2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE)最新文献_第6页

Power Grid Fault Location Method Based on Pretraining of Convolutional Autoencoder 基于卷积自编码器预训练的电网故障定位方法

2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE) Pub Date : 2021-08-20 DOI: 10.1109/CSAIEE54046.2021.9543368

Ling Zheng, Peixin Xu, Jiayin Bai

{"title":"Power Grid Fault Location Method Based on Pretraining of Convolutional Autoencoder","authors":"Ling Zheng, Peixin Xu, Jiayin Bai","doi":"10.1109/CSAIEE54046.2021.9543368","DOIUrl":"https://doi.org/10.1109/CSAIEE54046.2021.9543368","url":null,"abstract":"In the power grid system, the accurate determination and precise positioning of faulty equipment is an important foundation for realizing the self-healing function of the power grid. With the increasing complexity of the power grid topology, traditional fault location methods are prone to misjudge the faulty equipment and the normal equipment in the station, causing problems such as low positioning accuracy and poor stability. To locate faulty equipment quickly and accurately, this paper proposes a power grid fault location method based on pre-training of convolutional autoencoder. The method is based on the synchronous phasor measurement unit (PMU), which converts its sequence data into images to reduce noise interference. The method pre-trains lots of samples by using convolutional autoencoder, and then uses a classifier to fine-tune small batches of balanced samples. Finally, the faulty equipment in the power grid can be accurately located by dividing the area multiple times and positioning the faulty equipment gradually. Experiments and simulations show that compared with other mainstream methods, this method has higher robustness and stability, can effectively alleviate the impact of data imbalance, and improve the accuracy and accuracy of faulty equipment location.","PeriodicalId":376014,"journal":{"name":"2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116104669","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

Research on C4.5 Algorithm Optimization For User Churn 针对用户流失的C4.5算法优化研究

2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE) Pub Date : 2021-08-20 DOI: 10.1109/CSAIEE54046.2021.9543367

Chao Deng, Zhaohui Ma

{"title":"Research on C4.5 Algorithm Optimization For User Churn","authors":"Chao Deng, Zhaohui Ma","doi":"10.1109/CSAIEE54046.2021.9543367","DOIUrl":"https://doi.org/10.1109/CSAIEE54046.2021.9543367","url":null,"abstract":"Decision tree is a kind of machine learning method which can decide a corresponding result according to the probability of different eigenvalues, The effective decision number constructed can provide help for our data analysis. The generation of decision tree is a recursive process, It mainly uses the optimal partition attribute as the corresponding tree node, and then uses various values of the attribute to construct branches. In this way, until the data reaches a certain purity, the leaf nodes are obtained, and a decision tree in accordance with the rules is constructed. Among the traditional decision tree algorithms, C4.5 algorithm has a gain rate because of its attribute division. This leads to another obvious disadvantage, that is, it has a preference for the attributes with a small number of values, so that the accuracy of the decision tree is often not particularly ideal. In view of this, this paper proposes an improved E-C4.5 algorithm, which combines information gain and information gain rate to generate a new attribute partition criterion. The attribute partition method greatly eliminates the shortcoming of C4.5 algorithm which has a preference for the attributes with a small number of values, and further improves the decision accuracy of decision tree generation. In this paper, the actual data sets are used to verify the accuracy of the decision tree generated by the improved algorithm compared with the traditional C4.5 algorithm.","PeriodicalId":376014,"journal":{"name":"2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116906979","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

CTR Prediction Model Using xDeepFM and Bayesian optimization 基于xDeepFM和贝叶斯优化的点击率预测模型

2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE) Pub Date : 2021-08-20 DOI: 10.1109/CSAIEE54046.2021.9543277

Yiying Zhang

引用次数: 0

Video motions classification based on CNN 基于CNN的视频动作分类

2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE) Pub Date : 2021-08-20 DOI: 10.1109/CSAIEE54046.2021.9543398

Yue Luo, Boyuan Yang

引用次数: 2

Res-Attention Net: An Image Dehazing Network 重新注意网络:一种图像去雾网络

2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE) Pub Date : 2021-08-20 DOI: 10.1109/CSAIEE54046.2021.9543298

Shuai Song, Ren-Yuan Zhang, Zhipeng Qiu, Jiawei Jin, Shangbin Yu

{"title":"Res-Attention Net: An Image Dehazing Network","authors":"Shuai Song, Ren-Yuan Zhang, Zhipeng Qiu, Jiawei Jin, Shangbin Yu","doi":"10.1109/CSAIEE54046.2021.9543298","DOIUrl":"https://doi.org/10.1109/CSAIEE54046.2021.9543298","url":null,"abstract":"In the image dehazing task, there are three key subtasks need to be performed. The first one is extracting the finer scale features, e.g. the detail textures of objects, covered by haze. The second one is retaining the coarser scale features, e.g. the contours of objects, as complete as possible. And third one is fusing the finer scale features and the coarser scale features together. Aiming at the three points, we propose a single image dehazing network named Res-Attention Net based on the encoding-decoding structure similar to U -Net. The encoder and decoder of Res-Attention Net are designed for the objective that extracting the detail textures and retrieving the contours at the same time. We construct the encoder of the Res-Attention Net by the residual blocks (RBs) with different depths and downsampling for performing the first two subtasks, i.e. extracting the multiscale image features from the original hazy image. The decoder of the Res-Attention is based on the attention gates (AGs) and upsampling. The decoder can retrieve the coarser scale features from the output of the encoder and can also fuse them with the multiscale features from the encoder together. That is to say, the decoder is for performing the last two subtasks. The experimental results show that the Res-Attention Net proposed performs better than several state-of-the-art methods.","PeriodicalId":376014,"journal":{"name":"2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114273877","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

Teacher-Student Network for Low-quality Remote Sensing Ship Detection 面向低质量遥感船舶探测的师生网络

2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE) Pub Date : 2021-08-20 DOI: 10.1109/CSAIEE54046.2021.9543185

Shitian He, H. Zou, Runlin Li, Xu Cao, Fei Cheng, Juan Wei

引用次数: 0

Application of Active Learning in Decoding 主动学习在解码中的应用

2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE) Pub Date : 2021-08-20 DOI: 10.1109/CSAIEE54046.2021.9543424

Yunfei Quan

引用次数: 0

K-means clustering algorithm and Python implementation K-means聚类算法和Python实现

2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE) Pub Date : 2021-08-20 DOI: 10.1109/CSAIEE54046.2021.9543260

BoKai Wu

{"title":"K-means clustering algorithm and Python implementation","authors":"BoKai Wu","doi":"10.1109/CSAIEE54046.2021.9543260","DOIUrl":"https://doi.org/10.1109/CSAIEE54046.2021.9543260","url":null,"abstract":"K-means is a commonly used algorithm in machine learning. It is an unsupervised learning algorithm. It is regularly used for data clustering. Only the number of clusters are needed to be specified for it to automatically aggregate the data into multiple categories, the similarity between data in the same cluster is high, thus, the similarity of data in different clusters is low. K-means algorithm is a typical distance-based clustering algorithm. It takes distance as the evaluation index of similarity, that is, the closer the distance between two objects, the greater similarity. Clustering is also extremely extensive in practical applications, such as: market segmentation, social network analysis, organized computing clusters, and astronomical data analysis. This paper is my own attempt to make K-means code and API, using Python and Java to jointly complete a project. The Python is mainly used to write the framework of the core algorithm of K-means, and the Java to create experimental data. In this research report, I will describe the simple data model provided by K-means, as well as the design and implementation of K-means.","PeriodicalId":376014,"journal":{"name":"2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE)","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133035343","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

A multi-scale surface target recognition algorithm based on attention fusion mechanism 基于注意融合机制的多尺度表面目标识别算法

2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE) Pub Date : 2021-08-20 DOI: 10.1109/CSAIEE54046.2021.9543180

Runze Guo, Shaojing Su, Zhen Zuo, Bei Sun

引用次数: 1

Resonating response makes people feel better: An empathetic protocol in dialogue system 共鸣反应让人感觉更好:对话系统中的移情协议

2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE) Pub Date : 2021-08-20 DOI: 10.1109/CSAIEE54046.2021.9543448

Mingwei Shi

引用次数: 0