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Time Series Forecasting of Natural Inflow in Hydroelectric Power Plants Using Hyper-Tuned Temporal Fusion Transformer With Hodrick–Prescott Filter

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IET Generation, Transmission and Distribution 2025年第1期19卷

作者： Muniz, Rafael Ninno Buratto, William Gouvêa Nied, Ademir Cardoso, Rodolfo Finardi, Erlon Cristian Gonzalez, Gabriel Villarrubia Rio das Ostras Brazil Joinville Brazil Florianópolis Brazil Institute of Systems and Computer Engineering INESC R&D Brazil Santos Brazil Expert Systems and Applications Lab Faculty of Science University of Salamanca Salamanca Spain

The scheduling of the operation of the electricity system in Brazil is based on multi-criteria optimization that takes into account the forecast of the level of the dams of the hydroelectric plants, this variation is evaluated by the soil moisture active passive model. Considering the advances in using deep learning to forecast time series variations, this paper proposes a hybrid method for forecasting dam level variations. In particular, the temporal fusion transformer (TFT) is used for prediction with the Hodrick–Prescott filter for denoising. To enhance the model's performance, its hyperparameters are optimized by the Optuna framework based on the tree-structured Parzen estimator. For benchmarking, the multilayer perceptron, long short-term memory, recurrent neural network (RNN), Dilated RNN, temporal convolutional neural, neural hierarchical interpolation for time series forecasting, deep non-parametric time series forecaster, and the standard TFT are considered. The results show that the proposed model can make predictions with high performance compared to other methods, being 29.12% better than the second-best model, and 59.22% better than the original TFT model for very short-term forecasting, making it a promising alternative to be used as additional information for planning the operation of the electrical power system. © 2025 The Author(s). IET Generation, Transmission & Distribution published by John Wiley & Sons Ltd on behalf of The Institution of engineering and technology.

关键词： Long short-term memory

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“Is It Future or Is It Past?”: From Self-contained Microtasks to AI-driven Collaborative Crowdsourcing

“Is It Future or Is It Past?”: From Self-contained Microta...

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Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Daniel Schneider Marcos Antonio De Almeida Ramon Chaves Benjamim Fonseca Hesam Mohseni António Correia Postgraduate Program in Informatics (PPGI) Federal University of Rio de Janeiro Rio de Janeiro Brazil Systems Engineering and Computer Science Program Federal University of Rio de Janeiro Rio de Janeiro Brazil INESC TEC and University of Trás-os-Montes e Alto Douro Vila Real Portugal Faculty of Information Technology University of Jyväskylä Jyväskylä Finland

ISBN: (数字)9798331510886

ISBN: (纸本)9798331510893

Interest in artificial intelligence (AI)-driven crowd work has increased during the last few years as a line of inquiry that expands upon prior research on microtasking to represent a means of scaling up complex tasks through AI mediation. Despite the increasing attention to the macrotask phenomenon in crowdsourcing, there is a need to understand the processes, elements, and constraints underlying the infrastructural and behavioral aspects in such form of crowd work when involving collaboration. To this end, this paper provides a first attempt to characterize some of the research conducted in this direction to identify important paths for an agenda comprising key drivers, challenges, and prospects for integrating human-centered AI in collaborative crowdsourcing environments.

关键词： Crowdsourcing Human computer interaction Collaboration Artificial intelligence Mediation Robots Optimization

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ML-Assistant for Human Operators to Solve Faults and Classify Events Complexity in Electrical Grids 13

ML-Assistant for Human Operators to Solve Faults and Classif...

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13th Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion, MEDPOWER 2022

作者： Campos, Vasco Andrade, José R. Bessa, Ricardo J. Gouveia, Clara INESC TEC Institute for Systems and Computer Engineering Technology and Science Porto Portugal

ISBN: (纸本)9781839538445

Nowadays, human operators at grid control centers analyze a large volume of alarm information during outage’s events, and must act fast to restore the service. Currently, after the occurrence of short-circuit faults and its isolation via feeder protection, fault location and isolation is achieved via remotely controlled switching actions defined by operator’s experience. Despite operator’s experience and knowledge, this makes the process sub-optimal and slower. This paper proposes two novel machine learning-based algorithms to assist human operator decisions, aiming to: i) classify the complexity of a fault occurrence (Occurrences Classifier) based on its alarm events;ii) provide fast insights to the operator on how to solve it (Data2Actions). The Occurrences Classifier takes the alarm information of an occurrence and classifies it as a "simple" or "complex" occurrence. The Data2Actions takes a sequence of alarm information from the occurrence and suggests to the operator the more adequate sequence of switching actions to isolate the fault section on the overhead medium voltage line. Both algorithms were tested in real data from a Distribution System Operator between 2017 and 2020, and showed i) an accuracy of 86% for the Data2Actions, and ii) the Occurrences Classifier reached 74% accuracy for "simple" occurrences and 58% for "complex" ones, leading to an overall 65% accuracy. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Machine learning

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Enhancing Power Distribution Protection: A Comprehensive Analysis of Renewable Energy Integration Challenges and Mitigation Strategies 14

Enhancing Power Distribution Protection: A Comprehensive Ana...

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14th Mediterranean Conference on Power Generation Transmission, Distribution and Energy Conversion, MEDPOWER 2024

作者： Alves, Everton Reiz, Cleberton Melim, André Gouveia, Clara Institute for Systems and Computer Engineering Technology and Science - INESC TEC Porto Portugal

ISBN: (纸本)9781837242689

The integration of Distributed Energy Resources (DER) imposes challenges to the operation of distribution networks. This paper conducts a systematic assessment of the impact of DER on distribution network overcurrent protection, considering the behavior of Inverter Based Resources (IBR) during faults in the coordination of medium voltage (MV) feeders' overcurrent protection. Through a detailed analysis of various scenarios, we propose adaptive protection solutions that enhance the reliability and resilience of distribution networks in the face of growing renewable energy integration. Results highlight the advantages of using adaptive protection over traditional methods and topology changes, and delve into current protection strategies, identifying limitations and proposing mitigation strategies. © The Institution of engineering & technology 2024.

关键词： Power distribution networks

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Context-aware Rate Adaptation for Predictive Flying Networks using Contextual Bandits

arXiv

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arXiv 2025年

作者： Queiros, Ruben Kaneko, Megumi Fontes, Helder Campos, Rui INESC TEC Faculty of Engineering University of Porto Porto Portugal National Institute of Informatics Dept. of Computer Science The University of Tokyo Tokyo Japan

The increasing complexity of wireless technologies, such as Wi-Fi, presents significant challenges for Rate Adaptation (RA) due to the large configuration space of transmission parameters. While extensive research has been conducted on RA for low-mobility networks, existing solutions fail to adapt in flying networks, where high mobility and dynamic wireless conditions introduce additional uncertainty. We propose Linear Upper Confidence Bound for RA (LinRA), a novel Contextual Bandit-based approach that leverages real-time link context to optimize transmission rates. Designed for predictive flying networks, where future trajectories are known, LinRA proactively adapts to obstacles affecting channel quality. Simulation results demonstrate that LinRA converges 5.2× faster than state-of-the-art benchmarks and improves throughput by 80% in Non Line-of-Sight (NLoS) conditions, matching the performance of ideal algorithms. With low time complexity, LinRA is a scalable and efficient RA solution for predictive flying networks. © 2025, CC BY.

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State-of-the-art AI-based Learning Approaches for Deepfake Generation and Detection, Analyzing Opportunities, Threading through Pros, Cons, and Future Prospects

arXiv

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arXiv 2025年

作者： Goyal, Harshika Wajid, Mohammad Saif Wajid, Mohd Anas Khanday, Akib Mohi Ud Din Neshat, Mehdi Gandomi, Amir Indian Institute of Technology Kharagpur India Department of Computer Science and Intelligent Systems Tecnológico de Monterrey Monterrey Mexico Institute for the Future of Education TEC de Monterrey Monterrey Mexico Department of Computer Science and Software Engineering United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates Department of Computer Science Samarkand International University of Technology Samarkand Uzbekistan Faculty of Engineering and Information Technology University of Technology Sydney Ultimo SydneyNSW2007 Australia Obuda University Budapest1034 Hungary School of Computer and Mathematical Sciences University of Adelaide Adelaide5005 Australia

The rapid expansion and advancement of deepfake technology, which is specifically designed to create incredibly lifelike facial imagery and video content, has ignited a remarkable level of interest and curiosity across a multitude of fields, including but not limited to the realms of the entertainment industry, forensic analysis, cybersecurity and the innovative creation of digital characters. By harnessing the latest breakthroughs in deep learning methodologies, especially through the implementation of techniques such as Generative Adversarial Networks, Variational Autoencoders, Few-Shot Learning Strategies, and Transformer Neural Networks, the outcomes achieved in the generation of deepfakes have been nothing short of astounding and transformative. Furthermore, the recent advancements in diffusion models have served to invigorate and catalyze ongoing research efforts in this fascinating domain. At the same time, the ongoing evolution of detection technologies is being developed to counteract the potential for misuse associated with deepfakes, effectively addressing critical concerns that range from political manipulation to the dissemination of fake news and the ever-growing issue of cyberbullying. This comprehensive review paper meticulously investigates the most recent developments in both deepfake generation and detection, including around 400 publications, providing an in-depth analysis of the cutting-edge innovations that are shaping this rapidly evolving landscape. Starting with a thorough examination of systematic literature review methodologies, we embark on a journey that delves into the complex technical intricacies inherent in the various techniques used for deepfake generation, comprehensively addressing the challenges faced, potential solutions available, and the nuanced details surrounding manipulation formulations. Besides, task definitions are standardized to ensure clarity, while datasets and metrics are diligently introduced, paving the way for a

关键词： Generative adversarial networks

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AI-based Models to Predict the Heart Rate Using PPG and Accelerometer Signals During Physical Exercise

AI-based Models to Predict the Heart Rate Using PPG and Acce...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Ribeiro, Lucas Oliveira, Helder P. Hu, Xiao Pereira, Tania University of Porto Inesc Tec Inesc Tec - Institute for Systems and Computer Engineering Technology and Science Feup - Faculty of Engineering Porto Portugal University of Porto Inesc Tec - Institute for Systems and Computer Engineering Technology and Science Fcup - Faculty of Science Porto Portugal Emory University Nell Hodgson Woodruff School of Nursing Department of Biomedical Informatics School of Medicine Department of Computer Science College of Arts and Sciences Atlanta United States Technology and Science Inesc Tec - Institute for Systems and Computer Engineering Porto Portugal

ISBN: (纸本)9798350337488

PPG signal is a valuable resource for continuous heart rate monitoring;however, this signal suffers from artifact movements, which is particularly relevant during physical exercise and makes this biomedical signal difficult to use for heart rate detection during those activities. The purpose of this study was to develop learning models to determine heart rate using data from wearables (PPG and acceleration signals) and dealing with noise during physical exercise. Learning models based on CNNs and LSTMs were developed to predict the heart rate. The PPG signal was combined with data from accelerometers trying to overcome the noise movement on the PPG signal. Two datasets were used on this work: the 2015 IEEE Signal Processing Cup (SPC) dataset was used for training and testing, and another dataset was used for validation of the learning model (PPG-DaLiA dataset). The predictions obtained by the learning model represented a mean average error of 7.033±5.376 bpm for the SCP dataset, while a mean average error of 9.520±8.443 bpm for the validation set. The use of acceleration data increases the performance of the learning models on the prediction of the heart rate, showing the benefits of using this source of data to overcome the noise movement problem on the PPG signal. The combination of PPG signal with acceleration data could allow the learning models to use more information regarding the motion artifacts that affect the PPG and improve performance on the physiological event detections, which will largely spread the use of wearables on the healthcare applications for continuous monitor the physiological state allowing early and accurate detection of pathological events. © 2023 IEEE.

关键词： Convolutional Neural Network Heart Rate Estimation Long Short Term Memory Motion Artifacts Photoplethysmography

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The AppVox mobile application, a tool for speech and language training sessions

The AppVox mobile application, a tool for speech and languag...

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作者： Rocha, Tânia Gonçalves, Cirano Fernandes, Hugo Reis, Arsénio Barroso, João Vila Real Portugal INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal

AppVox is a mobile application that provides support for children with speech and language impairments in their speech therapy sessions, while also allowing autonomous training at home. The application simulates a vocalizer with an audio stimulus feature, which can be used to train and amend the pronunciation of specific words through repetition. In this paper, we aim to present the development of the application as an assistive technology option, by adding new features to the vocalizer as well as assessing it as a usable option for daily training interaction for children with speech and language impairments. In this regard, we invited 15 children with speech and language impairments and 20 with no impairments to perform training activities with the application. Likewise, we asked three speech therapists and three usability experts to interact, assess, and give their feedback. In this assessment, we include the following parameters: successful conclusion of the training tasks (effectiveness);number of errors made, as well as number and type of difficulties found (efficiency);and the acceptance and level of comfort in completing the requested tasks (satisfaction). Overall, the results showed that children conclude the training tasks successfully and helped to improve their language and speech capabilities. Therapists and children gave positive feedback to the AppVox interface. © 2019 John Wiley & Sons, Ltd

关键词： Speech

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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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2021 Telecoms Conference, ConfTELE 2021

作者： Marques, Joao Novais, Susana Silva, Susana Frazao, Orlando Faculty of Sciences University of Porto Inesc Tec Department of Physics and Astronomy Porto Portugal Inesc Tec Institute for Systems and Computer Engineering Technology and Science Porto Portugal

ISBN: (纸本)9781665415880

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 singlemode 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. © 2021 IEEE.

关键词： Endoscopy

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Exploring Large Language Models for Relevance Judgments in Tetun 1

Exploring Large Language Models for Relevance Judgments in T...

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1st Workshop on Large Language Models for Evaluation in Information Retrieval, LLM4Eval 2024

作者： de Jesus, Gabriel Nunes, Sérgio INESC TEC - Institute for Systems and Computer Engineering Technology and Science Portugal FEUP - Faculty of Engineering University of Porto Portugal

The Cranfield paradigm has served as a foundational approach for developing test collections, with relevance judgments typically conducted by human assessors. However, the emergence of large language models (LLMs) has introduced new possibilities for automating these tasks. This paper explores the feasibility of using LLMs to automate relevance assessments, particularly within the context of low-resource languages. In our study, LLMs are employed to automate relevance judgment tasks, by providing a series of query-document pairs in Tetun as the input text. The models are tasked with assigning relevance scores to each pair, where these scores are then compared to those from human annotators to evaluate the inter-annotator agreement levels. Our investigation reveals results that align closely with those reported in studies of high-resource languages. © 2024 CEUR-WS. All rights reserved.

关键词： Structured Query Language

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