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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
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
● 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
● 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
● 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
Luosi WEI, Zongxia JIAO
Frontiers of Mechanical Engineering 2009, Volume 4, Issue 2, Pages 184-191 doi: 10.1007/s11465-009-0034-9
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
● 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
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
Keywords: earthquake cone penetration test liquefaction support vector machine (SVM) prediction
Frontiers of Chemical Science and Engineering 2022, Volume 16, Issue 2, Pages 183-197 doi: 10.1007/s11705-021-2073-7
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
Keywords: big data machine learning artificial intelligence smart sensor cyber–physical system Industry 4.0
Frontiers of Environmental Science & Engineering 2022, Volume 16, Issue 3, doi: 10.1007/s11783-021-1472-9
• 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
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
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
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
Keywords: machine components reliability arbitrary distribution parameter principle of maximum entropy
Title Author Date Type Operation
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
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
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