2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)最新文献_第3页

Survivability Analysis and Optimization of Dependent Networks Based on Natural Connectivity 基于自然连通性的依赖网络生存性分析与优化

2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2020-06-01 DOI: 10.1109/ICAICA50127.2020.9182670

Ying Xu, Baihan Zou

{"title":"Survivability Analysis and Optimization of Dependent Networks Based on Natural Connectivity","authors":"Ying Xu, Baihan Zou","doi":"10.1109/ICAICA50127.2020.9182670","DOIUrl":"https://doi.org/10.1109/ICAICA50127.2020.9182670","url":null,"abstract":"In order to more fully evaluate the anti-destructive performance of the dependent network under different attacks, three one-to-one, one-to-many, and many-to-many edge-connection methods, as well as three types of degree homology, degree heterogeneity and randomness Coupling way to construct different network models. Under four different attack methods, the natural connectivity is used as a measure of the indestructibility of the dependent network, and the dependent network models of different connected edges are compared to each other based on the intentional attack of the degree and the median when they face different attack strategies. The anti-destructive effect is greater, and the dependent network has the best anti-destructive performance when the number of attack nodes is small. In addition, by changing the coupling strength between the sub-networks, it is found that the anti-destructiveness of the dependent network also increases as the coupling strength increases. Based on natural connectivity, the concept of node dispersion was proposed to evaluate the comprehensive survivability of dependent networks, and it was found that the dependent network was the most robust when one-to-many homogeneous coupling was used.","PeriodicalId":113564,"journal":{"name":"2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115234885","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

Research on an Improved KCF Target Tracking Algorithm Based on CNN Feature Extraction 基于CNN特征提取的改进KCF目标跟踪算法研究

2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2020-06-01 DOI: 10.1109/ICAICA50127.2020.9182522

J. Gong, Yong Mei, Yong Zhou

{"title":"Research on an Improved KCF Target Tracking Algorithm Based on CNN Feature Extraction","authors":"J. Gong, Yong Mei, Yong Zhou","doi":"10.1109/ICAICA50127.2020.9182522","DOIUrl":"https://doi.org/10.1109/ICAICA50127.2020.9182522","url":null,"abstract":"Target tracking is one of the most concerned computer problems, but it is also challenging with few training samples, fast moving objects and some other issues. The kernelized correlation filter (KCF) algorithm proposed by the team of Joao F. Henriques had applied to address this problem for tracking successfully. The method has expanded the number of negative samples to enhance the performance of the tracker and used the fast Fourier transform to accelerate the calculation of the algorithm. However, the features used by the KCF have limited ability to express the target with complex background. We propose improved KCF algorithm for tracking. The pre-trained deep convolutional neural network (CNN) is introduced in extracting the layer information respectively to describe the spatial and semantic features of the target. Experiments are performed on OTB-2015 benchmark datasets, and the results show that in comparison with the existing tracking algorithms, the proposed improved algorithm can deal with the challenges much better performance compared to original KCF and KCF-S method.","PeriodicalId":113564,"journal":{"name":"2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","volume":"243 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123437230","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: 6

Candidate Selection-based Deep Affinity Network for Multi-object Tracking 基于候选选择的深度关联网络多目标跟踪

2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2020-06-01 DOI: 10.1109/ICAICA50127.2020.9182622

Ming Tan, X. Zhong, Liang Xie, Bo Ma, Wenxuan Liu, Hongxia Xia

{"title":"Candidate Selection-based Deep Affinity Network for Multi-object Tracking","authors":"Ming Tan, X. Zhong, Liang Xie, Bo Ma, Wenxuan Liu, Hongxia Xia","doi":"10.1109/ICAICA50127.2020.9182622","DOIUrl":"https://doi.org/10.1109/ICAICA50127.2020.9182622","url":null,"abstract":"Deep Affinity Network (DAN) is a novel approach in multi-object tracking (MOT) designed to jointly modeling object appearances and affinities end to end. But tracking accuracy of DAN tracker is greatly limited since it neglects unreliable detection. Exploiting predictions of tracks has emerged as a popular approach to tackle the task of tracking-by-detection. However, it's observed that missing detection has not been solved well enough which would significantly influence tracking accuracy. Thus, obtaining more reliable tracking candidates is concerned to further address the problem of missing detection. In this paper, we propose Candidate Selection-based Deep Affinity Network (CSDAN) tracker for MOT. It collects candidates from detection, predictions of tracks and backward tracking simultaneously so that they can complement each other in different scenarios. Moreover, we propose a deep learned candidate selection model (DCSM) with a unified scoring function suitable for CSDAN, which can well handle candidates from three sources separately and select those for data association. Experiments conducted on MOT17 benchmark demonstrate that our extensions can significantly address the unreliable detection problem in DAN tracker, and our CSDAN tracker demonstrates competitive tracking performance.","PeriodicalId":113564,"journal":{"name":"2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123479190","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

Improved closed-loop detection and Octomap algorithm based on RGB-D SLAM 基于RGB-D SLAM的改进闭环检测和八元映射算法

2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2020-06-01 DOI: 10.1109/ICAICA50127.2020.9182601

Chungui Deng, Xiaonan Luo, Y. Zhong

引用次数: 5

Visible Light Communication System Based on White LED 基于白光LED的可见光通信系统

2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2020-06-01 DOI: 10.1109/ICAICA50127.2020.9182417

Yanyan Zhu, Xiao Chen

引用次数: 1

Rate Forecast about Aircraft Grounded due to Maintenance with Modified Neural Network 基于改进神经网络的飞机维修停飞率预测

2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2020-06-01 DOI: 10.1109/ICAICA50127.2020.9182645

Zhengwu Wang, Wansuo Liu

引用次数: 0

Clustering Anchor for Faster R-CNN to Improve Detection Results 聚类锚更快R-CNN提高检测结果

2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2020-06-01 DOI: 10.1109/ICAICA50127.2020.9182521

Han Wei-yue, L. Xiaohong

引用次数: 3

Research on User Behavior Analysis Model of Financial Industry in Big Data Environment 大数据环境下金融业用户行为分析模型研究

2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2020-06-01 DOI: 10.1109/ICAICA50127.2020.9182369

Junfeng Mei, Ying Chen, Taoli Ye, Chenglong Huang, H. Ye

{"title":"Research on User Behavior Analysis Model of Financial Industry in Big Data Environment","authors":"Junfeng Mei, Ying Chen, Taoli Ye, Chenglong Huang, H. Ye","doi":"10.1109/ICAICA50127.2020.9182369","DOIUrl":"https://doi.org/10.1109/ICAICA50127.2020.9182369","url":null,"abstract":"Along with the age of the Internet the rapid development of diversified business model and market segments, facing high customer cost and the double challenges of high turnover rate, a third-party service access to statistical data insecurity, buried point higher cost problems, be badly in need of precise positioning for guest channels, fine operation, and through the study of the statistics, analysis of these data, we may discover the laws of users to use the product, and the law and website marketing strategy, product features, operation strategy, the combination of optimization of user experience, to achieve more accurate operation and marketing, make products better growth. Based on the buried point and the mobile network environment detection tool based on the client SDK technology, this paper will provide the visual statistical effect through the analysis of the user behavior module, with simple operation and accurate data. Based on Eclipse, Hadoop, Spark and other technologies, the user behavior analysis platform is established to meet users' needs for data security and accuracy.","PeriodicalId":113564,"journal":{"name":"2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","volume":"330 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124651787","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

Cloud Computing Data Center Structure Based on Internet of Things and Its Scheduling Mechanism 基于物联网的云计算数据中心结构及其调度机制

2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2020-06-01 DOI: 10.1109/ICAICA50127.2020.9182508

Li Li

{"title":"Cloud Computing Data Center Structure Based on Internet of Things and Its Scheduling Mechanism","authors":"Li Li","doi":"10.1109/ICAICA50127.2020.9182508","DOIUrl":"https://doi.org/10.1109/ICAICA50127.2020.9182508","url":null,"abstract":"With the rapid development of new generation information technologies such as cloud computing, Internet of Things and big data, the fourth generation data center based on cloud computing is also growing rapidly. Cloud computing provides users with a flexible, reliable, ultra-large and scalable pool of computing resources. In the cloud computing environment, the Internet transfers information and data to direct transfer services. Since the emergence of cloud computing, through the continuous development of science and technology. Data center is an architecture that integrates network communication, storage and high performance computing. It can provide users with a variety of storage, computing and network services. How to deal with massive data and services, to facilitate users to complete all kinds of business more quickly and with higher experience, has become a major problem in the era of Internet development. This paper designs the cloud computing architecture of the Internet of things data center, hoping to propose a new scheduling mechanism for the intelligent management of the data center.","PeriodicalId":113564,"journal":{"name":"2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129367237","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: 2

Trajectory Optimization for UAV-Enabled Amplify-and-Forward Relaying System Based on Communication Performance 基于通信性能的无人机放大转发系统轨迹优化

2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2020-06-01 DOI: 10.1109/ICAICA50127.2020.9182646

Zhengdong Li

引用次数: 0