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

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Language Chatbot–The Design and Implementation of English Language Transfer Learning Agent Apps 语言聊天机器人——英语语言迁移学习代理app的设计与实现
2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE) Pub Date : 2020-11-20 DOI: 10.1109/AUTEEE50969.2020.9315567
Nuobei Shi, Qin Zeng, Raymond S. T. Lee
{"title":"Language Chatbot–The Design and Implementation of English Language Transfer Learning Agent Apps","authors":"Nuobei Shi, Qin Zeng, Raymond S. T. Lee","doi":"10.1109/AUTEEE50969.2020.9315567","DOIUrl":"https://doi.org/10.1109/AUTEEE50969.2020.9315567","url":null,"abstract":"Language Chatbot has widely used in customer service and personal assistant for task orientated, interactive chats in special domains, knowledge base for question-answer systems, in general, chatbot including automatic speech recognition (ASR), natural language understanding (NLU), dialogue management (DM), natural language generation (NLG), speech synthesis (SS). In our research, we proposed a transfer learning-based English Language learning chatbot with THREE levels learning system in real-world application, which integrate recognition service from Google and GPT-2 from Open AI with dialogue tasks in NLU and NLG at miniprogram of WeChat.","PeriodicalId":6767,"journal":{"name":"2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)","volume":"15 1","pages":"403-407"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87835785","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: 12
Automatic Algorithmically Generated Domain Detection with Deep Learning Methods 基于深度学习方法的自动算法生成域检测
2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE) Pub Date : 2020-11-20 DOI: 10.1109/AUTEEE50969.2020.9315559
Yihang Zhang
{"title":"Automatic Algorithmically Generated Domain Detection with Deep Learning Methods","authors":"Yihang Zhang","doi":"10.1109/AUTEEE50969.2020.9315559","DOIUrl":"https://doi.org/10.1109/AUTEEE50969.2020.9315559","url":null,"abstract":"Domain-Flux is the main way for malware or botnets to bypass most detection systems where static methods, such as blacklists, are relied on. The key point of Domain-Flux is Domain Generation Algorithm (DGA), which can generate thousands of domain names in a short time. Therefore, effective detection of DGA domain names plays an important role in cybersecurity defense. Traditional detection methods mainly depend on the reverse engineering of malware samples, which is tedious and inflexible. In this paper, we apply artificial intelligence methods in this field and detect DGA domains automatically. Specifically, we first discover the pseudo-randomness of DGA domain name strings through data analysis. Then, we build several DGA classifiers based on different machine learning and deep learning methods. Apart from separating DGA domains from benign ones, classifiers with deep learning models can also support multi-classification and identify DGA families. Finally, the experiment results on a well-known public dataset show that the classifier with CNN model is superior to the others with the consideration of accuracy, efficiency and robustness.","PeriodicalId":6767,"journal":{"name":"2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)","volume":"12 1","pages":"463-469"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87192867","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
Generating Reasoning Plan of SWRL Rule with Spark 用Spark生成SWRL规则推理方案
2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE) Pub Date : 2020-11-20 DOI: 10.1109/AUTEEE50969.2020.9315684
Wan Li, Dongbo Ma, Xiuhua Geng, Li Zhu, Zhong Wan, H. Li
{"title":"Generating Reasoning Plan of SWRL Rule with Spark","authors":"Wan Li, Dongbo Ma, Xiuhua Geng, Li Zhu, Zhong Wan, H. Li","doi":"10.1109/AUTEEE50969.2020.9315684","DOIUrl":"https://doi.org/10.1109/AUTEEE50969.2020.9315684","url":null,"abstract":"With the advent of the big data era, large-scale semantic data has emerged. Semantic data contains some important but usually implicit information, which can be derived through reasoning. The conventional single-machine reasoners inevitably have problems such as insufficient computing performance and scalability. And the existing largescale reasoners have limited functions, they cannot fully and effectively support Semantic Web Rule Language (SWRL). In this regard, we propose a scalable distributed reasoning method for SWRL using Spark SQL. This paper shows how to use Spark to generate reasoning plan of SWRL rules, which are used in subsequent reasoning stages. We define a hierarchical data structure to represent the reasoning plan, and designed some variant data structures to facilitate generating reasoning plan. We also use Spark to implement distributed generating the reasoning plan of rule base which composed of rules.","PeriodicalId":6767,"journal":{"name":"2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)","volume":"18 1","pages":"376-379"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87179715","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
Application of Deep Learning Algorithm in Generator Fault Prediction 深度学习算法在发电机故障预测中的应用
2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE) Pub Date : 2020-11-20 DOI: 10.1109/AUTEEE50969.2020.9315532
Xia Yun, Haiwei Wu
{"title":"Application of Deep Learning Algorithm in Generator Fault Prediction","authors":"Xia Yun, Haiwei Wu","doi":"10.1109/AUTEEE50969.2020.9315532","DOIUrl":"https://doi.org/10.1109/AUTEEE50969.2020.9315532","url":null,"abstract":"Recent rapid development of information and communication technology boosts the advance of distributed management and control system, especially for power system. Massive data and information have been accumulated, however, the meaningful fault information hidden in these data is not fully utilized, as the existing fault detection technologies are usually based on monitoring and diagnosis rather than prediction. In this paper, we introduce the deep learning algorithm into the fault prediction of generators in power system, and explore the validity and feasibility of generator operation data in fault prediction application. Our method includes two parts, the first is a Partial Least Square (PLS)-based pre-process module which is used to reduce the feature dimension, the second is a deep linear regression model which is dedicated to regressing the generator operation data and predicting the fault behavior of generators. Experimental results demonstrate the effectiveness of our method.","PeriodicalId":6767,"journal":{"name":"2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)","volume":"52 1","pages":"152-155"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83149041","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
Adaptive neural network control of hypersonic flight vehicle with actuator constraints 具有作动器约束的高超声速飞行器自适应神经网络控制
2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE) Pub Date : 2020-11-20 DOI: 10.1109/AUTEEE50969.2020.9315589
Aixue Wang, Shuquan Wang
{"title":"Adaptive neural network control of hypersonic flight vehicle with actuator constraints","authors":"Aixue Wang, Shuquan Wang","doi":"10.1109/AUTEEE50969.2020.9315589","DOIUrl":"https://doi.org/10.1109/AUTEEE50969.2020.9315589","url":null,"abstract":"An adaptive neural network controller based on the back-stepping is developed for a generic hypersonic flight vehicle. The controller addresses two main problems, including model uncertainty and input saturations. First, the longitudinal dynamic model is transformed into an altitude subsystem and a velocity subsystem with the strict feedback form. Then, the combination of the adaptive neural network controller via the back-stepping method and command filter is utilized to track the altitude and velocity command. The stability analysis of the closed-loop system is proved based on Lyapunov’s stability theorem. Simulation results display that the proposed controller is robust in terms of parametric uncertainty and meets the performance requirements with input saturation.","PeriodicalId":6767,"journal":{"name":"2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)","volume":"20 1","pages":"171-175"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75863526","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
Sparse KELM Online Prediction Model Based on Forgetting Factor 基于遗忘因子的稀疏KELM在线预测模型
2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE) Pub Date : 2020-11-20 DOI: 10.1109/AUTEEE50969.2020.9315722
Jinling Dai, Aiqiang Xu, Xing Liu, Ruifeng Li
{"title":"Sparse KELM Online Prediction Model Based on Forgetting Factor","authors":"Jinling Dai, Aiqiang Xu, Xing Liu, Ruifeng Li","doi":"10.1109/AUTEEE50969.2020.9315722","DOIUrl":"https://doi.org/10.1109/AUTEEE50969.2020.9315722","url":null,"abstract":"In the process of online prediction of nonstationary time series by kernel extreme learning machine (KELM), two problems appear that the order of kernel matrix is increasing and the system dynamic characteristics are difficult to be determined. A sparse KELM state prediction model based on forgetting factor (FF) is proposed. Firstly, by introducing the forgetting factor, a new objective function is constructed to make the elements in the sparse dictionary have different weights according to the time distance, so as to ensure the effective tracking of the dynamic changes of the model. By studying the relationship between KELM and kernel recursive least-squares (KRLS), KRLS is extended to the online sparse KELM framework. To control the growth of network structure, and realize the recursion and update of dictionary parameters, the samples are sparse by using approximate linear dependence (ALD) criterion. The experimental results show that compared with KB-KELM, FOKELM, NOS-KELM and KRLSELM, FF-KRLSELM can reduce the average root mean square error by 48% and 36%, and the average relative error by 37% and 36%, and has good dynamic tracking ability and adaptability.","PeriodicalId":6767,"journal":{"name":"2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)","volume":"73 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91111927","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 the Temporal and Spatial Law of Respiratory Dust Concentration in Fully-Mechanized Mine 综采矿山呼吸性粉尘浓度时空规律研究
2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE) Pub Date : 2020-11-20 DOI: 10.1109/AUTEEE50969.2020.9315539
Zheng Zhao
{"title":"Research on the Temporal and Spatial Law of Respiratory Dust Concentration in Fully-Mechanized Mine","authors":"Zheng Zhao","doi":"10.1109/AUTEEE50969.2020.9315539","DOIUrl":"https://doi.org/10.1109/AUTEEE50969.2020.9315539","url":null,"abstract":"At present, the temporal and spatial law of the concentration of respirable dust in fully mechanized mining faces had been generally obtained through numerical simulation and regional actual measurement, which are all qualitative researches. This article would conduct quantitative research on them. First, used CFD software to numerically simulate the spatial law of the total dust concentration of the fully mechanized face. Based on this, designed the three-dimensional space actual measurement point plan of the fully mechanized face; adopted the international standard method— weighing method, according to the working conditions (production and support) The total dust concentration and the particle size distribution of respirable dust were measured on site to establish the spatial law of total dust concentration and the particle size distribution of respirable dust and a three-dimensional mathematical model; combined the two to calculate the spatial law of respirable dust at the fully mechanized face; installed a respirable dust concentration sensor at the appropriate position of the fully mechanized face to monitor the characteristics of the change of respirable dust concentration over time, and explored the real-time and online spatial and temporal laws of respirable dust concentration in fully mechanized face based on the previous spatial laws of respirable dust.","PeriodicalId":6767,"journal":{"name":"2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)","volume":"406 1","pages":"214-218"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76324749","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
Design of embedded optical cable anti extemal damage early waming system based on lonely forest statistical algorithm 基于孤独森林统计算法的嵌入式光缆防外损预警系统设计
2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE) Pub Date : 2020-11-20 DOI: 10.1109/AUTEEE50969.2020.9315703
X. Yang, Shilun Zeng, Xiaofang Wang, Lipeng Zhu, Jianbing Li, Liang Chen
{"title":"Design of embedded optical cable anti extemal damage early waming system based on lonely forest statistical algorithm","authors":"X. Yang, Shilun Zeng, Xiaofang Wang, Lipeng Zhu, Jianbing Li, Liang Chen","doi":"10.1109/AUTEEE50969.2020.9315703","DOIUrl":"https://doi.org/10.1109/AUTEEE50969.2020.9315703","url":null,"abstract":"The buried cable laying method is gradually favored because it solves the environmental protection problems of urban construction. The new city basically chooses the buried type operation under the condition of pipeline permission, but it increases the maintenance difficulty for the distribution network operation and maintenance personnel. In order to meet the demand of this application, this paper designs a kind of external stress sensitive detection function of distributed sensing optical fiber, combined with the computing advantages of Fourier transform and lonely forest algorithm, transforms the time-domain information of optical fiber into frequency-domain information, and extracts the sensitive phase amplitude and position important parameters in the frequency domain, and compares them with the storage fault threshold value of power optical fiber external failure event In addition, the system can detect the failure event in time and send out warning information to avoid further cable damage. Compared with other data algorithms, the system detection and fault evaluation method designed in this paper has high accuracy, strong matching of application requirements and practicability to solve problems.","PeriodicalId":6767,"journal":{"name":"2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)","volume":"61 1","pages":"264-267"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84027490","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
Analysis, Application and Research of the Index of Urban Resumption of Work and Production Based on Energy Big Data 基于能源大数据的城市复工复产指标分析、应用与研究
2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE) Pub Date : 2020-11-20 DOI: 10.1109/AUTEEE50969.2020.9315585
Wang Wei, Z. Xie, Li Duanchao, Chen Shuo, Xiao Jiakai, Xu Zhong-ping
{"title":"Analysis, Application and Research of the Index of Urban Resumption of Work and Production Based on Energy Big Data","authors":"Wang Wei, Z. Xie, Li Duanchao, Chen Shuo, Xiao Jiakai, Xu Zhong-ping","doi":"10.1109/AUTEEE50969.2020.9315585","DOIUrl":"https://doi.org/10.1109/AUTEEE50969.2020.9315585","url":null,"abstract":"At present, the energy enterprises have realized the improvement of energy management by using their own energy data acquisition, analysis and application system, but there is information island phenomenon among the systems. With the diversification of enterprise energy consumption, the fusion analysis of various energy data becomes more and more important. The analysis only based on power data cannot provide comprehensive and accurate decision-making basis for the government and energy enterprises. In this paper, the energy big data center collects and fuses the data of electricity, water, gas and heat by using the index system analysis model of urban work and production resumption of work and production, analyzing and comparing the overall situation in Hefei with different regions and different industries in previous years, so as to effectively supports the scientific formulation of government policies.","PeriodicalId":6767,"journal":{"name":"2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)","volume":"1 1","pages":"428-432"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90873655","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
Fake account detection with attention-based graph convolution networks 基于注意力的图卷积网络的虚假账户检测
2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE) Pub Date : 2020-11-20 DOI: 10.1109/AUTEEE50969.2020.9315597
Peipei Yang, Zhuoyuan Zheng
{"title":"Fake account detection with attention-based graph convolution networks","authors":"Peipei Yang, Zhuoyuan Zheng","doi":"10.1109/AUTEEE50969.2020.9315597","DOIUrl":"https://doi.org/10.1109/AUTEEE50969.2020.9315597","url":null,"abstract":"Social networks are permeating all aspects of social life. As the number of users of social networks continues to expand and social relationships continue to enrich, the security risks faced by social network environments are becoming increasingly apparent. Most of the existing methods need to manually extract some descriptive features, which leads to the performance of the model depends largely on the quality of the extracted features. Aiming at the problem, this paper proposes an attention-based graph neural network, which uses the graph convolutional operator to capture the aggregation patterns in social networks automatically. We verified and discussed this proposed hypothesis on the cresci2017 dataset. Experimental results show that our attention based GNNs are better able to capture malicious account behavior than previous non-learning methods.","PeriodicalId":6767,"journal":{"name":"2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)","volume":"143 1","pages":"106-110"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85271248","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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