2022 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)最新文献_第2页

Research on Torque Control Algorithm for Path Planning of Free Floating Space Robots Capturing Target 自由漂浮空间机器人捕获目标路径规划的力矩控制算法研究

2022 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI) Pub Date : 2022-07-01 DOI: 10.1109/ICCEAI55464.2022.00164

Huazhong Li

引用次数: 0

Research on architecture and technology application of smart Park Based on 5G cloud network 基于5G云网络的智慧园区架构与技术应用研究

2022 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI) Pub Date : 2022-07-01 DOI: 10.1109/ICCEAI55464.2022.00182

Huibin Duan, Yuying Xue, Peng Ding, Yun Shen, Dan Liu, Qiuhong Zheng

引用次数: 1

A Review of the types of disturbances and suppression methods for SiC MOSFET driver SiC MOSFET驱动器的干扰类型及抑制方法综述

2022 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI) Pub Date : 2022-07-01 DOI: 10.1109/ICCEAI55464.2022.00019

Zhi Zheng, Xinyu Cui, Feng Wang, F. Zhuo

{"title":"A Review of the types of disturbances and suppression methods for SiC MOSFET driver","authors":"Zhi Zheng, Xinyu Cui, Feng Wang, F. Zhuo","doi":"10.1109/ICCEAI55464.2022.00019","DOIUrl":"https://doi.org/10.1109/ICCEAI55464.2022.00019","url":null,"abstract":"In recent years, wide-bandgap semiconductor devices have developed rapidly, and their usage in various power electronic devices is also increasing. Among them, SIC MOSFET devices have the advantages of high temperature resistance, high voltage resistance, and fast switching, so as switching devices, they have been widely used in various power electronic topologies. However, the higher switching frequency and various driving interference factors make the driving module of SIC MOSFET need higher quality. In order to make better use of SIC MOSFET and ensure that it can switch stably in the circuit, it is necessary to study the driving design method of SIC MOSFET. This article expounds and summarizes the types of disturbances encountered by SiC MOSFET drivers and their suppression methods. Firstly, the basic status and basic conditions of SiC MOSFET device driving are introduced. Then it is explained that under different driving conditions, the kinds of disturbances received by driving are different, and their effects are also different. Finally, the methods of suppressing interference are summarized and compared.","PeriodicalId":414181,"journal":{"name":"2022 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)","volume":"29 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114006419","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

Advance Algorithms of Secchi depth Remote Sensing 深度遥感的改进算法

2022 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI) Pub Date : 2022-07-01 DOI: 10.1109/ICCEAI55464.2022.00021

Yi Wang, Shudao Zhou

引用次数: 0

The Implementation of Movies and TV Plays Analysis System Combined with Knowledge Graph and Data Visualization 知识图谱与数据可视化相结合的影视剧分析系统的实现

2022 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI) Pub Date : 2022-07-01 DOI: 10.1109/ICCEAI55464.2022.00094

F. Yang, Yong Yue, Gangmin Li

{"title":"The Implementation of Movies and TV Plays Analysis System Combined with Knowledge Graph and Data Visualization","authors":"F. Yang, Yong Yue, Gangmin Li","doi":"10.1109/ICCEAI55464.2022.00094","DOIUrl":"https://doi.org/10.1109/ICCEAI55464.2022.00094","url":null,"abstract":"More and more movies and television plays have been produced in recent years, but a few have succeeded in the market. Therefore, the analysis and speculation of the success factors of movies and television plays are very important for the producers and investors. The existing analysis platform only analyzes the benefits generated by movies and television dramas in a certain period and lacks the ability of prediction and reasoning. To analyze the key factors affecting the success of movies and television dramas and provide a reference for producers and investors, we design and implement a movies and television plays analysis system combined with a knowledge graph and data visualization technology. First of all, we crawl the information of movies and television plays and user comments on the Douban website; Then, the entities and relationships are extracted by OpenUE toolkit, and Neo4j is used to construct and store the knowledge graph in movies and television plays. On this basis, we utilize the improved TransR algorithm for knowledge completion and reasoning. Finally, combined with the knowledge graph, we analyze the success factors of popular movies and TV plays and visualize the analysis results in various chart types.","PeriodicalId":414181,"journal":{"name":"2022 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134441298","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

Application of a New VAE-MF Generative Model in TCD Dataset 一种新的VAE-MF生成模型在TCD数据集中的应用

2022 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI) Pub Date : 2022-07-01 DOI: 10.1109/ICCEAI55464.2022.00032

Xueying Zhang, Xiaoyu Chen, Yuling Guo, Suzhe Wang, Wenhui Jia

{"title":"Application of a New VAE-MF Generative Model in TCD Dataset","authors":"Xueying Zhang, Xiaoyu Chen, Yuling Guo, Suzhe Wang, Wenhui Jia","doi":"10.1109/ICCEAI55464.2022.00032","DOIUrl":"https://doi.org/10.1109/ICCEAI55464.2022.00032","url":null,"abstract":"Transcranial Doppler (TCD) is a non-invasive method for detecting ischemic stroke disease and is widely used in clinical diagnosis. Due to the obvious imbalance of medical data, oversampling is needed to balance it. Although the oversampling method is one of the important means to solve the problem of imbalanced data classification, the traditional oversampling method will inevitably introduce noise, which will affect the classification results. To address this issue, we proposed a new oversampling method combining the membership function (MF) and the variational autoencoder (VAE). Our method utilized VAE as a generative model to generate new samples to reduce the Imbalance Ratio (IR) of the dataset. Then the new samples are filtered using MF to weaken the impact of the noise introduced by the oversampling method on classification performance. In addition, in view of the weak adaptability of traditional MFs to complex sample distributions in practical applications, a new MF is proposed by using kernel function mapping and hypersphere. The classification experiments on TCD imbalanced dataset prove that the oversampling method we proposed has improved classification performance on multiple evaluation criteria compared with traditional oversampling methods.","PeriodicalId":414181,"journal":{"name":"2022 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134445105","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

A BERT-based Text Sentiment Classification Algorithm through Web Data 基于bert的基于Web数据的文本情感分类算法

2022 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI) Pub Date : 2022-07-01 DOI: 10.1109/ICCEAI55464.2022.00105

Ganhua Li, Bo Kong, Jiancheng Li, Henghai Fan, Jian Zhang, Yuan An, Zhenglei Yang, Shengrong Danz, Jiancun Fan

{"title":"A BERT-based Text Sentiment Classification Algorithm through Web Data","authors":"Ganhua Li, Bo Kong, Jiancheng Li, Henghai Fan, Jian Zhang, Yuan An, Zhenglei Yang, Shengrong Danz, Jiancun Fan","doi":"10.1109/ICCEAI55464.2022.00105","DOIUrl":"https://doi.org/10.1109/ICCEAI55464.2022.00105","url":null,"abstract":"In order to analyze the sentiment tendency of public opinion, this paper conducts a textual sentiment classification research through web data. In the research, this paper uses the BERT (Bidirectional Encoder Representation from Transformers) model to replace the commonly used word2vec model as a text vectorization tool, which has stronger semantic representation capabilities and can realize polysemous words. For the multi-label classification problem of reviews, the BR (Binary Relevance) algorithm is used to transform the problem into multiple binary classification problems, which is directly and efficient for processing multi-label data. Design the BiLSTM-Attention model, which combines the bidirectional long and short-term memory network and the attention mechanism to achieve further extraction of text features. After multiple sets of comparative experiments, the effectiveness of the BiLSTM-Attention model is verified through performance evaluation. In order to further improve the performance of the model, the problem of unbalanced data set is solved by adjusting the loss function and various parameters so that a better classification effect is achieved.","PeriodicalId":414181,"journal":{"name":"2022 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131648038","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

Intelligent Ship Decision System Based on DDPG Algorithm 基于DDPG算法的船舶智能决策系统

2022 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI) Pub Date : 2022-07-01 DOI: 10.1109/ICCEAI55464.2022.00149

Zhewen Cui, Wei Guan, Wenzhe Luo

{"title":"Intelligent Ship Decision System Based on DDPG Algorithm","authors":"Zhewen Cui, Wei Guan, Wenzhe Luo","doi":"10.1109/ICCEAI55464.2022.00149","DOIUrl":"https://doi.org/10.1109/ICCEAI55464.2022.00149","url":null,"abstract":"With the development of robot technology, intelligent ship decision-making becomes particularly important. Toward this goal, this research proposes a path planning and manipulating approach based on deep reinforcement learning DDPG algorithm, which can drive a ship by itself without requiring any input from human experiences. At the very beginning, a ship is modelled with the Nomoto model in a simulation waterway. Then, distances, obstacles and prohibited areas are regularized as rewards or punishments, which are used to judge the performance, or manipulation decisions of the ship. Subsequently, DDPG is introduced to learn the action-reward model and the learning outcome is used to manipulate the ship's movement. By chasing higher reward values, the ship can find an appropriate path or navigation strategies by itself. After a sufficient number of rounds of training, a convincing path and manipulating strategies will likely be produced. By comparing the proposed approach with the existing methods. The results show that this approach is more effective in self-learning and continuous optimization and thus closer to human manipulation.","PeriodicalId":414181,"journal":{"name":"2022 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132943438","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

A novel Face Recognition Method based on Feature Fusion of Global and Local Structure 一种基于全局和局部结构特征融合的人脸识别方法

2022 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI) Pub Date : 2022-07-01 DOI: 10.1109/ICCEAI55464.2022.00116

Zhengkai Wang

引用次数: 1

Bearing fault diagnosis Based on 1-D DenseNet-LSTM 基于一维DenseNet-LSTM的轴承故障诊断

2022 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI) Pub Date : 2022-07-01 DOI: 10.1109/ICCEAI55464.2022.00126

Zhihong Zhao, Kejian Liu, Jingjiao Zhao

{"title":"Bearing fault diagnosis Based on 1-D DenseNet-LSTM","authors":"Zhihong Zhao, Kejian Liu, Jingjiao Zhao","doi":"10.1109/ICCEAI55464.2022.00126","DOIUrl":"https://doi.org/10.1109/ICCEAI55464.2022.00126","url":null,"abstract":"Rolling bearing, as a vulnerable part of rotating machinery, acts an essential role in mechanical equipment. Once fails, it will cause huge economic losses, even lead to major safety accidents which threaten human life. Considering that, it's of great significance for the bearing fault diagnosis. Aiming at the issue that the accuracy of the shallow diagnosis model is limited in big data set and generalization ability is weak, an end-to-end fault diagnosis method based on one-dimensional densely connected convolutional neural network and long short-term memory neural network (1-D DenseNet-LSTM) is proposed. First, the bearing vibration signal after preprocessing is input into the fault diagnosis model. Then, Convolution layer can effectively extract the local features of bearing vibration signal, in which, dense connection mechanism will fuse the low-level features and high-level features. After that, LSTM layer pays more attention to temporal information and extracts deep features. Finally, the fault feature information is fully extracted and input to the classification layer and output the fault type. Experiments showed that the fault diagnosis model based on 1-D DenseNet-LSTM can fully extract bearing fault feature information, and classify different fault types effectively. The accuracy of bearing fault diagnosis is more than 99%, which has higher accuracy of diagnosis compared with the depth network model.","PeriodicalId":414181,"journal":{"name":"2022 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)","volume":"2015 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132571327","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