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IEEE Latin American Robotics Symposium, LARS

作者： Guilherme Reis Miguel Marcos R. O. A. Maximo Izabela Batista Henriques Autonomous Computational Systems Lab Computer Science Division Aeronautics Institute of Technology São Josè dos Campos SP Brazil Mechanical Engineering Division Aeronautics Institute of Technology São Josè dos Campos SP Brazil

ISBN: (数字)9781665462808

ISBN: (纸本)9781665462815

This paper presents a study of the thermophysical properties of electric motors and of the structure of the robots used by ITAndroids' Very Small Size (VSS) team. To perform the modeling of the robot structure, the General Lumped Capacitance Analysis was used, assuming that the motor's internal temperature can be considered approximately uniform. This hypothesis was validated by means of Biot's Number, and the heat source term was evaluated in order to define which energy dissipation sources would be considered in the modeling. The obtained model was evaluated through numerical simulations to analyze the motors' temperature profile for different current values. We compared the obtained results with similar experiments found in the literature, concluding that the model could predict the system's behavior in a satisfactory manner. Therefore, this work contributes to the research area by providing a model able to predict the temperature of the electric motor under different workloads, thus, allowing the team to anticipate its overheating and prevent its premature burnout.

关键词： Analytical models Biological system modeling Windings Predictive models Capacitance Numerical models Thermal analysis

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Comparison of Encoder-Decoder Networks for Soccer Field Segmentation

Comparison of Encoder-Decoder Networks for Soccer Field Segm...

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IEEE Latin American Robotics Symposium, LARS

作者： Otávio H. R. Guimarães Marcos R. O. A. Maximo Jose Maria Parente de Oliveira École polytechnique Institut Polytechnique de Paris Route de Saclay Palaiseau Île-de-France France Autonomous Computational Systems Lab Computer Science Division Aeronautics Institute of Technology Praça Marechal Eduardo Gomes 50 Vila das Acaciás São José dos Campos SP Brazil Computer Science Division Aeronautics Institute of Technology Praça Marechal Eduardo Gomes 50 Vila das Acaciás São José dos Campos SP Brazil

Convolutional neural networks consist of state-of the-art models used for the solution of computer vision problems. This paper contributes by evaluating the efficiency of several encoder-decoder neural networks, trained to perform the segmentation of the soccer field in Humanoid KidSize Robot Soccer competitions. To compare the efficiency of several encoders, a total of fourteen neural network models, based on the U-Net and SegNet architectures, were tested and compared in terms of accuracy, cost function value, IoU, and average inference time. Based on that, the networks based on U-Net that utilized the MobileNetv3Small or the ResNet18 for the encoding process were found to be the optimal solution among the considered alternatives to segment the soccer field.

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Human Resource Management based Economic analysis using Data Mining 3

Human Resource Management based Economic analysis using Data...

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3rd International Conference on Intelligent Engineering and Management, ICIEM 2022

作者： Sindhura, K. Sabarirajan, A. Kumar Siddamallappa, U. Narang, Pravin Bhanushali, Mahesh Manohar Turai, Anjaneya Krishna Studies University of Madras Department of Management Tamilnadu Chennai India PSNA College of Engineering & Technology Department of Management Studies Tamilnadu Dindigul India Devangere University DOS in Computer Science Karnataka Davanagere India VPM's Dr. V. N. Bedekar Institute of Management Studies Management Studies Maharashtra Thane India Data Science Student Data Science Symbiosis Skill and Professional University Maharashtra Mumbai India

ISBN: (数字)9781665467568

ISBN: (纸本)9781665467568

Researchers in this field are looking at how data mining and HRM are intertwined to manage human resources better. An Ensemble Classifier-Decision Tree (EC-DT) approach may analyse Human Resource Management (HRM) data. C4.5, Random Tree, J48, and Simple Cart are the single decision tree algorithms employed. Based on the algorithm design, algorithms for evaluation and talent recommendation are then assessed in an HRM system. The algorithm under development is put through its last paces in terms of comparisons and testing. The EC-DT method has a classification accuracy of 79.97 %, whereas C4.5 has a classification accuracy of 76.69 %. DMRM's accuracy and recall were 35.2 % and 41.6 % better than CRM's and CFR's combined, proving that data mining may be a powerful tool for recommending products and services (CFRM). Thus, the data mining-based human resources management system may help organisations expand due to quantitative analyses. In this way, the data mining-based HRM system may encourage and direct businesses to grow under the outcomes of quantitative assessments. A starting point for further research on data mining-based HRM systems may be found in the abovementioned findings © 2022 IEEE.

关键词： Data mining

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Exploring DeepDream and XAI Representations for Classifying Histological Images

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SN computer science 2024年第4期5卷 362页

作者： Martinez, João Manoel Cardoso Neves, Leandro Alves Longo, Leonardo Henrique da Costa Rozendo, Guilherme Botazzo Roberto, Guilherme Freire Tosta, Thaína Aparecida Azevedo de Faria, Paulo Rogério Loyola, Adriano Mota Cardoso, Sérgio Vitorino Silva, Adriano Barbosa do Nascimento, Marcelo Zanchetta Department of Computer Science and Statistics (DCCE) São Paulo State University (UNESP) Rua Cristóvão Colombo 2265 SP São José do Rio Preto 15054-000 Brazil Department of Informatics Engineering - Faculty of Engineering University of Porto Dr. Roberto Frias sn Porto 4200-465 Portugal Science and Technology Institute (ICT) Federal University of São Paulo (UNIFESP) Av. Cesare Mansueto Giulio Lattes 1201 SP São José dos Campos 12247-014 Brazil Department of Histology and Morphology Institute of Biomedical Science Federal University of Uberlândia (UFU) Av. Amazonas S/N MG Uberlândia 38405-320 Brazil Area of Oral Pathology School of Dentistry Federal University of Uberlândia (UFU) R. Ceará - Umuarama MG Uberlândia 38402-018 Brazil Faculty of Computer Science (FACOM) Federal University of Uberlândia (UFU) Av. João Naves de Ávila 2121 MG Uberlândia 38400-902 Brazil

The application of machine learning methods to analyze histological images has led to significant advances in computer-aided diagnosis. In this work, a study was carried out to classify patterns present in histological samples stained with hematoxylin-eosin, representative of breast cancer, colorectal cancer, liver tissue, and oral dysplasia. The proposed method explored convolutional neural networks (VGG19, ResNet50, InceptionV3, and EfficientNet B2), gradient-weighted class activation mapping, local interpretable model-agnostic explanations and representations based on the inceptionism effect (DeepDream). The deep features were analyzed via multiple classifiers (rotation forest, multilayer perceptron, logistic, random forest, decorate, k-nearest neighbor, KStar, and support vector machine) to indicate the main associations. The best solution was obtained through the ResNet50 model with the representations from the VGG19 architecture, exploring feature vectors with 40 and 100 attributes. The results were AUC rates ranging from 0.994 (breast cancer dataset) to 1.0 (colorectal dataset). The most explored deep features were obtained via original images (highest occurrence), followed by local interpretable model-agnostic explanations, DeepDream, and gradient-weighted class activation mapping representations. These results can support the development of techniques addressed to pattern recognition of H &E images with more robust computer-aided diagnoses. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Deep features DeepDream representations Histological images XAI

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Deep Reinforcement Learning for Humanoid Robot Dribbling∗∗Alexandre Muzio acknowledges CAPES for his scholarship (number 88882.161989/2017-01). Takashi Yoneyama would like to thank CNPq - National Research Council of Brasil for the grant 304134/2-18-0. The authors thank ITAndroids' sponsors: Altium, Cenic, Intel, ITAEx, MathWorks, Metinjo, Micropress, Polimold, Rapid, SolidWorks, STMicroelectronics, Wildlife Studios, and ***. 17

Deep Reinforcement Learning for Humanoid Robot Dribbling∗∗...

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17th Latin American Robotics Symposium, 12th Brazilian Symposium on Robotics and 11th Workshop on Robotics in Education, LARS-SBR-WRE 2020

作者： Muzio, Alexandre F. V. Maximo, Marcos R.O. A. Yoneyama, Takashi Computer Science Division Sào José Dos Campos Sp 12228-900 Brazil Computer Science Division Sào José Dos Campos Sp 12228-900 Brazil Aeronautics Institute of Technology Praça Marechal Eduardo Gomes Electronic Engineering Division Sào José dos Campos SP12228-900 Brazil

ISBN: (纸本)9780738111537

Humanoid robot soccer is a very traditional competitive task that aims to push the boundaries of state-of-the-art robotics. One of the many challenges of playing soccer is walking and running while not losing balance. Deep Reinforcement Learning (DRL) has been used to solve complex continuous control problems such as those in robotics. In this work, we focused on learning humanoid robot behavior to dribble a ball against a single opponent. Instead of learning how to control joint commands directly, we adopt an approach where the learning agent interacts with a predefined walking engine. Using DRL model-free algorithms (namely, Deep Deterministic Policy Gradients, Trust Region Policy Optimization, and Proximal Policy Optimization), we effectively learn a high level policy that allows a humanoid robot to fulfill this task. Finally, the learned dribble policy was evaluated on a simulated Nao robot from the RoboCup 3D Soccer Simulation League. According to our results, the learned agent was able to surpass the hand-coded behavior effectively used by the ITAndroids robotics team in the RoboCup competition. © 2020 IEEE.

关键词： Anthropomorphic robots

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Deep Neural Network Algorithm to Control a Curved Kicking Mechanism in RoboCup Small Size League

Deep Neural Network Algorithm to Control a Curved Kicking Me...

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IEEE Latin American Robotics Symposium, LARS

作者： Francisco A. B. Azevedo Guilherme P. C. Leão Marcos R. O. A. Maximo Aeronautics Engineering Division Aeronautics Institute of Technology São José dos Campos Brazil Electronic Engineering Division Aeronautics Institute of Technology São José dos Campos Brazil Autonomous Computational Systems Lab (LAB-SCA) Computer Science Division Aeronautics Institute of Technology São José dos Campos Brazil

ISBN: (数字)9781665462808

ISBN: (纸本)9781665462815

Small Size League (SSL) is a robotics soccer league at the Robotics World Cup, RoboCup. In this league, the proposition of a mechanism capable of performing unpredictable kicks and passes inspired works of both the physical mechanism to implement this and the algorithms to extract the most of this mechanism. The present work's contribution is an improvement on a previously algorithm that aims to perform a real-time inversion of the non-linear ordinary differential equation that models the ball's trajectory to find the parameters to hit a target with the curved kick mechanism, by adding new concepts and by using a Deep Neural Network. New strategies are added to improve the efficiency of the algorithm and to make it easier to be integrated with other algorithms on the robot and also make it capable of finding a trajectory that avoids enemies on the field.

关键词： Deep learning Conferences Neural networks Education Ordinary differential equations Real-time systems Mathematical models

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ARMCHAIR: integrated inverse reinforcement learning and model predictive control for human-robot collaboration

arXiv

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arXiv 2024年

作者： Caregnato-Neto, Angelo Siebert, Luciano Cavalcante Zgonnikov, Arkady de Albuquerque Maximo, Marcos Ricardo Omena Afonso, Rubens Junqueira Magalhães Electronic Engineering Division Instituto Tecnológico de Aeronáutica São José dos Campos São Paulo Brazil Computer Science Division Instituto Tecnológico de Aeronáutica São José dos Campos São Paulo Brazil Intelligent Systems Department Interactive Intelligence Group Delft University of Technology Delft Netherlands Department of Cognitive Robotics Delft University of Technology Delft Netherlands

One of the key issues in human-robot collaboration is the development of computational models that allow robots to predict and adapt to human behavior. Much progress has been achieved in developing such models, as well as control techniques that address the autonomy problems of motion planning and decision-making in robotics. However, the integration of computational models of human behavior with such control techniques still poses a major challenge, resulting in a bottleneck for efficient collaborative human-robot teams. In this context, we present a novel architecture for human-robot collaboration: Adaptive Robot Motion for Collaboration with Humans using Adversarial Inverse Reinforcement learning (ARMCHAIR). Our solution leverages adversarial inverse reinforcement learning and model predictive control to compute optimal trajectories and decisions for a mobile multi-robot system that collaborates with a human in an exploration task. During the mission, ARMCHAIR operates without human intervention, autonomously identifying the necessity to support and acting accordingly. Our approach also explicitly addresses the network connectivity requirement of the human-robot team. Extensive simulation-based evaluations demonstrate that ARMCHAIR allows a group of robots to safely support a simulated human in an exploration scenario, preventing collisions and network disconnections, and improving the overall performance of the task. © 2024, CC BY-SA.

关键词： Model predictive control

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The relevance of IT Security awareness in Renewable Energy facilities

Renewable Energy and Power Quality Journal

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Renewable Energy and Power Quality Journal 2022年 20卷 530-535页

作者： Lourenço, Justino Zanghi, Eric Morais, José Neves, Nelson Vasques, César Figueiredo, Fernando Department of Computer Science Engineering Instituto Superior Politécnico Gaya Avenida dos Descobrimentos 333 Vila Nova de Gaia Portugal Department of Electronics and Automation Engineering Instituto Superior Politécnico Gaya Avenida dos Descobrimentos 333 Vila Nova de Gaia Portugal Department of Mechanical Engineering ISPGAYA Instituto Superior Politécnico Gaya Avenida dos Descobrimentos 333 Vila Nova de Gaia Portugal

In recent years several efforts have being made in bringing smart network connectivity to the Renewable Energy Plant (REP) environment. On the other hand, REP is extending in scale from specialized points where the energy provider acts as a supplier to home REP (self-energy producers). This enables new important features such as: process automation, monitoring, control and optimizations. On the other hand, and in particular during and after the Covid19 pandemics the cybersecurity menace is a massive concern. The digital literacy of a worker of such an infrastructure is relevant to the correct implementation of adequate security policies. This article describes the threats and challenges on the field and conducts an enquire for perceiving the awareness of Automation and Mechanics Engineering students for this relevant problem, as future player in the field. © 2022, European Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ). All rights reserved.

关键词： Cybersecurity

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Real-time motion planning and decision-making for a group of differential drive robots under connectivity constraints using robust MPC and mixed-integer programming

arXiv

引用

arXiv 2022年

作者： Caregnato-Neto, Angelo Maximo, Marcos O.A. Afonso, Rubens J.M. Electronic Engineering Division Instituto Tecnológico de Aeronáutica São José dos Campos São Paulo Brazil Computer Science Division Instituto Tecnológico de Aeronáutica São José dos Campos São Paulo Brazil

This work is concerned with the problem of planning trajectories and assigning tasks for a Multi-Agent System (MAS) comprised of differential drive robots. We propose a multirate hierarchical control structure that employs a planner based on robust Model Predictive Control (MPC) with mixed-integer programming (MIP) encoding. The planner computes trajectories and assigns tasks for each element of the group in real-time, while also guaranteeing the communication network of the MAS to be robustly connected at all times. Additionally, we provide a data-based methodology to estimate the disturbances sets required by the robust MPC formulation. The results are demonstrated with experiments in two obstacle-filled scenarios. © 2022, CC BY.

关键词： Multi agent systems

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Trainable and Explainable Simplicial Map Neural Networks

arXiv

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arXiv 2023年

作者： Paluzo-Hidalgo, Eduardo Gonzalez-Diaz, Rocio Gutiérrez-Naranjo, Miguel A. Department of Quantitative Methods Universidad Loyola Andalucía Campus Sevilla Dos Hermanas Seville Spain Department of Applied Mathematics I School of engineering University of Seville Seville Spain Department of Computer Science and Artificial Intelligence School of Engineering University of Seville Seville Spain

Simplicial map neural networks (SMNNs) are topology-based neural networks with interesting properties such as universal approximation ability and robustness to adversarial examples under appropriate conditions. However, SMNNs present some bottlenecks for their possible application in high-dimensional datasets. First, SMNNs have precomputed fixed weight and no SMNN training process has been defined so far, so they lack generalization ability. Second, SMNNs require the construction of a convex polytope surrounding the input dataset. In this paper, we overcome these issues by proposing an SMNN training procedure based on a support subset of the given dataset and replacing the construction of the convex polytope by a method based on projections to a hypersphere. In addition, the explainability capacity of SMNNs and effective implementation are also newly introduced in this paper. © 2023, CC BY.

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