J. Circuits Syst. Comput.最新文献_第5页

NER in Cyber Threat Intelligence Domain Using Transformer with TSGL 基于TSGL变压器的网络威胁情报领域NER研究

J. Circuits Syst. Comput. Pub Date : 2023-01-10 DOI: 10.1142/s0218126623502018

Yuhuang Huang, mang su, Yuting Xu, T. Liu

引用次数: 1

Multi-Objective Optimal Power Flow Solutions Using Improved Multi-Objective Mayfly Algorithm (IMOMA) 基于改进多目标蜉蝣算法的多目标最优潮流解

J. Circuits Syst. Comput. Pub Date : 2023-01-06 DOI: 10.1142/s0218126623502006

K. V. Bhaskar, S. Ramesh, K. Karunanithi, S. Raja

引用次数: 1

A 6.7 GHz, 89.33 μW Power and 81.26% Tuning Range Dual Input Ring VCO with PMOS Varactor 一种6.7 GHz、89.33 μW功率、81.26%调谐范围的PMOS变容管双输入环形压控振荡器

J. Circuits Syst. Comput. Pub Date : 2023-01-06 DOI: 10.1142/s0218126623501992

Mohd Saqib, Subodh Wairya, Anurag Yadav

引用次数: 0

Random Access Preamble Detection with Noise Suppression for 5G-Integrated Satellite Communication Systems 基于噪声抑制的5g综合卫星通信系统随机接入前导检测

J. Circuits Syst. Comput. Pub Date : 2023-01-06 DOI: 10.1142/s0218126623501979

Li Zhen, Yan Zhao, Yanyan Zhu, Chenchen Pei, Yinghua Li

引用次数: 0

Innovative Energy Management System for Energy Storage Systems of Multiple-Type with Cascade Utilization Battery 多级梯级利用电池储能系统的创新能量管理系统

J. Circuits Syst. Comput. Pub Date : 2023-01-06 DOI: 10.1142/s0218126623501980

Junhong Liu, Yongmi Zhang, Yanhong Li, Yulei Liu, Xingxing Wang, Lei Zhao, Qiguang Liang, Jun Ye

引用次数: 0

Comprehensive Performance Evaluation of Landing Gear Retraction Mechanism in a Certain Model of Aircraft Based on RPCA Method 基于RPCA方法的某型飞机起落架收放机构综合性能评价

J. Circuits Syst. Comput. Pub Date : 2022-12-23 DOI: 10.1142/s0218126623501955

Z. Sun, Jing Zhao

引用次数: 0

DRMT: A Decentralized IoT Device Recognition and Management Technology in Smart Cities DRMT:智慧城市中分散式物联网设备识别与管理技术

J. Circuits Syst. Comput. Pub Date : 2022-12-22 DOI: 10.1142/s0218126623501943

Yu Tang, Yi Sun, Bin Ning, Jun Wun, Zhaowen Lin

引用次数: 0

MMsRT: A Hardware Architecture for Ray Tracing in the Mobile Domain MMsRT:移动领域光线追踪的硬件架构

J. Circuits Syst. Comput. Pub Date : 2022-12-22 DOI: 10.1142/s021812662350192x

Run Yan, Libo Huang, Hui Guo, Yashuai Lü, Ling Yang, Nong Xiao, Li Shen, Mengqiao Lan, Yongwen Wang

引用次数: 0

A Big Data-Driven Intelligent Knowledge Discovery Method for Epidemic Spreading Paths 大数据驱动的流行病传播路径智能知识发现方法

J. Circuits Syst. Comput. Pub Date : 2022-12-20 DOI: 10.1142/s0218126623501931

Yibo Zhang, Jie Zhang

{"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}

引用次数: 0

A Neural Network-Based Method for Surface Metallization of Polymer Materials 基于神经网络的高分子材料表面金属化方法

J. Circuits Syst. Comput. Pub Date : 2022-12-19 DOI: 10.1142/s0218126623501670

Lina Liu, Yuhao Qiao, Dongxia Wang, Xiaoguang Tian, Feiyue Qin

{"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}

引用次数: 0