviors and testing limitations of reinforced concrete (RC) members in torsion, torsional mechanism exploration and torsional performance prediction have always been difficult. In the present paper, several machine learning models were applied to predict the torsional capacity of RC members. Experimental results of a total of 287 torsional specimens were collected through an overall literature review. Algorithms of extreme gradient boosting machine (XGBM), random forest regression, back propagation artificial neural network and support vector machine, were trained and tested by 10-fold cross-validation method. Predictive performances of proposed machine learning models were evaluated and compared, both with each other and with the calculated results of existing design codes, i.e., GB 50010, ACI 318-19, and Eurocode 2. The results demonstrated that better predictive performance was achieved by machine learning models, whereas GB 50010 slightly overestimated the torsional capacity, and ACI 318-19 and Eurocode 2 underestimated it, especially in the case of ACI 318-19. The XGBM model gave the most favorable predictions with R2 = 0.999, RMSE = 1.386, MAE = 0.86, and λ¯ = 0.976. Moreover, strength of concrete was the most sensitive input parameters affecting the reliability of the predictive model, followed by transverse-to-longitudinal reinforcement ratio and total reinforcement ratio.

Keywords： RC members     torsional capacity     machine learning models     design codes    

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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 self-organizing-map part maps the input data into multiple two-dimensional planes and sends thempreviously introduced self-organizing-map convolutional neural network model, thus leading to more accurate optimization

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

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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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Data-driven consumer-phase identification in low-voltage distribution networks considering prosumers

Frontiers in Energy doi: 10.1007/s11708-024-0946-4

Abstract: To overcome the above challenges, this paper develops a data-driven model to identify the phase connectivityinterpolation and singular value decomposition is adopted to improve the quality of the smart meter dataThen, using Kirchoff’s current law and correlation analysis, a discrete convolution optimization modelThe data sets utilized are obtained by performing power flow simulations on a modified IEEE-906 testThe robustness of the model is tested against data set size, missing smart meter data, measurement errors

Keywords： consumer-phase identification     data-driven     low-voltage distribution network     advanced metering infrastructure    

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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 optimization

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

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Secure Federated Evolutionary Optimization—A Survey Review

Qiqi Liu,Yuping Yan,Yaochu Jin,Xilu Wang,Peter Ligeti,Guo Yu,Xueming Yan

Engineering 2024, Volume 34, Issue 3,   Pages 23-42 doi: 10.1016/j.eng.2023.10.006

Abstract: However, the data privacy issue also occurs when solving optimization problems, which has received littledata-driven evolutionary optimization.optimization (BO).Finally, we conclude the survey by outlining open questions and remaining challenges in federated data-drivenoptimization.

Keywords： Federated learning     Privacy-preservation     Security     Evolutionary optimization     Data-driven optimization     Bayesianoptimization    

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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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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    

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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: The burgeoning era of machine learning (ML) and data-driven control (DDC) techniques promises an improvedThis paper reviews typical applications of ML and DDC at the level of monitoring, control, optimization

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

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Title Author Date Type Operation

Data-driven rolling eco-speed optimization for autonomous vehicles

Journal Article

Development of gradient boosting-assisted machine learning data-driven model for free chlorine residual

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

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

Journal Article

Predicting torsional capacity of reinforced concrete members by data-driven machine learning models

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

Data-Driven Anomaly Diagnosis for Machining Processes

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

Journal Article

Data-driven consumer-phase identification in low-voltage distribution networks considering prosumers

Journal Article

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

Journal Article

Secure Federated Evolutionary Optimization—A Survey

Qiqi Liu,Yuping Yan,Yaochu Jin,Xilu Wang,Peter Ligeti,Guo Yu,Xueming Yan

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

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

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

Li Sun, Fengqi You

Journal Article