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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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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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Novel Approach for Offshore Photovoltaic Panels Inspection with VTOL UAV

Novel Approach for Offshore Photovoltaic Panels Inspection w...

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OCEANS

作者： R. Morais João J. Martins P. Lima A. Dias A. Martins J. Almeida E. Silva Institute for Systems and Computer Engineering Technology and Science (INESC TEC) Instituto Superior de Engenharia do Porto (ISEP) Porto Portugal

ISBN: (数字)9798350362077

ISBN: (纸本)9798350362084

Solar energy will contribute to global economic growth, increasing worldwide photovoltaic (PV) solar energy production. More recently, one of the outstanding energy achievements of the last decade has been the development of floating photovoltaic panels. These panels differ from conventional (ter-restrial) panels because they occupy space in a more environmen-tally friendly way, i.e., aquatic areas. In contrast, land areas are saved for other applications, such as construction or agriculture. Developing autonomous inspection systems using unmanned aerial vehicles (UAV s) represents a significant step forward in solar PV technology. Given the frequently remote and difficult-to-access locations, traditional inspection methods are no longer practical or suitable. Responding to these challenges, an in-novative inspection framework was developed to autonomously inspect photovoltaic plants (offshore) with a Vertical Takeoff and Landing (VTOL) UAV. This work explores two different methods of autonomous aerial inspection, each adapted to specific scenarios, thus increasing the adaptability of the inspection process. During the flight, the aerial images are evaluated in real-time for the autonomous detection of the photovoltaic modules and the detection of possible faults. This mechanism is crucial for making decisions and taking immediate corrective action. An offshore simulation environment was developed to validate the implemented system.

关键词： Economics Oceans Solar energy Production Inspection Autonomous aerial vehicles Real-time systems

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12th International Conference on Mobile Communication and Healthcare, MobiHealth 2023

作者： Fontes, Miguel Leite, Danilo Dallyson, João Cunha, António UTAD—University of Trás-os-Montes and Alto Douro Vila Real5001-801 Portugal INESC TEC—Institute for Systems and Computer Engineering Technology and Science Porto4200-465 Portugal UFMA—Federal University of Maranhão São Luís Brazil

ISBN: (纸本)9783031606649

Artificial intelligence (AI) is playing a growing role today in several areas, especially in health, where understanding AI models and their predictions is extremely important for health professionals. In this context, Explainable AI (XAI) plays a crucial role in seeking to provide understandable explanations for these models. This article analyzes two different XAI approaches applied to analyzing gastric endoscopy images. The first, more conventional approach uses Grad CAM, while the second, even less explored but with great potential, is based on "similarity-based explanations". This example-based XAI technique aims to provide representative examples to support the decisions of AI models. In this study, we compare these two techniques applied to two different models: one based on the VGG16 architecture and the other based on ResNet50, designed to classify images from the KVASIR-capsule database. The results reveal that Grad-CAM provided intuitive explanations only for the VGG16 model, while the "similarity-based explanations" technique provided consistent explanations for both models. We conclude that exploring other XAI techniques can be a significant asset in improving the understanding of the various AI models. © ICST institute for computer sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Endoscopy

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Novel RSSI-based Localization System for Cattle and Animal Tracking

Novel RSSI-based Localization System for Cattle and Animal T...

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2019 International Conference on engineering Applications, ICEA 2019

作者： Santos, Victor D.N. Neves, Bernardino Ferreira, N.M. Fonseca Coimbra Polytechnic ISEC Rua Pedro Nunes-Quinta da Nora Coimbra3030-199 Portugal Institute for Systems and Computers Engineering at Coimbra INESC Coimbra Rua Sílvio Lima Pólo II DEEC Coimbra3030-790 Portugal Technology and Science-INESC TEC Institute for Systems and Computer Eng. Rua Dr. Roberto Frias Porto4200-465 Portugal

ISBN: (纸本)9781728129624

This paper presents a novel radio localization system based on radio signal strength measurements to estimate the location of animals on a farm. The system uses low-cost mobile devices based on a microcontroller, a radio transceiver and an antenna. The developed solution works on the 433 MHz band and comprises innovative protocols that use the radio wake-up concept in order to minimize the mobile unit energy consumption, promoting that way its autonomy. A software application was developed to track the animals on a real-time basis using the Internet of Things paradigm. © 2019 IEEE.

关键词： Animals

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A hybrid heuristics artificial intelligence feature selection for intrusion detection classifiers in cloud of things

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Cluster Computing 2023年第1期26卷 599-612页

作者： Sangaiah, Arun Kumar Javadpour, Amir Ja’fari, Forough Pinto, Pedro Zhang, Weizhe Balasubramanian, Sudha Department of Industrial Engineering and Management National Yunlin University of Science and Technology Yunlin Taiwan Harbin Institute of Technology Shenzhen China Department of Computer Engineering Sharif University of Technology Tehran Iran ADiT-Lab Electrotechnics and Telecommunications Department Instituto Politécnico de Viana do Castelo Porto4900-347 Portugal School of Computing Science and Engineering VIT University Vellore India Universidade da Maia Maia4475-690 Portugal INESC TEC Porto4200-465 Portugal

Cloud computing environments provide users with Internet-based services and one of their main challenges is security issues. Hence, using Intrusion Detection systems (IDSs) as a defensive strategy in such environments is essential. Multiple parameters are used to evaluate the IDSs, the most important aspect of which is the feature selection method used for classifying the malicious and legitimate activities. We have organized this research to determine an effective feature selection method to increase the accuracy of the classifiers in detecting intrusion. A Hybrid Ant-Bee Colony Optimization (HABCO) method is proposed to convert the feature selection problem into an optimization problem. We examined the accuracy of HABCO with BHSVM, IDSML, DLIDS, HCRNNIDS, SVMTHIDS, ANNIDS, and GAPSAIDS. It is shown that HABCO has a higher accuracy compared with the mentioned methods. © 2022, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Cloud computing

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Deep Spatio-Temporal Ensemble to Improve Wind Speed Forecasting

SSRN

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SSRN 2023年

作者： Raynaud-Sanchez, Jaime Garcia-Gutierrez, Jorge Gonçalves-Seco, Luis Division of Computer Science University of Seville Seville41012 Spain INESC TEC - Institute for Systems and Computer Engineering Technology and Science Maia Porto4475-690 Portugal

The accuracy of wind speed forecasting is critical for effectively integrating renewable energy sources, such as wind power, into the electrical grid. In recent years, deep learning techniques have gained popularity for forecasting renewable energy, especially in the field of spatio-temporal wind forecasting due to their ability to uncover the spatial and temporal correlations in wind speed data. In this context, an ensemble of three Recurrent Neural Networks is proposed for improving spatio-temporal wind speed forecasting, aiming to determine the efficacy of an ensemble approach in enhancing the precision of a state-of-the-art spatio-temporal wind speed forecasting method obtained from the literature. To establish solid results, experiments were conducted on 31 spatio-temporal wind speed datasets, obtained from the National Renewable Energies Laboratory, representing various locations worldwide. With the proposed approach, the ensemble algorithm presented demonstrates the ability to enhance the forecasting performance of the state-of-the-art technique from the literature upon which it is based, reducing its Mean Absolute Error on average by 45.82% and producing statistically significant results. © 2023, The Authors. All rights reserved.

关键词： Wind power

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A deep learning model for detection of traffic events based on social networks publications

A deep learning model for detection of traffic events based ...

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2022 International Conference on Industry sciences and computer science Innovation, iSCSi 2022

作者： Capela, Silvio Pereira, Vitor Duque, Jorge Filipe, Vitor School of Science and Technology Universidade de Tras-os-Montes e Alto Douro Vila Real5000-801 Portugal ISLA Instituto Politecnico de Gestao e Tecnologia Vila Nova de Gaia4400-107 Portugal INESC TEC Institute for Systems and Computer Engineering Technology and Science Porto4200-465 Portugal

Nowadays, social networks are one of the biggest ways of sharing real time information. These networks, have several groups focused on sharing information about road incidents and other traffic events. The work here presented aims the creation of an AI model capable of identifying publications related to traffic events in a specific road, based on publications shared on social networks. A predictive model was obtained by training a deep learning model for the detection of publications related with road incidents with an average accuracy of 95%. The model deployed as a service is already fully functional and is operating in 24/7 while awaits a final integration with the road management system of a company where it will be used to support the Control Center team in the decision making. © 2022 Elsevier B.V.. All rights reserved.

关键词： Decision making

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Adequacy of the long-term operational reserve of a system with wind power and electric vehicles under severe scenarios

Adequacy of the long-term operational reserve of a system wi...

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International Conference on Probabilistic Methods Applied to Power systems (PMAPS)

作者： L. E. Bremermann M. A. da Rosa M. Matos J. A. P. Lopes L. M. Carvalho I. C. Costa INESC TEC - Institute for Systems and Computer Engineering Technology and Science (formerly INESC Porto) Portugal UFSC Federal University of Santa Catarina Brazil Faculty of Engineering University of Porto Portugal

Combining conventional and unconventional generations with hard forecasting properties and consumption variability has made the task of fitting large amounts of wind generation into unit commitment procedures very challenging. In this context, massive integration of electric vehicles in the electric systems, is expected. This paper aims at evaluating the adequacy of a generating system taking into account the electric vehicles and high wind power integration level in its energy mix. This study was conducted on the European project MERGE framework, and the results have shown that the generating system evaluated is reliable. Nevertheless, the simulations carried out for scenarios with adverse weather conditions (dry years) have revealed specific circumstances that might jeopardize the system adequacy.

关键词： Wind power generation Load modeling Reliability Uncertainty Forecasting Wind forecasting Batteries

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