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A Method to Extract Causality for Safety Events in Chemical Accidents from Fault Trees and Accident Reports
المؤلفون المشاركون
Yu, Yangyang
Du, Junwei
Zhao, Hanrui
Hu, Qiang
المصدر
Computational Intelligence and Neuroscience
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-12، 12ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-06-19
دولة النشر
مصر
عدد الصفحات
12
التخصصات الرئيسية
الملخص EN
Chemical event evolutionary graph (CEEG) is an effective tool to perform safety analysis, early warning, and emergency disposal for chemical accidents.
However, it is a complicated work to find causality among events in a CEEG.
This paper presents a method to accurately extract event causality by using a neural network and structural analysis.
First, we identify the events and their component elements from fault trees by natural language processing technology.
Then, causality in accident events is divided into explicit causality and implicit causality.
Explicit causality is obtained by analyzing the hierarchical structure relations of event nodes and the semantics of component logic gates in fault trees.
By integrating internal structural features of events and semantic features of event sentences, we extract implicit causality by utilizing a bidirectional gated recurrent unit (BiGRU) neural network.
An algorithm, named CEFTAR, is presented to extract causality for safety events in chemical accidents from fault trees and accident reports.
Compared with the existing methods, experimental results show that our method has a higher accuracy and recall rate in extracting causality.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Du, Junwei& Zhao, Hanrui& Yu, Yangyang& Hu, Qiang. 2020. A Method to Extract Causality for Safety Events in Chemical Accidents from Fault Trees and Accident Reports. Computational Intelligence and Neuroscience،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1138802
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Du, Junwei…[et al.]. A Method to Extract Causality for Safety Events in Chemical Accidents from Fault Trees and Accident Reports. Computational Intelligence and Neuroscience No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1138802
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Du, Junwei& Zhao, Hanrui& Yu, Yangyang& Hu, Qiang. A Method to Extract Causality for Safety Events in Chemical Accidents from Fault Trees and Accident Reports. Computational Intelligence and Neuroscience. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1138802
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1138802
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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