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Understanding the demand predictability of bike share systems: A station-level analysis

Frontiers of Engineering Management   Pages 551-565 doi: 10.1007/s42524-023-0279-8

Abstract: prediction, which can help decision makers and system operators anticipate diverse station-level prediction errors

Keywords： bike share systems     demand prediction     prediction errors     machine learning     entropy    

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Modeling and simulating the impact of forgetting and communication errors on delays in civil infrastructure

Zhe SUN, Cheng ZHANG, Pingbo TANG

Frontiers of Engineering Management 2021, Volume 8, Issue 1,   Pages 109-121 doi: 10.1007/s42524-019-0084-6

Abstract: Handoff processes during civil infrastructure operations are transitions between sequential tasks. Typical handoffs constantly involve cognitive and communication activities among operations personnel, as well as traveling activities. Large civil infrastructures, such as nuclear power plants (NPPs), provide critical services to modern cities but require regular or unexpected shutdowns (i.e., outage) for maintenance. Handoffs during such an outage contain interwoven workflows and communication activities that pose challenges to the cognitive and communication skills of handoff participants and constantly result in delays. Traveling time and changing field conditions bring additional challenges to effective coordination among multiple groups of people. Historical NPP records studied in this research indicate that even meticulous planning that takes six months before each outage could hardly guarantee sufficient back-up plans for handling various unexpected events. Consequently, delays frequently occur in NPP outages and bring significant socioeconomic losses. A synthesis of previous studies on the delay analysis of accelerated maintenance schedules revealed the importance and challenges of handoff modeling. However, existing schedule representation methods could hardly represent the interwoven communication, cognitive, traveling, and working processes of multiple participants collaborating on completing scheduled tasks. Moreover, the lack of formal models that capture how cognitive, waiting, traveling, and communication issues affect outage workflows force managers to rely on personal experiences in diagnosing delays and coordinating multiple teams involved in outages. This study aims to establish formal models through agent-based simulation to support the analytical assessment of outage schedules with full consideration of cognitive and communication factors involved in handoffs within the NPP outage workflows. Simulation results indicate that the proposed handoff modeling can help predict the impact of cognitive and communication issues on delays propagating throughout outage schedules. Moreover, various activities are fully considered, including traveling between workspaces and waiting. Such delay prediction capability paves the path toward predictive and resilience outage control of NPPs.

Keywords： NPP outage     human error     team cognition     handoff modeling    

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The dynamic correction of collimation errors of CT slicing pictures

LIU Ya-xiong, Sekou Sing-are, LI Di-chen, LU Bing-heng

Frontiers of Mechanical Engineering 2006, Volume 1, Issue 2,   Pages 168-172 doi: 10.1007/s11465-006-0016-0

Abstract: To eliminate the motion artifacts of CT images caused by patient motions and other related errors, twoimages, which facilitates in eliminating or decreasing the motion artifacts and correcting other static errorsand image processing errors.
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Quantum security analysis of a lattice-basedoblivious transfer protocol Article

Mo-meng LIU, Juliane KRÄMER, Yu-pu HU, Johannes BUCHMANN

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 9,   Pages 1348-1369 doi: 10.1631/FITEE.1700039

Abstract: (CRYPTO, 2008), which is universally composably secure under theassumption of learning with errors hardness

Keywords： Oblivious transfer     Post-quantum     Lattice-based     Learning with errors     Universally composable    

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Rotation errors in numerical manifold method and a correction based on large deformation theory

Ning ZHANG, Xu LI, Qinghui JIANG, Xingchao LIN

Frontiers of Structural and Civil Engineering 2019, Volume 13, Issue 5,   Pages 1036-1053 doi: 10.1007/s11709-019-0535-5

Abstract: Numerical manifold method (NMM) is an effective method for simulating block system, however, significant errorsThree kinds of errors, as volume expansion, stress vibration, and attenuation of angular velocity, wereThe first two kind errors are owing to the small deformation assumption and the last one is due to theblock rotation, beam bending, and rock falling problems and the results prove that all three kinds of errors

Keywords： numerical manifold method     rotation     large deformation     Green strain     open-close iteration    

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Efficient hierarchical identity based encryption scheme in the standard model over lattices Article

Feng-he WANG,Chun-xiao WANG,Zhen-hua LIU

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 8,   Pages 781-791 doi: 10.1631/FITEE.1500219

Abstract: Based on the hardness of the learning with errors problem, we demonstrate that the scheme is secure under

Keywords： Hierarchical identity based encryption scheme     Lattice-based cryptography     Standard model     Learning witherrors problem     Gaussian    

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Planet position errors in planetary transmission: Effect on load sharing and transmission error

Miguel IGLESIAS, Alfonso FERNáNDEZ, Ana DE-JUAN, Ramón SANCIBRIáN, Pablo GARCíA

Frontiers of Mechanical Engineering 2013, Volume 8, Issue 1,   Pages 80-87 doi: 10.1007/s11465-013-0362-7

Abstract: transmissions developed by the authors is used to study the influence of carrier planet pin hole position errorsThe influence of carrier planet pin hole position errors on the planet load sharing is studied, and severalTangential and radial planet pin hole position errors are considered independently, and the effect of

Keywords： gear     planetary     epicyclic     transmission     load sharing     transmission error    

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A multi-functional dynamic state estimator for error validation: measurement and parameter errors and Article

Mehdi AHMADI JIRDEHI,Reza HEMMATI,Vahid ABBASI,Hedayat SABOORI

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 11,   Pages 1218-1227 doi: 10.1631/FITEE.1500301

Abstract: We propose a new and efficient algorithm to detect, identify, and correct measurement errors and branchparameter errors of power systems.The method uses three normalized vectors to process errors at each sampling time: normalized measurement

Keywords： Dynamic state estimation     Kalman filter     Measurement errors     Branch parameter errors     Sudden load changes    

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MSWNet: A visual deep machine learning method adopting transfer learning based upon ResNet 50 for municipal

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 6, doi: 10.1007/s11783-023-1677-1

Abstract:

● MSWNet was proposed to classify municipal solid waste.

Keywords： Municipal solid waste sorting     Deep residual network     Transfer learning     Cyclic learning rate     Visualization    

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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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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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Machine learning in building energy management: A critical review and future directions

Frontiers of Engineering Management 2022, Volume 9, Issue 2,   Pages 239-256 doi: 10.1007/s42524-021-0181-1

Abstract: Over the past two decades, machine learning (ML) has elicited increasing attention in building energy

Keywords： building energy management     machine learning     integrated framework     knowledge evolution    

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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 learning

Keywords： machine learning     flowsheet simulations     constraints     exploration    

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Machine learning for fault diagnosis of high-speed train traction systems: A review

Frontiers of Engineering Management doi: 10.1007/s42524-023-0256-2

Abstract: In recent years, machine learning has been widely used in various pattern recognition tasks and has demonstratedMachine learning has made considerably advancements in traction system fault diagnosis; however, a comprehensiveThis paper primarily aims to review the research and application of machine learning in the field ofThen, the research and application of machine learning in traction system fault diagnosis are comprehensivelydiagnosis under actual operating conditions are revealed, and the future research trends of machine learning

Keywords： high-speed train     traction systems     machine learning     fault diagnosis    

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Dynamic prediction of moving trajectory in pipe jacking: GRU-based deep learning framework

Frontiers of Structural and Civil Engineering   Pages 994-1010 doi: 10.1007/s11709-023-0942-5

Abstract: Hence, a gated recurrent unit (GRU)-based deep learning framework is proposed herein to dynamically predictdecision support for moving trajectory control and serve as a foundation for the application of deep learning

Keywords： dynamic prediction     moving trajectory     pipe jacking     GRU     deep learning    

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Title Author Date Type Operation

Understanding the demand predictability of bike share systems: A station-level analysis

Journal Article

Modeling and simulating the impact of forgetting and communication errors on delays in civil infrastructure

Zhe SUN, Cheng ZHANG, Pingbo TANG

Journal Article

The dynamic correction of collimation errors of CT slicing pictures

LIU Ya-xiong, Sekou Sing-are, LI Di-chen, LU Bing-heng

Journal Article

Quantum security analysis of a lattice-basedoblivious transfer protocol

Mo-meng LIU, Juliane KRÄMER, Yu-pu HU, Johannes BUCHMANN

Journal Article

Rotation errors in numerical manifold method and a correction based on large deformation theory

Ning ZHANG, Xu LI, Qinghui JIANG, Xingchao LIN

Journal Article

Efficient hierarchical identity based encryption scheme in the standard model over lattices

Feng-he WANG,Chun-xiao WANG,Zhen-hua LIU

Journal Article

Planet position errors in planetary transmission: Effect on load sharing and transmission error

Miguel IGLESIAS, Alfonso FERNáNDEZ, Ana DE-JUAN, Ramón SANCIBRIáN, Pablo GARCíA

Journal Article

A multi-functional dynamic state estimator for error validation: measurement and parameter errors and

Mehdi AHMADI JIRDEHI,Reza HEMMATI,Vahid ABBASI,Hedayat SABOORI

Journal Article

MSWNet: A visual deep machine learning method adopting transfer learning based upon ResNet 50 for municipal

Journal Article

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

Journal Article

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

Journal Article

Machine learning in building energy management: A critical review and future directions

Journal Article

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

Journal Article

Machine learning for fault diagnosis of high-speed train traction systems: A review

Journal Article

Dynamic prediction of moving trajectory in pipe jacking: GRU-based deep learning framework

Journal Article