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In order to solve the problem of evaluating the towing cost of submarine cable paths under arbitrary submarine terrain conditions, this paper adopted the particle swarm optimisation algorithm to plan submarine cable paths, evaluated the towing cost of submarine cable paths from the factors related to the towing cost of submarine cables, established the network structure of submarine cable paths for the evaluation of towing cost, and evaluated it with the static Bayesian network method. After the planning of the submarine cable path, the data related to the planned path were discretised and transformed into observation evidence for Bayesian network evaluation. Then, simulation experiments were conducted to evaluate the towing cost of the submarine cable path. Finally, the results of the simulation experiments were compared with the actual path results to verify the feasibility of Bayesian network application in this field. [ABSTRACT FROM AUTHOR]

In order to realize the intelligent inference of structural vulnerability, Bayesian networks(BNs)have been adopted for reconstructing the analysis system of a truss structure. Firstly, the external load combination, the truss system and its members are defined as the top parent nodes, the middle nodes and the bottom child nodes of the network, respectively. These nodes are connected by some directed edges representing the causality between them. Therefore, the BN topology of the truss structure is defined. Secondly, serious damage is suggested to replace the commonly-used assumption of conceptual removal in vulnerability analysis. Based on the uncertainty distributions of the parameters of members and external loads, the samples are randomly drawn from the probability distributions for learning the conditional probability tables between the different nodes, thereby realizing the BN establishment. Thirdly, the observed state of a specific member is used as the evidence into the established BN for synchronously inferring the state probabilities of the other members, based on which the member importance coefficient is calculated. The sum of all the importance coefficients is further defined as the system vulnerability index. Finally, a member vulnerability index is proposed to predict the most probable failure path of the truss system. The numerical and experimental examples have demonstrated that the proposed method can effectively evaluate the importance of each member within a truss system. The inferred failure path accords well with the experimental observation. The estimated value of the system vulnerability index of the experimental truss is far less than the number of members, and thus, it indicates the low possibility of progressive collapse of the system due to the damaged member. [ABSTRACT FROM AUTHOR]

Owing to the effects of rural tourism and urbanization, the frequent participation of external market activities in traditional villages has increased the sensitivity and fragility of villages. This study constructs a Bayesian network model for systemic risk adaptation in traditional village industries by selecting relevant driving factors based on the nexus causality of "risk perturbation-adaptation process-adaptation outcome," using 148 Traditional Villages in She Xian, Huangshan as the experimental sample. Through simulations of adaptation under low-, medium-, and high-risk scenarios, the relationship and degree of influence between the drivers in the network model were quantified, and the adaptation mechanisms of the risk of the traditional village industry system were predicted and analyzed. Finally, ideal development methods for the current traditional villages in She Xian were proposed using the selected primary drivers. The primary findings of the study are as follows: 1) Most of the traditional villages in She Xian are in the middle to high level of industrial system risk, and show a certain advantageous concentration in space. 2) Under the low risk scenario, the ecological and cultural resources of traditional villages are effectively protected and utilized. The allocation of all parts of the industrial system is coordinated, and the ability to adapt and self-organize plays a greater role in adaptive capacity. Key traditional village subjects should be linked internally and externally in future endeavors, and joint efforts should be to made build regional tourism brands. 3) Under the medium risk scenario, traditional villages have slow economic development but stable social organization base, wherein the adaptive capacity of learning self-organization and buffering capacity have a greater impact. Traditional villages with medium risk should concentrate on activating the internal dynamics of villages and improving public services and infrastructure. 4) Under the high risk scenario, traditional villages face difficulties in industrial development and that the risk-coping mechanisms and reproduction logic of traditional villages lack the requisite social foundation. It is therefore vital to make conservation and restoration a top priority and strengthen the intervention of external factors for high-risk villages. The incorporation of Bayesian network theory into the risk assessment of traditional villages can improve current understanding of the diversity and complexity of traditional village risk adaptation process subjects and the dynamics of uncertainty influencing factors, thereby providing new ideas and theoretical references for Industrial transformation and long-term risk management of traditional villages in similar regions.

Objective To explore the correlation between geriatric depression and its related factors, and to reveal their impact on depression through bayesian reasoning, ao as to achieve prevention and treatment of depression. Methods Based on the China Health and Elderly Care Longitudinal Survey (CHARLS)2018, this study employed the Depression Scale of the Center for Epidemiology (CES-D) to conduct a questionnaire survey on the depression of the elderly population, and incorporates it into demographics, health status, lifestyle, etc. Afterwards, Elastic Net was used to conduct a preliminary screening of 12,468 participants. Last, we used Bayesian networks with MMHC algorithm was constructed, and parameter learning was achieved by maximum likelihood estimation. Results Among the 12,468 participants, 5,035 (40. 38% ) participants were subjected to depression. After feature screening using Elastic Net, 9 variables finally entered the model, which was constructed with 10 nodes and 14 directed edges. The model showed that education levels, sleep duration, instrumental activities of daily living (IADL), and self -- reported health were directly related to depression, suggesting that there were parent nodes of depression occurrence. Conclusion BNs can well reveal the complex network connection between depression and its related factors, enabling the identification of direct or indirect risk factors for depression in the elderly. Accordingly, prevention and control of depression occurrence can be made in a targeted manner in the elderly, which provides a reference for reducing the risk of depression and improving the quality of life of the elderly. [ABSTRACT FROM AUTHOR]

Owing to the effects of rural tourism and urbanization, the frequent participation of external market activities in traditional villages has increased the sensitivity and fragility of villages. This study constructs a Bayesian network model for systemic risk adaptation in traditional village industries by selecting relevant driving factors based on the nexus causality of "risk perturbation-adaptation process-adaptation outcome," using 148 Traditional Villages in She Xian, Huangshan as the experimental sample. Through simulations of adaptation under low-, medium-, and high-risk scenarios, the relationship and degree of influence between the drivers in the network model were quantified, and the adaptation mechanisms of the risk of the traditional village industry system were predicted and analyzed. Finally, ideal development methods for the current traditional villages in She Xian were proposed using the selected primary drivers. The primary findings of the study are as follows: 1) Most of the traditional villages in She Xian are in the middle to high level of industrial system risk, and show a certain advantageous concentration in space. 2) Under the low risk scenario, the ecological and cultural resources of traditional villages are effectively protected and utilized. The allocation of all parts of the industrial system is coordinated, and the ability to adapt and self-organize plays a greater role in adaptive capacity. Key traditional village subjects should be linked internally and externally in future endeavors, and joint efforts should be to made build regional tourism brands. 3) Under the medium risk scenario, traditional villages have slow economic development but stable social organization base, wherein the adaptive capacity of learning self-organization and buffering capacity have a greater impact. Traditional villages with medium risk should concentrate on activating the internal dynamics of villages and improving public services and infrastructure. 4) Under the high risk scenario, traditional villages face difficulties in industrial development and that the risk-coping mechanisms and reproduction logic of traditional villages lack the requisite social foundation. It is therefore vital to make conservation and restoration a top priority and strengthen the intervention of external factors for high-risk villages. The incorporation of Bayesian network theory into the risk assessment of traditional villages can improve current understanding of the diversity and complexity of traditional village risk adaptation process subjects and the dynamics of uncertainty influencing factors, thereby providing new ideas and theoretical references for Industrial transformation and long-term risk management of traditional villages in similar regions. [ABSTRACT FROM AUTHOR]

Copyright of Transportation Science & Technolgy is the property of Transportation Science & Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Under the condition of Bayesian network (BN) small data set, the qualitative maximum a posteriori (QMAP) estimation tends to violate expert constraints, which causes the QMAP estimation to deviate the true value. In order to overcome the shortcomings of this algorithm, an improved QMAP algorithm was proposed. Firstly, the QMAP estimation was learned. Next, the parameters that violated the inequality constraints were regulated by the isotonic regression method. Then, the regulated parameters were further adjusted by using a fine-tuning strategy, in order to make the obtained parameters satisfy the expert constraints. Finally, a comparison was made with the maximum likelihood estimation (MLE) algorithm and QMAP algorithm. The simulation results show that under the condition of small data set, the proposed algorithm meets all the constraints:(1) the KL (Kullback-Leibler) divergence is always lower than the other two algorithms; (2) the running time is approximately 0.1 s more than the other two algorithms, which has little influence; and (3) the inference results are close to the real value, with the deviation maintaining between ±0.05. The comprehensive performance of the improved QMAP algorithm is better than that of MLE and QMAP algorithm, and it has good practicability.

Under the condition of Bayesian network（BN） small data set, the qualitative maximum a posteriori（QMAP） estimation tends to violate expert constraints, which causes the QMAP estimation to deviate the true value. In order to overcome the shortcomings of this algorithm, an improved QMAP algorithm was proposed. Firstly, the QMAP estimation was learned. Next, the parameters that violated the inequality constraints were regulated by the isotonic regression method. Then, the regulated parameters were further adjusted by using a fine-tuning strategy, in order to make the obtained parameters satisfy the expert constraints. Finally, a comparison was made with the maximum likelihood estimation (MLE） algorithm and QMAP algorithm. The simulation results show that under the condition of small data set, the proposed algorithm meets all the constraints:（1） the KL（Kullback-Leibler） divergence is always lower than the other two algorithms;（2） the running time is approximately 0.1 s more than the other two algorithms, which has little influence; and（3） the inference results are close to the real value, with the deviation maintaining between ±0.05. The comprehensive performance of the improved QMAP algorithm is better than that of MLE and QMAP algorithm, and it has good practicability. [ABSTRACT FROM AUTHOR]

Objective: To explore the risk factors of insomnia among employees in the thermal power generation industry and the network relationships between their interactions, and to provide scientific basis for personalized interventions for high-risk groups with insomnia. Methods: In November 2022, 860 employees of a typical thermal power generation enterprise were selected as the research subjects by cluster sampling. On-site occupational health field surveys and questionnaire surveys were used to collect basic information, occupational characteristics, anxiety, depression, stress, occupational stress, and insomnia. The interaction between insomnia and occupational health psychological factors was evaluated by using structural equation model analysis and Bayesian network construction. Results: The detection rates of anxiety, depression and stress were 34.0% (292/860), 32.1% (276/860) and 18.0% (155/860), respectively. The total score of occupational stress was (445.3±49.9) points, and 160 workers (18.6%) were suspected of insomnia, and 578 workers (67.2%) had insomnia. Structural equation model analysis showed that occupational stress had a significant effect on the occurrence of insomnia in thermal power generation workers (standardized load coefficient was 0.644), and occupational health psychology had a low effect on insomnia (standardized load coefficient was 0.065). However, the Bayesian network model further analysis found that anxiety and stress were the two parent nodes of insomnia, with direct causal relationships, the arc strength was-8.607 and -15.665, respectively. The model prediction results showed that the probability of insomnia occurring was predicted to be 0 in the cases of no stress and anxiety, low stress without anxiety, and no stress with low anxiety. When high stress with low anxiety and low stress with high anxiety occurred, the predicted probability of insomnia occurring were 0.38 and 0.47, respectively. When both high stress and high anxiety occurred simultaneously, the predicted probability of insomnia occurring was 0.51. Conclusion: Bayesian network risk assessment can intuitively reveal and predict the insomnia risk of thermal power generation workers and the network interaction relationship between the risks. Anxiety and stress are the direct causal risks of insomnia, and stress is the main risk of individual insomnia of thermal power generation workers. The occurrence of insomnia can be reduced based on scientific intervention of stress conditions.

The modeling of phased-mission systems is difficult and the solving process is complex because of the relevance of the phase tasks and the sharing of components existing in different phases or between phases. To solve the problem, based on the cumulative exposure model, the path sets combination method of phased-mission systems is proposed. Aiming at the problem of the cross-stage correlation of components and its different failure rate in each phase, the cumulative exposure model considering the historical damage of components is used to solve by obtaining the cumulative damage distribution of each component in each phase. Firstly, a phased-mission systems reliability model is build by mapping phased-mission system fault trees into a Bayesian network. By traversing the Bayesian network, the minimal path sets of each phase are obtained. Secondly, the disjoint formulas introduced by variable elimination method are used to do the disjoint operation of the minimal path sets of each phase and the conditional probability relations of the common components are used to reduce the minimal path sets scale. Finally, the minimum disjoint path sets of each phase are combined and summed according to the component conditional probability relation. The path sets combination method of phased-mission systems avoids the large conditional probability table, large storage and large computation problems caused by the excessive discrete states in the traditional Bayesian method and the problem that the PMS-BDD method has strict requirements for variable ordering and is difficult to solve the system reliability with multiple failure distribution types of components. In the end, a phased-mission systems reliability modeling and solving is carried out for a geosynchronous orbit satellite, and compared with the PMS-BDD method, which verifies the correctness of the method.

Bayesian network(BN) is a mathematical model based on probabilistic reasoning, which have a great advantages in solving the uncertainties between complex systems. Because of the complexity and uncertainty in air-combat, aiming at the problems in effectiveness evaluation, combined with the air-combat theory and reality, the effectiveness evaluation model of air-combat based on BN is build, the effectiveness evaluation method of air-combat based on BN is given, and simulation analysis is carried out. The results show that the model is valid and accurate, and the method is of a strong learning and reasoning ability.

A vulnerability Bayesian network approach for rapid earthquake damage prediction in urban group buildings is proposed. By establishing a categorical vulnerability Bayesian network, the categorical structure vulnerability matrix information is embedded into the model. Based on the probability distribution of various building properties, the seismic vulnerability of group buildings is obtained by Bayesian network inference, and the vulnerability index is calculated. On this basis, an enhanced multi structural type vulnerability Bayesian network is built by increasing the structure type node and the correlations with the vulnerability nodes. Bayesian network parameter learning is performed using a data-driven approach. This network can make prediction of unknown structure type information and rapid assessment of the seismic vulnerability of group buildings with multiple structure types. As an application of vulnerability Bayesian network, a rapid seismic damage prediction analysis is performed on part of the buildings in Pudong District, Shanghai. The feasibility and reliability of the method are demonstrated by comparison with traditional methods of earthquake damage prediction. [ABSTRACT FROM AUTHOR]

Ｔｈｅ ａｕｔｏｍａｔｉｃ ｌｅａｒｎｉｎｇ ｏｆ Ｂａｙｅｓｉａｎ ｎｅｔｗｏｒｋ ｇｒａｐｈ ｓｔｒｕｃｔｕｒｅ ｉｓ ａ ｃｈａｌｌｅｎｇｅ ｉｎ ｍａｃｈｉｎｅ ｌｅａｒｎｉｎｇ. Ａｉｍｉｎｇ ａｔ ｔｈｅ ｐｒｏｂｌｅｍｓ ｏｆ ｌｏｗ ｌｅａｒｎｉｎｇ ｅｆｆｉｃｉｅｎｃｙ ｏｆ ｔｒａｄｉｔｉｏｎａｌ ａｌｇｏｒｉｔｈｍ, ｄｉｆｆｉｃｕｌｔｙ ｉｎ ｒｅｍｏｖｉｎｇ ｒｅｄｕｎｄａｎｔ ｅｄｇｅｓ ａｎｄ ｄｅｔｅｒｍｉｎｉｎｇ ｔｈｅ ｄｉｒｅｃｔｉｏｎ ｏｆ ｔｈｅ ｅｄｇｅｓ ｉｎ ｔｈｅ ｓｔｒｕｃｔｕｒｅ, ａ Ｂａｙｅｓｉａｎ ｎｅｔｗｏｒｋ ｓｔｒｕｃｔｕｒｅ ｌｅａｒｎｉｎｇ ａｌｇｏｒｉｔｈｍ ｂａｓｅｄ ｏｎ Ｍａｒｋｏｖ ｂｌａｎｋｅｔ ｗａｓ ｐｒｏｐｏｓｅｄ. Ｔｈｅ ｐｒｏｐｏｓｅｄ ａｌｇｏｒｉｔｈｍ ｉｍｐｒｏｖｅｓ ｔｈｅ ｃｌａｓｓｉｃａｌ Ｍａｒｋｏｖ ｂｌａｎｋｅｔ ｌｅａｒｎｉｎｇ ａｌｇｏｒｉｔｈｍ, ｒｅｄｕｃｅｓ ｔｈｅ ｎｕｍｂｅｒ ｏｆ ｃｏｎｄｉｔｉｏｎａｌ ｉｎｄｅｐｅｎｄｅｎｔ ｉｎｓｐｅｃｔｉｏｎｓ, ａｎｄ ｉｓ ｍｏｒｅ ｓｕｉｔａｂｌｅ ｆｏｒ Ｂａｙｅｓｉａｎ ｎｅｔｗｏｒｋ ｓｔｒｕｃｔｕｒｅ ｌｅａｒｎｉｎｇ ｉｎ ｔｈｅ ｓｕｂｓｅｑｕｅｎｔ ｄｅｔｅｒｍｉｎａｔｉｏｎ ｏｆ ｄｉｒｅｃｔｅｄ ｓｔｒｕｃｔｕｒｅｓ. Ａｔ ｔｈｅ ｓａｍｅ ｔｉｍｅ, ａ ｇｅｎｅｒａｌ ｓｏｌｕｔｉｏｎ ｆｏｒ ｄｅｔｅｒｍｉｎｉｎｇ ｔｈｅ ｄｉｒｅｃｔｉｏｎ ｏｆ ｔｗｏ ｄｉｒｅｃｔｅｄ ｅｄｇｅｓ ｗａｓ ｇｉｖｅｎ, ｗｈｉｃｈ ｅｆｆｅｃｔｉｖｅｌｙ ｉｍｐｒｏｖｅｓ ｔｈｅ ｌｅａｒｎｉｎｇ ｅｆｆｉｃｉｅｎｃｙ ｏｆ ｔｈｅ ｌｅａｒｎｉｎｇ ａｌｇｏｒｉｔｈｍ. Ｆｉｎａｌｌｙ, ｔｈｅ Ｂａｙｅｓｉａｎ ｎｅｔｗｏｒｋ-ｂａｓｅｄ ｉｎｔｅｒｃｏｎｎｅｃｔｅｄ ｃｌｏｕｄ ＱｏＥ ｅｖａｌｕａｔｉｏｎ ｍｏｄｅｌ ｗａｓ ｅｓｔａｂｌｉｓｈｅｄ, ａｎｄ ｔｈｅ ｓｉｍｕｌａｔｉｏｎ ｅｘｐｅｒｉｍｅｎｔ ｗａｓ ｃａｒｒｉｅｄ ｏｕｔ. Ｔｈｅ ｒｅｓｕｌｔｓ ｓｈｏｗｅｄ ｔｈａｔ ｔｈｅ ｉｍｐｒｏｖｅｄ ｌｅａｒｎｉｎｇ ａｌｇｏｒｉｔｈｍ ｉｓ ｓｕｐｅｒｉｏｒ ｔｏ ｔｈｅ ｔｒａｄｉｔｉｏｎａｌ ａｌｇｏｒｉｔｈｍ ｉｎ ｐｒｅｄｉｃｔｉｏｎ ａｃｃｕｒａｃｙ ａｎｄ ｌｅａｒｎｉｎｇ ｅｆｆｉｃｉｅｎｃｙ. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Zhengzhou University (Natural Science Edition) is the property of Journal of Zhengzhou University (Natural Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the problems that the existing Bayesian network structure learning algorithms always had inadequacies such as easy to premature convergence，low learning efficiency，a Bayesian network structure learning strategy based on improved bacterial foraging optimization algorithm was proposed，which improved the chemotaxis operator，reproduction operator and elimination operator in the traditional bacterial foraging optimization. The adaptive theory was applied to the calculation of bacterial run step size and selection of reproduction individuals. In the migration probability calculation of the migration operator，the roulette method in the genetic algorithm was introduced. Based on mutual information theory，a new random evolution method of the network structure was created to take place of the random elimination method in the traditional bacterial optimization. Experiments on classical Bayesian networks of different scales were proceeded. The results indicate that the algorithm is effective in Bayesian network structure learning，the performance in convergence is inferior to other algorithms，but in the learning effect，especially for the complex structure of the network，the advantages are obvious. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of Beijing Institute of Technology is the property of Beijing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The max-product problem includes the most probable explanation (MPE) and the maximum a posteriori hypothesis (MAP), which are basic problems of Bayesian networks. Because the time complexity of the traditional algorithms was high, this paper introduced novel techniques to solve the max-product problem. This algorithm first transformed the problem of computing the max-product instantiations of Bayesian networks into a set of linear equations with one variable, which could be easily solved. Next this paper used factors to cache the intermediate results when computing the max-product probability. The with cached factors could be used to optimize the computation of the max-product instantiations without too much extra space overhead. Moreover, the cached factors could be shared among Bayesian network queries. Finally, the experimental results demonstrate the efficiencies of the algorithms in finding the max-product instantiations of Bayesian networks, and they are effective algorithms in computing the max-product instantiations. [ABSTRACT FROM AUTHOR]

Threat evaluation is a time-sensitive, uncertain and dynamic process. In order to access a profound understanding, a detailed description of 18 threat cues essential in the assessment process is given firstly. And Gaussian function is used as fuzzy membership function to deal with data required. At the same time, Bayesian network structure is designed for complex threat factors. Then a two-stage evaluation structure based on the above description is proposed; Important information unknown about distant target is speculated based on the association rules and then average linear weighted method is used on the parameters to get the assessment results; Bayesian network is used when information is complete. Finally simulation results verify the validity and rationality of the model. [ABSTRACT FROM AUTHOR]

There are a lot of unknown patterns within the large amounts of raw traffic data generated by the urban intelligent traffic information system which can be used in urban road safety management. In order to effectively use these data, this paper proposed a method of prediction on driving track of specific vehicles in potential group (SVPG-TP). This method leveraged the proposed searching algorithm to potential group of specific vehicles and sequential pattern discovery and Bayesian networks complementary predictable pattern, effectively addressed the two big problems of the most concerned that both the potential group finding and driving track in the current urban road safety, in order to assure that the reliable technological means provided in urban road safety. Finally, the real system built for experimental test verifies the effectiveness and practicality of the proposed algorithm on potential group of specific vehicles traveling trajectory prediction in urban road safety management. [ABSTRACT FROM AUTHOR]

In order to improve the accuracy of prediction of aircraft arrival time and the stability of queue in the terminal airspace, a probabilistic model for the same type aircraft crossing the same entry point was established through analysis of the main factors influenced the estimated time of arrival, and a probabilistic prediction method was proposed using a large amount of actual flight data. The uncertainty of estimated time of arrival was calculated using Bayesian network. The results show that the probabilistic method for estimated time of arrival could quantize the uncertainty exactly and be help to improve the stability of arrival queue. [ABSTRACT FROM AUTHOR]

Bayesian networks can describe better the uncertainty causality by using conditional probability and ratiocination. Because of the complexity of commercial bank's enterprise risk, it is difficult to build pre-warning system by traditional methods. This paper uses the Bayesian networks as a new way to analyze the topology frame of enterprise risk and builds a causal network with key risk drives and enterprise risk. After evaluating every node, we calculate the effect of every risk factor on enterprise risk and vividly show the effect through the signal lamp model of pre-warning system, which can help commercial bank take action to deal with risks. [ABSTRACT FROM AUTHOR]

Aiming at the complexity of modern battlefield, diversity, uncertainty and illegibility of battle damage assessment (BDA) data sources, the BDA fuzzy Bayesian networks is constructed by integrate the fuzzy set theory into Bayesian networks. The continuous node of this network is converted to corresponding membership degree, and then the membership degree is converted to probability knowledge which can be used to Bayesian networks inference by possibility transformation theory. The effect on BDA of weather, electronic jamming, optical jamming and weapon-target matching degree are considered in this model. The combat decision-maker can use the model to simulate on the target damage under the varied kinds of attack conditions, and make quick and correct assessment on target damage grade. It can get true target damage information by syncretizing the recon-naissance intelligence collected after the attack, and it also can make use of assessment accuracy to modify the assessment model. Finally, it will provide decision-making evidence for second strike and properly making fire-power configuration scheme. [ABSTRACT FROM AUTHOR]

According to the limitations of hypothesis of binary-state and statistical independence in event tree analysis, a new method based on Bayesian networks is proposed. The new method can deal with not only binary-state and statistically independent events, but multi-state and statistically dependent events. At the same time, the size of the graph can be kept down and some additional information can be obtained. An example taken from literature verifies the validity of the proposed method. [ABSTRACT FROM AUTHOR]

According to the difficulty of quantitative reliability analysis for binary-state system with multi-state components, a new method based on Bayesian networks is proposed. Multi-state fault tree was converted into Bayesian networks firstly, then the probability of each event and the importance of each component were calculated. And additional information used for failure diagnosis and real-time monitoring was obtained at the same time. The correctness of this method was validated through an example of power supply system. [ABSTRACT FROM AUTHOR]