Resource Type

Journal Article 231

Conference Videos 6

Year

2024 1

2023 35

2022 43

2021 23

2020 23

2019 18

2018 10

2017 11

2016 10

2015 7

2014 1

2013 7

2012 2

2011 4

2010 5

2009 3

2008 6

2007 3

2006 4

2005 4

open ︾

Keywords

Machine learning 50

machine learning 24

Artificial intelligence 8

Deep learning 6

artificial neural network 6

artificial intelligence 5

Support vector machine 4

Bayesian optimization 3

Big data 3

Collaborative filtering 2

Data-driven 2

Extreme learning machine 2

Feature selection 2

Materials design 2

Soft sensors 2

Support vector machine (SVM) 2

construction 2

deep learning 2

machine tool 2

open ︾

Search scope:

排序: Display mode:

Challenges of human–machine collaboration in risky decision-making

Frontiers of Engineering Management 2022, Volume 9, Issue 1,   Pages 89-103 doi: 10.1007/s42524-021-0182-0

Abstract: The purpose of this paper is to delineate the research challenges of human–machine collaboration in riskyTechnological advances in machine intelligence have enabled a growing number of applications in human–machineTherefore, it is desirable to achieve superior performance by fully leveraging human and machine capabilitiesAfterward, we review the literature on human–machine collaboration in a general decision context, fromthe perspectives of human–machine organization, relationship, and collaboration.

Keywords: human–machine collaboration     risky decision-making     human–machine team and interaction     task allocation     human–machine relationship    

Spatial prediction of soil contamination based on machine learning: a review

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 8, doi: 10.1007/s11783-023-1693-1

Abstract:

● A review of machine learning (ML) for spatial prediction of soil

Keywords: Soil contamination     Machine learning     Prediction     Spatial distribution    

Predicting the elemental compositions of solid waste using ATR-FTIR and machine learning

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 10, doi: 10.1007/s11783-023-1721-1

Abstract:

● A method based on ATR-FTIR and ML was developed to predict CHNS contents in waste.

Keywords: Elemental composition     Infrared spectroscopy     Machine learning     Moisture interference     Solid waste     Spectral    

State-of-the-art applications of machine learning in the life cycle of solid waste management

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 4, doi: 10.1007/s11783-023-1644-x

Abstract:

● State-of-the-art applications of machine learning (ML) in solid waste

Keywords: Machine learning (ML)     Solid waste (SW)     Bibliometrics     SW management     Energy utilization     Life cycle    

Research and application of visual location technology for solder paste printing based on machine vision

Luosi WEI, Zongxia JIAO

Frontiers of Mechanical Engineering 2009, Volume 4, Issue 2,   Pages 184-191 doi: 10.1007/s11465-009-0034-9

Abstract: Using machine vision technology to complete the location mission is new and very efficient.This paper presents an integrated visual location system for solder paste printing based on machine vision

Keywords: machine vision     visual location     solder paste printing     VisionPro    

Elucidate long-term changes of ozone in Shanghai based on an integrated machine learning method

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 11, doi: 10.1007/s11783-023-1738-5

Abstract:

● A novel integrated machine learning method to analyze O3

Keywords: Ozone     Integrated method     Machine learning    

Evaluation and prediction of slope stability using machine learning approaches

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 4,   Pages 821-833 doi: 10.1007/s11709-021-0742-8

Abstract: In this paper, the machine learning (ML) model is built for slope stability evaluation and meets the

Keywords: slope stability     factor of safety     regression     machine learning     repeated cross-validation    

Liquefaction prediction using support vector machine model based on cone penetration data

Pijush SAMUI

Frontiers of Structural and Civil Engineering 2013, Volume 7, Issue 1,   Pages 72-82 doi: 10.1007/s11709-013-0185-y

Abstract: A support vector machine (SVM) model has been developed for the prediction of liquefaction susceptibilityThe SVM, a novel learning machine based on statistical theory, uses structural risk minimization (SRM

Keywords: earthquake     cone penetration test     liquefaction     support vector machine (SVM)     prediction    

Using machine learning models to explore the solution space of large nonlinear systems underlying flowsheet

Frontiers of Chemical Science and Engineering 2022, Volume 16, Issue 2,   Pages 183-197 doi: 10.1007/s11705-021-2073-7

Abstract: exploration of the design variable space for such scenarios, an adaptive sampling technique based on machine

Keywords: machine learning     flowsheet simulations     constraints     exploration    

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: advanced data processing, storage and analysis, advanced process control, artificial intelligence and machineExploitation 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    

A fast antibiotic detection method for simplified pretreatment through spectra-based machine learning

Frontiers of Environmental Science & Engineering 2022, Volume 16, Issue 3, doi: 10.1007/s11783-021-1472-9

Abstract:

• A spectral machine learning approach is proposed for predicting mixed

Keywords: Antibiotic contamination     Spectral detection     Machine learning    

Coupling evaluation for material removal and thermal control on precision milling machine tools

Frontiers of Mechanical Engineering 2022, Volume 17, Issue 1,   Pages 12-12 doi: 10.1007/s11465-021-0668-9

Abstract: Machine tools are one of the most representative machining systems in manufacturing.The energy consumption of machine tools has been a research hotspot and frontier for green low-carbonExperimental study indicates that TC is the main energy-consuming process of the precision milling machineIt can provide a foundation for energy-efficient, high-precision machining of machine tools.

Keywords: machine tools     cutting energy efficiency     thermal stability     machining accuracy     coupling evaluation    

Energy saving design of the machining unit of hobbing machine tool with integrated optimization

Frontiers of Mechanical Engineering 2022, Volume 17, Issue 3, doi: 10.1007/s11465-022-0694-2

Abstract: The machining unit of hobbing machine tool accounts for a large portion of the energy consumption during

Keywords: energy saving design     energy consumption     machining unit     integrated optimization     machine tool    

A novel six-legged walking machine tool for

Jimu LIU, Yuan TIAN, Feng GAO

Frontiers of Mechanical Engineering 2020, Volume 15, Issue 3,   Pages 351-364 doi: 10.1007/s11465-020-0594-2

Abstract: maintenance of large parts in ships, trains, aircrafts, and so on create an increasing demand for mobile machineThis study proposes a novel six-legged walking machine tool consisting of a legged mobile robot and aportable parallel kinematic machine tool.advantage of the large workspace of the legged mobile platform and the high precision of the parallel machineFinally, an application scenario is shown in which the walking machine tool steps successfully over a

Keywords: legged robot     parallel mechanism     mobile machine tool     in-situ machining    

Principle of maximum entropy for reliability analysis in the design of machine components

Yimin ZHANG

Frontiers of Mechanical Engineering 2019, Volume 14, Issue 1,   Pages 21-32 doi: 10.1007/s11465-018-0512-z

Abstract: We studied the reliability of machine components with parameters that follow an arbitrary statisticalThis function was used to calculate the reliability of the machine components, including a connecting

Keywords: machine components     reliability     arbitrary distribution parameter     principle of maximum entropy    

Title Author Date Type Operation

Challenges of human–machine collaboration in risky decision-making

Journal Article

Spatial prediction of soil contamination based on machine learning: a review

Journal Article

Predicting the elemental compositions of solid waste using ATR-FTIR and machine learning

Journal Article

State-of-the-art applications of machine learning in the life cycle of solid waste management

Journal Article

Research and application of visual location technology for solder paste printing based on machine vision

Luosi WEI, Zongxia JIAO

Journal Article

Elucidate long-term changes of ozone in Shanghai based on an integrated machine learning method

Journal Article

Evaluation and prediction of slope stability using machine learning approaches

Journal Article

Liquefaction prediction using support vector machine model based on cone penetration data

Pijush SAMUI

Journal Article

Using machine learning models to explore the solution space of large nonlinear systems underlying flowsheet

Journal Article

Big data and machine learning: A roadmap towards smart plants

Journal Article

A fast antibiotic detection method for simplified pretreatment through spectra-based machine learning

Journal Article

Coupling evaluation for material removal and thermal control on precision milling machine tools

Journal Article

Energy saving design of the machining unit of hobbing machine tool with integrated optimization

Journal Article

A novel six-legged walking machine tool for

Jimu LIU, Yuan TIAN, Feng GAO

Journal Article

Principle of maximum entropy for reliability analysis in the design of machine components

Yimin ZHANG

Journal Article