2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)最新文献_第9页

Using binocular vision to measure wave characteristic 利用双目视觉测量波特性

2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI) Pub Date : 2021-08-01 DOI: 10.1109/ICCEAI52939.2021.00044

Lei Shi, Li Hui, Jing Shi, Bin Zhao, Xiao Cui, Shibo Chu

引用次数: 0

Hyperspectral image fusion by hybrid regularizations with local low-rank 局部低秩混合正则化的高光谱图像融合

2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI) Pub Date : 2021-08-01 DOI: 10.1109/ICCEAI52939.2021.00022

Zhaoyang Liu, Mingxi Ma, Zhaoming Wu

引用次数: 0

Anomaly detection of electricity load data based on MixMatch 基于MixMatch的电力负荷数据异常检测

2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI) Pub Date : 2021-08-01 DOI: 10.1109/ICCEAI52939.2021.00010

S. Sun, Yatong Zhou, Haonuo He, Jingfei He, Yue Chi

{"title":"Anomaly detection of electricity load data based on MixMatch","authors":"S. Sun, Yatong Zhou, Haonuo He, Jingfei He, Yue Chi","doi":"10.1109/ICCEAI52939.2021.00010","DOIUrl":"https://doi.org/10.1109/ICCEAI52939.2021.00010","url":null,"abstract":"With the development of power industry, electricity has become one of the most important energy sources in our country, related to the lifeline of the country's economy. The electricity system is becoming more and more mature, but abnormal electricity consumption behaviors are also emerging endlessly, causing potential safety hazards in the electricity industry and even the electricity supply system. Considering the lack of abnormal annotations in the electricity load data, this paper proposes a semi-supervised electricity load data anomaly detection method based on MixMatch. Firstly, data cleaning of electricity load data is used to remove incorrect data. Secondly, Convolutional Autoencoder (CAE) is used to extract its time-domain and frequency-domain features separately, and the features are combined through feature fusion. Thirdly, the Borderline Synthetic Minority Oversampling Technique (Borderline-SMOTE) is used to solve the problem of data imbalance. The MixMatch semi-supervised algorithm is used to label the abnormal data to realize the anomaly detection of the electricity load data. Finally, this paper uses the k-means clustering and T-Stochastic neighbour Embedding (T -SNE) to classify the abnormal data and visualize the data. The experimental results show that, compared with traditional machine learning methods, the proposed method has a significant improvement on AUC.","PeriodicalId":331409,"journal":{"name":"2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114672067","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

Terminal Micro-service Discovery Algorithm based on attractor model 基于吸引子模型的终端微服务发现算法

2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI) Pub Date : 2021-08-01 DOI: 10.1109/ICCEAI52939.2021.00086

Li Wang, Siyu Tang, Jiageng Zhang, Heshan Wang, Jiawei Han, Jianxiang Cao

{"title":"Terminal Micro-service Discovery Algorithm based on attractor model","authors":"Li Wang, Siyu Tang, Jiageng Zhang, Heshan Wang, Jiawei Han, Jianxiang Cao","doi":"10.1109/ICCEAI52939.2021.00086","DOIUrl":"https://doi.org/10.1109/ICCEAI52939.2021.00086","url":null,"abstract":"In the intensive computing scenario with the application of micro-service framework, it is too costly to allocate resources centrally to the micro-service instance on the server side, and it cannot adapt to the changes of the environment in real time. Therefore, allocating resource for micro-service instance distributed on the terminal becomes an effective way to solve the problem mentioned above. A client-based micro-service discovery algorithm with attractor selection model is proposed in this paper, which has the characters of simple and intelligent evolution. At first, this paper models micro-service discovery problem, defines optimization object and constraints; then, it re-defines the parameters of attractor selection and proposes the client-based micro-service discovery algorithm with attractor selection model; and at last, it analyzes the performance of the proposed algorithm with the compared ones in the really productive environment. The experiment results show that the proposed algorithm reduces almost 32 % resource fragments and 70% running time than the compared ones.","PeriodicalId":331409,"journal":{"name":"2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114936035","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 integrated development of stadiums and stadiums under the background of “Internet +” “互联网+”背景下体育场馆融合发展研究

2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI) Pub Date : 2021-08-01 DOI: 10.1109/ICCEAI52939.2021.00036

Zhuo-Ya Pan

引用次数: 0

Research on Deep Learning Based Optimal Combination of Multidimensional Features in Large-Scene Laser Point Clouds Classification 基于深度学习的大场景激光点云多维特征最优组合分类研究

2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI) Pub Date : 2021-08-01 DOI: 10.1109/ICCEAI52939.2021.00033

Lei Wang, Zhiyong Zhang, Xiaonan Li

引用次数: 1

A comparative study of deep learning approaches for Chinese Sentence Classification 中文句子分类的深度学习方法比较研究

2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI) Pub Date : 2021-08-01 DOI: 10.1109/ICCEAI52939.2021.00045

Zhu Zeng

{"title":"A comparative study of deep learning approaches for Chinese Sentence Classification","authors":"Zhu Zeng","doi":"10.1109/ICCEAI52939.2021.00045","DOIUrl":"https://doi.org/10.1109/ICCEAI52939.2021.00045","url":null,"abstract":"One of the most commonly used natural language processing technologies is text classification. Spam detection, news text classification, information retrieval, emotion analysis, and intention judgment, among other applications, are all popular text classification applications [25]. Text classification is the process of assigning pre-defined class labels to text documents in order to shape semantic classes. Engineering, medical science, life science, social sciences and humanities, marketing, and government are only a few of the real-world applications. Machine learning and deep learning algorithms have recently become common and efficient methods for dealing with text classification problems involving labeled data [26]. The primary goal of text classification is to automatically assign texts to pre-defined categories based on their content. In this study, we will conduct a comparative study of the accuracies of different deep learning methods that include Bidirectional Encoder Representations from Transformers (BERT), Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), and Region-based convolutional neural networks and compare the effectiveness of these deep-learning approaches in classifying Chinese news title text using the THUCNews dataset.","PeriodicalId":331409,"journal":{"name":"2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133320948","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 Information Visualization of Electronic Games 电子游戏信息可视化研究

2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI) Pub Date : 2021-08-01 DOI: 10.1109/ICCEAI52939.2021.00034

Lin Yuting, Wang Jianyao

引用次数: 0

Design and Implementation of a Business Domain Requirement Collection System 业务领域需求收集系统的设计与实现

2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI) Pub Date : 2021-08-01 DOI: 10.1109/ICCEAI52939.2021.00015

Binbin Fan, Fengzeng Liu, Yuzhao Huang

引用次数: 0

Stability Analysis and Application Research of Hybrid Dynamic System 混合动力系统稳定性分析及应用研究

2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI) Pub Date : 2021-08-01 DOI: 10.1109/ICCEAI52939.2021.00082

Qianqian Wang, Minan Tang, Aimin An, Weili Liu, Kaiyue Zhang, Jiandong Qiu

引用次数: 0