Pub Date : 2021-08-01DOI: 10.1109/ISCEIC53685.2021.00048
Dong Wang, Wenhao Xue, Lili Li, Jiangtao Li
As an important environment for data circulation within large enterprises, enterprise LAN carries a large amount of data of users in the industry. The importance of data security and privacy is increasing under the trend of digital development and has become an important work research direction for enterprises. While, the reliable methods for data protection during the process of penetrating and sharing LAN data to the Internet is rare. This paper proposes an enterprise LAN data desensitization penetration scheme. This scheme provides corresponding desensitization methods through the fine-grained control method of user permissions and the different degree of data confidentiality, so as to realize data application initiation, identity determination, permission control, data desensitization, and data sharing. The whole process data is Safe and controllable traceability. This solution provides new ideas and methods for enterprise intranet and extranet penetration.
{"title":"Research on data desensitization and penetration of intranet and extranet based on access control","authors":"Dong Wang, Wenhao Xue, Lili Li, Jiangtao Li","doi":"10.1109/ISCEIC53685.2021.00048","DOIUrl":"https://doi.org/10.1109/ISCEIC53685.2021.00048","url":null,"abstract":"As an important environment for data circulation within large enterprises, enterprise LAN carries a large amount of data of users in the industry. The importance of data security and privacy is increasing under the trend of digital development and has become an important work research direction for enterprises. While, the reliable methods for data protection during the process of penetrating and sharing LAN data to the Internet is rare. This paper proposes an enterprise LAN data desensitization penetration scheme. This scheme provides corresponding desensitization methods through the fine-grained control method of user permissions and the different degree of data confidentiality, so as to realize data application initiation, identity determination, permission control, data desensitization, and data sharing. The whole process data is Safe and controllable traceability. This solution provides new ideas and methods for enterprise intranet and extranet penetration.","PeriodicalId":342968,"journal":{"name":"2021 2nd International Symposium on Computer Engineering and Intelligent Communications (ISCEIC)","volume":"8 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":"115227764","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/ISCEIC53685.2021.00032
Mingqing Xue, Ming Huang, J. Yang, Ji Da Wu
In the face of a complex electromagnetic environment, the modulation mode of communication signals is becoming increasingly complicated. Existing modulation mode recognition methods of communication signals cannot accurately and quickly identify the modulation mode of communication signals. In this letter, we propose an efficient architecture for automatic modulation classification (AMC) based on residual neural network (ResNet). We combine the improved residual neural network with long short-term memory network (LSTM) to obtain a new network structure (MLResNet), which solves the problems of gradient disappearance and too many parameters. In the experiments, MLResNet reaches the overall 24-modulation classification rate of 96.60% at 18 dB SNR on the well-known DeepSig dataset.
{"title":"MLResNet: An Efficient Method for Automatic Modulation Classification Based on Residual Neural Network","authors":"Mingqing Xue, Ming Huang, J. Yang, Ji Da Wu","doi":"10.1109/ISCEIC53685.2021.00032","DOIUrl":"https://doi.org/10.1109/ISCEIC53685.2021.00032","url":null,"abstract":"In the face of a complex electromagnetic environment, the modulation mode of communication signals is becoming increasingly complicated. Existing modulation mode recognition methods of communication signals cannot accurately and quickly identify the modulation mode of communication signals. In this letter, we propose an efficient architecture for automatic modulation classification (AMC) based on residual neural network (ResNet). We combine the improved residual neural network with long short-term memory network (LSTM) to obtain a new network structure (MLResNet), which solves the problems of gradient disappearance and too many parameters. In the experiments, MLResNet reaches the overall 24-modulation classification rate of 96.60% at 18 dB SNR on the well-known DeepSig dataset.","PeriodicalId":342968,"journal":{"name":"2021 2nd International Symposium on Computer Engineering and Intelligent Communications (ISCEIC)","volume":"14 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":"127304022","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/ISCEIC53685.2021.00055
Jinling Xu, Ting Wang, Chenjie Su, Zengping Zhang, Xiaodong Cheng
In order to solve the problem of data sparseness and cold start of the collaborative filtering model, many methods have been proposed, but most of them ignore the user attribute similarity and the user preference. The accuracy of recommendation needs to be improved. Most of researches stay in simple linear modeling of the relationship between users and items, and does not consider the influence of auxiliary information on the recommendation algorithm. In our real life, users preferences are affected by age, gender, and personality. Environment, social circle, etc.In this work, we design a Top-N recommendation algorithm LNGCF-B (light neural graph collaborative filtering with user basic information). Firstly, different from traditional graph convolutional collaborative filtering algorithm, the simplified version is more explanatory, the training time is shortened. Secondly, this algorithm considers the attributes of the user, experiments show that LNGCF-B is better than the baseline algorithm. In our social life, there are many different types of networks, under different network models, the performance of the recommendation algorithm is also different. However, there are few researches on the performance of recommendation algorithms in different scenarios. We use LNGCF-B on two data sets belonging to different network models. The results show that the list recommended by the algorithm on the Movielens 100K data set belonging to the scale-free network has a higher degree of relevance, and the Facebook friend relationship data set belonging to the small world network has a higher recall rate.
为了解决协同过滤模型的数据稀疏性和冷启动问题，人们提出了许多方法，但大多数方法都忽略了用户属性相似度和用户偏好。推荐的准确性有待提高。大多数研究都停留在简单的用户与商品关系的线性建模上，没有考虑辅助信息对推荐算法的影响。在现实生活中，用户的偏好受到年龄、性别和个性的影响。在本工作中，我们设计了Top-N推荐算法LNGCF-B (light neural graph collaborative filtering with user basic information)。首先，与传统的图卷积协同过滤算法不同，简化版更具解释性，缩短了训练时间。其次，该算法考虑了用户的属性，实验表明LNGCF-B算法优于基线算法。在我们的社会生活中，有很多不同类型的网络，在不同的网络模型下，推荐算法的表现也是不同的。然而，关于推荐算法在不同场景下的性能研究却很少。我们在属于不同网络模型的两个数据集上使用了LNGCF-B。结果表明，算法推荐的列表在属于无尺度网络的Movielens 100K数据集上具有较高的相关度，而属于小世界网络的Facebook好友关系数据集具有较高的召回率。
{"title":"A Top-N recommendation algorithm based on graph convolutional network that integrates basic user information","authors":"Jinling Xu, Ting Wang, Chenjie Su, Zengping Zhang, Xiaodong Cheng","doi":"10.1109/ISCEIC53685.2021.00055","DOIUrl":"https://doi.org/10.1109/ISCEIC53685.2021.00055","url":null,"abstract":"In order to solve the problem of data sparseness and cold start of the collaborative filtering model, many methods have been proposed, but most of them ignore the user attribute similarity and the user preference. The accuracy of recommendation needs to be improved. Most of researches stay in simple linear modeling of the relationship between users and items, and does not consider the influence of auxiliary information on the recommendation algorithm. In our real life, users preferences are affected by age, gender, and personality. Environment, social circle, etc.In this work, we design a Top-N recommendation algorithm LNGCF-B (light neural graph collaborative filtering with user basic information). Firstly, different from traditional graph convolutional collaborative filtering algorithm, the simplified version is more explanatory, the training time is shortened. Secondly, this algorithm considers the attributes of the user, experiments show that LNGCF-B is better than the baseline algorithm. In our social life, there are many different types of networks, under different network models, the performance of the recommendation algorithm is also different. However, there are few researches on the performance of recommendation algorithms in different scenarios. We use LNGCF-B on two data sets belonging to different network models. The results show that the list recommended by the algorithm on the Movielens 100K data set belonging to the scale-free network has a higher degree of relevance, and the Facebook friend relationship data set belonging to the small world network has a higher recall rate.","PeriodicalId":342968,"journal":{"name":"2021 2nd International Symposium on Computer Engineering and Intelligent Communications (ISCEIC)","volume":"50 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":"126727062","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/ISCEIC53685.2021.00073
Jiangqi Hu, G. Cui, Xiukai Ruan, Yishan Jiang
In the paint industry, querying a certain texture image is usually done by employees visually with their personal experience or with the help of a common image retrieval system, which cannot meet the needs of paint companies to query images accurately. In order to improve the accuracy of retrieval, an image retrieval algorithm is proposed for paint images with a wide variety of colors and complex texture information. For color features, a color autocorrelogram is selected; for texture features, a direction-improved uniform local binary pattern that considers the comparison of gray values between neighboring pixels is proposed to enhance texture directional feature recognition. The color and texture features are fused as feature descriptors to retrieve 216 insulated decorative integrated panel images. The experimental results show that the fused features are more suitable for describing particular paint images and have a higher average finding accuracy than other descriptive feature algorithms.
{"title":"Painting Image Retrieval Method Based on Color and Texture Features","authors":"Jiangqi Hu, G. Cui, Xiukai Ruan, Yishan Jiang","doi":"10.1109/ISCEIC53685.2021.00073","DOIUrl":"https://doi.org/10.1109/ISCEIC53685.2021.00073","url":null,"abstract":"In the paint industry, querying a certain texture image is usually done by employees visually with their personal experience or with the help of a common image retrieval system, which cannot meet the needs of paint companies to query images accurately. In order to improve the accuracy of retrieval, an image retrieval algorithm is proposed for paint images with a wide variety of colors and complex texture information. For color features, a color autocorrelogram is selected; for texture features, a direction-improved uniform local binary pattern that considers the comparison of gray values between neighboring pixels is proposed to enhance texture directional feature recognition. The color and texture features are fused as feature descriptors to retrieve 216 insulated decorative integrated panel images. The experimental results show that the fused features are more suitable for describing particular paint images and have a higher average finding accuracy than other descriptive feature algorithms.","PeriodicalId":342968,"journal":{"name":"2021 2nd International Symposium on Computer Engineering and Intelligent Communications (ISCEIC)","volume":"13 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":"128391810","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/ISCEIC53685.2021.00072
Sheng Dong, Jiaxin Zhang, Zehui Qu
Small and multi-scale objects are always dilemmas for object detection. However, small objects may disappear and cannot be detected because it is arduous to differentiate information from a small part of the original image. To alleviate the issue, an image pyramid is utilized to build a feature pyramid to detect across a range of scales. Instead, we combine image pyramid and feature pyramid with a Contextually Enhanced Module (CEM) to extract contextual information. Furthermore, we propose Unidirectional Bottom-up Connections (UBC) to extract more distinct features. A novel Multi-path and Multi-scale Feature Pyramid Network (MM-FPN) is proposed to improve the performance of both small-sized and large-sized objects. Experiments and ablation studies are performed on PASCAL VOC, which surpass most of the existing competitive single-stage and two-stage methods.
{"title":"MM-FPN: Multi-path and Multi-scale Feature Pyramid Network for Object Detection","authors":"Sheng Dong, Jiaxin Zhang, Zehui Qu","doi":"10.1109/ISCEIC53685.2021.00072","DOIUrl":"https://doi.org/10.1109/ISCEIC53685.2021.00072","url":null,"abstract":"Small and multi-scale objects are always dilemmas for object detection. However, small objects may disappear and cannot be detected because it is arduous to differentiate information from a small part of the original image. To alleviate the issue, an image pyramid is utilized to build a feature pyramid to detect across a range of scales. Instead, we combine image pyramid and feature pyramid with a Contextually Enhanced Module (CEM) to extract contextual information. Furthermore, we propose Unidirectional Bottom-up Connections (UBC) to extract more distinct features. A novel Multi-path and Multi-scale Feature Pyramid Network (MM-FPN) is proposed to improve the performance of both small-sized and large-sized objects. Experiments and ablation studies are performed on PASCAL VOC, which surpass most of the existing competitive single-stage and two-stage methods.","PeriodicalId":342968,"journal":{"name":"2021 2nd International Symposium on Computer Engineering and Intelligent Communications (ISCEIC)","volume":"27 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":"129930766","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/ISCEIC53685.2021.00053
Yanjun Wei, Tonghe Ding, Tianping Li, Kaili Feng
With the development of deep learning, convolution neural networks have become the mainstream of computer vision algorithms. In recent years, the biggest problem of applying convolution neural network to image segmentation is that it can not achieve accurate segmentation at the last layer, and it will cause resolution loss when extracting features. In order to solve these two problems, we add jump feature fusion methods after Entry, Middle, ExitFlow and ASPP module respectively, so that the feature loss will not be serious when extracting features. In the process of feature restoration, a module combining bilinear upsampling and deconvolution is added to further enrich the feature graph and make the features robust. The experimental results show that the results exceed the performance of other previous algorithms. We demonstrate the effectiveness of the proposed model on PASCAL VOC 2012, achieving the test set performance of 85.5%.
{"title":"Image Segmentation Algorithm Based on Jump Feature Fusion and Rich Features","authors":"Yanjun Wei, Tonghe Ding, Tianping Li, Kaili Feng","doi":"10.1109/ISCEIC53685.2021.00053","DOIUrl":"https://doi.org/10.1109/ISCEIC53685.2021.00053","url":null,"abstract":"With the development of deep learning, convolution neural networks have become the mainstream of computer vision algorithms. In recent years, the biggest problem of applying convolution neural network to image segmentation is that it can not achieve accurate segmentation at the last layer, and it will cause resolution loss when extracting features. In order to solve these two problems, we add jump feature fusion methods after Entry, Middle, ExitFlow and ASPP module respectively, so that the feature loss will not be serious when extracting features. In the process of feature restoration, a module combining bilinear upsampling and deconvolution is added to further enrich the feature graph and make the features robust. The experimental results show that the results exceed the performance of other previous algorithms. We demonstrate the effectiveness of the proposed model on PASCAL VOC 2012, achieving the test set performance of 85.5%.","PeriodicalId":342968,"journal":{"name":"2021 2nd International Symposium on Computer Engineering and Intelligent Communications (ISCEIC)","volume":"20 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":"123376834","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/ISCEIC53685.2021.00039
Taizhi Lv, Jun Zhang, Chenyong He
The structural contradiction between talent supply and demand is the key problem to be solved in higher vocational colleges. Recruitment website provides massive recruitment data. The analysis of recruitment data has important practical significance for promoting the reform and innovation of talent training mode. Based on big data technology, the distributed real-time incremental collection of posts information is realized by Redis and Scrapy technology. The crawled posts information is stored in HBase database. The posts data is analyzed by spark platform, and the analysis result is stored in MySQL database. The charts are displayed by Flask framework and Echarts library. The system is closely linked to the pain spot of the current higher vocational talent training, and it is closely combined the skills required by the post with the courses offered by the school. It is helpful to improve the quality of talent training and cultivate more high-quality skilled talents.
{"title":"Research on posts analysis based on data process automation","authors":"Taizhi Lv, Jun Zhang, Chenyong He","doi":"10.1109/ISCEIC53685.2021.00039","DOIUrl":"https://doi.org/10.1109/ISCEIC53685.2021.00039","url":null,"abstract":"The structural contradiction between talent supply and demand is the key problem to be solved in higher vocational colleges. Recruitment website provides massive recruitment data. The analysis of recruitment data has important practical significance for promoting the reform and innovation of talent training mode. Based on big data technology, the distributed real-time incremental collection of posts information is realized by Redis and Scrapy technology. The crawled posts information is stored in HBase database. The posts data is analyzed by spark platform, and the analysis result is stored in MySQL database. The charts are displayed by Flask framework and Echarts library. The system is closely linked to the pain spot of the current higher vocational talent training, and it is closely combined the skills required by the post with the courses offered by the school. It is helpful to improve the quality of talent training and cultivate more high-quality skilled talents.","PeriodicalId":342968,"journal":{"name":"2021 2nd International Symposium on Computer Engineering and Intelligent Communications (ISCEIC)","volume":"15 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":"121284928","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/ISCEIC53685.2021.00047
Hui Wang, Jilong Wang
At present, most of the short-term wind speed forecasting researches directly use the original data as the input or break them down, and take the decomposed series as the input for forecasting model. There is a lack of feature analysis of the original data and the decomposed series. In this paper, from the perspective of feature analysis of wind speed, Ensemble Empirical Mode Decomposition (EEMD) and Convolutional Neural Networks (CNN) are used to decompose the sequence and extract features, and Multilayer Perceptron (MLP) is used to predict the wind speed. Firstly, EEMD is used to decompose the wind speed into a series of subsequences; Secondly, CNN is used to extract the features of each decomposition layer, and the input variables of each decomposition layer are constructed; Finally, MLP is used to predict each decomposition layer; At the same time, Adam is used to optimize the parameters of CNN and MLP. The results of case study and comparison show that EEMD-CNN-MLP-Adam has high prediction and good generalization, which can provide reference for wind speed prediction in different regions and periods.
{"title":"Short Term Wind Speed Forecasting Based on Feature Extraction by CNN and MLP","authors":"Hui Wang, Jilong Wang","doi":"10.1109/ISCEIC53685.2021.00047","DOIUrl":"https://doi.org/10.1109/ISCEIC53685.2021.00047","url":null,"abstract":"At present, most of the short-term wind speed forecasting researches directly use the original data as the input or break them down, and take the decomposed series as the input for forecasting model. There is a lack of feature analysis of the original data and the decomposed series. In this paper, from the perspective of feature analysis of wind speed, Ensemble Empirical Mode Decomposition (EEMD) and Convolutional Neural Networks (CNN) are used to decompose the sequence and extract features, and Multilayer Perceptron (MLP) is used to predict the wind speed. Firstly, EEMD is used to decompose the wind speed into a series of subsequences; Secondly, CNN is used to extract the features of each decomposition layer, and the input variables of each decomposition layer are constructed; Finally, MLP is used to predict each decomposition layer; At the same time, Adam is used to optimize the parameters of CNN and MLP. The results of case study and comparison show that EEMD-CNN-MLP-Adam has high prediction and good generalization, which can provide reference for wind speed prediction in different regions and periods.","PeriodicalId":342968,"journal":{"name":"2021 2nd International Symposium on Computer Engineering and Intelligent Communications (ISCEIC)","volume":"72 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":"134477035","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/ISCEIC53685.2021.00076
Cuilian Liu, Caijie Lin, Ruihong Lin, Yibo Li, Zexin Fan
After the epidemic, online and offline mixed teaching will become a norm, and video teaching will be the most commonly used online teaching mode. The fragmented way of paging recording and broadcasting designed by this system can solve the traditional misreading in the recording of the whole text or need a lot of editing work because of being interrupted, which can greatly improve the efficiency of recording classes. This system adopts B/S mode, chooses SpringBoot and SSM-(Spring_SpringMVC_Mybatis) framework and, Spring Cloud microservice framework. Using document cutting, stroke track monitoring, stroke track restoration, progress bar jump and multi-version recording algorithms, the text is paginated and recorded, and the recorded course is developed twice. It supports online and offline playback of recorded courses and saves most traffic mode. It is a real online recording and teaching synchronization of the Internet teaching platfom.
{"title":"Design of a Cloud-based Paging Intelligent Classroom Recording and Broadcasting System","authors":"Cuilian Liu, Caijie Lin, Ruihong Lin, Yibo Li, Zexin Fan","doi":"10.1109/ISCEIC53685.2021.00076","DOIUrl":"https://doi.org/10.1109/ISCEIC53685.2021.00076","url":null,"abstract":"After the epidemic, online and offline mixed teaching will become a norm, and video teaching will be the most commonly used online teaching mode. The fragmented way of paging recording and broadcasting designed by this system can solve the traditional misreading in the recording of the whole text or need a lot of editing work because of being interrupted, which can greatly improve the efficiency of recording classes. This system adopts B/S mode, chooses SpringBoot and SSM-(Spring_SpringMVC_Mybatis) framework and, Spring Cloud microservice framework. Using document cutting, stroke track monitoring, stroke track restoration, progress bar jump and multi-version recording algorithms, the text is paginated and recorded, and the recorded course is developed twice. It supports online and offline playback of recorded courses and saves most traffic mode. It is a real online recording and teaching synchronization of the Internet teaching platfom.","PeriodicalId":342968,"journal":{"name":"2021 2nd International Symposium on Computer Engineering and Intelligent Communications (ISCEIC)","volume":"33 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":"124561974","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/ISCEIC53685.2021.00011
Liu Yang
With the development of new energy vehicles, the capacity of residential areas for private charging piles continues to increase. But for most car owners, charging piles are not needed every day, and the charging piles of residents will be redundant. In response to this phenomenon, this paper analyzes the relevant attributes of new energy vehicles and the current use of cars under big data statistics, and proposes to calculate the number of new energy charging piles in residential areas through genetic algorithm in order to solve the problem of surplus charging piles.
{"title":"New energy charging pile planning in residential area based on improved genetic algorithm","authors":"Liu Yang","doi":"10.1109/ISCEIC53685.2021.00011","DOIUrl":"https://doi.org/10.1109/ISCEIC53685.2021.00011","url":null,"abstract":"With the development of new energy vehicles, the capacity of residential areas for private charging piles continues to increase. But for most car owners, charging piles are not needed every day, and the charging piles of residents will be redundant. In response to this phenomenon, this paper analyzes the relevant attributes of new energy vehicles and the current use of cars under big data statistics, and proposes to calculate the number of new energy charging piles in residential areas through genetic algorithm in order to solve the problem of surplus charging piles.","PeriodicalId":342968,"journal":{"name":"2021 2nd International Symposium on Computer Engineering and Intelligent Communications (ISCEIC)","volume":"45 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":"114622179","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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