2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC )最新文献_第7页

Active Magnetic Bearing Controller Design based on Radial Basis Function Neural Network 基于径向基函数神经网络的磁轴承主动控制器设计

2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC ) Pub Date : 2022-10-03 DOI: 10.1109/IAEAC54830.2022.9929453

Zixuan Xu, Hongze Xu

引用次数: 0

Driving mechanism of thermal environment based on Bayesian structural equation model 基于贝叶斯结构方程模型的热环境驱动机理研究

2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC ) Pub Date : 2022-10-03 DOI: 10.1109/IAEAC54830.2022.9929835

Fan Wu, Yong Chang

{"title":"Driving mechanism of thermal environment based on Bayesian structural equation model","authors":"Fan Wu, Yong Chang","doi":"10.1109/IAEAC54830.2022.9929835","DOIUrl":"https://doi.org/10.1109/IAEAC54830.2022.9929835","url":null,"abstract":"The urban thermal environment has an important impact on human health and sustainable development. Most of the current mainstream studies on the influence of thermal environment focus on the relationship analysis with a single factor, but there are fewer studies on multiple factors and their interactions, and there is a lack of analysis of human activities as an important factor. In this paper, the relationship between summer surface temperature and natural environment, surface cover, landscape structure, and human activities in Beijing-Tianjin-Hebei region is investigated in depth by using Bayesian structural equation model. The results show that: impervious surface (0.66) and air temperature (0.78) have significant positive effects on the thermal environment, while vegetation (-0.37) and transpiration (-0.27) have significant negative effects on the thermal environment; human activities indirectly affect the surface thermal environment through their effects on the natural environment and landscape structure, so the construction of the driving mechanism model of the surface thermal environment needs to consider the role of human activities The model results are optimal at 6k scales, with Rhat less than 1.01 for all parameters, Bulk_ESS and Tail_ESS values greater than 1500, and no effect value over 1. The LOOIC value is 67431.1. The remote sensing image data has the advantages of sufficient data volume and significant spatial and temporal effects, so it is feasible and effective to apply it to BSEM analysis.","PeriodicalId":349113,"journal":{"name":"2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC )","volume":"147 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126911132","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

Power Consumption Prediction via Improved Gaussian Mixture Clustering Model for Automatically Clustering 基于改进高斯混合聚类模型的自动聚类功耗预测

2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC ) Pub Date : 2022-10-03 DOI: 10.1109/IAEAC54830.2022.9929702

Buhua Chen, Hanjiang Liu

引用次数: 0

Design of petrochemical power distribution and substation digital delivery system based on microservice architecture 基于微服务架构的石化配电变电数字化交付系统设计

2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC ) Pub Date : 2022-10-03 DOI: 10.1109/IAEAC54830.2022.9929790

Songyi Zhu, Qiu Chen, Yuan Cheng, Yingying Yu, Zelong Li, Xuan Ji

引用次数: 0

Research on Image Classification Algorithm of Haematococcus Pluvialis Cells 雨红球菌细胞图像分类算法研究

2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC ) Pub Date : 2022-10-03 DOI: 10.1109/IAEAC54830.2022.9929530

Sun Chen, Cui Shigang, Zhang Yongli, He Lin, Li Xinqi, Zhang Jingyu

{"title":"Research on Image Classification Algorithm of Haematococcus Pluvialis Cells","authors":"Sun Chen, Cui Shigang, Zhang Yongli, He Lin, Li Xinqi, Zhang Jingyu","doi":"10.1109/IAEAC54830.2022.9929530","DOIUrl":"https://doi.org/10.1109/IAEAC54830.2022.9929530","url":null,"abstract":"Haematococcus pluvialis has the highest content of astaxanthin and is recognized as the best biological source for astaxanthin production in nature. Its culture stage is mainly divided into cell proliferation stage and astaxanthin accumulation stage. Because the color and cell radius of Haematococcus pluvialis cells are different at different stages, an efficient and accurate image classification algorithm is sought to identify the growth stage of Haematococcus pluvialis. In response to this problem, this paper selects three algorithms (C4.5 decision tree, SVM, KNN) with more balanced complexity and accuracy in the machine learning image classification algorithm to identify Haematococcus pluvialis cells, and explores the application of Pluvialis pluvialis cells. Algorithms for image classification of algal cells. The image classification is completed by extracting the features of the cell image and calculating the number of pixels in the image, and then using the corresponding algorithm to train the model. The experimental results show that the decision tree algorithm is significantly better than the other two algorithms, and the classification accuracy is about 97%. It can be seen that the classification model trained by the decision tree algorithm achieves a good classification effect, and provides an effective method for solving the problem of Haematococcus pluvialis cell image classification.","PeriodicalId":349113,"journal":{"name":"2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC )","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126476905","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

Digital twin modeling and temperature field analysis of transformer windings 变压器绕组的数字孪生建模与温度场分析

2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC ) Pub Date : 2022-10-03 DOI: 10.1109/IAEAC54830.2022.9929698

Li Huang, Peifeng Shen, Ting Chen, Yong Li, Xiaoyan Sun, Ling Ju, Tianyu He, Debao Tang

{"title":"Digital twin modeling and temperature field analysis of transformer windings","authors":"Li Huang, Peifeng Shen, Ting Chen, Yong Li, Xiaoyan Sun, Ling Ju, Tianyu He, Debao Tang","doi":"10.1109/IAEAC54830.2022.9929698","DOIUrl":"https://doi.org/10.1109/IAEAC54830.2022.9929698","url":null,"abstract":"Aiming at the lack of effective means to detect and evaluate the internal temperature field of transformer windings on site, this paper establishes a digital twin model of transformer windings and analyzes the heat distribution characteristics based on transformer winding losses. In this paper, the internal winding of a 220 kV oil-immersed transformer is taken as the research object, and the finite element method is used to calculate the winding losses of each layer. Taking the winding loss of each layer as the heat source, the electromagnetic-heat-flow coupling model is used to solve the spatial temperature distribution characteristics of the winding of each layer. Firstly, theoretical analysis is carried out to establish a real scale transformer simulation model; through parameter setting, the current of each layer winding is solved, the winding loss is calculated, and loaded into the fluid-temperature field as a heat source. Furthermore, a three-dimensional temperature digital twin model of transformer winding is established, and the temperature distribution characteristics of transformer winding are calculated. Finally, the research results can provide support for the internal temperature monitoring and analysis of the transformer.","PeriodicalId":349113,"journal":{"name":"2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC )","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126494649","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

DropMask: A data augmentation method for convolutional networks 一种卷积网络的数据增强方法

2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC ) Pub Date : 2022-10-03 DOI: 10.1109/IAEAC54830.2022.9929974

Diancheng Gong, Zhiling Wang, Hanqi Wang, Huawei Liang

{"title":"DropMask: A data augmentation method for convolutional networks","authors":"Diancheng Gong, Zhiling Wang, Hanqi Wang, Huawei Liang","doi":"10.1109/IAEAC54830.2022.9929974","DOIUrl":"https://doi.org/10.1109/IAEAC54830.2022.9929974","url":null,"abstract":"Convolutional neural networks can learn a powerful feature space and play a great role on the promotion of autonomous driving. On limited datasets, deep neural networks often overfit. Properly adding noise and regularization during training can alleviate this. DropBlock randomly removes blocks on the feature map, however, such randomness may lead to complete removal of objects due to excessive removal of one or a few blocks, as well as contextual information. To solve this problem, we propose a regularization method (DropMask), which strikes a reasonable balance between deletion and retention in the block to be deleted on the feature map. That will avoid excessive removal of contiguous blocks so that improve the accuracy and robustness of model. After extensive experiments, it has been shown that the DropMask proposed in the paper outperforms DropBlock on neural networks. On CIFAR-I0 classification, ResNet-18 architecture with DropMask achieves 95.34% on accuracy, 1.88% improvement over the baseline. On KITTI 2D detection task, Yolov5s with DropMask improves mAP from 77.6% to 79.2%.","PeriodicalId":349113,"journal":{"name":"2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC )","volume":"337 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120907359","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

Traffic Light Timing Optimization based on Improved Particle Swarm Optimization 基于改进粒子群算法的红绿灯配时优化

2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC ) Pub Date : 2022-10-03 DOI: 10.1109/IAEAC54830.2022.9929570

Chi Liu, Changqing Yan, Chenyin Ma, Qihan Suo

引用次数: 0

An ensemble approach to predict local lymph-node metastasis in cancer based on SMOTE strategy 基于SMOTE策略预测肿瘤局部淋巴结转移的综合方法

2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC ) Pub Date : 2022-10-03 DOI: 10.1109/IAEAC54830.2022.9929529

Tingrui Guo, Shihua Zhang, Yuan Zhu

{"title":"An ensemble approach to predict local lymph-node metastasis in cancer based on SMOTE strategy","authors":"Tingrui Guo, Shihua Zhang, Yuan Zhu","doi":"10.1109/IAEAC54830.2022.9929529","DOIUrl":"https://doi.org/10.1109/IAEAC54830.2022.9929529","url":null,"abstract":"Local lymph node metastasis is a standard mode of tumor metastasis. Accurate prediction of local lymph node metastasis in cancer patients can help select appropriate treat-ment. Long non-coding RNA (LncRNA) has been proved to play an essential role in cancer prediction. According to the characteristics of such data, problems such as data imbal-ance, high dimensionality, and small sample size may arise in the analysis involved. Besides, considering a large amount of adequate information in LncRNA expression profiles and the correlation between different features, the commonly used data dimensionality reduction method may not retain the expression profile information well. In this research, taking complete account of data imbalance, a comprehensive feature extraction method is proposed by combining an ensemble classification strategy with Synthetic Minority Oversampling Technique (SMOTE). The biometric selection and linear discriminant analysis were used to reduce the feature dimension and enhance computational complexity. Some comparative experiments were conducted on three tissue-specific cancer datasets, and the performance validated the effectiveness of the VotMeta in terms of accuracy.","PeriodicalId":349113,"journal":{"name":"2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC )","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115875627","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 A Frame Packer for Data FrameTransmission between Chips 用于芯片间数据帧传输的帧封隔器设计

2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC ) Pub Date : 2022-10-03 DOI: 10.1109/IAEAC54830.2022.9929540

Yuzhe Chen, Yubo Wang, Dejian Li, Changzheng Dong, Jie Gan, Jinlong Pan

引用次数: 0