Resource Type

Journal Article 614

Conference Videos 34

Year

2024 1

2023 94

2022 118

2021 73

2020 68

2019 50

2018 33

2017 46

2016 22

2015 25

2014 12

2013 8

2012 7

2011 7

2010 10

2009 1

2008 8

2007 10

2006 7

2005 3

open ︾

Keywords

Machine learning 50

Deep learning 36

machine learning 24

big data 20

Artificial intelligence 17

Reinforcement learning 15

deep learning 15

data mining 12

Big data 9

artificial intelligence 8

Blockchain 5

Active learning 4

Additive manufacturing 4

artificial neural network 4

Autonomous driving 3

Bayesian optimization 3

Data mining 3

Industry 4.0 3

convolutional neural network 3

open ︾

Search scope:

排序： Display mode:

Big data and machine learning: A roadmap towards smart plants

Frontiers of Engineering Management   Pages 623-639 doi: 10.1007/s42524-022-0218-0

Abstract: components are smart sensing, mobile communication, Internet of Things, modelling and simulation, advanced dataprocessing, storage and analysis, advanced process control, artificial intelligence and machine learningessential element to this transformation is the exploitation of large amounts of historical process dataand large volumes of data generated in real-time by smart sensors widely used in industry.Exploitation of the information contained in these data requires the use of advanced machine learning

Keywords： big data     machine learning     artificial intelligence     smart sensor     cyber–physical system     Industry 4.0    

HTML PDF Collect

Shear stress distribution prediction in symmetric compound channels using data mining and machine learning

Zohreh SHEIKH KHOZANI, Khabat KHOSRAVI, Mohammadamin TORABI, Amir MOSAVI, Bahram REZAEI, Timon RABCZUK

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 5,   Pages 1097-1109 doi: 10.1007/s11709-020-0634-3

Abstract: A set of, data mining and machine learning algorithms including Random Forest (RF), M5P, Random Committee, KStar and Additive Regression implemented on attained data to predict the shear stress distributionFinally, the most powerful data mining method which studied in this research compared with two well-known

Keywords： compound channel     machine learning     SKM model     shear stress distribution     data mining models    

HTML PDF Collect

Data Analytics and Machine Learning for Smart Process Manufacturing: Recent Advances and Perspectivesin the Big Data Era Perspective

Chao Shang、 Fengqi You

Engineering 2019, Volume 5, Issue 6,   Pages 1010-1016 doi: 10.1016/j.eng.2019.01.019

Abstract: The burgeoning era of big data is influencing the process industries tremendously, providing unprecedentedTo attain this goal, data analytics and machine learning are indispensable.In this paper, we review recent advances in data analytics and machine learning applied to the monitoringindustrial processes, paying particular attention to the interpretability and functionality of machine learning

Keywords： Big data     Machine learning     Smart manufacturing     Process systems engineering    

HTML PDF Collect

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    

HTML PDF Collect

Big Data Creates New Opportunities for Materials Research: A Review on Methods and Applications of MachineLearning for Materials Design 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: As 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 rational

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

HTML PDF Collect

The State of the Art of Data Science and Engineering in Structural Health Monitoring Article

Yuequan Bao, Zhicheng Chen, Shiyin Wei, Yang Xu, Zhiyi Tang, Hui Li

Engineering 2019, Volume 5, Issue 2,   Pages 234-242 doi: 10.1016/j.eng.2018.11.027

Abstract: of sensors and instruments, followed by a diagnosis of the structural health based on the collected dataThe techniques related to massive data are referred to as data science and engineering, and include acquisitionThis paper provides a brief review of the state of the art of data science and engineering in SHM asthe anomaly data diagnosis approach using a deep learning algorithm, crack identification approachesusing computer vision techniques, and condition assessment approaches for bridges using machine learning

Keywords： Structural health monitoring     Monitoring data     Compressive sampling     Machine learning     Deep learning    

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: 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    

HTML PDF Collect

Static-based early-damage detection using symbolic data analysis and unsupervised learning methods

João Pedro SANTOS,Christian CREMONA,André D. ORCESI,Paulo SILVEIRA,Luis CALADO

Frontiers of Structural and Civil Engineering 2015, Volume 9, Issue 1,   Pages 1-16 doi: 10.1007/s11709-014-0277-3

Abstract: large amount of researches and studies have been recently performed by applying statistical and machine learningThe present paper aims at detecting this type of damage by using static SHM data and by assuming thatTo achieve this objective a data driven strategy is proposed, consisting of the combination of advancedstatistical and machine learning methods such as principal component analysis, symbolic data analysis

Keywords： structural health monitoring     early-damage detection     principal component analysis     symbolic data     symbolic    

HTML PDF Collect

Physics-Informed Deep Learning-Based Real-Time Structural Response Prediction Method

Ying Zhou,Shiqiao Meng,Yujie Lou,Qingzhao Kong,

Engineering doi: 10.1016/j.eng.2023.08.011

Abstract: and efficiency of structural response prediction, this study proposes a novel physics-informed deep-learning-basedstructural response prediction method that can predict a large number of nodes in a structure through a data-driven

Keywords： seismic response prediction     Physics information informed     Real-time prediction     Earthquake engineering     Data-drivenmachine learning    

HTML PDF Collect

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

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 degradationsulfuric acid corrosive factor in wastewater systems to simulate concrete mass loss using five machine learningThe models include three different types of extreme learning machines, including the standard, onlineFor the first assessment, the machine learning models were developed using all the available cement andThe online sequential extreme learning machine model demonstrated superior performance over the other

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

HTML PDF Collect

Regional wind power forecasting model with NWP grid data optimized

Zhao WANG, Weisheng WANG, Bo WANG

Frontiers in Energy 2017, Volume 11, Issue 2,   Pages 175-183 doi: 10.1007/s11708-017-0471-9

Abstract: A locally weighted learning method is also proposed to utilize the processed feature set to produce the

Keywords： set     minimal-redundancy-maximal-relevance (mRMR)     principal component analysis (PCA)     locally weighted learning    

HTML PDF Collect

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    

HTML PDF Collect

Strategies and Principles of Distributed Machine Learning on Big Data Review

Eric P. Xing,Qirong Ho,Pengtao Xie,Dai Wei

Engineering 2016, Volume 2, Issue 2,   Pages 179-195 doi: 10.1016/J.ENG.2016.02.008

Abstract:

The rise of big data has led to new demands for machine learning (ML) systems to learn complex models

Keywords： Machine learning     Artificial intelligence big data     Big model     Distributed systems     Principles     Theory     Data-parallelism    

HTML PDF Collect

Design and Implementation of Intelligent Risk Control Platform Based on Big Data

Zhang Ming, Liu Pei

Strategic Study of CAE 2020, Volume 22, Issue 6,   Pages 111-120 doi: 10.15302/J-SSCAE-2020.06.015

Abstract: control platform with “five layers and two domains” based on the key technologies of big dataSpecifically, the framework vertically consists of a risk data layer, a feature computing layer, a riskThis design is helpful for commercial banks to realize the unified governance and management of risk dataplatform, it can also provide sufficient support for risk control experts in risk control operation, data

Keywords： risk control，big data，machine learning，real-time computation，financial industry    

HTML PDF Collect

Hybrid method integrating machine learning and particle swarm optimization for smart chemical process

Haoqin Fang, Jianzhao Zhou, Zhenyu Wang, Ziqi Qiu, Yihua Sun, Yue Lin, Ke Chen, Xiantai Zhou, Ming Pan

Frontiers of Chemical Science and Engineering 2022, Volume 16, Issue 2,   Pages 274-287 doi: 10.1007/s11705-021-2043-0

Abstract: Thus, this paper presents an efficient hybrid framework of integrating machine learning and particleFirstly, a data set was generated based on process mechanistic simulation validated by industrial dataSecondly, four well-known machine learning methods, namely, K-nearest neighbors, decision tree, supportmethods achieved highly accurate model by adjusting model parameters on the basis of high-coverage data

Keywords： smart chemical process operations     data generation     hybrid method     machine learning     particle swarm optimization    

HTML PDF Collect

Title Author Date Type Operation

Big data and machine learning: A roadmap towards smart plants

Journal Article

Shear stress distribution prediction in symmetric compound channels using data mining and machine learning

Zohreh SHEIKH KHOZANI, Khabat KHOSRAVI, Mohammadamin TORABI, Amir MOSAVI, Bahram REZAEI, Timon RABCZUK

Journal Article

Data Analytics and Machine Learning for Smart Process Manufacturing: Recent Advances and Perspectivesin the Big Data Era

Chao Shang、 Fengqi You

Journal Article

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

Journal Article

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

Teng Zhou, Zhen Song, Kai Sundmacher

Journal Article

The State of the Art of Data Science and Engineering in Structural Health Monitoring

Yuequan Bao, Zhicheng Chen, Shiyin Wei, Yang Xu, Zhiyi Tang, Hui Li

Journal Article

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

Li Sun, Fengqi You

Journal Article

Static-based early-damage detection using symbolic data analysis and unsupervised learning methods

João Pedro SANTOS,Christian CREMONA,André D. ORCESI,Paulo SILVEIRA,Luis CALADO

Journal Article

Physics-Informed Deep Learning-Based Real-Time Structural Response Prediction Method

Ying Zhou,Shiqiao Meng,Yujie Lou,Qingzhao Kong,

Journal Article

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

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

Journal Article

Regional wind power forecasting model with NWP grid data optimized

Zhao WANG, Weisheng WANG, Bo WANG

Journal Article

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

Journal Article

Strategies and Principles of Distributed Machine Learning on Big Data

Eric P. Xing,Qirong Ho,Pengtao Xie,Dai Wei

Journal Article

Design and Implementation of Intelligent Risk Control Platform Based on Big Data

Zhang Ming, Liu Pei

Journal Article

Hybrid method integrating machine learning and particle swarm optimization for smart chemical process

Haoqin Fang, Jianzhao Zhou, Zhenyu Wang, Ziqi Qiu, Yihua Sun, Yue Lin, Ke Chen, Xiantai Zhou, Ming Pan

Journal Article