Intelligent construction technology has been widely used in the field of railway engineering.This work first analyzes the connotation,function,and characteristics of intelligent construction of railway engineering(ICR...Intelligent construction technology has been widely used in the field of railway engineering.This work first analyzes the connotation,function,and characteristics of intelligent construction of railway engineering(ICRE)and establishes its system structure from three dimensions,namely,life cycle,layers of management,and intelligent function,to deeply understand the development situation of intelligent railway construction in China.Second,seven key technical support systems of ICRE,which include building information modeling(BIM)standard system for China’s railway sector,technology management platform and life cycle management based on BIM-hGIS(geography information system),ubiquitous intelligent perception system,intelligent Intemet-of-Things(IoT)communication system based on mobile interconnection,construction management platform based on cloud computing and big data,unmanned operation system based on artificial intelligence,intelligent machinery and robot,and intelligent operation and maintenance system based on BIM and PHM(prediction and health management),are established.Third,ICRE is divided into three development stages:primary(perception),intermediate(substitution),and advanced(intelligence).The evaluation index system of each stage is provided from the aspects of technology and function.Finally,this work summarizes and analyzes the application situation of ICRE in the entire railway sector of China,represented by Beijing-Zhangjiakou and Beijing-Xiong’an high-speed railways.Result shows that the technical support systems of the ICRE have emerged in China and are still in the process of deepening basic technology research and preliminary application.In the future,the ICRE of China’s railway sector will develop toward a higher stage.展开更多
Community Question Answering(CQA) in web forums, as a classic forum for user communication,provides a large number of high-quality useful answers in comparison with traditional question answering.Development of method...Community Question Answering(CQA) in web forums, as a classic forum for user communication,provides a large number of high-quality useful answers in comparison with traditional question answering.Development of methods to get good, honest answers according to user questions is a challenging task in natural language processing. Many answers are not associated with the actual problem or shift the subjects,and this usually occurs in relatively long answers. In this paper, we enhance answer selection in CQA using multidimensional feature combination and similarity order. We make full use of the information in answers to questions to determine the similarity between questions and answers, and use the text-based description of the answer to determine whether it is a reasonable one. Our work includes two subtasks:(a) classifying answers as good, bad, or potentially associated with a question, and(b) answering YES/NO based on a list of all answers to a question. The experimental results show that our approach is significantly more efficient than the baseline model, and its overall ranking is relatively high in comparison with that of other models.展开更多
文摘Intelligent construction technology has been widely used in the field of railway engineering.This work first analyzes the connotation,function,and characteristics of intelligent construction of railway engineering(ICRE)and establishes its system structure from three dimensions,namely,life cycle,layers of management,and intelligent function,to deeply understand the development situation of intelligent railway construction in China.Second,seven key technical support systems of ICRE,which include building information modeling(BIM)standard system for China’s railway sector,technology management platform and life cycle management based on BIM-hGIS(geography information system),ubiquitous intelligent perception system,intelligent Intemet-of-Things(IoT)communication system based on mobile interconnection,construction management platform based on cloud computing and big data,unmanned operation system based on artificial intelligence,intelligent machinery and robot,and intelligent operation and maintenance system based on BIM and PHM(prediction and health management),are established.Third,ICRE is divided into three development stages:primary(perception),intermediate(substitution),and advanced(intelligence).The evaluation index system of each stage is provided from the aspects of technology and function.Finally,this work summarizes and analyzes the application situation of ICRE in the entire railway sector of China,represented by Beijing-Zhangjiakou and Beijing-Xiong’an high-speed railways.Result shows that the technical support systems of the ICRE have emerged in China and are still in the process of deepening basic technology research and preliminary application.In the future,the ICRE of China’s railway sector will develop toward a higher stage.
基金developed by the NLP601 group at School of Electronics Engineering and Computer Science, Peking University, within the National Natural Science Foundation of China (No. 61672046)
文摘Community Question Answering(CQA) in web forums, as a classic forum for user communication,provides a large number of high-quality useful answers in comparison with traditional question answering.Development of methods to get good, honest answers according to user questions is a challenging task in natural language processing. Many answers are not associated with the actual problem or shift the subjects,and this usually occurs in relatively long answers. In this paper, we enhance answer selection in CQA using multidimensional feature combination and similarity order. We make full use of the information in answers to questions to determine the similarity between questions and answers, and use the text-based description of the answer to determine whether it is a reasonable one. Our work includes two subtasks:(a) classifying answers as good, bad, or potentially associated with a question, and(b) answering YES/NO based on a list of all answers to a question. The experimental results show that our approach is significantly more efficient than the baseline model, and its overall ranking is relatively high in comparison with that of other models.
