2022 2nd International Conference on Networking Systems of AI (INSAI)最新文献_第4页

A Single Input Algorithm for Transmission Synchronization of Multiple Memristor Chaotic Systems 多忆阻混沌系统传输同步的单输入算法

2022 2nd International Conference on Networking Systems of AI (INSAI) Pub Date : 2022-10-01 DOI: 10.1109/INSAI56792.2022.00029

Jing Luo, Jinxiang Wang, Yaya Xie

引用次数: 0

A Novel Transformer-based Framework for Multi-View 3D Human Mesh Reconstruction 一种新的基于变压器的多视图三维人体网格重建框架

2022 2nd International Conference on Networking Systems of AI (INSAI) Pub Date : 2022-10-01 DOI: 10.1109/INSAI56792.2022.00042

Entao Chen, Bobo Ju, Linhua Jiang, Dongfang Zhao

引用次数: 0

Research on Detection and Early Warning Method of Foreign Objects in Vehicle Gauge Based on Canny Edge Extraction 基于Canny边缘提取的车辆仪表异物检测预警方法研究

2022 2nd International Conference on Networking Systems of AI (INSAI) Pub Date : 2022-10-01 DOI: 10.1109/INSAI56792.2022.00046

Fei Peng, Yuqiang Shen, Xiaoli Yang

{"title":"Research on Detection and Early Warning Method of Foreign Objects in Vehicle Gauge Based on Canny Edge Extraction","authors":"Fei Peng, Yuqiang Shen, Xiaoli Yang","doi":"10.1109/INSAI56792.2022.00046","DOIUrl":"https://doi.org/10.1109/INSAI56792.2022.00046","url":null,"abstract":"In recent years, in urban traffic, vehicle gauge detection and early warning of abnormal conditions have attracted more and more attention. At present, the method based on edge detection is often used to detect foreign matters in vehicle gauge. However, this method requires manual detection, confirmation and classification, and its accuracy and accuracy are low; and the method of edge detection is only suitable for detecting objects with small vehicle edge, and there is a big problem of detection accuracy for foreign objects in vehicle clearance. Therefore, this paper studies the defects in the vehicle clearance foreign matter detection and early warning problem, and proposes a vehicle fault anomaly detection and early warning method based on Canny edge extraction. Compared with the traditional methods, the detection accuracy and false alarm rate of the detected abnormal vehicle fault information and defects after classification and post-processing are higher. In addition, due to the phenomenon of disconnection and collision in varying degrees, it is also of great significance for the detection of foreign matters in the vehicle limit and the detection and early warning of abnormalities. The experimental results show that this method has high detection speed and accuracy, and can avoid dropping lines, collisions and other phenomena.","PeriodicalId":318264,"journal":{"name":"2022 2nd International Conference on Networking Systems of AI (INSAI)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134402090","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 Low-Power Router for Wireless Sensor Networks Based on Zigbee 基于Zigbee的无线传感器网络低功耗路由器设计

2022 2nd International Conference on Networking Systems of AI (INSAI) Pub Date : 2022-10-01 DOI: 10.1109/INSAI56792.2022.00026

Chao Zhang, Qingyan Zhao

引用次数: 0

A Motion Imagery EEG Signal Recognition Algorithm Based on Power Spectral Density combined with Particle Swarm Optimization Algorithm Optimized Support Vector Machine 基于功率谱密度与粒子群优化算法相结合的运动图像脑电信号识别算法

2022 2nd International Conference on Networking Systems of AI (INSAI) Pub Date : 2022-10-01 DOI: 10.1109/INSAI56792.2022.00036

Ruijing Lin, Chaoyi Dong, Pengfei Ma, Xiaoyan Chen, Huanzi Liu, Dongyang Lei

{"title":"A Motion Imagery EEG Signal Recognition Algorithm Based on Power Spectral Density combined with Particle Swarm Optimization Algorithm Optimized Support Vector Machine","authors":"Ruijing Lin, Chaoyi Dong, Pengfei Ma, Xiaoyan Chen, Huanzi Liu, Dongyang Lei","doi":"10.1109/INSAI56792.2022.00036","DOIUrl":"https://doi.org/10.1109/INSAI56792.2022.00036","url":null,"abstract":"For the purpose of addressing the issue of low classification accuracy resulting from low signal-to-noise ratios in electroencephalogram (EEG) signals, in this paper, a classification algorithm for motion imagery EEG signals (PSD-PSO-SVM) utilizing power spectral density analysis (PSD) combined with particle swarm optimization method (PSO) improved support vector machine (SVM) is proposed. A first step of the algorithm is to extract features of the EEG signal in the frequency domain by PSD, and the energy spectral densities of 0-30Hz in delta, theta, alpha, and beta frequencies are selected as their frequency domain features. Following that, a support vector machine is utilized for the classification of the extracted features. A solution is provided for the problem that performance in classification of traditional SVM is greatly influenced by its k-function parameters. It is the parameters of the kernel function of the SVM that are optimized by using the global optimisation-seeking capability of PSO to achieve optimal performance in classification. Finally, the algorithm validity was assessed by analyzing both the laboratory dataset (IMUT data) and the open dataset III developed for the 2003 competition on brain-computer interfaces (III BCI 2003). These findings show that PSD-PSO_SVM can perform classifications up to a level of 80.63%. It is proposed in this article that the proposed algorithm is compared to other optimization SVM algorithms, such as genetic algorithm optimization SVM (GA_SVM), and shows that PSO_SVM outperforms GA _ SVM with an average classification accuracy of 5.63%. Thus the PSD-PSO_SVM was shown to have good classification performance.","PeriodicalId":318264,"journal":{"name":"2022 2nd International Conference on Networking Systems of AI (INSAI)","volume":"205 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123252401","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

Real-time Detection of DDoS Attacks Based on Hurst Index 基于Hurst指数的DDoS攻击实时检测

2022 2nd International Conference on Networking Systems of AI (INSAI) Pub Date : 2022-10-01 DOI: 10.1109/INSAI56792.2022.00018

Ying Ling, Chunyan Yang, Xin Li, Ming Xie, Shaofeng Ming, Jieke Lu, Fuchuan Tang

引用次数: 0

FCD-YOLO: Improved YOLOv5 Based on Decoupled Head and Attention Mechanism for Defect Detection on Printed Circuit Board FCD-YOLO:基于头部解耦和注意机制的改进YOLOv5印刷电路板缺陷检测

2022 2nd International Conference on Networking Systems of AI (INSAI) Pub Date : 2022-10-01 DOI: 10.1109/INSAI56792.2022.00011

Huijian Xu

{"title":"FCD-YOLO: Improved YOLOv5 Based on Decoupled Head and Attention Mechanism for Defect Detection on Printed Circuit Board","authors":"Huijian Xu","doi":"10.1109/INSAI56792.2022.00011","DOIUrl":"https://doi.org/10.1109/INSAI56792.2022.00011","url":null,"abstract":"Defect detection technology is an indispensable quality control technology in PCB manufacturing. However, the effect of the existing PCB defect detection algorithm is not good. Therefore, we propose an improved PCB defect detection algorithm FCD-YOLO based on YOLOv5. Firstly, we add a set of anchors suitable for detecting small objects, and at the same time add a shallow prediction layer. Secondly, we modify the neck network and integrate more shallow features to improve the detection effect of small objects and texture features. Thirdly, we integrate a CBAM attention module into the neck network to improve the ability of the model to extract the features of the region of interest on complex PCB background. Finally, we integrate a decoupled head mechanism into the head network to help the model converge quickly and improve the detection effect of the model. The experiment adopts the public PCB defect image released by the laboratory of Peking University. The experiment proves that the precision of this method is increased by 1.4 %, the recall is increased by 1.6 %, the mAP@0.5 is increased by 0.6 %, and the mAP@0.5:.95 is increased by 1.7% compared with YOLOv5s, which is more suitable for detecting PCB defects than YOLOv5s.","PeriodicalId":318264,"journal":{"name":"2022 2nd International Conference on Networking Systems of AI (INSAI)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117097827","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 Private Cloud Operation Evaluation Method Based on Low-Carbon Digital Grid 基于低碳数字网格的私有云运营评估方法

2022 2nd International Conference on Networking Systems of AI (INSAI) Pub Date : 2022-10-01 DOI: 10.1109/INSAI56792.2022.00033

Botao Wang, Xu Xu, Dai Zhang, JingJing Wang, Xian Luo, Weijing Yao, Xiangyu Yuan, Ronghao Yang, Fen Liu, Liang Dong, Wei Fu, Lei Zhang, Jun Liu

引用次数: 0

Mobile Object Detection Without Labels with Camera-LiDAR Fusion for Roadside Perception 基于摄像头-激光雷达融合的路边感知无标签移动目标检测

2022 2nd International Conference on Networking Systems of AI (INSAI) Pub Date : 2022-10-01 DOI: 10.1109/INSAI56792.2022.00038

Xuhua Chen, Xinhua Zeng, Liang Song

引用次数: 0

Research on Data Communication Monitoring System of Master and Slave Equipment in Avionics System Based on 1553B Bus 基于1553B总线的航电系统主从设备数据通信监控系统研究

2022 2nd International Conference on Networking Systems of AI (INSAI) Pub Date : 2022-10-01 DOI: 10.1109/INSAI56792.2022.00052

Zhitian Zhu, Yanfang Fu, Chengtao Lu, Yongxing Qiao, Zhipeng Cai

引用次数: 0