J. Circuits Syst. Comput.最新文献

Erratum: Diode Connected Transistor-Based Low PDP Adiabatic Full Adder in 7nm FINFET Technology for MIMO Applications 校正:基于二极管连接晶体管的低PDP绝热全加法器，用于MIMO应用的7nm FINFET技术

J. Circuits Syst. Comput. Pub Date : 2023-07-19 DOI: 10.1142/s0218126623920032

A. Venkatesan, P. Vanathi, M. Elangovan

引用次数: 0

An Efficient Control Strategy for an Extended Switched Coupled Inductor Quasi-Z-Source Inverter for 3Φ Grid Connected System 3Φ并网系统扩展开关耦合电感准z源逆变器的有效控制策略

J. Circuits Syst. Comput. Pub Date : 2023-05-19 DOI: 10.1142/s0218126624500117

P. Meenalochini, R. Karthick, E. Sakthivel

引用次数: 2

An Optimal Partitioning and Floor Planning for VLSI Circuit Design Based on a Hybrid Bio-Inspired Whale Optimization and Adaptive Bird Swarm Optimization (WO-ABSO) Algorithm 基于混合生物鲸鱼优化和自适应蜂群优化(WO-ABSO)算法的VLSI电路最优分区和布局设计

J. Circuits Syst. Comput. Pub Date : 2023-03-23 DOI: 10.1142/s0218126623502730

R. Karthick, A. Senthil Selvi, P. Meenalochini, S. Senthil Pandi

引用次数: 3

Mode Switching Technique for High Efficiency Buck Converter 高效降压变换器的模式切换技术

J. Circuits Syst. Comput. Pub Date : 2023-03-10 DOI: 10.1142/s0218126623501591

Xu Xiao, Junjie Guo, Changyuan Chang, Pengyu Guo, Li Lu

引用次数: 0

Cloud-Edge Computing-Based ICICOS Framework for Industrial Automation and Artificial Intelligence: A Survey 基于云边缘计算的工业自动化与人工智能ICICOS框架研究

J. Circuits Syst. Comput. Pub Date : 2023-03-06 DOI: 10.1142/s0218126623501682

Weibin Su, Gang Xu, Zheng He, I. K. Machica, V. Quimno, Yi Du, Yanchun Kong

{"title":"Cloud-Edge Computing-Based ICICOS Framework for Industrial Automation and Artificial Intelligence: A Survey","authors":"Weibin Su, Gang Xu, Zheng He, I. K. Machica, V. Quimno, Yi Du, Yanchun Kong","doi":"10.1142/s0218126623501682","DOIUrl":"https://doi.org/10.1142/s0218126623501682","url":null,"abstract":"Industrial Automation (IA) and Artificial Intelligence (AI) need an integrated platform. Due to the uncertainty of the time required for training or reasoning tasks, it is difficult to ensure the real-time performance of AI in the factory. Thus in this paper, we carry out a detailed survey on cloud-edge computing-based Industrial Cyber Intelligent Control Operating System (ICICOS) for industrial automation and artificial intelligence. The ICICOS is built based on IEC61499 programming method and used to replace the obsolete Programmable Logic Controller (PLC). It is widely known that the third industrial revolution produced an important device: PLC. But the finite capability of PLC just only adapts automation which will not be able to support AI, especially deep learning algorithms. Edge computing promotes the expansion of distributed architecture to the Internet of Things (IoT), but little effect has been achieved in the territory of PLC. Therefore, ICICOS focuses on virtualization for IA and AI, so we introduce our ICICOS in this paper, and give the specific details.","PeriodicalId":14696,"journal":{"name":"J. Circuits Syst. Comput.","volume":"36 1","pages":"2350168:1-2350168:25"},"PeriodicalIF":0.0,"publicationDate":"2023-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83190109","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

YOLOv5s-Cherry: Cherry Target Detection in Dense Scenes Based on Improved YOLOv5s Algorithm YOLOv5s-Cherry:基于改进YOLOv5s算法的密集场景樱桃目标检测

J. Circuits Syst. Comput. Pub Date : 2023-02-25 DOI: 10.1142/s0218126623502067

Rong-Li Gai, Mengke Li, Zu-Min Wang, Lingyan Hu, Xiaomei Li

{"title":"YOLOv5s-Cherry: Cherry Target Detection in Dense Scenes Based on Improved YOLOv5s Algorithm","authors":"Rong-Li Gai, Mengke Li, Zu-Min Wang, Lingyan Hu, Xiaomei Li","doi":"10.1142/s0218126623502067","DOIUrl":"https://doi.org/10.1142/s0218126623502067","url":null,"abstract":"Intelligent agriculture has become the development trend of agriculture in the future, and it has a wide range of research and application scenarios. Using machine learning to complete basic tasks for people has become a reality, and this ability is also used in machine vision. In order to save the time in the fruit picking process and reduce the cost of labor, the robot is used to achieve the automatic picking in the orchard environment. Cherry target detection algorithms based on deep learning are proposed to identify and pick cherries. However, most of the existing methods are aimed at relatively sparse fruits and cannot solve the detection problem of small and dense fruits. In this paper, we propose a cherry detection model based on YOLOv5s. First, the shallow feature information is enhanced by convolving the feature maps sampled by two times down in BackBone layer of the original network model to the input end of the second and third CSP modules. In addition, the depth of CSP module is adjusted and RFB module is added in feature extraction stage to enhance feature extraction capability. Finally, Soft-Non-Maximum Suppression (Soft-NMS) is used to minimize the target loss caused by occlusion. We test the performance of the model, and the results show that the improved YOLOv5s-cherry model has the best detection performance for small and dense cherry detection, which is conducive to intelligent picking.","PeriodicalId":14696,"journal":{"name":"J. Circuits Syst. Comput.","volume":"46 1","pages":"2350206:1-2350206:23"},"PeriodicalIF":0.0,"publicationDate":"2023-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76367178","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: 1

A Big Data-Driven Risk Assessment Method Using Machine Learning for Supply Chains in Airport Economic Promotion Areas 基于机器学习的机场经济促进区供应链大数据驱动风险评估方法

J. Circuits Syst. Comput. Pub Date : 2023-02-23 DOI: 10.1142/s0218126623501700

Zhijun Ma, Xiaobei Yang, Ruili Miao

{"title":"A Big Data-Driven Risk Assessment Method Using Machine Learning for Supply Chains in Airport Economic Promotion Areas","authors":"Zhijun Ma, Xiaobei Yang, Ruili Miao","doi":"10.1142/s0218126623501700","DOIUrl":"https://doi.org/10.1142/s0218126623501700","url":null,"abstract":"With the rapid development of economic globalization, population, capital and information are rapidly flowing and clustering between regions. As the most important transportation mode in the high-speed transportation systems, airports are playing an increasingly important role in promoting regional economic development, yielding a number of airport economic promotion areas. To boost effective development management of these areas, accurate risk assessment through data analysis is quite important. Thus in this paper, the idea of ensemble learning is utilized to propose a big data-driven assessment model for supply chains in airport economic promotion areas. In particular, we combine two aspects of data from different sources: (1) national economic statistics and enterprise registration data from the Bureau of Industry and Commerce; (2) data from the Civil Aviation Administration of China and other multi-source data. On this basis, an integrated ensemble learning method is constructed to quantitatively analyze the supply chain security characteristics in domestic airport economic area, providing important support for the security of supply chains in airport economic area. Finally, some experiments are conducted on synthetic data to evaluate the method investigated in this paper, which has proved its efficiency and practice.","PeriodicalId":14696,"journal":{"name":"J. Circuits Syst. Comput.","volume":"68 1","pages":"2350170:1-2350170:13"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80330917","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 Context-Aware Image Generation Method for Assisted Design of Movie Posters Using Generative Adversarial Network 基于生成对抗网络的电影海报辅助设计情境感知图像生成方法

J. Circuits Syst. Comput. Pub Date : 2023-02-17 DOI: 10.1142/s021812662350233x

Yuan Lu, Ruoxu Hou, Jingyao Zheng

引用次数: 0

A 0.8-Volt 29.52-μW Current Mirror-Based OTA Design for Biomedical Applications 基于0.8伏29.52 μ w电流镜像的生物医学应用OTA设计

J. Circuits Syst. Comput. Pub Date : 2023-02-17 DOI: 10.1142/s0218126623502341

Mansi Jhamb

引用次数: 0

A New Tunable Gyrator-C-Based Active Inductor Circuit for Low Power Applications 一种新型低功耗可调陀螺有源电感电路

J. Circuits Syst. Comput. Pub Date : 2023-02-17 DOI: 10.1142/s0218126623502353

Rasool Gardeshkhah, A. N. Saatlo

引用次数: 0