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arXiv 2023年

作者： Lanzetti, Nicolas Dörfler, Florian Pagan, Nicolò The Automatic Control Laboratory Department of Electrical Engineering and Information Technology ETH Zürich Zürich8003 Switzerland The Social Computing Group Department of Informatics University of Zürich Zürich8003 Switzerland

Recommendation systems are widely used in web services, such as social networks and e-commerce platforms, to serve personalized content to the users and, thus, enhance their experience. While personalization assists users in navigating through the available options, there have been growing concerns regarding its repercussions on the users and their opinions. Examples of negative impacts include the emergence of filter bubbles and the amplification of users’ confirmation bias, which can cause opinion polarization and radicalization. In this paper, we study the impact of recommendation systems on users, both from a microscopic (i.e., at the level of individual users) and a macroscopic (i.e., at the level of a homogenous population) perspective. Specifically, we build on recent work on the interactions between opinion dynamics and recommendation systems to propose a model for this closed loop, which we then study both analytically and numerically. Among others, our analysis reveals that shifts in the opinions of individual users do not always align with shifts in the opinion distribution of the population. In particular, even in settings where the opinion distribution appears unaltered (e.g., measured via surveys across the population), the opinion of individual users might be significantly distorted by the recommendation system. © 2023, CC BY.

关键词： Recommender systems

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A 75 GHz Brillouin Stokes Channels For High Data Rate DWDM Communication Systems

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Optik 2023年 295卷

作者： Awsaj, Mohammed K. Al-Mashhadani, Thamer Fahad Al-Mashhadani, Mohammed Kamil Salh Hammudi, Rabi Noori Ali, Ali Yaseen Ali, Sanaria Abd Zan, Mohd Saiful Dzulkefly Arsad, Norhana UKM—Photonics Technology Laboratory Department of Electrical Electronic and Systems Engineering Faculty of Engineering and Built Environment Universiti Kebangsaan Malaysia Bangi43600 Malaysia Department of Electrical Engineering Al-Anbar University Ramadi31001 Iraq Department of Laser Engineering and Optoelectronics Kut University College Al-Kut52001 Iraq Tikrit University Department of Electrical Engineering Saladdin Iraq Department of Petroleum Systems Control Engineering The College of Petroleum Processes Engineering Tikrit University Tikrit34001 Iraq Informatics and Telecommunications public company Ministry of Communication Iraq

The experimental results of a 75 GHz Brillouin Stokes channels are demonstrated in this paper. Three dispersion compensation fiber (DCF) spools and one single mode fiber (SMF) spool are used as a Brillouin gain medium. An erbium doped fiber amplifier (EDFA) sections are used to amplify the generated Stokes channels and to reduce the Brillouin Stokes threshold inside the cavities. Laser pump of 1480 nm with total power of 470 mW are used for the EDFA sections as a pump power unit. In our proposed setup, four channels with 75 GHz spacing, high Stokes power of about 10 mW, and large optical signal to noise ratio (OSNR) of about 52 dB are achieved at Brillouin pump power of 25 mW. The achieved channels are tuned over 15 nm from 1565 to 1580 nm. within the entire tuning range up to 25 Stokes channels with 75 GHz spacing can be utilized. High data rate of about 800 Gb/s can be obtained per single channel at this wide channel spacing. Therefore, the total data rate that could be achieved within the obtained tuning range is about 20 Tb/s. © 2023 Elsevier GmbH

关键词： Erbium doped fiber amplifiers

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Open and Closed Source Models for LLM-Generated Metaheuristics Solving engineering Optimization Problem 28th

Open and Closed Source Models for LLM-Generated Metaheuris...

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28th European Conference on Applications of Evolutionary Computation, EvoApplications 2025, held as part of EvoStar 2025

作者： Senkerik, Roman Viktorin, Adam Kadavy, Tomas Kovac, Jozef Janku, Peter Pekar, Libor Guzowski, Hubert Smolka, Maciej Byrski, Aleksander Pluhacek, Michal A.I.Lab Faculty of Applied Informatics Tomas Bata University Zlin Czech Republic Department of Automation and Control Engineering Faculty of Applied Informatics Tomas Bata University Zlin Czech Republic Faculty of Computer Science AGH University of Krakow Krakow Poland Center of Excellence in Artificial Intelligence AGH University of Krakow Krakow Poland

ISBN: (纸本)9783031900648

This paper explores the applicability of generative AI (genAI), specifically Large Language Models (LLMs), for the automatic generation and configuration of metaheuristic algorithms to address a real-world engineering problem: the optimal parameter estimation of time-delay systems in interconnected heating-cooling loops. The study introduces a pioneering workflow and iterative architecture with feedback within the emerging field of genAI-driven optimization for real optimization problems, eliminating the need for manually crafted or modified algorithms. This automated system empowers domain experts in engineering to solve complex optimization problems with minimal knowledge of optimization algorithms, lowering the barrier to entry for sophisticated algorithm use. We demonstrate how LLMs can generate effective optimizers under conditions like connstrained optimization problems where the solution lies near the boundaries of the search space. Four state-of-the-art LLMs (closed and open-sourced) have been selected for experiments. These are GPT-4o, GPT-4o mini, Claude Sonnet 3.5 and Llama 3.1. All studied LLMs generated metaheuristics that outperformed the initialization baseline optimization method (Random Search and CMA-ES). Notably, the Claude Sonnet 3.5 model generated a metaheuristic with the best mean results, almost matching the performance of the tuned state-of-the-art DISH algorithm, as an example of adaptive Differential Evolution. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Time of arrival

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Estimating the AGV load and a battery lifetime based on the current measurement and random forest application

Estimating the AGV load and a battery lifetime based on the ...

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IEEE International Conference on Big Data

作者： Krzysztof Hanzel Damian Grzechca Adam Ziebinski Lukas Chruszczyk Artur Janus Department of Telecommunications and Teleinformatics Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland Department of Electronics Electrical Engineering and Microelectronics Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland Department of Distributed Systems and Informatics Devices Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland Student of Electrical Engineering Faculty of Electrical Engineering Silesian University of Technology Gliwice Poland

The paper describes the energy consumption from the battery based on the current measurements for various cases, i.e., speed (PWL adjustment) and loads. The main purpose of the research is to have additional and reliable information about power consumption and battery life estimation for autonomous guided vehicles (AGV). The authors propose a two-step algorithm. In the first step, a linear classifier was proposed. Then, the KNN classifier was tested; however, it did not give satisfactory results, so it was finally decided to use the random forest to estimate the load and PWL. The time domain current measurement is evaluated, and the beforementioned algorithms process the selected statistical measures. It has been proven that a two-step algorithm allows for achieving high accuracy. Based on the current observation, the paper is a good starting point for further investigation of the AGV because it is usually implemented in the AGV – so it does not require additional hardware. Moreover, it can lead to better energy management and increase battery lifetime.

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Adaptive UAV-Assisted Hierarchical Federated Learning: Optimizing Energy, Latency, and Resilience for Dynamic Smart IoT Networks

arXiv

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arXiv 2025年

作者： Yang, Xiaohong Liwang, Minghui Fu, Liqun Su, Yuhan Hosseinalipour, Seyyedali Wang, Xianbin Hong, Yiguang School of Informatics Xiamen University Fujian China Department of Control Science and Engineering The National Key Laboratory of Autonomous Intelligent Unmanned Systems China Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Tongji University Shanghai China School of Electronic Science and Engineering Xiamen University Xiamen China Department of Electrical Engineering University at Buffalo-SUNY BuffaloNY United States Department of Electrical and Computer Engineering Western University ON Canada

Hierarchical Federated Learning (HFL) introduces intermediate aggregation layers, addressing the limitations of conventional Federated Learning (FL) in geographically dispersed environments with limited communication infrastructure. An application of HFL is in smart IoT systems, such as remote monitoring, disaster response, and battlefield operations, where cellular connectivity is often unreliable or unavailable. In these scenarios, UAVs serve as mobile aggregators, providing connectivity to the terrestrial IoT devices. This paper studies an HFL architecture for energy-constrained UAVs in smart IoT systems, pioneering a solution to minimize global training cost increased caused by UAV disconnection. In light of this, we formulate a joint optimization problem involving learning configuration, bandwidth allocation, and device-to-UAV association, and perform global aggregation in time before UAV drops disconnect and redeployment of UAVs. The problem explicitly accounts for the dynamic nature of IoT devices and their interruptible communications and is unveiled to be NP-hard. To address this, we decompose it into three subproblems. First, we optimize the learning configuration and bandwidth allocation using an augmented Lagrangian function to reduce training costs. Second, we propose a device fitness score, integrating data heterogeneity (via Kullback-Leibler divergence), device-to-UAV distances, and IoT device resources, and develop a twin-delayed deep deterministic policy gradient (TD3)-based algorithm for dynamic device-to-UAV assignment. Third, We introduce a low-complexity two-stage greedy strategy for finding the location of UAVs redeployment and selecting the appropriate global aggregator UAV. Experiments on real-world datasets demonstrate significant cost reductions and robust performance under communication interruptions. © 2025, CC BY-NC-ND.

关键词： Federated learning

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Bio-inspired control by overlapping adaptive clusters: a vehicle platoon case study

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IFAC-PapersOnLine 2023年第2期56卷 1460-1465页

作者： Ovidiu Pauca Anca Maxim José M. Maestre Constantin F. Caruntu “Gheorghe Asachi” Technical University of Iasi Department of Automatic Control and Applied Informatics Blvd. D. Mangeron 27 700050 Iasi Romania University of Seville Department of Systems and Automation Engineering Camino Descubrimientos S/N.- Isla Cartuja 41092 Seville Spain

Clustering vehicles in a platoon by exchanging information through communication networks leads to an improvement in their performance with a moderate cooperation effort. In particular, in this paper, a bio-inspired methodology for overlapping coalitions of vehicles is proposed, which optimizes communication and control performance. The results obtained using the proposed adaptive clusters method are compared with the ones obtained by a conventional coalitional control strategy, and the comparison illustrates the improved efficiency of the proposed method.

关键词： Bio-inspired control coalitional control overlapping topology vehicle platoon

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Sim2Real Grasp Pose Estimation for Adaptive Robotic Applications

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IFAC-PapersOnLine 2023年第2期56卷 5233-5239页

作者： Dániel Horváth Kristóf Bocsi Gábor Erdős Zoltán Istenes Centre of Excellence in Production Informatics and Control Institute for Computer Science and Control Eőtvős Loránd Research Network Budapest Hungary CoLocation Center for Academic and Industrial Cooperation Eőtvős Loránd University Budapest Hungary Department of Manufacturing Science and Engineering Budapest University of Technology and Economics Budapest Hungary

Adaptive robotics plays an essential role in achieving truly co-creative cyber physical systems. In robotic manipulation tasks, one of the biggest challenges is to estimate the pose of given workpieces. Even though the recent deep-learning-based models show promising results, they require an immense dataset for training. In this paper, two vision-based, multi-object grasp pose estimation models (MOGPE), the MOGPE Real-Time and the MOGPE High-Precision are proposed. Furthermore, a sim2real method based on domain randomization to diminish the reality gap and overcome the data shortage. Our methods yielded an 80% and a 96.67% success rate in a real-world robotic pick-and-place experiment, with the MOGPE Real-Time and the MOGPE High-Precision model respectively. Our framework provides an industrial tool for fast data generation and model training and requires minimal domain-specific data.

关键词： adaptive robotics robot vision sim2real knowledge transfer smart manufacturing cyber physical production systems

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Learning-Based Joint Recommendation, Caching, and Transmission Optimization for Cooperative Edge Video Caching in Internet of Vehicles

SSRN

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SSRN 2024年

作者： Cheng, Zhipeng Liu, Lu Liwang, Minghui Chen, Ning Fan, Xuwei School of Future Science and Engineering Soochow University Suzhou China School of Journalism and Communication Xiamen University Xiamen China The Department of Control Science and Engineering The Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China School of Informatics Xiamen University Xiamen China

In an era dominated by multimedia information, achieving efficient video transmission in the Internet of Vehicles (IoV) is crucial because of the inherent bandwidth constraints and network volatility within vehicular environments. In this paper, we propose a cooperative edge video caching framework designed to enhance video delivery efficiency in IoV by integrating joint recommendation, caching, and transmission optimization. Leveraging deep reinforcement learning with the discrete soft actor-critic algorithm, our methodology dynamically adapts to fluctuating network conditions and diverse user preferences, aiming to optimize both content delivery efficiency and quality of experience. The proposed approach seamlessly combines recommendation and caching strategies with transmission optimization to provide a comprehensive solution for high-performance video services. Extensive simulation results demonstrate that our framework significantly outperforms traditional baseline methods, achieving superior outcomes in terms of service utility, delivery rate, and delay reduction. These results highlight the robust potential of our solution to facilitate seamless and high-quality video experiences in the complex and dynamic landscape of vehicular networks, advancing the capabilities of IoV content delivery. © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Improved method of segmentation of images from space-based optoelectronic observation systems based on Otsu's algorithm with global and adaptive thresholds 4

Improved method of segmentation of images from space-based o...

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4th IEEE KhPI Week on Advanced Technology, KhPI Week 2023

作者： Khudov, Hennadii Kalimulin, Temir Khudov, Rostyslav Butko, Ihor Burtseva, Viktoriia Burtsev, Valeriy Department of Radar Troops Tactic Ivan Kozhedub Kharkiv National Air Force University Kharkiv Ukraine Department of Theoretical and Applied Informatics V.N.Karazin Kharkiv National University Kharkiv Ukraine Department of Computer Sciences and Software Engineering Academician Y. Bugay International Scientific and Technical University Kyiv Ukraine Scientific Research Department of Military Standards Metrology Center of Military Standards Kharkiv Ukraine Department of Control Systems of Antiaircraft Missile Troops Ivan Kozhedub Kharkiv National Air Force University Kharkiv Ukraine

ISBN: (纸本)9798350395532

An analysis of optoelectronic image, received from space-based observation system has been carried out. It has been found out, that this image has a complex structure and a unimodal intensity histogram. Otsu method is chosen as to segment the image. This method is easy and relatively quick, when talking of its realization. Methods of global and adaptive thresholding have been researched to perform image segmentation. Results of experimental research of segmentation of optoelectronic image from space-based observation system with use of Otsu method are developed. Ways for future research in this area are defined. © 2023 IEEE.

关键词： Image segmentation

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A Computationally Efficient Algorithm for Estimating Respiratory Rate from Seismocardiogram

SSRN

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SSRN 2024年

作者： Ku, Tienhsiung Lin, Yue-Der Department of Anesthesiology Changhua Christian Hospital Changhua50051 Taiwan Artificial Intelligence Development Center Changhua Christian Hospital Changhua50051 Taiwan Department of Automatic Control Engineering Feng Chia University Taichung40724 Taiwan Master’s Program of Biomedical Informatics and Biomedical Engineering Feng Chia University Taichung40724 Taiwan

Respiratory rate (RR) monitoring is crucial in clinical and healthcare settings. With the advancements in microelectromechanical system (MEMS) technology, monitoring respiration using seismocardiogram (SCG) is emerging as a promising approach. This study presents a computationally efficient algorithm for estimating RR from SCG signal. The proposed algorithm consists of two primary steps: firstly applying Gaussian averaging filter for pre-processing and next computing the averaged spectrum from the scalogram based on complex Morlet wavelet within the specified frequency range. RR is then estimated from the dominant frequency appeared in the averaged spectrum. This study also establishes the analytical relationship between the parameters of Gaussian averaging filter and the sampling frequency of SCG signal, which serves as the design criterion for pre-processing filter. The proposed algorithm undergoes testing on two SCG datasets and has demonstrated its feasibility for estimating RR from SCG signal. Furthermore, the algorithm is also tested on both finger and wrist PPG datasets for RR estimation. The test results present high agreement between the RR estimations from SCG (or PPG) signal and those from respiration signal. Based on these testing results, the proposed algorithm shows significant potential in clinical and healthcare applications. © 2024, The Authors. All rights reserved.

关键词： Computational efficiency

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