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Corrigendum to “High-Speed Railway Train Timetable Conflict Prediction Based on Fuzzy Temporal

He Zhuang, Liping Feng, Chao Wen, Qiyuan Peng, Qizhi Tang

Engineering 2017, Volume 3, Issue 1,   Pages 150-150 doi: 10.1016/J.ENG.2017.01.002

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High-Speed Railway Train Timetable Conflict Prediction Based on Fuzzy Temporal Knowledge Reasoning

He Zhuang, Liping Feng, Chao Wen, Qiyuan Peng, Qizhi Tang

Engineering 2016, Volume 2, Issue 3,   Pages 366-373 doi: 10.1016/J.ENG.2016.03.019

Abstract: Therefore, reliable conflict prediction results can be valuable references for dispatchers in makingIn contrast to the traditional approach to conflict prediction that involves introducing random disturbancesTo measure conflict prediction results more comprehensively, we divided conflicts into potential conflictsBased on the temporal fuzzy reasoning method, with some adjustment, a new conflict prediction methodThe results prove that conflict prediction after fuzzy processing of the time intervals of a train timetable

Keywords： High-speed railway     Train timetable     Conflict prediction     Fuzzy temporal knowledge reasoning    

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The Detonation and Resoluble Architecture of Conflict Management in Collaborative Environment

Wang Chonghai,Hao Yongping,Zhang Deyu

Strategic Study of CAE 2002, Volume 4, Issue 4,   Pages 83-85

Abstract: Meanwhile, this also increases the incidence of conflict.In this paper, through analyzing the present conflict management, its origin and the whole architectureand the topology of conflict management are described.Furthermore, the authors discusse the managed hierarchy and tri-layer service architecture of conflictThis offers the technological support to manage conflict problems in Web-based collaborative environment

Keywords： CSCW     conflict     conflict management    

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Ground-to-Air Radar Jamming Syste——Functions in Current Local Conflict and Its Development Trend

Zhang Xixiang

Strategic Study of CAE 2000, Volume 2, Issue 7,   Pages 55-65

Abstract:

This paper describes the development of the ground-to-air jammer. Initially this equipment can only jam the bomb-aiming radar, but now it has evolved into a ground-to-air jamming system or series, which can jam a variety of radars , including airborne radar on combat aircraft, missile guidance radar, precise guid-ance radar, AW ACS radar and sat ellite-borne radar, etc. The paper analyzes the jamming conception, methods and jamming station configuration against these types of radars.

The paper provides the conceptual analysis of the jamming suppression factors taken for jamming new types of radars, derives their mathematical expressions, and gives both the computer-simulated result and real-test result to further prove the confidence of the theoretical analysis.

The paper notes that in 2 current conflicts in 1990s, the intruders won the wars with very low loss rate (only several parts of ten thousands), because the other side had not equipped with the ground-to-air jamming systems. If they had got the ground-to-air jamming equipment deployed in their air defense system, they would have supported their air defense system to function well, reduced their own loss rate, increased the enemy´s loss, and made intruders not to win so easily.

The paper concludes that the ground-to-air jamming system plays a very important role in the national defense. It has both defensive and offensive functions with its 3D jamming capabilities. Also it is very, cost effective and quite suitable for the developing countries. The ground-to-air jamming system will have a bright future.

Keywords： ground-to-air jamming system     functions in current conflict     development trend    

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An Empirical Study on the Generation Mechanism of NIMBY Conflicts of Construction Projects

Guang-she Jia,Song-yu Yan,Wen-jun Wang,Ralf Müller,Chen Lin

Frontiers of Engineering Management 2016, Volume 3, Issue 1,   Pages 39-49 doi: 10.15302/J-FEM-2016015

Abstract: From the perspective of social conflict theory, the authors built a process model of the NIMBY conflicts

Keywords： NIMBY conflicts     social conflict     construction projects     mechanism     empirical study    

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

Frontiers of Medicine 2022, Volume 16, Issue 3,   Pages 496-506 doi: 10.1007/s11684-021-0828-7

Abstract: 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.

Keywords： XGBoost     deep neural network     healthcare     risk prediction    

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

Frontiers of Mechanical Engineering 2021, Volume 16, Issue 4,   Pages 855-867 doi: 10.1007/s11465-021-0649-z

Abstract: Aiming at surface roughness prediction in the machining process, this paper proposes a position-varyingsurface roughness prediction method based on compensated acceleration by using regression analysis.i>R-square proving the effectiveness of the filtering features, is selected as the input of the predictionMoreover, the prediction curve matches and agrees well with the actual surface state, which verifies

Keywords： 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

Frontiers of Structural and Civil Engineering doi: 10.1007/s11709-023-0961-2

Abstract: 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.

Keywords： 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

Frontiers in Energy 2016, Volume 10, Issue 4,   Pages 479-488 doi: 10.1007/s11708-016-0425-7

Abstract: In this paper a novel method for reliability prediction and validation of nuclear power units in serviceThe accuracy of the reliability prediction can be evaluated according to the comparison between the predictedFurthermore, the reliability prediction method is validated using the nuclear power units in North American

Keywords： 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

Frontiers of Mechanical Engineering 2010, Volume 5, Issue 2,   Pages 171-175 doi: 10.1007/s11465-009-0091-0

Abstract: Trend prediction technology is the key technology to achieve condition-based maintenance of mechanicalTo ensure the normal operation of units and save maintenance costs, trend prediction technology is studiedThe main methods of the technology are given, the trend prediction method based on neural network isThe industrial site verification shows that the proposed trend prediction technology can reflect the

Keywords： 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

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

Abstract: Developing prediction models to support drivers in performing rectifications in advance can effectivelysubsequently, they are preprocessed and used to establish GRU-based multivariate multistep-ahead direct predictionIn addition, the effects of the activation function and input time-step length on the prediction performance

Keywords： dynamic prediction     moving trajectory     pipe jacking     GRU     deep learning    

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Prediction of the shear wave velocity

Amoroso SARA

Frontiers of Structural and Civil Engineering 2014, Volume 8, Issue 1,   Pages 83-92 doi: 10.1007/s11709-013-0234-6

Abstract: 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 .

Keywords： 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

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 susceptibilityThis paper examines the potential of SVM model in prediction of liquefaction using actual field coneUsing cone resistance ( ) and cyclic stress ratio ( ), model has been developed for prediction of liquefactionto simplify the model, requiring only two parameters ( and maximum horizontal acceleration ), for predictionThe study shows that SVM can be used as a practical tool for prediction of liquefaction potential, based

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

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Machine learning-based solubility prediction and methodology evaluation of active pharmaceutical ingredients

Frontiers of Chemical Science and Engineering 2022, Volume 16, Issue 4,   Pages 523-535 doi: 10.1007/s11705-021-2083-5

Abstract: Solubility prediction, as an alternative to experiments which can reduce waste and improve crystallizationHerein we used seven descriptors based on understanding dissolution behavior to establish two solubility predictionThe solubility data of 120 active pharmaceutical ingredients (APIs) in ethanol were considered in the predictionFurthermore, a comparison with traditional prediction methods including the modified solubility equationThe highest accuracy shown by the testing set proves that the ML models have the best solubility prediction

Keywords： solubility prediction     machine learning     artificial neural network     random decision forests    

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Title Author Date Type Operation

Corrigendum to “High-Speed Railway Train Timetable Conflict Prediction Based on Fuzzy Temporal

He Zhuang, Liping Feng, Chao Wen, Qiyuan Peng, Qizhi Tang

Journal Article

High-Speed Railway Train Timetable Conflict Prediction Based on Fuzzy Temporal Knowledge Reasoning

He Zhuang, Liping Feng, Chao Wen, Qiyuan Peng, Qizhi Tang

Journal Article

The Detonation and Resoluble Architecture of Conflict Management in Collaborative Environment

Wang Chonghai,Hao Yongping,Zhang Deyu

Journal Article

Ground-to-Air Radar Jamming Syste——Functions in Current Local Conflict and Its Development Trend

Zhang Xixiang

Journal Article

An Empirical Study on the Generation Mechanism of NIMBY Conflicts of Construction Projects

Guang-she Jia,Song-yu Yan,Wen-jun Wang,Ralf Müller,Chen Lin

Journal Article

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

Journal Article

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

Journal Article

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

Journal Article

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

Journal Article

Reliability prediction and its validation for nuclear power units in service

Jinyuan SHI,Yong WANG

Journal Article

Trend prediction technology of condition maintenance for large water injection units

Xiaoli XU, Sanpeng DENG

Journal Article

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

Journal Article

Prediction of the shear wave velocity

Amoroso SARA

Journal Article

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

Pijush SAMUI

Journal Article

Machine learning-based solubility prediction and methodology evaluation of active pharmaceutical ingredients

Journal Article