Symposium on Advances in Electrical, Electronics and Computer Engineering最新文献_第3页

Analysis and research of phase-shift push-pull forward ZVS converter 移相推挽正激ZVS变换器的分析与研究

Symposium on Advances in Electrical, Electronics and Computer Engineering Pub Date : 2023-05-31 DOI: 10.1117/12.2680515

Guo-Hui Wei, Longjie Sun, Jiaan Yi, Yaqiang Yang

引用次数: 0

Electroencephalography artifact removal based on an autoencoder deep network 基于自编码器深度网络的脑电图伪影去除

Symposium on Advances in Electrical, Electronics and Computer Engineering Pub Date : 2023-05-31 DOI: 10.1117/12.2680455

You Luo, Siyuan Wang, Hui Shen

{"title":"Electroencephalography artifact removal based on an autoencoder deep network","authors":"You Luo, Siyuan Wang, Hui Shen","doi":"10.1117/12.2680455","DOIUrl":"https://doi.org/10.1117/12.2680455","url":null,"abstract":"The electroencephalography (EEG) signal acquisition process is inevitably affected by a variety of physiological noise signals, including electrooculogram (EOG), electromyography (EMG). The traditional methods of removing EOG and EMG rely heavily on the subjective experience and prior knowledge of the user. However, the ambiguity of artificial judgments can lead to erroneous and misleading interpretations that are insufficient for qualitative analysis. This inaccurate denoising may affect the true information of the signals in the time domain and spectral domain, leading to a decline in the accuracy of the BCI system. In recent years, a variety of EEG denoising methods based on deep learning have been proposed, but their denoising performance needs to be further improved. In this paper, we design a novel autoencoder (AE) neural network to remove artifacts in EEG. The network includes an encoder and a decoder module. The encoder contains five convolutional layers with increasing feature dimension as depth increases, which are responsible for detecting and suppressing artifacts. The decoder contains five deconvolution layers, whose feature dimension decreases gradually, and is used for EEG reconstruction after denoising. The experimental results on semi-synthetic EEG datasets demonstrate that the proposed algorithm outperforms the four benchmark models.","PeriodicalId":201466,"journal":{"name":"Symposium on Advances in Electrical, Electronics and Computer Engineering","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127362623","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

The core technique and application of knowledge graph in power grid company administrative duty 知识图谱在电网公司管理任务中的核心技术及应用

Symposium on Advances in Electrical, Electronics and Computer Engineering Pub Date : 2023-05-31 DOI: 10.1117/12.2680494

Chenying Feng, Xiaodong Xu, Liang Chen, Miao Yu, Xirui Guo

引用次数: 0

Short-term power load forecasting based on GRU neural network optimized by an improved sparrow search algorithm 基于改进麻雀搜索算法优化GRU神经网络的短期电力负荷预测

Symposium on Advances in Electrical, Electronics and Computer Engineering Pub Date : 2023-05-31 DOI: 10.1117/12.2680053

Xu Song, Qiutong Wu, Yinong Cai

引用次数: 0

Portable package detection imaging method based on planar ECT technology 基于平面ECT技术的便携式包装检测成像方法

Symposium on Advances in Electrical, Electronics and Computer Engineering Pub Date : 2023-05-31 DOI: 10.1117/12.2680501

Yongbo He, Zhaoyang Chen, Shuhao Zhang

引用次数: 0

Knowledge tracking model based on recurrent neural network and transformer 基于递归神经网络和变压器的知识跟踪模型

Symposium on Advances in Electrical, Electronics and Computer Engineering Pub Date : 2023-05-31 DOI: 10.1117/12.2680016

Yan Cheng, Songhua Zhao, Jiansheng Hu, Haifeng Zou, Pin Luo, Yan Fu, Linhui Zhong, Chunlei Liu

{"title":"Knowledge tracking model based on recurrent neural network and transformer","authors":"Yan Cheng, Songhua Zhao, Jiansheng Hu, Haifeng Zou, Pin Luo, Yan Fu, Linhui Zhong, Chunlei Liu","doi":"10.1117/12.2680016","DOIUrl":"https://doi.org/10.1117/12.2680016","url":null,"abstract":"With the continuous development of online education platform, knowledge tracking (KT) has become a key technology to help online education platform provide personalized education. However, the existing knowledge tracking model based on recurrent neural network is difficult to be used for the input of long sequence, and has the problem of long-term dependence. Secondly, although the knowledge tracking model based on Transformer does not have the problem of long-term dependence, it is difficult to capture the input sequence information. Therefore, this paper proposes a knowledge tracking model based on recurrent neural network and transformer. A new position coding method is designed, and LSTM is used to replace the position coding method of Transformer to encode sequence features, so that the model in this paper can not only capture the input sequence information, but also get rid of the long-term dependency problem based on the recurrent neural network, and use GRU network to capture the context information. In addition, an adaptive fusion gate is designed to fuse the global features and context features obtained by Transformer, and use the fused features to predict the students' answers to the next question. In addition, an adaptive fusion gate is designed to fuse the global features and context features obtained by Transformer, and use the fused features to predict the students' answers to the next question.","PeriodicalId":201466,"journal":{"name":"Symposium on Advances in Electrical, Electronics and Computer Engineering","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125602667","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 click fraud prediction based on multi-algorithm fusion 基于多算法融合的点击欺诈预测研究

Symposium on Advances in Electrical, Electronics and Computer Engineering Pub Date : 2023-05-31 DOI: 10.1117/12.2680157

Ganglong Duan, Jianjun Liu, Weiwei Kong, B. Cui, Jiahao Li

引用次数: 0

Research on image recognition of power control system based on BHS-CTPN and AA-CRNN 基于BHS-CTPN和AA-CRNN的功率控制系统图像识别研究

Symposium on Advances in Electrical, Electronics and Computer Engineering Pub Date : 2023-05-31 DOI: 10.1117/12.2680450

Dali Xue, Desheng Wang, S. Zhou, Fan Chen, Datie Huang

引用次数: 0

A modeling technique for generalized power quality data 广义电能质量数据的建模技术

Symposium on Advances in Electrical, Electronics and Computer Engineering Pub Date : 2023-05-31 DOI: 10.1117/12.2680243

Ruichen Sun, K. Dong, Jianfeng Zhao

{"title":"A modeling technique for generalized power quality data","authors":"Ruichen Sun, K. Dong, Jianfeng Zhao","doi":"10.1117/12.2680243","DOIUrl":"https://doi.org/10.1117/12.2680243","url":null,"abstract":"Power quality data mining is of great potential in both supply-side and demand-side energy management system. In recent decades, with the wide application of flexible AC/DC power grid and grid-connected renewable energy generation, power quality data has been unified as a generalized model for improving power quality. Meanwhile, power quality monitoring system has also been deployed on a large scale. In order to further highlight the availability and usability of power quality data, the paper integrates various types of information to support power quality analysis. A multimodal data system is constructed to process information collected in different forms into a multi-dimensional data model, which can be pretrained to provide integrated features for various power quality analysis tasks. Firstly, the three data types of voltage waveforms, texts and images are embedded through feature extraction, low-dimensional spatial representation and CNNbased representation, respectively. Then all information is fused with the interaction model based on Attention mechanism. The output of the data model can be sent to networks specific to certain downstream tasks.","PeriodicalId":201466,"journal":{"name":"Symposium on Advances in Electrical, Electronics and Computer Engineering","volume":"52 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116693645","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 optimal configuration method of new generation and high-capacity synchronous condenser for enhancing the power grid voltage support 一种新一代大容量同步电容器的优化配置方法，以增强电网电压支持

Symposium on Advances in Electrical, Electronics and Computer Engineering Pub Date : 2023-05-31 DOI: 10.1117/12.2680514

Xiaoxiao Qi, Yongjun Yu, Xiaoyun Wang, Yue Ma, Jingling Cheng

引用次数: 0