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arXiv 2025年

作者： Agorio, Leopoldo Van Alen, Sean Paternain, Santiago Calvo-Fullana, Miguel Bazerque, Juan Andres Dept. of Electrical and Computer Engineering Univ. of Pittsburgh PittsburghPA United States Dept. of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY United States Dept of Engineering Universitat Pompeu Fabra Barcelona Spain

Constrained multi-agent reinforcement learning offers the framework to design scalable and almost surely feasible solutions for teams of agents operating in dynamic environments to carry out conflicting tasks. We address the challenges of multi-agent coordination through an unconventional formulation in which the dual variables are not driven to convergence but are free to cycle, enabling agents to adapt their policies dynamically based on real-time constraint satisfaction levels. The coordination relies on a light single-bit communication protocol over a network with stochastic connectivity. Using this gossiped information, agents update local estimates of the dual variables. Furthermore, we modify the local dual dynamics by introducing a contraction factor, which lets us use finite communication buffers and keep the estimation error bounded. Under this model, we provide theoretical guarantees of almost sure feasibility and corroborate them with numerical experiments in which a team of robots successfully patrols multiple regions, communicating under a time-varying ad-hoc network. Copyright © 2025, The Authors. All rights reserved.

关键词： Stochastic systems

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Mm-wave 3D-printed DRAs for Cost-effective Emerging 5G-and SatCom Applications

Mm-wave 3D-printed DRAs for Cost-effective Emerging 5G-and S...

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2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022

作者： El-Sawaf, Heba Abdel-Wahab, Wael M. Safavi-Naeini, Safieddin University of Waterloo Dept. of Electrical and Computer Engineering WaterlooON Canada C-COM Satellite Systems Inc. Ottawa Canada

ISBN: (数字)9781665496582

ISBN: (纸本)9781665496582

This paper presents a demonstration for low cost dielectric resonator antenna (DRA) manufacturing, using 3D-printing technology for mm-wave applications. DRAs are high-efficient radiators, as compared to metallic antennas, especially at high frequency applications. Moreover, 3D-printing technology facilitates DRA machining and positioning on the printed circuit board. Two DRAs, designed at 20 GHz, and 30 GHz, are 3D-printed, and measured realizing good performance. © 2022 IEEE.

关键词： 3D printers

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Supervised Learning Approaches for Vibration Analysis and Anomaly Detection in Rotating Machines

Supervised Learning Approaches for Vibration Analysis and An...

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Emerging Trends in Electrical, control, and Telecommunication engineering (ETECTE), International Conference on

作者： Syed Zulfiqar Ali Syed Hasnat Ali Anees Javeed Ihsan Ullah Sufyan Ali Memon Sajjad Manzoor Dept. of Electrical and Computer Engineering CUI Abbottabad Campus Abbottabad Pakistan Dept. of Defence Systems Engineering Sejong University Seoul Republic of Korea Dept. of Electrical Engineering MUST Mirpur AJK Pakistan

ISBN: (数字)9798331504861

ISBN: (纸本)9798331504878

Safety and reliability are crucial for modern systems and technologies. This paper presents an approach for real-time anomaly detection in rotating machines using vibration analysis and machine learning algorithms for predictive maintenance. Rotating machines such as motors, engines, gearboxes and turbines are widely used systems and faults in these systems can result in complete shutdowns. Preventing unexpected downtime while ensuring equipment reliability and safety is essential. In this context, anomaly detection plays a vital role in monitoring the healthy operation of rotating machines. A test-bed was designed to generate datasets for both balanced (healthy) and unbalanced (faulty) motor conditions. The experimental setup consists of a three-phase induction motor, a signal acquisition system (Raspberry Pi), a vibration sensor (MPU6050) and a coupling system capable of adjusting motor balance. Supervised machine learning algorithms, Support Vector Machine (SVM) and Random Forest (RF) were applied to classify and detect anomalies in the collected datasets. A grid search algorithm was employed for hyperparameter tuning of the SVM and RF models. The proposed technique achieved a maximum accuracy of approximately 98% using SVM and 100% using the RF algorithm leveraging nine optimally selected temporal features.

关键词： Support vector machines Vibrations Radio frequency Machine learning algorithms Approximation algorithms Vectors Safety Classification algorithms Reliability Anomaly detection

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Downlink Covariance Estimation in URA FDD Massive MIMO systems

Downlink Covariance Estimation in URA FDD Massive MIMO Syste...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Salime Bameri Khalid Almahrog Ramy H. Gohary Amr El-Keyi Yahia Ahmed Dept. of Systems and Computer Engineering Carleton University Canada Ericsson Canada

We propose a low-complexity downlink channel covariance matrix estimation for massive multiple-input multiple-output systems in which the base station (BS) is equipped with a uniform rectangular antenna array (URA). This scheme can be expressed in the form of an affine transformation which depends only on the uplink and downlink carrier frequencies, and the BS array configurations. An upper bound on the estimation error is derived, which shows that the accuracy of the proposed scheme increases with the number of URA antennas and the compactness and differentiability class of the periodic extension of a non-linearly transformed version of the angular power spread. The performance superiority of the proposed scheme over its existing counterparts is confirmed through simulations.

关键词： Geometry Estimation error Upper bound Massive MIMO Signal processing Downlink Numerical simulation

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Using High-Level Patterns to Estimate How Humans Predict a Robot will Behave

arXiv

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arXiv 2024年

作者： Parekh, Sagar Bramblett, Lauren Bezzo, Nicola Losey, Dylan P. Dept. of Mechanical Engineering Virginia Tech BlacksburgVA24061 United States Dept. of Systems & Information Engineering Dept. of Electrical & Computer Engineering University of Virginia CharlottesvilleVA22903 United States

Humans interacting with robots often form predictions of what the robot will do next. For instance, based on the recent behavior of an autonomous car, a nearby human driver might predict that the car is going to remain in the same lane. It is important for the robot to understand the human’s prediction for safe and seamless interaction: e.g., if the autonomous car knows the human thinks it is not merging — but the autonomous car actually intends to merge — then the car can adjust its behavior to prevent an accident. Prior works typically assume that humans make precise predictions of robot behavior. However, recent research on human-human prediction suggests the opposite: humans tend to approximate other agents by predicting their high-level behaviors. We apply this finding to develop a second-order theory of mind approach that enables robots to estimate how humans predict they will behave. To extract these high-level predictions directly from data, we embed the recent human and robot trajectories into a discrete latent space. Each element of this latent space captures a different type of behavior (e.g., merging in front of the human, remaining in the same lane) and decodes into a vector field across the state space that is consistent with the underlying behavior type. We hypothesize that our resulting high-level and course predictions of robot behavior will correspond to actual human predictions. We provide initial evidence in support of this hypothesis through proof-of-concept simulations, testing our method’s predictions against those of real users, and experiments on a real-world interactive driving dataset. © 2024, CC BY.

关键词： Prediction models

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Online 3D Bin Packing Using a Combination of Tree Structures and Deep Reinforcement Learning

Online 3D Bin Packing Using a Combination of Tree Structures...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Fukuda, Kenji Yamakita, Masaki Oyama, Hiroyuki Institute of Science Tokyo Dept. of Systems and Control Engineering 1-11-1 Oh-Okayama Meguro Tokyo151-8551 Japan NEC Corporation Data Science Laboratories 1753 Shimonumabe Nakahara-ku Kanagawa211-8666 Japan

ISBN: (纸本)9781665481090

The online 3D bin packing problem (online 3D-BPP) is crucial for optimizing packing efficiency in various industrial applications. In this work, we introduce two external areas: an item buffer for the selection of packing items, and a temporary backup space to allow the rearrangement of items already packed in the bin. We propose a novel method that combines a deep reinforcement learning-based placement position planner with a movement sequence planner structured as a tree with the use of these external areas. This approach enables efficient planning of item placement and movement sequences without expanding the learning space, regardless of the size of external areas. Through numerical experiments, our method demonstrates a consistent improvement in spatial filling rates and robustness against constraints like physical stability. Notably, by utilizing these external areas, we elevated the performance of existing DRL methods by approximately 15 % in the scenario involving complex constraints such as physical stability. These results suggest that our approach is highly effective for complex, real-time packing scenarios in logistics and warehousing. © 2024 IEEE.

关键词： Deep reinforcement learning

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Development of a teaching model based on lifelong learning through active methodological strategies 12

Development of a teaching model based on lifelong learning t...

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12th International Conference on Virtual Campus, JICV 2022

作者： Trujillo-Aguilera, F D. Blazquez-Parra, E.B. Almendros, J. Calderon Marti-Campoy, A. Plaza-Garcia, I. University of Malaga Dept. Electronic Technology Malaga Spain University of Malaga Dept. Graphic Engineering Design and Projects Malaga Spain Polytechnic University of Valencia Dept. of Computer and Systems Informatics Valencia Spain University of Zaragoza Dept. of Electronic Engineering and Communications Zaragoza Spain

ISBN: (数字)9781665451062

ISBN: (纸本)9781665451062

Based on one of the fundamental principles of the educational model of the European Higher Education Area concerning the development of the capacity to achieve lifelong learning, this project has been developed in which a set of active teaching activities have been implemented for the acquisition of key competences in the challenge of achieving one of the most important goals that any educational system must provide: lifelong learning, thereby facilitating the incorporation of new university graduates into the world of work. This project has been implemented on the subject of Power Electronics. However, to facilitate the transfer to other subjects, the catalogue of active teaching activities is sufficiently varied to make it easy to extrapolate and use in other subjects, thanks to the fact that teachers have a wide variety of activities to choose from the set. Finally, a survey has been drawn to evaluate this new active methodology's satisfaction, impact and improvement to know the students' perspectives once they have worked with this catalogue of activities. © 2022 IEEE.

关键词： E-learning

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Embracing the Future: Unveiling the Revolution of Human-AI Interaction in the Digital Education Era

Embracing the Future: Unveiling the Revolution of Human-AI I...

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Virtual Campus (JICV), X International Conference on

作者： Andrea E. Cotino Arbelo Carina S. González-González Jezabel M. Molina Gil Dept. of Computer Engineering and Systems University of La Laguna Tenerife Spain

ISBN: (数字)9798350343441

ISBN: (纸本)9798350343458

The recent and emerging introduction of generative AI in all areas of society poses new challenges to be overcome, particularly in the educational setting. The intimate and affective relationship that may develop between students and generative AI devices raises critical questions about the psychological and emotional impact of such interactions. This issue becomes particularly significant when the interactions with AI-based systems begin at an early age, and these systems assume roles as digital tutors, digital secretaries, motivator agents, and/or mentors. The aim of this work in progress was to address a recent challenge within the digital educational context: the psychological and emotional impact that the introduction of AI-based systems in digital education may have on students. However, we have faced limitations in terms of data availability and resources, particularly when addressing the issue from early ages, where there is a notable lack of studies. Although the results may not have been significant, the research has provided a general overview and a solid theoretical foundation for future studies in this field. Therefore, we suggest conducting more rigorous studies with representative samples and complete data, alongside developing reliable and validated methodologies and assessment tools to address the issues involved.

关键词： Generative AI Education Knowledge based systems Psychology Medical services Reliability theory Solids

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Evaluating the Solutions to Predict the Impact of Lung Cancer with an Advanced Intelligent Computing Method 5

Evaluating the Solutions to Predict the Impact of Lung Cance...

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5th International Conference on Smart systems and Inventive Technology, ICSSIT 2023

作者： Sundar, R. Ramadass, Sudhir Meeha, D. Subramanian, Balambigai Shankar, S. Siva Parasa, Gayatri Madanapalle Institute of Technology and Science Dept. of Computer Science and Engineering India Sterck Systems Chennai India Dr. Rangarajan Dr. Sakunthala Engg College Vel Tech Multi Tech Dept. of Information Technology Avadi Chennai India Dept. of Ece Kongu Engineering College Perundurai India Kg Reddy College of Engineering and Technology Dept. of Cse -AIandML Telangana Chilukuru India Koneru Lakshmaiah Education Foundation Dept. of Cse Vaddeswaram Andhra Pradesh Guntur India

ISBN: (纸本)9781665474672

Using symptoms as a basis for diagnosing lung cancer, Lung cancer detection was accomplished using several different machine-learning regression strategies. By comparing the efficacy of several regression algorithms for predicting lung cancer, considering factors including age, gender, chest discomfort, shortness of breath, alcohol intake, chronic illness, trouble swallowing, anxiety, and peer pressure. Lung cancer predictions and evaluations are made using regression methods such as the linear algorithm, polynomial regression, logistic regression, logarithmic regression, and multiple regression. With a predictive accuracy of 96%, multiple regression is superior to other regression techniques when identifying future lung cancer cases. The r-squared value, which can be calculated using several regression machine learning approaches, may also be used to evaluate the association between the various symptoms and lung cancer. Lung cancer is diagnosed using the r-squared value, which is calculated using several algorithms and takes into symptoms, including chronic illness. © 2023 IEEE.

关键词： Forecasting

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Utilizing low-cost optical sensor for the measurement of particulate matter and calculating Pearson's correlation coefficient 16

Utilizing low-cost optical sensor for the measurement of par...

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16th International Conference on Correlation Optics, COR 2023

作者： Kirešová, Simona Rusyn, Volodymyr Guzan, Milan Vorobets, George Sobota, Branislav Vorobets, Oleksandr Dept. of Theoretical and Industrial Electrical Engineering Technical University of Košice Letná 9 Košice042 00 Slovakia Dept. of Radio Engineering and Information Security Yuriy Fedkovych Chernivtsi National University 2 Kotsjubynskyi Str. Chernivtsi58000 Ukraine Dept. of Computer Systems and Networks Yuriy Fedkovych Chernivtsi National University 2 Kotsjubynskyi Str. Chernivtsi58000 Ukraine Dept. of Computers and Informatics Technical University of Košice Letná 9 Košice042 00 Slovakia

ISBN: (纸本)9781510671829

This article discusses the possibilities of measuring Particulate Matter using optical low-cost sensors. Depending on the sensor used, not only can there be problems with the positioning of the sensor with respect to the incoming sunlight, but it may also be possible to measure ultrafine particles. While PM2.5 and PM10 are commonly used to characterize air quality, in this paper we point out the need to pay more attention to smaller particles up to 0.1 μm in size (also referred to as ultrafine particulates) due to their highly negative effects on the human health. Attention is also paid to the correlation between particulate matter and meteorological factors like pressure, temperature, and humidity. However, the sudden change in Pearson's correlation coefficient unveiled a need to look into the effect of the wind on particulate matter. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Optical sensors

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