检索结果-内蒙古大学图书馆

Proceedings of the 5th International Conference on information Technologies and Electrical Engineering

作者： Min Xu Xinhe Chen Zhimin Guo Lin Peng Weijian Zhang State Grid Key Laboratory of Information & Network Security State Grid Smart Grid Research Institute Co.LTD China Institute of Electrical Engineering Chinese Academy of Sciences China State Grid Henan Electric Power Co. LTD China

ISBN: (纸本)9781450396806

In view of the difficulties in sharing 3D digital resources, insufficient interconnection and interaction of massive heterogeneous 3D data resources, and lack of spatial data analysis foundation for equipment diagnosis in the power grid infrastructure operation inspection stage, combined with the actual needs of the company for deepening the application of 3D digital resources and through collaboration, this paper analyzes the characteristics of typical GIM models, point cloud models and other multi-media heterogeneous models and their problems in the infrastructure construction and operation inspection applications; Aiming at the deficiency of GIM model in texture, material and illumination, the discrete sampling of equipment GIM model and its attitude registration method with scanning point cloud model are studied to estimate the spatial pose transformation relationship between heterogeneous models, so as to realize the refinement of GIM model; On the basis of attitude registration, a unified coordinate system is established to realize the comprehensive reconstruction of the multi-source medium model and the plant and station digital model combining appearance and internal, provide three-dimensional digital resource sharing for power grid operation and maintenance, technical transformation and overhaul, support the digital connection of three-dimensional capital construction and operation inspection resources, improve the comprehensive utilization level of three-dimensional digital resources of the company, and promote the transformation of power grid infrastructure acceptance and operation inspection operation mode.

关键词： Attitude registration Pose transformation Model comprehensive reconstruction

来源：评论

学校读者我要写书评

暂无评论

IEdroid:Detecting Malicious Android network Behavior Using Incremental Ensemble of Ensembles

IEdroid:Detecting Malicious Android Network Behavior Using I...

引用

International Conference on Parallel and Distributed Systems (ICPADS)

作者： Cong Liu Anli Yan Zhenxiang Chen Haibo Zhang Qiben Yan Lizhi Peng Chuan Zhao Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan China School of Information Science and Engineering University of Jinan China School of Cyberspace Security (School of Cryptology) Hainan University China University of Otago Dunedin New Zealand Michigan State University USA

ISBN: (纸本)9781665408790

Malware detection has attracted widespread attention due to the growing malware sophistication. Machine learning based methods have been proposed to find traces of malware by analyzing network traffic. However, network traffic exhibits a series of growing and changing states, which makes it challenging to design a detection model that can detect malicious traffic over a long period without the need for costly retraining. In this paper, we present, IEdroid, an Android malicious network behavior detection method that leverages incremental ensembles for model update. Specifically, we train multiple classifiers to form an interim ensemble in distributed cluster environment, and update the interim ensemble by removing and adding classifiers. The generated model is composed of multiple interim ensembles that can adapt to the network traffic. We evaluated the performance of IEdroid using a dataset consisting of 98,565 benign and 41,267 malicious flows. Results show that IEdroid can effectively detect malicious traffic compared with state-of-the-art detection models. The experiment trained IEdroid on datasets incrementally for 10 times without a significant loss on accuracy, precision, recall, and F-Measure, compared with re-training from scratch with full data.

关键词： Training Adaptation models Distributed databases Prototypes Telecommunication traffic Machine learning Malware

来源：评论

学校读者我要写书评

暂无评论

Video rain/snow removal by transformed online multiscale convolutional sparse coding

arXiv

引用

arXiv 2019年

作者： Li, Minghan Cao, Xiangyong Zhao, Qian Zhang, Lei Gao, Chenqiang Meng, Deyu School of Mathematics and Statistics Ministry of Education Key Lab of Intelligent Networks and Network Security Xi'an Jiaotong University Shaanxi China Department of Computing Hong Kong Polytechnic University Hung Hom Hong Kong Chongqing Key Laboratory of Signal and Information Processing Chongqing University of Posts and Telecommunications Chongqing400065 China

Video rain/snow removal from surveillance videos is an important task in the computer vision community since rain/snow existed in videos can severely degenerate the performance of many surveillance system. Various methods have been investigated extensively, but most only consider consistent rain/snow under stable background scenes. Rain/snow captured from practical surveillance camera, however, is always highly dynamic in time with the background scene transformed occasionally. To this issue, this paper proposes a novel rain/snow removal approach, which fully considers dynamic statistics of both rain/snow and background scenes taken from a video sequence. Specifically, the rain/snow is encoded as an online multi-scale convolutional sparse coding (OMS-CSC) model, which not only finely delivers the sparse scattering and multi-scale shapes of real rain/snow, but also well encodes their temporally dynamic configurations by real-time ameliorated parameters in the model. Furthermore, a transformation operator imposed on the background scenes is further embedded into the proposed model, which finely conveys the dynamic background transformations, such as rotations, scalings and distortions, inevitably existed in a real video sequence. The approach so constructed can naturally better adapt to the dynamic rain/snow as well as background changes, and also suitable to deal with the streaming video attributed its online learning mode. The proposed model is formulated in a concise maximum a posterior (MAP) framework and is readily solved by the ADMM algorithm. Compared with the state-of-the-art online and offline video rain/snow removal methods, the proposed method achieves better performance on synthetic and real videos datasets both visually and quantitatively. Specifically, our method can be implemented in relatively high efficiency, showing its potential to real-time video rain/snow removal. Copyright © 2019, The Authors. All rights reserved.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

EEG based Parkinson Detection through Supervised information Enhanced Contrastive Learning

EEG based Parkinson Detection through Supervised Information...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jian Song Xiang Li Wenjing Jiang Chunxiao Wang Zhigang Zhao Jialiang Lv Bin Hu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Department of Geriatric Neurology Key Laboratory of Cardiovascular Proteomics of Shandong Province Qilu Hospital of Shandong University Jinan China Institute of Engineering Medicine Beijing Institute of Technology Beijing China

This study presents a novel Supervised information Enhanced Contrastive Learning Algorithm for Parkinson’s Disease Detection (SI-CLAPD) based on Electroencephalography (EEG). SI-CLAPD performs contrastive learning in a multi-granularity manner on enhanced contextual views to achieve robust contextual representations for EEG, thus could contribute to improving the effectiveness of PD detection. Unlike existing methods for constructing PD detection models guided solely by classification loss, we propose a joint learning model that combines self-supervised contrastive learning with supervised classification learning. This model is optimized using both contrastive loss and classification loss, allowing it to capture subtle differences between EEG signals and representations that are specific to PD detection. Through extensive experimental evaluations, we demonstrate that SI-CLAPD achieves robust and high accuracy in PD detection tasks on three benchmark datasets. To the best of our knowledge, this study represents the first effort in validating the effectiveness of contrastive learning for the detection of PD. Besides, within the realm of contrastive learning research in EEG, it also represents the first endeavor to fuse supervised learning with self-supervised contrastive learning for EEG classification. This investigation unveils an universally applicable approach to EEG signal processing, with the potential to confer advantages to a multitude of EEG classification tasks.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Visible Light Positioning Method for Indoor Line-of-Sight Scene Based on Time Series Coding

Visible Light Positioning Method for Indoor Line-of-Sight Sc...

引用

IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Yonghao Yu Yongde Guo Dawei Zhao Kexue Fu Yongwei Tang Qinyuan Wang Faculty of Data Science City University of Macau China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center(National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China School of Mechanical Engineering Key Laboratory of High Efficiency and Clean Mechanical Manufacture Ministry of Education Shandong University Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

Existing indoor visible light positioning methods, such as those based on fingerprint database algorithms, are complex to construct and consume significant resources. In response to this issue, the text proposes an end-to-end indoor visible light positioning method that samples continuous signals at a small number of random coordinate positions in indoor environments. Through a deep learning method based on attention mechanism, a nonlinear mapping between the received signal and spatial position coordinates is constructed, and the time series properties in the received signal are encoded as a feature in the data. The method is further improved by combining residual networks to improve accuracy. Through simulation experiments, it has been verified that the deep learning based localization method described in this article can achieve sub meter level accuracy on small sample datasets, and it has been proven that time series features have a significant impact on localization accuracy in indoor visible light positioning algorithms based on deep learning.

关键词： Location awareness Deep learning Meters Training Accuracy Attention mechanisms Time series analysis Training data Encoding Data models

来源：评论

学校读者我要写书评

暂无评论

Several constructions of optimal LCD codes over small finite fields

arXiv

引用

arXiv 2022年

作者： Li, Shitao Shi, Minjia Liu, Huizhou School of Mathematical Sciences Anhui University Hefei230601 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Mathematical Sciences Anhui University Hefei230601 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China State Grid Anhui Electric Power Co. Ltd. Hefei China

Linear complementary dual (LCD) codes are linear codes which intersect their dual codes trivially, which have been of interest and extensively studied due to their practical applications in computational complexity and information protection. In this paper, we give some methods for constructing LCD codes over small finite fields by modifying some typical methods for constructing linear codes. We show that all odd-like binary LCD codes, ternary LCD codes and quaternary Hermitian LCD codes can be constructed using the modified methods. Our results improve the known lower bounds on the largest minimum distances of LCD codes. Furthermore, we give two counterexamples to disprove the conjecture proposed by Bouyuklieva (Des. Codes Cryptogr. 89(11): 2445-2461, 2021). Copyright © 2022, The Authors. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Deep Learning for Password Guessing and Password Strength Evaluation, A Survey

Deep Learning for Password Guessing and Password Strength Ev...

引用

IEEE International Conference on Trust, security and Privacy in computing and Communications (TrustCom)

作者： Tao Zhang Zelei Cheng Yi Qin Qiang Li Lin Shi Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis Guangxi University for Nationalities Nanning China Department of Computer and Information Technology Purdue University West Lafayette USA PLA Information Engineering University Zhengzhou China Henan Key Laboratory of Network Cryptography Technology Zhengzhou China Cloud Computing and Big Data Research Institute China Academy of Information and Communications Technology Beijing China

ISBN: (数字)9781665403924

ISBN: (纸本)9781665403931

Text passwords are the most widely used authentication methods and will also be used in the future. Text passwords can be regarded as meaningful strings, and deep learning methods have an advantage of text processing. LSTM, RNN, GAN and other deep learning models have been using in password guessing and password strength measurements. In the paper, we make a survey on state-of-the-art deep learning methods for password guessing and password strength evaluation, including password pattern extraction, candidate password generation and password strength measurement. Compared with traditional methods, neural networks based methods can achieve better results and performance.

关键词： Deep learning Training Transfer learning security Gallium nitride Password Text processing

来源：评论

学校读者我要写书评

暂无评论

Research and Implementation of User Behavior Simulation Technology Based on power Industry Cyber Range

Research and Implementation of User Behavior Simulation Tech...

引用

information Technology,Big Data and Artificial Intelligence (ICIBA), International Conference on IEEE International Conference on

作者： Huiting Yang Tao Chen Yunxiao Bai Feng Li Mingxuan Li Ruxia Yang State Grid Xinjiang Electric Power Research Institute Urumqi China State Grid Xinjiang Electric Power Co. Ltd Urumqi China State Grid Key Laboratory of Information & Network Security Nanjing China Global Energy Interconnection Research Institute Co. Ltd Nanjing Jiangsu China

ISBN: (纸本)9781665428781

The current cyber range of the electric power industry has background traffic simulation problems, prospect behavior generation problems, and target simulation diversity problems in large-scale network environments. In response to these problems, this paper proposes a user behavior simulation technology suitable for power industry cyber range. We perform large-scale, high-fidelity network user behavior simulation by combining real traffic playback, inject user behavior traffic in the real network into the virtual target network, then model the user behavior in multiple scenarios in the power network space and generate it based on the method of sequence prediction, and finally realize the diversity simulation of user behavior.

关键词： Adaptation models Virtual links Diversity reception Telecommunication traffic Predictive models Traffic control Data models

来源：评论

学校读者我要写书评

暂无评论

Research on the Distribution Characteristics of Standardized Big Data Resources Based on Data Visualization——Taking Standardization Practice in Shandong Province as an Example

Research on the Distribution Characteristics of Standardized...

引用

IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Jinyang Sun Dongdong Peng Zhe Lu Mengran Zhai Ning Qi Yuanhua Qi SHANDONG SCICOM Information and Economy Research Institute Co. Ltd Jinan China Jinan Municipal Bureau of Big Data Jinan China Shandong Institute of Economy and Informatization Development Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology Shandong Academy of Sciences Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

Standardized big data, as a product of the combination of big data and standardization work, objectively reflects the status quo and trends of standardization work. An in-depth study of the distribution characteristics of standardized big data resources has important value for improving the efficiency of standardization. Therefore, this paper explores the characteristics of the distribution of standardization resources from the dimensions of spatial and temporal distribution, industry distribution, standard type distribution, and distribution of think tank experts through visual analysis methods. The study shows that: (1) the distribution of standardization resources in Shandong Province presents significant hierarchical and geographical characteristics; (2) from the viewpoint of the distribution of industry types and standard types, the distribution of standardization resources in Shandong Province presents a typical "long-tailed distribution"; (3) the distribution of think-tank experts presents obvious "agglomeration effect".

关键词： Industries Technological innovation Heavily-tailed distribution Animals Urban areas Data visualization Forestry Big Data Fisheries Standards

来源：评论

学校读者我要写书评

暂无评论

Parallel Optimization of Computational Fluid Dynamics Application code_saturne Based on Next Generation Sunway Supercomputer

Parallel Optimization of Computational Fluid Dynamics Applic...

引用

International Symposium on Parallel Architectures, Algorithms and Programming (PAAP)

作者： Anjun Liu Yin Liao Xihao Xu YanYan Chen Hongjun Zhong Jianming Wu Bo Kong Alexander Raikov Meng Guo Jinan Key Laboratory of High Performance Industrial Software Jinan Institute of Supercomputing Technology Jinan China Jinan Supercomputing-Zhejiang Yuansuan Industrial Simulation System Joint Laboratory Zhejiang Yuansuan Technology Co. Ltd Hangzhou China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan ) Qilu University of Technology (Shandong Academy of Sciences) Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

code_saturne is an open-source general large-scale computational fluid dynamics software independently developed by Electricite de France since 1997. Due to its high computing performance, code_saturne has been widely used in the nuclear industry, heavy fuel and biomass combustion, particle tracking and other computational fluid dynamics fields. In this work, we implemented code_saturne on the new generation Sunway supercomputer (SW26010pro) at the first time. By applying the data value-taking technology of double cache, combined with Sunway shared memory technology, we have adapted the data structure and Modular programming of code_saturne conducively to the new generation Sunway supercomputer (SW26010pro) and accelerated the software hotspots the software hotspots by 1–2 times. After optimizing and porting, this software has successfully served in the numerical simulation of the lateral flow of fuel assemblies in the nuclear industry.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：