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Assessing Air Traffic Management Performance Interdependencies through bayesian networks： Preliminary Applications and Results

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Journal of Traffic and Transportation Engineering 2016年第1期4卷 34-48页

作者： Andrada Bujor Andrea Ranieri Advanced Logistics Group (ALG) Minsait Barcelona 08018 Spain SESAR Joint Undertaking Brussels 1000 Belgium

The performance model proposed by this study represents an innovative approach to deal with performance assessment in ATM （air traffic management）. It is based on bayesian networks methodology, which presents several advantages but also some drawbacks as highlighted along the paper. We illustrate the main steps required for building the model and present a number of interesting results. The contribution of the paper is two-fold：（1） It presents a new methodological approach to deal with a problem which is of strategic importance for ANSPs （air navigation service providers）; （2） It provides insights on the interdependencies between factors influencing performance. Both results are considered particularly important nowadays, due to the SES （Single European Sky） performance scheme and its related target setting process.

关键词： Key performance area interdependencies performance target bayesian network air traffic management.

Analysis of traffic safety in airport aircraft activity areas based on bayesian networks and fault trees

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Digital Transportation and Safety

Digital Transportation and Safety 2024年第1期3卷 8-18页

作者： Ruijun Guo Jiawen Wu Fan Ji Wanxiang Wang Yuan Yin School of Transportation Engineering Dalian Jiaotong UniversityDalian 116028China

To assess road traffic safety risk in civil aviation airports and develop effective accident prevention measures,this study proposed a risk assessment method based on accident tree and bayesian network for airport aircraft activity *** identified influencing factors in the aircraft activity area from the perspectives of person-vehicle-road-environment-management and analyzed their *** bayesian network was utilized to determine initial probabilities for each influencing *** indicated a relatively high overall safety level in the airport's road traffic *** trees were employed to qualitatively and quantitatively analyze common human-vehicle accident *** initial probabilities obtained from the bayesian network served as basic event probabilities in the accident tree to determine the occurrence probability of the top *** a 4F airport in China as an example,accident cause analysis identified five important risk sources in human-vehicle accidents,including blind spots for special vehicles,illegal driving by drivers,pedestrians violating regulations,passengers entering restricted areas,and blind spots at *** safety management measures were *** study concluded that the integration of bayesian networks and accident trees effectively determines accident probabilities and offers specific solutions,thus playing a crucial role in enhancing road traffic safety management within aviation airports.

关键词： bayesian network fault tree analysis minimum cut set structural importance accident cause analysis

Probabilistic Safety Analysis of High Speed and Conventional Lines Using bayesian networks

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Journal of Traffic and Transportation Engineering 2022年第3期10卷 118-125页

作者： Zacarias Grande Enrique Castillo Maria Nogal Alan O’Connor Department of Applied Mathematics and Computational Sciences University of CantabriaCantabriaSpain Royal Academy of Engineering MadridSpain

A bayesian network approach is presented for probabilistic safety analysis(PSA)of railway *** idea consists of identifying and reproducing all the elements that the train encounters when circulating along a railway line,such as light and speed limit signals,tunnel or viaduct entries or exits,cuttings and embankments,acoustic sounds received in the cabin,curves,*** addition,since the human error is very relevant for safety evaluation,the automatic train protection(ATP)systems and the driver behaviour and its time evolution are modelled and taken into account to determine the probabilities of human *** nodes of the bayesian network,their links and the associated probability tables are automatically constructed based on the line data that need to be carefully *** conditional probability tables are reproduced by closed formulas,which facilitate the modelling and the sensitivity analysis.A sorted list of the most dangerous elements in the line is obtained,which permits making decisions about the line safety and programming maintenance operations in order to optimize them and reduce the maintenance costs *** proposed methodology is illustrated by its application to several cases that include real lines such as the Palencia-Santander and the Dublin-Belfast lines.

关键词： Railway probabilistic safety analysis bayesian network

RISK REDUCTION IN A PROJECT PORTFOLIO

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Journal of Systems Science and Systems Engineering 2017年第1期26卷 3-22页

作者： Dujuan Guan Peng Guo Keith W. Hipel Liping Fang School of Management Northwestern Polytechnical University Xi 'an 710072 China Department of Management Hefei University Hefei 230601 China Department of Systems Design Engineering University of Waterloo Waterloo Ontario Canada N2L 3G1 Centre for International Governance Innovation Waterloo Ontario Canada N2L 6C2 Department of Mechanical and Industrial Engineering Ryerson University Toronto Ontario Canada M5B 2K3

The positive impacts of managing projects as a portfolio are quantified by comparing the value of the integrated risk of a project portfolio and the aggregation of single project risks implemented separately. Firstly, the integrated risk is defined by proposing risky events based on set theory. Secondly, as projects interact with each other in a project portfolio, the integrated risk is evaluated by using a bayesian network structure learning algorithm to construct an interdependent network of risks. Finally, the integrated risk of a practical case is assessed using this method, and the results show that the proposed method is an effective tool for calculating the extent of risk reduction of implementing a project portfolio and identifying the most risky project, so as to assist companies in making comprehensive decisions in the phase of portfolio selection and portfolio controlling.

关键词： Project portfolio integrated risk set theory bayesian network risk assessment

A Heuristic Sampling Method for Maintaining the Probability Distribution

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Journal of Computer Science & Technology 2021年第4期36卷 896-909页

作者： Jiao-Yun Yang Jun-Da Wang Yi-Fang Zhang Wen-Juan Cheng Lian Li Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei 230601China National Smart EldeTca/re International Science and Technology Coopemtion Base Hefei University of Technology Hefei 230601China School of Computer Science and Information Engirwering Hefei University of TechnologyHefei 230601China School of Mathematics Hefei University of TechnologyHefei 230601China

Sampling is a fundamental method for generating data *** many data analysis methods are developed based on probability distributions,maintaining distributions when sampling can help to ensure good data analysis ***,sampling a minimum subset while maintaining probability distributions is still a *** this paper,we decompose a joint probability distribution into a product of conditional probabilities based on bayesian networks and use the chi-square test to formulate a sampling problem that requires that the sampled subset pass the distribution test to ensure the ***,a heuristic sampling algorithm is proposed to generate the required subset by designing two scoring functions:one based on the chi-square test and the other based on likelihood *** on four types of datasets with a size of 60000 show that when the significant difference level,a,is set to 0.05,the algorithm can exclude 99.9%,99.0%,93.1%and 96.7%of the samples based on their bayesian networks-ASIA,ALARM,HEPAR2,and ANDES,*** subsets of the same size are sampled,the subset generated by our algorithm passes all the distribution tests and the average distribution difference is approximately 0.03;by contrast,the subsets generated by random sampling pass only 83.8%of the tests,and the average distribution difference is approximately 0.24.

关键词： bayesian network chi-square test sampling probability distribution

A model for automatic identification of human pulse signals

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2008年第10期9卷 1382-1389页

作者： Hui-yan WANG Pei-yong ZHANG College of Computer Science and Information Engineering Zhejiang Gongshang University Hangzhou 310018 China Institute of VLSI Design Zhejiang University Hangzhou 310027 China

This paper presents a quantitative method for automatic identification of human pulse signals. The idea is to start with the extraction of characteristic parameters and then to construct the recognition model based on bayesian networks. To identify depth, frequency and rhythm, several parameters are proposed. To distinguish the strength and shape, which cannot be represented by one or several parameters and are hard to recognize, the main time-domain feature parameters are computed based on the feature points of the pulse signal. Then the extracted parameters are taken as the input and five models for automatic pulse signal identification are constructed based on bayesian networks. Experimental results demonstrate that the method is feasible and effective in recognizing depth, frequency, rhythm, strength and shape of pulse signals, which can be expected to facilitate the modernization of pulse diagnosis.

关键词： Pulse signal identification Feature extraction bayesian network Quantitative diagnosis Wavelet transform

A STUDY ON DECISION-MAKING OF FOOD SUPPLY CHAIN BASED ON BIG DATA

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Journal of Systems Science and Systems Engineering 2017年第2期26卷 183-198页

作者： Guojun Ji Limei Hu Kim Hua Tan Collaborative Innovation Center for Peaceful Development of Corss-Strait Relations School of Management Xiamen University Xiamen Fujian361005 China Operations Management & Information Systems Division Nottingham University Business School

As more and more companies have captured and analyzed huge volumes of data to improve the performance of supply chain, this paper develops a big data harvest model that uses big data as inputs to make more informed production decisions in the food supply chain. By introducing a method of bayesian network, this paper integrates sample data and finds a cause-and-effect between data to predict market demand. Then the deduction graph model that translates products demand into processes and divides processes into tasks and assets is presented, and an example of how big data in the food supply chain can be combined with bayesian network and deduction graph model to guide production decision. Our conclusions indicate that the analytical framework has vast potential for supporting support decision making by extracting value t^om big data.

关键词： Big data bayesian network deduction graph model food supply chain

Robust Non-Frontal Face Alignment with Edge Based Texture

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Journal of Computer Science & Technology 2005年第6期20卷 849-854页

作者：李华颜水成彭立中 LMAM School of Mathematical Sciences Peking University Beijing 100871 P.R. China Department of Information Engineering The Chinese University of Hong KongHong Kong Special Administration Region P.R. China

This paper proposes a new algorithm, called Edge-based Texture Driven Shape Model （E-TDSM）, for nonfrontal face alignment task. First, the texture is defined as the un-warped edge image contained in the shape rectangle; then, a bayesian network is constructed to describe the relationship between the shape and texture models; finally, ExpectationMaximization （EM） approach is utilized to infer the optimal texture and position parameters from the observed shape and texture information. Compared with the traditional shape localization algorithms, E-TDSM has the following advantages： 1） the un-warped edge-based texture can better predict the shape and is more robust to the illumination and expression variation than the conventional warped gray-level based texture; 2） the presented bayesian network indicates the logic structure of the face alignment task; and 3） the mutually enhanced shape and texture observations are integrated to infer the optimal parameters of the proposed bayesian network using face alignment task demonstrate the effectiveness and robustness EM approach. The extensive experiments on non-frontal of the proposed E-TDSM algorithm.

关键词： computer vision face and gesture recognition feature evaluation and selection bayesian network

The Coupling Relationships and Influence Mechanisms of Green Credit and Energy-Environment-Economy Under China’s Goal of Carbon Neutrality

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Journal of Systems Science & Complexity 2023年第1期36卷 360-374页

作者： CHAI Jian WANG Yabo HU Yi ZHANG Xuejun ZHANG Xiaokong School of Economics and Management Xidian UniversityXi’an 710126China School of Economics and Management University of Chinese Academy of SciencesBeijing 100190China

Under the goal of carbon neutrality,it is critical for China to give full play to the role of green credit,and promote the coordinated development of energy-environment-economy(3E)*** on the data of China from 2000 to 2020,the authors build the environmental pollution index,energy transformation index and high-quality economic development *** using bayesian network model(BN),the authors investigate the coupling relationships and influence mechanisms of green credit and 3E *** results show that the main cause of environmental pollution is the annual increase of carbon dioxide *** credit can reduce carbon emissions to a certain extent,and alleviate environmental pollution through energy structure,technological progress and per capita *** energy utilization and per capita GDP growth also help to control environmental *** credit can stimulate technological progress and accelerate energy transformation together with technological *** energy utilization can facilitate the upgrading of industrial structure,industrial structure upgrading and green credit can restrict the level of opening *** progress promotes per capita GDP *** capita GDP growth can reduce energy intensity and improve urbanization and per capita energy consumption.

关键词： bayesian network carbon neutrality coupling nexus energy-environment-economy green credit