2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC )最新文献_第10页

Research on Key Technologies of high power DC charging module 大功率直流充电模块关键技术研究

2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC ) Pub Date : 2022-10-03 DOI: 10.1109/IAEAC54830.2022.9930064

Zengdong Jia, Yong Yang, Lianjun Duan, Lei Liu, Mingmao Hu

引用次数: 0

Hybrid Flow Scheduling with Additional Simple Compensation Mechanisms in Time-Sensitive Networks 时间敏感网络中具有附加简单补偿机制的混合流调度

2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC ) Pub Date : 2022-10-03 DOI: 10.1109/IAEAC54830.2022.9929463

Xianqiong Yao, Zhong Gan, Yilong Chen, Lei Guo, Wei Wang

{"title":"Hybrid Flow Scheduling with Additional Simple Compensation Mechanisms in Time-Sensitive Networks","authors":"Xianqiong Yao, Zhong Gan, Yilong Chen, Lei Guo, Wei Wang","doi":"10.1109/IAEAC54830.2022.9929463","DOIUrl":"https://doi.org/10.1109/IAEAC54830.2022.9929463","url":null,"abstract":"With the increasing of distributed energy resources, in order to ensure system security and flexibility, the same switching device must support the mixed transmission of multiple streams. Therefore, the mechanism of hybrid streaming is urgently needed. The IEEE Time-Sensitive Networking (TSN) Task Force is working to standardize key mechanisms to enable simultaneous coexistence of time-sensitive and non-time-sensitive flows in Ethernet. In this paper, we propose a hybrid traffic scheduling mechanism for HFSM-C. This mechanism combines TAS with CQF, and utilizes flow ordering, injection slot adjustment and the simple compensation mechanism proposed in this paper to schedule time-sensitive flow and large-bandwidth flow respectively. Through experiments, we found that the HFSM-C mechanism significantly improves the schedule-ability compared with other mechanisms, and under the same TSN system settings, it schedules more flows than the HSTC mechanism, the FITS mechanism and the Tabu-ITP mechanism when scheduling large-bandwidth flows, the success rate is higher, and the efficient scheduling of mixed flows is realized.","PeriodicalId":349113,"journal":{"name":"2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC )","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128930345","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

Efficiency Study of SSAG on RNN Framework RNN框架下SSAG的有效性研究

2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC ) Pub Date : 2022-10-03 DOI: 10.1109/IAEAC54830.2022.9929855

Xiaowei Xie, Aixiang Chen

引用次数: 0

Algorithm Research Based on 2D LiDAR-Binocular Camera Fusion 基于二维激光雷达-双目摄像机融合的算法研究

2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC ) Pub Date : 2022-10-03 DOI: 10.1109/IAEAC54830.2022.9929493

Chuyue Wang, Meiling Wang, Wenfeng Zhan, Xiaodong Ye

引用次数: 0

FDTD Algorithm for Bistatic RCS Prediction of 3-D Target on two GPUs 双gpu上三维目标双基地RCS预测的FDTD算法

2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC ) Pub Date : 2022-10-03 DOI: 10.1109/IAEAC54830.2022.9929994

Pengfei Wang, Haifu Zhang

引用次数: 0

Research on Personal Privacy Security Detection Technology for Android Application Android应用的个人隐私安全检测技术研究

2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC ) Pub Date : 2022-10-03 DOI: 10.1109/IAEAC54830.2022.9930036

Wang Chao, Dong Jiahan, Wang Xiaohu, Ren Tianyu

{"title":"Research on Personal Privacy Security Detection Technology for Android Application","authors":"Wang Chao, Dong Jiahan, Wang Xiaohu, Ren Tianyu","doi":"10.1109/IAEAC54830.2022.9930036","DOIUrl":"https://doi.org/10.1109/IAEAC54830.2022.9930036","url":null,"abstract":"With the intensive release and implementation of relevant laws, regulations and standards on personal information protection, mobile APP, as the key application carrier and entrance and exit of personal information, has attracted more and more attention from government regulators and the public. In order to improve the accuracy of security detection of personal privacy violation in Android Apps, an APP sensitive privacy behavior detection method based on Frida hook monitoring was proposed. The method calls the system function interface through Frida hook APP to intercept and monitor the access to sensitive privacy data such as external storage, address book, SMS and geographical location, and construct the APP sensitive behavior access list. Combined with the application type, comprehensively analyze the permission list and sensitive behavior list to judge whether the APP has the problem of collecting personal privacy information beyond the scope. By selecting common mobile APPs such as audio-visual, online games and social chat, the verification test is carried out. The personal privacy security detection method can find the violation problems of the notified APP, which has certain practicality.","PeriodicalId":349113,"journal":{"name":"2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC )","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125275651","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

Mental workload recognition using ECG and machine learning in simulated flight tasks 在模拟飞行任务中使用ECG和机器学习进行心理工作量识别

2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC ) Pub Date : 2022-10-03 DOI: 10.1109/IAEAC54830.2022.9930029

Zebin Jiang, Ke Zhang, Kuijun Wu, Jie Xu, Xinyan Li, Yu Sun, Xianliang Ge, Ming Mao

引用次数: 0

Cable Tension Measurement System Based on Lora Module 基于Lora模块的电缆张力测量系统

2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC ) Pub Date : 2022-10-03 DOI: 10.1109/IAEAC54830.2022.9929664

Liangbo Li, Hairong Wang, Fangfang Lu, Shenglan Gu, Li Li, Kaixiang Guo

引用次数: 0

IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference IEEE第六届先进信息技术、电子与自动化控制会议

2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC ) Pub Date : 2022-10-03 DOI: 10.1109/iaeac54830.2022.9929804

引用次数: 0

Semi-supervised Malicious Traffic Detection with Improved Wasserstein Generative Adversarial Network with Gradient Penalty 基于梯度惩罚的改进Wasserstein生成对抗网络半监督恶意流量检测

2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC ) Pub Date : 2022-10-03 DOI: 10.1109/IAEAC54830.2022.9929762

Jiafeng Wang, Ming Liu, Xiaokang Yin, Yuhao Zhao, Shengli Liu

{"title":"Semi-supervised Malicious Traffic Detection with Improved Wasserstein Generative Adversarial Network with Gradient Penalty","authors":"Jiafeng Wang, Ming Liu, Xiaokang Yin, Yuhao Zhao, Shengli Liu","doi":"10.1109/IAEAC54830.2022.9929762","DOIUrl":"https://doi.org/10.1109/IAEAC54830.2022.9929762","url":null,"abstract":"With the development of artificial intelligence, malicious traffic detection technology based on deep learning has become mainstream with its powerful detection performance. Most existing deep learning-based detection methods require sufficient labeled data to train classifiers. But much labeled traffic is difficult to obtain in practical applications. To solve this problem, we propose and implement a semi-supervised malicious traffic detection method based on improved Wasserstein Generative Adversarial Network with Gradient Penalized (WGAN-GP), denoted as SEMI-WGAN-GP. First, we construct a pseudo- feature map (PFM) for each stream in the dataset using the time-series properties of consecutive packets in a given stream. Second, we fix the generator and only train the discriminator on a few labeled PFMs, which obtain a discriminator that can distinguish malicious from benign traffic. Finally, the generator and discriminator are trained unsupervisedly in the adversarial setting, which allows the discriminator to improve detection performance by generator-generated PFMs. Experiments on the publicly available UNSW-NB15 dataset demonstrate that SEMI-WGAN-GP can achieve 90.53% accuracy using a few labeled samples (20% of the samples in the dataset are marked), exceeding the 79.92% and 84.94% of fully supervised multilayer perceptron network (MLP) and 2- dimensional convolutional neural network (2DCNN). In addition, SEMI-WGAN-GP also achieves better detection performance than SEMI-DCGAN by generating better samples.","PeriodicalId":349113,"journal":{"name":"2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC )","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129180164","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: 2