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44th Annual Conference of the Association for Computer Aided Design in Architecture: Designing Change, ACADIA 2024

作者： Nejur, Andrei Szentesi-Nejur, Szende Taghavifard, Reza Balaban, Thomas Harrop, Patrick University of Montreal School of Architecture Laboratory for Architecture Informatics and Robotics LAIR Canada Laval University School of Architecture Canada University of Montreal School of Architecture Canada

ISBN: (纸本)9798989176472

Cnidaria is a research pavilion developed in the University of Montreal, School of Architecture ARC 6801H research master's studio and unveiled in May 2024. This 25 sqm shell showcases an innovative method for constructing large-span, doubly curved, loadbearing architectural surfaces using low-tech manual folding and snap-fit assembly. Its design, inspired by sea anemones, and sound focusing acoustic shells, emerged from a soap-bubble membrane simulation that optimizes material use by evenly distributing structural stress across the surface. Constructed from dual layers of 1mm-thick aluminum stripes, Cnidaria's outer layer acts as a structural and aesthetic membrane, providing its shelter function and handling axial loads. The inner layer, sparsely arranged, addresses bending and shearing stresses, with a biologically inspired pattern that educates and enhances the aesthetic. This approach combines lightweight, aesthetic qualities with structural integrity through a novel snap-fit connection that allows for easy assembly without requiring inter-layer access. The workflow employs novel, cutting-edge algorithms to efficiently segment complex curved geometries into non-overlapping, clash-free stripes, streamlining the design process. The constructed shell serves as a projection screen for digital art, highlighting its dual function as an electronic arts venue. © 2024 Association for Computer Aided Design in Architecture. All rights reserved.

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Segment-Based Trajectory Prediction and Risk Assessment for RSU-assisted CAVs at Signalized Intersections

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-19页

作者： Cao, Yue Shangguan, Wei Visser, Arnoud Chen, Junjie Chai, Linguo Cai, Baigen School of Automation and Intelligence Beijing Jiaotong University Beijing China School of Automation and Intelligence and State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China Intelligent Robotics and Computer Vision Lab of the Informatics Institute Faculty of Science University of Amsterdam The Netherlands

Detecting surrounding situations and reacting accordingly to avoid collisions remains a challenging task for autonomous driving. This task requires predicting the trajectories of surrounding agents and assessing the potential risk of future situations, which can be difficult to achieve solely through onboard vehicle devices. Therefore, this paper proposes a cooperative architecture for trajectory prediction and risk assessment conducted on roadside devices (RSUs) to assist Connected and Autonomous Vehicles (CAVs). Firstly, we develop a segmentbased prediction model (SegNet) tailored to hub signalized intersections. Intersections are divided into multiple segments, and the Curvilinear coordinates are utilized to indicate the geometric road features. The model leverages individual interaction cues in the ego segment and group features in the merging segments, while also incorporating traffic signal information to generate multimodal prediction results. In terms of risk assessment, we utilize the prediction results to provide hierarchical assistance, such as risk values, risk maps, and reference trajectories. Offline experimental results demonstrate that our SegNet model achieves competitive and well-balanced performance compared to stateof-the-art methods on the CitySim Database, with more accurate and smooth prediction trajectories. Through real-time CARLA and SUMO co-simulation, the performance of assisted CAVs indicates that they can safely and effectively navigate with the support of the proposed architecture. IEEE

关键词： Real time systems

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Human-Social Robot Online Co-Tutoring Enhances Students’ Learning & Engagement: Interaction Guidelines

Research Square

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Research Square 2024年

作者： Velentza, Anna-Maria Lefkos, Ioannis Fachantidis, Nikolaos Laboratory of Informatics and Robotics Applications in Education and Society – LIRES University of Macedonia Greece

— Online classes are increasingly gaining popularity in both children's and adult education, although, the challenge of the students’ engagement remains. Evidence has shown that co-teaching is beneficial for both tutors and students, however, especially on a university level, tutors are not always able to co-teach classes. Additionally, socially assistive robots (SAR) have been proven advantageous in educational roles, as they deliver teaching material and provide support to students across various educational levels. This research paper specifically examines the performance of SAR in place of university co-tutors in online classes, in the field of computer science, measuring three outcomes, knowledge acquisition (academic test), enjoyment level (questionnaire), and engagement (qualitative and quantitative analysis of verbal and chat discussions and threads during class). We also aim to fill in the gap of having structured human-robot collaboration guidelines for educational environments. This will be accomplished through a sequence of three experiments: 1. A co-tutored lecture involving one human and one robot, 2. A co-tutored lecture involving two humans, 3. A second instance of a co-tutored lecture involving both human and robot. Results indicated that students taught by a collaborative team of human and robot tutors exhibited a greater level of knowledge acquisition, while maintaining comparable levels of enjoyment, in comparison to those instructed by a collaborative team of two human tutors. Moreover, engagement remained high during the lectures, and we are addressing the differences in students’ communication channels and discussed topics when lectured by two humans vs a human and a robot. Finally, the human-robot collaboration managed to eliminate the negative outcomes of the presence of a robot, such as the surprise effect based on the literature the students face the first time they are being lectured by a robot-tutor. © 2024, CC BY.

关键词： Adversarial machine learning

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6-DOF AUV Identification with MIMO-NARX

6-DOF AUV Identification with MIMO-NARX

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2023 International Conference on Industrial Engineering, Applications and Manufacturing, ICIEAM 2023

作者： Kabanov, Aleksey A. Lipko, Ivan Y. Dementiev, Kirill V. Sevastopol State University Department of Informatics and Control in Technical Systems Sevastopol Russia Sevastopol State University Robotics and Intelligent Control System Laboratory Sevastopol Russia

ISBN: (纸本)9781665475952

This paper applies the MIMO NARX neural model to identify AUV with six degrees of freedom. Strongly nonlinear dependencies are modeled using a MIMO NARX model trained using «input-output» experimental data. This results in a grey box model with a partially known internal structure that can be supervised. This is achieved by specifying a certain regressors type, which is feedback to the model input. Obtained results show that the neural model trained by the Levenberg-Marquardt numerical algorithm provides acceptable performance and accuracy. Validation of the model was performed using data from full-scale experiments, which allowed to identify the weaknesses and advantages of the resulting system more accurately. © 2023 IEEE.

关键词： Degrees of freedom (mechanics)

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A Position-Force Control Approach for an Underwater Vehicle-Manipulator System

A Position-Force Control Approach for an Underwater Vehicle-...

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2023 International Conference on Ocean Studies, ICOS 2023

作者： Kabanov, Aleksey A. Dementiev, Kirill V. Sevastopol State University Department of Informatics and Control in Technical Systems Sevastopol Russia Sevastopol State University Robotics and Intelligent Control System Laboratory Sevastopol Russia

ISBN: (纸本)9798350382457

The paper proposes an approach to position-force control in the underwater vehicle-manipulator systems. There are two different strategies to realize position-force control systems: through a decoupled or coupled controller. The first one is more flexible and is used in the present article as a PID-controllers array. The paper considers the generalized system design methodology applicable to control objects of any kinematic structure. Compensation blocks for manipulator disturbances and calculation of additional end-effector offset to maintain a constant force on the operating point are proposed. The system also utilizes the desired underwater vehicle position calculation based on the relativity of the vehicle position and operating point for a heading set point. The modeling results showed that the system is able to perform qualitatively force operations in a given point of the working space. In addition, the graphs revealed the compensator unit efficiency and confirmed the necessity of the additional offset calculation unit. © 2023 IEEE.

关键词： Manipulators

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‘Do you trust me with your secret if I wear the clothes you made for me’? Kids knitting clothes for SARs and choosing one to share a secret wish

‘Do you trust me with your secret if I wear the clothes you...

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2024 ALTRUIST Workshop on SociAL RoboTs for PeRsonalized, ContinUous and AdaptIve ASsisTance, BAILAR Workshop on Behavior Adaptation and Learning for Assistive robotics, SCRITA Workshop on Trust, Acceptance and Social Cues in Human-Robot Interaction, and WARN Workshop on Weighing the Benefits of Autonomous Robot PersoNalisation, ALTRUIST-BAILAR-SCRITA-WARN 2024

作者： Gioumatzidou, Sofia Velentza, Anna-Maria Fischer, Katrin Fachantidis, Nikolaos School of Educational & Social Policies University of Macedonia Thessaloniki Greece Laboratory of Informatics and Robotics in Education and Society Thessaloniki Greece Communication Department University of Southern California Los AngelesCA United States

Recent studies in child-robot interaction indicate a propensity for children to trust robots with humanoid characteristics, fostering social relationships that enhance interaction quality. Trust is fundamental in Human-Robot Interaction (HRI) and can be affected by parameters such as the robot’s appearance, texture, or performed activity. In the current paper, we investigate whether children’s trust in a SAR, specifically the Nao robot, increases after they design and dress it with handmade clothes using the felting technique. Children had the chance to interact with robots both naked and dressed in and without their creations and share a secret wish with one of them. Behavioral observations and wish content analysis provided insights into children’s emotional engagement and trust preferences. Most children chose to confide in the robot wearing their crafted outfit, using wish statements that reflect aspirational desires, contrasting with immediate wants expressed to the’naked’ robot. This study underscores the role of co-design and initial interaction quality in fostering trust and emotional engagement between children and SARs, highlighting avenues for enhancing HRI outcomes. © 2024 Copyright for this paper by its authors.

关键词： Social robots

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Multi-Class Gait Phase Recognition using Machine Learning Models with Two Training Methods 15

Multi-Class Gait Phase Recognition using Machine Learning Mo...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Mekni, Amal Narayan, Jyotindra Gritli, Hassene Borj Cedria Tunis1164 Tunisia Indian Institute of Technology Guwahati Mechtronics and Robotics Laboratory Department of Mechanical Engineering Assam Guwahati781039 India University of Tunis El Manar le Belvédère Laboratory of Robotics Informatics and Complex Systems RISC Lab-LR16ES07 National Engineering School of Tunis Tunis1002 Tunisia

ISBN: (纸本)9798350370249

Walking is a fundamental human activity, yet understanding its intricacies is crucial for diagnosing and treating various gait abnormalities and musculoskeletal disorders. This study investigates the machine learning-based classification of gait phases into five subphases with two training methods. Using data from 100 individuals obtained from an open-source platform. The gait cycle was divided into the following subphases: Initial Contact, Loading Response, Mid Stance, Terminal Stance, and Pre-Swing. In the first approach, a stratified random sampling method was employed, allocating 80% of data from each subphase for training and 20% for testing. The second method involved training with data from 80% participants and testing with data from 20% participants. Out of different ML algorithms, the Random Forest (RF) achieves the highest performance (99.2%) with similar results across both methods. The k-Nearest Neighbors (k-NN), the Logistic Regression (LR), the Decision Tree (DT), and the Support Vector Machine (SVM) show comparable performance ( 91%) with marginal improvement with the second method. Naive Bayes (NB) has a significantly lower performance ( 77.7%). These findings suggest that RF is a robust and effective algorithm for this task. Furthermore, the study indicates that applying machine learning techniques to datasets divided by subjects presents promising opportunities for creating precise gait analysis systems. This can assist clinicians and researchers in enhancing patient care and gaining deeper insights into human locomotion. © 2024 IEEE.

关键词： Adversarial machine learning

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Vascular-confined multi-passage discoidal nanoconstructs for the low-dose docetaxel inhibition of triple-negative breast cancer growth

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Nano Research 2022年第1期15卷 482-491页

作者： Alessia Felici Daniele Di Mascolo Miguel Ferreira Simone Lauciello Luca Bono Andrea Armirotti Arunkumar Pitchaimani Anna Lisa Palange Paolo Decuzzi Laboratory of Nanotechnology for Precision Medicine Fondazione Istituto Italiano di TecnologiaGenoa 16163Italy Department of Informatics BioengineeringRobotics and System EngineeringUniversity of GenoaGenoa 16126Italy Nanochemistry Laboratory Fondazione Istituto Italiano di TecnologiaGenoa 16163Italy Analytical Chemistry Laboratory Fondazione Istituto Italiano di TecnologiaGenoa 16163Italy

Taxane efficacy in triple negative breast cancer(TNBC)is limited by insufficient tumor accumulation and severe off-target *** offer a unique opportunity to enhance the anti-cancer potency of this ***,1,000 nm×400 nm discoidal polymeric nanoconstructs(DPN)encapsulating docetaxel(DTXL)and the near infrared compound Iipid-Cy5 were *** were obtained by filling multiple times cylindrical wells in a poly(vinyl alcohol)template with a polymer mixture comprising poly(lactic-co-glycolic acid)(PLGA)and poly(ethylene glycol)diacrylate(PEG-DA)chains together with therapeutic and imaging *** resulting“multi-passage”DPN exhibited higher DTXL loading,Iipid-Cy5 stability,and stiffness as compared to the conventional"single-passage"*** microscopy confirmed that DTXL-DPN were not taken up by MDA-MB-231 cells but would rather sit next to the cell membrane and slowly release DTXL *** DPN had no toxicity on TNBC cells,whereas DTXL-DPN presented a cytotoxic potential comparable to free DTXL(IC_(50)=2.6 nM±1.0 nM vs.7.0 nM±1.09 nM at 72 h).In orthotopic murine models,DPN accumulated in TNBC more efficiently than *** only 2 mg/kg DTXL,intravenously administered every 2 days for a total of 13 treatments,DTXL-DPN induced tumor regression and were associated to an overall 80%survival rate as opposed to a 30%survival rate for free-DTXL,at 120 *** untreated mice succumbed before 90 ***,this data demonstrates that vascular confined multi-passage DPN,biomimicking the behavior of circulating platelets,can efficiently deliver chemotherapeutic molecules to malignant tissues and effectively treat orthotopic TNBC at minimal taxane doses.

关键词： hydrogel particles template strategy vascular targeting biomimicry cancer therapy

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Classification of a nanocomposite using a combination between Recurrent Neural Network based on Transformer and Bayesian Network for testing the conductivity property

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Expert Systems with Applications 2025年 270卷

作者： Gazehi, Wejden Loukil, Rania Besbes, Mongi Higher Institute of Information and Communication Technologies University of Carthage Tunisia Laboratory of Robotics Informatics and Complex Systems University of Tunis Manar Tunisia

To enhance trust and adoption of machine learning models, particularly neural Networks, it is crucial to develop methods that allow quantifying and reporting epistemic uncertainties and the random uncertainties which lead to the non accuracy of RNN. To transcend these boundaries, we suggest to use Bayesian techniques, where probability distributions are employed to represent not only the model predictions but also the uncertainty associated with these predictions. Our study encapsulates the use of Bayesian Networks, RNNs based on the transformer and propose an hybrid approach for improving the classification of nanocomposites for evaluating the conductivity. In fact, it presents a comprehensive approach to classify nanocomposites using advanced Deep Learning techniques. Initially, a Bayesian Network (BN) model is employed to analyze and classify the nanocomposites, providing probabilistic insights into their conductive behavior based on the distribution and interaction of nanoparticles. Subsequently, a Recurrent Neural Network (RNN) based on the Transformer architecture is used to enhance the classification accuracy by capturing the sequential dependencies and complex patterns in the data. Finally, we propose a hybrid model that combines the strengths of both Bayesian Networks and RNNs. This integrated approach leverages the probabilistic reasoning of BNs and the Deep Learning capabilities of RNNs to achieve a more robust and accurate classification framework. The experimental results based on metric values indicate that the hybrid model significantly outperforms individual models, providing a powerful tool for the predictive classification of nanocomposites’ conductivity. © 2025 Elsevier Ltd

关键词： Distribution transformers

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A Multi - Task Control Approach for Active-Assisting Rehabilitation

A Multi - Task Control Approach for Active-Assisting Rehabil...

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International Conference on Advanced Systems and Electric Technologies (IC_ASET)

作者： Afef Hfaiedh Sondes Jaziri Nahla Khraief Safya Belghith Laboratory of Robotics Informatics and Complex Systems (RISC lab-LR16ES07) University of Tunis EL Manar ENIT Tunisia Laboratory of Robotics Informatics and Complex Systems (RISC lab-LR16ES07) Tunisia

ISBN: (数字)9798350384895

ISBN: (纸本)9798350384901

Control of robotic exoskeleton presents an inter-esting research topic in the last years. However, in the field of healthcare, making informed decisions can be challenging because there is not enough data available describing real-world scenarios. In these cases, we can profit from intelligent algorithms that learn from data and aid decision-making to obtain more robust and reliable decisions. In this work, we propose a control approach for active-assisting rehabilitation. It is based on Reinforcement Learning method namely PILCO, short for Probabilistic Inference for Learning Control. It overcomes the high data challenge and does not require an extensive amount of data to design a model that closely approximates the real-world environment. However, PILCO is limited to learn only single task. Therefore, to alleviate the user's workload in the learning process across multiple tasks, we generalize the predicted policy to encompass novel tasks. The novelty in the proposed approach is that the set of tasks used to test the method are predicted from an electromyography signals by the radial basis function (RBF) algorithm. The test of the proposed approach is applied in a simulation environment of a one Degree of Freedom I-DoF elbow-exoskeleton (EE). The simulation results prove the efficiency of the method and show that tasks performed are most often close to target positions.

关键词： Training Simulation Exoskeletons Reinforcement learning Prediction algorithms Search problems Electromyography

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