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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Wafa Akermi Nouha Alyaoui Awatef Benfradj Guiloufi Karim Chabir Computer Science and Telecommunications Department Higher Institute of Applied Science and Technology Gafsa University Gafsa Tunisia Laboratory of Modeling Analysis and Control of Systems (MACS) National Engineering School of Gabes (ENIG) Gabes University Gabes Tunisia Departement of Computer Engineering College of Computer Qassim University Buraidh Saudi Ababia

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

High node mobility, rapid topology changes provide specific challenges for vehicular ad hoc networks (VANETs), which have an immediate impact on the routing protocols' performance. Traditional approaches, like the Optimized Link State Routing (OLSR) protocol, provide proactive route management but fail to fully account for critical dynamic parameters like link stability, relative vehicle speed, node distance, and bandwidth availability. This work proposes a hybrid method combining OLSR with Q-learning to facilitate real-time adaptive routing. The model leverages dynamic metrics to proactively evaluate links while employing Q-learning to optimize routing decisions based on rewards computed from performance factors like delay, packet loss rate, and link duration. Simulation results demonstrate that our approach significantly outperforms classic OLSR. The improvements include reduced packet loss rates, increased average throughput, lower average latency, and a reduction in control overhead. These findings confirm that integrating dynamic metrics and adaptive learning effectively addresses the challenges posed by VANETs.

关键词： Q-learning Simulation Vehicular ad hoc networks Packet loss Routing Throughput Routing protocols Stability analysis Topology Vehicle dynamics

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Effect of Ignition Voltage Amplitude on Breakdown Delay Time of Ring Laser Discharge Gap

Effect of Ignition Voltage Amplitude on Breakdown Delay Time...

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International Youth Conference on Radio Electronics, Electrical and Power engineering (REEPE)

作者： Anton O. Sinelnikov Ilya A. Smetanin Bogdan A. Basov Egor A. Smetanin Uliana F. Bykanova Computer science and control systems department Laboratory of Technologies of Polymer Ferroelectrics Laboratory of Microelectronics and Nanotechnology Mechanical engineering technologies department Bauman Moscow State Technical University Moscow Russian Federation Department of Nanotechnology and Microsystems Engineering Department of Mechanics and Control Processes RUDN University Moscow Russian Federation

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

The modern space industry requires autonomous and high-precision control systems for scientific and commercial missions. Strapdown inertial navigation systems based on laser gyroscopes play a key role in spacecraft control, especially in environments where satellite navigation is unavailable. In this paper, ring lasers with an active medium made of a helium-neon mixture, their design and operating modes are investigated. The influence of voltage on the ignition electrodes (4.2-5 kV) on the breakdown time of the active medium has been analyzed. It was found that increasing the voltage reduces the breakdown time, providing reliable activation of gyroscopes. The experimental results allowed to determine the optimal ignition parameters that increase operation reliability and reduce production losses. This contributes to the development of highly efficient spacecraft control systems, opening up new prospects for deep space exploration and control of satellite systems.

关键词： Space vehicles Breakdown voltage Electric breakdown Power lasers Ignition Ring lasers Laser modes control systems Gyroscopes Reliability

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Optimal Sensor Placement for Full-Set TDOA Localization Accounting for Sensor Location Errors

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IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Zhang, Chengjie Han, Xinyang Kong, He Ho, K.C. Southern University of Science and Technology Shenzhen Key Laboratory of Control Theory and Intelligent Systems Guangdong Provincial Key Laboratory of Fully Actuated System Control Theory and Technology Shenzhen518055 China University of Missouri Electrical Engineering and Computer Science Department ColumbiaMO65211 United States

The positioning accuracy of a signal source is influenced by where the sensors are deployed. Studies in the literature for the optimal sensor placement (OSP) of localization often ignore the presence of sensor position errors. In practice, however, it is impossible to place the sensors precisely at the desired locations. This work designs OSP by accounting for the presence of Gaussian sensor position errors that are independent and identically distributed (IID), with the full set of Time Difference of Arrival measurements corrupted by Gaussian IID noise. The criterion is the A-optimality equal to the trace of the source position Cramer-Rao Bound (CRB). We show that the presence of sensor position errors introduces a multiplication factor to the same criterion for the case without sensor position errors. The latter factor is dependent on the ratio of the sensor position error power to the range difference noise power but independent of the sensor positions, implying that the A-optimality OSP in the presence of sensor position errors is the same as in the absence. To our knowledge, this is the first time such results have been derived. Simulation studies validate the derived OSP, show the minimal OSP performance gap with sensor positioning error and without, and illustrate the better source localization accuracy by OSP. © 1965-2011 IEEE.

关键词： Cramer-Rao bounds

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Fowler-Nordheim stress-induced degradation in n-channel MOSFETs

Fowler-Nordheim stress-induced degradation in n-channel MOSF...

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International Youth Conference on Radio Electronics, Electrical and Power engineering (REEPE)

作者： Igor V. Chukhraev Dmitry V. Mastykash Vladimir E. Drach Computer Science and Control Department Bauman Moscow State Technical University Moscow Russian Federation Department of Innovative Engineering and Digital Technologies Sochi State University Sochi Russian Federation

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

This study investigates the degradation mechanisms of gate oxide in n-channel MOSFETs subjected to Fowler-Nordheim constant current stress to introduce uniformly distributed defects. Different techniques were used to characterise the impact of stress-induced defects on the electrical properties of MOSFETs. The findings reveal distinct degradation behaviors, with early-stage negative shifts in threshold voltage attributed to hole trapping, transitioning to positive shifts due to interface state generation at higher electron fluence. The study also emphasises the factors that influence device performance. The results enhance our understanding of gate oxide reliability under high-field stress, as well as the mechanisms of defect creation and charge trapping in MOSFETs.

关键词： Degradation Electron traps Logic gates Threshold voltage Interface states Reliability Stress Thermal stability Thermal stresses Thermal degradation

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Efficient Coordination and Synchronization of Multi-Robot Systems Under Recurring Linear Temporal Logic

arXiv

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arXiv 2025年

作者： Peron, Davide Fernandez-Ayala, Victor Nan Vlahakis, Eleftherios E. Dimarogonas, Dimos V. Department of Information Engineering University of Padova Padova35122 Italy Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm10044 Sweden

We consider multi-robot systems under recurring tasks formalized as linear temporal logic (LTL) specifications. To solve the planning problem efficiently, we propose a bottom-up approach combining offline plan synthesis with online coordination, dynamically adjusting plans via real-time communication. To address action delays, we introduce a synchronization mechanism ensuring coordinated task execution, leading to a multi-agent coordination and synchronization framework that is adaptable to a wide range of multi-robot applications. The software package is developed in Python and ROS2 for broad deployment. We validate our findings through lab experiments involving nine robots showing enhanced adaptability compared to previous methods. Additionally, we conduct simulations with up to ninety agents to demonstrate the reduced computational complexity and the scalability features of our work. © 2025, CC BY.

关键词： Multipurpose robots

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Integrated design of system structure and delayed resonator towards efficient non-collocated vibration absorption

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Journal of Sound and Vibration 2025年 612卷

作者： Peichl, Adam Kuře, Matěj Michiels, Wim Vyhlídal, Tomáš Department of Instrumentation and Control Engineering Faculty of Mechanical Engineering Czech Technical University in Prague Technická 4 Prague 6166 07 Czech Republic Department of Computer Science KU Leuven Celestijnenlaan 200A HeverleeB-3001 Belgium

The problem of non-collocated vibration absorption by a delayed resonator is addressed with emphasis on system fatigue resistance and energy efficiency of control actions. The analysis is performed for a system consisting of an arbitrary large series of flexibly linked single-degree-of-freedom masses. For the stage where the vibration of the target mass is fully absorbed by the non-collocated resonator, key forces, motion amplitudes and potential energies across the system structure are assessed. Next, a complete parameter set of the resonator gain and delay is derived, and the actuation force and power needed by the resonator for the full vibration absorption is determined. The derived quantities are utilized in forming an optimization problem to balance minimal risk of fatigue across the system structure and power needed by the resonator, under the closed loop stability and parameter constraints. Next to the gain and delay of the resonator, selected structural parameters of the system are used as variables in the constrained nonlinear optimization problem. Experimental and numerical case studies are included to demonstrate benefits of the proposed integrated structural and control design. © 2025 The Authors

关键词： Structural health monitoring

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Periodic-Enhanced Informer Model for Short-Term Wind Power Forecasting Using SCADA Data

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IEEE Transactions on Sustainable Energy 2025年

作者： Liu, Zhao-Hua Li, Long-Wei Wei, Hua-Liang Li, Ming Lv, Ming-Yang Zhang, Ying-Jie Hunan University of Science and Technology School of Information and Electrical Engineering Xiangtan411201 China University of Sheffield Department of Automatic Control and Systems Engineering SheffieldS1 3JD United Kingdom Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

Supervisory control and Data Acquisition (SCADA) systems can collect abundant information about wind farm operation and environment. To better make use of SCADA data, a periodic-enhanced informer model for short-term wind power forecasting using scada data is proposed. Firstly, to effectively filter out noise in SCADA data, a v-p curve-based method is adopted by incorporating a quartile approach to remove sparse outliers;a density-based spatial clustering of applications with noise (DBSCAN) algorithm is then employed to eliminate stacked outliers from the power curve. Secondly, a multi-feature input set selection method based on Maximization Information Coefficient is introduced to make better use of the SCADA system data by reducing the number of features. Thirdly, a Temporal Convolutional Network (TCN) is designed to extract the scalar projection of the input set, followed by fusing the local time stamp and global time stamp to build the periodic information enhanced prediction model embedding layer. Subsequently, the enhanced input set is fed into an informer model to predict future wind power. Finally, considering the multiple temporal scales structure characteristics present in the dataset, a multi-scale deep fusion module is established in the informer model to deeply integrate the features of different scales. It can simultaneously avoid the resource waste and overfitting problems caused by increasing the network depth. The experimental results, which are obtained from several deep learning methods on real SCADA data, demonstrate that the suggested approach has good predictive capability. © 2010-2012 IEEE.

关键词： Wind forecasting

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Correction to: Generalized Robot Vision-Language Model via Linguistic Foreground-Aware Contrast (International Journal of computer Vision, (2025), 10.1007/s11263-024-02340-z)

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International Journal of computer Vision 2025年 1-1页

作者： Liu, Kangcheng Wang, Chaoqun Han, Xiaodong Liu, Yong-Jin Chen, Baoquan Changsha China Pasadena United States The School of Control Science and Engineering Shandong University Jinan China The School of Control Engineering Minjiang University Fuzhou China The Department of Computer Science and Technology Tsinghua University Beijing China The School of Artificial Intelligence Peking University Beijing China

The original version of this article contained inadvertent typos requiring the following revisions. The article title was given as "Generalized Robotic Vision-Language Learning Model via Linguistic Foreground-Aware Contrast" but should have been "Generalized Robot Vision-Language Model via Linguistic Foreground-Aware Contrast". The affiliation details for Author Chaoqun Wang were given as "The department of computer science and Technology, Tsinghua University, Beijing, China" but should have been "The School of control science and engineering, Shandong University, Jinan, China". The affiliation details for Author Xiaodong Han were given as "The School of control science and engineering, Shandong University, Jinan, China" but should have been "The School of control engineering, Minjiang University, Fuzhou, China". The affiliation details for Author Yong-Jin Liu were given as "The School of control engineering, Minjiang University, Fuzhou, China" but should have been "The department of computer science and Technology, Tsinghua University, Beijing, China". In this article, the part of the caption to Fig. 3 "Please refer to the colored figures online for detailed information" was inadvertently omitted. The complete caption of Fig. 3 is given here. Figure 3 Visualizations of projected point correlation maps over the indoor ScanNet (1st-4th rows) and the outdoor KITTI (5th-8th rows) with respect to the query points highlighted by yellow crosses. The View 1 and View 2 in each sample show the intra-view and cross-view correlations, respectively. We compare FAC with the state-of-the-art CSC (Hou et al., 2021) on segmentation (rows 1–4) and ProCo (Yin et al., 2022) on detection (rows 5–8). FAC clearly captures better feature correlations within and across views (columns 3–4). Please refer to the colored figures online for detailed information In this article, the footnote "The bold highlights the results of our proposed approaches" to Table 1 was inadvertently omitted. In Tab

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Energy-Aware Task Allocation for Teams of Multi-mode Robots

arXiv

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arXiv 2025年

作者： Ito, Takumi Funada, Riku Sampei, Mitsuji Notomista, Gennaro Department of Systems and Control Engineering Institute of Science Tokyo Tokyo152-8550 Japan Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

This work proposes a novel multi-robot task allocation framework for robots that can switch between multiple modes, e.g., flying, driving, or walking. We first provide a method to encode the multi-mode property of robots as a graph, where the mode of each robot is represented by a node. Next, we formulate a constrained optimization problem to decide both the task to be allocated to each robot as well as the mode in which the latter should execute the task. The robot modes are optimized based on the state of the robot and the environment, as well as the energy required to execute the allocated task. Moreover, the proposed framework is able to encompass kinematic and dynamic models of robots alike. Furthermore, we provide sufficient conditions for the convergence of task execution and allocation for both robot models. Copyright © 2025, The Authors. All rights reserved.

关键词： Constrained optimization

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Mixture-of-Experts as Continual Knowledge Adapter for Mobile Vision Understanding

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IEEE Transactions on Mobile Computing 2025年

作者： Guo, Bicheng Zhou, Conghao He, Shibo Chen, Jiming Shen, Xuemin Zhejiang University College of Control Science and Engineering Zhejiang Hangzhou310027 China Xidian University School of Telecommunications Engineering China University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

Continual machine learning in the context of limited computational resources and data availability is critical in the connected digital world. Current intelligent applications predominantly rely on deep learning models requiring labor/computation-intensive training. These models often struggle to adapt effectively to new data while preserving performance on previously learned knowledge. In this paper, we introduce a lightweight method for continual knowledge adaptation that can address these challenges. To prevent disruption of the existing services, we propose a Mixture-of-Experts (MoE) adapter that integrates seamlessly with the existing vision model to encode new data. The weights of the original model are kept fixed during the adaptation process, ensuring the preservation of previously learned knowledge. The MoE technique enables scaling up the parameters of the adapter while maintaining a relatively low computation, making it fit for constrained devices in mobile computation scenarios. Furthermore, to enhance learning efficiency and accelerate convergence with new data, we implement a knowledge fusion mechanism that facilitates interaction between the existing knowledge and the information extracted from new data. The timing of employing the fusion module is further investigated. We find that it is conducive in scenarios where the task's performance requirements are enhanced. The MoE adapter and knowledge fusion module are integrated at each stage with minimal trainable parameters, efficiently optimizing resource usage. Extensive experiments and ablation studies validate the effectiveness of the proposed method. Specifically, the proposed method prevents an accuracy drop of 43.02% on the previous data compared to the continual train method, while achieving an accuracy of 44.81% on the new data, which is even 0.34% higher than fully training a new model. © 2002-2012 IEEE.

关键词： Deep learning

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