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

《工程管理前沿（英文）》   页码 551-565 doi: 10.1007/s42524-023-0279-8

摘要： Predicting demand for bike share systems (BSSs) is critical for both the management of an existing BSS and the planning for a new BSS. While researchers have mainly focused on improving prediction accuracy and analysing demand-influencing factors, there are few studies examining the inherent randomness of stations’ observed demands and to what degree the demands at individual stations are predictable. Using Divvy bike-share one-year data from Chicago, USA, we measured demand entropy and quantified the station-level predictability. Additionally, to verify that these predictability measures could represent the performance of prediction models, we implemented two commonly used demand prediction models to compare the empirical prediction accuracy with the calculated entropy and predictability. Furthermore, we explored how city- and system-specific temporally-constant features would impact entropy and predictability to inform estimating these measures when historical demand data are unavailable. Our results show that entropy of demands across stations is polarized as some stations exhibit high uncertainty (a low entropy of 0.65) and others have almost no check-out demand uncertainty (a high entropy of around 1.0). We also validated that the entropy and predictability are a priori model-free indicators for prediction error, given a sequence of bike usage demands. Lastly, we identified that key factors contributing to station-level entropy and predictability include per capita income, spatial eccentricity, and the number of parking lots near the station. Findings from this study provide more fundamental understanding of BSS demand prediction, which can help decision makers and system operators anticipate diverse station-level prediction errors from their prediction models both for existing stations and for new ones.

关键词： 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

《工程管理前沿（英文）》 2021年 第8卷 第1期   页码 109-121 doi: 10.1007/s42524-019-0084-6

摘要： 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.

关键词： 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

《机械工程前沿（英文）》 2006年 第1卷 第2期   页码 168-172 doi: 10.1007/s11465-006-0016-0

摘要： To eliminate the motion artifacts of CT images caused by patient motions and other related errors, two kinds of correctors (A type and U type) are proposed to monitor the scanning process and correct the motion artifacts of the original images via reverse geometrical transformation such as reverse scaling, moving, rotating and offsetting. The results confirm that the correction method with any of the correctors can improve the accuracy and reliability of CT images, which facilitates in eliminating or decreasing the motion artifacts and correcting other static errors and image processing errors. This provides a foundation for the 3D reconstruction and accurate fabrication of the customized implants.
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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

《结构与土木工程前沿（英文）》 2019年 第13卷 第5期   页码 1036-1053 doi: 10.1007/s11709-019-0535-5

摘要： Numerical manifold method (NMM) is an effective method for simulating block system, however, significant errors are found in its simulation of rotation problems. Three kinds of errors, as volume expansion, stress vibration, and attenuation of angular velocity, were observed in the original NMM. The first two kind errors are owing to the small deformation assumption and the last one is due to the numerical damping. A large deformation NMM is proposed based on large deformation theory. In this method, the governing equation is derived using Green strain, the large deformation iteration and the open-close iteration are combined, and an updating strategy is proposed. The proposed method is used to analyze block rotation, beam bending, and rock falling problems and the results prove that all three kinds of errors are eliminated in this method.

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

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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

《机械工程前沿（英文）》 2013年 第8卷 第1期   页码 80-87 doi: 10.1007/s11465-013-0362-7

摘要：

In this paper an advanced model of spur gear transmissions developed by the authors is used to study the influence of carrier planet pin hole position errors on the behaviour of the transmission. The model, initially conceived for external gear modeling, has been extended with internal meshing features, and thus increasing its capabilities to include planetary transmission modeling. The new features are presented, along with the summary of the model general approach. The parameters and characteristics of the planetary transmission used in the paper are introduced. The influence of carrier planet pin hole position errors on the planet load sharing is studied, and several static cases are given as examples in order to show the ability of the model. Tangential and radial planet pin hole position errors are considered independently, and the effect of the load level is also taken into account. It is also given attention to the effect on the transmission error of the transmission. Two different configurations for the planetary transmission are used, attending to the fixed or floating condition of the sun, and the differences in terms of load sharing ratio are shown.

关键词： gear     planetary     epicyclic     transmission     load sharing     transmission error    

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一种针对测试误差、参数误差和负荷突变故障分析的多功能动态状态估计器 Article

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

《信息与电子工程前沿（英文）》 2016年 第17卷 第11期   页码 1218-1227 doi: 10.1631/FITEE.1500301

摘要： 本文提出了一种基于卡尔曼滤波理论的动态状态估计新算法，可有效探测、识别并校正电力系统中的测试误差和支路参数误差。同时，该算法亦可成功探测和识别电力系统中的负荷突变。该方法在每段取样时间内，采用三种归一化向量对误差进行处理，包括归一化测量残差，归一化拉格朗日乘子，以及归一化新息向量。在IEEE14节点测试系统上对所提出的算法进行了可行性和效能验证，并通过对数值结果的呈示和讨论，说明了该方法的精确度。

关键词： 动态状态估计器；卡尔曼滤波；测试误差；支路参数误差；负荷突变    

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

《环境科学与工程前沿（英文）》 2023年 第17卷 第8期 doi: 10.1007/s11783-023-1693-1

摘要：

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

关键词： Soil contamination     Machine learning     Prediction     Spatial distribution    

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Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis

《医学前沿（英文）》 2022年 第16卷 第3期   页码 496-506 doi: 10.1007/s11684-021-0828-7

摘要： The fracture risk of patients with diabetes is higher than those of patients without diabetes due to hyperglycemia, usage of diabetes drugs, changes in insulin levels, and excretion, and this risk begins as early as adolescence. Many factors including demographic data (such as age, height, weight, and gender), medical history (such as smoking, drinking, and menopause), and examination (such as bone mineral density, blood routine, and urine routine) may be related to bone metabolism in patients with diabetes. However, most of the existing methods are qualitative assessments and do not consider the interactions of the physiological factors of humans. In addition, the fracture risk of patients with diabetes and osteoporosis has not been further studied previously. In this paper, a hybrid model combining XGBoost with deep neural network is used to predict the fracture risk of patients with diabetes and osteoporosis, and investigate the effect of patients’ physiological factors on fracture risk. A total of 147 raw input features are considered in our model. The presented model is compared with several benchmarks based on various metrics to prove its effectiveness. Moreover, the top 18 influencing factors of fracture risks of patients with diabetes are determined.

关键词： XGBoost     deep neural network     healthcare     risk prediction    

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Position-varying surface roughness prediction method considering compensated acceleration in milling

《机械工程前沿（英文）》 2021年 第16卷 第4期   页码 855-867 doi: 10.1007/s11465-021-0649-z

摘要： Machined surface roughness will affect parts’ service performance. Thus, predicting it in the machining is important to avoid rejects. Surface roughness will be affected by system position dependent vibration even under constant parameter with certain toolpath processing in the finishing. Aiming at surface roughness prediction in the machining process, this paper proposes a position-varying surface roughness prediction method based on compensated acceleration by using regression analysis. To reduce the stochastic error of measuring the machined surface profile height, the surface area is repeatedly measured three times, and Pauta criterion is adopted to eliminate abnormal points. The actual vibration state at any processing position is obtained through the single-point monitoring acceleration compensation model. Seven acceleration features are extracted, and valley, which has the highest R-square proving the effectiveness of the filtering features, is selected as the input of the prediction model by mutual information coefficients. Finally, by comparing the measured and predicted surface roughness curves, they have the same trends, with the average error of 16.28% and the minimum error of 0.16%. Moreover, the prediction curve matches and agrees well with the actual surface state, which verifies the accuracy and reliability of the model.

关键词： surface roughness prediction     compensated acceleration     milling     thin-walled workpiece    

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Improved prediction of pile bending moment and deflection due to adjacent braced excavation

《结构与土木工程前沿（英文）》 doi: 10.1007/s11709-023-0961-2

摘要： Deep excavations in dense urban areas have caused damage to nearby existing structures in numerous past construction cases. Proper assessment is crucial in the initial design stages. This study develops equations to predict the existing pile bending moment and deflection produced by adjacent braced excavations. Influential parameters (i.e., the excavation geometry, diaphragm wall thickness, pile geometry, strength and small-strain stiffness of the soil, and soft clay thickness) were considered and employed in the developed equations. It is practically unfeasible to obtain measurement data; hence, artificial data for the bending moment and deflection of existing piles were produced from well-calibrated numerical analyses of hypothetical cases, using the three-dimensional finite element method. The developed equations were established through a multiple linear regression analysis of the artificial data, using the transformation technique. In addition, the three-dimensional nature of the excavation work was characterized by considering the excavation corner effect, using the plane strain ratio parameter. The estimation results of the developed equations can provide satisfactory pile bending moment and deflection data and are more accurate than those found in previous studies.

关键词： pile responses     excavation     prediction     deflection     bending moments    

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Reliability prediction and its validation for nuclear power units in service

Jinyuan SHI,Yong WANG

《能源前沿（英文）》 2016年 第10卷 第4期   页码 479-488 doi: 10.1007/s11708-016-0425-7

摘要： In this paper a novel method for reliability prediction and validation of nuclear power units in service is proposed. The equivalent availability factor is used to measure the reliability, and the equivalent availability factor deducting planed outage hours from period hours and maintenance factor are used for the measurement of inherent reliability. By statistical analysis of historical reliability data, the statistical maintenance factor and the undetermined parameter in its numerical model can be determined. The numerical model based on the maintenance factor predicts the equivalent availability factor deducting planed outage hours from period hours, and the planed outage factor can be obtained by using the planned maintenance days. Using these factors, the equivalent availability factor of nuclear power units in the following 3 years can be obtained. Besides, the equivalent availability factor can be predicted by using the historical statistics of planed outage factor and the predicted equivalent availability factor deducting planed outage hours from period hours. The accuracy of the reliability prediction can be evaluated according to the comparison between the predicted and statistical equivalent availability factors. Furthermore, the reliability prediction method is validated using the nuclear power units in North American Electric Reliability Council (NERC) and China. It is found that the relative errors of the predicted equivalent availability factors for nuclear power units of NERC and China are in the range of –2.16% to 5.23% and –2.15% to 3.71%, respectively. The method proposed can effectively predict the reliability index in the following 3 years, thus providing effective reliability management and maintenance optimization methods for nuclear power units.

关键词： nuclear power units in service     reliability     reliability prediction     equivalent availability factors    

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Trend prediction technology of condition maintenance for large water injection units

Xiaoli XU, Sanpeng DENG

《机械工程前沿（英文）》 2010年 第5卷 第2期   页码 171-175 doi: 10.1007/s11465-009-0091-0

摘要： Trend prediction technology is the key technology to achieve condition-based maintenance of mechanical equipment. Large-sized water injection units are key equipment in oilfields. The traditional preventive maintenance is not economical and cannot completely avoid vicious accidents. To ensure the normal operation of units and save maintenance costs, trend prediction technology is studied to achieve condition-based maintenance for water injection units. The main methods of the technology are given, the trend prediction method based on neural network is put forward, and the expert system based on the knowledge is developed. The industrial site verification shows that the proposed trend prediction technology can reflect the operating condition trend change of the water injection units and provide technical means to achieve condition-based predictive maintenance.

关键词： water injection units     condition-based maintenance     trend prediction    

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

《结构与土木工程前沿（英文）》   页码 994-1010 doi: 10.1007/s11709-023-0942-5

摘要： The moving trajectory of the pipe-jacking machine (PJM), which primarily determines the end quality of jacked tunnels, must be controlled strictly during the entire jacking process. Developing prediction models to support drivers in performing rectifications in advance can effectively avoid considerable trajectory deviations from the designed jacking axis. Hence, a gated recurrent unit (GRU)-based deep learning framework is proposed herein to dynamically predict the moving trajectory of the PJM. In this framework, operational data are first extracted from a data acquisition system; subsequently, they are preprocessed and used to establish GRU-based multivariate multistep-ahead direct prediction models. To verify the performance of the proposed framework, a case study of a large pipe-jacking project in Shanghai and comparisons with other conventional models (i.e., long short-term memory (LSTM) network and recurrent neural network (RNN)) are conducted. In addition, the effects of the activation function and input time-step length on the prediction performance of the proposed framework are investigated and discussed. The results show that the proposed framework can dynamically and precisely predict the PJM moving trajectory during the pipe-jacking process, with a minimum mean absolute error and root mean squared error (RMSE) of 0.1904 and 0.5011 mm, respectively. The RMSE of the GRU-based models is lower than those of the LSTM- and RNN-based models by 21.46% and 46.40% at the maximum, respectively. The proposed framework is expected to provide an effective decision support for moving trajectory control and serve as a foundation for the application of deep learning in the automatic control of pipe jacking.

关键词： dynamic prediction     moving trajectory     pipe jacking     GRU     deep learning    

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Prediction of the shear wave velocity

Amoroso SARA

《结构与土木工程前沿（英文）》 2014年 第8卷 第1期   页码 83-92 doi: 10.1007/s11709-013-0234-6

摘要： The paper examines the correlations to obtain rough estimates of the shear wave velocity from non-seismic dilatometer tests (DMT) and cone penetration tests (CPT). While the direct measurement of is obviously preferable, these correlations may turn out useful in various circumstances. The experimental results at six international research sites suggest that the DMT predictions of from the parameters (material index), (horizontal stress index), (constrained modulus) are more reliable and consistent than the CPT predictions from (cone resistance), presumably because of the availability, by DMT, of the stress history index .

关键词： horizontal stress index     shear wave velocity     flat dilatometer test     cone penetration test    

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

Pijush SAMUI

《结构与土木工程前沿（英文）》 2013年 第7卷 第1期   页码 72-82 doi: 10.1007/s11709-013-0185-y

摘要： A support vector machine (SVM) model has been developed for the prediction of liquefaction susceptibility as a classification problem, which is an imperative task in earthquake engineering. This paper examines the potential of SVM model in prediction of liquefaction using actual field cone penetration test (CPT) data from the 1999 Chi-Chi, Taiwan earthquake. The SVM, a novel learning machine based on statistical theory, uses structural risk minimization (SRM) induction principle to minimize the error. Using cone resistance ( ) and cyclic stress ratio ( ), model has been developed for prediction of liquefaction using SVM. Further an attempt has been made to simplify the model, requiring only two parameters ( and maximum horizontal acceleration ), for prediction of liquefaction. Further, developed SVM model has been applied to different case histories available globally and the results obtained confirm the capability of SVM model. For Chi-Chi earthquake, the model predicts with accuracy of 100%, and in the case of global data, SVM model predicts with accuracy of 89%. The effect of capacity factor ( ) on number of support vector and model accuracy has also been investigated. The study shows that SVM can be used as a practical tool for prediction of liquefaction potential, based on field CPT data.

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

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标题 作者 时间 类型 操作

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

期刊论文

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

Zhe SUN, Cheng ZHANG, Pingbo TANG

期刊论文

The dynamic correction of collimation errors of CT slicing pictures

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

期刊论文

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

Ning ZHANG, Xu LI, Qinghui JIANG, Xingchao LIN

期刊论文

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

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

期刊论文

一种针对测试误差、参数误差和负荷突变故障分析的多功能动态状态估计器

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

期刊论文

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

期刊论文

Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis

期刊论文

Position-varying surface roughness prediction method considering compensated acceleration in milling

期刊论文

Improved prediction of pile bending moment and deflection due to adjacent braced excavation

期刊论文

Reliability prediction and its validation for nuclear power units in service

Jinyuan SHI,Yong WANG

期刊论文

Trend prediction technology of condition maintenance for large water injection units

Xiaoli XU, Sanpeng DENG

期刊论文

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

期刊论文

Prediction of the shear wave velocity

Amoroso SARA

期刊论文

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

Pijush SAMUI

期刊论文