Using knowledge distillation to compress pre-trained models such as Bert has proven to be highly effective in text classification tasks. However, the overhead of tuning parameters manually still hinders their application in practice. To alleviate the cost of manual tuning of parameters in training tasks, inspired by the inverse decrease of the word frequency of TF-IDF, this paper proposes an adaptive knowledge distillation method (AKD). This core idea of the method is based on the Cosine similarity score which is calculated by the probabilistic outputs similarity measurement in two networks. The higher the score, the closer the student model's understanding of knowledge is to the teacher model, and the lower the degree of imitation of the teacher model. On the contrary, we need to increase the degree to which the student model imitates the teacher model. Interestingly, this method can improve distillation model quality. Experimental results show that the proposed method significantly improves the precision, recall and F1 value of text classification tasks. However, training speed of AKD is slightly slower than baseline models. This study provides new insights into knowledge distillation.
{"title":"An adaptive knowledge distillation algorithm for text classification","authors":"Zuqin Chen, Tingkai Hu, Chao Chen, Jike Ge, Chengzhi Wu, Wenjun Cheng","doi":"10.1109/ICESIT53460.2021.9696948","DOIUrl":"https://doi.org/10.1109/ICESIT53460.2021.9696948","url":null,"abstract":"Using knowledge distillation to compress pre-trained models such as Bert has proven to be highly effective in text classification tasks. However, the overhead of tuning parameters manually still hinders their application in practice. To alleviate the cost of manual tuning of parameters in training tasks, inspired by the inverse decrease of the word frequency of TF-IDF, this paper proposes an adaptive knowledge distillation method (AKD). This core idea of the method is based on the Cosine similarity score which is calculated by the probabilistic outputs similarity measurement in two networks. The higher the score, the closer the student model's understanding of knowledge is to the teacher model, and the lower the degree of imitation of the teacher model. On the contrary, we need to increase the degree to which the student model imitates the teacher model. Interestingly, this method can improve distillation model quality. Experimental results show that the proposed method significantly improves the precision, recall and F1 value of text classification tasks. However, training speed of AKD is slightly slower than baseline models. This study provides new insights into knowledge distillation.","PeriodicalId":164745,"journal":{"name":"2021 IEEE International Conference on Emergency Science and Information Technology (ICESIT)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129937823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-22DOI: 10.1109/ICESIT53460.2021.9696520
Wang Bo, Wu Yang, Wei Wei, Su Yaofeng, Mu Xiaofeng
Current Chinese Event Detection (ED) models often incorporate character-level features to improve model performance. In this paper, to incorporate span-level features, we propose a span-based Chinese event detection model (BiLSTM-span, Bidirectional Long Short-Term Memory-span) and its extension (BiLSTM-span-ext). The BiLSTM-span model regards Chinese ED as a span classification problem rather than the sequence labeling problem, and the BiLSTM-span-ext, based on the BiLSTM-span, adds a component that could incorporate span-level information. The experimental results show that our model could achieve the F1 score, which is approximate to the current state-of-the-art Chinese ED model. Besides, we conduct extensive experiments to study span representation methods and show that span representation methods influence the BiLSTM-span model's performance dramatically. Last, we show that BiLSTM-span-ext's ability to incorporate dictionary information at the span-level.
{"title":"Span-based Model for Chinese Event Detection","authors":"Wang Bo, Wu Yang, Wei Wei, Su Yaofeng, Mu Xiaofeng","doi":"10.1109/ICESIT53460.2021.9696520","DOIUrl":"https://doi.org/10.1109/ICESIT53460.2021.9696520","url":null,"abstract":"Current Chinese Event Detection (ED) models often incorporate character-level features to improve model performance. In this paper, to incorporate span-level features, we propose a span-based Chinese event detection model (BiLSTM-span, Bidirectional Long Short-Term Memory-span) and its extension (BiLSTM-span-ext). The BiLSTM-span model regards Chinese ED as a span classification problem rather than the sequence labeling problem, and the BiLSTM-span-ext, based on the BiLSTM-span, adds a component that could incorporate span-level information. The experimental results show that our model could achieve the F1 score, which is approximate to the current state-of-the-art Chinese ED model. Besides, we conduct extensive experiments to study span representation methods and show that span representation methods influence the BiLSTM-span model's performance dramatically. Last, we show that BiLSTM-span-ext's ability to incorporate dictionary information at the span-level.","PeriodicalId":164745,"journal":{"name":"2021 IEEE International Conference on Emergency Science and Information Technology (ICESIT)","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131086986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-22DOI: 10.1109/ICESIT53460.2021.9696701
P. Yao
Some studies show that the closure and reopening orders brought by covid-19 have had a negative impact on the residential real estate market. Generally speaking, real estate sales decreased significantly during this period, such as office buildings, shopping centers and family houses. Although the overall situation is declining, there are also some new situations. For example, people's desire for spacious family space caused by home office leads to an increase in the demand for large houses in the suburbs. This paper mainly compares the sales differences between suburban family houses and urban family houses in San Francisco and New York in the real estate market during covid-19. The data come from multiple dimensions such as house listing price on the real estate sales website, Machine learning methods could be used for analysis. This paper proposed a multi-modal joint attention seq2seq method to analyze these differences and the reasons for the differences. The experimental results show that one of the possible reasons the house price change in San Francisco is that there are more high-tech job position and their family income is higher than the average level of other regions.
{"title":"A Multi-modal Attention-based Seq2eq Model for Predicting Real-estate Prices","authors":"P. Yao","doi":"10.1109/ICESIT53460.2021.9696701","DOIUrl":"https://doi.org/10.1109/ICESIT53460.2021.9696701","url":null,"abstract":"Some studies show that the closure and reopening orders brought by covid-19 have had a negative impact on the residential real estate market. Generally speaking, real estate sales decreased significantly during this period, such as office buildings, shopping centers and family houses. Although the overall situation is declining, there are also some new situations. For example, people's desire for spacious family space caused by home office leads to an increase in the demand for large houses in the suburbs. This paper mainly compares the sales differences between suburban family houses and urban family houses in San Francisco and New York in the real estate market during covid-19. The data come from multiple dimensions such as house listing price on the real estate sales website, Machine learning methods could be used for analysis. This paper proposed a multi-modal joint attention seq2seq method to analyze these differences and the reasons for the differences. The experimental results show that one of the possible reasons the house price change in San Francisco is that there are more high-tech job position and their family income is higher than the average level of other regions.","PeriodicalId":164745,"journal":{"name":"2021 IEEE International Conference on Emergency Science and Information Technology (ICESIT)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128871931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-22DOI: 10.1109/ICESIT53460.2021.9696712
Xiaozhong Chen
The significant advantage of human long bone feature model is anatomical semantics, which can help medical personnel quickly identify and describe individual bone feature information. In this study, a constraint construction method of femoral surface model based on hierarchical parameter mapping is proposed to ensure the rationality of the deformed model by associating the variation range of characteristic parameters. The experimental results show that based on the modification of feature parameters, users can realize the rapid deformation of local feature regions and the constraint reconstruction of the whole model.
{"title":"A novel method for constraint creation of human long bone model based on hierarchical semantic mapping","authors":"Xiaozhong Chen","doi":"10.1109/ICESIT53460.2021.9696712","DOIUrl":"https://doi.org/10.1109/ICESIT53460.2021.9696712","url":null,"abstract":"The significant advantage of human long bone feature model is anatomical semantics, which can help medical personnel quickly identify and describe individual bone feature information. In this study, a constraint construction method of femoral surface model based on hierarchical parameter mapping is proposed to ensure the rationality of the deformed model by associating the variation range of characteristic parameters. The experimental results show that based on the modification of feature parameters, users can realize the rapid deformation of local feature regions and the constraint reconstruction of the whole model.","PeriodicalId":164745,"journal":{"name":"2021 IEEE International Conference on Emergency Science and Information Technology (ICESIT)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127720155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-22DOI: 10.1109/ICESIT53460.2021.9696808
Xie Xikun, Liang Changjiang, Xu Meng
Common feature engineering method and traditional machine visual detection algorithm have problems with strong subjective dependence, low detection accuracy and limited detection range in the detection of metal surface defects. Integrated the ECA attention mechanism to realize the adaptive weight assignment in the important areas of the image will form ECAMobileNetV2 as the model backbone feature extraction network, then use the PANet module of YOLOV4 to enhance the defect feature-one lightweight Yolo V 4 model (ECA_MobileNetV2_yoloV4, abb EMV2yoloV4) integrated ECA and MobileNet. Our method got highest detection accuracy, applied the datasets of metal surface defects for defect types in GCT10 and NED_DET, with mAP of 0.86 and 0.68 respectively. it's significantly higher than MV2yoloV4 and MV3yoloV 4 integrating attention mechanism SE. The model parameter reaching 10.4M is less lightweight than novel detection networks such as Efficientdet and Ghost etc. Experexperiment shows that EMV2yolo V 4 better solves the problem of low recognition accuracy caused by background pixels and brightness. The single image inference time of 18.44ms and frame rate up to 54.25f/s. It can meet the requirements of lightweight deployment and accuracy requirements of metal surface defect detection.
{"title":"Application of attention YOLOV 4 algorithm in metal defect detection","authors":"Xie Xikun, Liang Changjiang, Xu Meng","doi":"10.1109/ICESIT53460.2021.9696808","DOIUrl":"https://doi.org/10.1109/ICESIT53460.2021.9696808","url":null,"abstract":"Common feature engineering method and traditional machine visual detection algorithm have problems with strong subjective dependence, low detection accuracy and limited detection range in the detection of metal surface defects. Integrated the ECA attention mechanism to realize the adaptive weight assignment in the important areas of the image will form ECAMobileNetV2 as the model backbone feature extraction network, then use the PANet module of YOLOV4 to enhance the defect feature-one lightweight Yolo V 4 model (ECA_MobileNetV2_yoloV4, abb EMV2yoloV4) integrated ECA and MobileNet. Our method got highest detection accuracy, applied the datasets of metal surface defects for defect types in GCT10 and NED_DET, with mAP of 0.86 and 0.68 respectively. it's significantly higher than MV2yoloV4 and MV3yoloV 4 integrating attention mechanism SE. The model parameter reaching 10.4M is less lightweight than novel detection networks such as Efficientdet and Ghost etc. Experexperiment shows that EMV2yolo V 4 better solves the problem of low recognition accuracy caused by background pixels and brightness. The single image inference time of 18.44ms and frame rate up to 54.25f/s. It can meet the requirements of lightweight deployment and accuracy requirements of metal surface defect detection.","PeriodicalId":164745,"journal":{"name":"2021 IEEE International Conference on Emergency Science and Information Technology (ICESIT)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123052801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-22DOI: 10.1109/ICESIT53460.2021.9696753
Linyu Wang, Haiyan Jiang, Yibo Jiang
Increasing proportion of centralized wind power integrated into partial areas of China leads to requirement in sharing both energy and reserve among areas under its inherent hierarchical control structure, and the unbalance power introduced by wind power uncertainty lead to requirement of correction from day ahead to intra-day along with the improvement of wind power prediction precision. In order to address these problems, this paper develops an information integration method integrating complicated relations among fuel cost, total thermal power output, reserve capacity, owned reserve and expectations of loading shedding and wind curtailment within this area into three types of time-related relation curves in different time scale. Furthermore, a multi-time scale tie-line energy and reserve allocation model is proposed, which contains two levels in control structure, two time scales in dispatch sequence and multiple areas integrated with wind farms. The efficiency of the proposed method is tested in 9-bus test system and IEEE 118-bus system. The results show that cross-regional control centre is able to allocate both energy and reserve among areas efficiently with the integrated relation curves. The proposed model not only relieves energy and reserve shortage in partial areas but also allocates them to more urgent areas in a high effectivity manner in both day-ahead and intraday time scale.
{"title":"A Multi-time Scale Tie-line Energy and Reserve Allocation Model Considering Wind Power Uncertainty for Multi-area System in Hierarchical Control Structure","authors":"Linyu Wang, Haiyan Jiang, Yibo Jiang","doi":"10.1109/ICESIT53460.2021.9696753","DOIUrl":"https://doi.org/10.1109/ICESIT53460.2021.9696753","url":null,"abstract":"Increasing proportion of centralized wind power integrated into partial areas of China leads to requirement in sharing both energy and reserve among areas under its inherent hierarchical control structure, and the unbalance power introduced by wind power uncertainty lead to requirement of correction from day ahead to intra-day along with the improvement of wind power prediction precision. In order to address these problems, this paper develops an information integration method integrating complicated relations among fuel cost, total thermal power output, reserve capacity, owned reserve and expectations of loading shedding and wind curtailment within this area into three types of time-related relation curves in different time scale. Furthermore, a multi-time scale tie-line energy and reserve allocation model is proposed, which contains two levels in control structure, two time scales in dispatch sequence and multiple areas integrated with wind farms. The efficiency of the proposed method is tested in 9-bus test system and IEEE 118-bus system. The results show that cross-regional control centre is able to allocate both energy and reserve among areas efficiently with the integrated relation curves. The proposed model not only relieves energy and reserve shortage in partial areas but also allocates them to more urgent areas in a high effectivity manner in both day-ahead and intraday time scale.","PeriodicalId":164745,"journal":{"name":"2021 IEEE International Conference on Emergency Science and Information Technology (ICESIT)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123283155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-22DOI: 10.1109/ICESIT53460.2021.9696643
Li-Jung Weng
The in-depth implementation of the “One Belt, One Road” has improved the development of the port economy and perfected the the functions of ports in Guangdong. Therefore, accurate forecasting of the port container throughput is essential for port planning and resource coordination. Taking Guangdong port as an example, the article uses ARIMA, GM (1, 1), ES, ES-GM (1, 1) and ES-ARIMA models to simulate and predict port container throughput. The results show that the optimal model for port throughput prediction is ES-GM (1, 1). In the next five months, the average increase in container port throughput was 2.14 wTEU. Finally, based on the forecast results, suggestions are made for the future development of the port.
{"title":"Prediction of Container Throughput in Guangdong Province Based on Different Model","authors":"Li-Jung Weng","doi":"10.1109/ICESIT53460.2021.9696643","DOIUrl":"https://doi.org/10.1109/ICESIT53460.2021.9696643","url":null,"abstract":"The in-depth implementation of the “One Belt, One Road” has improved the development of the port economy and perfected the the functions of ports in Guangdong. Therefore, accurate forecasting of the port container throughput is essential for port planning and resource coordination. Taking Guangdong port as an example, the article uses ARIMA, GM (1, 1), ES, ES-GM (1, 1) and ES-ARIMA models to simulate and predict port container throughput. The results show that the optimal model for port throughput prediction is ES-GM (1, 1). In the next five months, the average increase in container port throughput was 2.14 wTEU. Finally, based on the forecast results, suggestions are made for the future development of the port.","PeriodicalId":164745,"journal":{"name":"2021 IEEE International Conference on Emergency Science and Information Technology (ICESIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129373895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-22DOI: 10.1109/ICESIT53460.2021.9696455
Hongcheng Liao, Wenwen Zhu, Benzhu Zhang, Xiang Zhang, Yu Sun, Cending Wang, Jie Li
Aiming at solving the natural gas leakage detection issue, we propose an improved method based on deep residual network with channel-wise thresholds (DRSN-CW) to improve the detection accuracy with GPLA-12 dataset. In the approach, larger and unequal convolution kernel size are designed in all convolution layers to extend the receptive field in the process of extracting fault feature. Moreover, considering that datasets of natural gas pipeline leakage typically contain large amounts of ambient noise, the soft threshold module of DRSN-CW is combined with designed kernel size to reduce the influence of noise on accuracy of gas pipeline leakage detection. Compared with the-state-of-art techniques (e.g., CNN, DRSN-CW and DRSN-CS), experimental results show that our method outperforms the compared methods.
{"title":"Application of Natural Gas Pipeline Leakage Detection Based on Improved DRSN-CW","authors":"Hongcheng Liao, Wenwen Zhu, Benzhu Zhang, Xiang Zhang, Yu Sun, Cending Wang, Jie Li","doi":"10.1109/ICESIT53460.2021.9696455","DOIUrl":"https://doi.org/10.1109/ICESIT53460.2021.9696455","url":null,"abstract":"Aiming at solving the natural gas leakage detection issue, we propose an improved method based on deep residual network with channel-wise thresholds (DRSN-CW) to improve the detection accuracy with GPLA-12 dataset. In the approach, larger and unequal convolution kernel size are designed in all convolution layers to extend the receptive field in the process of extracting fault feature. Moreover, considering that datasets of natural gas pipeline leakage typically contain large amounts of ambient noise, the soft threshold module of DRSN-CW is combined with designed kernel size to reduce the influence of noise on accuracy of gas pipeline leakage detection. Compared with the-state-of-art techniques (e.g., CNN, DRSN-CW and DRSN-CS), experimental results show that our method outperforms the compared methods.","PeriodicalId":164745,"journal":{"name":"2021 IEEE International Conference on Emergency Science and Information Technology (ICESIT)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130061693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-22DOI: 10.1109/ICESIT53460.2021.9696941
Yan Wang, Wanxia Zhong, Hang Su, Fujian Zheng, Yiran Pang, Hongchuan Wen, Kun Cai
The multi-view convolutional neural network architecture represented by MVCNN has achieved great success in 3D shape recognition. Taking the MVCNN architecture as the research goal, this paper proposes a novel 3D shape recognition convolutional neural network Attention-MVCNN that integrates channel attention mechanism, residual structure and Mish activation function. The channel attention machine is used to make the feature extraction network for Attention-MVCNN, which can reduce the feature redundancy caused by traditional convolution. The residual structure can reduce the network over-fitting problem and achieve better gradient information, thereby improving the performance of Attention-MVCNN. We replace the activation function in the Attention-MVCNN network with Mish, a self-regular non-monotonic neural activation function. The smooth activation function allows better information to penetrate the neural network, resulting in better accuracy and generalization. Experiments show that the improved Attention-MVCNN attains the competitive results on ModelNet40 dataset.
{"title":"An Improved MVCNN for 3D Shape Recognition","authors":"Yan Wang, Wanxia Zhong, Hang Su, Fujian Zheng, Yiran Pang, Hongchuan Wen, Kun Cai","doi":"10.1109/ICESIT53460.2021.9696941","DOIUrl":"https://doi.org/10.1109/ICESIT53460.2021.9696941","url":null,"abstract":"The multi-view convolutional neural network architecture represented by MVCNN has achieved great success in 3D shape recognition. Taking the MVCNN architecture as the research goal, this paper proposes a novel 3D shape recognition convolutional neural network Attention-MVCNN that integrates channel attention mechanism, residual structure and Mish activation function. The channel attention machine is used to make the feature extraction network for Attention-MVCNN, which can reduce the feature redundancy caused by traditional convolution. The residual structure can reduce the network over-fitting problem and achieve better gradient information, thereby improving the performance of Attention-MVCNN. We replace the activation function in the Attention-MVCNN network with Mish, a self-regular non-monotonic neural activation function. The smooth activation function allows better information to penetrate the neural network, resulting in better accuracy and generalization. Experiments show that the improved Attention-MVCNN attains the competitive results on ModelNet40 dataset.","PeriodicalId":164745,"journal":{"name":"2021 IEEE International Conference on Emergency Science and Information Technology (ICESIT)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126846034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-22DOI: 10.1109/ICESIT53460.2021.9696997
Liang Kaiwu, Chen Xingyu, Wang Maolin
Pipeline transportation, as a common method of oil and gas transportation in China, is generating economic and social benefits, while inhabiting huge potential risks at the same time. In order to make sure the acceptable level of social risks in oil and gas pipeline, it is extremely necessary to formulate a reasonable and feasible standard for acceptable risks. This essay, on the basis of as-low-as-reasonably-practicable (ALARP) principle, proposes a relation chart between “cumulative death frequency (F)” and “the number of death (N)” - improved F-N curve, which is combined with the risk chart. While drawing the curve, four variables in the risk chart are used to grade the risk level, the oil and gas pipeline related regulations are also considered. The research indicates that, improved F-N curve has a more objective and reasonable drawing process as well as a more accurate acceptable level of social risks. With stronger universality, it is able to be applied to different regions and industries.
{"title":"Research on Acceptable Level of Social Risks in Oil and Gas Pipeline","authors":"Liang Kaiwu, Chen Xingyu, Wang Maolin","doi":"10.1109/ICESIT53460.2021.9696997","DOIUrl":"https://doi.org/10.1109/ICESIT53460.2021.9696997","url":null,"abstract":"Pipeline transportation, as a common method of oil and gas transportation in China, is generating economic and social benefits, while inhabiting huge potential risks at the same time. In order to make sure the acceptable level of social risks in oil and gas pipeline, it is extremely necessary to formulate a reasonable and feasible standard for acceptable risks. This essay, on the basis of as-low-as-reasonably-practicable (ALARP) principle, proposes a relation chart between “cumulative death frequency (F)” and “the number of death (N)” - improved F-N curve, which is combined with the risk chart. While drawing the curve, four variables in the risk chart are used to grade the risk level, the oil and gas pipeline related regulations are also considered. The research indicates that, improved F-N curve has a more objective and reasonable drawing process as well as a more accurate acceptable level of social risks. With stronger universality, it is able to be applied to different regions and industries.","PeriodicalId":164745,"journal":{"name":"2021 IEEE International Conference on Emergency Science and Information Technology (ICESIT)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127575788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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