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Roedunet International Conference (RoEduNet)

作者： Khirota Gorgees Yalda Diyar Jamal Hamad Nicolae Tapus Ibrahim Taner Okumus Faculty of Automatic Control and Computer Science Politehnica University of Bucharest Bucharest Romania IT department Soran Technical college Erbil Polytechnic University Kurdistan Region Erbil Iraq Computer Engineering Faculty of engineering Kahramanmaras Sutcu Imam University Kahramanmaras Turkey

ISBN: (数字)9798331540388

ISBN: (纸本)9798331540395

Software-Defined Networking (SDN) represents a significant shift in network architecture, providing exceptional programmability, flexibility, and simplified management. However, this paradigm shift introduces a unique set of security challenges that must be addressed to fully realize the potential of SDN. This paper examines the security issues in SDN environments, detailing the threats and vulnerabilities at various layers of the SDN architecture, including the control plane, data plane, and application plane. Through an extensive review of current literature, critical security challenges such as controller attacks, data plane breaches, and vulnerabilities in inter-plane communications are identified. Existing security solutions and mitigation strategies, such as authentication and authorization mechanisms, encryption techniques, and intrusion detection systems, are also explored. Furthermore, the paper discusses recent advances and emerging trends in SDN security, offering insights into ongoing research and future directions. The findings underscore the importance of robust security measures in ensuring the reliability and integrity of SDN deployments, providing a foundation for future innovation and development in this dynamic field.

关键词： Technological innovation Prevention and mitigation Intrusion detection Distributed databases Organizations Robustness Encryption Security Software defined networking computer crime

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Mixed Strategy PSO for Global Optimization

Mixed Strategy PSO for Global Optimization

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2021 IEEE International Conference on Space-Air-Ground Computing, SAGC 2021

作者： Pang, Jinwei Li, Xiaohui Han, Shuang School of Computer and Control Engineering Yantai University Yantai China Harbin Vocational & Technical College Harbin China Harbin Engineering University Department of Computer Science and Technology Harbin China

ISBN: (纸本)9781665400114

Particle Swarm Optimization (PSO) is an evolutionary algorithm for solving global optimization problems. In recent two decades, a lot of researches have been working on improving the performance of PSO and numerous PSO variants have been presented. According to a recent theory, the hardest problem to one evolutionary algorithm may be the easiest to another algorithm, and vice versa. Thus, mixed strategy PSO algorithms might be more efficient than pure strategy algorithms. A mixed strategy PSO algorithm (MSPSO) was proposed which integrates five different PSO variants. In MSPSO, an adaptive selection strategy is used to adjust the probability of selecting different variants according to the rate of the fitness value change between offspring generated by each variant and the personal best position of particles to guide the selection probabilities of variants. The rate of the fitness value change is a more effective indicator of good strategies than the number of previous successes and failures of each variant. The proposed algorithm is tested on CEC 2014 benchmark suites with 10 variables. Experimental results demonstrate that the solution accuracy of the proposed algorithm is overall better than those of comparative algorithms. © 2021 IEEE.

关键词： Global optimization

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Robust Remote Sensing Scene Classification with Multi-View Voting and Entropy Ranking

arXiv

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arXiv 2023年

作者： Wang, Jinyang Wang, Tao Gan, Min Hadjichristofi, George Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering International Digital Economy College Minjiang University Fuzhou350108 China College of Computer and Data Science Fuzhou University Fuzhou350108 China Department of Computer Science and Engineering European University Cyprus Nicosia1516 Cyprus

Deep convolutional neural networks have been widely used in scene classification of remotely sensed images. In this work, we propose a robust learning method for the task that is secure against partially incorrect categorization of images. Specifically, we remove and correct errors in the labels progressively by iterative multi-view voting and entropy ranking. At each time step, we first divide the training data into disjoint parts for separate training and voting. The unanimity in the voting reveals the correctness of the labels, so that we can train a strong model with only the images with unanimous votes. In addition, we adopt entropy as an effective measure for prediction uncertainty, in order to partially recover labeling errors by ranking and selection. We empirically demonstrate the superiority of the proposed method on the WHU-RS19 dataset and the AID dataset. Copyright © 2023, The Authors. All rights reserved.

关键词： Entropy

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Data-driven SOC Estimation with Moving Window Adaptive Residual Generator for Li-ion Battery

Data-driven SOC Estimation with Moving Window Adaptive Resid...

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2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes, SAFEPROCESS 2021

作者： Xu, Xiaoyi Luo, Hao Wang, Hao Chow, Mo-Yuen Huang, Cong-Sheng Harbin Institute of Technology Department of Control Science and Engineering Harbin China North Carolina State University Department of Electrical and Computer Engineering RaleighNC United States

ISBN: (纸本)9781665401159

Lithium battery has been widely used in electric vehicle (EV) and microgrid applications as an outstanding energy storage device. To optimally control the batteries, the nonlinear battery dynamics and the battery State-of-Charge (SOC) need to be real-time and accurately estimated by the battery management systems (BMSs). Compared with traditional estimation algorithms, the observer-based adaptive residual generator has the advantages of small calculation amount, stability, and adaptability. It can accurately estimate the SOC of a battery with a high excited load current. However, in the case of a low excited load current, the SOC estimation effect is poor. Besides, as a data-driven algorithm, the adaptive residual generator's estimation effect will deteriorate when affected by measurement noise. This paper proposes a moving window scheme for the adaptive residual generator and analyzes the parameter setting range to ensure the stability and convergence of the algorithm, which provides guidance for parameter selection when applying the algorithm. On the one hand, the proposed moving window scheme effectively reduces the high excitation requirement of the adaptive residual generator for the load current. Even under low excitation, the moving window adaptive residual generator can still accurately estimate the SOC. On the other hand, the addition of a moving window effectively improves the anti-disturbance ability of the adaptive residual generator. The performance of the proposed method is verified by lithium battery simulation experiments. © 2021 IEEE.

关键词： Lithium-ion batteries

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Distributed Optimization with Efficient Communication, Event-Triggered Solution Enhancement, and Operation Stopping

arXiv

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arXiv 2025年

作者： Rikos, Apostolos I. Jiang, Wei Charalambous, Themistoklis Johansson, Karl H. Guangzhou China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Clear Water Bay Hong Kong Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo Finland Department of Electrical and Computer Engineering University of Cyprus Nicosia1678 Cyprus Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Finland Division of Decision and Control Systems KTH Royal Institute of Technology StockholmSE-100 44 Sweden Digital Futures United Kingdom

In modern large-scale systems with sensor networks and IoT devices it is essential to collaboratively solve complex problems while utilizing network resources efficiently. In our paper we present three distributed optimization algorithms that exhibit efficient communication among nodes. Our first algorithm presents a simple quantized averaged gradient procedure for distributed optimization, which is shown to converge to a neighborhood of the optimal solution. Our second algorithm incorporates a novel event-triggered refinement mechanism, which refines the utilized quantization level to enhance the precision of the estimated optimal solution. It enables nodes to terminate their operation according to predefined performance guarantees. Our third algorithm is tailored to operate in environments where each message consists of only a few bits. It incorporates a novel event-triggered mechanism for adjusting the quantizer basis and quantization level, allowing nodes to collaboratively decide operation termination based on predefined performance criteria. We analyze the three algorithms and establish their linear convergence. Finally, an application on distributed sensor fusion for target localization is used to demonstrate their favorable performance compared to existing algorithms in the literature. © 2025, CC BY.

关键词： Dynamic programming

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MTP-GO: Graph-Based Probabilistic Multi-Agent Trajectory Prediction with Neural ODEs

arXiv

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arXiv 2023年

作者： Westny, Theodor Oskarsson, Joel Olofsson, Björn Frisk, Erik Department of Electrical Engineering Linköping University Sweden Department of Computer and Information Science Linköping University Sweden Department of Automatic Control Lund University Sweden

Enabling resilient autonomous motion planning requires robust predictions of surrounding road users' future behavior. In response to this need and the associated challenges, we introduce our model titled MTP-GO. The model encodes the scene using temporal graph neural networks to produce the inputs to an underlying motion model. The motion model is implemented using neural ordinary differential equations where the state-transition functions are learned with the rest of the model. Multimodal probabilistic predictions are obtained by combining the concept of mixture density networks and Kalman filtering. The results illustrate the predictive capabilities of the proposed model across various data sets, outperforming several state-of-the-art methods on a number of metrics. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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Towards backward compatibility of ADRC: revisiting classical state-feedback control with integral compensator

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Bulletin of the Polish Academy of sciences: Technical sciences 2025年第2期73卷

作者： Mrotek, Mikolaj Michalski, Jacek Madonski, Rafal Pazderski, Dariusz Retinger, Marek Faculty of Control Robotics and Electrical Engineering Institute of Robotics and Machine Intelligence Poznan University of Technology Piotrowo 3a Poznan60-965 Poland Faculty of Automatic Control Electronics and Computer Science Department of Automatic Control and Robotics Silesian University of Technology Akademicka 16 Gliwice44-100 Poland Faculty of Control Robotics and Electrical Engineering Institute of Automatic Control and Robotics Poznan University of Technology Piotrowo 3a Poznan60-965 Poland

In this paper, the problem of backward compatibility of active disturbance rejection control (ADRC) is investigated. The goal is to contextualize ADRC to deliver its interpretations from the established field of linear control systems. For this study, a control algorithm, denoted here as integral disturbance rejection control (IDRC), is considered that combines classical state-feedback control with an integral compensator. At first, an interpretation of ADRC is involved in terms of existing state-space control approaches. Next, a transition to the frequency domain is performed, which is justified as a significant part of practical control engineering is conducted in that domain. For assumed specific plant structures, both ADRC and IDRC are then holistically compared in terms of transfer function representation and frequency characteristics, as well as steady-state convergence conditions. Such a juxtaposition helps to highlight the similarities and differences of both approaches, whereas the utilized bandwidth parameterization is shown to bring the control system to the same form, thus indicating some interesting practical aspects. Finally, the theoretical results concerning both considered control structures are validated in a set of numerical simulations and experiments conducted on a laboratory hardware testbed. © 2025 The Author(s).

关键词： Disturbance rejection

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A Comprehensive Methodology of Field-Oriented control Design With Parameter Variation Analysis for Interior Permanent Magnet Synchronous Machine Drives

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IEEE Access 2025年 13卷 89524-89541页

作者： Tiago Davi Curi Busarello Abdullah Bubshait Oruganti V. S. R. Varaprasad Abdulhakeem Alsaleem Marcelo Godoy Simões Department of Control Automation and Computation Engineering Federal University of Santa Catarina Florianopolis Brazil Department of Electrical Engineering College of Engineering King Faisal University Al Ahsa Saudi Arabia Smart Transportation Electrification and Energy Research (STEER) Group Department of Electrical Computer and Software Engineering Faculty of Engineering and Applied Science Ontario Tech University Oshawa ON Canada Department of Electrical Engineering College of Engineering Qassim University Buraydah Saudi Arabia School of Technology and Innovations University of Vaasa Vaasa Finland

This paper proposes a comprehensive methodology for Field-Oriented control (FOC) with parameter variation analysis for Interior Permanent Magnet Synchronous Machines (IPMSM). The modeling approach for an IPMSM is first presented, followed by a step-by-step procedure for designing a vector-controlled strategy. The formulation is based on a dq-rotating reference frame aligned with the rotor shaft position. A key distinction of this methodology from traditional approaches is that the current controller is designed in the time domain based on desired time constants, while the speed control is formulated within a frequency response domain framework. The proposed hybrid approach enables accurate tuning of the Proportional-Integrator (PI) controllers for both current and speed control loops. Additionally, a parameter variation analysis is conducted to enhance the proposed methodology. The validity region for the design procedure is presented, ensuring that for any wide speed variation of the machine, loop gains are properly tuned. One of the main advantages of the proposed methodology is that it provides a fast, reliable, and accurate technique for implementing IPMSM drive systems. Results from a controller Hardware-in-the-Loop (C-HIL) setup with an external microcontroller are presented. The comprehensive design approach is validated under two different IPMSM parameter sets, demonstrating its effectiveness.

关键词： PI control Stators Tuning Mathematical models Velocity control Rotors Vectors Stator windings Robustness Microcontrollers

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Language-driven Grasp Detection with Mask-guided Attention

Language-driven Grasp Detection with Mask-guided Attention

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Tuan Van Vo Minh Nhat Vu Baoru Huang An Vuong Ngan Le Thieu Vo Anh Nguyen FPT Software AI Center Vietnam Automation & Control Institute TU Wien Austria Austrian Institute of Technology (AIT) GmbH Austria Imperial College London UK Department of Computer Science & Computer Engineering University of Arkansas USA National University of Singapore Singapore Department of Computer Science University of Liverpool UK

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Grasp detection is an essential task in robotics with various industrial applications. However, traditional methods often struggle with occlusions and do not utilize language for grasping. Incorporating natural language into grasp detection remains a challenging task and largely unexplored. To address this gap, we propose a new method for language-driven grasp detection with mask-guided attention by utilizing the transformer attention mechanism with semantic segmentation features. Our approach integrates visual data, segmentation mask features, and natural language instructions, significantly improving grasp detection accuracy. Our work introduces a new framework for language-driven grasp detection, paving the way for language-driven robotic applications. Intensive experiments show that our method outperforms other recent baselines by a clear margin, with a 10.0% success score improvement. We further validate our method in real-world robotic experiments, confirming the effectiveness of our approach.

关键词： Visualization Attention mechanisms Accuracy Service robots Source coding Semantic segmentation Natural languages Grasping Transformers Feature extraction

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Evaluation of Differentially Constrained Motion Models for Graph-Based Trajectory Prediction

Evaluation of Differentially Constrained Motion Models for G...

引用

IEEE Symposium on Intelligent Vehicle

作者： Theodor Westny Joel Oskarsson Björn Olofsson Erik Frisk Department of Electrical Engineering Linköping University Sweden Department of Computer and Information Science Linköping University Sweden Department of Automatic Control Lund University Sweden

Given their flexibility and encouraging performance, deep-learning models are becoming standard for motion prediction in autonomous driving. However, with great flexibility comes a lack of interpretability and possible violations of physical constraints. Accompanying these data-driven methods with differentially-constrained motion models to provide physically feasible trajectories is a promising future direction. The foundation for this work is a previously introduced graph-neural-network-based model, MTP-GO. The neural network learns to compute the inputs to an underlying motion model to provide physically feasible trajectories. This research investigates the performance of various motion models in combination with numerical solvers for the prediction task. The study shows that simpler models, such as low-order integrator models, are preferred over more complex, e.g., kinematic models, to achieve accurate predictions. Further, the numerical solver can have a substantial impact on performance, advising against commonly used first-order methods like Euler forward. Instead, a second-order method like Heun’s can greatly improve predictions.

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