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A new item-based deep network structure using a restricted Boltzmann machine for collaborative filtering Article

Yong-ping DU, Chang-qing YAO, Shu-hua HUO, Jing-xuan LIU

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 5,   Pages 658-666 doi: 10.1631/FITEE.1601732

Abstract: We propose a new item-based restricted Boltzmann machine (RBM) approach for CF and use the deep multilayer

Keywords： Restricted Boltzmann machine     Deep network structure     Collaborative filtering     Recommendation system    

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Simulation of interfacial Marangoni convection in gas-liquid mass transfer by lattice Boltzmann method

Shuyong CHEN, Xigang YUAN, Bo FU, Kuotsung YU

Frontiers of Chemical Science and Engineering 2011, Volume 5, Issue 4,   Pages 448-454 doi: 10.1007/s11705-011-1142-8

Abstract: In this paper, an approach based on lattice Boltzmann method is established and two perturbation models

Keywords： interfacial Marangoni convection     lattice Boltzmann method     gas-liquid mass transfer    

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Simulation on Flow Field Characteristic of Restricted Wall-attached Jet in Heading Face

Wang Haiqiao,Liu Ronghua,Chen Shiqiang

Strategic Study of CAE 2004, Volume 6, Issue 8,   Pages 45-49

Abstract:

Pressed ventilation in heading face is actually a restrained wall-attached jet ventilation in confined space. In this paper, based on hydrcxlynamic and jet theory, the turbulence k-ε model of restrained wall-attached jet ventilation in heading face is set up and calculation boundary is analyzed. Combining with practice, and using PHOENICS3. 4 software of computational fluid dynamics (CFD), three - dimensional airflow field of the jet ventilation in heading face is simulated. The flow field characteristics such as the flow field zoning, the starting segment of jet, the attaching course of jet and the changing pattern of jet velocity are obtained by numerical simulation. According to result of numerical simulation, there are four zones in heading face, i. e. wall-attached jet zone, impact jet wall-attached zone, back flow zone and eddy zone. The starting segment length of attached jet is shorter than thar of free attached jet. After wall-attached jet formed completely, the axis velocity of wall is higher than that in air outlet. The result of numerical simulation agrees with that of experiment, which provides a dependable basis for further study of the air mass transport process, the reason of methane accumulation and the efficiency of air displacement in heading face.

Keywords： heading face     restricted wall-attached jet     k - ε model     numerical simulation    

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Application of Porous Aerostatic Bearings in Three-axis Table

Du Jinming

Strategic Study of CAE 2005, Volume 7, Issue 1,   Pages 65-68

Abstract: Porous aerostatic bearings have higher load capacity, static stiffness and highed damping than other restricted

Keywords： aerostatic bearings     porous     orifice restricted     three-axis table    

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Numerical study on natural convection in a square enclosure containing a rectangular heated cylinder

Jianhua LU, Zhaoli GUO, Zhenhua CHAI, Baochang SHI,

Frontiers in Energy 2009, Volume 3, Issue 4,   Pages 373-380 doi: 10.1007/s11708-009-0078-x

Abstract: convection in a square enclosure with a rectangular heated cylinder is investigated via the lattice Boltzmann

Keywords： natural convection     buoyant regime     heated cylinder     lattice Boltzmann    

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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

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    

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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    

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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    

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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    

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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    

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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    

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The research of connectivity-credibility restricted clustering algorithm in wireless sensor networks

Yu Jiming,Sun Yamin,Lei Yanjing,Yang Yuwang

Strategic Study of CAE 2010, Volume 12, Issue 9,   Pages 73-77

Abstract:

This paper proposed a speeding clustering algorithm of connectivity-credibility constrained random dispose which based on some other clustering algorithms. Simulation shows that this algorithm can get large cover of clustering, logical distributing and good stability. Comparing to the lowerst-ID clustering and highest-connectivity clustering algorithm, the algorithm can get less number of cluster-heads, more logical clustering, good communication between nodes and cluster-heads, steady networks, reduce communication cost of rebuilding, and can balance network's energy consume, prolong the networks life.

Keywords： wireless sensor networks     connectivity-credibility     clustering algorithm    

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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    

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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    

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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    

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Title Author Date Type Operation

A new item-based deep network structure using a restricted Boltzmann machine for collaborative filtering

Yong-ping DU, Chang-qing YAO, Shu-hua HUO, Jing-xuan LIU

Journal Article

Simulation of interfacial Marangoni convection in gas-liquid mass transfer by lattice Boltzmann method

Shuyong CHEN, Xigang YUAN, Bo FU, Kuotsung YU

Journal Article

Simulation on Flow Field Characteristic of Restricted Wall-attached Jet in Heading Face

Wang Haiqiao,Liu Ronghua,Chen Shiqiang

Journal Article

Application of Porous Aerostatic Bearings in Three-axis Table

Du Jinming

Journal Article

Numerical study on natural convection in a square enclosure containing a rectangular heated cylinder

Jianhua LU, Zhaoli GUO, Zhenhua CHAI, Baochang SHI,

Journal Article

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

The research of connectivity-credibility restricted clustering algorithm in wireless sensor networks

Yu Jiming,Sun Yamin,Lei Yanjing,Yang Yuwang

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