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共 6619 条记录，以下是161-170 订阅

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Extended State Observer-based Hybrid Dynamic Event-Triggered Consensus Control with External Disturbances 24

Extended State Observer-based Hybrid Dynamic Event-Triggered...

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2nd international conference on Frontiers of Intelligent Manufacturing and Automation, CFIMA 2024

作者： Pei, Wenliang Hua, Changchun Cui, Hailong Zhao, Guanglei Ma, Zhuang The Institute of Electrical Engineering The Key Lab of Intelligent Rehabilitation and Neuroregulation in Hebei Province Key Laboratory of Intelligent Control and Neural Information Processing Ministry of Education Yanshan University Hebei China Tangshan University Hebei China

ISBN: (纸本)9798400710681

This paper solves the event-triggered robust consensus problem of disturbed multi-agent systems (MASs) in fully distributed fashion. first, a distributed extended state observer (DESO) is developed for appropriately estimating system states and external disturbances. Then, by utilising these estimates, a fully distributed event-triggering control protocol, that composes of an adaptive distributed controller to avoid using global information while maintaining desired consensus property, and a novel hybrid dynamic event-triggering mechanism (ETM) to save communication resources, is put forward for the MASs. It is notable that an auxiliary differential equation is incorporated in the triggering condition, with which, positive minimum interevent times (MIETs) are available to ease the hardware implementation, while being with adaptive coefficients, the proposed hybrid dynamic ETM can be independent of any global information. Finally, the obtained main results are testified via a numerical example. © 2024 Copyright held by the owner/author(s).

关键词： State estimation

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Constraint-Forcing Recursive Generalized Maximum Correntropy Algorithm with Forgetting Factor for Adaptive Filtering 29

Constraint-Forcing Recursive Generalized Maximum Correntropy...

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29th IEEE international conference on parallel and distributed systems, ICPADS 2023

作者： Li, Wenyue Zhao, Ji Li, Qiang Tang, Lingli Zhang, Hongbin Southwest University of Science and Technology School of Information Engineering Mianyang621010 China Southwest University of Science and Technology Department of Social Sciences Mianyang621010 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China

ISBN: (纸本)9798350330717

In this paper, jointly with the exponential weighted generalized maximum correntropy (GMC) criterion and the linear constraint framework, we derive a recursive constrained adaptive filtering algorithm named recursive constrained GMC with forgetting factor (FF-RCGMC). In addition, due to a lack of constraint information during the learning process, FF-RCGMC will diverge or even fail to work after some iterations. Therefore, we propose a more stable version by introducing a constraint-forcing strategy into FF-RCGMC and call this robust type as constraint-forcing FF-RCGMC (CFFF-RCGMC). Some simulation results in system identification under non-Gaussian noisy environments validate the effectiveness of CFFF-RCGMC. © 2023 IEEE.

关键词： Constrained adaptive filtering generalized correntropy non-Gaussian noise system identification

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ProDE: Interpretable APT Detection Method Based on Encoder-decoder Architecture 29

ProDE: Interpretable APT Detection Method Based on Encoder-d...

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29th IEEE international conference on parallel and distributed systems, ICPADS 2023

作者： Zhou, Fengxi Chang, Baoming Wen, Yu Meng, Dan University of Chinese Academy of Sciences Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security Beijing China Chinese Academy of Sciences Institute of Information Engineering Beijing China

ISBN: (纸本)9798350330717

The detection and analysis of Advanced Persistent Threats (APTs) are pivotal for contemporary network security. Provenance graphs, constructed from audit logs, offer a wealth of contextual information to identify and analyze threats and are popular in APT detection field. However, existing approaches frequently fall short in offering explanatory capabilities for their detection results, placing an additional burden on security analysts. Confronted with coarse-grained detection outcomes, analysts must delve into provenance graphs or audit logs to precisely pinpoint attack entities and events, which can significantly delay the response to threats. In this paper, we propose ProDE, a novel approach that enhances APT detection by providing interpretable results using an encoder-decoder architecture. ProDE initiates the detection process by comparing the encoded representations of the true graph and the predicted graph. Upon detecting abnormalities, the encoder-decoder model is able to decode the encodings into provenance graphs, thereby revealing inconsistencies between the decoded graph and real graph that serve as interpretable results. We evaluate ProDE on two widely used datasets, while taking into account the detection performance, the result shows ProDE can provided the more detailed detection results which provide the interpretation for analysts compared with existing approaches. © 2023 IEEE.

关键词： Advanced Persistent Threats (APTs) encoder-decoder interpretable

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An Autoencoder-Driven FDIA Detection Method for Virtual Coupling systems of Heavy-Haul Trains 21

An Autoencoder-Driven FDIA Detection Method for Virtual Coup...

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21st IEEE international Symposium on parallel and distributed Processing with Applications, 13th IEEE international conference on Big Data and Cloud Computing, 16th IEEE international conference on Social Computing and Networking and 13th international conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Ren, Wanwan Peng, Jun Zhang, Rui Rong, Jieqi Shu, Boyu Liu, Yongting Yang, Yingze Central South University School of Computer Science and Engineering Changsha China Central South University School of Electronic Information Changsha China Changsha University School of Electronic Information and Electrical Engineering Changsha China Central South University School of Automation Changsha China

ISBN: (纸本)9798350329223

The virtual coupling system provides a solution for improving the transport capacity of heavy-haul trains, and its implementation relies on reliable train-to-train communication. However, communications are vulnerable to cyberattacks. Among them, false data injection attacks (FDIAs) are a common and highly concealed form of attack. They interfere with train information interaction by destroying the authenticity of data, disrupting the cooperative control objectives of the virtual coupling system, and thus affecting the stable operation of the train. In order to ensure the safe, reliable, and efficient operation of heavy-haul trains, an autoencoder-driven FDIA detection method for virtual coupling systems of heavy-haul trains is proposed. first, the virtual coupling system of heavy-haul trains is modeled. Next, the collaborative control law of the virtual coupling system is designed, and the potential impact of false data injection on the system is analyzed. Afterward, a stacked autoencoder model is developed. Using the unsupervised idea, the model is trained to learn to reconstruct normal operating data, and the reconstruction error of the trained model is used as an attack detection indicator. Finally, the effectiveness of the proposed scheme is evaluated against several attack scenarios. © 2023 IEEE.

关键词： Learning systems

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A Novel Gold Futures Price Prediction Model based on PCA-AGRU 29

A Novel Gold Futures Price Prediction Model based on PCA-AGR...

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29th IEEE international conference on parallel and distributed systems, ICPADS 2023

作者： Wang, Jingyang Lu, Tong Liu, Jindian Sun, Qiuhong Qi, Lianyong Zhou, Xiaokang Hebei University of Science and Technology School of Information Science and Engineering Shijiazhuang China Shiga University Faculty of Data Science Hikone522-8522 Japan Qingdao China RIKEN Center for Advanced Intelligence Project RIKEN Tokyo103-0027 Japan

ISBN: (纸本)9798350330717

As an important financial investment commodity, the price fluctuations of gold significantly impact the global economy and the stability of the financial market. Therefore, it is of great significance to accurately predict the price of gold futures. This paper presents a novel gold futures price prediction model, PCA-AGRU, based on Principal Component Analysis (PCA) and Adaptive Gated Recurrent Unit (AGRU). PCA is utilized for the dimensionality reduction of data while extracting essential features. AGRU bases on Gated Recurrent Unit (GRU), embeds Self-Attention (SA), and adds an adaptive adjustment mechanism, making the model more effective in capturing long-term dependencies within time series data. This paper uses the international gold futures market data as the experimental dataset, and uses Maximal information Coefficient (MIC) to analyze the correlation of the influencing factors. The PCA-AGRU model is compared with five prediction models of GRU, SA-GRU, AGRU, PCA-GRU, and PCA-SA-GRU. The experimental results show that the PCA-AGRU model performs best in forecasting gold futures prices. © 2023 IEEE.

关键词： AGRU deep learning Gold Futures PCA Price Prediction SA

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An Efficient Network Flow Rules Compression Method Based On In-Network Computation On Cloud Computing 29

An Efficient Network Flow Rules Compression Method Based On ...

引用

29th IEEE international conference on parallel and distributed systems, ICPADS 2023

作者： Zhang, Penghao Zhang, Xinyi Shanxi University China Chinese Academy of Sciences Computer Network Information Center China

ISBN: (纸本)9798350330717

The development of cloud computing has led to the explosion of network traffic. The switches of cloud computing make it hard to process large-scale network traffic. Prior approaches proposed flow rules compression methods with splitting matching fields, expanding switch storage space, or reconstructing network architecture. The methods incurred little performance improvement in the network. The technology of innetwork computation(INC) has been widely used to accelerate the network of cloud computing and data-intensive distributed applications. The computing tasks performed on servers are offloaded to the network through INC. We implement a flow rules compression method(NetFR) based on INC to accelerate the network performance of cloud computing. NetFR adopts heuristic methods which include rule-deduplication and optimal coverage to compress flow rules, and offloads compressed rules into switches to accelerate the performance of the network on cloud computing. Finally, we evaluate NetFR and show that the performance of NetFR is 1.3x better than other methods of network packet matching. Furthermore, NetFR can achieve 98% compression ratio on average and 99.99% on highest. © 2023 IEEE.

关键词： Cloud Computing In-Network Computing Network Flow Rules Compression Programmable Switch

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The Model for the Formation of Time Intervals for Receiving Messages in distributed information systems

The Model for the Formation of Time Intervals for Receiving ...

引用

2024 international Russian Automation conference, RusAutoCon 2024

作者： Tanygin, Maxim O. Tanygina, Elena A. Southwest State University Faculty of Fundamental and Applied Informatics Department of Information Security Kursk Russia Southwest State University Faculty of Law Department of Foreign Languages Kursk Russia

The article discusses the transmission of groups of messages in modes that do not require delivery confirmation. It is expected that it can be used in messaging systems that involve a high percentage of messages transmitted with errors, such as sensor networks and Internet of Things networks. The message source transmits a sequence of messages, while the receiver forms a certain time window in which their delivery is expected. Increasing the time interval increases the number of messages arriving at the receiver from other sources operating as part of a distributed system, which increases the likelihood of message authentication code collisions. Reducing the size of the time interval increases the probability that at least one message from the group will not be included in it. The purpose of the work is to create a mathematical model based on the theory of random processes that allows to determine the appropriate boundaries of the time interval for receiving messages using the laws of distribution of the time of message delivery from the source to the receiver and the average intensity of message generation by the source. The relationship between the value of the time interval between the time of arrival of the first message of the group and the preceding beginning of the time window for receiving the message was determined. It has been established that the appropriate value of this interval is in the range of 0.3... 0.4 of the average time between receiving messages. © 2024 IEEE.

关键词： Instant messaging

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Efficient Node Selection for Coding-based Timely Computation over Heterogeneous systems 21

Efficient Node Selection for Coding-based Timely Computation...

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作者： Lin, Yuxuan Tang, Bin Zhou, Siyuan Xie, Zaipeng Ye, Baoliu Hohai University Key Laboratory of Water Big Data Technology Ministry of Water Resources Nanjing211100 China Hohai University School of Computer and Information Nanjing211100 China Nanjing University National Key Laboratory for Novel Software Technology Nanjing210023 China

ISBN: (纸本)9798350329223

In large-scale distributed systems, computational nodes often experience random slowdown which can degrade the performance of timely computation tasks significantly. Recently, coded computing has emerged as a promising approach where coded subtasks are generated for parallel execution over different nodes. In this paper, we focus on exploring the tradeoff between the performance of coding-based timely computation tasks and the computation cost in distributed systems with heterogeneous nodes. We introduce a node selection problem (NSP), which aims at minimizing the task failure probability by selecting proper participating nodes under a total cost constraint. We show that NSP is NP-hard due to the total cost constraint, and is more challenging since the task failure probability is a complicated multivariate polynomial. By approximating the task failure probability using central limit theorem and establishing relationships with certain integer linear programming problems, we then propose a linear programming relaxation based algorithm with performance guarantee, as well as an efficient Lagrangian relaxation inspired heuristic algorithm. The effectiveness of the proposed node selection algorithms is further demonstrated via both trace-driven and model-driven simulations. © 2023 IEEE.

关键词： Heuristic algorithms

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Transaction Data Management Optimization Based on Multi-Partitioning in Blockchain systems 21

Transaction Data Management Optimization Based on Multi-Part...

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作者： Zhang, Jingyu Hou, Xingxing Teng, Yilong Luo, Lailong Changsha University of Science and Technology School of Computer and Communication Engineering Changsha China National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory School of Systems Engineering Changsha China

ISBN: (纸本)9798350329223

Unspent Transaction Output (UTXO) is part of the transaction data set, which represents the digital cryptocurrency asset in transaction-based blockchain systems. The data management capability, storage method and occupied space of UTXOs will greatly affect the running efficiency and the verification performance of blockchain systems. Especially, with the popularity of blockchain technology, the relevant UTXO data sets have been growing, and all the stored data can no longer be almost completely stored in memory. How should the UTXO transaction data be stored and managed at this time, it is an urgent issue to be solved in bitcoin-like blockchain systems. This paper provides a blockchain transaction data management optimization mechanism based on multi-partitioning. first, we analyze the influencing factors of transactions through real blockchain data. The proposed method can evaluate the time interval and transaction frequency factors, and use the received information to realize the efficient transaction data storage. In our design, UTXOs with lower likelihood to be used in new transactions will be stored in the disk, and the other UTXOs with higher likelihood to be used in new generated transactions should be stored in the cache. This approach aims to minimize memory consumption for the transaction data sets, accelerate UTXO access time during block verification, and ultimately decrease the overall time required for verification, leading to efficient UTXO transaction data management. Finally, the effectiveness of the proposed optimization mechanism is verified through theoretical analysis and simulation experiments, and the UXTO access time has been reduced compared with state-of-the-art methods. © 2023 IEEE.

关键词： information management

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proceedings of the 2023 IEEE international conference on Computer, information, and Telecommunication systems, CITS 2023

Proceedings of the 2023 IEEE International Conference on Com...

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2023 IEEE international conference on Computer, information, and Telecommunication systems, CITS 2023

ISBN: (纸本)9798350336092

The proceedings contain 33 papers. The topics discussed include: deep learning polar convolutional parallel concatenated (DL-PCPC) channel decoding for 6G communications;cyber deception system based on Monte Carlo simulation in the mobile edge computing (MEC);lightweight hash function based on Fermat number transform (FNT);automatic recognition for LPI radar waveform using sequence-based deep learning model;a transfer learning based CSI indoor localization using GASF image construction;profit aware resource allocation in fog computing: a Stackelberg game approach;proposal for a security model applying artificial intelligence for administrative management in a higher education institution;on implementation of sparse finite impulse response secondary path for active noise control;high layer coding for video transmission;deep neural network for user attribute recognition in metaverse hand images;lifetime estimation and reparameterization of GPS devices for tracking applications;a reparameterization and monitoring platform for energy efficient GPS trackers in behavioral analysis of wildlife;and scheduling delay-sensitive bag-of-task jobs with imprecise computations on distributed resources.

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