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2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)最新文献

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A Fault Location Method Based on Improved Wavelet Denoising for Open-line Fault in the Low-voltage Distribution Network 基于改进小波去噪的低压配电网开路故障定位方法
2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE) Pub Date : 2020-11-20 DOI: 10.1109/AUTEEE50969.2020.9315564
Peng Fu, Lihui Yang, Jinzhu Shi
{"title":"A Fault Location Method Based on Improved Wavelet Denoising for Open-line Fault in the Low-voltage Distribution Network","authors":"Peng Fu, Lihui Yang, Jinzhu Shi","doi":"10.1109/AUTEEE50969.2020.9315564","DOIUrl":"https://doi.org/10.1109/AUTEEE50969.2020.9315564","url":null,"abstract":"In this paper, the traveling wave method is used to accurately locate the open-line fault in the low-voltage distribution network where many branch lines and noise interference exist. An improved wavelet threshold function (TF) method is proposed to reduce the noise of the collected traveling wave signal. Then the wavelet singularity theory is applied to identify the time interval between the injected wave head and the reflected wave head, based on which the location of the open-line fault can be calculated. The actual cable models are built in the PSCAD to represent the power lines of the low-voltage distribution network and the open-line fault simulation results show that the proposed denoising method in this paper can increase the signal to noise ratio (SNR) of the collected traveling wave and improve the accuracy of fault location.","PeriodicalId":6767,"journal":{"name":"2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)","volume":"15 1","pages":"111-115"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80879080","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
Blind Image Quality Assessment Based on Active Learning 基于主动学习的盲图像质量评价
2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE) Pub Date : 2020-11-20 DOI: 10.1109/AUTEEE50969.2020.9315621
Lihao Zheng, Genyuan Zhang, Yaping Cai
{"title":"Blind Image Quality Assessment Based on Active Learning","authors":"Lihao Zheng, Genyuan Zhang, Yaping Cai","doi":"10.1109/AUTEEE50969.2020.9315621","DOIUrl":"https://doi.org/10.1109/AUTEEE50969.2020.9315621","url":null,"abstract":"Image quality assessment (IQA) is a task of measuring the perceptual quality of a given image and it is a hot study direction in the region of video and image processing and blind image quality assessment (BIQA) model is one of the most utilized full models. Among the existing BIQA models, the CORNIA model has achieved good performance. In the CORNIA model, Ye et. al. utilized d k-means clustering to cluster the pre-processed image patch s, and utilized d the clustering center as a dictionary. However, experimental observations have shown that when the dictionary size is small, the Superiority of CORNIA decreases rapidly. To address this, we introduce active learning strategies into unsupervised character learning, proposing an active character learning (AFL) framework, and apply it to BIQA model.","PeriodicalId":6767,"journal":{"name":"2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)","volume":"134 1","pages":"147-151"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88974968","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
Blockchain Based on PoR Consensus Mechanism 基于PoR共识机制的区块链
2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE) Pub Date : 2020-11-20 DOI: 10.1109/AUTEEE50969.2020.9315612
Wenyu Shen, Xuebing Huang, Yang Yu, Lintian Gu, Jiangluhai Pan, Li Ling
{"title":"Blockchain Based on PoR Consensus Mechanism","authors":"Wenyu Shen, Xuebing Huang, Yang Yu, Lintian Gu, Jiangluhai Pan, Li Ling","doi":"10.1109/AUTEEE50969.2020.9315612","DOIUrl":"https://doi.org/10.1109/AUTEEE50969.2020.9315612","url":null,"abstract":"Using Bitcoin as a reference, we’ve created a new consensus mechanism named PoR (Proof of Rest). We built a blockchain with six core nodes for testing and simulated certain user transactions, which proved to be efficient. PoR is an asynchronous network that solves the massive waste of computing power in the Bitcoin system, but still retains the ability to resist forks. Compared with the PBFT consensus mechanism, the requirements for the network are also lower. But it would be a bit tricky when changing the total number of core nodes.","PeriodicalId":6767,"journal":{"name":"2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)","volume":"16 1","pages":"73-76"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90479221","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 Power Quality Monitoring System Based on Smart Grid Information Processing 基于智能电网信息处理的电能质量监测系统研究
2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE) Pub Date : 2020-11-20 DOI: 10.1109/AUTEEE50969.2020.9315642
Meiming Fu, Kang Wang, Bo Xu, Shuming Xu
{"title":"Research on Power Quality Monitoring System Based on Smart Grid Information Processing","authors":"Meiming Fu, Kang Wang, Bo Xu, Shuming Xu","doi":"10.1109/AUTEEE50969.2020.9315642","DOIUrl":"https://doi.org/10.1109/AUTEEE50969.2020.9315642","url":null,"abstract":"There are a large number of nonlinear, fluctuating, and shock loads in the power system, which will cause the degradation of power quality. With the development of computers and microprocessors, the power sector has become more and more important to power quality monitoring, especially in terms of voltage, current, and frequency for power monitoring. The paper proposes a system scheme for power quality monitoring chip. SoC chip integrates functions such as signal acquisition, power quality parameter calculation, wave recording and control communication. Various modules use the high-speed bus on the chip for data exchange. Combining GPRS communication technology, ASP.NET technology and database technology, a remote monitoring system for power quality based on B/S structure is developed. This system is used to realize local and remote real-time monitoring of power quality of smart grid. After simulation and testing, the device can effectively monitor the power quality of the power grid.","PeriodicalId":6767,"journal":{"name":"2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)","volume":"1 1","pages":"219-222"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90717241","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
Chinese Text Error Correction Method Based on Prefix Tree Merging 基于前缀树合并的中文文本纠错方法
2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE) Pub Date : 2020-11-20 DOI: 10.1109/AUTEEE50969.2020.9315643
Zongyu Yang, Hao Zeng, Hongyan Li
{"title":"Chinese Text Error Correction Method Based on Prefix Tree Merging","authors":"Zongyu Yang, Hao Zeng, Hongyan Li","doi":"10.1109/AUTEEE50969.2020.9315643","DOIUrl":"https://doi.org/10.1109/AUTEEE50969.2020.9315643","url":null,"abstract":"In order to solve the problem of high computational complexity and repetitive calculation of the Long Short-Term Memory (LSTM) language model in the task of Chinese text automatic proofreading, a Chinese text error correction method based on prefix tree merging is proposed in this paper. The method has made the following improvements: different from the traditional error correction method based on N-gram model, the method use LSTM language model to evaluate the rationality of the candidate sentences, and the candidate sentences with higher similarity are combined into a tree structure and then scored. Repetitive calculations can be reduced by merging the same calculations when the model calculates the probability of candidate sentences, which thereby could improve the calculation efficiency of the LSTM language model. The experimental results show that this method can not only achieve good error correction accuracy, but also shorten the time consumed and improve the error correction efficiency.","PeriodicalId":6767,"journal":{"name":"2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)","volume":"10 1","pages":"272-276"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84548076","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 and Implemention of System of Electric Business Expanding Installation Process Based on Graph Database 基于图数据库的电商扩容安装流程系统的研究与实现
2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE) Pub Date : 2020-11-20 DOI: 10.1109/AUTEEE50969.2020.9315680
Junfeng Qiao, Aihua Zhou, Hongbin Qiu
{"title":"Research and Implemention of System of Electric Business Expanding Installation Process Based on Graph Database","authors":"Junfeng Qiao, Aihua Zhou, Hongbin Qiu","doi":"10.1109/AUTEEE50969.2020.9315680","DOIUrl":"https://doi.org/10.1109/AUTEEE50969.2020.9315680","url":null,"abstract":"The expansion and installation of electric transformer plays a very important role in the marketing management of electric company, which is closely related to the quality of electric services. With the rapid improvement of the level of science and technology, higher requirements are put forward for the expansion and installation of the electric industry. It is necessary for the expansion and installation of electric transformer to work out a more reasonable and efficient plan with the help of scientific and technological means. The graph database has technical advantages of high efficiency of topological data organization, low redundancy of topological data storage, strong expansibility of topological data model and high efficiency of topological analysis and calculation, which can meet the needs of rapid topological analysis and efficient calculation in the expansion and installation of electric transformer. The results show that the auxiliary system based on graph database can greatly improve the service quality of electric company.","PeriodicalId":6767,"journal":{"name":"2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)","volume":"25 1","pages":"10-13"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79428239","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 air training evaluation model based on improved KNN algorithm 基于改进KNN算法的空中训练评估模型研究
2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE) Pub Date : 2020-11-20 DOI: 10.1109/AUTEEE50969.2020.9315617
Fengqin Wang, Tingxue Xu, Jing Yu
{"title":"Research on air training evaluation model based on improved KNN algorithm","authors":"Fengqin Wang, Tingxue Xu, Jing Yu","doi":"10.1109/AUTEEE50969.2020.9315617","DOIUrl":"https://doi.org/10.1109/AUTEEE50969.2020.9315617","url":null,"abstract":"How to realize the automatic evaluation of pilots’ air training performance has become a research hotspot in the field of air training. Firstly, the index model of flight action evaluation is established, and then the automatic air training evaluation model based on improved k-nearest neighbor algorithm is constructed. Finally, Python language implementation of the evaluation model is given. The experimental results show that the evaluation model has high accuracy, and is easy to find out the problems existing in flight training, so as to improve flight and improve flight training performance.","PeriodicalId":6767,"journal":{"name":"2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)","volume":"154 1","pages":"380-384"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86156065","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 Feature Extraction Method of Fundus Image Based on Deep Learning 基于深度学习的眼底图像特征提取方法研究
2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE) Pub Date : 2020-11-20 DOI: 10.1109/AUTEEE50969.2020.9315604
Xiao-hui Cheng, Xianfeng Feng, Wei Li
{"title":"Research on Feature Extraction Method of Fundus Image Based on Deep Learning","authors":"Xiao-hui Cheng, Xianfeng Feng, Wei Li","doi":"10.1109/AUTEEE50969.2020.9315604","DOIUrl":"https://doi.org/10.1109/AUTEEE50969.2020.9315604","url":null,"abstract":"Image feature extraction is a key step in medical image processing, and it also plays an important role in the recognition and classification of medical images. A feature extraction method of fundus images using deep learning technology is proposed to recognize and classify fundus images. Firstly, the fundus image in the data set needs to be preprocessed. Then, the fundus image is extracted in the VGG16 network for the optic cup and disc region. Finally, the fundus feature image is input into the improved Softmax classifier for recognition and classification. The experimental results show that the fundus image feature extraction method used in the fundus images of glaucoma patients and normal people has a higher classification accuracy rate and the classification effect is better.","PeriodicalId":6767,"journal":{"name":"2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)","volume":"37 1","pages":"443-447"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74905139","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
Automatic Chinese Proofreading Based on Deep Learning 基于深度学习的中文自动校对
2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE) Pub Date : 2020-11-20 DOI: 10.1109/AUTEEE50969.2020.9315729
Haozhe Chen
{"title":"Automatic Chinese Proofreading Based on Deep Learning","authors":"Haozhe Chen","doi":"10.1109/AUTEEE50969.2020.9315729","DOIUrl":"https://doi.org/10.1109/AUTEEE50969.2020.9315729","url":null,"abstract":"The rapid development of the Internet enables a large number of users to generate a large amount of network text. Correcting the grammatical errors implied in the text can make the text smoother and easier to read. It is obviously unrealistic to process massive text based on manual proofreading, which makes the text automatic. Proofreading tasks have received more attention in recent years. The rise of deep learning makes the sequence-to-sequence learning method widely used in text proofreading tasks. This paper improves the existing Chinese text automatic proofreading method based on deep learning, mainly from the following aspects: This paper uses depth-based Learning to correct and correct texts, compared to the traditional n-gram method in detecting sentence fluency, detecting incorrect words, and also generating alternative words and other accuracy has been greatly improved. The experimental results show that the training of this model can converge quickly, and the training accuracy rate is as high as 90.64%, which can effectively and accurately correct Chinese text.","PeriodicalId":6767,"journal":{"name":"2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)","volume":"260 1","pages":"470-473"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76388529","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
Electric IoT Perception Layer Data Privacy-preserving Using Multi-identity-based Fully Homomorphic Encryption 基于多身份全同态加密的电物联网感知层数据隐私保护
2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE) Pub Date : 2020-11-20 DOI: 10.1109/AUTEEE50969.2020.9315709
Rixuan Qiu, Jiawen Yu, Fuyong Zheng, Liang Liang, Yuancheng Li
{"title":"Electric IoT Perception Layer Data Privacy-preserving Using Multi-identity-based Fully Homomorphic Encryption","authors":"Rixuan Qiu, Jiawen Yu, Fuyong Zheng, Liang Liang, Yuancheng Li","doi":"10.1109/AUTEEE50969.2020.9315709","DOIUrl":"https://doi.org/10.1109/AUTEEE50969.2020.9315709","url":null,"abstract":"The edge perception layer of electric Internet of Things (eIoT) involves a large amount of user’s data, including identity, location and other sensitive data, facing the risk of privacy disclosure. To solve this problem, we first established an eIoT model based on edge IoT agent, and propose a dynamic privacy preserving scheme using Multi-identity-based Fully Homomorphic Encryption (MIBFHE). Different from the exiting schemes, the system is no longer limited to the same secret key. Each user generating their own secret key on the basis of identity, and key management, revocation are much easier to achieve. In addition, our scheme is dynamically faulttolerant, and it can still be normal to calculate even if the device is added or failed. The experimental results verify the effectiveness and advantage in the cost of computational and communication compared to the existing schemes.","PeriodicalId":6767,"journal":{"name":"2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)","volume":"36 1","pages":"30-34"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76663694","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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