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2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA)最新文献

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Uncertainty Modeling of Source Load and Analysis of Park Energy Use Optimization 源负荷不确定性建模与园区能源利用优化分析
2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA) Pub Date : 2021-05-01 DOI: 10.1109/AIEA53260.2021.00086
Yuxin Zhao, Tongtong Yan
{"title":"Uncertainty Modeling of Source Load and Analysis of Park Energy Use Optimization","authors":"Yuxin Zhao, Tongtong Yan","doi":"10.1109/AIEA53260.2021.00086","DOIUrl":"https://doi.org/10.1109/AIEA53260.2021.00086","url":null,"abstract":"Aiming at the uncertainty of the system operation caused by the demand response mechanism introduced by Energy Park (EP) on both sides of the source and load, this paper proposes an optimized scheduling method for EH energy consumption based on the improved PSO algorithm. The paper analyzes the operating characteristics of wind power, one of the interactive subjects in the EH, and constructs an uncertain response model of wind power output. According to the energy use evaluation plan of the park, energy storage operation equipment is introduced. The paper establishes a source-load-storage interaction response mechanism, and sets a multiobjective optimization function for energy consumption satisfaction and wind power consumption net income; the paper uses the SAPSO algorithm to solve the EP source and storage Interactive optimization model, the paper verifies the validity and rationality of the model method through simulation examples, and provides reference for EP energy dispatch managers.","PeriodicalId":223292,"journal":{"name":"2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA)","volume":"424 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123048827","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
Harmonic suppression of LCL grid-connected inverter based on PI control 基于PI控制的LCL并网逆变器谐波抑制
2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA) Pub Date : 2021-05-01 DOI: 10.1109/AIEA53260.2021.00011
Hao Hou, Shengguo Zhang, Jiawei Yang, Weiwen Hu
{"title":"Harmonic suppression of LCL grid-connected inverter based on PI control","authors":"Hao Hou, Shengguo Zhang, Jiawei Yang, Weiwen Hu","doi":"10.1109/AIEA53260.2021.00011","DOIUrl":"https://doi.org/10.1109/AIEA53260.2021.00011","url":null,"abstract":"In recent years, energy issues have become more prominent. To solve energy problems, distributed power generation has received great attention. Therefore, distributed power generation has also received extensive attention and rapid development. This puts higher requirements on the quality of the output power of the inverter. How to control the LCL type grid-connected inverter to achieve the goal of better output power quality is a hot and difficult point that needs to be overcome in recent years.In this paper, a simulation model of the LCL grid-connected inverter is established in the MATLAB/Simulink environment, the advantages of PI control are analyzed, and the LCL grid-connected inverter based on PI control is designed. Finally, simulation verification was performed under Simulink.","PeriodicalId":223292,"journal":{"name":"2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115763576","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
Stacked Autoencoder for Wavelet-Based the EMI Signal analysis 基于小波的电磁干扰信号分析的堆叠自编码器
2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA) Pub Date : 2021-05-01 DOI: 10.1109/AIEA53260.2021.00034
Hongyi Li, Yuan Yang, Z.K. Peng, Di Zhao
{"title":"Stacked Autoencoder for Wavelet-Based the EMI Signal analysis","authors":"Hongyi Li, Yuan Yang, Z.K. Peng, Di Zhao","doi":"10.1109/AIEA53260.2021.00034","DOIUrl":"https://doi.org/10.1109/AIEA53260.2021.00034","url":null,"abstract":"One way to analyze and improve the electromagnetic compatibility (EMC) of an electronic system is to sample multiple signals and then locating the electromagnetic interference (EMI) sources. First, each signal is decomposed into sub-signals using wavelet transform in which contains potential interesting EMI components. Then, the cluster algorithm is used to identify EMI sources. However, due to the inconsistency imposed by different sampling time and space, sampling signals belong to the same source may take on diverse patterns, thus deteriorating the clustering accuracy. This paper proposed a feature extraction method based on Stacked Autoencoder (SAE) in order to yield a robust signal representation. An additional robust feature based on Fréchet distance is also proposed. Experimental results demonstrate that the proposed method could significantly promote the clustering performance compared to existing methods.","PeriodicalId":223292,"journal":{"name":"2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131383297","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 analysis of Network Traffic Identification based on Decision Tree 基于决策树的网络流量识别分析
2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA) Pub Date : 2021-05-01 DOI: 10.1109/AIEA53260.2021.00072
Jiahao Dai, Yongqun Chen, Yucheng Chen, Aodong Meng
{"title":"An analysis of Network Traffic Identification based on Decision Tree","authors":"Jiahao Dai, Yongqun Chen, Yucheng Chen, Aodong Meng","doi":"10.1109/AIEA53260.2021.00072","DOIUrl":"https://doi.org/10.1109/AIEA53260.2021.00072","url":null,"abstract":"With the strong development momentum of the Internet, network not only brings convenience to our life, but also poses threats to our network security. Users can hide their IP and identity information through encryption algorithm, and carry out various illegal activities and transactions in the network. Therefore, it will be of great value to identify special traffic which impact on network security and exploring abnormal situations in the network. Special traffic is important to the security of the network space. In this paper, we establish a decision tree model based on C4.5 and random forest to compare the accuracy of the two decision tree algorithms in traffic classification. Experimental results show that these two methods can identify traffic effectively.","PeriodicalId":223292,"journal":{"name":"2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA)","volume":"5 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131626334","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
Image recognition based on deep residual shrinkage Network 基于深度残差收缩网络的图像识别
2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA) Pub Date : 2021-05-01 DOI: 10.1109/AIEA53260.2021.00077
Yu Li, Hongguan Chen
{"title":"Image recognition based on deep residual shrinkage Network","authors":"Yu Li, Hongguan Chen","doi":"10.1109/AIEA53260.2021.00077","DOIUrl":"https://doi.org/10.1109/AIEA53260.2021.00077","url":null,"abstract":"Deep residual shrinkage network is a novel and improved algorithm based on deep residual shrinkage network by introducing attention mechanism and soft threshold function. The core of the deep residual shrinkage network can be expressed as: when processing the image sample data, the important information features are extracted by the attention mechanism, and then the negligible information features are eliminated by the soft threshold function, only important information is retained, and these important information features are processed to get the final results, so as to improve the accuracy. The deep residual shrinkage network was applied to MNIST dataset and CIFAR-10 dataset to determine the feasibility of this method in image recognition, and then the network was applied to the facial expression dataset made by the author based on FER2013. Experimental results show that this method can effectively improve the accuracy of image recognition.","PeriodicalId":223292,"journal":{"name":"2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134159367","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: 5
Research on Information Extraction in Judicial Field 司法领域信息提取研究
2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA) Pub Date : 2021-05-01 DOI: 10.1109/AIEA53260.2021.00060
Song Mao, Zhe Li, Jia-Jun Cheng, Wenjing Zhang, Chen Wang, Bo Li
{"title":"Research on Information Extraction in Judicial Field","authors":"Song Mao, Zhe Li, Jia-Jun Cheng, Wenjing Zhang, Chen Wang, Bo Li","doi":"10.1109/AIEA53260.2021.00060","DOIUrl":"https://doi.org/10.1109/AIEA53260.2021.00060","url":null,"abstract":"In the era of information explosion, how to effectively identify the named entities and the relationships between them from the massive judicial data is a key step to realize automatic trial, and it is also the focus of this paper. The traditional way is to use the pipeline method to identify named entities first, and then extract relationships, which will lead to the loss of related information between tasks and ignore the overlapping relationships between entities. For this reason, this paper designs a joint extraction algorithm for entities and relations in the judicial field. It uses the BiLSTM network model based on the BERT pre-training language algorithm and combines it with the attention mechanism, realizes joint learning through parameter sharing, and makes full use of the connections between tasks to optimize the results. In order to verify the effectiveness of this method, this paper constructs a small judicial data set to verify its generalization ability. Experimental results show that the model proposed in this paper can extract entities and relationships without manually defining complex features, and the average F value can reach 0.7.","PeriodicalId":223292,"journal":{"name":"2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130322610","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
Stability study of Distribution Network with Distributed Generation based on SVPWM algorithm 基于SVPWM算法的分布式发电配电网稳定性研究
2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA) Pub Date : 2021-05-01 DOI: 10.1109/AIEA53260.2021.00057
Jiawei Yang, Shengguo Zhang, Hao Hou, Weiwen Hu
{"title":"Stability study of Distribution Network with Distributed Generation based on SVPWM algorithm","authors":"Jiawei Yang, Shengguo Zhang, Hao Hou, Weiwen Hu","doi":"10.1109/AIEA53260.2021.00057","DOIUrl":"https://doi.org/10.1109/AIEA53260.2021.00057","url":null,"abstract":"Distributed power sources have attracted much attention because of their small scale and cleanliness. The development of smart grids based on distributed power sources is also the development trend of the world today. Therefore, this article is of great significance to the construction of the distributed power distribution network model and the research on the stability of the model. This paper builds a model on the basis of SVPWM (Space Vector Pulse Width Modulation) algorithm, simulates the distribution process of distributed power, and after the delay setting changes the power demand of inverter 3, it verifies that the master-slave control is in the conversion process. The power regulation function that the converter can play during power conversion. Therefore, the SVPWM algorithm and master-slave control method used in the modeling in this paper can ensure the stability of the distribution network with distributed power sources.","PeriodicalId":223292,"journal":{"name":"2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114506812","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
[Title page i] [标题页i]
2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA) Pub Date : 2021-05-01 DOI: 10.1109/aiea53260.2021.00001
{"title":"[Title page i]","authors":"","doi":"10.1109/aiea53260.2021.00001","DOIUrl":"https://doi.org/10.1109/aiea53260.2021.00001","url":null,"abstract":"","PeriodicalId":223292,"journal":{"name":"2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125488231","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
Harmonic Detection Algorithm based on Adaptive Variational Modal Decomposition 基于自适应变分模态分解的谐波检测算法
2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA) Pub Date : 2021-05-01 DOI: 10.1109/AIEA53260.2021.00085
Liang Guoxin, Zhu Tangyu, Li Guoqing, Wang Jian
{"title":"Harmonic Detection Algorithm based on Adaptive Variational Modal Decomposition","authors":"Liang Guoxin, Zhu Tangyu, Li Guoqing, Wang Jian","doi":"10.1109/AIEA53260.2021.00085","DOIUrl":"https://doi.org/10.1109/AIEA53260.2021.00085","url":null,"abstract":"Aiming at the shortcoming of harmonic detection in power systems, a method based on Variational Modal Decomposition (VMD) is presented in this paper. This method combines VMD with Fast Fourier Transform (FFT) so that VMD can adaptively find the modal decomposition number ‘K’. K intrinsic mode functions (IMF) can be obtained by adaptive VMD decomposition of harmonic signals and Teager energy operator (TEO) analysis can be performed. By calculating the three sample points of the signal via TEO, the instantaneous amplitude and instantaneous frequency of one sample point can be acquired, so as to realize the harmonic detection of the power grid signal. Through the simulation of transient signal, the results of adaptive VMD are tested. At the same time, TEO and Hilbert transform (HT) are used to calculate the IMF respectively. Through contrastive analysis of the caculation, the feasibility and accuracy of the method mentioned in the article can be verified.","PeriodicalId":223292,"journal":{"name":"2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121729436","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
Volume adaptive under-actuated pickup based on self-tuning principle of trailer body 基于拖车车身自调谐原理的体积自适应欠驱动皮卡
2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA) Pub Date : 2021-05-01 DOI: 10.1109/AIEA53260.2021.00036
W. Ke, Yi Jie, Y. Zihao, Yang Zehang, Ma Mingrun
{"title":"Volume adaptive under-actuated pickup based on self-tuning principle of trailer body","authors":"W. Ke, Yi Jie, Y. Zihao, Yang Zehang, Ma Mingrun","doi":"10.1109/AIEA53260.2021.00036","DOIUrl":"https://doi.org/10.1109/AIEA53260.2021.00036","url":null,"abstract":"At present, the electromechanical system which is compatible with all kinds of ball picking has no practical application value and low flexibility, and gradually becomes the Short Board of the stadium management automation. This paper presents a volume adaptive under-actuated pickup device based on the self-tuning principle of trailer body. The volume is always selfa-daptively maintained at the minimum. Based on the semi-trailer steering principle, a 360 ° self-tuning steering can be achieved by dragging the pickup on the chassis to adjust the collection port orientation rapidly. The whole car only two steering gear control chassis rotation, small range of rotation to achieve direction adjustment, large range of rotation through screw nut to control the volume of ball pickup, the final realization can be deep into the gap to pick up different kinds of spheres.","PeriodicalId":223292,"journal":{"name":"2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124969708","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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