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2021 International Conference on Computer Engineering and Application (ICCEA)最新文献

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Research on seismic impedance inversion method based on pre-training and improved residual network 基于预训练和改进残差网络的地震阻抗反演方法研究
2021 International Conference on Computer Engineering and Application (ICCEA) Pub Date : 2021-06-01 DOI: 10.1109/ICCEA53728.2021.00036
J. Meng, Shoudong Wang, G. Niu
{"title":"Research on seismic impedance inversion method based on pre-training and improved residual network","authors":"J. Meng, Shoudong Wang, G. Niu","doi":"10.1109/ICCEA53728.2021.00036","DOIUrl":"https://doi.org/10.1109/ICCEA53728.2021.00036","url":null,"abstract":"In this paper, we design a novel network architecture based on the principle of seismic impedance inversion. The network combines convolutional neural network and residual network. In seismic impedance inversion, there are usually only a few Wells. However, supervised learning requires a large number of labeled data training networks. In order to solve the above problems, this paper uses two steps to train the network. The first step, the network is pre-trained by using seismic records as the input and a large number of labeled low-frequency information as the output of the network. The second step is to train the network with seismic records as input and a small amount of well data as output of the network. The network can learn the low frequency trend of impedance through pre-training and capture the high frequency characteristics of impedance through re-training. In summary, the network learns the full-band information of impedance through two-step training. We used two typical models with different geological characteristics to prove the effectiveness of the inversion method. We used two typical models of Marmousi II and Overthrust with different geological features to prove the effectiveness of the inversion method.","PeriodicalId":325790,"journal":{"name":"2021 International Conference on Computer Engineering and Application (ICCEA)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131627885","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
Real-time Linkage and Active Defense Architecture of Electric Power Industrial Control System 电力工业控制系统实时联动与主动防御体系结构
2021 International Conference on Computer Engineering and Application (ICCEA) Pub Date : 2021-06-01 DOI: 10.1109/ICCEA53728.2021.00103
Wei Li, Xin-hang Xu, Xiaoliang Zhang, Zhuo Lv, Cen Chen
{"title":"Real-time Linkage and Active Defense Architecture of Electric Power Industrial Control System","authors":"Wei Li, Xin-hang Xu, Xiaoliang Zhang, Zhuo Lv, Cen Chen","doi":"10.1109/ICCEA53728.2021.00103","DOIUrl":"https://doi.org/10.1109/ICCEA53728.2021.00103","url":null,"abstract":"Electric power industry is the most important fundamental energy industry in the development of national economy, and it is the cornerstone of economic development and social progress. The traditional security defense system of industrial control system adopts the passive working mode composed of traditional security products, which is difficult to resist the increasingly serious network attacks of industrial control system and eliminate hidden dangers fundamentally. In this regard, it is necessary to explore the active security defense system architecture to improve the comprehensive defense capabilities of industrial control systems and effectively resist cyberspace threats. Through in-depth analysis of the security status, protection strategy, particularity and vulnerability of power industrial control system, this paper proposes a real-time linkage active defense system including five modules of prediction, defense, detection, response and learning, and expounds the architecture and technology of each part. The active defense system proposed in this paper can prevent the vast majority of external attacks, and can flexibly adapt to and predict changes in enemy attacks.","PeriodicalId":325790,"journal":{"name":"2021 International Conference on Computer Engineering and Application (ICCEA)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123985188","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
Malicious Account Detection based on intelligent algorithm Research 基于智能算法的恶意账户检测研究
2021 International Conference on Computer Engineering and Application (ICCEA) Pub Date : 2021-06-01 DOI: 10.1109/ICCEA53728.2021.00091
Xun Huang, Haibo Luo
{"title":"Malicious Account Detection based on intelligent algorithm Research","authors":"Xun Huang, Haibo Luo","doi":"10.1109/ICCEA53728.2021.00091","DOIUrl":"https://doi.org/10.1109/ICCEA53728.2021.00091","url":null,"abstract":"Malicious account detection has always been a hot issue. This paper mainly identifies the malicious account at the registration level. After feature extraction of the collected data, a weighted undirected graph is constructed. Node2Vec is used to convert each node into a multidimensional vector. K-means algorithms and DBSCAN algorithms are used to obtain the model of garbage account. Finally, Euclidean distance is used to identify whether the malicious account is.","PeriodicalId":325790,"journal":{"name":"2021 International Conference on Computer Engineering and Application (ICCEA)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124583072","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 New Approach for End to End Automation Testing Platform with Cloud Computing for 5G Product 基于云计算的5G产品端到端自动化测试平台新思路
2021 International Conference on Computer Engineering and Application (ICCEA) Pub Date : 2021-06-01 DOI: 10.1109/ICCEA53728.2021.00070
Suqiong Zhang, Dongyi Fan, Jun He, P. Zhang
{"title":"A New Approach for End to End Automation Testing Platform with Cloud Computing for 5G Product","authors":"Suqiong Zhang, Dongyi Fan, Jun He, P. Zhang","doi":"10.1109/ICCEA53728.2021.00070","DOIUrl":"https://doi.org/10.1109/ICCEA53728.2021.00070","url":null,"abstract":"Fast development, fast integration, fast delivery is extremely critical for any organizations. It is essential to develop end to end automation platform for quick delivery of software features in the entire software lifecycle management. Developing one platform can help to reduce the separation between developer, tester and operators which including continuous integration, continuous testing and continuous delivery. In addition, it will reduce the software development lifecycle, delivery frequent and high quality software versions in automated way. Meanwhile, moving the platform to cloud will speed up the platform deployment and enhance the portability. Besides, it promote the process more and more agile at software development and operations.This paper presents an entire automated pipeline, trigger from source code change by developers, create resource from data center to deploy the containerized testing framework. It follows the best practice of DevOps and base on Jenkins for Cl stage. The innovation here is we use the resource from data center which is a private cloud resource, thus it fully support the scalability and overall ease of use.","PeriodicalId":325790,"journal":{"name":"2021 International Conference on Computer Engineering and Application (ICCEA)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116813378","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
Multi-temporal remote sensing image registration based on siamese network 基于暹罗网络的多时相遥感图像配准
2021 International Conference on Computer Engineering and Application (ICCEA) Pub Date : 2021-06-01 DOI: 10.1109/ICCEA53728.2021.00072
Junjie Liu, Yuanzhuo Li, Yu Chen
{"title":"Multi-temporal remote sensing image registration based on siamese network","authors":"Junjie Liu, Yuanzhuo Li, Yu Chen","doi":"10.1109/ICCEA53728.2021.00072","DOIUrl":"https://doi.org/10.1109/ICCEA53728.2021.00072","url":null,"abstract":"Multi-temporal remote sensing image registration aims to find the optimal alignment between images acquired from different times. The complexity and disparity of features in remote sensing images bring great difficulties to image registration. We propose a deep learning method based on the Siamese network to address this problem. Unlike traditional methods doing feature extraction and feature matching separately. We pair patches from sensed and reference images, and directly learn the mapping relationship between those image patch pairs and their matching labels. This end-to-end network architecture helps us optimize the entire network, which is what traditional methods lack. Besides, we use the spatial scale convolution layer in the feature extraction network to improve scale variations’ adaptability. Extensive experiments are conducted on a multi-temporal satellite image dataset from google earth. The results of the experiment indicate that our method can obtain more correct matched points and effectively improve the registration accuracy than traditional image registration methods.","PeriodicalId":325790,"journal":{"name":"2021 International Conference on Computer Engineering and Application (ICCEA)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117262484","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
Trusted Internet of Things Based on Trusted Computing 基于可信计算的可信物联网
2021 International Conference on Computer Engineering and Application (ICCEA) Pub Date : 2021-06-01 DOI: 10.1109/ICCEA53728.2021.00011
Weihua Cheng, Chao Xu, Yiqing Cheng, Mingyuan Zhang, Haihang Niu
{"title":"Trusted Internet of Things Based on Trusted Computing","authors":"Weihua Cheng, Chao Xu, Yiqing Cheng, Mingyuan Zhang, Haihang Niu","doi":"10.1109/ICCEA53728.2021.00011","DOIUrl":"https://doi.org/10.1109/ICCEA53728.2021.00011","url":null,"abstract":"Thanks to the rapid development of mobile Internet, the application and popularity of the IoT are becoming wider and wider. However, new security threats and attacks are constantly emerging, IoT has the characteristics of massive terminal devices, diversification and intelligence of devices. Therefore, it becomes a hot research direction to ensure the security and trustworthiness of the IoT computing environment today. Trusted IoT applies trusted computing technology to IoT scenarios to ensure trustworthiness. The software and hardware resources are limited in IoT scenario, such that low power consumption and availability are required. Therefore, the design and implementation of a trusted IoT will confront many security challenges. This paper summarizes the research ideas and states of the trusted IoT from three aspects: the lightweight root of trust system, software attestation, and secure code update, and introduces representative research results for trusted IoT.","PeriodicalId":325790,"journal":{"name":"2021 International Conference on Computer Engineering and Application (ICCEA)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115613190","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
Malicious code detection method based on image segmentation and deep residual network RESNET 基于图像分割和深度残差网络RESNET的恶意代码检测方法
2021 International Conference on Computer Engineering and Application (ICCEA) Pub Date : 2021-06-01 DOI: 10.1109/ICCEA53728.2021.00099
Lidong Xin, L. Chao, Liang He
{"title":"Malicious code detection method based on image segmentation and deep residual network RESNET","authors":"Lidong Xin, L. Chao, Liang He","doi":"10.1109/ICCEA53728.2021.00099","DOIUrl":"https://doi.org/10.1109/ICCEA53728.2021.00099","url":null,"abstract":"Many existing malicious code detection methods based on deep learning basically have high accuracy, but when detecting malicious code families with high similarity, due to the lack of obvious training features, the detection accuracy is seriously reduced. To solve this problem, this paper proposes a malicious code detection method based on image segmentation and deep residual network. Firstly, the original gray image is transformed into more distinctive sample data by image segmentation technology, which makes the data set increase the distance between classes and reduce the distance within classes, and then the feature extraction and training are carried out through the deep residual network. In the paper, Malimg data set is used to test. Compared with the sample data set without image segmentation technology, the detection accuracy is improved from 95.86% to 98.94%, and the detection accuracy of similar malicious code family is increased from 51.85% to 81.48%","PeriodicalId":325790,"journal":{"name":"2021 International Conference on Computer Engineering and Application (ICCEA)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115055455","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: 4
Hierarchical Multi-turn Dialogue Generation Model Based on Double-layer Decoding 基于双层解码的分层多回合对话生成模型
2021 International Conference on Computer Engineering and Application (ICCEA) Pub Date : 2021-06-01 DOI: 10.1109/ICCEA53728.2021.00030
Siyu Gong, Biqing Zeng, Xiaomin Chen, Mayi Xu, Shengzhou Luo
{"title":"Hierarchical Multi-turn Dialogue Generation Model Based on Double-layer Decoding","authors":"Siyu Gong, Biqing Zeng, Xiaomin Chen, Mayi Xu, Shengzhou Luo","doi":"10.1109/ICCEA53728.2021.00030","DOIUrl":"https://doi.org/10.1109/ICCEA53728.2021.00030","url":null,"abstract":"Intelligent and accurate human-machine dialogue systems can help reduce labor costs in business. Existing models of multi-turn dialogue generation, despite their successes, still suffer from lack of contextual relevance and coherence in the generated responses. In this paper, we propose a hierarchical multi-turn dialogue generation model based on double-layer decoding (HMDM-DD) to exploit the positional relationship and contextual information of the dialogues. First, we use relative position embedding to obtain the sequence of context information, then applying the self-attention mechanism to get long-distance dependencies. Finally, we use double-layer decoding to scrutinize the generated dialogue repeatedly. Experiments on two datasets demonstrate that our model has robust superiority over compared methods in generating informative and fluent dialogues.","PeriodicalId":325790,"journal":{"name":"2021 International Conference on Computer Engineering and Application (ICCEA)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129924689","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
Implement Music Generation with GAN: A Systematic Review 用GAN实现音乐生成:系统回顾
2021 International Conference on Computer Engineering and Application (ICCEA) Pub Date : 2021-06-01 DOI: 10.1109/ICCEA53728.2021.00075
Haohang Zhang, Letian Xi, Kaiyi Qi
{"title":"Implement Music Generation with GAN: A Systematic Review","authors":"Haohang Zhang, Letian Xi, Kaiyi Qi","doi":"10.1109/ICCEA53728.2021.00075","DOIUrl":"https://doi.org/10.1109/ICCEA53728.2021.00075","url":null,"abstract":"Music generation has a long history, which can be a tool to decrease human intervention in the process. Recently, it is widely achieved to generate mellifluous music based on generative adversarial network (GAN), which is one of the deep learning models on unsupervised learning. One of the advantages of GAN is that it uses generative model and discriminative model to learn mutually with more realistic and higher accuracy. In this review, we focus on the overview achievement with GAN to generate music. Specifically, the definition and GAN methods are introduced first. Subsequently, the application in music generation as well as the corresponding drawbacks are discussed accordingly. These results will offer a guideline for future research in music generation with machine learning techniques.","PeriodicalId":325790,"journal":{"name":"2021 International Conference on Computer Engineering and Application (ICCEA)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134361800","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: 4
Acoustic impedance inversion base on dual learning 基于双重学习的声阻抗反演
2021 International Conference on Computer Engineering and Application (ICCEA) Pub Date : 2021-06-01 DOI: 10.1109/ICCEA53728.2021.00080
Zixu Wang, Shoudong Wang, Chen Zhou, Zhiyong Wang
{"title":"Acoustic impedance inversion base on dual learning","authors":"Zixu Wang, Shoudong Wang, Chen Zhou, Zhiyong Wang","doi":"10.1109/ICCEA53728.2021.00080","DOIUrl":"https://doi.org/10.1109/ICCEA53728.2021.00080","url":null,"abstract":"Acoustic impedance inversion is an effective way to predict oil and gas reservoirs, but the acoustic impedance inversion based on traditional convolution neural network is limited by the number of labeled data. In order to solve this problem of insufficient labeled data in acoustic impedance inversion, we proposed an acoustic impedance inversion method base on dual learning. This method can be used for impedance inversion under the constraint of the small number of labeled data, and can obtain accurate inversion results.","PeriodicalId":325790,"journal":{"name":"2021 International Conference on Computer Engineering and Application (ICCEA)","volume":"178 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114848967","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
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