This paper presents a new approach for offshore risk analysis that is capable of dealing with linguistic probabilities in Bayesian networks （ BNs）. In this paper, linguistic probabilities are used to describe occurr...This paper presents a new approach for offshore risk analysis that is capable of dealing with linguistic probabilities in Bayesian networks （ BNs）. In this paper, linguistic probabilities are used to describe occurrence likelihood of hazardous events that may cause possible accidents in offshore operations. In order to use fuzzy information, an f-weighted valuation function is proposed to transform linguistic judgements into crisp probability distributions which can be easily put into a BN to model causal relationships among risk factors. The use of linguistic variables makes it easier for human experts to express their knowledge, and the transformation of linguistic judgements into crisp probabilities can significantly save the cost of computation, modifying and maintaining a BN model. The flexibility of the method allows for multiple forms of information to be used to quantify model relationships, including formally assessed expert opinion when quantitative data are lacking, or when only qualitative or vague statements can be made. The model is a modular representation of uncertain knowledge caused due to randomness, vagueness and ignorance. This makes the risk analysis of offshore engineering systems more functional and easier in many assessment contexts. Specifically, the proposed f-weighted valuation function takes into account not only the dominating values, but also the a-level values that are ignored by conventional valuation methods. A case study of the collision risk between a Floating Production, Storage and Off-loading （FPSO） unit and the anthorised vessels due to human elements during operation is used to illustrate the application of the proposed model.展开更多
The learning Bayesian network （BN） structure from data is an NP-hard problem and still one of the most exciting chal- lenges in the machine learning. In this work, a novel algorithm is presented which combines ideas...The learning Bayesian network （BN） structure from data is an NP-hard problem and still one of the most exciting chal- lenges in the machine learning. In this work, a novel algorithm is presented which combines ideas from local learning, constraint- based, and search-and-score techniques in a principled and ef- fective way. It first reconstructs the junction tree of a BN and then performs a K2-scoring greedy search to orientate the local edges in the cliques of junction tree. Theoretical and experimental results show the proposed algorithm is capable of handling networks with a large number of variables. Its comparison with the well-known K2 algorithm is also presented.展开更多
Since the outbreak and spread of corona virus disease 2019(COVID-19),the prevalence of mental disorders,such as depression,has continued to increase.To explore the abnormal changes of brain functional connections in p...Since the outbreak and spread of corona virus disease 2019(COVID-19),the prevalence of mental disorders,such as depression,has continued to increase.To explore the abnormal changes of brain functional connections in patients with depression,this paper proposes a depression analysis method based on brain function network(BFN).To avoid the volume conductor effect,BFN was constructed based on phase lag index(PLI).Then the indicators closely related to depression were selected from weighted BFN based on small-worldness(SW)characteristics and binarization BFN based on the minimum spanning tree(MST).Differences analysis between groups and correlation analysis between these indicators and diagnostic indicators were performed in turn.The resting state electroencephalogram(EEG)data of 24 patients with depression and 29 healthy controls(HC)was used to verify our proposed method.The results showed that compared with HC,the information processing of BFN in patients with depression decreased,and BFN showed a trend of randomization.展开更多
This study used multinomial logistic regression and Bayesian belief networks(BBN)to analyze factors influenc-ing the functionality of the community-based rural drinking water supply and sanitation program(PAMSIMAS)in ...This study used multinomial logistic regression and Bayesian belief networks(BBN)to analyze factors influenc-ing the functionality of the community-based rural drinking water supply and sanitation program(PAMSIMAS)in Indonesia.28,936 PAMSIMAS projects in 33 provinces in Indonesia were analyzed.The data indicates that 85.4%of the water supply systems were fully functioning,9.1%were partially functioning,and 5.5%were not functioning.In the regression analysis,good management is positively associated with functionality and a high investment per capita is negatively associated with the functionality.The latter suggests the need for comprehen-sive economic analysis in the feasibility study in scattered housing sites and remote-undeveloped areas.We also found that high community participation at the beginning of the project was associated with the not functioning system,while women’s participation was positively associated with the functionality.Furthermore,the household connection is more likely to be functioning than communal connection.BBN analysis shows if the beneficiaries do not pay for water,the probability of not functioning systems is 20 times higher than systems with fee collec-tion.Moreover,the combination of strong management,strong financial status,and household connection rather than communal connection increases the probability of fully functioning to 98%.Improvement of data collection is also necessary to monitor the current conditions of all PAMSIMAS systems in Indonesia.This study offers a country-level perspective for better implementation of the community-based rural water supply and sanitation program in developing countries.展开更多
基金This project is funded bythe UK Engineering and Physical Sciences Research Council (EPSRC) under Grant Refer-ences:GR/S85504 and GR/S85498
文摘This paper presents a new approach for offshore risk analysis that is capable of dealing with linguistic probabilities in Bayesian networks （ BNs）. In this paper, linguistic probabilities are used to describe occurrence likelihood of hazardous events that may cause possible accidents in offshore operations. In order to use fuzzy information, an f-weighted valuation function is proposed to transform linguistic judgements into crisp probability distributions which can be easily put into a BN to model causal relationships among risk factors. The use of linguistic variables makes it easier for human experts to express their knowledge, and the transformation of linguistic judgements into crisp probabilities can significantly save the cost of computation, modifying and maintaining a BN model. The flexibility of the method allows for multiple forms of information to be used to quantify model relationships, including formally assessed expert opinion when quantitative data are lacking, or when only qualitative or vague statements can be made. The model is a modular representation of uncertain knowledge caused due to randomness, vagueness and ignorance. This makes the risk analysis of offshore engineering systems more functional and easier in many assessment contexts. Specifically, the proposed f-weighted valuation function takes into account not only the dominating values, but also the a-level values that are ignored by conventional valuation methods. A case study of the collision risk between a Floating Production, Storage and Off-loading （FPSO） unit and the anthorised vessels due to human elements during operation is used to illustrate the application of the proposed model.
基金supported by the National Natural Science Fundation of China (6097408261075055)the Fundamental Research Funds for the Central Universities (K50510700004)
文摘The learning Bayesian network （BN） structure from data is an NP-hard problem and still one of the most exciting chal- lenges in the machine learning. In this work, a novel algorithm is presented which combines ideas from local learning, constraint- based, and search-and-score techniques in a principled and ef- fective way. It first reconstructs the junction tree of a BN and then performs a K2-scoring greedy search to orientate the local edges in the cliques of junction tree. Theoretical and experimental results show the proposed algorithm is capable of handling networks with a large number of variables. Its comparison with the well-known K2 algorithm is also presented.
基金supported by the National Natural Science Foundation of China(Nos.61962034,61862058)Longyuan Youth Innovation and Entrepreneurship Talent(Individual)Project and Tianyou Youth Talent Lift Program of Lanzhou Jiaotong Univesity。
文摘Since the outbreak and spread of corona virus disease 2019(COVID-19),the prevalence of mental disorders,such as depression,has continued to increase.To explore the abnormal changes of brain functional connections in patients with depression,this paper proposes a depression analysis method based on brain function network(BFN).To avoid the volume conductor effect,BFN was constructed based on phase lag index(PLI).Then the indicators closely related to depression were selected from weighted BFN based on small-worldness(SW)characteristics and binarization BFN based on the minimum spanning tree(MST).Differences analysis between groups and correlation analysis between these indicators and diagnostic indicators were performed in turn.The resting state electroencephalogram(EEG)data of 24 patients with depression and 29 healthy controls(HC)was used to verify our proposed method.The results showed that compared with HC,the information processing of BFN in patients with depression decreased,and BFN showed a trend of randomization.
文摘This study used multinomial logistic regression and Bayesian belief networks(BBN)to analyze factors influenc-ing the functionality of the community-based rural drinking water supply and sanitation program(PAMSIMAS)in Indonesia.28,936 PAMSIMAS projects in 33 provinces in Indonesia were analyzed.The data indicates that 85.4%of the water supply systems were fully functioning,9.1%were partially functioning,and 5.5%were not functioning.In the regression analysis,good management is positively associated with functionality and a high investment per capita is negatively associated with the functionality.The latter suggests the need for comprehen-sive economic analysis in the feasibility study in scattered housing sites and remote-undeveloped areas.We also found that high community participation at the beginning of the project was associated with the not functioning system,while women’s participation was positively associated with the functionality.Furthermore,the household connection is more likely to be functioning than communal connection.BBN analysis shows if the beneficiaries do not pay for water,the probability of not functioning systems is 20 times higher than systems with fee collec-tion.Moreover,the combination of strong management,strong financial status,and household connection rather than communal connection increases the probability of fully functioning to 98%.Improvement of data collection is also necessary to monitor the current conditions of all PAMSIMAS systems in Indonesia.This study offers a country-level perspective for better implementation of the community-based rural water supply and sanitation program in developing countries.
