Pub Date : 2019-11-01DOI: 10.1109/IICSPI48186.2019.9095889
Shaotong Luan, Cong Lu, Liang Bai, Haoran Wang
In the application of spatial data clustering, the density-based clustering method can achieve good results. DPC algorithm is a density-based clustering algorithm, which can discover the clustering of irregular shapes. The algorithm is trustworthy of clustering results, simple to implement, and parameter robust. However, the DPC algorithm needs to calculate the distance between the two pairs. It takes a long time to calculate the local density and high-density distance for large-scale spatial data sets. To solve the problem of low efficiency in large datasets, this paper improved the DPC algorithm and proposed a density peak clustering algorithm, DPSCGNS, based on grid neighborhood search. DPSCGNS map raw data to grid cells and redefine the local distance and high-density distance of grid cells. By using the grid to index neighborhood information, the local density and high-density distance of grid cells can be calculated rapidly. Experiments on several data sets demonstrate that the efficiency of DPSCGNS algorithm is improved without decline on clustering effect.
{"title":"Density Peaks Spatial Clustering by Grid Neighborhood Search","authors":"Shaotong Luan, Cong Lu, Liang Bai, Haoran Wang","doi":"10.1109/IICSPI48186.2019.9095889","DOIUrl":"https://doi.org/10.1109/IICSPI48186.2019.9095889","url":null,"abstract":"In the application of spatial data clustering, the density-based clustering method can achieve good results. DPC algorithm is a density-based clustering algorithm, which can discover the clustering of irregular shapes. The algorithm is trustworthy of clustering results, simple to implement, and parameter robust. However, the DPC algorithm needs to calculate the distance between the two pairs. It takes a long time to calculate the local density and high-density distance for large-scale spatial data sets. To solve the problem of low efficiency in large datasets, this paper improved the DPC algorithm and proposed a density peak clustering algorithm, DPSCGNS, based on grid neighborhood search. DPSCGNS map raw data to grid cells and redefine the local distance and high-density distance of grid cells. By using the grid to index neighborhood information, the local density and high-density distance of grid cells can be calculated rapidly. Experiments on several data sets demonstrate that the efficiency of DPSCGNS algorithm is improved without decline on clustering effect.","PeriodicalId":318693,"journal":{"name":"2019 2nd International Conference on Safety Produce Informatization (IICSPI)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114310047","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 : 2019-11-01DOI: 10.1109/IICSPI48186.2019.9095941
Yingyuan Jiang, Yuping Li
Work-piece recognition is a typical application of computer vision in the field of industry. In order to accomplish the task of work-piece assorting and assembling, the position and posture of work-pieces need to be obtained. However, the occlusion between several work-pieces is often occurred in industrial production sites, which brings a great challenge to their recognition. We proposed a novel work-piece recognition and localization method based on adversarial network and template matching, which can defend the much occlusion in the production line. Compared with most of current recognition methods on occlusion work-pieces, the proposed method is more robust to occlusion and light change, and achieves plausible performance.
{"title":"Occluded Work-piece Localization via Adversarial Network and Template Matching","authors":"Yingyuan Jiang, Yuping Li","doi":"10.1109/IICSPI48186.2019.9095941","DOIUrl":"https://doi.org/10.1109/IICSPI48186.2019.9095941","url":null,"abstract":"Work-piece recognition is a typical application of computer vision in the field of industry. In order to accomplish the task of work-piece assorting and assembling, the position and posture of work-pieces need to be obtained. However, the occlusion between several work-pieces is often occurred in industrial production sites, which brings a great challenge to their recognition. We proposed a novel work-piece recognition and localization method based on adversarial network and template matching, which can defend the much occlusion in the production line. Compared with most of current recognition methods on occlusion work-pieces, the proposed method is more robust to occlusion and light change, and achieves plausible performance.","PeriodicalId":318693,"journal":{"name":"2019 2nd International Conference on Safety Produce Informatization (IICSPI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121908961","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 : 2019-11-01DOI: 10.1109/IICSPI48186.2019.9095929
J. Feng, Yang Feng, Luo Ningzhao, Wu Benxiang
In order to make up for the shortcomings of traditional fire detectors and improve the reliability of fire alarm, based on the Raspberry Pi hardware conditions and the Keras deep learning framework, this paper uses the lightweight direct regression detection algorithm YOLO v3tiny to implement a small local video identification system for ship fire. Based on video test and fire simulation, the RpiFire system has achieved high accuracy in high recall rate and can meet the needs of ship fire detection.
{"title":"Design and experimental research of video detection system for ship fire","authors":"J. Feng, Yang Feng, Luo Ningzhao, Wu Benxiang","doi":"10.1109/IICSPI48186.2019.9095929","DOIUrl":"https://doi.org/10.1109/IICSPI48186.2019.9095929","url":null,"abstract":"In order to make up for the shortcomings of traditional fire detectors and improve the reliability of fire alarm, based on the Raspberry Pi hardware conditions and the Keras deep learning framework, this paper uses the lightweight direct regression detection algorithm YOLO v3tiny to implement a small local video identification system for ship fire. Based on video test and fire simulation, the RpiFire system has achieved high accuracy in high recall rate and can meet the needs of ship fire detection.","PeriodicalId":318693,"journal":{"name":"2019 2nd International Conference on Safety Produce Informatization (IICSPI)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127988789","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 : 2019-11-01DOI: 10.1109/IICSPI48186.2019.9096026
Xiaohua Xu, Changxi Chen
In order to solve the shortage of “company + farmer” pig raising mode in China, improve the quality and safety of pork and expand the economic benefit of pig raising enterprises, the methods of adopting standardized technology for production and e-commerce for sales in the pig industry chain are put forward and practiced. Applied to several pig breeding and slaughtering enterprises in Tianjin, China, the pig quality and safety level have been improved, the production cost of the enterprises has been reduced, the supervision capacity has been improved, the industrial structure has been optimized, and the purpose of increasing agricultural efficiency and increasing farmers’ income has been achieved.
{"title":"Integrated application of standardization technology and e-commerce in livestock and poultry safe production - take pig as an example","authors":"Xiaohua Xu, Changxi Chen","doi":"10.1109/IICSPI48186.2019.9096026","DOIUrl":"https://doi.org/10.1109/IICSPI48186.2019.9096026","url":null,"abstract":"In order to solve the shortage of “company + farmer” pig raising mode in China, improve the quality and safety of pork and expand the economic benefit of pig raising enterprises, the methods of adopting standardized technology for production and e-commerce for sales in the pig industry chain are put forward and practiced. Applied to several pig breeding and slaughtering enterprises in Tianjin, China, the pig quality and safety level have been improved, the production cost of the enterprises has been reduced, the supervision capacity has been improved, the industrial structure has been optimized, and the purpose of increasing agricultural efficiency and increasing farmers’ income has been achieved.","PeriodicalId":318693,"journal":{"name":"2019 2nd International Conference on Safety Produce Informatization (IICSPI)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134147078","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 : 2019-11-01DOI: 10.1109/IICSPI48186.2019.9096016
Feng Xiao-rong, Jia Shizhun, Mai Songtao
with the rapid development of V2X technology, intelligent network connected vehicle combines multidisciplinary emerging technology, where the function, performance and security characters present new features, thus it is necessary to put forward higher requirements on software quality. New combination of multi software testing methods on intelligent connected vehicle is discussed in this paper. In combination with cloud testing platform, software testing framework based on cloud computing is proposed, and the function of each module is elaborated. Finally software quality of safety and reliability evaluation system is summarized to apply on the intelligent connected vehicle, which provides further guidance on the accuracy and efficiency of the software testing.
{"title":"Research on Intelligent Network Auto Software Testing Technology","authors":"Feng Xiao-rong, Jia Shizhun, Mai Songtao","doi":"10.1109/IICSPI48186.2019.9096016","DOIUrl":"https://doi.org/10.1109/IICSPI48186.2019.9096016","url":null,"abstract":"with the rapid development of V2X technology, intelligent network connected vehicle combines multidisciplinary emerging technology, where the function, performance and security characters present new features, thus it is necessary to put forward higher requirements on software quality. New combination of multi software testing methods on intelligent connected vehicle is discussed in this paper. In combination with cloud testing platform, software testing framework based on cloud computing is proposed, and the function of each module is elaborated. Finally software quality of safety and reliability evaluation system is summarized to apply on the intelligent connected vehicle, which provides further guidance on the accuracy and efficiency of the software testing.","PeriodicalId":318693,"journal":{"name":"2019 2nd International Conference on Safety Produce Informatization (IICSPI)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134369804","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 : 2019-11-01DOI: 10.1109/IICSPI48186.2019.9095957
Bing Zhang, Zhaojun Hao, Xingjun Yuan, Lifang Wang
With the improvement of science and technology, the manufacturing technology of CNC machine tools has become one of the irreplaceable important technologies in the manufacturing field. However, the manufacturing process, in actual production, often makes the accuracy of the product unsatisfactory for a variety of reasons. The importance evaluation is an effective means to identify the weak link of reliability, and the analysis results can be used as the basis for design and maintenance. However, the existing methods of importance evaluation assume that the degradation process is accurate and known. Due to the uncertainty of the parameters of the degradation model caused by the lack of failure data and the unclear mechanism of the CNC machine tool in the manufacturing process, a Bimbaum importance evaluation method of the multi-state CNC machine tool under uncertainty is proposed. On the one hand, based on the Markov model, the reliability modeling of multi-state CNC machine tools is carried out. On the other hand, Dempster-Shafer evidence theory is used to quantify the uncertainty of model parameters. This method is applied to the reliability evaluation of the cutter feeding control system of CNC machine tools. And the result reflect the effect of uncertainty on importance evaluation and ranking.
{"title":"Birnbaum importance evaluation method for multistate CNC machine tools under uncertainty title","authors":"Bing Zhang, Zhaojun Hao, Xingjun Yuan, Lifang Wang","doi":"10.1109/IICSPI48186.2019.9095957","DOIUrl":"https://doi.org/10.1109/IICSPI48186.2019.9095957","url":null,"abstract":"With the improvement of science and technology, the manufacturing technology of CNC machine tools has become one of the irreplaceable important technologies in the manufacturing field. However, the manufacturing process, in actual production, often makes the accuracy of the product unsatisfactory for a variety of reasons. The importance evaluation is an effective means to identify the weak link of reliability, and the analysis results can be used as the basis for design and maintenance. However, the existing methods of importance evaluation assume that the degradation process is accurate and known. Due to the uncertainty of the parameters of the degradation model caused by the lack of failure data and the unclear mechanism of the CNC machine tool in the manufacturing process, a Bimbaum importance evaluation method of the multi-state CNC machine tool under uncertainty is proposed. On the one hand, based on the Markov model, the reliability modeling of multi-state CNC machine tools is carried out. On the other hand, Dempster-Shafer evidence theory is used to quantify the uncertainty of model parameters. This method is applied to the reliability evaluation of the cutter feeding control system of CNC machine tools. And the result reflect the effect of uncertainty on importance evaluation and ranking.","PeriodicalId":318693,"journal":{"name":"2019 2nd International Conference on Safety Produce Informatization (IICSPI)","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131026501","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 : 2019-11-01DOI: 10.1109/IICSPI48186.2019.9095945
Qiuping Wang, J. Guo, Yanting Xiao
K-means clustering is a clustering technique based on partition. It is widely used in practice because of its simplicity and efficiency. However, it has shortcomings of highly relying on initial clustering center and possibly trapping into local optimum. Firstly, a revised moth flame optimization algorithm with quadratic interpolation is proposed to overcome the defects of K-means and to revise the quality of solution and iterative efficiency of the basic algorithm. The initial population with better diversity is generated by using tent chaotic map to ameliorate the exploration ability of the algorithm. Arithmetical crossover operation for flame location produces new flame with better diversity to guide the moth finding the optimal solution so that the iterative efficiency of the algorithm is ameliorated. Selecting the moths of population to perform quadratic interpolation is helpful for the algorithm to converge rapidly near the optimal solution. It can polish up exploitation ability of the algorithm. The high performance of the improved algorithm is then employed for optimize the location of cluster centers and is examined by five UCI datasets. The experiment results indicate that the improved technique is suitable for finishing problem clustering via k-means, and good clustering results are obtained.
{"title":"Moth flame optimization algorithm based on quadratic interpolation for data clustering","authors":"Qiuping Wang, J. Guo, Yanting Xiao","doi":"10.1109/IICSPI48186.2019.9095945","DOIUrl":"https://doi.org/10.1109/IICSPI48186.2019.9095945","url":null,"abstract":"K-means clustering is a clustering technique based on partition. It is widely used in practice because of its simplicity and efficiency. However, it has shortcomings of highly relying on initial clustering center and possibly trapping into local optimum. Firstly, a revised moth flame optimization algorithm with quadratic interpolation is proposed to overcome the defects of K-means and to revise the quality of solution and iterative efficiency of the basic algorithm. The initial population with better diversity is generated by using tent chaotic map to ameliorate the exploration ability of the algorithm. Arithmetical crossover operation for flame location produces new flame with better diversity to guide the moth finding the optimal solution so that the iterative efficiency of the algorithm is ameliorated. Selecting the moths of population to perform quadratic interpolation is helpful for the algorithm to converge rapidly near the optimal solution. It can polish up exploitation ability of the algorithm. The high performance of the improved algorithm is then employed for optimize the location of cluster centers and is examined by five UCI datasets. The experiment results indicate that the improved technique is suitable for finishing problem clustering via k-means, and good clustering results are obtained.","PeriodicalId":318693,"journal":{"name":"2019 2nd International Conference on Safety Produce Informatization (IICSPI)","volume":"171 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131109215","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}
The development of computer networks is making it become more complex and dynamic. How to achieve efficient package-routing in the SDN (Software Design Network) has become hot research field. SARSA-Learning is a typical Reinforcement Learning algorithm. Through the on-policy exploration and learning of the network environment, it can be used to derive the optimal decision in an unknown network environment, in this way, the network data routing and forwarding can be effectively completed. This paper yields a SARSA-Learning Routing algorithm with variable greedy function (Variable $varepsilon$-Greedy function within SARSA-Learning Routing, V-S Routing). The V-S Routing algorithm preserves the efficiency of the SARSA-Leaning framework. The purpose of V-S Routing introduces a variable factor to $varepsilon$-Greedy function. The V-S Routing algorithm can be dynamically calculated to represent the priority of the current state in the SDN network and to solve the problem of SDN network optimal route selection, which can avoid long package waiting queue and reduce SDN network congestion and improve the link transmission speed. The Variable $varepsilon$-Greedy function makes the algorithm more suitable to the network environment, and it also makes V-S Routing algorithm having better generalization ability. The experimental results verify the effectiveness of the algorithm.
{"title":"Research on Routing Optimization of SDN Network Using Reinforcement Learning Method","authors":"Zhengwu Yuan, Peng Zhou, Shanshan Wang, Xiaojian Zhang","doi":"10.1109/IICSPI48186.2019.9095940","DOIUrl":"https://doi.org/10.1109/IICSPI48186.2019.9095940","url":null,"abstract":"The development of computer networks is making it become more complex and dynamic. How to achieve efficient package-routing in the SDN (Software Design Network) has become hot research field. SARSA-Learning is a typical Reinforcement Learning algorithm. Through the on-policy exploration and learning of the network environment, it can be used to derive the optimal decision in an unknown network environment, in this way, the network data routing and forwarding can be effectively completed. This paper yields a SARSA-Learning Routing algorithm with variable greedy function (Variable $varepsilon$-Greedy function within SARSA-Learning Routing, V-S Routing). The V-S Routing algorithm preserves the efficiency of the SARSA-Leaning framework. The purpose of V-S Routing introduces a variable factor to $varepsilon$-Greedy function. The V-S Routing algorithm can be dynamically calculated to represent the priority of the current state in the SDN network and to solve the problem of SDN network optimal route selection, which can avoid long package waiting queue and reduce SDN network congestion and improve the link transmission speed. The Variable $varepsilon$-Greedy function makes the algorithm more suitable to the network environment, and it also makes V-S Routing algorithm having better generalization ability. The experimental results verify the effectiveness of the algorithm.","PeriodicalId":318693,"journal":{"name":"2019 2nd International Conference on Safety Produce Informatization (IICSPI)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132661859","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 : 2019-11-01DOI: 10.1109/IICSPI48186.2019.9096018
Qi Han, Rui Cao, GangQiang Ye, Guorong Chen
Cellular neural networks have been applied in many fields and have shown good results. Therefore, this article studies the leader-following consensus problem (MSALFC) of multi-agent systems with fixed directed topology under cellular neural networks framework (CNNF). Compared with the common consensus problem, agents' communication topology is reconstructed through CNNF. Then, the breadth-first algorithm is used to cut unnecessary connections in the system. So that the undirected communication topology based on CNNF is transformed into a directed topology with only one spanning tree and communication protocol are constructed. Finally, an effective control protocol is designed to help the system achieve leader-follower consensus and Lyapunov stability theory is used to establish the sufficient condition to guarantee the realization of MSALFC. The communication protocol based on CNNF can not only effectively achieve the consistency of the system, but also reduce the communication traffic in the System. Numerical simulation is given to illustrate the theoretical results.
{"title":"Leader-following consensus of multi-agent system based on cellular neural networks framework","authors":"Qi Han, Rui Cao, GangQiang Ye, Guorong Chen","doi":"10.1109/IICSPI48186.2019.9096018","DOIUrl":"https://doi.org/10.1109/IICSPI48186.2019.9096018","url":null,"abstract":"Cellular neural networks have been applied in many fields and have shown good results. Therefore, this article studies the leader-following consensus problem (MSALFC) of multi-agent systems with fixed directed topology under cellular neural networks framework (CNNF). Compared with the common consensus problem, agents' communication topology is reconstructed through CNNF. Then, the breadth-first algorithm is used to cut unnecessary connections in the system. So that the undirected communication topology based on CNNF is transformed into a directed topology with only one spanning tree and communication protocol are constructed. Finally, an effective control protocol is designed to help the system achieve leader-follower consensus and Lyapunov stability theory is used to establish the sufficient condition to guarantee the realization of MSALFC. The communication protocol based on CNNF can not only effectively achieve the consistency of the system, but also reduce the communication traffic in the System. Numerical simulation is given to illustrate the theoretical results.","PeriodicalId":318693,"journal":{"name":"2019 2nd International Conference on Safety Produce Informatization (IICSPI)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127656311","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 : 2019-11-01DOI: 10.1109/IICSPI48186.2019.9096033
Mengyuan Ren, Xiaolong Chen, Haoyu Yu
With the increased number and extended length of oil pipelines in China, accidents caused by pipeline damages show the same trend. Traditional monitoring methods mainly adopt regular manual detection which, though easy to operate, yet cannot realize real-time monitoring and deployment. This paper puts forwards a real-time monitoring method based on the internet of things technology for monitoring pipeline deformation. In this method, the pipelines’ attitude angles at certain location are firstly obtained by the inclinometers-based sensing nodes. The measurement results are then delivered to the sink nodes by short-distance wireless communication modules. The sink nodes can upload the measured data to a cloud server by long-distance wireless communication modules. At the same time, anyone who are permitted to get access to the server’ database can obtain the real-time data and then process these data with proper way. In this paper, cubic spline interpolation algorithm is used so as to obtain a deflection curve of the pipeline’s profile. Visual graphics technique is utilized to display the real-time deformation of pipelines. At last, the feasibility of the proposed method is verified by a series of testing.
{"title":"Real-time monitoring method of pipeline deformation based on Internet of things","authors":"Mengyuan Ren, Xiaolong Chen, Haoyu Yu","doi":"10.1109/IICSPI48186.2019.9096033","DOIUrl":"https://doi.org/10.1109/IICSPI48186.2019.9096033","url":null,"abstract":"With the increased number and extended length of oil pipelines in China, accidents caused by pipeline damages show the same trend. Traditional monitoring methods mainly adopt regular manual detection which, though easy to operate, yet cannot realize real-time monitoring and deployment. This paper puts forwards a real-time monitoring method based on the internet of things technology for monitoring pipeline deformation. In this method, the pipelines’ attitude angles at certain location are firstly obtained by the inclinometers-based sensing nodes. The measurement results are then delivered to the sink nodes by short-distance wireless communication modules. The sink nodes can upload the measured data to a cloud server by long-distance wireless communication modules. At the same time, anyone who are permitted to get access to the server’ database can obtain the real-time data and then process these data with proper way. In this paper, cubic spline interpolation algorithm is used so as to obtain a deflection curve of the pipeline’s profile. Visual graphics technique is utilized to display the real-time deformation of pipelines. At last, the feasibility of the proposed method is verified by a series of testing.","PeriodicalId":318693,"journal":{"name":"2019 2nd International Conference on Safety Produce Informatization (IICSPI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131266684","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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