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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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An automL-based approach to multimodal image sentiment analysis

arXiv

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arXiv 2021年

作者： Ferrinho, Vasco Lopes Gaspar, António Alexandre, Luís A. Cordeiro, João NOVA LINCS Universidade da Beira Interior Portugal LIAAD INESC TEC – Institute for Systems and Computer Engineering Technology and Science Portugal

Sentiment analysis is a research topic focused on analysing data to extract information related to the sentiment that it causes. Applications of sentiment analysis are wide, ranging from recommendation systems, and marketing to customer satisfaction. Recent approaches evaluate textual content using Machine Learning techniques that are trained over large corpora. However, as social media grown, other data types emerged in large quantities, such as images. Sentiment analysis in images has shown to be a valuable complement to textual data since it enables the inference of the underlying message polarity by creating context and connections. Multimodal sentiment analysis approaches intend to leverage information of both textual and image content to perform an evaluation. Despite recent advances, current solutions still flounder in combining both image and textual information to classify social media data, mainly due to subjectivity, inter-class homogeneity and fusion data differences. In this paper, we propose a method that combines both textual and image individual sentiment analysis into a final fused classification based on AutoML, that performs a random search to find the best model. Our method achieved state-of-the-art performance in the B-T4SA dataset, with 95.19% accuracy. © 2021, CC BY.

关键词： Sentiment analysis

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Sagittal Lower Limb Joint Angular Phase-Plane Analysis at Long, Short and No-Countermovement

Sagittal Lower Limb Joint Angular Phase-Plane Analysis at Lo...

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Bioengineering (ENBENG), Portuguese Meeting in

作者： Carlos Rodrigues Miguel Correia João Abrantes Marco Rodrigues Jurandir Nadal C-BER - Centre for Biomedical Engineering Research INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal MovLab - Laboratory of Technologies for Interactions and Interfaces CICANT - Centre for Research in Applied Communication Culture and New Technologies Lisbon Portugal Department of Electronic and Systems Federal University of Pernambuco Recife Brazil Biomedical Engineering Program Federal University of Rio de Janeiro Rio de Janeiro Brazil

This study presents non-invasive subject specific analysis using innovative tools from dynamic systems theory and image processing for sagittal plane anatomical marker tracking and digital filtering for detection of normalized phase differences of lower limb joint angular displacement and angular velocity coordination during long and short countermovement (CM) and muscle stretch-shortening cycle. Applied metrics captured at low-dimensional level (one variable - the phase) differences of CM neuromuscular control of lower limb joint coordination with greater dissimilarity between long and short CM, whereas no CM condition shares higher phase coordination at the hip, knee, ankle.

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A Human-computer Interaction Perspective on Clinical Decision Support systems: A Systematic Review of Usability, Barriers, and Recommendations for Improvement

A Human-Computer Interaction Perspective on Clinical Decisio...

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IEEE International Conference on Serious Games and Applications for Health (SeGAH)

作者： Gonçalo Ferreira Eva Oliveira John Stamper António Coelho Hugo Paredes Nuno F. Rodrigues Faculty of Engineering University of Porto Porto Portugal Human-Computer Interaction Polytechnic Institute of Cávado and Ave Braga Portugal Human-Computer Interaction Institute Carnegie Melon University Pittsburgh USA INESC TEC Vila Real Portugal Escola Superior de Tecnologia Polytechnic Institute of Cávado and Ave Braga Portugal

Clinical decision support systems have been increasingly utilized in the healthcare industry to improve patient outcomes and enhance clinical decision-making, taking advantage of the growing digital medical data. Despite their potential, there are still obstacles in an extensive adoption of these systems, such as low usability and human factors. In this systematic review, several articles describing clinical decision support systems with clinical validation are used to address some of the gaps, as well as to map the current academic landscape for the given context. The selected articles are observed through a Human-computer Interaction perspective, aiming to identify the state-of-the-art, as well as barriers to the application of these principles. From an initial database search resulting in 121 articles, 16 articles were selected that fulfilled the chosen criteria: (1) article must be available and written in English, (2) article must report experimental work, (3) the reported system must be clinically validated. The research strategy followed the PRISMA framework. We highlight the need for clinical validation, a standardized clinical decision support taxonomy and the evaluation of these tools across multiple variables. Based on the found results, a list of recommendations can be formed to aid the development of future CDSS, or the improvement of current ones.

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Evaluating YOLO Models for Grape Moth Detection in Insect Traps

Evaluating YOLO Models for Grape Moth Detection in Insect Tr...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Ana Cláudia Teixeira Gabriel Carneiro Raul Morais Joaquim J. Sousa António Cunha University of Trás-os-Montes and Alto Douro Vila Real Portugal Institute for Systems and Computer Engineering Technology and Science (INESC TEC) Porto Portugal Centre for the Research and Technology of Agro-Environmental and Biological Sciences University of Trás-os-Montes e Alto Douro Vila Real Portugal

The grape moth is a common pest that affects grapevines by consuming both fruit and foliage, rendering grapes deformed and unsellable. Integrated pest management for the grape moth heavily relies on pheromone traps, which serve a crucial function by identifying and tracking adult moth populations. This information is then used to determine the most appropriate time and method for implementing other control techniques. This study aims to find the best method for detecting small insects. We evaluate the following recent YOLO models: v5, v6, v7, and v8 for detecting and counting grape moths in insect traps. The best performance was achieved by YOLOv8, with an average precision of 92.4% and a counting error of 8.1%.

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JEMA: A JOINT EMBEDDING FRAMEWORK FOR SCALABLE CO-LEARNING WITH MULTIMODAL ALIGNMENT

arXiv

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arXiv 2024年

作者： Sousa, João Darabi, Roya Sousa, Armando Brueckner, Frank Reis, Luís Paulo Reis, Ana Porto Portugal FEUP Porto Portugal INEGI Porto Portugal Fraunhofer Institute for Material and Beam Technology IWS Dresden Germany FEUP Porto Portugal Centre for Robotics in Industry and Intelligent Systems INESC TEC Porto Portugal Department of Engineering Sciences and Mathematics Luleå University of Technology Luleå Sweden

This work introduces JEMA (Joint Embedding with Multimodal Alignment), a novel co-learning framework tailored for laser metal deposition (LMD), a pivotal process in metal additive manufacturing. As Industry 5.0 gains traction in industrial applications, efficient process monitoring becomes increasingly crucial. However, limited data and the opaque nature of AI present challenges for its application in an industrial setting. JEMA addresses this challenges by leveraging multimodal data, including multi-view images and metadata such as process parameters, to learn transferable semantic representations. By applying a supervised contrastive loss function, JEMA enables robust learning and subsequent process monitoring using only the primary modality, simplifying hardware requirements and computational overhead. We investigate the effectiveness of JEMA in LMD process monitoring, focusing specifically on its generalization to downstream tasks such as melt pool geometry prediction, achieved without extensive fine-tuning. Our empirical evaluation demonstrates the high scalability and performance of JEMA, particularly when combined with Vision Transformer models. We report an 8% increase in performance in multimodal settings and a 1% improvement in unimodal settings compared to supervised contrastive learning. Additionally, the learned embedding representation enables the prediction of metadata, enhancing interpretability and making possible the assessment of the added metadata's contributions. Our framework lays the foundation for integrating multisensor data with metadata, enabling diverse downstream tasks within the LMD domain and beyond. © 2024, CC BY.

关键词： Embeddings

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Organizational Enablers to the Governance of Collaborative University-Industry RD Programs

Organizational Enablers to the Governance of Collaborative U...

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2020 IEEE International Conference on engineering, technology and Innovation, ICE/ITMC 2020

作者： Fernandes, Gabriela Leite, Sara Araujo, Madalena Simoes, Ana Correia University of Coimbra Cemmpre Department of Mechanical Engineering Coimbra Portugal University of Minho Department of Production and Systems Guimarães Portugal Inesc Tec - Institute for Systems and Computer Engineering Technology and Science Porto Portugal

ISBN: (纸本)9781728170374

Governance has a significant impact on the success of programs and projects. However, governance of collaborative university-industry projects and programs in literature, is a rather scarce topic. Based on an ethnographic study of a large university-industry collaboration, this paper proposes a conceptual framework of Organizational Enablers (OEs) to improve the governance of collaborative university-industry RD programs. An exploratory research was carried out, aiming to learn from the experience of program and project managers and other program stakeholders of the case under study. Qualitative data was collected using participant observation and document analysis. The framework highlights nine OEs: 'Established governance policies and values', 'Formal Governance support structures', 'Flexible organization structures', 'Standardization of program and project management practices', 'Different management approaches to fit the project needs', 'Clearly defined roles and responsibilities', 'Different means of communication and interaction', 'Top management Support' and 'Projects strategic alignment within the industry and university roadmaps'. © 2020 IEEE.

关键词： Collaborative programs and projects Governance Organizational Enablers University-industry

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Crowd and Urban Storytelling: Evaluating a Collective Intelligence Model to Support Discussions about the City 25

Crowd and Urban Storytelling: Evaluating a Collective Intell...

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25th IEEE International Conference on computer Supported Cooperative Work in Design, CSCWD 2022

作者： Chaves, Ramon Motta, Claudia Correia, Antonio Paredes, Hugo Caetano, Barbara De Souza, Jano Moreira Schneider, Daniel Postgraduate Program in Informatics PPGI/UFRJ Rio de Janeiro Brazil Federal University of Rio de Janeiro Brazil Inesc Tec University of Tr s-os-Montes e Alto Douro Utad Quinta de Prados Apartado 1013 Vila Real Portugal Systems Engineering and Computer Science Program PESC/COPPE/UFRJ Rio de Janeiro Brazil

ISBN: (纸本)9781665405270

In recent years, digital technologies have been used to support discussions about the city and also to involve citizens in participatory public processes. However, despite the widespread use of social media platforms, old issues related to engagement and participation still persist in digital initiatives. The main goal of this study is to carry out an empirical evaluation of a collective intelligence model that combines crowdsourcing and social storytelling to support discussions about the city from a bottom-up perspective. Within a design science research approach we designed a participatory action study that was carried out through a workshop with students and professionals from different areas, such as architecture, urban design and information technology. As a result, we were able to assess whether the collective intelligence model was acceptable to the participants by investigating whether the behavioral assumptions were valid and thus outlining some contributions to the field of urban informatics. © 2022 IEEE.

关键词： Crowdsourcing

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LiDAR-based Power Assets Extraction based on Point Cloud Data

LiDAR-based Power Assets Extraction based on Point Cloud Dat...

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IEEE International Conference on Autonomous Robot systems and Competitions (ICARSC)

作者： M. Amado F. Lopes A. Dias A. Martins ISEP Instituto Superior de Engenharia do Porto Porto Portugal INESC TEC Institute for Systems and Computer Engineering Technology and Science Porto Portugal

The detection and extraction of individual pylons and power lines from high-density point cloud (PC) LiDAR data are a relevant tool for evaluating the power lines utility corridors. Moreover, the presence of high vegetation and hilly terrain is a research challenger in the available methods. The paper presents a novel method for the extraction of pylons and power lines. Two steps compose the proposed approach: a pylon detection step based on top view projection, denoted by DFSS - Detect Filled Square Shapes, and a pylon arms detection step with the DPA - Detect Pylon Arm algorithm. The results show that the proposed method could accurately and automatically extract pylons and the associated power lines, even if the dataset has low quality with downsampling, to reduce the processing time. Field tests were performed with a ground static LiDAR and a point cloud affected by downsampling voxel grid and Gaussian noise to simulate the expected LiDAR data from a UAV.

关键词： Three-dimensional displays Laser radar Shape Gaussian noise Conferences Poles and towers Vegetation mapping

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Graph-SLAM Approach for Indoor UAV Localization in Warehouse Logistics Applications

Graph-SLAM Approach for Indoor UAV Localization in Warehouse...

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IEEE International Conference on Autonomous Robot systems and Competitions (ICARSC)

作者： A. Moura J. Antunes A. Dias A. Martins J. Almeida ISEP Instituto Superior de Engenharia do Porto Porto Portugal INESC TEC Institute for Systems and Computer Engineering Technology and Science Porto Portugal

ISBN: (纸本)9781665431989;9781665431996

Unmanned Aerial Vehicles (UAVs) are a key ingredient in the industry and in warehouse logistics digital transformation process, providing the ability to perform automatic cyclic counting and real-time inventory, localize hard-to-find items and reach narrow storage areas. The use of UAVs poses new challenges, such as indoor autonomous localization and navigation, collision avoidance and automated UAV fleet management. This paper addresses the development of a vision-based Graph-SLAM approach for UAV indoor localization without predefined warehouse markers positions. A framework is proposed and developed to support different commercial UAV platforms, allowing the estimation in real-time of the UAV position and attitude. Indoor experimental tests were carried out in order to evaluate the performance of the developed method, comparing the results obtained with an approach based on the pre-mapped markers position indoor localization method.

关键词： Location awareness Visualization Estimation Unmanned aerial vehicles Real-time systems Task analysis Trajectory optimization

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