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2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP)最新文献

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Transfer-learning-based Network Traffic Automatic Generation Framework 基于迁移学习的网络流量自动生成框架
2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP) Pub Date : 2021-04-09 DOI: 10.1109/ICSP51882.2021.9408767
Yanjie Li, Tianrui Liu, Dongxiao Jiang, Tao Meng
{"title":"Transfer-learning-based Network Traffic Automatic Generation Framework","authors":"Yanjie Li, Tianrui Liu, Dongxiao Jiang, Tao Meng","doi":"10.1109/ICSP51882.2021.9408767","DOIUrl":"https://doi.org/10.1109/ICSP51882.2021.9408767","url":null,"abstract":"Nowadays, there is an increasing number of attacks against the network system. The intrusion detection system is a standard method to prevent attack. In essence intrusion detection is a classification problem to judge normal or abnormal behaviors according to network traffic characteristics, and deep learning has been applied to intrusion detection recently. However, due to the lack of training data in some systems such as industrial control systems and smart grid, the deep-learning algorithm cannot give full play to its advantages. To solve this problem, we propose a transfer-learning-based network flow generation framework for deep-learning-based intrusion detection, which uses invariant extraction and sequence to sequence generation, to extract the attack invariant of the existing attack data set and transfer the knowledge to the target network system. We use the open-source data set on real systems and carry out relevant experiments, proving that our method can generate effective anomaly traffic as well as improve the accuracy of intrusion detection.","PeriodicalId":117159,"journal":{"name":"2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116246362","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: 6
Classification of images by using TensorFlow 使用TensorFlow进行图像分类
2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP) Pub Date : 2021-04-09 DOI: 10.1109/ICSP51882.2021.9408796
Ye Jingyi, Si Rui, Wei Tianqi
{"title":"Classification of images by using TensorFlow","authors":"Ye Jingyi, Si Rui, Wei Tianqi","doi":"10.1109/ICSP51882.2021.9408796","DOIUrl":"https://doi.org/10.1109/ICSP51882.2021.9408796","url":null,"abstract":"In the project of image classification, one model was built to sort images. By using the model which was built in the project, pictures can be classified effectively and quickly. At the beginning of the project, an appropriate data set was chosen. Then, the model was created by using TensorFlow. Next, the model would be trained to get the parameters with good fitting. Finally, in order to evaluate the model effectively, several graphs of validation accuracy were created. In the process of completing this project, the members of Team have mastered the ability to construct convolutional neural network models using python. What’s more, the members of the team also develop a good ability of data analysis.","PeriodicalId":117159,"journal":{"name":"2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115119946","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: 3
Intelligent Data Processing of Marine Target Tracking Process Based on Fuzzy clustering 基于模糊聚类的海洋目标跟踪过程智能数据处理
2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP) Pub Date : 2021-04-09 DOI: 10.1109/ICSP51882.2021.9408814
Zheng Zhang, Yanwei Du
{"title":"Intelligent Data Processing of Marine Target Tracking Process Based on Fuzzy clustering","authors":"Zheng Zhang, Yanwei Du","doi":"10.1109/ICSP51882.2021.9408814","DOIUrl":"https://doi.org/10.1109/ICSP51882.2021.9408814","url":null,"abstract":"Aiming at the inaccurate tracking and the difficulty of the track initialization during multi-target tracking in marine rescue scenes, this article proposes an intelligent processing method for marine tracking data based on fuzzy clustering. The measurement information received by the sensor of the measuring device is subjected to the fuzzy clustering process in a period of time, then the entropy from the subjection matrix is used to judge the change in the number of targets. In this way, the number of the target quantity can be determined and the target can be tracked more accurate. This article also analyzes and improves the method through simulation experiments to solve to problems such as inaccurate clustering in real situations. And the method can meet the needs of tracking velocity and tracking accuracy.","PeriodicalId":117159,"journal":{"name":"2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123226732","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
Modeling and experimental verification of space-time multipath wireless channel based on ray tracing 基于光线追踪的空时多径无线信道建模与实验验证
2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP) Pub Date : 2021-04-09 DOI: 10.1109/ICSP51882.2021.9408992
Yi Jiang, Musheng Wang, Shudie Ren
{"title":"Modeling and experimental verification of space-time multipath wireless channel based on ray tracing","authors":"Yi Jiang, Musheng Wang, Shudie Ren","doi":"10.1109/ICSP51882.2021.9408992","DOIUrl":"https://doi.org/10.1109/ICSP51882.2021.9408992","url":null,"abstract":"Aiming at the problems of deterministic modeling, such as limited scenes, large environmental impact, insufficient prediction accuracy, etc.. First of all, this paper has carried out related theoretical research on ray tracing technology, expounding its research status, theoretical basis and method content, algorithm model, etc.. This paper constructs an indoor channel model based on ray tracing, and obtains channel characteristic parameters through accurate simulation analysis of indoor scene buildings. This model can accurately analyze the propagation of indoor wireless signals and guarantees accuracy and accuracy. In addition, this article also built a channel measurement system, using the system to measure and analyze the channel parameter characteristics in the same scene to verify the accuracy of the channel model based on the ray tracing method.","PeriodicalId":117159,"journal":{"name":"2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124812949","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
Feature Extraction of Loader Operation Based on Kernel Principal Component Analysis 基于核主成分分析的加载器操作特征提取
2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP) Pub Date : 2021-04-09 DOI: 10.1109/ICSP51882.2021.9408684
Ren-bin Yu, Hui Ji-zhuang, S. Ze, Zhang Ze-yu, Zhang Xu-hui, Fan Hong-wei
{"title":"Feature Extraction of Loader Operation Based on Kernel Principal Component Analysis","authors":"Ren-bin Yu, Hui Ji-zhuang, S. Ze, Zhang Ze-yu, Zhang Xu-hui, Fan Hong-wei","doi":"10.1109/ICSP51882.2021.9408684","DOIUrl":"https://doi.org/10.1109/ICSP51882.2021.9408684","url":null,"abstract":"The response characteristics of the vehicle are complex under different working conditions. To facilitate the calculation and analysis of a variety of attributes, based on the kernel principal component analysis method, loaders are used as the research object of this article, data signals of 11 attributes such as throttle signal, speed, pressure, etc. are collected. After denoising and reconstruction of the original signal, the variance contribution rates of principal components 1-4 are 50.99 %, 29.90 %, 14.50 % and 4.27 % by using the kernel principal component analysis. The variance contribution rate of the original 11 attributes is 99.66 %, which could accurately reflect the working condition information. The research methods of multiattribute dimension reduction in this paper could be applied to road construction machinery, mining machinery, petroleum machinery and other engineering fields.","PeriodicalId":117159,"journal":{"name":"2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123055407","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 adaptive imaging method for ultrasound coherent plane-wave compounding based on the polar coherence factor 基于极性相干系数的超声相干平面波复合自适应成像方法
2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP) Pub Date : 2021-04-09 DOI: 10.1109/ICSP51882.2021.9408812
Mengjia Chang, Zhenkun Lu
{"title":"An adaptive imaging method for ultrasound coherent plane-wave compounding based on the polar coherence factor","authors":"Mengjia Chang, Zhenkun Lu","doi":"10.1109/ICSP51882.2021.9408812","DOIUrl":"https://doi.org/10.1109/ICSP51882.2021.9408812","url":null,"abstract":"Coherent Plane-Wave Compounding (CPWC) is a compromise between the frame rate and image quality after a single plane wave imaging, which emits plane waves at different angles to form a backscattered signal through the target, and then forms an image by superimposing it. Although this technology effectively improves the imaging quality, the unfocused nature of the plane waves leads to a strong coherence, especially when the two angles are similar, the artifacts in the image become more obvious. Therefore, this paper proposes a method of coherent plane-wave compounding based on the polar coherence factor (PCF), which takes the imaging results of plane waves at different angles as the weighting object, and then calculates the PCF factor. Finally, multiply the PCF factor and the result is the final weighted pixel value. The experimental results show that the PCF-CPWC algorithm greatly improves the resolution and contrast of the image, and it has better imaging quality.","PeriodicalId":117159,"journal":{"name":"2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121903332","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
Nonlinear Fiber Compensation Based on Neural Network in Reflective Coherent Detection System 反射相干检测系统中基于神经网络的非线性光纤补偿
2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP) Pub Date : 2021-04-09 DOI: 10.1109/ICSP51882.2021.9408701
Chengqi Bi, Shuqiang Chen, Jiacheng Fu
{"title":"Nonlinear Fiber Compensation Based on Neural Network in Reflective Coherent Detection System","authors":"Chengqi Bi, Shuqiang Chen, Jiacheng Fu","doi":"10.1109/ICSP51882.2021.9408701","DOIUrl":"https://doi.org/10.1109/ICSP51882.2021.9408701","url":null,"abstract":"We study artificial neural network used for fiber nonlinear mitigation in single-fiber bidirectional reflective coherent detection system. In this system, the transmitter of this system is located at the receiving end, and the transmission distance of carrier is double the length of actual optical fiber channel, the required optical emission power increases which leads to intensifying of nonlinear effect. Also, using neural network to compensate fiber nonlinear can be processed without knowing the system parameters preferentially. The simulation results show that fiber nonlinear mitigation based on neural network can effectively reduce the bit error rate and improve the transmission performance of the system.","PeriodicalId":117159,"journal":{"name":"2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP)","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124223745","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 Chinese Word Separation Based on Deep Learning 基于深度学习的汉语分词研究
2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP) Pub Date : 2021-04-09 DOI: 10.1109/ICSP51882.2021.9408899
Yuanyi Chen
{"title":"Research on Chinese Word Separation Based on Deep Learning","authors":"Yuanyi Chen","doi":"10.1109/ICSP51882.2021.9408899","DOIUrl":"https://doi.org/10.1109/ICSP51882.2021.9408899","url":null,"abstract":"Currently, a large amount of information is generated every day, and natural language processing techniques can help people to get the information they need quickly. For natural language processing of Chinese, Chinese word separation is a fundamental task in natural language processing. At present, research on Chinese word separation is basically based on machine learning methods, with the disadvantage that a large number of features need to be constructed manually. To address the shortcomings of current Chinese word sorting, this paper first analyzes the common methods and deep learning models for Chinese word sorting, and proposes an improvement scheme based on the Chinese word sorting model Bi LSTM+textbf CRF. And experiments are designed to verify the correctness and superiority of the model proposed in the paper on Chinese word separation on three datasets.","PeriodicalId":117159,"journal":{"name":"2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP)","volume":"264 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124303896","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
Big Data Analysis of Accounting Forecasting Based on Machine Learning 基于机器学习的会计预测大数据分析
2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP) Pub Date : 2021-04-09 DOI: 10.1109/ICSP51882.2021.9408921
W. Zhai, Guanlin Wu, Weidong Bao, Liyuan Niu
{"title":"Big Data Analysis of Accounting Forecasting Based on Machine Learning","authors":"W. Zhai, Guanlin Wu, Weidong Bao, Liyuan Niu","doi":"10.1109/ICSP51882.2021.9408921","DOIUrl":"https://doi.org/10.1109/ICSP51882.2021.9408921","url":null,"abstract":"Artificial intelligence, big data and other technologies will bring transformative significance to the traditional accounting and audit process, but the relevant research on the specific application methods of such technologies in the field of accounting and audit is still basically blank. In this paper, we combine 5 million pieces of user behavior information of Ant Financials’ Yu Ebao business to predict the company’s total daily capital inflow as the goal, and discuss the value of machine learning method in artificial intelligence in accounting prediction and specific implementation methods. In addition, based on the construction of application examples and the comparison between machine learning method and traditional method for accounting prediction, this paper also provides theoretical support for the application of machine learning method in the field of accounting. The results of this study show that machine learning is a very special method to predict the problem compared with traditional accounting, because it transcends the existing accounting information model to some extent. Although there has been a long history of using data to support accounting prediction, the depth, breadth, cost performance and potential of accounting prediction through artificial intelligence machine learning are unprecedented. The fundamental goal of machine learning technologies is to help a wide variety of organizations use modern data sets to gain valuable insights, and this is partly in line with the role that accounting has played in business operations in the past.","PeriodicalId":117159,"journal":{"name":"2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125211544","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
Research on Recognition Method of Neutron-Gamma Single Pulse Pileup Based on Mathematical Morphology 基于数学形态学的中子-伽马单脉冲叠加识别方法研究
2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP) Pub Date : 2021-04-09 DOI: 10.1109/ICSP51882.2021.9408914
Yao Huang, Mingzhe Liu, Xianghe Liu, Rui Luo, Xueyao Jiang, Tao Wang
{"title":"Research on Recognition Method of Neutron-Gamma Single Pulse Pileup Based on Mathematical Morphology","authors":"Yao Huang, Mingzhe Liu, Xianghe Liu, Rui Luo, Xueyao Jiang, Tao Wang","doi":"10.1109/ICSP51882.2021.9408914","DOIUrl":"https://doi.org/10.1109/ICSP51882.2021.9408914","url":null,"abstract":"In this study, the open operation of mathematical morphology of neutron-gamma pulse data with complex structural elements is proposed, and the pileup discrimination is carried out according to the maximum amplitude after the open operation. For single pulse signals, the method proposed in this paper can effectively reduce the occurrence of misidentification events of pulse pileup.","PeriodicalId":117159,"journal":{"name":"2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125335344","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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