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International Joint Conference on Neural Networks (IJCNN)

作者： Gonçalo Ferreira Marco Teixeira Raquel Belo Wilson Silva Jaime S. Cardoso FEUP - Faculty of Engineering University of Porto Porto Portugal INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal

The application of machine learning algorithms to predict the mechanism of action (MoA) of drugs can be highly valuable and enable the discovery of new uses for known molecules. The developed methods are usually evaluated with small subsets of MoAs with large support, leading to deceptively good generalization. However, these datasets may not accurately represent a practical use, due to the limited number of target MoAs. Accurate predictions for these rare drugs are important for drug discovery and should be a point of focus. In this work, we explore different training strategies to improve the performance of a well established deep learning model for rare drug MoA prediction. We explored transfer learning by first learning a model for common MoAs, and then using it to initialize the learning of another model for rarer MoAs. We also investigated the use of a cascaded methodology, in which results from an initial model are used as additional inputs to the model for rare MoAs. Finally, we proposed and tested an extension of Mixup data augmentation for multilabel classification. The baseline model showed an AUC of 73.2% for common MoAs and 62.4% for rarer classes. From the investigated methods, Mixup alone failed to improve the performance of a baseline classifier. Nonetheless, the other proposed methods outperformed the baseline for rare classes. Transfer Learning was preferred in predicting classes with less than 10 training samples, while the cascaded classifiers (with Mixup) showed better predictions for MoAs with more than 10 samples. However, the performance for rarer MoAs still lags behind the performance for frequent MoAs and is not sufficient for the reliable prediction of rare MoAs.

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Grey wolf, gravitational search and particle swarm optimizers: A comparison for PID controller design 12th

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12th Portuguese Conference on Automatic Control, CONTROLO 2016

作者： Oliveira, Paulo Moura Vrančić, Damir INESC TEC—INESC Technology and Science Department of Engineering School of Sciences and Technology Universidade de Trás-os-Montes e Alto Douro UTAD Vila Real5001-801 Portugal Department of Systems and Control Jožef Stefan Institute Jamova cesta 39 Ljubljana Slovenia

ISBN: (纸本)9783319436708

Nature and biologically inspired metaheuristics can be powerful tools to design PID controllers. The grey wolf optimization is one of these promising and interesting metaheuristics, recently introduced. In this study the grey wolf optimization algorithm is proposed to design PID controllers, and the results obtained compared with the ones obtained with gravitational search and particle swarm optimization algorithms. Simulation results obtained with these three bio-inspired metaheuristics applied to a set of benchmark linear plants are presented, considering the design objective of set-point tracking. The results are also compared with two non-iterative PID tuning techniques. © Springer International Publishing Switzerland 2017.

关键词： Three term control systems

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A Multimodal Learning-based Approach for Autonomous Landing of UAV

A Multimodal Learning-based Approach for Autonomous Landing ...

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IEEE/ASME International Conference on Mechtronic and Embedded systems and Applications, MESA

作者： Francisco S. Neves Luís M. Branco Maria I. Pereira Rafael M. Claro Andry M. Pinto Department of Electrical and Computer Engineering (DEEC) Faculty of Engineering of University of Porto (FEUP) Institute for Systems and Computer Engineering - Technology and Science (INESC TEC) Porto Portugal

ISBN: (数字)9798331516239

ISBN: (纸本)9798331516246

In the field of autonomous Unmanned Aerial Vehicles (UAVs) landing, conventional approaches fall short in delivering not only the required precision but also the resilience against environmental disturbances. Yet, learning-based algorithms can offer promising solutions by leveraging their ability to learn the intelligent behaviour from data. On one hand, this paper introduces a novel multimodal transformer-based Deep Learning detector, that can provide reliable positioning for precise autonomous landing. It surpasses standard approaches by addressing individual sensor limitations, achieving high reliability even in diverse weather and sensor failure conditions. It was rigorously validated across varying environments, achieving optimal true positive rates and average precisions of up to 90%. On the other hand, it is proposed a Reinforcement Learning (RL) decision-making model, based on a Deep Q-Network (DQN) rationale. Initially trained in simulation, its adaptive behaviour is successfully transferred and validated in a real outdoor scenario. Furthermore, this approach demonstrates rapid inference times of approximately 5ms, validating its applicability on edge devices.

关键词： Training Wind Image edge detection Decision making Detectors Reinforcement learning Robot sensing systems Autonomous aerial vehicles Transformers Resilience

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Characterization of an Hollow Core PCF for Endoscopy Applications: A Proof Concept

Characterization of an Hollow Core PCF for Endoscopy Applica...

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Telecoms Conference (ConfTELE)

作者： João Marques Susana Novais Susana Silva Orlando Frazão Faculty of Sciences of University of Porto Porto Portugal INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal

Two distinct optical fibers for endoscope-based configurations are demonstrated and studied in this work. The fibers used for the experiment consist of: a conventional single-mode fiber (SMF 28e) and a hollow core photonic crystal fiber (HC-PCF) based on silica. Two studies that allowed the characterization of these fibers, according to their optical output power and when subjected to curvature, were carried out. The intensity power profile was also analysed in relation to the propagation distance, transversal displacement and incidence angle. After this study it can be concluded that the most suitable solution for the endoscope is the HC-PCF fiber working as a transmission probe. For the proof of concept of the fiber-based endoscope, a cleaved multimode fiber (MMF) tip was used as a reception probe and its reflection efficiency was also analysed.

关键词： Optical fibers Silicon compounds Power measurement Endoscopes Displacement measurement Reflection Telecommunications

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Wearable biomonitoring platform for the assessment of stress and its impact on cognitive performance of firefighters: An experimental study

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Clinical Practice and Epidemiology in Mental Health 2018年第1期14卷 250-262页

作者： Rodrigues, Susana Paiva, Joana S. Dias, Duarte Pimentel, Gonçalo Kaiseler, Mariana Cunha, João Paulo S. Institute for Systems Engineering and Computers – Technology and Science (INESC TEC) Porto Portugal Faculty of Engineering (FEUP) University of Porto Porto Portugal Astronomy and Physics Department Sciences Faculty University of Porto Porto Portugal Institute for Sport Physical Activity and Leisure Leeds Beckett University Leeds United Kingdom

Background: Stress is a complex process with an impact on health and performance. The use of wearable sensor-based monitoring systems offers interesting opportunities for advanced health care solutions for stress analysis. Considering the stressful nature of firefighting and its importance for the community’s safety, this study was conducted for firefighters. Objectives: A biomonitoring platform was designed, integrating different biomedical systems to enable the acquisition of real time Electrocardiogram (ECG), computation of linear Heart Rate Variability (HRV) features and collection of perceived stress levels. This platform was tested using an experimental protocol, designed to understand the effect of stress on firefighter’s cognitive performance, and whether this effect is related to the autonomic response to stress. Method: The Trier Social Stress Test (TSST) was used as a testing platform along with a 2-Choice Reaction Time Task. Linear HRV features from the participants were acquired using an wearable ECG. Self-reports were used to assess perceived stress levels. Results: The TSST produced significant changes in some HRV parameters (AVNN, SDNN and LF/HF) and subjective measures of stress, which recovered after the stress task. Although these short-term changes in HRV showed a tendency to normalize, an impairment on cognitive performance was found after performing the stress event. Conclusion: Current findings suggested that stress compromised cognitive performance and caused a measurable change in autonomic balance. Our wearable biomonitoring platform proved to be a useful tool for stress assessment and quantification. Future studies will implement this biomonitoring platform for the analysis of stress in ecological settings. © 2018 Rodrigues et al.

关键词： Biomonitoring platform Cognitive performance Firefighter Firefighters Heart rate variability Stress

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Optimizing the fluidity in thin-section investment casting for vacuum induction molten AZ91D-1 %wt. CaO

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Journal of Magnesium and Alloys 2025年

作者： V.H. Carneiro V. Lopes I. Duarte H. Puga INESC TEC - Institute for Systems and Computer Engineering Technology and Science R. Dr. Roberto Frias 4200-465 Porto Portugal Department of Engineering School of Science and Technology - Universidade de Trás-os-Montes e Alto Douro Quinta de Prados 5001-801 Vila Real Portugal INL – International Iberian Nanotechnology Laboratory Avenida Mestre José Veiga 4715-330 Braga Portugal CMEMS-UMinho Department of Mechanical Engineering University of Minho Campus de Azurém 4800-058 Guimarães Portugal TEMA - Centre for Mechanical Technology and Automation Department of Mechanical Engineering University of Aveiro Campus Universitário de Santiago 3810-193 Aveiro Portugal LASI - Intelligent Systems Associate Laboratory Campus Azurém 4800-058 Guimarães Portugal

This paper discusses an experimental investigation into the fluidity of AZ91D-1 %wt. CaO magnesium melt via induction for thin-section investment casting. Plaster molds with thin spiral cavities (0.5 to 1.5 mm square sections) were designed and manufactured to assess the impact of casting conditions on filling length, as magnesium alloys cause severe melting and melt-mold exothermic reactions, making investment casting challenging. Combinations of traditional Mg-mold reaction mitigation techniques, such as applying a protective mold coating (Yttria) and vacuum, were examined to determine their role in the filling process. The results suggest that when induction is employed to melt reactive alloys, these methods are not always beneficial, as initially thought. Particularly at higher melt temperatures, the combination of Yttria-coated molds with low-pressure vacuum induction significantly reduce fluidity: vacuum induced melt levitation which promotes oxidation with the residual atmosphere; and Yttria-coating cracking due to thermal stress during the mold fabrication slows filling and promotes significant melt-mold reaction. This study shows that best results to investment cast thin-sections are obtained by avoiding both vacuum and protective coatings, providing a viable route for the precision manufacturing of stent biomedical devices.

关键词： Az91d-1 %wt. cao Fluidity Investment casting Yttria-coating Vacuum

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A wearable system for the stress monitoring of air traffic controllers during an air traffic control refresher training and the trier social stress test: A comparative study

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Open Bioinformatics Journal 2018年第1期11卷 106-116页

作者： Rodrigues, Susana Paiva, Joana S. Dias, Duarte Aleixo, Marta Filipe, Rui Cunha, João Paulo Silva Institute for Systems Engineering and Computers – Technology and Science (INESC TEC) Porto Portugal Faculty of Engineering University of Porto Porto Portugal Physics and Astronomy Department Sciences Faculty University of Porto Porto Portugal Navegação Aérea de Portugal (NAV) EPE Lisbon Portugal

Background: Air Traffic Control (ATC) is a complex and demanding process, exposing Air Traffic Controllers (ATCs) to high stress. Recently, efforts have been made in ATC to maintain safety and efficiency in the face of increasing air traffic demands. computer simulations have been a useful tool for ATC training, improving ATCs skills and consequently traffic safety. Objectives: This study aims to: a) evaluate psychophysiological indices of stress in an ATC simulation environment using a wearable biomonitoring platform. In order to obtain a measure of ATCs stress levels, results from an experimental study with the same participants, that included a stress-induced task were used as a stress ground truth;b) understand if there are differences in stress levels of ATCs with different job functions (“advisors” vs “operationals”) when performing an ATC Refresher Training, in a simulator environment. Methods: Two studies were conducted with ATCs: Study 1, that included a stress-induced task - the Trier Social Stress Test (TSST) and Study 2, that included an ATC simulation task. Linear Heart Rate Variability (HRV) features from ATCs were acquired using a medical grade wearable Electrocardiogram (ECG) device. Self-reports were used to measure perceived stress. Results: TSST was self-reported as being much more stressful than the simulation task. Physiological data supports these results. Results from study 2 showed more stress among the “advisors” group when comparing to the “operational” group. Conclusion: Results point to the importance of the development of quantified Occupational Health (qOHealth) devices to allow monitoring and differentiation of ATCs stress responses. © 2018 Donato and Denaro.

关键词： Air traffic controllers ECG Heart rate variability Simulation training Stress Wearable biomonitoring platform

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Individual’s Neutral Emotional Expression Tracking for Physical Exercise Monitoring 22nd

Individual’s Neutral Emotional Expression Tracking for Phys...

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22nd International Conference on Human computer Interaction,HCII 2020

作者： Khanal, Salik Ram Sampaio, Jaime Barroso, João Filipe, Vitor Universidade de Trás-os-Montes e Alto Douro Vila Real Portugal Institute for Systems and Computer Engineering Technology and Science INESC TEC Porto Portugal Research Center in Sports Sciences Health Sciences and Human Development CIDESD Vila Real Portugal

ISBN: (纸本)9783030601164

Facial expression analysis is a widespread technology applied in various research areas, including sports science. In the last few decades, facial expression analysis has become a key technology for monitoring physical exercise. In this paper, a deep neural network is proposed to recognize seven basic emotions and their corresponding probability values (scores). The score of the neutral emotion was tracked throughout the exercise and related with heart rate and power generation by a stationary bicycle. It was found that in a certain power range, a participant changes his/her expression drastically. Twelve university students participated in the sub-maximal physical exercise in stationary bicycles. A facial video, heart rate,and power generation were recorded throughout the exercise. All the experiments, including the facial expression analysis, were carried out offline. The score of the neutral emotion and its derivative was plotted against maxHR% and maxPower%. The threshold point was determined by calculating the local minima, with the threshold power for all the participants being within 80% to 90% of its maximum value. From the results, it is concluded that the facial expression was different from one individual to another, but it was more consistant with power generation. The threshold point can be a useful cue for various purposes, such as: physiological parameter prediction and automatic load control in the exercise equipment, such as treadmill and stationary bicycle. © 2020, Springer Nature Switzerland AG.

关键词： Bicycles

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Implementation of Consensus-ADMM Approach for Fast DC-OPF Studies

Implementation of Consensus-ADMM Approach for Fast DC-OPF St...

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International Conference on Smart Energy systems and technologies (SEST)

作者： Mohammad Javadi Ali Esmaeel Nezhad Matthew Gough Mohamed Lotfi João P.S. Catalão Institute for Systems and Computer Engineering Technology and Science (INESC TEC) Porto Portugal Department of Electrical Electronic and Information Engineering University of Bologna Italy

This paper proposes a novel method for solving the Optimal Power Flow (OPF) problem in conditions close to realtime. The linearized cost function of the generating units is used to this end. Besides, the presented linear model is solved using the Consensus Alternating Direction Method of Multipliers (C-ADMM) approach. This technique would provide the possibility of modeling the problem both in centralized and decentralized manners. The suggested method exploits the power flow results obtained from the previous iteration to considerably improve the rate of convergence. As the C-ADMM method uses an iterative technique, Lagrange multipliers, and the norm function, the rate of convergence highly depends upon assigning the initial conditions and the optimality gap. Thus, using the operating points of the previous instant due to being close to the operating point of the current instant would enhance the results. The proposed model has been implemented on two case studies including the Pennsylvania-New Jersey-Maryland (PJM) network to verify the results and the 9-bus system to evaluate the performance of the model for the daily operation.

关键词： Load flow Convergence Linear programming Cost function Power transmission lines

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A Machine Learning Approach for Predicting Microsatellite Instability using RNA-seq

A Machine Learning Approach for Predicting Microsatellite In...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Miguel Simões Tania Pereira Francisco Silva José Carlos Machado Hélder P. Oliveira INESC TEC - Institute for Systems and Computer Engineering Technology and Science FEUP - Faculty of Engineering University of Porto Porto Portugal INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal INESC TEC - Institute for Systems and Computer Engineering Technology and Science FCUP - Faculty of Science University of Porto Porto Portugal IPATIMUP - Institute of Molecular Pathology and Immunology i3S - Instituto de Investigação e Inovação em Saúde FMUP - Faculty of Medicine University of Porto Porto Portugal

Microsatellite Instability (MSI) is an important biomarker in cancer patients, showing a defective DNA mismatch repair system. Its detection allows the use of immunotherapy to treat cancer, an approach that is revolutionizing cancer treatment. MSI is especially relevant for three types of cancer: Colon Adenocarcinoma (COAD), Stomach Adenocarcinoma (STAD), and Uterus corpus endometrial cancer (UCEC). In this work, learning algorithms were employed to predict MSI using RNA-seq data from The Cancer Genome Atlas (TCGA) database, with a focus on the selection of the most informative genomic features. The Multi-Layer Perceptron (MLP) obtained the best score (AUC = 98.44%), showing that it is possible to exploit information from RNA-seq data to find relevant relationships with the instability levels of microsatellites (MS). The accurate prediction of MSI with transcription data from cancer patients will help with the correct determination of MSI status and adequate prescription of immunotherapy, creating more precise and personalized patient care. At the genetic level, the study revealed a high expression of genes related to cell regulation functions, and a low expression of genes responsible for Mismatch Repair functions, in patients with high instability.

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