【目的】海底石油生产设施在特定条件下可能面临极端或意外事件(如石油泄漏),相关的工程设计和科学研究面临诸多挑战。应用风险评估和可靠性研究,分析工程应用中管汇存在的缺陷,以有效地提高海底管汇系统的可靠性和使用寿命。【方法】...【目的】海底石油生产设施在特定条件下可能面临极端或意外事件(如石油泄漏),相关的工程设计和科学研究面临诸多挑战。应用风险评估和可靠性研究,分析工程应用中管汇存在的缺陷,以有效地提高海底管汇系统的可靠性和使用寿命。【方法】针对海底管汇生产过程中存在的风险因素进行定性和定量分析,并基于故障树分析(Fault Tree Analysis,FTA)方法和可靠性分析模型确定系统薄弱环节/风险点。【结果】生产系统中较为复杂模块的可靠性下降较快,将决定整个系统的稳定性、可靠性、操作性及使用寿命。【结论】管汇系统中,生产系统模块对整个系统的可靠性影响最大。在生产系统模块中,球阀是最薄弱的环节,对顶事件的可靠性影响最大,是管汇系统中的风险点。展开更多
Automatic classification of sentiment data（e.g., reviews, blogs） has many applications in enterprise user management systems, and can help us understand people＇s attitudes about products or services. However, it is...Automatic classification of sentiment data（e.g., reviews, blogs） has many applications in enterprise user management systems, and can help us understand people＇s attitudes about products or services. However, it is difficult to train an accurate sentiment classifier for different domains. One of the major reasons is that people often use different words to express the same sentiment in different domains, and we cannot easily find a direct mapping relationship between them to reduce the differences between domains. So, the accuracy of the sentiment classifier will decline sharply when we apply a classifier trained in one domain to other domains. In this paper, we propose a novel approach called words alignment based on association rules（WAAR） for cross-domain sentiment classification,which can establish an indirect mapping relationship between domain-specific words in different domains by learning the strong association rules between domain-shared words and domain-specific words in the same domain. In this way, the differences between the source domain and target domain can be reduced to some extent, and a more accurate cross-domain classifier can be trained. Experimental results on Amazon~ datasets show the effectiveness of our approach on improving the performance of cross-domain sentiment classification.展开更多
文摘【目的】海底石油生产设施在特定条件下可能面临极端或意外事件(如石油泄漏),相关的工程设计和科学研究面临诸多挑战。应用风险评估和可靠性研究,分析工程应用中管汇存在的缺陷,以有效地提高海底管汇系统的可靠性和使用寿命。【方法】针对海底管汇生产过程中存在的风险因素进行定性和定量分析,并基于故障树分析(Fault Tree Analysis,FTA)方法和可靠性分析模型确定系统薄弱环节/风险点。【结果】生产系统中较为复杂模块的可靠性下降较快,将决定整个系统的稳定性、可靠性、操作性及使用寿命。【结论】管汇系统中,生产系统模块对整个系统的可靠性影响最大。在生产系统模块中,球阀是最薄弱的环节,对顶事件的可靠性影响最大,是管汇系统中的风险点。
基金Project supported by the National Natural Science Foundation of China(Nos.61703013,91546111,91646201,61672070,and61672071)the Beijing Municipal Natural Science Foundation(No.4152005)+1 种基金the Key Projects of Beijing Municipal Education Commission(Nos.KZ201610005009 and KM201810005024)the International Cooperation Seed Grant from Beijing University of Technology of 2016(No.007000514116520)
文摘Automatic classification of sentiment data（e.g., reviews, blogs） has many applications in enterprise user management systems, and can help us understand people＇s attitudes about products or services. However, it is difficult to train an accurate sentiment classifier for different domains. One of the major reasons is that people often use different words to express the same sentiment in different domains, and we cannot easily find a direct mapping relationship between them to reduce the differences between domains. So, the accuracy of the sentiment classifier will decline sharply when we apply a classifier trained in one domain to other domains. In this paper, we propose a novel approach called words alignment based on association rules（WAAR） for cross-domain sentiment classification,which can establish an indirect mapping relationship between domain-specific words in different domains by learning the strong association rules between domain-shared words and domain-specific words in the same domain. In this way, the differences between the source domain and target domain can be reduced to some extent, and a more accurate cross-domain classifier can be trained. Experimental results on Amazon~ datasets show the effectiveness of our approach on improving the performance of cross-domain sentiment classification.
