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arXiv 2025年

作者： Sanati, Afsaneh L. Nikitin, Timur Fausto, Rui Majidi, Carmel Tavakoli, Mahmoud Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Coimbra3030–290 Portugal Dept. de Química Universidade de Coimbra Portugal Faculty of Sciences and Letters Dept. of Physics Istanbul Kultur University Ataköy Campus Bakirköy Istanbul34156 Turkey Soft Machines Lab Department of Mechanical Engineering Carnegie Mellon University PittsburghPA15213 United States

Energy storage devices with liquid metal electrodes have attracted interest in recent years due to their potential for mechanical resilience, self-healing, dendrite-free operation, and fast reaction kinetics. Gallium alloys such as Eutectic Gallium Indium (EGaIn) are particularly interesting due to their low melting point and high theoretical specific capacity. However, when exposed to a highly alkaline electrolyte, thin-film electrodes composed of EGaIn droplets are not stable due to the dissolution of Gallium oxide. In this letter, we address this bottleneck by introducing chemically stable films in which nanoscale droplets of EGaIn are coated with trace amounts of graphene oxide (GO). We demonstrated that a GO to EGaIn weight ratio as low as 0.01 provides enough protection for a thin film formed by GO@EGaIn nanocomposite against significantly acidic or alkaline environments (pH 1-14). We show that the GO coating significantly enhances the surface stability in such environments, and thus improves the energy storage capacity by over 10 times. We perform detailed microstructural analysis of various GO@EGaIn composites and analyze their electrochemical cycling performance under various conditions. We then use this technique to fabricate a thin-film supercapacitor. The results indicate that when coating the EGaIn by a GO to EGaIn ratio of 0.001, the areal capacitance is improved by 10x, reaching 20.02 mF/cm2. This discovery can lay the foundation for the development of the next generation of high-performance liquid metal based thin-film electrodes for energy storage applications. © 2025, CC BY-NC-ND.

关键词： Gallium alloys

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Sparse Gaussian Process Regression using Progressively Growing Learning Representations

Sparse Gaussian Process Regression using Progressively Growi...

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IEEE Conference on Decision and Control

作者： Christos N. Mavridis George P. Kontoudis John S. Baras Department of Electrical and Computer Engineering Institute for Systems Research University of Maryland College Park USA Dept. of Aerospace Engineering Maryland Robotics Center Inst. of Systems Research University of Maryland College Park MD USA

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

We present a new sparse Gaussian process regression model whose covariance function is parameterized by the locations of a progressively growing set of pseudo-inputs generated by an online deterministic annealing optimization algorithm. A series of entropy-regularized optimization problems is solved sequentially, introducing a bifurcation phenomenon, according to which, pseudo-inputs are gradually generated. This results in an active learning approach, which, in contrast to most existing works, can modify already selected pseudo-inputs and is trained using a recursive gradient-free stochastic approximation algorithm. Finally, the proposed algorithm is able to incorporate prior knowledge in the form of a probability density, according to which new observations are sampled. Experimental results showcase the efficacy and potential advantages of the proposed methodology.

关键词： Annealing Gaussian processes Bifurcation Approximation algorithms Optimization

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Forewarning Crossing Intention of Pedestrians Using Multimodal Deep Learning Approach

Forewarning Crossing Intention of Pedestrians Using Multimod...

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International Conference on Green Technology and Sustainable Development (GTSD)

作者： Chi-Tam Nguyen Minh-Thien Duong Thanh-Danh Phan Van-Binh Nguyen My-Ha Le Intelligent System Laboratory HCMC University of Technology and Education Ho Chi Minh City Vietnam Dept. of Information and Telecommunication Engineering Soongsil University Seoul South Korea Dept. of Intelligent Systems and Robotics Chungbuk National University Cheongju South Korea Institute of Engineering-Technology Thu Dau Mot University Thu Dau Mot City Vietnam Faculty of Electrical and Electronics Engineering HCMC University of Technology and Education Ho Chi Minh City Vietnam

ISBN: (数字)9798350386073

ISBN: (纸本)9798350386080

Nowadays, traffic accidents not only come from public transportation but also from pedestrians. Therefore, designing a solution to protect pedestrian safety is essential. This article proposes a system for forewarning the crossing intentions of pedestrians, comprising the tasks of detection, tracking, and intention prediction. Firstly, our proposed system performs the detection and pose estimation of pedestrians using the YOLOv7-Pose model. Subsequently, the OC-SORT algorithm is employed for the tracking task. Ultimately, an efficient architecture based on the VoVNet backbone and Temporal Shift Module is provided to forewarn whether pedestrians will cross the road or not. The input of the proposed network in multimodality includes RGB image or semantic image map, keypoints, and vehicle speed. In addition, the bag-of-freebies technique is applied to enhance the model's overall performance and address the issue of biased model creation caused by imbalanced data. Comprehensive experiments performed on the JAAD benchmark dataset demonstrate that our proposed method performs better than existing benchmark classification models.

关键词： Pedestrians Roads Semantics Pose estimation Benchmark testing Prediction algorithms Data models

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Survey on Deep Fuzzy systems in regression applications: a view on interpretability

arXiv

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arXiv 2022年

作者： Silveira, Jorge Sampaio Mendes, Jerome Souza, Francisco Premebida, Cristiano Department of Electrical and Computer Engineering Pólo II University of Coimbra Institute of Systems and Robotics Coimbra3030-290 Portugal Dept. Analytical Chemistry & Chemometrics Radboud University Nijmegen6525 AJ Netherlands

Regression problems have been more and more embraced by deep learning (DL) techniques. The increasing number of papers recently published in this domain, including surveys and reviews, shows that deep regression has captured the attention of the community due to efficiency and good accuracy in systems with high-dimensional data. However, many DL methodologies have complex structures that are not readily transparent to human users. Accessing the interpretability of these models is an essential factor for addressing problems in sensitive areas such as cyber-security systems, medical, financial surveillance, and industrial processes. Fuzzy logic systems (FLS) are inherently interpretable models, well known in the literature, capable of using nonlinear representations for complex systems through linguistic terms with membership degrees mimicking human thought. Within an atmosphere of explainable artificial intelligence, it is necessary to consider a trade-off between accuracy and interpretability for developing intelligent models. This paper aims to investigate the state-of-the-art on existing methodologies that combine DL and FLS, namely deep fuzzy systems, to address regression problems, configuring a topic that is currently not sufficiently explored in the literature and thus deserves a comprehensive survey. © 2022, CC BY.

关键词： Surveys

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Sensitivity Analysis for Uncertain Linear systems: A Set-Membership Approach

SSRN

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SSRN 2023年

作者： Silvestre, Daniel Rosa, Paulo Hespanha, João P. Silvestre, Carlos Caparica2829-516 Portugal COPELABS The Lusófona University Portugal The Institute for Systems and Robotics Instituto Superior Técnico University of Lisbon Portugal Deimos Engenharia Lisbon Portugal The Department of Electrical and Computer Engineering The Faculty of Science and Technology The University of Macau China The Dept. of Electrical and Computer Eng. University of California Santa BarbaraCA93106-9560 United States

This paper deals with the problem of deciding which inputs or parameters in a dynamic model have the greatest impact on the state or output of a linear system. In over-parameterized models or Multi-Agent systems (MAS), a sensitivity analysis tool would allow for reduced representations of the same system or to decide which agents should be monitored. The proposed approach consists in constructing the exact reachable set for a linear system (uncertain in the case of parameter sensitivity) and projecting onto the desired coordinates associated with the state or output in question. Given the exact representation, under mild assumptions, sensitivity values are optimal for the proposed functions, although others can be specified using the same sets. Illustrative examples are presented in order to provide insights on the proposed method. © 2023, The Authors. All rights reserved.

关键词： Sensitivity analysis

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Multi-agent approach to foster regular physical activity in elderly users

Multi-agent approach to foster regular physical activity in ...

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IEEE International Conference on Autonomous Robot systems and Competitions (ICARSC)

作者： Paulo Menezes Rui P. Rocha Institute of Systems and Robotics Dept. of Electrical and Computer Engineering University of Coimbra Coimbra Portugal

ISBN: (数字)9781728170787

ISBN: (纸本)9781728170794

The increase of life expectancy in last decades brought with it an increase in the number of technologies developed to assist this growing population segment. Some of the developed technologies focus on assisting older adults age in a more active way, remaining active and independent for longer. In accordance with the concept of active ageing we present a multi-agent approach, intended to foster healthy exercise habits among the elderly. This paper presents a multi-agent exercise system that positively affects elders' motivation to engage and maintain exercise habits. It details the theoretical foundations for our approach as well as the implementation details of each agent. A pilot study was conducted on 12 elderly users, testing three variants of the system, each involving a different combination of agents, and evaluating their impact on the participants. Results show that all three variants of the system were able to guide the participants through a diverse exercise routine, with the most complete system being the one most highly evaluated by the users.

关键词： Avatars Senior citizens Cameras Robot vision systems Robot kinematics Conferences

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Overcoming the Fear of the Dark: Occlusion-Aware Model-Predictive Planning for Automated Vehicles Using Risk Fields

arXiv

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arXiv 2023年

作者： van der Ploeg, Chris Nyberg, Truls Sánchez, José Manuel Gaspar Silvas, Emilia van de Wouw, Nathan Netherlands Organisation for Applied Scientific Research Integrated Vehicle Safety Group Helmond5700 AT Netherlands Eindhoven University of Technology Dynamics and Control Group Mechanical Engineering Dept. P.O. Box 513 Eindhoven5600 MB Netherlands Eindhoven University of Technology Control Systems Technology Group Mechanical Engineering Dept. P.O. Box 513 Eindhoven5600 MB Netherlands KTH Royal Institute of Technology Division of Robotics Perception and Learning School of Electrical Engineering and Computer Science Stockholm100 44 Sweden Scania CV AB Södertälje151 87 Sweden

As vehicle automation advances, motion planning algorithms face escalating challenges in achieving safe and efficient navigation. Existing Advanced Driver Assistance systems (ADAS) primarily focus on basic tasks, leaving unexpected scenarios for human intervention, which can be error-prone. Motion planning approaches for higher levels of automation in the state-of-the-art are primarily oriented toward the use of risk- or anti-collision constraints, using over-approximates of the shapes and sizes of other road users to prevent collisions. These methods however suffer from conservative behavior and the risk of infeasibility in high-risk initial conditions. In contrast, our work introduces a novel multi-objective trajectory generation approach. We propose an innovative method for constructing risk fields that accommodates diverse entity shapes and sizes, which allows us to also account for the presence of potentially occluded objects. This methodology is integrated into an occlusion-aware trajectory generator, enabling dynamic and safe maneuvering through intricate environments while anticipating (potentially hidden) road users and traveling along the infrastructure toward a specific goal. Through theoretical underpinnings and simulations, we validate the effectiveness of our approach. This paper bridges crucial gaps in motion planning for automated vehicles, offering a pathway toward safer and more adaptable autonomous navigation in complex urban contexts. Copyright © 2023, The Authors. All rights reserved.

关键词： Motion planning

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CNN-based approaches for cross-subject classification in motor imagery: From the state-of-the-art to DynamicNet

arXiv

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arXiv 2021年

作者： Zancanaro, Alberto Cisotto, Giulia Paulo, João Ruivo Pires, Gabriel Nunes, Urbano J. Dept. Information Engineering University of Padova Italy National Centre for Neurology and Psychiatry Tokyo Japan Padova Italy Institute of Systems and Robotics University of Coimbra Coimbra Portugal Engineering Department Polytechnic Institute of Tomar Tomar Portugal Department of Electrical and Computer Engineering University of Coimbra Coimbra Portugal

Motor imagery (MI)-based brain-computer interface (BCI) systems are being increasingly employed to provide alternative means of communication and control for people suffering from neuro-motor impairments, with a special effort to bring these systems out of the controlled lab environments. Hence, accurately classifying MI from brain signals, e.g., from electroencephalography (EEG), is essential to obtain reliable BCI systems. However, MI classification is still a challenging task, because the signals are characterized by poor signal-to-noise-ratio, high intra-subject and cross-subject variability. Deep learning approaches have started to emerge as valid alternatives to standard machine learning techniques, e.g., filter bank common spatial pattern (FBCSP), to extract subject-independent features and to increase the cross-subject classification performance of MI BCI systems. In this paper, we first present a review of the most recent studies using deep learning for MI classification, with particular attention to their cross-subject performance. Second, we propose DynamicNet, a Python-based tool for quick and flexible implementations of deep learning models based on convolutional neural networks. We show-case the potentiality of DynamicNet by implementing EEGNet, a well-established architecture for effective EEG classification. Finally, we compare its performance with FBCSP in a 4-class MI classification over public datasets. To explore its cross-subject classification ability, we applied three different cross-validation schemes. From our results, we demonstrate that DynamicNet-implemented EEGNet outperforms FBCSP by about 25%, with a statistically significant difference when cross-subject validation schemes are applied. We conclude that deep learning approaches might be particularly helpful to provide higher cross-subject classification performance in multi-class MI classification problems with respect to FBCSP. Moreover, in the future DynamicNet could be useful to imp

关键词： Brain computer interface

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Movement Detection and Moving Object Distinction Based on Optical Flow

Movement Detection and Moving Object Distinction Based on Op...

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2019 World Congress on engineering, WCE 2019

作者： Mendes, Paulo A.S. Mendes, Mateus Coimbra, A. Paulo Crisóstomo, Manuel M. Polytechnic Institute of Coimbra - ESTGOH Portugal ISR - Institute of Systems and Robotics Dept. of Electrical and Computer Engineering University of Coimbra Portugal

ISBN: (纸本)9789881404862

Detection of moving objects in sequences of images is an important research field, with applications for surveillance, tracking and object recognition among others. An algorithm to estimate motion in video image sequences, with moving object distinction and differentiation, is proposed. The motion estimation is based in three consecutive RGB image frames, which are converted to gray scale and filtered, before being used to calculate optical flow, applying Gunnar Farnebäck's method. The areas of higher optical flow are maintained and the areas of lower optical flow are discarded using Otsu's adaptive threshold method. To distinguish between different moving objects, a border following method was applied to calculate each object's contour. The method was successful detecting and distinguishing moving objects in different types of image datasets, including datasets obtained from moving cameras. © 2019 Newswood Limited. All rights reserved.

关键词： Object detection

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SAHRTA: A Supervisory-Based Adaptive Human-Robot Teaming Architecture

arXiv

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arXiv 2020年

作者： Heard, Jamison Fortune, Julian Adams, Julie A. Dept. of Electrical Engineering Rochester Institute of Technology RochesterNY School of Electrical Engineering and Computer Science Oregon State University CorvallisOR Collaborative Robotics and Intelligent Systems Institute Oregon State University CorvallisOR

Supervisory-based human-robot teams are deployed in various dynamic and extreme environments (e.g., space exploration). Achieving high task performance in such environments is critical, as a mistake may lead to significant monetary loss or human injury. Task performance may be augmented by adapting the supervisory interface’s interactions or autonomy levels based on the human supervisor’s workload level, as workload is related to task performance. Typical adaptive systems rely solely on the human’s overall or cognitive workload state to select what adaptation strategy to implement;however, overall workload encompasses many dimensions (i.e., cognitive, physical, visual, auditory, and speech) called workload components. Selecting an appropriate adaptation strategy based on a complete human workload state (rather than a single workload dimension) may allow for more impactful adaptations that ensure high task performance. A Supervisory-Based Adaptive Human-Robot Teaming Architecture (SAHRTA) that selects an appropriate level of autonomy or system interaction based on a complete real-time multi-dimensional workload estimate and predicted future task performance is introduced. SAHRTA was shown to improve overall task performance in a physically expanded version of the NASA Multi-Attribute Task Battery. Copyright © 2020, The Authors. All rights reserved.

关键词： Adaptive systems

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