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An Isomerism Learning Model to Solve Time-Varying Problems Through Intelligent Collaboration

IEEE/CAA Journal of Automatica Sinica 2023年第8期10卷 1772-1774页

作者： Zhihao Hao Guancheng Wang Bob Zhang Leyuan Fang Haisheng Li the Department of Computer and Information Science University of MacaoMacao 999078China the School of Data Science the Chinese University of Hong KongShenzhen 518172 Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen 518000 China Industrial Control Systems Cyber Emergency Response Team Beijing 100040China IEEE the College of Electrical and Information Engineering Hunan UniversityChangsha 410082 the Peng Cheng Laboratory Shenzhen 518000China the Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business UniversityBeijing 100048 the School of Computer Science and Engineering Beijing Technology and Business UniversityBeijing 100048China

Dear Editor,This letter deals with a solution for time-varying problems using an intelligent computational(IC)algorithm driven by a novel decentralized machine learning approach called isomerism *** order to meet the challenges of the model’s privacy and security brought by traditional centralized learning models,a private permissioned blockchain is utilized to decentralize the model in order to achieve an effective coordination,thereby ensuring the credibility of the overall model without exposing the specific parameters and solution process.

关键词： ism thereby letter

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Inter-Stream Dependencies in Time-Sensitive Networking

Inter-Stream Dependencies in Time-Sensitive Networking

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International Conference on Intelligence in Next Generation Networks (ICIN)

作者： Hamza Chahed Mark Yarvis Florian Frick Andreas Kassler Intel Deutschland GmbH Germany Department of Mathematics and Computer Science Karlstad University Karlstad Sweden Intel Corporation USA Institute for Control Technology of Machine Tools and Manufacturing Equipment University of Stuttgart Germany Faculty of Applied Computer Science Deggendorf Institute of Technology Deggendorf Germany

ISBN: (数字)9798331542399

ISBN: (纸本)9798331542405

Effective configuration of Time-Sensitive Networks is crucial for providing timeliness and reliability guarantees for real-time industrial applications, where many inter-dependent streams may co-exist. However, existing methods for specifying the characteristics of real-time streams to the Centralized Network Configuration element (CNC) do not allow the specification of stream dependencies. Consequently, the CNC may schedule the real-time streams in a sub-optimal way, leading to low number of real-time applications supported. To address this pressing issue, we present methods that allow to model and express such inter-stream dependencies and propose extensions to the user network interface (UNI) to convey these dependencies to the CNC, transforming it into an intent-based UNI. We define a novel metric to evaluate a specific network configuration regarding the latency budget available for scheduling the transmission of a stream. Using a set of numerical simulations, we show that when exploiting the knowledge of inter-stream dependencies, we can derive better network schedules, which results in significant increase in applications onboarding up to 2.2x and increased network utilization for real-time streams up to 3x.

关键词： Schedules Technological innovation Sequential analysis Pressing Reliability engineering Real-time systems Network interfaces Synchronization Streams Optimization

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Research on Real-Time Linux Task Scheduling Algorithm for Multiprocessors Based on Particle Swarm Optimisation 9

Research on Real-Time Linux Task Scheduling Algorithm for Mu...

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9th International Conference on computer and Communications, ICCC 2023

作者： Qiu, Tong Yin, Zhenyu Wang, Jun Wang, Zisong School of Computer Science and Technology Shenyang University of Chemical Technology Shenyang China Liaoning Key Laboratory of Domestic Industrial Control Platform Technology on Basic HardwareSoftware Shenyang China Liaoning Provincial Key Laboratory of Industrial Intelligent Technology for Chemical Processes Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9798350317251

Schedulability analysis in real-time operating systems is one of the hot research topics in the field. While Linux has become the preferred choice for industrial systems due to its excellent portability, it cannot meet the hard real-time requirements of specific industrial environments as a time-sharing operating system. To address this issue, we introduce the Real-Time Application Interface (RTAI) 5.2 real-time operating system kernel patch and combine it with Linux 4.4.176 to form the RTAI-Linux dual-core real-time system. Building upon this foundation, we proposes a Particle Swarm Optimization algorithm based on Earliest Deadline First (PSO-EDF) to improve task scheduling in real-time Linux multiprocessor environments. This method introduces a metric for evaluating processor utilization, when a new task arrives, it considers the allocation of free processor resources. Compared with the original method EDF, PSO-EDF provides a broader search space with better global characteristics. The algorithm finds the globally optimal scheduling scheme through iteration, which effectively improves the processor resource utilisation and alleviates the task deadline missing problem encountered in the original method to a certain extent, thus improving the real-time task completion rate. Through extensive simulation experiments, the PSO-EDF scheduling algorithm proves that the scheduling model can further improve the real-time performance of the RTAI-Linux system compared with the classical EDF algorithm. © 2023 IEEE.

关键词： Linux

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SS-DBSCAN: Epsilon Estimation with Stratified Sampling for Density-Based Spatial Clustering of Applications with Noise

SS-DBSCAN: Epsilon Estimation with Stratified Sampling for D...

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Automation, control and Electronics Engineering (CACEE), International Conference on

作者： Gloriana Joseph Monko Masaomi Kimura Department of Functional Control Systems Graduate School Shibaura Institute of Technology Tokyo Japan Department of Computer Science and Engineering Shibaura Institute of Technology Tokyo Japan

Clustering algorithms are crucial in uncovering hidden patterns and structures within datasets. Among the density-based clustering algorithms, DBSCAN (Density-Based Spatial Clustering of Applications with Noise) has gained considerable attention for its effectiveness in various applications. However, determining appropriate parameter values for this algorithm remains a challenging task. This paper presents a novel methodology for eps parameter estimation for an improved DBSCAN, namely SS-DBSCAN. The experimental results across nine datasets demonstrate the efficacy of our proposed method in accurately determining clusters with eps value from SS-DBSCAN algorithm. The clusters identified using estimated eps values by SS-DBSCAN align well with the inherent structure of the datasets, yielding better cluster results than the manually set parameters and other methods used for automatic estimations of the eps for DBSCAN. Our approach adapted well to the peculiarities of each dataset, whether dealing with different scales, dimensions, or densities; it proved the versatility and robustness across various datasets, thereby emphasizing its generalizability and potential for broader applications.

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ECG Signal Anomaly Detection Algorithm Based on CNN-BiLSTM 11

ECG Signal Anomaly Detection Algorithm Based on CNN-BiLSTM

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11th International Conference of Information and Communication technology, ICTech 2022

作者： Cui, Kai Xing Xia, Xiao Jun School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing100049 China Shenyang Institute of Computing Technology Chinese Academy of Sciences Liaoning Shenyang110168 China

ISBN: (数字)9781665496940

ISBN: (纸本)9781665496940

Aiming at the problems of low feature extraction efficiency and low detection accuracy of traditional ECG signal detection algorithms, this paper proposes a convolutional neural network (CNN) and bi-directional long short-term memory (Bi-directional long short-term memory, LSTM) network hybrid ECG signal anomaly detection algorithm. This model effectively utilizes the ability of CNN to automatically extract features and BiLSTM's ability to efficiently process time series data. Through experimental verification on the arrhythmia data set in the MIT-BIH database, the overall accuracy of the model is 98.56%. Compared with support vector machine (SVM) and bidirectional long short-term memory neural network (BiLSTM), the accuracy and F1 value of this model are improved. © 2022 IEEE.

关键词： Long short-term memory

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Carbon-Aware Computing in a Network of Data Centers: A Hierarchical Game-Theoretic Approach

Carbon-Aware Computing in a Network of Data Centers: A Hiera...

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European control Conference (ECC)

作者： Enno Breukelman Sophie Hall Giuseppe Belgioioso Florian Dörfler Division of Decision and Control Systems KTH Royal Institute of Technology School of Electrical Engineering and Computer Science Stockholm Sweden Department of Electrical Engineering and Information Technology Automatic Control Laboratory ETH Zurich Zürich Switzerland

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

Over the past decade, the continuous surge in cloud computing demand has intensified data center workloads, leading to significant carbon emissions and driving the need for improving their efficiency and sustainability. This paper focuses on the optimal allocation problem of batch compute loads with temporal and spatial flexibility across a global network of data centers. We propose a bilevel game-theoretic solution approach that captures the inherent hierarchical relationship between supervisory control objectives, such as carbon reduction and peak shaving, and operational objectives, such as priority-aware scheduling. Numerical simulations with real carbon intensity data demonstrate that the proposed approach success-fully reduces carbon emissions while simultaneously ensuring operational reliability and priority-aware scheduling.

关键词： Data centers Processor scheduling Computational modeling Carbon dioxide Games Supervisory control Resource management

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Direct data-driven state-feedback control of general nonlinear systems

arXiv

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arXiv 2023年

作者： Verhoek, Chris Koelewijn, Patrick J.W. Haesaert, Sofie Tóth, Roland The Dept. of Electrical Engineering The Eindhoven University of Technology Netherlands The Institute for Computer Science and Control Budapest Hungary

Through the use of the Fundamental Lemma for linear systems, a direct data-driven state-feedback control synthesis method is presented for a rather general class of nonlinear (NL) systems. The core idea is to develop a data-driven representation of the so-called velocity-form, i.e., the time-difference dynamics, of the NL system, which is shown to admit a direct linear parameter-varying (LPV) representation. By applying the LPV extension of the Fundamental Lemma in this velocity domain, a state-feedback controller is directly synthesized to provide asymptotic stability and dissipativity of the velocity-form. By using realization theory, the synthesized controller is realized as a NL state-feedback law for the original unknown NL system with guarantees of universal shifted stability and dissipativity, i.e., stability and dissipativity w.r.t. any (forced) equilibrium point, of the closed-loop behavior. This is achieved by the use of a single sequence of data from the system and a predefined basis function set to span the scheduling map. The applicability of the results is demonstrated on a simulation example of an unbalanced disc. © 2023, CC BY-NC-SA.

关键词： Linear systems

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A heuristic mixed real-time task allocation of virtual utilization in multi-core processor

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Journal of Information and Intelligence 2023年第2期1卷 156-177页

作者： Hongbiao Liu Mengfei Yang Tingyu Wang Chenghao Song Shenghui Zhu Xi Chen School of Computer Science and Technology Xidian UniversityXi'an 710071China China Academy of Space Technology Beijing 100094China Beijing Institute of Control Engineering Beijing 100190China

Multi-core processor is widely used as the running platform for safety-critical real-time systems such as spacecraft,and various types of real-time tasks are dynamically added at *** order to improve the utilization of multi-core processors and ensure the real-time performance of the system,it is necessary to adopt a reasonable real-time task allocation method,but the existing methods are only for single-core processors or the performance is too low to be *** at the task allocation problem when mixed real-time tasks are dynamically added,we propose a heuristic mixed real-time task allocation algorithm of virtual utilization VU-WF(Virtual Utilization Worst Fit)in multi-core ***,a 4-tuple task model is established to describe the fixedpoint task and the sporadic task in a unified ***,a VDS(Virtual Deferral Server)for serving execution requests of fixed-point task is constructed and a schedulability test of the mixed task set is ***,combined with the analysis of VDS's capacity,VU-WF is proposed,which selects cores in ascending order of virtual utilization for the schedulability *** show that the overall performance of VU-WF is better than available algorithms,not only has a good schedulable ratio and load balancing but also has the lowest runtime *** a 4-core processor,compared with available algorithms of the same schedulability ratio,the load balancing is improved by 73.9%,and the runtime overhead is reduced by 38.3%.In addition,we also develop a visual multi-core mixed task scheduling simulator RT-MCSS(open source)to facilitate the design and verification of multi-core scheduling for *** the high performance,VU-WF can be widely used in resource-constrained and safety-critical real-time systems,such as spacecraft,self-driving cars,industrial robots,etc.

关键词： Mixed real-time task set Multi-core scheduling Task dynamical adding Virtual utilization Scheduling simulator

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A Survey of fMRI to Image Reconstruction

arXiv

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arXiv 2025年

作者： Guo, Weiyu Sun, Guoying He, Jianxiang Shao, Tong Wang, Shaoguang Chen, Ziyang Hong, Meisheng Sun, Ying Xiong, Hui Thrust of Artificial Intelligence The Hong Kong University of Science and Technology Guangzhou China College of Computer Science and Technology Harbin Institute of Technology Shenzhen China School of Control Science and Engineering Shandong University China

Functional magnetic resonance imaging (fMRI)based image reconstruction plays a pivotal role in decoding human perception, with applications in neuroscience and brain-computer interfaces. While recent advancements in deep learning and large-scale datasets have driven progress, challenges such as data scarcity, cross-subject variability, and low semantic consistency persist. To address these issues, we introduce the concept of fMRI-to-Image Learning (fMRI2Image) and present the first systematic review in this field. This review highlights key challenges, categorizes methodologies such as fMRI signal encoding, feature mapping, and image generator. Finally, promising research directions are proposed to advance this emerging frontier, providing a reference for future studies. © 2025, CC BY.

关键词： Image reconstruction

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On the notion of strong relative degree and its implications on the design of extended PD-controllers for time-delay systems*

On the notion of strong relative degree and its implications...

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European control Conference (ECC)

作者： Wim Michiels Zhou Bin Zhou Department of Computer Science KU Leuven Leuven Belgium Center for Control Theory and Guidance Technology Harbin Institute of Technology Harbin China

The fundamental notion of relative degree is studied for a class of linear time-delay systems of retarded type, where the standard assumption of commensurate delays is dropped. Even though the analyzed systems do not have a non-trivial feedthrough from input to output, it is shown that the relative degree may be sensitive to infinitesimal delay perturbations. This observation is at the basis of a novel notion of relative degree, called strong relative degree, which is characterized algebraically and computationally. It plays a key role in guaranteeing well-posedness of systems controlled with extended PD controllers, for which a design procedure and numerical experiments are presented.

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