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

Research on Dynamic Monitoring and Early Warning Methods of Company Management Driven by Artificial Intelligence 基于人工智能驱动的企业管理动态监测预警方法研究

2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE) Pub Date : 2021-08-20 DOI: 10.1109/CSAIEE54046.2021.9543389

Yuantian Zhu

{"title":"Research on Dynamic Monitoring and Early Warning Methods of Company Management Driven by Artificial Intelligence","authors":"Yuantian Zhu","doi":"10.1109/CSAIEE54046.2021.9543389","DOIUrl":"https://doi.org/10.1109/CSAIEE54046.2021.9543389","url":null,"abstract":"The rapid development of science and technology in our country has led to the rapid development of Chinese enterprises, and the scale of enterprise production has continued to expand. However, as enterprises continue to accelerate their expansion and development, it follows that a huge scale is generated during the operation of the enterprise. The amount of data is increasing, and the hidden dangers of enterprises are also escalating. With the continuous competition among enterprises, the predictive and early warning technology under artificial intelligence has become the most advantageous competitiveness among enterprises. At the same time, the immeasurable losses caused by risks have made enterprises' desire for artificial intelligence monitoring and early warning technology more intense and urgent. Nowadays, most companies are still using more traditional corporate management methods. This traditional management method has many problems: mostly based on experience and visual inspection, thus ignoring the attention to some risks that cannot be visually observed, and cannot pay attention to the current corporate risks. Quantitative analysis and evaluation of the status can not achieve the effect of accurately preventing risks, so that the potential value of a large amount of data cannot be fully explored.","PeriodicalId":376014,"journal":{"name":"2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE)","volume":"37 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":"117139275","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

Insincere Question Classification by Deep Neural Networks 基于深度神经网络的非真诚问题分类

2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE) Pub Date : 2021-08-20 DOI: 10.1109/CSAIEE54046.2021.9543161

C. Chen

引用次数: 2

Student behavior detection based on YOLOv4-Bi 基于YOLOv4-Bi的学生行为检测

2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE) Pub Date : 2021-08-20 DOI: 10.1109/CSAIEE54046.2021.9543310

Xiao-ling Ren, Deyi Yang

{"title":"Student behavior detection based on YOLOv4-Bi","authors":"Xiao-ling Ren, Deyi Yang","doi":"10.1109/CSAIEE54046.2021.9543310","DOIUrl":"https://doi.org/10.1109/CSAIEE54046.2021.9543310","url":null,"abstract":"With the continuous development of the times, intelligent teaching assistance is also attracting more and more attention in education. The intelligent detection algorithm for student behavior is gradually becoming more precise. In university classrooms, students sleep on mobile phones and are more serious. Intelligent education can more accurately identify student behaviors to help teachers optimize teaching methods, thereby improving students' classroom learning effects. This paper studies and improves YOLOv4, and proposes a network structure called YOLOv4-Bi, which mainly adds the enhanced feature extraction network of YOLOv4 to the feature extraction structure of jumping and top-down, bottom-up combined paths. The used student classroom recording video is enhanced by taking the frame data and training, and testing on this data set. The original YOLOv4 is compared with the network of the improved PANet module and the Faster R-CNN network, and the data is carried out in the data set. It is verified that the mAP of the improved YOLOv4 network is higher than the mAP of the original unimproved YOLOv4 network. Compared with the original network, YOLOv4 is more suitable for student detection and recognition.","PeriodicalId":376014,"journal":{"name":"2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE)","volume":"738 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":"122952410","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

Wood acoustic emission signal classification based on IMF's features 基于IMF特征的木材声发射信号分类

2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE) Pub Date : 2021-08-20 DOI: 10.1109/CSAIEE54046.2021.9543325

Meilin Zhang, Junqiu Li, Qinghui Zhang, Jiale Xu

引用次数: 0

Face Restoration Network with Feature Prior 基于特征先验的人脸恢复网络

2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE) Pub Date : 2021-08-20 DOI: 10.1109/CSAIEE54046.2021.9543344

Yu Liu

引用次数: 1

CycleGAN -based Cloud2painting Translation 基于CycleGAN的Cloud2painting翻译

2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE) Pub Date : 2021-08-20 DOI: 10.1109/CSAIEE54046.2021.9543229

Jingshuo Liu, Shu Zhang, Xinrui Ma, Maoxuan Feng

引用次数: 0

Optimized XGBoost based sparrow search algorithm for short-term load forecasting 优化基于XGBoost的麻雀搜索算法的短期负荷预测

2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE) Pub Date : 2021-08-20 DOI: 10.1109/CSAIEE54046.2021.9543453

Jialei Song, Lijun Jin, Yingpeng Xie, Congmou Wei

{"title":"Optimized XGBoost based sparrow search algorithm for short-term load forecasting","authors":"Jialei Song, Lijun Jin, Yingpeng Xie, Congmou Wei","doi":"10.1109/CSAIEE54046.2021.9543453","DOIUrl":"https://doi.org/10.1109/CSAIEE54046.2021.9543453","url":null,"abstract":"To address the problem that the difficulty of selecting parameters in the XGBoost model makes it difficult to optimize the regression effect, a short-term load forecasting model based on the sparrow search algorithm to optimize XGBoost is proposed. Similar days are selected as the training set by the GRA algorithm, the mean absolute error obtained by cross-validation is used as the fitness function, the sparrow search algorithm (SSA) is used to optimize the XGBoost covariate selection process, and the SSA-XGBoost load forecasting model is constructed, and finally the load is corrected by the compensation model to obtain the final load forecasting data. Taking the load data of a region in Zhejiang Province from January 2019 to December 2020 as an example, the prediction ability of the SSA-XGBoost load forecasting model is examined through five experiments. The experimental results show that (i) the parameters of SVM, RF, and XGBoost models can be optimized using the SSA algorithm, and SSA-SVM, SSA-RF, and SSA-XGBoost can quickly calculate the load forecasting data, among which the SSA-XGBoost model has the highest accuracy. Compared with kmeans and other clustering methods, this paper uses the GRA algorithm to select similar days more reasonably, with smaller prediction errors and a controllable number of training sets. The compensation model improves the prediction accuracy of the model by correcting the SSA-XGBoost load prediction data.","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":"134449703","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: 10

Back Propagation Optimization of Convolutional Neural Network Based on the left and the right hands Identification 基于左右手辨识的卷积神经网络反向传播优化

2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE) Pub Date : 2021-08-20 DOI: 10.1109/CSAIEE54046.2021.9543137

Taifen Bao, Huimin Jiao, Su Gao, Jifei Cai, Yuansheng Qi

引用次数: 0

Optimization of GNSS Signals Acquisition Algorithm Complexity Using Comb Decimation Filter 利用梳状抽取滤波器优化GNSS信号采集算法复杂度

2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE) Pub Date : 2021-08-20 DOI: 10.1109/CSAIEE54046.2021.9543328

Yong Li, Chu He, Qile Zhao, Jiarui Hu

引用次数: 0

Recent research trends on Model Compression and Knowledge Transfer in CNNs cnn中模型压缩和知识转移的研究进展

2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE) Pub Date : 2021-08-20 DOI: 10.1109/CSAIEE54046.2021.9543192

Haoqian Xue, Keyu Ren

{"title":"Recent research trends on Model Compression and Knowledge Transfer in CNNs","authors":"Haoqian Xue, Keyu Ren","doi":"10.1109/CSAIEE54046.2021.9543192","DOIUrl":"https://doi.org/10.1109/CSAIEE54046.2021.9543192","url":null,"abstract":"Convolutional neural network (CNN) is the main tool for deep learning and computer vision, and it has many applications in face recognition, sign language recognition and speech recognition. As deep learning becomes more and more mature, the application of convolutional neural networks will become more and more widespread. As we know, the deeper a neural network is, the higher its memory and computational power overhead. Many neural networks used in medicine, autonomous driving, and language recognition have large model complexity, which makes it difficult to apply these CNNs to people's daily life. Therefore, the development of simple, lightweight and small neural networks has become the focus of researchers nowadays. In this paper, we summarize the development of convolutional neural networks in recent years, as well as various methods for compressing models and migrating data from large models to small ones. In general, the main convolutional neural network compression approaches are: pruning, knowledge distillation, aggregating neurons of different scales, proposing new structures, etc. We start from the structure of neural networks, introduce the major structural changes experienced from the development of convolutional neural networks, and then analyze various pruning, compression and knowledge distillation methods. For specific methods, we run different models and compare the improvements of the new methods with respect to the old ones. We also debugged models on adversarial generative pruning, teacher-student networks, and other compressed CNNs during this period, and drew some constructive conclusions. Finally, we summarize the trends in CNN development in recent years and the challenges we may face in the future.","PeriodicalId":376014,"journal":{"name":"2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE)","volume":"67 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":"126210127","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