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arXiv 2022年

作者： Rikos, Apostolos I. Charalambous, Themistoklis Hadjicostis, Christoforos N. Johansson, Karl H. The Division of Decision and Control Systems KTH Royal Institute of Technology StockholmSE-100 44 Sweden The Department of Electrical and Computer Engineering University of Cyprus Nicosia1678 Cyprus

We consider the problems of computing the average degree and the size of a given network in a distributed fashion under quantized communication. We present two distributed algorithms which rely on quantized operation (i.e., nodes process and transmit quantized messages), and are able to calculate the exact solutions in a finite number of steps. Furthermore, during the operation of our algorithms, each node is able to determine in a distributed manner whether convergence has been achieved and thus terminate its operation. To the best of the authors’ knowledge these algorithms are the first to find the exact solutions under quantized communication (i.e., there is no error in the final calculation). Additionally, note that our network size calculation algorithm is the first in the literature which calculates the exact size of a network in a finite number of steps without introducing a final error. This error in other algorithms can be either due to quantization or asymptotic convergence. In our case, no error is introduced since the desired result is calculated in the form of a fraction involving a quantized numerator and a quantized denominator. Finally, we demonstrate the operation of our algorithms and their potential advantages. © 2022, CC BY.

关键词： Errors

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automatic removal algorithm of electrooculographic artifacts in non-invasive braincomputer interface based on independent component analysis

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Shengwu Yixue Gongchengxue Zazhi/Journal of Biomedical Engineering 2022年第6期39卷 1074-1081页

作者： Song, Hao Xu, Song Liu, Guoming Liu, Jing Xiong, Peng Key Laboratory of Digital Medical Engineering of Hebei Province Hebei Baoding071002 China School of Quality and Technical Supervision Hebei University Hebei Baoding071002 China Beijing Aerospace Automatic Control Institute Beijing100854 China National Key Laboratory of Aerospace Intelligent Control Technology Beijing100854 China College of Computer and Cyber Security Hebei Normal University Shijiazhuang050024 China College of Electronic and Information Engineering Hebei University Hebei Baoding071002 China

The non-invasive brain-computer interface (BCI) has gradually become a hot spot of current research, and it has been applied in many fields such as mental disorder detection and physiological monitoring. However, the electroencephalography (EEG) signals required by the non-invasive BCI can be easily contaminated by electrooculographic (EOG) artifacts, which seriously affects the analysis of EEG signals. Therefore, this paper proposed an improved independent component analysis method combined with a frequency filter, which automatically recognizes artifact components based on the correlation coefficient and kurtosis dual threshold. In this method, the frequency difference between EOG and EEG was used to remove the EOG information in the artifact component through frequency filter, so as to retain more EEG information. The experimental results on the public datasets and our laboratory data showed that the method in this paper could effectively improve the effect of EOG artifact removal and improve the loss of EEG information, which is helpful for the promotion of non-invasive BCI. © 2022 West China Hospital, Sichuan institute of Biomedical Engineering. All rights reserved.

关键词： Electroencephalography

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AttriReBoost: A Gradient-Free Propagation Optimization Method for Cold Start Mitigation in Attribute Missing Graphs

arXiv

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arXiv 2025年

作者： Li, Mengran Ding, Chaojun Chen, Junzhou Xing, Wenbin Ye, Cong Zhang, Ronghui Zhuang, Songlin Hu, Jia Qiu, Tony Z. Gao, Huijun School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen Campus Shenzhen518107 China School of Transportation Science and Engineering Harbin Institute of Technology Harbin150080 China Yongjiang Laboratory Ningbo315202 China Laboratory of Road and Traffic Engineering The Ministry of Education Tongji University Shanghai201804 China Department of Civil and Environmental Engineering University of Alberta EdmontonABT6G 2R3 Canada Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin150080 China

Missing attribute issues are prevalent in the graph learning, leading to biased outcomes in Graph Neural Networks (GNNs). Existing methods that rely on feature propagation are prone to cold start problem, particularly when dealing with attribute resetting and low-degree nodes, which hinder effective propagation and convergence. To address these challenges, we propose AttriReBoost (ARB), a novel method that incorporates propagation-based method to mitigate cold start problems in attribute-missing graphs. ARB enhances global feature propagation by redefining initial boundary conditions and strategically integrating virtual edges, thereby improving node connectivity and ensuring more stable and efficient convergence. This method facilitates gradient-free attribute reconstruction with lower computational overhead. The proposed method is theoretically grounded, with its convergence rigorously established. Extensive experiments on several real-world benchmark datasets demonstrate the effectiveness of ARB, achieving an average accuracy improvement of 5.11% over state-of-the-art methods. Additionally, ARB exhibits remarkable computational efficiency, processing a large-scale graph with 2.49 million nodes in just 16 seconds on a single GPU. Our code is available at https://***/limengran98/ARB. © 2025, CC BY.

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Distributed Optimal Data Allocation in Finite Time with Efficient Communication and Transmission Stopping over Dynamic Networks

Distributed Optimal Data Allocation in Finite Time with Effi...

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IEEE Conference on Decision and control

作者： Apostolos I. Rikos Christoforos N. Hadjicostis Karl H. Johansson Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden Digital Futures Stockholm Sweden Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

In this paper, we focus on the problem of data sharing over a wireless computer network (i.e., a wireless grid). Given a set of available data, we present a distributed algorithm, which operates over a dynamically changing network and allows each node to calculate the optimal allocation of data in a finite number of time steps. We show that our proposed algorithm (i) converges to the optimal solution in finite time with very high probability, and (ii) once the optimal solution is reached, each node is able to cease transmissions without needing knowledge of a global parameter such as the network diameter. Furthermore, our algorithm (i) operates exclusively with quantized values (i.e., each node processes and transmits quantized information), (ii) relies on event-driven updates, and (iii) calculates the optimal solution in the form of a quantized fraction which avoids errors due to quantization. Finally, we demonstrate the operation, performance, and potential advantages of our algorithm over random dynamic networks.

关键词： Wireless communication Knowledge engineering Quantization (signal) Heuristic algorithms Distributed databases Dynamic scheduling Computer networks

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Data-Driven Predictive control for Linear Parameter-Varying systems ⁎

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IFAC-PapersOnLine 2021年第8期54卷 101-108页

作者： Chris Verhoek Hossam S. Abbas Roland Tóth Sofie Haesaert Control Systems Group Dept. of Electrical Engineering Eindhoven University of Technology Eindhoven 5600MB The Netherlands Institute for Electrical Engineering in Medicine Universität zu Lübeck 23558 Lübeck Germany Systems and Control Lab Institute for Computer Science and Control (SZTAKI) Kende u. 13-17 1111 Budapest Hungary

Based on the extension of the behavioral theory and the Fundamental Lemma for Linear Parameter-Varying (LPV) systems, this paper introduces a Data-driven Predictive control (DPC) scheme capable to ensure reference tracking and satisfaction of Input-Output (IO) constraints for an unknown system under the conditions that (i) the system can be represented in an LPV form and (ii) an informative data-set containing measured IO and scheduling trajectories of the system is available. It is shown that if the data set satisfies a persistence of excitation condition, then a data-driven LPV predictor of future trajectories of the system can be constructed from the IO data set and online measured data. The approach represents the first step towards a DPC solution for nonlinear and time-varying systems due to the potential of the LPV framework to represent them. Two illustrative examples, including reference tracking control of a nonlinear system, are provided to demonstrate that the data-based LPV-DPC scheme, achieves similar performance as LPV model-based predictive control.

关键词： Predictive control Data-Driven control Linear Parameter-Varying systems Non-Parametric Methods

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A Multi-Model Fusion Approach for Transformer Fault Detection

A Multi-Model Fusion Approach for Transformer Fault Detectio...

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Information technology,Big Data and Artificial Intelligence (ICIBA), International Conference on IEEE International Conference on

作者： Ke Zhou Wei Zhang Ligang Li Shaoming Pan Chengwei Huang Guangxi Power Grid Equipment Monitoring and Diagnosis Technology Innovation Center Guangxi Key Laboratory of Intelligent Control and Maintenance of Power Equipment Electric Power Research Institute of Guangxi Power Grid CO. LTD. Nanning State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences China Power Supply Bureau of Guangxi Hezhou Power Grid Co. Ltd.

ISBN: (数字)9798350363609

ISBN: (纸本)9798350363616

Addressing the weak generalization capability and suboptimal prediction performance of single mechanistic and data-driven models, this paper proposes a transformer fault detection method based on the fusion of mechanism and data models. Firstly, vibration and current data are collected on-site to train the SVM vibration model and the Adaboost current model. Secondly, current simulation data is obtained using laboratory simulation software. This simulation data, combined with the current data collected under the same conditions on-site, is used as input to train the GBDT error compensation model. Finally, these three models are used as inputs to the GRNN, completing the overall model design. This fusion of mechanistic and data-driven models not only addresses the parameter mismatch issue of the mechanistic model under complex working conditions but also deeply mines time-series data. Experimental results show that The mean squared error of the fusion model is 0.002 lower than that of the data model, and the coefficient of determination is improved by 5.07% compared to the mechanistic model, enhancing the overall predictive accuracy of the model.

关键词： Vibrations Support vector machines Training Accuracy Biological system modeling Fault detection Predictive models Transformers Prediction algorithms Data models

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secRTK -A Jamming Resistant RTK-Receiver: Prototype Architecture and Results of First Measurement Campaigns

secRTK -A Jamming Resistant RTK-Receiver: Prototype Architec...

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ESA Workshop on Satellite Navigation Technologies and European Workshop on GNSS Signals and Signal Processing (NAVITEC)

作者： Gö tz C. Kappen Shutao Zhang Shuchen Liu Markus Biermann Tobias Gemmeke Dennis Borgmann Communication Engineering Laboratory NTLab FH M&#x00FC nster Steinfurt Germany Chair of Integrated Digital Systems and Circuit Design RWTH Aachen Aachen Germany RWTH Aachen Institute of Automatic Control Aachen Germany trilogik GmbH Greven Germany

ISBN: (数字)9781665416160

ISBN: (纸本)9781665416177

GNSS receivers featuring both high accuracy and interferer resistance would be a very promising option for existing markets and could be the basis for new applications in the field of satellite navigation. This paper presents an architecture based on the concept of controlled radiation pattern antennas. The general idea and a receiver setup, which is based mainly on of-the-shelf hardware, are introduced. Central components of the presented demonstrator are a 2-by-2 rectangular multi-an-tenna array, a programmable 4-channel analog GNSS-frontend and FPGA-used for processing at intermediate frequency, an up-converter and RTK-receiver. This real-time demonstrator is used during measurement campaigns with and without interfering signals. Measurements without interfering signals prove that the overall architecture is functional, and the integrated RTK-receiver provides consistent and high accuracy localization. For a scenario without interfering signal, the results in this paper show scatter plots for single and multi-antenna configuration, which prove that the RTK fix state can still be reached within seconds for the combined antenna data streams. These measurements are done using a connection to the SAPOS® NRW service. A first test campaign on the vehicle dynamics area in Aldenho-ven Testing Center shows the behavior with controlled interfer-ence conditions for the static and dynamic case. Results compared with a commercial single antenna receiver show that spa-tial processing successfully rejects the interferer signals. Results under certain conditions of the test campaign are presented. While high-end hardware in the demonstrator provides sufficient processing capability for the prototype, algorithmic opti-mizations enable mapping to low-cost commodity FPGAs or highly optimized CMOS chip implementation for commerciali-zation.

关键词： Resistance Semiconductor device measurement Satellites Prototypes Estimation Signal processing algorithms Hardware

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LogisticsVISTA: 3D Terminal Delivery Services With UAVs, UGVs and USVs Based on Foundation Models and Scenarios Engineering

LogisticsVISTA: 3D Terminal Delivery Services With UAVs, UGV...

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IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI

作者： Yonglin Tian Fei Lin Xinyuan Zhang Jingwei Ge Yutong Wang Xingyuan Dai Yisheng Lv Fei-Yue Wang State Key Laboratory of Multimodal Artificial Intelligence Systems and State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Department of Engineering Science Faculty of Innovation Engineering Macau University of Science and Technology Macau China University of Chinese Academy of Sciences Beijing China Department of Automation Tsinghua University Beijing China

ISBN: (数字)9798350379167

ISBN: (纸本)9798350379174

Intelligent logistics with autonomous vehicles is promising to improve the efficiency of rapidly expanding delivery systems. However, complex environments, diverse packages, and irregular human interaction have imposed challenges to the application of unmanned delivery vehicles. Combining various forms of vehicles and developing knowledge-driven delivery services have been the key to further facilitating the upgrading of logistics systems. In this paper, we propose an integrated framework named LogisticsVISTA (Logistics on Vehicles with Intelligent systems for Transport Automation) comprised of UAVs (Unmanned Aerial Vehicles), UGVs (Unmanned Ground Vehicles), and USVs (Unmanned Surface Vehicles) to provide timely and flexible 3D delivery services. In LogisticsVISTA, we design Large language models (LLMs) and large multimodal models (LMMs) for task planning, active perception, autonomous decision, and human-machine interaction. Besides, virtual-real integrated parallel scenarios are constructed to provide the playing ground for embodied agents efficiently and flexibly.

关键词： Human computer interaction Training Three-dimensional displays Foundation models Transportation Transforms Real-time systems Resource management Logistics Testing

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Dissipativity verification with guarantees for polynomial systems from noisy input-state data

arXiv

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arXiv 2020年

作者： Martin, Tim Allgöwer, Frank Institute for Systems Theory and Automatic Control University of Stuttgart Germany

In this paper, we investigate the verification of dissipativity properties for polynomial systems without explicit knowledge of a model but directly from noise-corrupted measurements. Contrary to most data-driven approaches for nonlinear systems, we determine dissipativity properties over infinite time horizon using input-state data. To this end, we propose two characterizations of the noise that affects the system and deduce from each characterization a data-based set-membership representation of the ground-truth system. Each representation then serves as a framework to derive computationally attractive conditions to verify dissipativity properties with rigorous guarantees from noise-corrupted data using SOS optimization. Copyright © 2020, The Authors. All rights reserved.

关键词： Machine learning

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A review of FDM nozzle system for additional materials manufacturing technology 10th

A review of FDM nozzle system for additional materials manuf...

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10th China Academic Conference on Printing and Packaging, 2019

作者： Wu, Yanzhi Hou, Heping Liu, Shanhui Wen, Qing Faculty of Printing Packaging Engineering and Digital Media Technology Xi’an University of Technology Xi’an China North Automatic Control Technology Institute China North Industries Group Corporation Taiyuan China

ISBN: (纸本)9789811518638

Compared with the traditional material reduction manufacturing, additive manufacturing technology is a great change, which brings new opportunities for the production models. Fused Deposition Molding (FDM) equipment is one of the most widely used to additive manufacturing equipment. The nozzle system is the core component of FDM technology. Its performance directly affects the product forming accuracy and efficiency. Therefore, taking FDM nozzle system as the research object, this paper reviews the research process and development trends of the nozzle system at home and abroad in recent years from the aspects of its flow channel structure, heat sink, heating device and nozzle, points out the hotspots and challenges of nozzle system research, which provides important reference for the optimization design of nozzle system. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Deposition

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