2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE)最新文献_第3页

Research and application of credit risk of small and medium-sized enterprises based on random forest model 基于随机森林模型的中小企业信用风险研究与应用

2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE) Pub Date : 2021-01-15 DOI: 10.1109/ICCECE51280.2021.9342572

Wang Liu-yi, Zhu Li-gu

引用次数: 1

ICCECE 2021 Cover Page ICCECE 2021封面

2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE) Pub Date : 2021-01-15 DOI: 10.1109/iccece51280.2021.9342523

引用次数: 0

An Efficient Deep Reinforcement Learning Based Distributed Channel Multiplexing Framework for V2X Communication Networks 基于深度强化学习的V2X通信网络分布式信道复用框架

2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE) Pub Date : 2021-01-15 DOI: 10.1109/ICCECE51280.2021.9342305

R. Hu, Xinguo Wang, Yuyuan Su, Bin Yang

{"title":"An Efficient Deep Reinforcement Learning Based Distributed Channel Multiplexing Framework for V2X Communication Networks","authors":"R. Hu, Xinguo Wang, Yuyuan Su, Bin Yang","doi":"10.1109/ICCECE51280.2021.9342305","DOIUrl":"https://doi.org/10.1109/ICCECE51280.2021.9342305","url":null,"abstract":"It is crucial to multiplex channel resources efficiently in wireless networks due to the link interference and wireless spectrum scarcity. In this paper, we study the allocation problem of channel resources in Vehicle-to-Everything communication networks. We model this problem as a decentralized Markov Decision Process, where each V2V Agent independently decides its channel and power level based on the local environmental observations and global network reward. Then, a multi-agent distributed channel resource multiplexing framework based on Deep Reinforcement Learning is proposed to derive the best joint resources allocation solution. Furthermore, Prioritized DDQN algorithm is used to provide a more accurate estimation target for the action evaluation and can effectively reduce Q-Values’ overestimation. The extensive experimental results show that the proposed framework can achieve better performances than the existing works in terms of both the capacity sum of V2I channels and the package delivery success ratios of V2V links.","PeriodicalId":229425,"journal":{"name":"2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117280465","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: 4

Research on Intrusion Detection Based on BP Neural Network 基于BP神经网络的入侵检测研究

2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE) Pub Date : 2021-01-15 DOI: 10.1109/ICCECE51280.2021.9342479

Haonan Chen, Yiyang Liu, Jianming Zhao, Xianda Liu

{"title":"Research on Intrusion Detection Based on BP Neural Network","authors":"Haonan Chen, Yiyang Liu, Jianming Zhao, Xianda Liu","doi":"10.1109/ICCECE51280.2021.9342479","DOIUrl":"https://doi.org/10.1109/ICCECE51280.2021.9342479","url":null,"abstract":"The purpose of network security is to prevent the data transmitted over the Internet from being stolen and tampered with, and to ensure the security of the data. It is not only necessary to ensure that the information entering and exiting the network is not stolen or tampered with, but also to ensure the integrity and confidentiality of the information in the information system. The network environment is becoming more and more complex, and the attack methods are becoming more and more diverse. Therefore, intrusion detection systems have some common problems, such as low detection rate and high false alarm rate, and it is difficult to meet the real-time requirements of intrusion detection systems. Currently, deep learning is increasingly used in intrusion detection. In order to solve the problems existing in the current intrusion detection system, this paper studies the application of deep learning in intrusion detection. First, it analyzes the BP neural network (BP-NN) technology, and proposes an improvement method for the shortcomings of the current BP-NN, and finally conducts an empirical analysis. Experimental results show that intrusion detection based on BP-NN has a high accuracy rate, and the false alarm rate and false alarm rate are both at a low level.","PeriodicalId":229425,"journal":{"name":"2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE)","volume":"452 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125785302","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: 2

Research on the Information Credibility Modeling Method Based on Big Data 基于大数据的信息可信度建模方法研究

2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE) Pub Date : 2021-01-15 DOI: 10.1109/ICCECE51280.2021.9342249

Yijun Liulst, Wenqi Cai, Zhigang Xu

引用次数: 0

Adaptive waveform optimization method for OFDM radar communication jamming OFDM雷达通信干扰的自适应波形优化方法

2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE) Pub Date : 2021-01-15 DOI: 10.1109/ICCECE51280.2021.9342472

Shengkun Zhu, Xiaobai Li, Rui-juan Yang, Yating Ding

引用次数: 4

Fully Convolutional Network Variations and Method on Small Dataset 小数据集上的全卷积网络变化与方法

2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE) Pub Date : 2021-01-15 DOI: 10.1109/ICCECE51280.2021.9342059

Tianyou Hu, Yancong Deng, Yuwei Deng, Anmin Ge

{"title":"Fully Convolutional Network Variations and Method on Small Dataset","authors":"Tianyou Hu, Yancong Deng, Yuwei Deng, Anmin Ge","doi":"10.1109/ICCECE51280.2021.9342059","DOIUrl":"https://doi.org/10.1109/ICCECE51280.2021.9342059","url":null,"abstract":"Fully Convolutional Network (FCN) labels each pixel in an image with its category by up-sampling convolutional layer to the exact shape of input image. This paper presents a detailed evaluation on Fully Convolutional Network variations and method on small dataset. The paper mainly discusses three FCN models based on VGG16, containing FCN-32s, FCN-16s and FCN-8s, which are different in their up-sample multiple and process of fusing skipped layers. FCN based on ResNet and vanilla Convolutional Neural Network (CNN) are discussed as well for comparative experiment. Because of the small dataset, FCN method is quite different from the general, therefore arguments containing kernel size and up-sample method are tuned to increase accuracy for each kind of model. Arguments with highest accuracy are picked for comparative experiment among different kinds of model, which are FCN based on VGG16, ResNet and vanilla CNN. Mean Intersection over Union (mIoU) metric is computed as well to contrast segmentation performance among models and among classes. Loss, accuracy and mIoU after 300 epochs of training are compared. optimize processes of models are recorded to evaluate converge trend. Among all models implemented in our experiment, FCN-8s stands out, reaching the accuracy of 86.79% after 300 epochs, only by training a small dataset including 367 train images and 101 test images.","PeriodicalId":229425,"journal":{"name":"2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129045796","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

Application of Convolutional Neural Networks to the Classification of Agricultural Technology Articles 卷积神经网络在农业科技文章分类中的应用

2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE) Pub Date : 2021-01-15 DOI: 10.1109/ICCECE51280.2021.9342271

Wu Qimeng, Qi Qiuyang, Xin Ping, Zhang Enhui

引用次数: 0

Pneumothorax Image Segmentation and Prediction with UNet++ and MSOF Strategy 基于UNet++和MSOF的气胸图像分割与预测

2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE) Pub Date : 2021-01-15 DOI: 10.1109/ICCECE51280.2021.9342193

Zhongzhi Li, Jiankai Zuo, Chunhong Zhang, Yifan Sun

{"title":"Pneumothorax Image Segmentation and Prediction with UNet++ and MSOF Strategy","authors":"Zhongzhi Li, Jiankai Zuo, Chunhong Zhang, Yifan Sun","doi":"10.1109/ICCECE51280.2021.9342193","DOIUrl":"https://doi.org/10.1109/ICCECE51280.2021.9342193","url":null,"abstract":"Deep learning is becoming more and more popular to solve image segmentation tasks in medical image processing community because of the incredible advantages in deep feature representation and nonlinear problem modeling. However, most existing deep learning methods based segmentation are implemented by combing deep, semantic, coarse-grained feature maps from the decoder sub network with shallow, low-level, fine-grained feature maps from the encoder sub-network, which are not up to the mustard of medical image segmentation. To solve the above-mentioned problem, an innovative end-to-end Pneumothorax Segmentation (PS) method based on UNet++ is proposed, where change maps could be learned from scratch using existing annotated datasets. And the fusion strategy of multiple side outputs is applied to combine change maps from different semantic levels. The high efficiency and availability of our proposed method are proved with SIIM-ACR Pneumothorax Segmentation dataset. Plenty of experimental results have shown that our proposed approach outperforms many cutting-edge methods.","PeriodicalId":229425,"journal":{"name":"2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131735566","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: 7

A Data Mining Based System For Transaction Fraud Detection 基于数据挖掘的交易欺诈检测系统

2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE) Pub Date : 2021-01-15 DOI: 10.1109/ICCECE51280.2021.9342376

Wenkai Deng, Ziming Huang, Jiachen Zhang, Junyan Xu

引用次数: 8