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International Conference on Smart Energy systems and technologies (SEST)

作者： Mahsa Rezaei Maryam Dehghani Navid Vafamand Bita Shayanfard Mohammad Sadegh Javadi João P. S. Catalão School of Electrical and Computer Engineering Shiraz University Shiraz Iran Fars Regional Electric Company Shiraz Iran Institute for Systems and Computer Engineering Technology and Science (INESC TEC.) Porto Portugal Faculty of Engineering of the University of Porto (FEUP) and INESC TEC Porto Portugal

ISBN: (数字)9781728147017

ISBN: (纸本)9781728147024

Phasor Measurement Unit (PMU) provides beneficial information for dynamic power system stability, analysis and control. One main application of such useful information is data-driven control. This paper is devoted to presenting an approach for optimal signal selection in PMU-based power system stabilizer (PSS) design. In this paper, for selecting the optimal input and output signals for PSS, an algorithm is suggested in which the combination of clustering the generators and the buses of the system with ICA, modal analysis and PCA techniques is used. The solution for optimal PSS input-output selection is found to increase the observability and damping of the power system. This method is simulated on a 68 buses system with 16 machines. To compare the results with the previous methods, the system is simulated and the results of two previously-developed algorithms are compared with the proposed approach. The results show the benefit of the suggested method in reducing the required signals, which lowers the number of required PMUs while the system damping is not deteriorated.

关键词： Covariance matrices Phasor measurement units Generators Modal analysis Principal component analysis Observability Power system stability

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Simulation environment for multi-robot cooperative 3D target perception

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8810卷 98-109页

作者： Dias, André Almeida, Jose Dias, Nuno Lima, Pedro Silva, Eduardo INESC TEC INESC Technology and Science ISEP/IPP School of Engineering Porto Portugal Institute for Systems and Robotics Instituto Superior Técnico Universidade de Lisboa Lisbon Portugal

Field experiments with a team of heterogeneous robots require human and hardware resources which cannot be implemented in a straightforward manner. Therefore, simulation environments are viewed by the robotic community as a powerful tool that can be used as an intermediate step to evaluate and validate the developments prior to their integration in real robots. This paper evaluates a novel multi-robot heterogeneous cooperative perception framework based on monocular measurements under the MORSE robotic simulation environment. The simulations are performed in an outdoor environment using a team of Micro Aerial Vehicles (MAV) and an Unmanned Ground Vehicle (UGV) performing distributed cooperative perception based on monocular measurements. The goal is to estimate the 3D target position. © Springer International Publishing Switzerland 2014.

关键词： Ground vehicles

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Joint analysis of the Portuguese and Spanish NECP for 2021-2030

Joint analysis of the Portuguese and Spanish NECP for 2021-2...

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International Conference on European Electricity Market, EEM

作者： André Rodrigues de Oliveira José Villar Collado João Abel Peças Lopes João Paulo Tomé Saraiva Nuno Soares Fonseca Salvador Doménech Fco. Alberto Campos INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal Institute for Research in Technology Comillas Pontifical University c/ Santa Cruz de Marcenado Madrid Spain

ISBN: (数字)9781728169194

ISBN: (纸本)9781728169200

The European Union (EU) energy strategy towards decarbonization led EU countries to elaborate their corresponding National Energy and Climate Plans (NECP) for the period 2021 to 2030. This paper analyzes the Portuguese and Spanish NECPs concerning their power systems. CEVESA, a model for the long-term planning and operation of the Iberian electricity system, is used. The analysis is based on simulating the reference NECP scenario, as well as other alternative scenarios with different solar and wind generation shares, CO2 prices and fuel costs. Results provide insights on the MIBEL electricity market evolution under the current decarbonization national strategies.

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Reconstruction and generation of virtual heritage sites

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Digital Applications in Archaeology and Cultural Heritage 2014年第3-4期1卷 92-102页

作者： Rodrigues, N. Magalhães, L. Moura, J. Chalmers, A. School of Technology and Management Computer Science and Communication Research Centre Polytechnic Institute of Leiria Leiria Portugal Universidade de Trás-os-Montes e Alto Douro Vila Real Portugal Institute for Systems and Computer Engineering of Porto (INESC Porto) Porto Portugal Digital Laboratory WMG University of Warwick United Kingdom

Traditionally procedural modelling techniques are commonly used to generate new structures and are presently established in several areas such as video games and computer animated movies. However they may also be used in heritage applications to efficiently produce models of non-existing worlds for which there is some kind of knowledge (e.g. floor plans, photographs) to support the reconstruction of realistic environments. Similarly they may also be used to support the generation of distinct possibilities that allow experts to draw some conclusions or conceive different hypotheses about lost worlds. The present paper shows the benefits and constraints that may arise from the use of such techniques in virtual heritage applications. Furthermore, a whole method is proposed, for the reconstruction and generation of virtual heritage traversable house models, provided through the means of a grammar, demonstrated with the reconstruction and generation of several Roman houses from the heritage site of Conimbriga, Portugal. © 2014 Elsevier Ltd. All rights reserved.

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Street Light Segmentation in Satellite Images Using Deep Learning

Street Light Segmentation in Satellite Images Using Deep Lea...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Ana Cláudia Teixeira Gabriel Carneiro Vitor Filipe António Cunha Joaquim J. Sousa University of Trás-os-Montes and Alto Douro Vila Real Portugal Institute for Systems and Computer Engineering Technology and Science (INESC TEC) Porto Portugal

Public lighting plays a very important role for society's safety and quality of life. The identification of faults in public lighting is essential for the maintenance and prevention of safety. Traditionally, this task depends on human action, through checking during the day, representing expenditure and waste of energy. Automatic detection with deep learning is an innovative solution that can be explored for locating and identifying of this kind of problem. In this study, we present a first approach, composed of several steps, intending to obtain the segmentation of public lighting, using Seville (Spain) as case study. A dataset called NLight was created from a nighttime image taken by the JL1-3B satellite, and four U-Net and FPN architectures were trained with different backbones to segment part of the NLight. The U-Net with InceptionResNetv2 proved to be the model with the best performance, obtained 761 of 815, correct locations (93.4%). This model was used to predict the segmentation of the remaining dataset. This study provides the location of lamps so that we can identify patterns and possible lighting failures in the future.

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Short-term Load Forecasting based on Wavelet Approach

Short-term Load Forecasting based on Wavelet Approach

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International Conference on Smart Energy systems and technologies (SEST)

作者： Ali Karami Ghanavati Amir Afsharinejad Navid Vafamand Mohammad Mehdi Arefi Mohammad Sadegh Javadi João P. S. Catalão School of Electrical and Computer Engineering Shiraz University Shiraz Iran Institute for Systems and Computer Engineering Technology and Science (INESC TEC) Porto Portugal Faculty of Engineering University of Porto (FEUP) Porto Portugal

ISBN: (数字)9781728147017

ISBN: (纸本)9781728147024

This paper develops a novel short-term load forecasting technique to predict the demanding power for the next hour. In this study, a linear equation-error Auto Regressive Auto Regressive Moving Average Exogenous (ARARMAX) model is trained to specify power consumption as a function of a few past hours. The parameters of the candidate mathematical model are estimated by using two least squares-based iterative algorithms. The main difference with these algorithms is the total number of past data involved in the modeling. Whereas practical data are always subject to noise and un-accurate measuring, a wavelet de-noising technique is utilized to reduce the effect of noise on forecasting which leads to more precise predictions. The superiority of the proposed approach is validated by utilizing practical data from a power utility in Canada in January 1995. The first three days' data are utilized to train the selected model and the fourth-day data are dedicated to test the prediction of the provided model. The L 2 and L ∞ norms error and MAPE, MAE, and RMSE are selected as criteria to show the merits of the proposed approach.

关键词： Mathematical model Load modeling Load forecasting Autoregressive processes Computational modeling Wavelet transforms Data models

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Flexibility-Oriented Scheduling of Microgrids Considering the Risk of Uncertainties

Flexibility-Oriented Scheduling of Microgrids Considering th...

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International Conference on Smart Energy systems and technologies (SEST)

作者： Mohammad MansourLakouraj Mohammad Sadegh Javadi João P. S. Catalão Department of Electrical and Computer Engineering Babol Noshirvani University of Technology Babol Iran Institute for Systems and Computer Engineering Technology and Science (INESC TEC) Porto Portugal Faculty of Engineering University of Porto (FEUP) Porto Portugal

ISBN: (数字)9781728147017

ISBN: (纸本)9781728147024

Increasing the penetration of renewable resources has aggravated the operational flexibility at distribution level. In this study, a flexibility-oriented scheduling of microgrids (MGs) is suggested to reduce the power fluctuations in distribution feeders caused by the high penetration of wind turbines (WTs) in MGs. A flexibility constraint as viable and practical solution is used in MG scheduling to address this challenge. The presented scheduling model, implemented using mixed integer linear programming (MILP) and a stochastic framework, exercises risk constraints to capture the uncertainties associated with wind turbines, loads and market prices. The effectiveness of the model is investigated on a MG with high penetration of WTs in the presence of demand response (DR) and energy storage systems (ESSs). Numerical studies show the influence of risk parameters' changing on operation costs. In addition, the flexibility constraint mitigates the sharp variation of the net load at distribution level, which improves the flexibility of the distribution system.

关键词： Uncertainty Stochastic processes Wind turbines Scheduling Load modeling Power systems Processor scheduling

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Industrial Digitalization Solutions for Precision Forestry Towards Forestry 4.0 1

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7th International Conference on Smart City Applications, SCA 2022

作者： Torres, Pedro M. B. Spencer, Geoffrey Neto, Luís Gonçalves, Gil Dionísio, Rogério Instituto Politécnico de Castelo Branco Castelo Branco6000-084 Portugal SYSTEC - Research Center for Systems and Technologies Porto4200-465 Portugal DiSAC - R &D Unit for Digital Services Applications and Content Castelo Branco6000-767 Portugal INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto4200-465 Portugal

ISBN: (数字)9783031268526

ISBN: (纸本)9783031268519

This paper presents machine digitalization solutions with particular application in forest machines, such as harvesters and wood processing machines. In line with all the requirements of Industry 4.0, this type of machines also needs digitization to align with the concept defined as Forestry 4.0, where we think of a smarter forest in which all stakeholders, humans, forest producers, machines and factories communicate. For machine manufacturers is a step that must be taken to modernize machines, enabling remote access services for maintenance, productivity monitoring, and management of forest operations. It consists of developing cyber-physical systems around the machines with digital twins that allow the simulation and identification of faults that may occur. A solution is presented to enable CAN Bus communication between the controller, operator joysticks, and sensors/actuators, as well as a Digital Twin solution to emulate machine operations. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Industry 4.0

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Automatic Identification of Public Lighting Failures in Satellite Images: A Case Study in Seville, Spain

Automatic Identification of Public Lighting Failures in Sate...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Ana Cláudia Teixeira Leonor Batista Gabriel Carneiro António Cunha Joaquim J. Sousa University of Trás-os-Montes and Alto Douro Vila Real Portugal Institute for Systems and Computer Engineering Technology and Science (INESC TEC) Porto Portugal

Public lighting is crucial for maintaining the safety and well-being of communities. Current inspection methods involve examining the luminaires during the day, but this approach has drawbacks, including energy consumption, delay in detecting issues, and high costs and time investment. Utilising deep learning based automatic detection is an advanced method that can be used for identifying and locating issues in this field. This study aims to use deep learning to automatically detect burnt-out street lights, using Seville (Spain) as a case study. The study uses high-resolution night time imagery from the JL1-3B satellite to create a dataset called NLight, which is then divided into three subsets: NL1, NL2, and NT. The NL1 and NL2 datasets are used to train and evaluate YOLOv5 and YOLOv7 segmentation models for instance segmentation of streets. And then, distance outliers were detected to find the lights off. Finally, the NT dataset is used to evaluate the effectiveness of the proposed methodology. The study finds that YOLOv5 achieved a mask mAP of 57.7%, and the proposed methodology had a precision of 30.8% and a recall of 28.3%. The main goal of this work is accomplished, but there is still space for future work to improve the methodology.

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Patch-based CNN Models for Bone Marrow Edema Detection Using MRI

Patch-based CNN Models for Bone Marrow Edema Detection Using...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： André Gomes Tania Pereira Francisco Silva Pedro Franco Diogo Costa Carvalho Sílvia Costa Dias 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 FCUP - Faculty of Science University of Porto Porto Portugal CHUSJ - Centro Hospitalar e Universitário de São João Porto Portugal Faculty of Medicine University of Porto Porto Portugal

Bone marrow edema (BME) or bone marrow lesion is the term attributed to an observed signal change within the bone marrow in magnetic resonance imaging (MRI). BME can be originated from multiple mechanisms, with pain being the main symptom. The presence of BME is an unspecific but sensitive sign with a wide differential diagnosis, that may act as a guide that leads to a systematic and correct interpretation of the magnetic resonance examination. An automatic approach for BME detection and quantification aims to reduce the overload of clinicians, decreasing human error and accelerating the time to the correct diagnosis. In this work, the bone region on the MRI slice was split into several patches and a CNN-based model was trained to detect BME in each patch from the MRI slice. The learning model developed achieved an AUC of 0.853 ± 0.056, showing that the CNN-based model can be used to detect BME in the MRI and confirming the patch strategy implemented to deal with the small data size and allowing the neural network to learn the specific information related with the classification task by reducing the region of the image to be considered. A learning model that can help clinicians with BME identification will decrease the time and the error for the diagnosis, and represent the first step for a more objective assessment of the BME.

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