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.
{"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":"14 2","pages":"0"},"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}
Pub Date : 2021-08-01DOI: 10.1109/ICCEAI52939.2021.00094
Li Ruiyu, Li Yue, L. Xing, L. Meng, Guo Weiya, Zhang Chenyu, L. Qingwen, Hou Jinjie
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.
{"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":"26 1","pages":"0"},"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}
Objective to explore the nursing intervention points of postoperative hypoglycemia in elderly patients with endometrial cancer and diabetes mellitus. Method: 50 elderly patients with hypoglycemia after endometrial cancer and diabetes were selected. During the nursing process, the author made a detailed record and follow-up observation, and summarized the data. Result: After treatment, the condition of some patients were under control, including 44 cases with obvious effect, 4 cases with general effect, 2 cases with no effect, and the effective rate was 98%. Conclusion: In the clinical nursing of endometrial cancer patients with diabetes mellitus, effective nursing can greatly improve the recovery effect of patients, and can effectively control the deterioration of the disease, so efficient nursing methods can be popularized in clinical.
{"title":"Nursing intervention of postoperative hypoglycemia in elderly patients with endometrial cancer and diabetes mellitus","authors":"Ting Sun, Huiqing Hua, Lijuan Gao, Fengju Chen, Lingling Wu","doi":"10.1109/ICCEAI52939.2021.00102","DOIUrl":"https://doi.org/10.1109/ICCEAI52939.2021.00102","url":null,"abstract":"Objective to explore the nursing intervention points of postoperative hypoglycemia in elderly patients with endometrial cancer and diabetes mellitus. Method: 50 elderly patients with hypoglycemia after endometrial cancer and diabetes were selected. During the nursing process, the author made a detailed record and follow-up observation, and summarized the data. Result: After treatment, the condition of some patients were under control, including 44 cases with obvious effect, 4 cases with general effect, 2 cases with no effect, and the effective rate was 98%. Conclusion: In the clinical nursing of endometrial cancer patients with diabetes mellitus, effective nursing can greatly improve the recovery effect of patients, and can effectively control the deterioration of the disease, so efficient nursing methods can be popularized in clinical.","PeriodicalId":331409,"journal":{"name":"2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114078674","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}
Pub Date : 2021-08-01DOI: 10.1109/ICCEAI52939.2021.00074
Yusu Wang, Zhipeng Ma, Huaqi Fang, Can Hu, Yihao Cao, Dingxin He
The security system is an important guarantee for the safety of citizens' lives and property. In recent years, security robots have been more and more widely used in security systems. At present, domestic security robots generally lack of pedestrian recognition ability under complex circumstances. Therefore, this paper designs and implements pedestrian recognition system for smart security robots using improved pedestrian re-identification algorithm. Experiment result shows that the system has success rate of 90 % and response speed compliance rate of 94.4% under real circumstances, which is much better than traditional system.
{"title":"Pedestrian Recognition System for Smart Security Robot using Pedestrian Re-identification Algorithm","authors":"Yusu Wang, Zhipeng Ma, Huaqi Fang, Can Hu, Yihao Cao, Dingxin He","doi":"10.1109/ICCEAI52939.2021.00074","DOIUrl":"https://doi.org/10.1109/ICCEAI52939.2021.00074","url":null,"abstract":"The security system is an important guarantee for the safety of citizens' lives and property. In recent years, security robots have been more and more widely used in security systems. At present, domestic security robots generally lack of pedestrian recognition ability under complex circumstances. Therefore, this paper designs and implements pedestrian recognition system for smart security robots using improved pedestrian re-identification algorithm. Experiment result shows that the system has success rate of 90 % and response speed compliance rate of 94.4% under real circumstances, which is much better than traditional system.","PeriodicalId":331409,"journal":{"name":"2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134540926","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}
Pub Date : 2021-08-01DOI: 10.1109/ICCEAI52939.2021.00092
Hongbo Guo, Li Zhang, Haohan Hu, Ting Yu, Yan Li, Ning Li
Indium oxide (In2O3) material has decent electronic characteristics as the channel layer of the thin film transistor (TFT), but obviously it has the improvement possibility in terms of stability and threshold voltage. We use poly tetra fluoroethylene (PTFE) as a passivation layer to prepare it above the In2O3TFT through solution treatment which protects the device and improves its performance. We performed electronic characterization of In2O3with different solute weight ratio PTFT passivation layer, analyzed the types and contents of the constituent elements in the micro area of the material by energy dispersive spectrometry and observed the dynamic and static response of the inverter drive. Got a proper conversion effect. It is concluded that the appropriate solute weight ratio of the PTFE passivation layer is helpful to improve the various electronic characteristics of the In2O3TFT and the relative content of each element under the optimal state.
{"title":"Analysis of Different Passivation Solute Weight Ratio on Performance Influence of Indium Oxide Electronic Characteristics","authors":"Hongbo Guo, Li Zhang, Haohan Hu, Ting Yu, Yan Li, Ning Li","doi":"10.1109/ICCEAI52939.2021.00092","DOIUrl":"https://doi.org/10.1109/ICCEAI52939.2021.00092","url":null,"abstract":"Indium oxide (In2O3) material has decent electronic characteristics as the channel layer of the thin film transistor (TFT), but obviously it has the improvement possibility in terms of stability and threshold voltage. We use poly tetra fluoroethylene (PTFE) as a passivation layer to prepare it above the In2O3TFT through solution treatment which protects the device and improves its performance. We performed electronic characterization of In2O3with different solute weight ratio PTFT passivation layer, analyzed the types and contents of the constituent elements in the micro area of the material by energy dispersive spectrometry and observed the dynamic and static response of the inverter drive. Got a proper conversion effect. It is concluded that the appropriate solute weight ratio of the PTFE passivation layer is helpful to improve the various electronic characteristics of the In2O3TFT and the relative content of each element under the optimal state.","PeriodicalId":331409,"journal":{"name":"2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)","volume":"363 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130232496","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}
Pub Date : 2021-08-01DOI: 10.1109/ICCEAI52939.2021.00009
Wang Haiyang, Li Yujiao
This paper mainly studies the financial sustainability of China's pension system. Focusing on the current pension system, this paper adopts the actuarial model and the strategy of layer-by-layer analysis to establish the macro model of urban and rural residents' pension income and expenses. Thereby, the pension gap is forecasted. At the same time, based on the confidence interval theory, the range of replacement rate and contribution rate is controlled to safeguard the sustain ability of China's pension system. The reliability is 95%. Therefore, the results show that by adjusting the replacement rate and contribution rate can roughly ensure the sustainable development of China's pension system. Improving the pension's overall level and system is also recommended.
{"title":"Analysis of China's Pension Financial Sustainability Based on Actuarial Model and Confidence Interval Theory","authors":"Wang Haiyang, Li Yujiao","doi":"10.1109/ICCEAI52939.2021.00009","DOIUrl":"https://doi.org/10.1109/ICCEAI52939.2021.00009","url":null,"abstract":"This paper mainly studies the financial sustainability of China's pension system. Focusing on the current pension system, this paper adopts the actuarial model and the strategy of layer-by-layer analysis to establish the macro model of urban and rural residents' pension income and expenses. Thereby, the pension gap is forecasted. At the same time, based on the confidence interval theory, the range of replacement rate and contribution rate is controlled to safeguard the sustain ability of China's pension system. The reliability is 95%. Therefore, the results show that by adjusting the replacement rate and contribution rate can roughly ensure the sustainable development of China's pension system. Improving the pension's overall level and system is also recommended.","PeriodicalId":331409,"journal":{"name":"2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134294387","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}
Pub Date : 2021-08-01DOI: 10.1109/ICCEAI52939.2021.00049
Chaohui Chai, Dong-Ru Ruan
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.
{"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":"1 1","pages":"0"},"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}
Pub Date : 2021-08-01DOI: 10.1109/ICCEAI52939.2021.00077
Cuiqian Yang, Yatong Zhou, H. He, Jingfei He, Yue Chi
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.
{"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":"2 1","pages":"0"},"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}
Pub Date : 2021-08-01DOI: 10.1109/ICCEAI52939.2021.00033
Lei Wang, Zhiyong Zhang, Xiaonan Li
As the self-occlusion or occluded 3D point clouds objects in complex scenes, which could affect the accuracy of objects classification, we propose Optimal Combination of Multidimensional Features based on deep learning for large-scene laser point clouds in classification. We construct the optimal combination matrix of multidimensional features by extracting the three-dimensional features of the three-dimensional point cloud and the two-dimensional features in multiple directions. The multidimensional optimal combination features are introduced into the convolutional network. The experimental results show that effectiveness of classification for large-scale point clouds, the effectiveness of 3D feature of point cloud is higher than that of 2D feature. The classification accuracy of our method can reach 98.8% on the Large-Scene Point Cloud Oakland data set, which obtains the better classification accuracy than other classification algorithms the paper mentioned.
针对复杂场景中自遮挡或遮挡的三维点云对象，影响目标分类精度的问题，提出了基于深度学习的大场景激光点云多维特征最优组合分类方法。通过在多个方向上提取三维点云的三维特征和二维特征，构建最优的多维特征组合矩阵。在卷积网络中引入了多维最优组合特征。实验结果表明，对大规模点云进行分类的有效性，点云的三维特征分类的有效性高于二维特征分类的有效性。本文方法在Large-Scene Point Cloud Oakland数据集上的分类准确率可达98.8%，获得了比本文提到的其他分类算法更好的分类准确率。
{"title":"Research on Deep Learning Based Optimal Combination of Multidimensional Features in Large-Scene Laser Point Clouds Classification","authors":"Lei Wang, Zhiyong Zhang, Xiaonan Li","doi":"10.1109/ICCEAI52939.2021.00033","DOIUrl":"https://doi.org/10.1109/ICCEAI52939.2021.00033","url":null,"abstract":"As the self-occlusion or occluded 3D point clouds objects in complex scenes, which could affect the accuracy of objects classification, we propose Optimal Combination of Multidimensional Features based on deep learning for large-scene laser point clouds in classification. We construct the optimal combination matrix of multidimensional features by extracting the three-dimensional features of the three-dimensional point cloud and the two-dimensional features in multiple directions. The multidimensional optimal combination features are introduced into the convolutional network. The experimental results show that effectiveness of classification for large-scale point clouds, the effectiveness of 3D feature of point cloud is higher than that of 2D feature. The classification accuracy of our method can reach 98.8% on the Large-Scene Point Cloud Oakland data set, which obtains the better classification accuracy than other classification algorithms the paper mentioned.","PeriodicalId":331409,"journal":{"name":"2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128621306","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}
Pub Date : 2021-08-01DOI: 10.1109/ICCEAI52939.2021.00079
Yi Liu, Qi Chang, Qinghai Gan, Fengyun Xie
As the growing of individual demand and the requirement of environmental protection, the species of modern rail traffic equipment are increasing. System integration and coupling complexity require the maintenance personnel's knowledge, which also increases the economic burden of the enterprise. This study proposes a new 3D visualization and collaborative maintenance method for the product maintenance of rail traffic equipment manufacturing enterprises, which enables maintenance staff and experts to complete the collaborative operation or guiding behavior by integrating the information models of maintenance process and maintenance schedules based on human-computer interaction, information visualization and sharing methods. This technical scheme will establish a good platform that avoids the disadvantage of the traditional maintenance process guidance way. This work can improve the efficiency of maintenance work and reduce the waste of resources in the maintenance process.
{"title":"Research on 3D Visual Cooperative Maintenance Method for Bogie of Urban Rail Vehicle","authors":"Yi Liu, Qi Chang, Qinghai Gan, Fengyun Xie","doi":"10.1109/ICCEAI52939.2021.00079","DOIUrl":"https://doi.org/10.1109/ICCEAI52939.2021.00079","url":null,"abstract":"As the growing of individual demand and the requirement of environmental protection, the species of modern rail traffic equipment are increasing. System integration and coupling complexity require the maintenance personnel's knowledge, which also increases the economic burden of the enterprise. This study proposes a new 3D visualization and collaborative maintenance method for the product maintenance of rail traffic equipment manufacturing enterprises, which enables maintenance staff and experts to complete the collaborative operation or guiding behavior by integrating the information models of maintenance process and maintenance schedules based on human-computer interaction, information visualization and sharing methods. This technical scheme will establish a good platform that avoids the disadvantage of the traditional maintenance process guidance way. This work can improve the efficiency of maintenance work and reduce the waste of resources in the maintenance process.","PeriodicalId":331409,"journal":{"name":"2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)","volume":"142 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127322800","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}
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