2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET)最新文献_第6页

Hierarchical Feature Fusion and Multi-scale Cost Aggregation for Stereo Matching 层次特征融合与多尺度代价聚合立体匹配

2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET) Pub Date : 2022-08-19 DOI: 10.1109/CCET55412.2022.9906319

Jiaquan Zhang, Pengfei Li, Xin'an Wang, Yong Zhao

引用次数: 0

A Lightweight Attribute-based Encryption Scheme for Data Access Control in Smart Grids 基于属性的智能电网数据访问控制轻量级加密方案

2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET) Pub Date : 2022-08-19 DOI: 10.1109/CCET55412.2022.9906352

Guocong Feng, Tingting Mu, Huahui Lyu, Hang Yang, Yuyang Lai, Huijuan Li

引用次数: 0

Research on Intelligent Communication Scheduling System Based on Network Slices 基于网络切片的智能通信调度系统研究

2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET) Pub Date : 2022-08-19 DOI: 10.1109/CCET55412.2022.9906378

Chenchen Dou, Xue Wang, Rui Dai

{"title":"Research on Intelligent Communication Scheduling System Based on Network Slices","authors":"Chenchen Dou, Xue Wang, Rui Dai","doi":"10.1109/CCET55412.2022.9906378","DOIUrl":"https://doi.org/10.1109/CCET55412.2022.9906378","url":null,"abstract":"The rapid development of smart grid has put forward higher requirements on communication technology. With the rapid development of power wireless communication network, the demand of power business becomes more and more complicated and diverse. However, the current single-standard network has poor coverage, poor power service carrying capacity, and weak transmission stability and security. To address the above problems, this paper explores the network slicing technology for intelligent power distribution services. Based on the analysis of existing network access selection methods, a network selection strategy for quality of service (QoS) assurance of power services is proposed for the characteristics of power distribution services. Combining its own QoS requirements and the characteristics of the network to be accessed, the power service can realize the access selection of the optimal adaptation network. In order to better meet various vertical industry applications, network slicing is required in both the core network and the radio access network, i.e., end-to-end network slicing is realized, and network slicing can be customized to tailor network functions and reasonably allocate network resources according to service scenario requirements. After receiving a request for network slicing, operators need to map the virtual network functions required for network slicing to the underlying physical network, a process that involves the optimal allocation of physical resources.","PeriodicalId":329327,"journal":{"name":"2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET)","volume":"84 11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130699861","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

Fake News Detection Based on Two-Branch Network and Domain Adversarial 基于双分支网络和领域对抗的假新闻检测

2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET) Pub Date : 2022-08-19 DOI: 10.1109/CCET55412.2022.9906330

Ying Guo, Hong Ge, Jinhong Li

{"title":"Fake News Detection Based on Two-Branch Network and Domain Adversarial","authors":"Ying Guo, Hong Ge, Jinhong Li","doi":"10.1109/CCET55412.2022.9906330","DOIUrl":"https://doi.org/10.1109/CCET55412.2022.9906330","url":null,"abstract":"Fake news detection is essential for society, however, implicit state information in features is ignored in multimodal fake news detection, resulting in inefficient of feature. There are also poor domain generality of features problems. So, a Two-Branch Network with Domain Adversarial (TBNDA), is proposed. Firstly, a pre-trained language model is used to encode features on textual information, and the hidden layer of word information and sentence information in the features is extracted separately using a two-branch network. Secondly, a pre-trained residual network model is used to encode the image information, and a two-branch network model is used to extract the different hidden layer image feature information. Finally, a domain adversarial network module is constructed to extract generic features between domains. The accuracy of the proposed model is S9.6% and S4.7% on the Weibo dataset and Twitter dataset respectively. The two-branch network improves the feature representation of images and text, and the domain adversarial network extracts features with generality, enhancing the migration performance of the model and improving the detection of fake news.","PeriodicalId":329327,"journal":{"name":"2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131051812","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

Non-intrusive Load Monitoring for Consistent Shape Loads Based on Convolutional Neural Network 基于卷积神经网络的非侵入式一致形状载荷监测

2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET) Pub Date : 2022-08-19 DOI: 10.1109/CCET55412.2022.9906390

Xiang Li, Y. Guo, Meng Yan, Xin Wu

{"title":"Non-intrusive Load Monitoring for Consistent Shape Loads Based on Convolutional Neural Network","authors":"Xiang Li, Y. Guo, Meng Yan, Xin Wu","doi":"10.1109/CCET55412.2022.9906390","DOIUrl":"https://doi.org/10.1109/CCET55412.2022.9906390","url":null,"abstract":"As the key method for demand-side management in power grid, non-intrusive load monitoring (NILM) keep up to the power consumption of various users in real time and provides data to support the formulation of relevant power policies. In order to achieve accurate resident load monitoring, this paper proposes a NILM architecture focus on consistent shape loads (CSL). Loads in CSL meet the following conditions: 1) current waveform images of different load individuals in the same type are highly similar. 2) different types of load waveform images are different in shapes which are distinguishable. Besides, a non-intrusive load monitoring method based on convolutional neural network (CNN) to identify CSL load is proposed and carried out on actual users. Power consumption data of CSL with different operating environments is taken as training samples. The outcome of our experiment shows the effectiveness of the method in accurately distinguishing CSL and high-precision identification which reaches 97.06%. The method ensures the real-time performance and accuracy of load monitoring.","PeriodicalId":329327,"journal":{"name":"2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET)","volume":"212 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115772868","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 optimization Research of the Washout Algorithm for Flight Simulators 飞行模拟器洗出算法的智能优化研究

2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET) Pub Date : 2022-08-19 DOI: 10.1109/CCET55412.2022.9906381

Jingyi Wang, Zhengping Li, Lijun Wang, Shujun Guo

引用次数: 0

Granularity Classification and Feature Fusion Methods in Traffic Sign Detection 交通标志检测中的粒度分类与特征融合方法

2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET) Pub Date : 2022-08-19 DOI: 10.1109/CCET55412.2022.9906331

Wei Huang, Xiaohong Shi, Qi Xu, Qingshu Li, Peng Yang

{"title":"Granularity Classification and Feature Fusion Methods in Traffic Sign Detection","authors":"Wei Huang, Xiaohong Shi, Qi Xu, Qingshu Li, Peng Yang","doi":"10.1109/CCET55412.2022.9906331","DOIUrl":"https://doi.org/10.1109/CCET55412.2022.9906331","url":null,"abstract":"Nowadays, deep learning based on detection algorithms have replaced the traditional manual feature extraction target algorithms and have achieved amazing results in many places with their powerful automatic feature extraction capabilities. However, the results are not ideal for the detection of small targets with low resolution and a lot of noise, such as traffic signs. To address the current problems of slow detection speed and low detection accuracy in small target detection, this paper adopts a feedback-driven mechanism to solve the image level imbalance of the input feature space under the original data distribution. At the same time, this paper designs a novel and flexible two-stage traffic sign recognition framework. The complex task of traffic sign detection and recognition is decomposed into two stages: 1) designing a superclass classifier to more accurately separate traffic signs in complex natural scene images; 2) The idea of similarity metric learning is used to design fine-grained classifiers to recognize traffic signs. Finally, to verify the effectiveness of the model, the model was first compared with Faster R-CNN and found to possess higher detection accuracy; then the model was experimented with the R-FCN model on TTIOOK dataset and CCTSDB dataset respectively, and the comparison of the experimental results revealed that the model improved over the R-FCN model in most of the metrics in the traffic sign detection task.","PeriodicalId":329327,"journal":{"name":"2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129758231","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

An Overview of SRv6 Standardization and Application towards 5G-Advanced and 6G 面向5G-Advanced和6G的SRv6标准化与应用综述

2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET) Pub Date : 2022-08-19 DOI: 10.1109/CCET55412.2022.9906338

Zhiwei Mo, Biao Long

引用次数: 0

GNSS Receiver Anti-jamming Algorithm Based on Estimation of Array Steering Vector 基于阵列转向矢量估计的GNSS接收机抗干扰算法

2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET) Pub Date : 2022-08-19 DOI: 10.1109/CCET55412.2022.9906341

Dawei Wang, Xibin Wang, Junhong Wang, Peng Du, Xiaoqiong Yao

引用次数: 1