This study utilizes a semantic-level computer vision-based detection to characterize fracture traces of hard rock pillars in underground space.The trace images captured by photogrammetry are used to establish the data...This study utilizes a semantic-level computer vision-based detection to characterize fracture traces of hard rock pillars in underground space.The trace images captured by photogrammetry are used to establish the database for training two convolutional neural network(CNN)-based models,i.e.,U-Net(University of Freiburg,Germany)and DeepLabV3+(Google,USA)models.Chain code technology,polyline approximation algorithm,and the circular window scanning approach are combined to quantify the main characteristics of fracture traces on flat and uneven surfaces,including trace length,dip angle,density,and intensity.The extraction results indicate that the CNN-based models have better performances than the edge detection methods-based Canny and Sobel operators for extracting the trace and reducing noise,especially the DeepLabV3+model.Furthermore,the quantization results further prove the reliability of extracting the fracture trace.As a result,a case study with two types of traces(i.e.,on flat and uneven surfaces)demonstrates that the applied semantic-level computer vision detection is an accurate and efficient approach for characterizing the fracture trace of hard rock pillars.展开更多
Purpose:In order to annotate the semantic information and extract the research level information of research papers,we attempt to seek a method to develop an information extraction system.Design/methodology/approach:S...Purpose:In order to annotate the semantic information and extract the research level information of research papers,we attempt to seek a method to develop an information extraction system.Design/methodology/approach:Semantic dictionary and conditional random field model(CRFM)were used to annotate the semantic information of research papers.Based on the annotation results,the research level information was extracted through regular expression.All the functions were implemented on Sybase platform.Findings:According to the result of our experiment in carbon nanotube research,the precision and recall rates reached 65.13%and 57.75%,respectively after the semantic properties of word class have been labeled,and F-measure increased dramatically from less than 50%to60.18%while added with semantic features.Our experiment also showed that the information extraction system for research level(IESRL)can extract performance indicators from research papers rapidly and effectively.Research limitations:Some text information,such as that of format and chart,might have been lost due to the extraction processing of text format from PDF to TXT files.Semantic labeling on sentences could be insufficient due to the rich meaning of lexicons in the semantic dictionary.Research implications:The established system can help researchers rapidly compare the level of different research papers and find out their implicit innovation values.It could also be used as an auxiliary tool for analyzing research levels of various research institutions.Originality/value:In this work,we have successfully established an information extraction system for research papers by a revised semantic annotation method based on CRFM and the semantic dictionary.Our system can analyze the information extraction problem from two levels,i.e.from the sentence level and noun(phrase)level of research papers.Compared with the extraction method based on knowledge engineering and that on machine learning,our system shows advantages of the both.展开更多
基金This research is partially supported by the National Natural Science Foundation Project of China(Grant No.42177164)the Outstanding Youth Project of Hunan Provincial Department of Education(Grant No.23B0008)the Distinguished Youth Science Foundation of Hunan Province of China(2022JJ10073).
文摘This study utilizes a semantic-level computer vision-based detection to characterize fracture traces of hard rock pillars in underground space.The trace images captured by photogrammetry are used to establish the database for training two convolutional neural network(CNN)-based models,i.e.,U-Net(University of Freiburg,Germany)and DeepLabV3+(Google,USA)models.Chain code technology,polyline approximation algorithm,and the circular window scanning approach are combined to quantify the main characteristics of fracture traces on flat and uneven surfaces,including trace length,dip angle,density,and intensity.The extraction results indicate that the CNN-based models have better performances than the edge detection methods-based Canny and Sobel operators for extracting the trace and reducing noise,especially the DeepLabV3+model.Furthermore,the quantization results further prove the reliability of extracting the fracture trace.As a result,a case study with two types of traces(i.e.,on flat and uneven surfaces)demonstrates that the applied semantic-level computer vision detection is an accurate and efficient approach for characterizing the fracture trace of hard rock pillars.
基金supported by the National Social Science Foundation of China(Grant No.12CTQ032)
文摘Purpose:In order to annotate the semantic information and extract the research level information of research papers,we attempt to seek a method to develop an information extraction system.Design/methodology/approach:Semantic dictionary and conditional random field model(CRFM)were used to annotate the semantic information of research papers.Based on the annotation results,the research level information was extracted through regular expression.All the functions were implemented on Sybase platform.Findings:According to the result of our experiment in carbon nanotube research,the precision and recall rates reached 65.13%and 57.75%,respectively after the semantic properties of word class have been labeled,and F-measure increased dramatically from less than 50%to60.18%while added with semantic features.Our experiment also showed that the information extraction system for research level(IESRL)can extract performance indicators from research papers rapidly and effectively.Research limitations:Some text information,such as that of format and chart,might have been lost due to the extraction processing of text format from PDF to TXT files.Semantic labeling on sentences could be insufficient due to the rich meaning of lexicons in the semantic dictionary.Research implications:The established system can help researchers rapidly compare the level of different research papers and find out their implicit innovation values.It could also be used as an auxiliary tool for analyzing research levels of various research institutions.Originality/value:In this work,we have successfully established an information extraction system for research papers by a revised semantic annotation method based on CRFM and the semantic dictionary.Our system can analyze the information extraction problem from two levels,i.e.from the sentence level and noun(phrase)level of research papers.Compared with the extraction method based on knowledge engineering and that on machine learning,our system shows advantages of the both.
