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Causal Analysis of Futures Sugar Prices in Zhengzhou

Acta Mathematicae Applicatae Sinica 2012年第1期28卷 39-48页

作者： Fang WANG Xing-wei TONG Jing XU Jian-ping CHEN School of Mathematical Science Beijing Normal UniversityBeijing 100875China School of Information Technology and Management Engineering University of International Business and EconomicsBeijing 100029China China International Science and Technology Convention Center No.12 Yumin RoadChaoyang DistrictBeijing 100029China

In this paper, we are interested in investigating the causal relationships among futures sugar prices in the Zhengzhou futures exchange market （ZF）, the spot sugar prices in Zhengzhou （ZS） and the futures sugar prices in New York futures exchange market （NF）. A useful tool called bayesian network is introduced to analyze the problem. Since there are only three variables in our bayesian network, the algorithm is straightforward： we display all the 25 possible network structures and adopt certain scoring metrics to evaluate them. We applied five different scoring metrics in total. Firstly, for each metric, we obtained 24 scores, each calculated from one of the 24 possible structures i.e. a Directed Acyclic Graph （DAG）. Then we eliminated the network structure which represents the independence of the three variables according to our prior knowledge concerning the futures sugar market. After that, the optimal network structure which implies the causal relationships was selected according to the corresponding scoring metric. Finally, after comparing the results from different scoring metrics, we obtained the relatively affirmative conclusion that ZS causes ZF from both the bayesian Dirichlet （BD） metric, bayesian Dirichlet-Akaike Information Criterion （BD-AIC） metric, bayesian Dirichlet-bayesian Information Criterion （BD-BIC） metric and bayesian Information Criterion （BIC） metric. The conclusions that NF causes ZF and ZF causes ZS from the Akaike Information Criterion （AIC） metric and ZF causes ZS from the BIC metric were useful and significant to our investigation.

关键词： causal relationship bayesian network BD metric AIC metric BIC metric

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Risk assessment of large-scale winter sports sites in the context of a natural disaster

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Journal of Safety Science and Resilience 2022年第3期3卷 263-276页

作者： Jiansong Wu Yuxuan Xing Yiping Bai Xiaofeng Hu Shuaiqi Yuan School of Emergency Management and Safety Engineering China University of Mining and TechnologyBeijing 100083China School of Information Technology People’s Public Security University of ChinaBeijing 100038China Safety and Security Science Section Faculty of TechnologyPolicy and ManagementTU DelftDelft2628 BXthe Netherlands

Accidents induced by natural disasters at sports sites may cause catastrophic loss of great ***,previous studies on risk assessments of sports sites have only focused on operational risk and equipment *** the frequent occurrence of extreme disasters,the risk of domino chains caused by natural disasters at large-scale events,such as large-scale winter sports sites,cannot be *** this study,a natural disaster-induced accident-chain evolution analysis model(NAEA model)is *** on the results of the NAEA model,a fuzzy bayesian network for domino accidents triggered by an earthquake at large-scale winter sports sites was *** sensitivity analysis and scenario analysis,it was found that fire and explosion accidents and crowded stampede accidents are the main causes of serious loss in domino disaster chains in large-scale sports ***,improving the early warning capability,reliability of electrical equipment,and automatic sprinkler systems are the most effective ways to prevent and control major *** addition,an optimal safety strategy improvement analysis was performed to facilitate the decision-making of safety managers to prevent serious accidents and reduce accident loss.

关键词： Risk assessment Domino disaster chain Large-scale sports sites bayesian network Natural disaster Fuzzy logic

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Self-Awakened Particle Swarm Optimization BN Structure Learning Algorithm Based on Search Space Constraint

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Computers, Materials & Continua 2023年第9期76卷 3257-3274页

作者： Kun Liu Peiran Li Yu Zhang Jia Ren Xianyu Wang Uzair Aslam Bhatti School of Information and Communication Engineering Hainan UniversityHaikou570228China Playload Research Center for Space Electronic Information System Academy of Space Electronic Information TechnologyXi’an710100China Research Center CRSC Communication&Information Group Co.Ltd.Beijing100070China

To obtain the optimal bayesian network(BN)structure,researchers often use the hybrid learning algorithm that combines the constraint-based(CB)method and the score-and-search(SS)*** hybrid method has the problemthat the search efficiency could be improved due to the ample search *** search process quickly falls into the local optimal solution,unable to obtain the global *** on this,the Particle SwarmOptimization(PSO)algorithm based on the search space constraint process is *** the first stage,the method uses dynamic adjustment factors to constrain the structure search space and enrich the diversity of the initial *** the second stage,the update mechanism is redefined,so that each step of the update process is consistent with the current structure which forms a one-to-one *** the same time,the“self-awakened”mechanism is added to prevent precocious particles frombeing part of the *** the fitness value of the particle converges prematurely,the activation operation makes the particles jump out of the local optimal values to prevent the algorithmfromconverging too quickly into the local ***,the standard network dataset was compared with other *** experimental results showed that the algorithmcould find the optimal solution at a small number of iterations and a more accurate network structure to verify the algorithm’s effectiveness.

关键词： bayesian network structure learning particle swarm optimization

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Inferring causal protein signalling networks from single-cell data based on parallel discrete artificial bee colony algorithm

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1587-1604页

作者： Jinduo Liu Jihao Zhai Junzhong Ji Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology Beijing Institute of Artificial IntelligenceFaculty of Information TechnologyBeijing University of TechnologyBeijingChina

Inferring causal protein signalling networks from human immune system cell data is a promising approach to unravel the underlying tissue signalling biology and dysfunction in diseased cells,which has attracted considerable attention within the bioinformatics ***,bayesian network(BN)techniques have gained significant popularity in inferring causal protein signalling networks from multiparameter single-cell ***,current BN methods may exhibit high computational complexity and ignore interactions among protein signalling molecules from different single cells.A novel BN method is presented for learning causal protein signalling networks based on parallel discrete artificial bee colony(PDABC),named ***,PDABC is a score-based BN method that utilises the parallel artificial bee colony to search for the global optimal causal protein signalling networks with the highest discrete K2 *** experimental results on several simulated datasets,as well as a previously published multi-parameter fluorescence-activated cell sorter dataset,indicate that PDABC surpasses the existing state-of-the-art methods in terms of performance and computational efficiency.

关键词： bayesian network causal protein signaling networks parallel discrete artificial bee colony single-cell data

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Structure Learning of bayesian networks Based On the LARS-MMPC Ordering Search Method

Structure Learning of Bayesian Networks Based On the LARS-MM...

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第37届中国控制会议

作者： Chu-chao He Xiao-guang Gao Department of System Engineering Northwestern Polytechnical University （NWPU）

A given ordering among variables can significantly improve the accuracy of learning in bayesian network *** this study, we propose using a combined Least Angle Regression（LARS） and Max-Min Parent and Children（MMPC） algorithm based on known root nodes specified by domain experts in order to obtain the optimal ordering. First, with a fixed root node, a partial ordering is tailored from the entire ordering by using the LARS algorithm. A further sequence is then obtained by combining all the different partial orderings. Parent and children sets are detected among the remaining nodes by the MMPC algorithm. Finally, a complete ordering is derived from the sequence and the parent and children sets, and the optimal structure is learnt by the K2 algorithm based on the ordering. Experiments showed that compared with other competitive methods, the proposed algorithm performed well in terms of balancing the learning accuracy with time consumption.

关键词： bayesian network least angle regression max-min parent and children ordering search structure learning

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Restricted bayesian classification networks

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Science China(Information Sciences) 2013年第7期56卷 210-224页

作者： WANG ShuangCheng XU GuangLin DU RuiJie School of Mathematics and Information Shanghai Lixin University of Commerce Open Economic and Trade Research Center Shanghai Lixin University of Commerce

bayesian networks are graphical models that describe dependency relationships between variables, and are powerful tools for studying probability classifiers. At present, the causal bayesian network learning method is used in constructing bayesian network classifiers while the contribution of attribute to class is over-looked. In this paper, a bayesian network specifically for classification-restricted bayesian classification networks is proposed. Combining dependency analysis between variables, classification accuracy evaluation criteria and a search algorithm, a learning method for restricted bayesian classification networks is presented. Experiments and analysis are done using data sets from UCI machine learning repository. The results show that the restricted bayesian classification network is more accurate than other well-known classifiers.

关键词： restricted bayesian classification network bayesian network classifier classification accuracy dependency analysis

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Identifying online traffic based on property of TCP flow

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The Journal of China Universities of Posts and Telecommunications 2009年第3期16卷 84-88页

作者： HONG Min-huo,GU Ren-tao,WANG Hong-xiang,SUN Yong-mei,JI Yue-feng Key Laboratory of Optical Communications and Lightwave Technologies,Beijing University of Posts and Telecommunications,Beijing 100876,China [a]Key Laboratory of Optical Communications and Lightwave Technologies Beijing University of Posts and Telecommunications Beijing 100876 China

Classification of network traffic using port-based or payload-based analysis is becoming increasingly difficult when many applications use dynamic port numbers, masquerading techniques, and encryption to avoid detection. In this article, an approach is presented for online traffic classification relying on the observation of the first n packets of a transmission control protocol （TCP） connection. Its key idea is to utilize the properties of the observed first ten packets of a TCP connection and bayesian network method to build a classifier. This classifier can classify TCP flows dynamically as packets pass through it by deciding whether a TCP flow belongs to a given application. The experimental results show that the proposed approach performs well in online Internet traffic classification and that it is superior to naive bayesian method.

关键词： network traffic classification inter-arrival time TCP flow bayesian network

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Target distribution in cooperative combat based on bayesian optimization algorithm

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Journal of Systems Engineering and Electronics 2006年第2期17卷 339-342页

作者： Shi Zhi fu Zhang An Wang Anli Electronic Information School Northwestern Polytechnical Univ. Xi'an 710072 P. R. China

Target distribution in cooperative combat is a difficult and emphases. We build up the optimization model according to the rule of fire distribution. We have researched on the optimization model with BOA. The BOA can estimate the joint probability distribution of the variables with bayesian network, and the new candidate solutions also can be generated by the joint distribution. The simulation example verified that the method could be used to solve the complex question, the operation was quickly and the solution was best.

关键词： target distribution bayesian network bayesian optimization algorithm cooperative air combat.

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Influence of surface properties and antecedent environmental conditions on particulate-associated metals in surface runoff

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Environmental Science and Ecotechnology 2020年第2期2卷 9-15页

作者： Zhenyu Wang Pei Hua Heng Dai Rui Li Beidou Xi Dongwei Gui Jin Zhang Peter Krebs Institute of Urban Water Management Technische Universit€at Dresden01062DresdenGermany The Environmental Research Institute MOE Key Laboratory of Environmental Theoretical ChemistrySouth China Normal University510006GuangzhouChina Institute of Groundwater and Earth Sciences Jinan University510632GuangzhouChina State Environmental Protection Key Laboratory of Simulation and Control of Groundwater Pollution Chinese Research Academy of Environmental SciencesBeijing100012China State Key Laboratory of Desert and Oasis Ecology Xinjiang Institute of Ecology and GeographyChinese Academy of SciencesUrumqi830011XinjiangChina

Particulate-associated trace metals have been regarded as an important pollution source for urban surface ***,Pb,Cu,Zn and total solids(TS)washed off two different surfaces(low-elevated facade and road surfaces)under two kinds of antecedent environmental conditions(dry and snow-melting)were determined in this ***-vacuuming sweeping(WVS)and surface washing(SW)methods,representing the maximum pollution potential and common rainfall-induced wash-off condition respectively,were used to collect the particulate *** result shows that the wash-off concentrations of trace metals were found in the order of Cd(2.28±2.08 mg/l)38% and >79% of metals and TS concentrations in the facade surface and road surface runoff *** wash-off concentrations of Cd,Cu,and Zn on the road surface 45e780% higher than those on the facade *** sensitivity analysis based on the bayesian network indicates that the wash-off concentrations of metals were mainly dependent on the antecedent environmental conditions or the surface properties while the sampling methods had a minor ***,to accurately model the pollutant migration in the surface runoff requires an improving method considering different surfaces and antecedent environment conditions.

关键词： Trace metal Road and facade Wash-off Snowmelt bayesian network

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Inferring gene regulatory networks by PCA-CMI using Hill climbing algorithm based on MIT score and SORDER method

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International Journal of Biomathematics 2016年第3期9卷 139-156页

作者： Rosa Aghdam MohsenAlijanpour Mehrdad Azadi Ali Ebrahimi Changiz Eslahchit Abolfazl Rezvan Institute for Research in Fundamental Sciences (IPM) Iran Department of Mathematics and Computer ScienceAmirkabir University of Technology Tehran Iran Department of Mathematics Faculty of Mathematical SciencesCentral Tehran Branch Islamic Azad University Tehran Iran Department of Mathematics Faculty of Mathematical Science Shahid Beheshti University Tehran Iran Department of Computer Science Faculty of Mathematical Sciences Shahid Beheshti University Tehran Iran

Inferring gene regulatory networks （GRNs） is a challenging task in Bioinformatics. In this paper, an algorithm, PCHMS, is introduced to infer GRNs. This method applies the path consistency （PC） algorithm based on conditional mutual information test （PCA-CMI）. In the PC-based algorithms the separator set is determined to detect the dependency between variables. The PCHMS algorithm attempts to select the set in the smart way. For this purpose, the edges of resulted skeleton are directed based on PC algorithm direction rule and mutual information test （MIT） score. Then the separator set is selected according to the directed network by considering a suitable sequential order of genes. The effectiveness of this method is benchmarked through several networks from the DREAM challenge and the widely used SOS DNA repair network of Escherichia coll. Results show that applying the PCHMS algorithm improves the precision of learning the structure of the GRNs in comparison with current popular approaches.

关键词： Inferring gene regulatory networks bayesian network PC algorithm conditional mutual independent test MIT score.

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