Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering最新文献_第3页

Platform of Formal Modeling and Analysis for Airborne Software Requirements 机载软件需求形式化建模与分析平台

Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering Pub Date : 2022-10-21 DOI: 10.1145/3573428.3573646

Jiarun Lyu, Jun Hu, Lisong Wang

{"title":"Platform of Formal Modeling and Analysis for Airborne Software Requirements","authors":"Jiarun Lyu, Jun Hu, Lisong Wang","doi":"10.1145/3573428.3573646","DOIUrl":"https://doi.org/10.1145/3573428.3573646","url":null,"abstract":"Airborne software systems play very important roles in modern civil aircraft systems, and there are several safety standards, including DO-178B/C, etc., that are compulsory to be satisfied before airborne software can be certificated by the authority of government. According to the DO-178B/C, the consistency and integrity of airborne software requirements must be analyzed and verified in the early stage of software development. In this paper, we introduce a formal modeling and analysis tool platform (ART: Avionics Requirement Tools) for airborne software natural language requirements, and a case study of the requirements of the software subsystem of the Indication-Recording System (IRS) is provided. Firstly, we give the semantics of a formal Variable Relationship Model (VRM), the platform architecture, and toolchain of ART. Then a methodology of formal analysis of requirement consistency and integrity based on a multi-paradigm is given. After that, some details of the case study of IRS are shown including: how to make a preproccessing of original requirements and the automatic analysis process of the requirement model, such as the preprocessing and standardization of original requirement items, automatic generation of VRM models and multi-paradigm based formal analysis, etc. Lastly, some experiences of this case study are shown.","PeriodicalId":314698,"journal":{"name":"Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering","volume":"2013 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121634490","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

Research on UAV swarm target search algorithm based on prior information 基于先验信息的无人机群目标搜索算法研究

Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering Pub Date : 2022-10-21 DOI: 10.1145/3573428.3573778

Xiao-Yang Liu, Xiang-hui Shen

引用次数: 0

Analysis of Tourist Destination Image Perception Based on Web Data Mining Technology: – Take Wuzhen Scenic Area in Zhejiang Province as an Example 基于Web数据挖掘技术的旅游目的地形象感知分析——以浙江乌镇景区为例

Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering Pub Date : 2022-10-21 DOI: 10.1145/3573428.3573664

Yi Liu

引用次数: 0

Design and Optimization of RF Impedance Matching Network Based on ADS and SIwave

Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering Pub Date : 2022-10-21 DOI: 10.1145/3573428.3573431

Jianhua Li, Kang Zhuo, Yifan Hu, Bin You, Changjian Liu, Shanxu Cao, Lehou Xi

引用次数: 0

Measurement of buried depth and inclination rate of concrete poles based on binocular vision 基于双目视觉的混凝土杆体埋深和倾斜率测量

Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering Pub Date : 2022-10-21 DOI: 10.1145/3573428.3573504

Chaoxin Chen, Hengbo Xu, Lei Guo, Peng Shen, Jiangyi Chen

{"title":"Measurement of buried depth and inclination rate of concrete poles based on binocular vision","authors":"Chaoxin Chen, Hengbo Xu, Lei Guo, Peng Shen, Jiangyi Chen","doi":"10.1145/3573428.3573504","DOIUrl":"https://doi.org/10.1145/3573428.3573504","url":null,"abstract":"Aiming at the low efficiency of the traditional detection methods of buried depth and inclination rate of concrete poles, a method of measuring concrete poles based on binocular vision was proposed. Firstly, an improved DeeplabV3+ semantic segmentation algorithm is proposed. Based on the original model structure, the backbone feature extraction network is modified, the feature fusion method is optimized, and the improved CBAM attention mechanism is added to reduce the model complexity and improved the accuracy of concrete pole area segmentation. Secondly, the sub-pixel edge extraction algorithm based on local area effect is used to determine the edge of the concrete pole in the image segmentation area, and the least squares method is used to fit the edge to determine the precise feature points. Finally, the coordinate transformation of binocular vision is used to calculate the depth and inclination of the concrete pole. Experiments show that the method has a buried depth measurement error of less than 10 cm, an error rate of less than 5%, and an inclination measurement error of less than 0.3°, which provides an automated concrete pole buried depth and inclination rate measurement solution","PeriodicalId":314698,"journal":{"name":"Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126460666","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

Point-based Attention Convolutional Neural Networks for Point Clouds Semantic Segmentation 基于点注意卷积神经网络的点云语义分割

Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering Pub Date : 2022-10-21 DOI: 10.1145/3573428.3573718

Y. Li, Qing Li

{"title":"Point-based Attention Convolutional Neural Networks for Point Clouds Semantic Segmentation","authors":"Y. Li, Qing Li","doi":"10.1145/3573428.3573718","DOIUrl":"https://doi.org/10.1145/3573428.3573718","url":null,"abstract":"Convolutional neural network (CNNs) have achieved success in processing data with regular grid structures, demonstrating the great potential of applying CNN to point cloud data. However, the disorder and irregularity of 3D point cloud data hinder this progress. To address this issue, we propose a point-based attention convolutional neural network, which consists of a dynamic attention convolution module (DAC) and a point-based feature relation matrix aggregation module (PRA). DAC is used to extract features. At each layer of the network, DAC recomputes the dynamic update graph and assigns attention weights to each edge across the feature space of different points, and finally updates the features by the weighted sum of adjacent points. PRA utilizes the raw and aggregated features of points to generate a global relation matrix, which can adjust the aggregated features for biases from DAC, while obtaining long-range contextual information. Our network structure consists of an encoder and a decoder, and in order to enhance the results of multi-scale feature fusion, we optimize the feature fusion process after upsampling to form a more detailed end-to-end trainable network. Through segmentation and classification experiments on challenging 3D point cloud benchmarks, we demonstrate that our algorithm can meet or outperform the performance of existing state-of-the-art methods.","PeriodicalId":314698,"journal":{"name":"Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering","volume":"20 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125839832","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

Recent Deep Learning Techniques on Short Text Classification 短文本分类的深度学习新技术

Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering Pub Date : 2022-10-21 DOI: 10.1145/3573428.3573468

Ziyuan Yang

引用次数: 0

A Secure Data Aggregation Scheme Enabling Abnormal Smart Meters Traceback for Smart Grid 智能电网智能电表异常溯源的安全数据聚合方案

Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering Pub Date : 2022-10-21 DOI: 10.1145/3573428.3573780

Shiying Yao, Jian Zeng, Shuang Wang, Xiaolong Yang, Jingtang Luo, Ziqi Wang

{"title":"A Secure Data Aggregation Scheme Enabling Abnormal Smart Meters Traceback for Smart Grid","authors":"Shiying Yao, Jian Zeng, Shuang Wang, Xiaolong Yang, Jingtang Luo, Ziqi Wang","doi":"10.1145/3573428.3573780","DOIUrl":"https://doi.org/10.1145/3573428.3573780","url":null,"abstract":"In order to prevent smart meter data from being stolen by attackers during transmission, it is common practice to securely aggregate the data and report it to the power company. Although the existing aggregation scheme can protect users' electricity consumption privacy, it cannot distinguish the data that has been attacked by false data injection (FDI), meaning it is difficult to trace and exclude abnormal data sources. To solve this problem, the study proposes a smart meter data aggregation scheme that can trace abnormal nodes. The aggregation center (AC) divides the smart meter (SM) into multiple groups, and the SM in the same group detect the abnormal behavior with each other by calculating whether the Hellinger distance of the power consumption of two adjacent timespan of their counterparts exceeds the set threshold, then feedback to the AC. Through multiple “grouping-detection” iterations, AC locates the groups which contains abnormal SMs. Then AC excludes the abnormal nodes and calculates the normal SMs’ power consumption aggregate value in the group by employing EC-EIGamal homomorphic encryption. Experimental results show that the detection accuracy is 73.3%∼100% under multiple FDI attacks, and attacked SMs can be effectively traced and excluded.","PeriodicalId":314698,"journal":{"name":"Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering","volume":"136 9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131347827","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

RSSI 3D Location Algorithm Based on Multiple Communication Radius and Distance Correction 基于多通信半径和距离校正的RSSI三维定位算法

Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering Pub Date : 2022-10-21 DOI: 10.1145/3573428.3573471

Jun Wang, He Lu, Hai-Di Sheng

{"title":"RSSI 3D Location Algorithm Based on Multiple Communication Radius and Distance Correction","authors":"Jun Wang, He Lu, Hai-Di Sheng","doi":"10.1145/3573428.3573471","DOIUrl":"https://doi.org/10.1145/3573428.3573471","url":null,"abstract":"Three-dimensional ranging and positioning algorithms in wireless sensor networks mostly rely on the principle of two-dimensional positioning algorithm. At present, RSSI-based positioning algorithm is widely used because of its simplicity, but RSSI algorithm has high energy consumption and large ranging error when positioning nodes, which affects the service life of the network and the positioning accuracy of nodes. To solve this problem, On the basis of the existing RSSI localization algorithms, this paper proposes a RSSI three-dimensional localization algorithm based on multiple communication radius and distance corrections (MD-RSSI), which selects anchor nodes by five-step communication mechanism and distance correction factor corrects the estimated distance. Firstly, the multi-communication radius is used to communicate with the anchor node step by step, and the distance between the anchor node and the node to be measured is calculated by RSSI's own ranging model. Then, a correction factor is introduced to correct the distance and the free space loss model of energy is used to calculate the remaining energy of the anchor node after communication. Finally, three anchor nodes with a small number of partners and relatively high energy are selected through constraints to locate unknown nodes. Theoretical analysis and experimental results show that the improved MD-RSSI localization algorithm can reduce the energy consumption of node localization and improve the localization accuracy.","PeriodicalId":314698,"journal":{"name":"Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering","volume":"553 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134004179","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

Traffic Classification Method Based on Federated Semi-Supervised Learning 基于联邦半监督学习的流量分类方法

Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering Pub Date : 2022-10-21 DOI: 10.1145/3573428.3573586

Chongxin Sun, Bo Chen, Youjun Bu, Desheng Zhang

{"title":"Traffic Classification Method Based on Federated Semi-Supervised Learning","authors":"Chongxin Sun, Bo Chen, Youjun Bu, Desheng Zhang","doi":"10.1145/3573428.3573586","DOIUrl":"https://doi.org/10.1145/3573428.3573586","url":null,"abstract":"In order to protect the data privacy of network users and solve the training difficulties caused by traffic distribution, this paper based on federal semi-supervised learning presents a traffic classification method to solve the problem of a small number of labeled traffic distributed in server, and a large number of non-labeled traffic distributed independently and identically in clients and not shared. On the one hand, this paper adopts the parameter decomposition strategy to avoid interference between different tasks. On the other hand, this paper uses consistency regularization between clients to maximize consensus between similar segment clients to solve the learning problem of variable small sample data. In addition, method in this paper only transfer parameter differences during the federated learning parameter transfer process. The experimental results show that the accuracy gap between our method and the supervised learning training method is minimal, which can effectively protect user privacy and does not require a large amount of labeled data and communication costs.","PeriodicalId":314698,"journal":{"name":"Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134059697","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