International Journal of Circuits, Systems and Signal Processing最新文献_第10页

Preventive Maintenance Strategy of Environmental Test Chamber Based on Particle Swarm Optimization Algorithm 基于粒子群优化算法的环境试验室预防性维护策略

International Journal of Circuits, Systems and Signal Processing Pub Date : 2022-01-10 DOI: 10.46300/9106.2022.16.77

Xianwen Zhou, Chaoyang Gu, Yuyu Sun, Che Han, W. Gu, Wangqiang Niu

{"title":"Preventive Maintenance Strategy of Environmental Test Chamber Based on Particle Swarm Optimization Algorithm","authors":"Xianwen Zhou, Chaoyang Gu, Yuyu Sun, Che Han, W. Gu, Wangqiang Niu","doi":"10.46300/9106.2022.16.77","DOIUrl":"https://doi.org/10.46300/9106.2022.16.77","url":null,"abstract":"With the development of various physical industries, people pay more attention to reliability tests and test equipment. To solve the problem of making maintenance strategy of an environmental test chamber for reliability test, a periodic preventive maintenance strategy based on RCM(Reliability Centre Maintenance) is proposed. Firstly, a multi-objective optimization model of reliability and maintenance cost is established by combining reliability theory and life distribution theory, and two objectives of equipment reliability and maintenance cost are considered. Secondly, the actual environmental test chamber fault maintenance data is analyzed, and it is found the fault distribution meets the dual parameter Weibull. Finally, the particle swarm optimization algorithm is used to solve the multi-objective model optimization, and a series of Pareto optimal solutions are obtained, that is, the number of maintenance times and the corresponding time interval in the operation cycle of the environmental test chamber, and these solutions might be good references for maintenance management personnel.","PeriodicalId":13929,"journal":{"name":"International Journal of Circuits, Systems and Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85505788","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

Design and Implementation of Image Edge Detection Algorithm on FPGA 基于FPGA的图像边缘检测算法设计与实现

International Journal of Circuits, Systems and Signal Processing Pub Date : 2022-01-10 DOI: 10.46300/9106.2022.16.78

N. Shylashree, M. A. Naik, A. Mamatha, V. Sridhar

{"title":"Design and Implementation of Image Edge Detection Algorithm on FPGA","authors":"N. Shylashree, M. A. Naik, A. Mamatha, V. Sridhar","doi":"10.46300/9106.2022.16.78","DOIUrl":"https://doi.org/10.46300/9106.2022.16.78","url":null,"abstract":"Image processing is an important task in data processing systems for applications such as medical sectors, remote sensing, and microscopy tomography. Edge recognition is a sort of image division method that is used to simplify the image records so as to reduce the amount of data to be processed. Edges are considered the most important in image processing because they are used to characterize the boundaries of an image. The performance of the Canny edge recognition algorithm remarkably surpasses the present edge recognition technology in various computer visualization methods. The main drawback of using Canny edge boundary is that it consumes lot of period due to its complex computation. In order to tackle this problem a hybrid edge recognition method is proposed in block stage to locate edges with no loss. It employs the Sobel operator estimate method to calculate the value and direction of the gradient by substituting complex processes by hardware cost savings, traditional non-maximum suppression adaptive thresholding block organization, and conventional hysteresis thresholding. Pipeline was presented to lessen latency. The planned strategy is simulated using Xilinx ISE Design Suite14.2 running on a Xilinx Spartan-6 FPGA board. The synthesized architecture uses less hardware to detect edges and operates at maximum frequency of 935 MHz.","PeriodicalId":13929,"journal":{"name":"International Journal of Circuits, Systems and Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89276151","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

Intelligent Network Traffic Control Based on Deep Reinforcement Learning 基于深度强化学习的智能网络流量控制

International Journal of Circuits, Systems and Signal Processing Pub Date : 2022-01-10 DOI: 10.46300/9106.2022.16.73

Fei Wu, Ting Li, Fucai Luo, ShuLin Wu, Chuanqi Xiao

{"title":"Intelligent Network Traffic Control Based on Deep Reinforcement Learning","authors":"Fei Wu, Ting Li, Fucai Luo, ShuLin Wu, Chuanqi Xiao","doi":"10.46300/9106.2022.16.73","DOIUrl":"https://doi.org/10.46300/9106.2022.16.73","url":null,"abstract":"This paper studies the problems of load balancing and flow control in data center network, and analyzes several common flow control schemes in data center intelligent network and their existing problems. On this basis, the network traffic control problem is modeled with the goal of deep reinforcement learning strategy optimization, and an intelligent network traffic control method based on deep reinforcement learning is proposed. At the same time, for the flow control order problem in deep reinforcement learning algorithm, a flow scheduling priority algorithm is proposed innovatively. According to the decision output, the corresponding flow control and control are carried out, so as to realize the load balance of the network. Finally, experiments show, the network traffic bandwidth loss rate of the proposed intelligent network traffic control method is low. Under the condition of random 60 traffic density, the average bisection bandwidth obtained by the proposed intelligent network traffic control method is 4.0mbps and the control error rate is 2.25%. The intelligent network traffic control method based on deep reinforcement learning has high practicability in the practical application process, and fully meets the research requirements.","PeriodicalId":13929,"journal":{"name":"International Journal of Circuits, Systems and Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77304883","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

Design of E-commerce Data Scalable Storage System Based on Mobile Internet Communication Technology 基于移动互联网通信技术的电子商务数据可扩展存储系统设计

International Journal of Circuits, Systems and Signal Processing Pub Date : 2022-01-10 DOI: 10.46300/9106.2022.16.74

Jiejie Cui, Xiang Li, Yang Wang

{"title":"Design of E-commerce Data Scalable Storage System Based on Mobile Internet Communication Technology","authors":"Jiejie Cui, Xiang Li, Yang Wang","doi":"10.46300/9106.2022.16.74","DOIUrl":"https://doi.org/10.46300/9106.2022.16.74","url":null,"abstract":"The traditional encrypted storage system is inefficient when it encrypts the data of the Internet of Things, and there are few IOT data nodes that can be encrypted in a short time. In order to solve the above problems, a new Internet of Things data effective information encryption storage system is proposed. The hardware and software of the system are mainly designed. The chip selected for the collector is TTSAD251, which can expand the collection range. The processor is set with multiple cores to reduce the system power consumption. The memory uses SPRTAN-2 chip as the structure chip. The software work consists of three parts: collecting effective information of Internet of Things big data, establishing encrypted documents and storing effective information of big data of Internet of Things. In order to detect the working effect of the system, the experimental comparison with the traditional system shows that the proposed encryption storage system can improve the storage range of big data effective information of the Internet of Things by 20.58%, and the work efficiency by 5.64%. Compared with the traditional system, the designed system also has obvious advantages in the number of big data node secrets. In different files, the average number of big data information node encryption in this system is about 166,700. The experimental data show that the designed system has ideal application performance and provides a reliable basis for related fields.","PeriodicalId":13929,"journal":{"name":"International Journal of Circuits, Systems and Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82976215","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

Design of sEMG Acquisition Circuit and Its Adaptive EEMD Denosing Research 表面肌电信号采集电路设计及其自适应EEMD去噪研究

International Journal of Circuits, Systems and Signal Processing Pub Date : 2022-01-10 DOI: 10.46300/9106.2022.16.63

Wei Li, Wei Hu, Kun Hu, Qiang Qin

{"title":"Design of sEMG Acquisition Circuit and Its Adaptive EEMD Denosing Research","authors":"Wei Li, Wei Hu, Kun Hu, Qiang Qin","doi":"10.46300/9106.2022.16.63","DOIUrl":"https://doi.org/10.46300/9106.2022.16.63","url":null,"abstract":"The Surface electromyography (sEMG) signal is a kind of electrical signal which generated by human muscles during contraction. It is prone to being affected by noise because of its small amplitude, so it is necessary to remove the noise in its original signal with an appropriate algorithm. Based on the traditional signal denoising indicators, a new complex indicator r has been proposed in this paper which combines three different indicator parameters, that is, Signal to Noise Ratio (SNR), correlation coefficient (R), and standard error (SE). At the same time, an adaptive ensemble empirical mode decomposition (EEMD) method named AIO-EEMD which based on the proposed indicator is represented later. To verify the effective of the proposed algorithm, an electromyography signal acquisition circuit is designed firstly for collecting the original sEMG signal. Then, the denosing performance from the designed method is been compared with empirical mode decomposition (EMD) method and wavelet transform noise reduction method, respectively. The experiment results shown that the designed algorithm can not only automatically get the numbers of the reconstructed signal numbers, but also obtain the best reduction performance.","PeriodicalId":13929,"journal":{"name":"International Journal of Circuits, Systems and Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79701508","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

Machine Learning Techniques for Automated Tremor Detection in the Presence of External Stressors 机器学习技术在存在外部压力的情况下自动检测震颤

International Journal of Circuits, Systems and Signal Processing Pub Date : 2022-01-10 DOI: 10.46300/9106.2022.16.69

K. Vanitha, Viswanath Talasila

引用次数: 0

Operation Status Monitoring of Transmission Tower in Power System based on Data Fusion 基于数据融合的电力系统输电塔运行状态监测

International Journal of Circuits, Systems and Signal Processing Pub Date : 2022-01-10 DOI: 10.46300/9106.2022.16.68

Haiting Ji, Jianfeng Liu

引用次数: 1

A Sensing Method of Network Security Situation Based on Markov Game Model 基于马尔可夫博弈模型的网络安全态势感知方法

International Journal of Circuits, Systems and Signal Processing Pub Date : 2022-01-10 DOI: 10.46300/9106.2022.16.66

Bingjie Lin, Jie Cheng, Jiahui Wei, Ang Xia

引用次数: 0

Detection of IoT based DDoS Attacks by Network Traffic Analysis using Feedforward Neural Networks 基于前馈神经网络的网络流量分析检测物联网DDoS攻击

International Journal of Circuits, Systems and Signal Processing Pub Date : 2022-01-10 DOI: 10.46300/9106.2022.16.81

Vanya Ivanova, T. Tashev, I. Draganov

引用次数: 5

Radiation Pattern of a Microstrip Antenna on a Trapezoidal Substrate 梯形基板上微带天线的辐射方向图

International Journal of Circuits, Systems and Signal Processing Pub Date : 2022-01-10 DOI: 10.46300/9106.2022.16.67

Suchana Mishra, R. K. Mishra, S. Patnaik

引用次数: 0