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Deep-Learning Based Classification of Engagement for Child-Robot Interaction

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Deep-Learning Based Classification of Engagement for Child-R...

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

作者： Gabriela Simões Ana Lopes Carlos Carona Ricardo Pereira Urbano J. Nunes Department of Electrical and Computer Engineering Institute of Systems and Robotics University of Coimbra Portugal Polytechnic Institute of Tomar Portugal Center for Research in Neuropsychology and Cognitive and Behavioral Intervention University of Coimbra

This work aims to describe the research and development of an engagement classification system based on emotion recognition through the use of facial expressions, to be used in robotic applications for children interacting with a NAO humanoid robot during therapy sessions. An emotion classification model based on different Convolution Neural Networks (CNN) architectures was first fed by faces provided from different public datasets to train and validate the emotion classification models. Two engagement classification methodologies are proposed: Method-1 first classifies 7-classes of emotions, using the ConvNeXt network, and then infers the levels of engagement based on the affective model developed by [1]; Method-2 employs the same classification network to directly classify the four levels of engagement. The results obtained for both engagement classification methodologies using the CAFE dataset proved their effectiveness, however Method-1 performs slightly better in terms of engagement classification accuracy. A novel child-robot activity with a NAO humanoid robot, that consists in a breathing-based relaxation exercise, is also proposed and the results of offline classification system showed to be promising, and with potential to be applied in psychology therapies in the future.

关键词： Emotion recognition Convolution Neural networks Medical treatment Humanoid robots Psychology Human-robot interaction

GEOMETRIC DATA FUSION FOR COLLABORATIVE ATTITUDE ESTIMATION

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arXiv 2024年

作者： Ge, Yixiao Zamani, Behzad van Goor, Pieter Trumpf, Jochen Mahony, Robert Systems Theory and Robotics Group School of Engineering Australian National University Canberra Australia Department of Electrical and Electronic Engineering University of Melbourne Melbourne Australia Group EEMCS Faculty University of Twente Enschede Netherlands School of Engineering Australian National University Canberra Australia

In this paper, we consider the collaborative attitude estimation problem for a multi-agent system. The agents are equipped with sensors that provide directional measurements and relative attitude measurements. We present a bottom-up approach where each agent runs an extended Kalman filter (EKF) locally using directional measurements and augments this with relative attitude measurements provided by neighbouring agents. The covariance estimates of the relative attitude measurements are geometrically corrected to compensate for relative attitude between the agent that makes the measurement and the agent that uses the measurement before being fused with the local estimate using the convex combination ellipsoid (CCE) method to avoid data incest. Simulations are undertaken to numerically evaluate the performance of the proposed algorithm. Copyright © 2024, The Authors. All rights reserved.

关键词： Extended Kalman filters

Learning-based Balancing of Model-based and Feedback Control for Second-order Mechanical systems

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Learning-based Balancing of Model-based and Feedback Control...

IEEE Conference on Decision and Control

作者： Thomas Beckers Leonardo J. Colombo Manfred Morari George J. Pappas Department of Electrical and Systems Engineering University of Pennsylvania Philadelphia USA Centre for Automation and Robotics (CSIC-UPM) Madrid Spain

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

High-performance tracking control of mechanical systems typically requires model-based control as it enables to counteract undesirable dynamics in a timely fashion. The quality of the compensation depends on the accuracy of the underlying model. However, the dynamics are often (partially) infeasible for complex systems in a-priori unknown environments. Due to the favorable properties of model-based control, the goal is to apply feedback control for error compensation only when necessary. In this paper, we present an online learning-based balancing (OLBB) framework to adapt the feedback gains such that the controlled system with state-dependent uncertainties satisfies given performance specifications. For this purpose, an oracle predicts the unknown dynamics and an error model adapts the feedback gains. The framework can be easily applied to existing control approaches to improve the safety and performance of the closed-loop.

关键词： Adaptation models Uncertainty Torque control Training data Predictive models Control systems Feedback control

Microfluidic Formation of Porous Microgels with Interior Spiral Canals for Mesenchymal Stem Cell Culture

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Microfluidic Formation of Porous Microgels with Interior Spi...

IEEE International Conference on Cyborg and Bionic systems (CBS)

作者： Zhen Zhan Tanyong Wei Zhan Yang Chengzhi Hu Department of Mechanical and Energy Engineering Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Southern University of Science and Technology Shenzhen China Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen China School of Mechanical and Electrical Engineering Soochow University Suzhou China

ISBN: (数字)9798350388039

ISBN: (纸本)9798350388046

Stem cell delivery is pivotal in tissue engineering, enabling the regeneration and repair of damaged tissues and advancing therapeutic solutions. As an innovative stem cell delivery system, microgels provide a supportive, protective environment that enhances cell viability and promotes targeted tissue regeneration. However, conventional microgels face limitations due to the lack of microporous structures and interior canals, which hinder efficient nutrient and oxygen exchange, thereby reducing cell viability and proliferation. To overcome these challenges, we developed a droplet microfluidic system for the stable and continuous production of porous microgels with interior spiral canals by introducing a pore-forming agent. This controlled formation process results in microgels with excellent porosity and monodispersity. With their unique structure and biocompatibility, these porous microgels are excellent microcarriers for 3D cell culture, enhancing nutrient and oxygen exchange and providing a conducive environment for cell growth and proliferation.

关键词： Irrigation Spirals Three-dimensional displays Microprocessors Tissue engineering Computer architecture Stem cells Nutrients Microfluidics Surface treatment

Approaching Metaheuristic Deep Learning Combos for Automated Data Mining

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arXiv 2024年

作者： Assunção, Gustavo Menezes, Paulo Institute of Systems and Robotics University of Coimbra R. Silvio Lima Coimbra3030-194 Portugal Department of Electrical and Computer Engineering University of Coimbra R. Silvio Lima Coimbra3030-194 Portugal

Lack of data on which to perform experimentation is a recurring issue in many areas of research, particularly in machine learning. The inability of most automated data mining techniques to be generalized to all types of data is inherently related with their dependency on those types which deems them ineffective against anything slightly different. Meta-heuristics are algorithms which attempt to optimize some solution independently of the type of data used, whilst classifiers or neural networks focus on feature extrapolation and dimensionality reduction to fit some model onto data arranged in a particular way. These two algorithmic fields encompass a group of characteristics which when combined are seemingly capable of achieving data mining regardless of how it is arranged. To this end, this work proposes a means of combining meta-heuristic methods with conventional classifiers and neural networks in order to perform automated data mining. Experiments on the MNIST dataset for handwritten digit recognition were performed and it was empirically observed that using a ground truth labeled dataset’s validation accuracy is inadequate for correcting labels of other previously unseen data instances. © 2024, CC BY.

关键词： Adversarial machine learning

Self-Organized Sensor Eggs for Decentralized Localization and Sensing on Vulcano Island – A Glimpse into Future Space Exploration with Swarms

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Self-Organized Sensor Eggs for Decentralized Localization an...

International Conference on Information Fusion

作者： Fabio Broghammer Thomas Wiedemann Siwei Zhang Armin Dammann Christian Gentner Petar Djuric Institute of Communications and Navigation German Aerospace Center (DLR) Germany Perception for Intelligent Systems Munich Institute of Robotics and Machine Intelligence TU Munich Germany Department of Electrical and Computer Engineering Stony Brook University USA

ISBN: (数字)9781737749769

ISBN: (纸本)9798350371420

Robotic swarms or portable sensor networks are emerging technologies for sensing physical processes that are spatially distributed- and temporally dynamic, both on Earth and in future Moon/Mars exploration missions. We develop a portable network composed of a multitude of self-organized “sensor eggs”. These eggs are equipped with ultra-wideband (UWB) transceivers, providing precise time and position information without additional infrastructures like Global Navigation Satellite systems (GNSSs). Each egg is additionally equipped with environmental sensors, for example, a Sulfur dioxide gas sensor to explore volcanic activity. We use a real time decentralized particle filter (DPF) to estimate the a-posteriori probability density functions (PDFs) of the egg positions. These PDFs are then used in a static state binary Bayes filter for estimating the gas sources with potentially complex structures such as cracks on the volcano surface. The proposed sensor network is verified with an in-field experiment at La Fossa volcano on the island of Vulcano, Italy, in 2023.

关键词： Location awareness Volcanic activity Probability density function Robot sensing systems Particle filters Transceivers Sensors Timing Surface cracks Ultra wideband technology

Continuous-Time Ultra-Wideband-Inertial Fusion

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arXiv 2023年

作者： Li, Kailai Cao, Ziyu Hanebeck, Uwe D. The Division of Automatic Control Department of Electrical Engineering Linköping University Sweden The Intelligent Sensor-Actuator-Systems Laboratory Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Germany

We introduce a novel framework of continuous-time ultra-wideband-inertial sensor fusion for online motion estimation. Quaternion-based cubic cumulative B-splines are exploited for parameterizing motion states continuously over time. Systematic derivations of analytic kinematic interpolations and spatial differentiations are further provided. Based thereon, a new sliding-window spline fitting scheme is established for asynchronous multi-sensor fusion and online calibration. We conduct a dedicated validation of the quaternion spline fitting method, and evaluate the proposed system, SFUISE (spline fusion-based ultra-wideband-inertial state estimation), in real-world scenarios using public data set and experiments. The proposed sensor fusion system is real-time capable and delivers superior performance over state-of-the-art discrete-time schemes. We release the source code and own experimental data at https://***/KIT-ISAS/SFUISE . Copyright © 2023, The Authors. All rights reserved.

关键词： Motion estimation

Choosing the suitable working fluid to recover heat from low-temperature sources 3

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Choosing the suitable working fluid to recover heat from low...

3rd International Youth Conference on Radio Electronics, electrical and Power engineering, REEPE 2021

作者： Dakkah, Baydaa Bo Sultanguzin, Ildar A. Yavorovsky, Yury V. Badran, Bassam E. Tina, Hussein A. Alsabbagh, Mohammad Yman Yassin National Research University MPEI Industrial Thermal Engineering Systems Department Moscow Russia Damascus University Faculty of Mechanical Electrical General Mechanical Engineering Department Damascus Syria

ISBN: (纸本)9781728183985

A considerable amount of heat is wasted in many industrial applications at 180 to 250°C temperature levels that can be recycled and reduce the Green-House Gases (GHGs) effects. Organic Rankine Cycle (ORC), nowadays, provides a feasible and efficient recovery solution for these applications. In this research paper, we have investigated and studied the effect of several working fluids (R22, R134a, R123, R245fa, and Acetone, Water, Ammonia) on the cycle thermal efficiency with respect to the heat source temperature. The research showed that the ORC thermal efficiency and the working fluids that could be used depends on the temperature of the heat source primarily. The research also showed the working fluids can be compared in each thermal range and then choose the most suitable working fluid and its thermal efficiency. © 2021 IEEE.

关键词： Temperature

A Hybrid Training-time and Run-time Defense Against Adversarial Attacks in Modulation Classification

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arXiv 2024年

作者： Zhang, Lu Lambotharan, Sangarapillai Zheng, Gan Liao, Guisheng Demontis, Ambra Roli, Fabio School of Electronic Engineering Xidian University Xi’an710071 China Wolfson School of Mechanical Electrical and Manufacturing Engineering Loughborough University Loughborough United Kingdom Dept. of Electrical and Electronic Engineering University of Cagliari Piazza d’Armi Cagliari09123 Italy Department of Informatics Bioengineering Robotics and Systems Engineering University of Genova Genoa16145 Italy

Motivated by the superior performance of deep learning in many applications including computer vision and natural language processing, several recent studies have focused on applying deep neural network for devising future generations of wireless networks. However, several recent works have pointed out that imperceptible and carefully designed adversarial examples (attacks) can significantly deteriorate the classification accuracy. In this paper, we investigate a defense mechanism based on both training-time and run-time defense techniques for protecting machine learning-based radio signal (modulation) classification against adversarial attacks. The training-time defense consists of adversarial training and label smoothing, while the run-time defense employs a support vector machine-based neural rejection (NR). Considering a white-box scenario and real datasets, we demonstrate that our proposed techniques outperform existing state-of-the-art technologies. Copyright © 2024, The Authors. All rights reserved.

关键词： Support vector machines