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2023 5th International Conference on Communications, Information System and Computer Engineering (CISCE)最新文献

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An Optimization Algorithm of Charging Station Siting to Balance Pressure & Safety of Power Grid 平衡电网压力与安全的充电站选址优化算法
2023 5th International Conference on Communications, Information System and Computer Engineering (CISCE) Pub Date : 2023-04-14 DOI: 10.1109/CISCE58541.2023.10142788
Bokai Li, Wei Quan, Dongdong Ma, Junwei Xin, Jing Ning
{"title":"An Optimization Algorithm of Charging Station Siting to Balance Pressure & Safety of Power Grid","authors":"Bokai Li, Wei Quan, Dongdong Ma, Junwei Xin, Jing Ning","doi":"10.1109/CISCE58541.2023.10142788","DOIUrl":"https://doi.org/10.1109/CISCE58541.2023.10142788","url":null,"abstract":"A reasonable charging station layout can not only alleviate the difficult problem of electric vehicle (EV) charging, but also balance the grid pressure and ensure the safety of electricity consumption. In this paper, the sparrow search algorithm (SSA) is used to optimize the charging station siting process and obtain suitable charging station siting results. This can meet the needs of customers, lower the cost of building charging stations, and have the least negative effects on the electrical system. The experiment results show the SSA has the best capacity to search globally and the fastest convergence speed compared with the genetic algorithm (GA) and moth-flame optimization algorithm (MFO).","PeriodicalId":145263,"journal":{"name":"2023 5th International Conference on Communications, Information System and Computer Engineering (CISCE)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117313733","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
UAV Forest Smoke and Flame Recognition System Based on YOLOv5 基于YOLOv5的无人机森林烟焰识别系统
2023 5th International Conference on Communications, Information System and Computer Engineering (CISCE) Pub Date : 2023-04-14 DOI: 10.1109/CISCE58541.2023.10142825
Fan Huang, Kai Fu, Zhihui Liu, Chengjun Zou, Kang Li, Siyong Fu
{"title":"UAV Forest Smoke and Flame Recognition System Based on YOLOv5","authors":"Fan Huang, Kai Fu, Zhihui Liu, Chengjun Zou, Kang Li, Siyong Fu","doi":"10.1109/CISCE58541.2023.10142825","DOIUrl":"https://doi.org/10.1109/CISCE58541.2023.10142825","url":null,"abstract":"At present, forest fire prevention is still facing a severe and complicated situation. In order to solve the problem of low efficiency of traditional forest fire detection, a forest fire and fire detection system is proposed, which uses uAV to collect digital images and PC for real-time intelligent recognition. The UAV is equipped with a high-definition camera and transmits digital images to the PC in real time through wireless image transmission technology. The PC recognizes the image through the YOLOv5 target detection algorithm and displays the results on the PC page in time, finally realizing real-time monitoring of forest fires.","PeriodicalId":145263,"journal":{"name":"2023 5th International Conference on Communications, Information System and Computer Engineering (CISCE)","volume":"173 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115441563","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
Sensitive Information Identification Method of Power System Based on Deep Learning 基于深度学习的电力系统敏感信息识别方法
2023 5th International Conference on Communications, Information System and Computer Engineering (CISCE) Pub Date : 2023-04-14 DOI: 10.1109/CISCE58541.2023.10142374
L. Chen, Yong Qiao, Shikan Fu, Jing Cao, Lei Wang, Zenghui Xiang, Xuan Chen, Keren Wu, Jinhui Li
{"title":"Sensitive Information Identification Method of Power System Based on Deep Learning","authors":"L. Chen, Yong Qiao, Shikan Fu, Jing Cao, Lei Wang, Zenghui Xiang, Xuan Chen, Keren Wu, Jinhui Li","doi":"10.1109/CISCE58541.2023.10142374","DOIUrl":"https://doi.org/10.1109/CISCE58541.2023.10142374","url":null,"abstract":"Power system is one of the most important infrastructures in modern society. In the power system, various sensitive information such as power supply status, load data and fault information need to be protected. In recent years, the methods based on deep learning has been widely used in the identification and protection of sensitive information in power systems. We propose a convolution neural network model based on pre-trained model and attention mechanism to classify and label power system data. Convolution neural network is a deep learning model, which offers a powerful and flexible tool for electric sensitive information detection. Pre-trained model and attention mechanism are two common technical means, which can improve the feature extraction and generalization ability of the model, thus providing effective support for image classification, target detection and other tasks. In the training process, the model we proposed realizes accurate and automatic recognition of sensitive information by learning the characteristics of input text information.","PeriodicalId":145263,"journal":{"name":"2023 5th International Conference on Communications, Information System and Computer Engineering (CISCE)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115445448","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
Government affairs message text classification based on RoBerta and TextCNN 基于RoBerta和TextCNN的政务信息文本分类
2023 5th International Conference on Communications, Information System and Computer Engineering (CISCE) Pub Date : 2023-04-14 DOI: 10.1109/CISCE58541.2023.10142573
Yan Lai, Lin Zhang
{"title":"Government affairs message text classification based on RoBerta and TextCNN","authors":"Yan Lai, Lin Zhang","doi":"10.1109/CISCE58541.2023.10142573","DOIUrl":"https://doi.org/10.1109/CISCE58541.2023.10142573","url":null,"abstract":"With the arrival of the era of big data, the number of messages on the government affairs platform has grown rapidly. To better solve the urgent problems reflected by the public, this paper takes some real messages from a provincial government affairs platform as the research object and constructs a model of RoBerta and TextCNN fusion to classify the text of government affairs messages. Firstly, the message text is pre-processed, including de-duplication and noise reduction. Second, the RoBerta-TextCNN model is constructed to classify the message text, and the message text vector obtained from the RoBerta layer is input to the TextCNN layer for feature extraction, and then the captured features are classified using the softmax classifier. Finally, the classification results are compared with those of other models. The experimental results show that the RoBerta-TextCNN model constructed in this paper achieves better classification results in this task, with an accuracy rate of 89.63%, a recall rate of 89.95%, and an F1 value of 90.31%.","PeriodicalId":145263,"journal":{"name":"2023 5th International Conference on Communications, Information System and Computer Engineering (CISCE)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115468244","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
Robust Reversible Database Watermarking Scheme for Local Distortion 局部失真的鲁棒可逆数据库水印方案
2023 5th International Conference on Communications, Information System and Computer Engineering (CISCE) Pub Date : 2023-04-14 DOI: 10.1109/CISCE58541.2023.10142826
Shan Wu
{"title":"Robust Reversible Database Watermarking Scheme for Local Distortion","authors":"Shan Wu","doi":"10.1109/CISCE58541.2023.10142826","DOIUrl":"https://doi.org/10.1109/CISCE58541.2023.10142826","url":null,"abstract":"To address the problem of excessive data distortion caused by watermark embedding in existing database watermarking methods, a robust and reversible data watermarking scheme with local distortion is proposed. The scheme proposes to use only part of the attribute column data as the watermark carrier to reduce data distortion, while the watermark is encoded using the corrective coding technique to enhance the robustness of the watermark, and the watermark is embedded into the data by the difference expansion technique to achieve reversible data recovery. Experiments based on real data show that the proposed scheme produces less data distortion, the watermark is robust after attacks, and the usability and copyright protection of shared data are enhanced.","PeriodicalId":145263,"journal":{"name":"2023 5th International Conference on Communications, Information System and Computer Engineering (CISCE)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123156889","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
Cross-Scale Dilated Residual Network for Image Compressed Sensing 图像压缩感知的跨尺度扩展残差网络
2023 5th International Conference on Communications, Information System and Computer Engineering (CISCE) Pub Date : 2023-04-14 DOI: 10.1109/CISCE58541.2023.10142651
Yanhe Chen
{"title":"Cross-Scale Dilated Residual Network for Image Compressed Sensing","authors":"Yanhe Chen","doi":"10.1109/CISCE58541.2023.10142651","DOIUrl":"https://doi.org/10.1109/CISCE58541.2023.10142651","url":null,"abstract":"Deep Learning based Compressed Sensing (DCS) algorithms are able to accomplish better rebuilding images compared to classical Compressed Sensing (CS). However, the majority of DCS algorithms focus on recovery and information transfer over network depth, while ignoring the communication between networks and losing some information about the traits. To enhance the exchange of data between networks of different scales, the cross-scale dilated residual network (CDRNet) is proposed. In the reconstruction section we use a parallel network and incorporate a dilated residual block (DRB) as a method to expand the convolutional field to obtain features at different scales, and then a cross-scale information exchange block(CIEB) to superimpose the information at different scales to achieve a better detailed reconstruction performance. We experimentally compare the CDRNet $*$ (without CIEB) and the CDRNet (with CIEB). The results show that information exchange helps image reconstruction, and our algorithm achieves better quality of rebuild and texture recovery than other CS algorithms.","PeriodicalId":145263,"journal":{"name":"2023 5th International Conference on Communications, Information System and Computer Engineering (CISCE)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126329233","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
Miniaturization design of L-band Class-F power amplifier module l波段f类功率放大器模块的小型化设计
2023 5th International Conference on Communications, Information System and Computer Engineering (CISCE) Pub Date : 2023-04-14 DOI: 10.1109/CISCE58541.2023.10142730
Hanrui Huang, Yannan Jiang, T. Liu
{"title":"Miniaturization design of L-band Class-F power amplifier module","authors":"Hanrui Huang, Yannan Jiang, T. Liu","doi":"10.1109/CISCE58541.2023.10142730","DOIUrl":"https://doi.org/10.1109/CISCE58541.2023.10142730","url":null,"abstract":"The miniaturization development of traditional Class-F power amplifiers is limited due to the independent design of harmonic suppression and impedance matching networks. In this paper, the method of comprehensive design of harmonic suppression and impedance matching networks is adopted to reduce the size of a Class-F power amplifier. To verify the feasibility of the proposed approach, a three-stage power amplifier module operating at 1.575 GHz and 40 dBm was designed, with the Class-F amplifier as the final stage amplifier. The saturation power added efficiency (PEA) of the final stage amplifier is 60% and its size is only 20%-42% of that reported for similar amplifiers.","PeriodicalId":145263,"journal":{"name":"2023 5th International Conference on Communications, Information System and Computer Engineering (CISCE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129310351","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 comprehensive assessment method for the power structured data classification and grading based on hierarchical analysis 一种基于层次分析法的电力结构化数据分类分级综合评价方法
2023 5th International Conference on Communications, Information System and Computer Engineering (CISCE) Pub Date : 2023-04-14 DOI: 10.1109/CISCE58541.2023.10142598
Yujia Zhai, Xiuli Huang, Congcong Shi, Shenglong Liu, Hao Zhang
{"title":"A comprehensive assessment method for the power structured data classification and grading based on hierarchical analysis","authors":"Yujia Zhai, Xiuli Huang, Congcong Shi, Shenglong Liu, Hao Zhang","doi":"10.1109/CISCE58541.2023.10142598","DOIUrl":"https://doi.org/10.1109/CISCE58541.2023.10142598","url":null,"abstract":"With the transformation of the digital development of power grids, the classification and grading of structured data for electricity is an important basis for data security protection. Existing methods for assessing data classification and grading results are mostly always based on individual indicators of the model's operational results. However, such methods can only evaluate a single performance indicator of that result, and cannot provide a comprehensive evaluation of all performance indicators. This paper proposes a comprehensive assessment method for the power structured data classification and grading based on hierarchical analysis, which allows various quality elements of the model to be extracted according to different needs, establishes a hierarchical indicator system, and determines the weights of each indicator in the system by means of hierarchical analysis, and finally calculates a comprehensive quality score for the classification and grading results. Finally, the method is systematically validated using different classification and grading models.","PeriodicalId":145263,"journal":{"name":"2023 5th International Conference on Communications, Information System and Computer Engineering (CISCE)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127265095","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 Photovoltaic Power Prediction Method for Power Grid Safety 面向电网安全的光伏发电功率预测方法研究
2023 5th International Conference on Communications, Information System and Computer Engineering (CISCE) Pub Date : 2023-04-14 DOI: 10.1109/CISCE58541.2023.10142818
Mingkang Guo, Wenxuan Ji, Bingling Gu, Peiyuan Li, Lin Tian
{"title":"Research on Photovoltaic Power Prediction Method for Power Grid Safety","authors":"Mingkang Guo, Wenxuan Ji, Bingling Gu, Peiyuan Li, Lin Tian","doi":"10.1109/CISCE58541.2023.10142818","DOIUrl":"https://doi.org/10.1109/CISCE58541.2023.10142818","url":null,"abstract":"When integrating large-scale photovoltaic systems with the power grid, variability and intermittency of photovoltaic power may potentially endanger the secure and stable operation of the power system as well as its scheduling management. So a new photovoltaic power prediction method using logistic chaotic mapping (LCM) improving atomic search optimization algorithm (ASO) to optimize back propagation neural network (LCM-ASO-BPNN) is proposed to solve this problem. The ASO algorithm is used to solve the defect that BPNN is likely to be trapped in a local optimum, and the initial population of the ASO algorithm is optimized by introducing logistic chaotic mapping, subsequently, the model's predictive accuracy is greatly enhanced. The experimental results demonstrate a significant improvement in the prediction accuracy of the proposed model when compared with the traditional prediction model.","PeriodicalId":145263,"journal":{"name":"2023 5th International Conference on Communications, Information System and Computer Engineering (CISCE)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127265899","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 Malicious Words Detection based on Neural Network 基于神经网络的恶意词检测研究
2023 5th International Conference on Communications, Information System and Computer Engineering (CISCE) Pub Date : 2023-04-14 DOI: 10.1109/CISCE58541.2023.10142537
Xiao-Chuang Chang
{"title":"Research on Malicious Words Detection based on Neural Network","authors":"Xiao-Chuang Chang","doi":"10.1109/CISCE58541.2023.10142537","DOIUrl":"https://doi.org/10.1109/CISCE58541.2023.10142537","url":null,"abstract":"At present, malicious words spreading not only damages the network environment but also bring an unsatisfied experience for all Internet users. Therefore, immediately detecting the malicious words becomes an important issue for existing Internet social networks and can assist the administrator to dispose the emergency issues. Traditional methods are primary concentrated on the several certain key words extraction, which may identify wrong malicious words and cost numerous computation costs. Subsequently, machine learning is utilized to detect malicious words, which enhances the identification accuracy with trained machine learning model. In this paper, we establish a neural network model to achieve malicious words identification with reasonable computation costs. We initially extract the sentence features through a multiple-layer perceptron and divide the malicious features to a trained neural network. Indeed, the features are divided to precisely identify the malicious words. From our extensive experimental results, we can conclude that our proposed methods can automatically identify the malicious words with more than 85% detection accuracy.","PeriodicalId":145263,"journal":{"name":"2023 5th International Conference on Communications, Information System and Computer Engineering (CISCE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131458658","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
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