Pub Date : 2023-01-10DOI: 10.1142/s0218126623502018
Yuhuang Huang, mang su, Yuting Xu, T. Liu
{"title":"NER in Cyber Threat Intelligence Domain Using Transformer with TSGL","authors":"Yuhuang Huang, mang su, Yuting Xu, T. Liu","doi":"10.1142/s0218126623502018","DOIUrl":"https://doi.org/10.1142/s0218126623502018","url":null,"abstract":"","PeriodicalId":14696,"journal":{"name":"J. Circuits Syst. Comput.","volume":"15 1","pages":"2350201:1-2350201:16"},"PeriodicalIF":0.0,"publicationDate":"2023-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87254515","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 : 2023-01-06DOI: 10.1142/s0218126623502006
K. V. Bhaskar, S. Ramesh, K. Karunanithi, S. Raja
{"title":"Multi-Objective Optimal Power Flow Solutions Using Improved Multi-Objective Mayfly Algorithm (IMOMA)","authors":"K. V. Bhaskar, S. Ramesh, K. Karunanithi, S. Raja","doi":"10.1142/s0218126623502006","DOIUrl":"https://doi.org/10.1142/s0218126623502006","url":null,"abstract":"","PeriodicalId":14696,"journal":{"name":"J. Circuits Syst. Comput.","volume":"64 1","pages":"2350200:1-2350200:30"},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81409163","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 : 2023-01-06DOI: 10.1142/s0218126623501980
Junhong Liu, Yongmi Zhang, Yanhong Li, Yulei Liu, Xingxing Wang, Lei Zhao, Qiguang Liang, Jun Ye
{"title":"Innovative Energy Management System for Energy Storage Systems of Multiple-Type with Cascade Utilization Battery","authors":"Junhong Liu, Yongmi Zhang, Yanhong Li, Yulei Liu, Xingxing Wang, Lei Zhao, Qiguang Liang, Jun Ye","doi":"10.1142/s0218126623501980","DOIUrl":"https://doi.org/10.1142/s0218126623501980","url":null,"abstract":"","PeriodicalId":14696,"journal":{"name":"J. Circuits Syst. Comput.","volume":"s3-44 1","pages":"2350198:1-2350198:19"},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90835860","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 : 2022-12-23DOI: 10.1142/s0218126623501955
Z. Sun, Jing Zhao
{"title":"Comprehensive Performance Evaluation of Landing Gear Retraction Mechanism in a Certain Model of Aircraft Based on RPCA Method","authors":"Z. Sun, Jing Zhao","doi":"10.1142/s0218126623501955","DOIUrl":"https://doi.org/10.1142/s0218126623501955","url":null,"abstract":"","PeriodicalId":14696,"journal":{"name":"J. Circuits Syst. Comput.","volume":"31 5","pages":"2350195:1-2350195:18"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91404196","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 : 2022-12-22DOI: 10.1142/s0218126623501943
Yu Tang, Yi Sun, Bin Ning, Jun Wun, Zhaowen Lin
{"title":"DRMT: A Decentralized IoT Device Recognition and Management Technology in Smart Cities","authors":"Yu Tang, Yi Sun, Bin Ning, Jun Wun, Zhaowen Lin","doi":"10.1142/s0218126623501943","DOIUrl":"https://doi.org/10.1142/s0218126623501943","url":null,"abstract":"","PeriodicalId":14696,"journal":{"name":"J. Circuits Syst. Comput.","volume":"46 1","pages":"2350194:1-2350194:25"},"PeriodicalIF":0.0,"publicationDate":"2022-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90312712","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 : 2022-12-22DOI: 10.1142/s021812662350192x
Run Yan, Libo Huang, Hui Guo, Yashuai Lü, Ling Yang, Nong Xiao, Li Shen, Mengqiao Lan, Yongwen Wang
{"title":"MMsRT: A Hardware Architecture for Ray Tracing in the Mobile Domain","authors":"Run Yan, Libo Huang, Hui Guo, Yashuai Lü, Ling Yang, Nong Xiao, Li Shen, Mengqiao Lan, Yongwen Wang","doi":"10.1142/s021812662350192x","DOIUrl":"https://doi.org/10.1142/s021812662350192x","url":null,"abstract":"","PeriodicalId":14696,"journal":{"name":"J. Circuits Syst. Comput.","volume":"13 1 1","pages":"2350192:1-2350192:14"},"PeriodicalIF":0.0,"publicationDate":"2022-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83437835","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 : 2022-12-20DOI: 10.1142/s0218126623501931
Yibo Zhang, Jie Zhang
The prevention and control of communicable diseases such as COVID-19 has been a worldwide problem, especially in terms of mining towards latent spreading paths. Although some communication models have been proposed from the perspective of spreading mechanism, it remains hard to describe spreading mechanism anytime. Because real-world communication scenarios of disease spreading are always dynamic, which cannot be described by time-invariant model parameters, to remedy such gap, this paper explores the utilization of big data analysis into this area, so as to replace mechanism-driven methods with big data-driven methods. In modern society with high digital level, the increasingly growing amount of data in various fields also provide much convenience for this purpose. Therefore, this paper proposes an intelligent knowledge discovery method for critical spreading paths based on epidemic big data. For the major roadmap, a directional acyclic graph of epidemic spread was constructed with each province and city in mainland China as nodes, all features of the same node are dimension-reduced, and a composite score is evaluated for each city per day by processing the features after principal component analysis. Then, the typical machine learning model named XGBoost carries out processing of feature importance ranking to discriminate latent candidate spreading paths. Finally, the shortest path algorithm is used as the basis to find the critical path of epidemic spreading between two nodes. Besides, some simulative experiments are implemented with use of realistic social network data. [ FROM AUTHOR]
{"title":"A Big Data-Driven Intelligent Knowledge Discovery Method for Epidemic Spreading Paths","authors":"Yibo Zhang, Jie Zhang","doi":"10.1142/s0218126623501931","DOIUrl":"https://doi.org/10.1142/s0218126623501931","url":null,"abstract":"The prevention and control of communicable diseases such as COVID-19 has been a worldwide problem, especially in terms of mining towards latent spreading paths. Although some communication models have been proposed from the perspective of spreading mechanism, it remains hard to describe spreading mechanism anytime. Because real-world communication scenarios of disease spreading are always dynamic, which cannot be described by time-invariant model parameters, to remedy such gap, this paper explores the utilization of big data analysis into this area, so as to replace mechanism-driven methods with big data-driven methods. In modern society with high digital level, the increasingly growing amount of data in various fields also provide much convenience for this purpose. Therefore, this paper proposes an intelligent knowledge discovery method for critical spreading paths based on epidemic big data. For the major roadmap, a directional acyclic graph of epidemic spread was constructed with each province and city in mainland China as nodes, all features of the same node are dimension-reduced, and a composite score is evaluated for each city per day by processing the features after principal component analysis. Then, the typical machine learning model named XGBoost carries out processing of feature importance ranking to discriminate latent candidate spreading paths. Finally, the shortest path algorithm is used as the basis to find the critical path of epidemic spreading between two nodes. Besides, some simulative experiments are implemented with use of realistic social network data. [ FROM AUTHOR]","PeriodicalId":14696,"journal":{"name":"J. Circuits Syst. Comput.","volume":"23 1","pages":"2350193:1-2350193:14"},"PeriodicalIF":0.0,"publicationDate":"2022-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75017667","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}
It’s no secret that polymers have been employed extensively in a variety of industries. Polymers, on the other hand, have faced difficulties in their development because of their complicated chemical composition and structure. Data-driven approaches in polymer science and technology have resulted in new directions in research leading to the implementation of deep learning models and vast data assets. In the growing area of polymer informatics, deep learning methods based on factual data are being used to speed up the performance assessment and process improvement of new polymers. Using a deep neural network (DNN), we can now forecast the surface metallization properties of polymer materials, which we describe in this research. First, we collect a raw dataset of polymer materials’ characteristics. The raw data are filtered and normalized using the min–max normalization approach. To convert normalized data into numerical characteristics, principal component analysis (PCA) is employed. Polymer surface metallization characteristics can then be predicted using a suggested DNN technique. The proposed and conventional approaches are also compared so that our research can be done to its full potential.
{"title":"A Neural Network-Based Method for Surface Metallization of Polymer Materials","authors":"Lina Liu, Yuhao Qiao, Dongxia Wang, Xiaoguang Tian, Feiyue Qin","doi":"10.1142/s0218126623501670","DOIUrl":"https://doi.org/10.1142/s0218126623501670","url":null,"abstract":"It’s no secret that polymers have been employed extensively in a variety of industries. Polymers, on the other hand, have faced difficulties in their development because of their complicated chemical composition and structure. Data-driven approaches in polymer science and technology have resulted in new directions in research leading to the implementation of deep learning models and vast data assets. In the growing area of polymer informatics, deep learning methods based on factual data are being used to speed up the performance assessment and process improvement of new polymers. Using a deep neural network (DNN), we can now forecast the surface metallization properties of polymer materials, which we describe in this research. First, we collect a raw dataset of polymer materials’ characteristics. The raw data are filtered and normalized using the min–max normalization approach. To convert normalized data into numerical characteristics, principal component analysis (PCA) is employed. Polymer surface metallization characteristics can then be predicted using a suggested DNN technique. The proposed and conventional approaches are also compared so that our research can be done to its full potential.","PeriodicalId":14696,"journal":{"name":"J. Circuits Syst. Comput.","volume":"21 1","pages":"2350167:1-2350167:17"},"PeriodicalIF":0.0,"publicationDate":"2022-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78932765","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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