2022 International Conference on Computing, Communication, Perception and Quantum Technology (CCPQT)最新文献

Research on an Improved DSR Protocol Based on the MarKov Model 基于MarKov模型的改进DSR协议研究

2022 International Conference on Computing, Communication, Perception and Quantum Technology (CCPQT) Pub Date : 2022-08-01 DOI: 10.1109/CCPQT56151.2022.00009

Ting-dong Hu

{"title":"Research on an Improved DSR Protocol Based on the MarKov Model","authors":"Ting-dong Hu","doi":"10.1109/CCPQT56151.2022.00009","DOIUrl":"https://doi.org/10.1109/CCPQT56151.2022.00009","url":null,"abstract":"Wireless Mesh Network (WMN) requires end-to-end performance of data transmission while reducing routing overhead. Therefore, an improved DSR (Dynamic Source Routing) protocol based on the MarKov model is designed. First, WCETT (Weighted Cumulative Expected Transmission Time) is applied to the DSR protocol as a routing parameter to select the link quality optimal path with the smallest bottleneck channel for data transmission, and locally optimize the traditional DSR protocol into an improved DSR (IDSR) protocol. Then, the MarKov model is used in the IDSR protocol to predict the geographic location of the node at the next moment, and determine if the original route is invalid base on this location, reroute to the next best route of WCETT if the original route is no longer valid and so on, until the optimal route available is selected. The performance is evaluated using NS2 simulation software, and the simulation results show that under the same wireless transmission and network size conditions, the IDSR protocol based on the MarKov model results in higher packet delivery rate, significantly reduced end-to-end average delay, route initiation frequency and routing overhead, when the node movement speed is accelerated, the IDSR protocol improves the network performance more significantly as the nodes move faster.","PeriodicalId":235893,"journal":{"name":"2022 International Conference on Computing, Communication, Perception and Quantum Technology (CCPQT)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117121482","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

Cashmere and Wool Classification with Large Kernel Attention and Deep Learning 基于大核关注和深度学习的羊绒和羊毛分类

2022 International Conference on Computing, Communication, Perception and Quantum Technology (CCPQT) Pub Date : 2022-08-01 DOI: 10.1109/CCPQT56151.2022.00058

Can Zeng, Qiao Kang, Pcngfei Hu, Mintao Dong, F. Dong, Kewei Chen

{"title":"Cashmere and Wool Classification with Large Kernel Attention and Deep Learning","authors":"Can Zeng, Qiao Kang, Pcngfei Hu, Mintao Dong, F. Dong, Kewei Chen","doi":"10.1109/CCPQT56151.2022.00058","DOIUrl":"https://doi.org/10.1109/CCPQT56151.2022.00058","url":null,"abstract":"Aiming at the problems of low manual detection efficiency and low automatic detection accuracy of cashmere and wool, a method is proposed to realize the image detection of cashmere and wool single fiber by using large kernel attention(LKA) mechanism and deep convolutional neural network. Based on the ConvNeXt network structure paradigm with simple structure, good scalability and high accuracy on ImageNet large datasets, the inverted residual structure improvement avoids information loss, and large kernel attention mechanism is added to make the model more accurate to pay attention to differentiated regions on the feature map space and channel, while considering the sparse amount of fiber image information, in order to avoid redundant training parameters and overfitting, the network is lightweight while maintaining the proportion of the ConvNeXt network hierarchy, and the LKA-RConvNeXt model is established. Finally, after training on 15,000 cashmere and wool datasets, the highest classification accuracy can reach 96.1%. Through ablation experiments and model comparison analysis, it is verified that the improved method used is beneficial to the accuracy of the model. The model can be used for cashmere and wool in automatic classification tasks, and contributes to backbone network for the subsequent fiber object detection task.","PeriodicalId":235893,"journal":{"name":"2022 International Conference on Computing, Communication, Perception and Quantum Technology (CCPQT)","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116363005","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

Theoretical Analysis of an In-line Thin-core Fiber Based Refractometer 直列薄芯光纤折光计的理论分析

2022 International Conference on Computing, Communication, Perception and Quantum Technology (CCPQT) Pub Date : 2022-08-01 DOI: 10.1109/CCPQT56151.2022.00072

Lu Cai, Jun Liu, Fu-cheng Xiang, Shan Li

{"title":"Theoretical Analysis of an In-line Thin-core Fiber Based Refractometer","authors":"Lu Cai, Jun Liu, Fu-cheng Xiang, Shan Li","doi":"10.1109/CCPQT56151.2022.00072","DOIUrl":"https://doi.org/10.1109/CCPQT56151.2022.00072","url":null,"abstract":"A highly sensitive liquid refractive index (RI) sensor with a single-mode fiber-multimode fiber-tapered thin-core fiber-single-mode fiber (SMTTS) structure can be simulated and the method is the beam propagation method (BPM). The modal perturbation between core and cladding mode happened because of modal mismatch and the transmission spectrum is calculated by BPM. Liquid RI and spectral dip wavelength shift for demodulating RI values is obtained and their relationship is approximately linear. To make a comparison, transmissions of single mode fiber-thin-core fiber-single mode fiber (STS) as well as single mode fiber-multimode fiber-thin-core fiber-single mode fiber (SMTS) are also calculated. It is significantly shown that the interference is improved by the segment of multimode fiber (MMF). And the tapered thin-core fiber (TCF) has the functions not only further enhance the fringe visibility, but also increase the RI sensitivity, which is higher with the thinner thin-core taper diameter. The average RI sensitivity would reach up to −116.7nm/RIU within the RI range of 1.33 to 1.39 if the taper diameter is $53.5 mumathrm{m}$. This is much higher than the value of SMTS structure. And in the practice application, it could be a slimmer taper in the SMTTS structure to improve the sensitivity just achieving by a simple fabrication process. All process could be finished using fusion splicer, cutter and hydrogen flame.","PeriodicalId":235893,"journal":{"name":"2022 International Conference on Computing, Communication, Perception and Quantum Technology (CCPQT)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126388558","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

Automatic Modulation Mode Recognition of Communication Signals Based on Complex-Valued Neural Network 基于复值神经网络的通信信号调制模式自动识别

2022 International Conference on Computing, Communication, Perception and Quantum Technology (CCPQT) Pub Date : 2022-08-01 DOI: 10.1109/CCPQT56151.2022.00012

Xiaobo Yang, Ruonan Zhang, Hongmei Xie, Huakui Sun, Huanling Li

{"title":"Automatic Modulation Mode Recognition of Communication Signals Based on Complex-Valued Neural Network","authors":"Xiaobo Yang, Ruonan Zhang, Hongmei Xie, Huakui Sun, Huanling Li","doi":"10.1109/CCPQT56151.2022.00012","DOIUrl":"https://doi.org/10.1109/CCPQT56151.2022.00012","url":null,"abstract":"Intelligent transportation systems (ITS) are designed to provide efficient and comfortable transportation. The development of ITS has brought new communication challenges, which require faster and more reliable transmission of information. In this paper, we investigate the modulation mode recognition method of communication signals based on a complex-valued neural network (CVNN). By combining a complex-valued convolutional neural network (CVCNN) with complex-valued long short-term memory (CVLSTM) and adding a residual learning unit, a modulation recognition model is established. The model can automatically learn from complex-valued signals without manual feature extraction and can recognize 11 modulation modes (3 analog modulation modes and 8 digital modulation modes) with a signal-to-noise ratio (SNR) between −20 dB and 18 dB. We design a Gaussian filter, and divide the signal to be identified into high SNR signal and low SNR signal through SNR estimation. The low SNR signal is Gaussian filtered before modulation recognition, so as to improve its modulation recognition accuracy. The algorithm proposed in this paper directly recognizes the modulation mode of the complex-valued signal without any preprocessing, and the recognition accuracy is better than the existing algorithms. This work is of great significance to the improvement of information transmission speed and the construction of ITS.","PeriodicalId":235893,"journal":{"name":"2022 International Conference on Computing, Communication, Perception and Quantum Technology (CCPQT)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126432671","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

Cybersecurity Analysis of Wind Farm Industrial Control System Based on Hierarchical Threat Analysis Model Framework 基于层次威胁分析模型框架的风电场工业控制系统网络安全分析

2022 International Conference on Computing, Communication, Perception and Quantum Technology (CCPQT) Pub Date : 2022-08-01 DOI: 10.1109/CCPQT56151.2022.00008

Baihua Yang, Yue Zhang

{"title":"Cybersecurity Analysis of Wind Farm Industrial Control System Based on Hierarchical Threat Analysis Model Framework","authors":"Baihua Yang, Yue Zhang","doi":"10.1109/CCPQT56151.2022.00008","DOIUrl":"https://doi.org/10.1109/CCPQT56151.2022.00008","url":null,"abstract":"Cybersecurity threat identification and analysis of wind farm industrial control systems (ICS-WF) is an important part of security work. The article innovatively proposes the STRIDE threat model as the basic structure to establish a framework of information system cybersecurity hierarchical threat analysis model for identifying and analyzing cyber security threats to ICS-WF. The framework model system covers three levels and six dimensions. The three levels refer to the Data Stream Layer, Context Layer, and Component Layer, which correspond to three parts of System Devices, Trust Boundary, and Application Process respectively; the six dimensions refer to six dimensions of Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service and Elevation of Privilege. Through this model framework for system analysis in a typical wind farm industrial control network environment, not only can we accurately identify and analyze cybersecurity threats, but also provide an effective basis for threat abatement and system design through the visualization of threats.","PeriodicalId":235893,"journal":{"name":"2022 International Conference on Computing, Communication, Perception and Quantum Technology (CCPQT)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125626799","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

Impact of Phase Noise on Secrecy Performance of Cell-Free Massive MIMO Networks with Multi-Antenna Users 相位噪声对多天线无小区大规模MIMO网络保密性能的影响

2022 International Conference on Computing, Communication, Perception and Quantum Technology (CCPQT) Pub Date : 2022-08-01 DOI: 10.1109/CCPQT56151.2022.00043

Xianyu Zhang, Tao Liang, K. An, Xiaoqiang Qiao, Xiaoyu Wang, Xiaoli Sun

引用次数: 0

Outlier Detection and Trust based Distributed Cooperative Spectrum Sensing in Internet of Vehicles 车联网中基于离群点检测和信任的分布式协同频谱感知

2022 International Conference on Computing, Communication, Perception and Quantum Technology (CCPQT) Pub Date : 2022-08-01 DOI: 10.1109/CCPQT56151.2022.00027

Haoshuang Zhao, Wen-hui Zhang, Xiuqiang Wu, Hongning Li, Liu He

引用次数: 2

An Improved A* Algorithm for Roadless Networks Based on Triangular Mesh 基于三角网格的无路网络改进A*算法

2022 International Conference on Computing, Communication, Perception and Quantum Technology (CCPQT) Pub Date : 2022-08-01 DOI: 10.1109/CCPQT56151.2022.00030

L. Wang, Bo Jiang, Zhe Sun

引用次数: 0

Multitask Semantic Segmentation Network Using Adaptive Multiscale Feature Fusion 基于自适应多尺度特征融合的多任务语义分割网络

2022 International Conference on Computing, Communication, Perception and Quantum Technology (CCPQT) Pub Date : 2022-08-01 DOI: 10.1109/CCPQT56151.2022.00018

Huilin Chen, Shengsong Yang, Ting Lyu

引用次数: 1

TD3-based Algorithm for Node Selection on Multi-tier Federated Learning 基于td3的多层联邦学习节点选择算法

2022 International Conference on Computing, Communication, Perception and Quantum Technology (CCPQT) Pub Date : 2022-08-01 DOI: 10.1109/CCPQT56151.2022.00034

Haojie Lin, Hong Wen, Wenjing Hou, Wenxin Lei

{"title":"TD3-based Algorithm for Node Selection on Multi-tier Federated Learning","authors":"Haojie Lin, Hong Wen, Wenjing Hou, Wenxin Lei","doi":"10.1109/CCPQT56151.2022.00034","DOIUrl":"https://doi.org/10.1109/CCPQT56151.2022.00034","url":null,"abstract":"Federated learning enables distributed devices to conduct cooperative training models while protecting data privacy, so it is widely promoted in big data scenario and the scope of the Internet of Things. Federated learning in multi-tier computing can integrate the resources of the device-edge-fog-cloud layer to interact and cooperate. For example, in addition to offloading training locally, the tasks of the device layer can also be uploaded to the edge layer or the fog layer for training, while the global aggregation node can be selected at the edge or fog or cloud. However, due to the uncertainty of network bandwidth, computing resources and terminal training tasks at each layer, it brings challenges to resource allocation and task offloading under federated learning in multi-tier computing. Therefore, we propose a TD3-based algorithm which aims to solve how to select training nodes and aggregation nodes during joint training on multi-tier federated learning to minimize the average task delay. Numerical experiments show that our method has better performance in terms of energy consumption and delay compared with edge federated learning and traditional federated learning.","PeriodicalId":235893,"journal":{"name":"2022 International Conference on Computing, Communication, Perception and Quantum Technology (CCPQT)","volume":"131 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114662620","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