Pub Date : 2020-11-20DOI: 10.1109/ICCSNT50940.2020.9304984
Xu Liang, Yifan Liu, Ming Huang
The NSGA2 algorithm is one of the effective methods to solve multi-objective flexible job shop scheduling problems (MOFJSP). An improved NSGA2 algorithm is proposed to solve the MOFJSP model that aims to minimize the maximum completion time, the total workload of all machines, the total workshop carbon emissions, the total workshop energy consumption, and the delivery time. Firstly, the improved algorithm performs neighborhood search and cross-mutation operation respectively according to the nondominated ranking level and randomly generated probability of individuals to balance their local search and global search ability of the algorithm. Then, in order to further enrich the diversity of the population and improve the solving ability of the improved algorithm, an elite retention combined with random retention is proposed to retain the parent individuals. At last, the experiment proves the effectiveness of the improved NSGA2 algorithm for solving multi-objective flexible job shop scheduling problems.
{"title":"Improved NSGA2 Algorithm to Solve Multi-Objective Flexible Job Shop Scheduling Problem","authors":"Xu Liang, Yifan Liu, Ming Huang","doi":"10.1109/ICCSNT50940.2020.9304984","DOIUrl":"https://doi.org/10.1109/ICCSNT50940.2020.9304984","url":null,"abstract":"The NSGA2 algorithm is one of the effective methods to solve multi-objective flexible job shop scheduling problems (MOFJSP). An improved NSGA2 algorithm is proposed to solve the MOFJSP model that aims to minimize the maximum completion time, the total workload of all machines, the total workshop carbon emissions, the total workshop energy consumption, and the delivery time. Firstly, the improved algorithm performs neighborhood search and cross-mutation operation respectively according to the nondominated ranking level and randomly generated probability of individuals to balance their local search and global search ability of the algorithm. Then, in order to further enrich the diversity of the population and improve the solving ability of the improved algorithm, an elite retention combined with random retention is proposed to retain the parent individuals. At last, the experiment proves the effectiveness of the improved NSGA2 algorithm for solving multi-objective flexible job shop scheduling problems.","PeriodicalId":6794,"journal":{"name":"2020 IEEE 8th International Conference on Computer Science and Network Technology (ICCSNT)","volume":"40 1","pages":"22-25"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88362539","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 : 2020-11-20DOI: 10.1109/ICCSNT50940.2020.9305018
Yan-sen Zhou, Jianquan Cui
Aiming at the problems of low recognition rate and long training time of deep convolution neural network Alexnet in encrypted traffic classification, some improvement measures are put forward for the classical Alexnet network, mainly including the introduction of multi-scale convolution, deconvolution operation and batch standardization, which can extract more comprehensive features and reduce convolution kernel parameters. The performance of the improved Alexnet convolutional neural network is tested by encrypting the traffic dataset. The test results show that the recognition accuracy and precision of the improved model on the selected test set are 83.9% and 84% respectively, which are about 7.2% and 8% higher than those of the classical model. The improved Alex net model has a certain improvement in the performance of network encryption traffic classification recognition.
{"title":"Research and Improvement of Encrypted Traffic Classification Based on Convolutional Neural Network","authors":"Yan-sen Zhou, Jianquan Cui","doi":"10.1109/ICCSNT50940.2020.9305018","DOIUrl":"https://doi.org/10.1109/ICCSNT50940.2020.9305018","url":null,"abstract":"Aiming at the problems of low recognition rate and long training time of deep convolution neural network Alexnet in encrypted traffic classification, some improvement measures are put forward for the classical Alexnet network, mainly including the introduction of multi-scale convolution, deconvolution operation and batch standardization, which can extract more comprehensive features and reduce convolution kernel parameters. The performance of the improved Alexnet convolutional neural network is tested by encrypting the traffic dataset. The test results show that the recognition accuracy and precision of the improved model on the selected test set are 83.9% and 84% respectively, which are about 7.2% and 8% higher than those of the classical model. The improved Alex net model has a certain improvement in the performance of network encryption traffic classification recognition.","PeriodicalId":6794,"journal":{"name":"2020 IEEE 8th International Conference on Computer Science and Network Technology (ICCSNT)","volume":"1 1","pages":"150-154"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85187489","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 : 2020-11-20DOI: 10.1109/ICCSNT50940.2020.9305009
Liang Xu, Chen Jiabao, Huang Ming
An improved genetic algorithm with elitist strategy (INSGA-II) is proposed to solve the multi-objective problem for low-carbon job shop scheduling. In this paper, a heuristic algorithm is introduced in the initial population stage, and the weight aggregation method is used to constrain the total completion time and carbon emissions. The elite strategy is improved by using simulated annealing method to replace the son with the parent to improve the quality of the replacement population. The improved non dominated sorting genetic algorithm with elitist strategy can obtain Pareto optimal solution set faster and obtain higher population diversity in the initial stage. The experimental results show that the convergence speed and diversity of the algorithm have been improved to a certain extent. On the basis of considering the machine load, the maximum completion time is minimized. When two machines with different carbon emissions in the same processing time are processed, the machine with low carbon emission will be selected optimally.
{"title":"Research on Low-carbon Application of Improved Non-dominated Sorting Genetic Algorithm","authors":"Liang Xu, Chen Jiabao, Huang Ming","doi":"10.1109/ICCSNT50940.2020.9305009","DOIUrl":"https://doi.org/10.1109/ICCSNT50940.2020.9305009","url":null,"abstract":"An improved genetic algorithm with elitist strategy (INSGA-II) is proposed to solve the multi-objective problem for low-carbon job shop scheduling. In this paper, a heuristic algorithm is introduced in the initial population stage, and the weight aggregation method is used to constrain the total completion time and carbon emissions. The elite strategy is improved by using simulated annealing method to replace the son with the parent to improve the quality of the replacement population. The improved non dominated sorting genetic algorithm with elitist strategy can obtain Pareto optimal solution set faster and obtain higher population diversity in the initial stage. The experimental results show that the convergence speed and diversity of the algorithm have been improved to a certain extent. On the basis of considering the machine load, the maximum completion time is minimized. When two machines with different carbon emissions in the same processing time are processed, the machine with low carbon emission will be selected optimally.","PeriodicalId":6794,"journal":{"name":"2020 IEEE 8th International Conference on Computer Science and Network Technology (ICCSNT)","volume":"91 1","pages":"26-31"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85244460","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 : 2020-11-20DOI: 10.1109/ICCSNT50940.2020.9304986
Hanmin Wang, Xinglu Liu, Wai Kin Victor Chan
In this paper, we propose a method that implicitly contains multiple features to describe the relationship between two variables, which was referred to as trend prediction. This method takes into account the relationship between the slot page view and multiple features. The data of different advertising slots are constructed by predicting methods such as tree model and neural network with category feature densification by auto-encoder model. To reduce the smooth time in the prediction curve, we take the logarithm function as a priori to regress the predicted curve. Finally, compared with our previous prediction model, the trend prediction model effectively improves the overall page view under the unconstrained mixed-integer programming model.
{"title":"Prediction and Application of Slot Attributes In Contract Display Advertisement","authors":"Hanmin Wang, Xinglu Liu, Wai Kin Victor Chan","doi":"10.1109/ICCSNT50940.2020.9304986","DOIUrl":"https://doi.org/10.1109/ICCSNT50940.2020.9304986","url":null,"abstract":"In this paper, we propose a method that implicitly contains multiple features to describe the relationship between two variables, which was referred to as trend prediction. This method takes into account the relationship between the slot page view and multiple features. The data of different advertising slots are constructed by predicting methods such as tree model and neural network with category feature densification by auto-encoder model. To reduce the smooth time in the prediction curve, we take the logarithm function as a priori to regress the predicted curve. Finally, compared with our previous prediction model, the trend prediction model effectively improves the overall page view under the unconstrained mixed-integer programming model.","PeriodicalId":6794,"journal":{"name":"2020 IEEE 8th International Conference on Computer Science and Network Technology (ICCSNT)","volume":"55 1","pages":"97-101"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81381831","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 : 2020-11-20DOI: 10.1109/ICCSNT50940.2020.9305011
Qiang Xu, C. Ai, Dunyang Geng, G. Ren, Zhiyong Wang
In order to meet the needs of modern factories for low-speed and heavy-duty cross-workshop transporting truck AGVs that operate outdoors, research on the truck AGV control system has been carried out from both hardware systems and software algorithms. STM32F407VET6 is selected as the main controller of the system, and a simplified model of the truck AGV is established. The pure pursuit algorithm is used for path tracking to control the vehicle to travel along the desired path. After actual vehicle experiments, under the control system of this article, the lateral tracking deviation of the vehicle after reaching the destination is no more than ±25mm, and the longitudinal deviation is no more than ±19mm, which shows that the control system of this article has good stability and repeat positioning accuracy; When the initial position has a lateral deviation of 1m from the desired path, after the driving distance exceeds 3.5m, the vehicle can be controlled to quickly and accurately converge to the desired path; at the same time, it also has a better tracking effect for more complex continuous curves. It can meet the motion control requirements of automatic transporting trucks operating on complex outdoor routes, which is of great significance for realizing factory handling automation and intelligence.
{"title":"Research on Truck AGV Control System","authors":"Qiang Xu, C. Ai, Dunyang Geng, G. Ren, Zhiyong Wang","doi":"10.1109/ICCSNT50940.2020.9305011","DOIUrl":"https://doi.org/10.1109/ICCSNT50940.2020.9305011","url":null,"abstract":"In order to meet the needs of modern factories for low-speed and heavy-duty cross-workshop transporting truck AGVs that operate outdoors, research on the truck AGV control system has been carried out from both hardware systems and software algorithms. STM32F407VET6 is selected as the main controller of the system, and a simplified model of the truck AGV is established. The pure pursuit algorithm is used for path tracking to control the vehicle to travel along the desired path. After actual vehicle experiments, under the control system of this article, the lateral tracking deviation of the vehicle after reaching the destination is no more than ±25mm, and the longitudinal deviation is no more than ±19mm, which shows that the control system of this article has good stability and repeat positioning accuracy; When the initial position has a lateral deviation of 1m from the desired path, after the driving distance exceeds 3.5m, the vehicle can be controlled to quickly and accurately converge to the desired path; at the same time, it also has a better tracking effect for more complex continuous curves. It can meet the motion control requirements of automatic transporting trucks operating on complex outdoor routes, which is of great significance for realizing factory handling automation and intelligence.","PeriodicalId":6794,"journal":{"name":"2020 IEEE 8th International Conference on Computer Science and Network Technology (ICCSNT)","volume":"5 1","pages":"176-180"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83376933","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 safe and reliable power supply provided by State Grid gives convenience for our life. At the same time, it also plays a central role in the construction and development of country. However, the working environment of State Grid is under high voltage. In order to prevent personal electric shock, damage equipment and lines, prevent fire and lightning, prevent electrostatic damage and ensure the power system operation, the staff must install grounding piles according to the power operation specification. To tackle this problem, this paper proposes a grounding pile detection system based on deep learning network. First, cameras can acquire images of these monitored areas in real time. Then, these images are transmitted to the grounding pile detection system for detection. A warning will be given if it is found that workers have not installed the grounding piles in the monitored areas in accordance with the specifications. At present, there is no research on grounding pile detection. So we created our own dataset. Through experiments, our system achieves 92.00% accuracy, 97.50% accuracy and 13.5% false alarm rate in our dataset.
{"title":"Grounding Pile Detection System based on Deep Learning","authors":"Jun Zhang, Miao Jin, Zhiwei Guo, Jian-xing Li, Tianfu Huang, Xiwen Chen, Zhuo Chen, Bing Lu, Wei Zhou, Zijuan Guo","doi":"10.1109/ICCSNT50940.2020.9304982","DOIUrl":"https://doi.org/10.1109/ICCSNT50940.2020.9304982","url":null,"abstract":"The safe and reliable power supply provided by State Grid gives convenience for our life. At the same time, it also plays a central role in the construction and development of country. However, the working environment of State Grid is under high voltage. In order to prevent personal electric shock, damage equipment and lines, prevent fire and lightning, prevent electrostatic damage and ensure the power system operation, the staff must install grounding piles according to the power operation specification. To tackle this problem, this paper proposes a grounding pile detection system based on deep learning network. First, cameras can acquire images of these monitored areas in real time. Then, these images are transmitted to the grounding pile detection system for detection. A warning will be given if it is found that workers have not installed the grounding piles in the monitored areas in accordance with the specifications. At present, there is no research on grounding pile detection. So we created our own dataset. Through experiments, our system achieves 92.00% accuracy, 97.50% accuracy and 13.5% false alarm rate in our dataset.","PeriodicalId":6794,"journal":{"name":"2020 IEEE 8th International Conference on Computer Science and Network Technology (ICCSNT)","volume":"29 1","pages":"107-110"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74561239","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}
Flexible manufacturing workshops (FMWs) are important part of modern manufacturing enterprises. An optimized layout of radio frequency identifications (RFIDs) in an FMW is of great significance to improve information perception quality and reduce RFID deployment cost. Therefore, the RFID network planning for an FMW is studied in this paper. Firstly, three common coverage requirements are analyzed, and a RFID reader radiation model and an FMW discrete grids model are constructed. Secondly, a 0–1 integer programming-based RFID network planning model is established with the optimization objectives of the RFID deployment cost, reader interference, and reading efficiency. Thirdly, a hierarchical clustering and gradient descent-based network planning approach is proposed to solve the network planning model. An FMW case in a flexible manufacturing enterprise is taken to verify the RFID network planning model and the hierarchical clustering approach. The results show that the proposed model and approach are effective.
{"title":"RFID Network Planning for Flexible Manufacturing Workshop with Multiple Coverage Requirements","authors":"Lihui Wu, Junfei Ren, Yuansheng Li, Zhengzheng Dai, Zhongwei Zhang, Zhaoyun Wu","doi":"10.1109/ICCSNT50940.2020.9305001","DOIUrl":"https://doi.org/10.1109/ICCSNT50940.2020.9305001","url":null,"abstract":"Flexible manufacturing workshops (FMWs) are important part of modern manufacturing enterprises. An optimized layout of radio frequency identifications (RFIDs) in an FMW is of great significance to improve information perception quality and reduce RFID deployment cost. Therefore, the RFID network planning for an FMW is studied in this paper. Firstly, three common coverage requirements are analyzed, and a RFID reader radiation model and an FMW discrete grids model are constructed. Secondly, a 0–1 integer programming-based RFID network planning model is established with the optimization objectives of the RFID deployment cost, reader interference, and reading efficiency. Thirdly, a hierarchical clustering and gradient descent-based network planning approach is proposed to solve the network planning model. An FMW case in a flexible manufacturing enterprise is taken to verify the RFID network planning model and the hierarchical clustering approach. The results show that the proposed model and approach are effective.","PeriodicalId":6794,"journal":{"name":"2020 IEEE 8th International Conference on Computer Science and Network Technology (ICCSNT)","volume":"19 1","pages":"86-91"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74459680","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 : 2020-11-20DOI: 10.1109/ICCSNT50940.2020.9304999
Maosong Ma, Xin-wang Chen, Jianbin Liu
Most DRAMs are tested with ATE at laboratory, or SOC at real system environment. ATE is flexible enough to characterize almost all DRAM features, but the price is very high. SOC is low cost, however most times the memory controller features are not open to users. In this paper, FPGA-based DRAM test solutions are surveyed. The study shows now FPGA can test many DRAM internal parameters with the advanced FPGA features. And the flexibility and programmability allow user to fully understand the DRAM characteristics.
{"title":"Characterize the DRAM with FPGA","authors":"Maosong Ma, Xin-wang Chen, Jianbin Liu","doi":"10.1109/ICCSNT50940.2020.9304999","DOIUrl":"https://doi.org/10.1109/ICCSNT50940.2020.9304999","url":null,"abstract":"Most DRAMs are tested with ATE at laboratory, or SOC at real system environment. ATE is flexible enough to characterize almost all DRAM features, but the price is very high. SOC is low cost, however most times the memory controller features are not open to users. In this paper, FPGA-based DRAM test solutions are surveyed. The study shows now FPGA can test many DRAM internal parameters with the advanced FPGA features. And the flexibility and programmability allow user to fully understand the DRAM characteristics.","PeriodicalId":6794,"journal":{"name":"2020 IEEE 8th International Conference on Computer Science and Network Technology (ICCSNT)","volume":"54 1","pages":"142-145"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86871668","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 : 2020-11-20DOI: 10.1109/ICCSNT50940.2020.9305006
Yuejianan Gu, Y. Piao, Ying Wang, Miaomiao Xu, Che Liu
In order to use a depth camera to simplify the acquisition process of integral imaging, this paper proposes a depth map restoration algorithm combining pixel filling and improved joint bilateral filtering(DIJBF). The depth image collected by the depth camera has the problems of background noise and holes in the foreground and background of the object. Firstly, the background filling method or the neighborhood value filling method is adopted to complete the preliminary restoration of the large edge void area according to the situation, and then combined with the improved joint bilateral filtering algorithm that adds the depth image depth value similarity factor to optimize the secondary restoration of the preliminary restoration depth map. After the depth image is restored and optimized, the contour of the three-dimensional object is clear and the edge is smooth. Combined with the color image, a high-quality elemental image array can be subsequently generated for the integral imaging display system.
{"title":"Depth Map Restoration Method based on Improved Bilateral Filtering for Integral Imaging","authors":"Yuejianan Gu, Y. Piao, Ying Wang, Miaomiao Xu, Che Liu","doi":"10.1109/ICCSNT50940.2020.9305006","DOIUrl":"https://doi.org/10.1109/ICCSNT50940.2020.9305006","url":null,"abstract":"In order to use a depth camera to simplify the acquisition process of integral imaging, this paper proposes a depth map restoration algorithm combining pixel filling and improved joint bilateral filtering(DIJBF). The depth image collected by the depth camera has the problems of background noise and holes in the foreground and background of the object. Firstly, the background filling method or the neighborhood value filling method is adopted to complete the preliminary restoration of the large edge void area according to the situation, and then combined with the improved joint bilateral filtering algorithm that adds the depth image depth value similarity factor to optimize the secondary restoration of the preliminary restoration depth map. After the depth image is restored and optimized, the contour of the three-dimensional object is clear and the edge is smooth. Combined with the color image, a high-quality elemental image array can be subsequently generated for the integral imaging display system.","PeriodicalId":6794,"journal":{"name":"2020 IEEE 8th International Conference on Computer Science and Network Technology (ICCSNT)","volume":"17 1","pages":"37-40"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86180539","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 : 2020-11-20DOI: 10.1109/iccsnt50940.2020.9305014
Zhenguang Liang, Yuze Jiang
Due to advantages of increase of safety and reduction of short circuit, overhead transmission lines with covered conductors have spread gradually. Analytical expressions of electric field to overhead transmission lines with covered conductors are presented. Calculation methods of allowable current and temperature to covered conductors are presented. Calculations of electric field and temperature of overhead lines with bare and covered conductors are taken. Results show differences of electric field and temperature to overhead lines with bare conductors and covered conductors.
{"title":"Calculation of Electric Field and Temperature of Overhead Transmission Lines with Covered Conductors","authors":"Zhenguang Liang, Yuze Jiang","doi":"10.1109/iccsnt50940.2020.9305014","DOIUrl":"https://doi.org/10.1109/iccsnt50940.2020.9305014","url":null,"abstract":"Due to advantages of increase of safety and reduction of short circuit, overhead transmission lines with covered conductors have spread gradually. Analytical expressions of electric field to overhead transmission lines with covered conductors are presented. Calculation methods of allowable current and temperature to covered conductors are presented. Calculations of electric field and temperature of overhead lines with bare and covered conductors are taken. Results show differences of electric field and temperature to overhead lines with bare conductors and covered conductors.","PeriodicalId":6794,"journal":{"name":"2020 IEEE 8th International Conference on Computer Science and Network Technology (ICCSNT)","volume":"1 1","pages":"50-54"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89477003","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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