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13th International Conference on Biomedical Electronics and Devices, BIODEVICES 2020 - Part of 13th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2020

作者： de Azevedo Fernandes, Lucas Brito, Thadeu Piardi, Luis Lima, José Leitão, Paulo Universidade Tecnológica Ferderal do Paraná Paraná Brazil CeDRI - Research Centre in Digitalization and Intelligent Robotics Portugal INESC TEC - INESC Technology and Science CeDRI - Research Centre in Digitalization and Intelligent Robotics Polytechnic Institute of Bragança Portugal

ISBN: (纸本)9789897583988

Rehabilitation is an important recovery process from dysfunctions that improves the patient’s activities of daily living. On the other hand, collaborative robotic applications, where humans and machines can share the same space, are increasing once it allows splitting a task between the accuracy of a robot and the ability and flexibility of a human. This paper describes an innovative approach that uses a collaborative robot to support the rehabilitation of the upper limb of patients, complemented by an intelligent system that learns and adapts its behaviour according to the patient’s performance during the therapy. This intelligent system implements the reinforcement learning algorithm, which makes the system robust and independent of the path of motion. The validation of the proposed approach uses a UR3 collaborative robot training in a real environment. The main control is the resistance force that the robot is able to do against the movement performed by the human arm. Copyright © 2020 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

关键词： Reinforcement learning

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Integrating a graph builder into python tutor 2

Integrating a graph builder into python tutor

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2nd International Computer Programming Education Conference, ICPEC 2021

作者： Soares, Diogo Pereira, Maria João Varanda Henriques, Pedro Rangel Research Centre in Digitalization and Intelligent Robotics Polythechnic Insitute of Bragança Portugal University of Minho Braga Portugal

ISBN: (纸本)9783959771948

Analysing unknown source code to comprehend it is quite hard and expensive task. Therefore, the Program Comprehension (PC) subject has always been an area of interest as it helps to realize how a program works by identifying the code that implements each functionality. This means being able to map the problem domain with the program domain. PC is a complex area, but its importance for programmers is so high that many approaches and tools were proposed along the last two decades. Program Animation is one of those approaches requiring specialized techniques. For each programming language, there are already tools that enable us to execute a program step by step, visualize its execution path, observe the effect of each instruction on its data structures, and inspect the value of its variables at any point. In the present context, we sustain the idea that PC techniques and tools can also be of great value for students taking the first steps in programming using a specific language. To this end, we aim to improve Python Tutor, a well-known program visualization tool, with graph-based representations of source code such as Control Flow Graph (CFG), Data Flow Graph (DFG), Function Call Graph (FCG) and System Control Graph (SCG). This helps novice programmers to understand the source code analyzing not only the variable contents but also a set of automatically generated graph-based visualizations, that were not included in Python Tutor so far. This will allow the students to be focused on certain aspects of the program (depending on the graph), abstracting others such as details of its syntax. © Diogo Soares, Maria João Varanda Pereira, and Pedro Rangel Henriques;licensed under Creative Commons License CC-BY 4.0 Second International Computer Programming Education Conference (ICPEC 2021).

关键词： Python

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Lyntax - A grammar-based tool for linguistics 10

Lyntax - A grammar-based tool for linguistics

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10th Symposium on Languages, Applications and Technologies, SLATE 2021

作者： de Sousa, Manuel Gouveia Carneiro Pereira, Maria João Varanda Henriques, Pedro Rangel University of Minho Braga Portugal Research Centre in Digitalization and Intelligent Robotics Polythechnic Insitute of Bragança Portugal

ISBN: (纸本)9783959772020

This paper is focused on using the formalism of attribute grammars to create a tool that allows Linguistic teachers to construct automatically their own processors totally adapted to each linguistic exercise. The system developed, named Lyntax, is a compiler for a domain specific language which intends to enable the teacher to specify different kinds of sentence structures, and then, ask the student to test his own sentences against those structures. The processor Lyntax validates the grammar (DSL program) written by the teacher, generating a processor every time the student defines a new sentence. For that ANTLR is used in both steps, generating not only the specialized processor but also the visualization of the syntax tree for analysis purposes. An interface that supports the specification of the language was built, also allowing the use of the processor and the generation of the specific grammar, abstracting the user of any calculations. © 2021 Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing. All rights reserved.

关键词： Trees (mathematics)

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An Innovative Optimization Approach for Energy Management of a Microgrid System

An Innovative Optimization Approach for Energy Management of...

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2023 International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023

作者： Amoura, Yahia Pereira, Ana I. Lima, José Ferreira, Ângela Boukli-Hacene, Fouad Torres, Santiago University of Laguna Research Centre in Digitalization and Intelligent Robotics Instituto Politécnico de Bragança Bragança Portugal Algoritmi Center University of Minho Braga Research Centre in Digitalization and Intelligent Robotics Instituto Politécnico de Bragança Bragança Portugal Inesc Tec Inesc Technology and Science Porto Research Centre in Digitalization and Intelligent Robotics Instituto Politécnico de Bragança Bragança Portugal Instituto Politécnico de Bragança Research Centre in Digitalization and Intelligent Robotics Bragança Portugal Higher School of Applied Sciences of Tlemcen-Algeria-TLEMCEN Algeria University of Laguna Tenerife Spain

ISBN: (纸本)9798350322972

The local association of electrical generator including renewable energies and storage technologies approximately installed to the client made way for a small-scale power grid called a microgrid. In certain cases, the random nature of renewable energy sources, combined with the variable pattern of demand, results in issues concerning the sustainability and reliability of the microgrid system. Furthermore, the cost of the energy coming from conventional sources is considering as matter to the private consumer due to its high fees. An improved methodology combining the simplex-based linear programming with the particle swarm optimisation approach is employed to implement an integrated power management system. The energy scheduling is done by assuming the consumption profile of a smart city. two scenarios of energy management have been suggested to illustrate the behaviour of cost and gas emissions for an optimised energy management. The results showed the reliability of the energy management system using an improvemed approach in scheduling of the energy flows for the microgrid producers, limiting the utility's cost versus an experiment that had already been done for a similar system using the identical data. The outcome of the computation identified the ideal set points of the power generators in a smart city supplied by a microgrid, while guaranteeing the comfort of the customers i.e without intermetency in the supply, also, reducing the emissions of greenhouse gases and providing an optimal exploitation cost for all smart city users. Morover, the proposed energy management system gave an inverse relation between economic and environmental aspects, in fact, a multi-objective optimization approach is performed as a continuation of the work proposed in this paper to identify all possible scenarios of the microgrid operation. © 2023 IEEE.

关键词： Linear programming

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Impact of hyper-parameter tuning on CNN accuracy in agricultural image classification

Smart Agricultural Technology

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Smart Agricultural Technology 2025年 11卷

作者： João Mendes José Lima Lino Costa Eligius M.T. Hendrix Ana I. Pereira Research Centre in Digitalization and Intelligent Robotics (CeDRI) Laboratório Associado para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC) Instituto Politécnico de Bragança 5300-253 Bragança Portugal ALGORITMI Research Centre/LASI University of Minho 4710-057 Braga Portugal Universidad de Málaga Málaga Spain

This study explores the impact of hyper-parameter optimization on the performance of convolutional neural networks (CNNs) for olive cultivar classification using transfer learning. Pre-trained ImageNet models such as VGG16, InceptionV3, and ResNet50 were adapted to a proprietary dataset, with VGG16 selected for detailed evaluation. Key hyper-parameters, including layer count, neurons per layer, dropout rate, learning rate, and batch size, were tuned using random search. The best configuration achieved a validation accuracy of 87.5%, significantly outperforming the control model. Sensitivity analyses with Morris and Sobol methods identified the number of layers as the most influential factor, followed by dropout and learning rates through interaction effects. These findings demonstrate the importance of tailoring CNN architecture and regularization settings to the problem domain. These results underscore the importance of tuning architectural depth and regularization mechanisms for performance optimization. As a practical guideline, models with fewer layers and intermediate dropout levels demonstrated higher robustness and generalization, offering an effective strategy for adapting CNNs to agricultural classification tasks.

关键词： Hyper-parameter optimization Convolutional neural networks Sensitivity analysis

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Deep Learning Techniques Applied to Skin Lesion Classification: A Review

Deep Learning Techniques Applied to Skin Lesion Classificati...

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2022 International Conference on Machine Learning, Control, and robotics, MLCR 2022

作者： Silva, Giuliana M. Lazzaretti, Andre E. Monteiro, Fernando C. Federal University of Technology Paraná Curitiba Brazil Research Centre in Digitalization and Intelligent Robotics Instituto Politécnico de Bragança Bragança Portugal

ISBN: (纸本)9781665454599

Skin cancer is one of the most common cancers in the world. The most dangerous type of skin cancer is melanoma, which can be lethal if not treated early. However, diagnosing skin lesions can be a difficult task. Therefore, deep learning techniques applied to the diagnosis of skin lesions have been explored by researchers, given their effectiveness in extracting features and classifying input data. In this work, we present a review of latest approaches that apply deep learning techniques to skin lesion classification task. In addition, some datasets used for training and validating the models are introduced, informing their characteristics and specificities, as well as popular pre-processing steps and skin lesion segmentation approaches. Finally, we comment the effectiveness of the proposed models. © 2022 IEEE.

关键词： Diagnosis

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Portuguese Twitter Dataset on COVID-19 14

Portuguese Twitter Dataset on COVID-19

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14th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022

作者： Jonker, Richard Adolph Aires Poudel, Roshan Fajarda, Olga Matos, Sergio Oliveira, Jose Luis Lopes, Rui Pedro Institute of Electronics and Informatics Engineering of Aveiro University of Aveiro Aveiro Portugal Research Centre in Digitalization and Intelligent Robotics Polytechnic Institute of Bragança Bragança Portugal

ISBN: (纸本)9781665456616

Over the last two years, the COVID-19 pandemic has affected hundreds of millions of people around the world. As in many crises, people turn to social media platforms, like Twitter, to communicate and share information. Twitter datasets have been used over the years in many research studies to extract valuable information. Therefore, several large COVID-19 Twitter datasets have been released over the last two years. However, none of these datasets contains only Portuguese Tweets, despite the Portuguese Language being reported as one of the top five languages used on Twitter. In this paper, we present the first large-scale Portuguese COVID-19 Twitter dataset. The dataset contains over 19 million Tweets spanning 2020 and 2021, allowing the entire pandemic to be analyzed. We also conducted a sentiment analysis on the dataset and correlated the various spikes in Tweet count and sentiment scores to various news articles and government announcements in Portugal and Brazil. The dataset is available at: https://***/bioinformatics-ua/Portuguese-Covid19-Dataset © 2022 IEEE.

关键词： COVID-19

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A Machine Learning-Based Early Forest Fire Detection System Utilizing Vision and Sensors' Fusion Technologies 4

A Machine Learning-Based Early Forest Fire Detection System ...

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4th IEEE Middle East and North Africa COMMunications Conference, MENACOMM 2022

作者： Khalifeh, Ala' Nassar, AbdelHamid Alajlouni, Mohammad M. Alnabelsi, Anas Alrawashdeh, Zaid Hejazi, Bashar Alwardat, Radi Lima, Jose German Jordanian University Amman Jordan Amman Jordan Instituto Politécnico de Bragança Bragança Research Centre in Digitalization and Intelligent Robotics Portugal

ISBN: (纸本)9781665494113

The paper aims at utilizing machine learning (ML) towards designing an early warning forest fire detection system. With the aid of the Internet of Things (IoT) and smart edge computing, an embedded system that utilizes sensors' fusion technology, machine vision and ML to early detect forest fire has been proposed. Different from most of the proposed fire detection systems in the literature, which either utilize vision or sensors'-based approaches to detect the fire, the proposed system utilizes both approaches jointly, which in turn will make it more accurate for fire detection. Furthermore, this paper focuses on implementing the proposed system utilizing a smart edge node and discusses the incurred technical challenges and how they have been solved. © 2022 IEEE.

关键词： Machine learning

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An AI-based Object Detection Approach for Robotic Competitions

An AI-based Object Detection Approach for Robotic Competitio...

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2023 International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023

作者： Pilarski, Leonardo Luiz, Luiz E. Braun, João Nakano, Alberto Y. Pinto, Vítor Costa, Paulo Lima, José Utfpr Toledo Brazil Polytechnic Institute of Bragança Research Centre in Digitalization and Intelligent Robotics Bragança Portugal Porto Portugal Feup Inesc Tec Porto Portugal

ISBN: (纸本)9798350322972

Artificial Intelligence has been introduced in many applications, namely in artificial vision-based systems with object detection tasks. This paper presents an object localization system with a motivation to use it in autonomous mobile robots at robotics competitions. The system aims to allow robots to accomplish their tasks more efficiently. Object detection is performed using a camera and artificial intelligence based on the YOLOv4 Tiny detection model. An algorithm was developed that uses the data from the system to estimate the parameters of location, distance, and orientation based on the pinhole camera model and trigonometric modelling. It can be used in smart identification procedures of objects. Practical tests and results are presented, constantly locating the objects and with errors between 0.16 and 3.8 cm, concluding that the object localization system is adequate for autonomous mobile robots. © 2023 IEEE.

关键词： Object detection

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Using a low-cost robotic manipulator towards the study of over-sensored systems and state estimation 8

Using a low-cost robotic manipulator towards the study of ov...

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8th International Conference on Technological Ecosystems for Enhancing Multiculturality, TEEM 2020

作者： Moreira, João H. Pinto, Vítor Gonçalves, José Costa, Paulo Department of Electrical and Computers Engineering Faculty of Engineering University of Porto Porto Portugal CeDRI - Research Centre in Digitalization and Intelligent Robotics Polytechnic Institute of Bragança Portugal

ISBN: (纸本)9781450388504

In this paper it is described a low-cost robotic manipulator that was developed to be applied in the study of over-sensored systems and state estimation. The prototype was developed to be a teaching aid in advanced courses, such as a robotics Doctoral Program, although it is also suitable to be applied in some other educational contexts, to support experiments, for graduate and undergraduate students. Its features and software implementation are described, as well as two possible approaches to the problem of estimating its pose, based on a wide variety of sensors use. © 2020 ACM.

关键词： State estimation

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