2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)最新文献_第8页

Study on the Rapid Prediction Model of Water Quality for Emergency Water Pollution 应急水污染水质快速预测模型研究

2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI) Pub Date : 2021-08-01 DOI: 10.1109/ICCEAI52939.2021.00041

Liting Zhang, Wensi Wang, Qiang Gao, Mei Yang, Yanping Ji, Shuqin Geng

{"title":"Study on the Rapid Prediction Model of Water Quality for Emergency Water Pollution","authors":"Liting Zhang, Wensi Wang, Qiang Gao, Mei Yang, Yanping Ji, Shuqin Geng","doi":"10.1109/ICCEAI52939.2021.00041","DOIUrl":"https://doi.org/10.1109/ICCEAI52939.2021.00041","url":null,"abstract":"Water quality is a basic work in environmental governance, which has vital significance in promoting the sustainable utilization of water resources and instant pollution prevention and precise control. Water quality data is dynamic and frequently fluctuating with different temporal and spatial dimensions, therefore it can be challenging to predict. A hybrid AM-ConvLSTM deep learning algorithm is proposed in this paper to rapidly predict the trend of water quality which can run faster and require low computing power rather than the traditional MIKE 21 hydrological method. The ConvLSTM method and the attention mechanism are assembled to build AM-ConvLSTM model to better capture spatial correlation. Moreover, the statistic methods are used to evaluate the effectiveness of the model and then compared with varieties of deep learning baseline methods. The results reveal that the hybrid AM-ConvLSTM model can effectively replace MIKE 21 model to predict the future trend of water quality, and then the local environmental protection agencies will respond quickly to emergency water pollution.","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":"116349540","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

Evaluation of Motor Vehicle Driver Fatigue Based on Eye Movement Signals 基于眼动信号的机动车驾驶员疲劳评价

2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI) Pub Date : 2021-08-01 DOI: 10.1109/ICCEAI52939.2021.00018

Xing Liu, Lecai Cai, Zhiming Wu, Shaosong Duan, Keyuan Tang, Chaoyang Zhang

{"title":"Evaluation of Motor Vehicle Driver Fatigue Based on Eye Movement Signals","authors":"Xing Liu, Lecai Cai, Zhiming Wu, Shaosong Duan, Keyuan Tang, Chaoyang Zhang","doi":"10.1109/ICCEAI52939.2021.00018","DOIUrl":"https://doi.org/10.1109/ICCEAI52939.2021.00018","url":null,"abstract":"Fatigue driving is one of the main causes of traffic accidents, and it has a great impact on road safety. One of the most effective fatigue driving detection methods is based on the machine vision using the characteristics of driver's eye. However, this accuracy of the method is influenced by the light environment during driving. In order to address the problem, a method is proposed to detect the fatigue driving. In this method, the homomorphic filtering is first used to preprocess the image to eliminate the effect of various lighting environment;then the face of driver is detected based on the Local Binary Pattern(LBP) based method considering its rapid image processing speed and the key points of the face and eyes were extracted using direct shape regression network (DSRN); finally, the features such as blink frequency, blink duration and PERCLOS are calculated based on the key points and the support vector machine is used to build classifier to identify the fatigue state of drivers. The results show that the proposed method can identify the fatigue state with relatively high accuracy.","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":"121014472","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

Changes of Parathyroid Hormone and Osteocalcin in Diabetic Patients with Different Syndromes of Deafness 糖尿病耳聋不同证型患者甲状旁腺激素和骨钙素的变化

2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI) Pub Date : 2021-08-01 DOI: 10.1109/ICCEAI52939.2021.00094

Li Ruiyu, Li Yue, L. Xing, L. Meng, Guo Weiya, Zhang Chenyu, L. Qingwen, Hou Jinjie

{"title":"Changes of Parathyroid Hormone and Osteocalcin in Diabetic Patients with Different Syndromes of Deafness","authors":"Li Ruiyu, Li Yue, L. Xing, L. Meng, Guo Weiya, Zhang Chenyu, L. Qingwen, Hou Jinjie","doi":"10.1109/ICCEAI52939.2021.00094","DOIUrl":"https://doi.org/10.1109/ICCEAI52939.2021.00094","url":null,"abstract":"Objective: To observe the changes of parathyroid hormone and osteocalcin in diabetic patients with different syndromes. Methods: The changes of parathyroid hormone (I-PTH) and osteocalcin (BGP) in 39 patients (50 ears) with different types of diabetes mellitus and deafness were detected. The control group was 20 normal people. Results: The level of I-PTH in diabetic patients with deafness had no significant change compared with the normal control group (P >0.05), but the level of I-PTH in diabetic patients with deafness had significant change (P>0.05). The level of BGP in qi-yin deficiency group and yin-yang deficiency group was significantly changed compared with the normal group (P<0.05, P<0.01). Compared with Yin deficiency and desiccation (P<0.05); Compared with Yin deficiency and desiccation heat (P<0.01) and qi deficiency and Yin deficiency (P<0.01), the two groups of Yin deficiency and desiccation heat (P<0.01) were compared. Conclusion: The changes of parathyroid hormone and osteocalcin in diabetic patients with different syndromes will provide objective evidence for the prevention and treatment of diabetes.","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":"127887765","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

Pedestrian Recognition System for Smart Security Robot using Pedestrian Re-identification Algorithm 基于行人再识别算法的智能安防机器人行人识别系统

2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI) Pub Date : 2021-08-01 DOI: 10.1109/ICCEAI52939.2021.00074

Yusu Wang, Zhipeng Ma, Huaqi Fang, Can Hu, Yihao Cao, Dingxin He

引用次数: 0

Analysis of Different Passivation Solute Weight Ratio on Performance Influence of Indium Oxide Electronic Characteristics 分析不同钝化溶质质量比对氧化铟电子特性性能的影响

2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI) Pub Date : 2021-08-01 DOI: 10.1109/ICCEAI52939.2021.00092

Hongbo Guo, Li Zhang, Haohan Hu, Ting Yu, Yan Li, Ning Li

引用次数: 0

Analysis of China's Pension Financial Sustainability Based on Actuarial Model and Confidence Interval Theory 基于精算模型和置信区间理论的中国养老金财务可持续性分析

2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI) Pub Date : 2021-08-01 DOI: 10.1109/ICCEAI52939.2021.00009

Wang Haiyang, Li Yujiao

引用次数: 0

Pyramid Residual Neural Network with Attention for Seismic Data Denoising 关注地震数据去噪的金字塔残差神经网络

2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI) Pub Date : 2021-08-01 DOI: 10.1109/ICCEAI52939.2021.00077

Cuiqian Yang, Yatong Zhou, H. He, Jingfei He, Yue Chi

{"title":"Pyramid Residual Neural Network with Attention for Seismic Data Denoising","authors":"Cuiqian Yang, Yatong Zhou, H. He, Jingfei He, Yue Chi","doi":"10.1109/ICCEAI52939.2021.00077","DOIUrl":"https://doi.org/10.1109/ICCEAI52939.2021.00077","url":null,"abstract":"In recent years, seismic data processing based on deep convolutional neural networks (CNN) has made great progress. However, most of these methods rely on the feature information on the same scale and cannot make full use of the self-similarity of seismic data. In order to solve this problem, this paper proposes a novel Pyramid Attention Residual Neural Network (PARNet) for seismic data denoising. Specifically, the main framework of the network includes the residual block (ResBlock), the residual block with multi-core convolutional layer, the parallel space and channel attention (MSCARB) and the pyramid module(Pyramid Module). Among them, MSACRB can not only extract more abundant features, but also focus on the features of channel and spatial dimension, so as to achieve stronger feature representation. The pyramid module captures multi-scale features through dilated convolution with different expansion rates. At the same time, the global context module can capture the global information of the feature map. The combination of the above two modules can achieve the purpose of capturing multi-scale global context features. This method has been verified on synthetic seismic data and field seismic data. The experiments use PSNR and SSIM as evaluation indicators. A large number of experiments have demonstrated that PARNet has efficient denoising ability and a competitive advantage compared with the latest seismic data denoising methods.","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":"129820167","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 sentiment classification algorithm of Bi-LSTM model fused with weighted word vectors 一种融合加权词向量的Bi-LSTM模型情感分类算法

2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI) Pub Date : 2021-08-01 DOI: 10.1109/ICCEAI52939.2021.00049

Chaohui Chai, Dong-Ru Ruan

{"title":"A sentiment classification algorithm of Bi-LSTM model fused with weighted word vectors","authors":"Chaohui Chai, Dong-Ru Ruan","doi":"10.1109/ICCEAI52939.2021.00049","DOIUrl":"https://doi.org/10.1109/ICCEAI52939.2021.00049","url":null,"abstract":"As a hot topic in the field of natural language processing, sentiment classification has always attracted much attention. With more and more comments from users in different fields, it is necessary to build a more accurate and efficient sentiment classification model. The traditional distributed word vector representation method cannot well represent the ability of words to distinguish text and cannot capture the emotional information in sentences. We use Word2vec to obtain semantic information between words, obtains word distribution representation characteristics, and then combines emotional dictionary to judge word emotional information, uses TF -IDF algorithm to construct the word distribution characteristics of weighted word vectors, so as to effectively capture the emotional information of contextual sentences. Finally, Combining the bi-directionallong short-term memory network (Bi-LSTM) model can get more accurate sentiment classification results. The experimental results show that after selecting appropriate model parameters, the weighted word vector method combined with emotional information and the distributed feature vector method based on semantic relations have improved accuracy and other indicators; through the feature representation of the weighted word vector Methods Compared the Bi-LSTM model with other text classification models, it is concluded that the feature representation method of the weighted word vector has improved the classification results in each model, and the classification effect is the best in the Bi-LSTM model.","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":"131021632","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 3D Visual Cooperative Maintenance Method for Bogie of Urban Rail Vehicle 城市轨道车辆转向架三维视觉协同维修方法研究

2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI) Pub Date : 2021-08-01 DOI: 10.1109/ICCEAI52939.2021.00079

Yi Liu, Qi Chang, Qinghai Gan, Fengyun Xie

引用次数: 0

Evaluating the Speedup of Multicore Architecture on the Topological Characteristics of On-chip Memory 基于片上存储器拓扑特性的多核结构加速评估

2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI) Pub Date : 2021-08-01 DOI: 10.1109/ICCEAI52939.2021.00098

Hong Zhang, Xiaojun Wang

引用次数: 0