ISEL - Eng. Elect. Tel. Comp. - Artigos
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Percorrer ISEL - Eng. Elect. Tel. Comp. - Artigos por Domínios Científicos e Tecnológicos (FOS) "Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática"
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- Assessing machine learning techniques for intrusion detection in cyber-physical systemsPublication . Santos, Vinicius F.; Albuquerque, Célio; Passos, Diego; Ereno Quincozes, Silvio; Mossé, DanielCyber-physical systems (CPS) are vital to key infrastructures such as Smart Grids and water treatment, and are increasingly vulnerable to a broad spectrum of evolving attacks. Whereas traditional security mechanisms, such as encryption and firewalls, are often inadequate for CPS architectures, the implementation of Intrusion Detection Systems (IDS) tailored for CPS has become an essential strategy for securing them. In this context, it is worth noting the difference between traditional offline Machine Learning (ML) techniques and understanding how they perform under different IDS applications. To answer these questions, this article presents a novel comparison of five offline and three online ML algorithms for intrusion detection using seven CPS-specific datasets, revealing that offline ML is superior when attack signatures are present without time constraints, while online techniques offer a quicker response to new attacks. The findings provide a pathway for enhancing CPS security through a balanced and effective combination of ML techniques.
- An autonomic parallel strategy for exhaustive search tree algorithms on shared or heterogeneous systemsPublication . Gonçalves de Oliveira Passos, Fernanda; Rebello, Vinod E. F.Backtracking branch-and-prune (BP) algorithms and their variants are exhaustive search tree techniques widely employed to solve optimization problems in many scientific areas. However, they characteristically often demand significant amounts of computing power for problem sizes representative of real-world scenarios. Given that their search domains can often be partitioned, these algorithms are frequently designed to execute in parallel by harnessing distributed computing systems. However, to achieve efficient parallel execution times, an effective strategy is required to balance the nonuniform partition workloads across the available resources. Furthermore, with the increasing integration of servers with heterogeneous resources and the adoption of resource sharing, balancing workloads is becoming complex. This paper proposes a strategy to execute parallel BP algorithms more efficiently on even shared or heterogeneous distributed systems. The approach integrates a self-adjusting dynamic partitioning method in the BP algorithm with a dynamic scheduler, provided by an application middleware, which manages the parallel execution while addressing any issues of imbalance. Empirical results indicate better scalability with efficiencies above 90% for instances of an application case study for the discretizable molecular distance geometry problem (DMDGP). Improvements of up to 38% were obtained in execution speed-ups compared to a more traditional parallel BP implementation for DMDGP.
- Cognitive radio with machine learning to increase spectral efficiency in indoor application on the 2.5 GHz bandPublication . Soares, Marilson Duarte; Passos, Diego; Castellanos, Pedro Vladimir GonzalezDue to the propagation characteristics in the 2.5 GHz band, the signal is significantly degraded by building entry loss (BEL), making coverage in indoor environments in some cases non-existent. Signal degradation inside buildings is a challenge for planning engineers, but it can be seen as a spectrum usage opportunity for a cognitive radio communication system. This work presents a methodology based on statistical modeling of data collected by a spectrum analyzer and the application of machine learning (ML) to leverage the use of those opportunities by autonomous and decentralized cognitive radios (CRs), independent of any mobile operator or external database. The proposed design targets using as few narrowband spectrum sensors as possible in order to reduce the cost of the CRs and sensing time, as well as improving energy efficiency. Those characteristics make our design especially interesting for internet of things (IoT) applications or low-cost sensor networks that may use idle mobile spectrum with high reliability and good recall.
- Convolutional neural network approach for fault detection and characterization in medium voltage distribution networksPublication . Shafei, Atefeh Pour; Silva, J. Fernando A.; Monteiro, J.; ElsevierPower outages significantly impact the power industry by disrupting social welfare and economic stability. Still, existing methods for fault detection face challenges due to load and network topology, conditions, and installed equipment. However, recent advances in artificial intelligence (AI) are enabling researchers to create alternative approaches for fault detection and location strategies. Therefore, this paper introduces a novel method for detecting, classifying, and locating faults in power systems through voltage waveform analysis using a convolutional neural network (CNN) integrated with the Piecewise Function Put Together (PFPT) algorithm for fault detection and fault zone localization in a power distribution network. Utilizing Park's transformation, noise reduction PFPT sine fitting, and CNNs, the proposed method distinguishes between 'healthy' and 'faulty' conditions. Simulation results reveal that while the voltage Park's vector time behavior of a healthy system remains stable, it exhibits circular or mixed patterns under faulty conditions. These patterns enable the identification of four types of short circuit faults—single-line-to-ground (LG), line-to-line (LL), line-to-line-to-ground (LLG), and three-line (3L) faults—by analyzing 3D voltage Park's waveforms at network buses. The study validates fault type identification through the observation of rotating Park vectors from sine fitting of time-based voltage waveforms. By converting 3D voltage waveforms into high-resolution images, the method utilizes a CNN for fault recognition, achieving an accuracy of 93.1%. This innovative approach underscores the robustness and precision of combining traditional electrical engineering techniques with modern AI.
- DC-DC converter with Buck-Boost characteristics and high voltage gain based on the differential conceptPublication . Pires, V. Fernão; Foito, Daniel; Cordeiro, Armando; Monteiro, Joaquim; Pinto, Sónia; Silva, J. Fernando; Monteiro, Joaquim; Cordeiro, Armando; IEEEThis paper introduces a novel DC-DC converter based on the differential concept. This topology can operate in interleaved mode, which, together with the differential concept, allows for a reduction in the size of the inductors. This converter is also characterized by their continuous input current. Moreover, it is also characterized by an extended voltage range when compared with the classical Buck-Boost converter. Another aspect, is that the voltage stress across the switches is reduced when compared with the output voltage. The same regarding the voltage stress across the capacitors. The proposed converter will be tested through the one of the most used simulation tools, as well as, by a laboratory prototype.
- Decentralized multi-agent reinforcement learning with visible light communication for robust urban traffic signal controlPublication . Augusto Vieira, Manuel; Gonçalo Galvão; Vieira, Manuela; Véstias, Mário; Louro, Paula; Vieira, PedroThe rapid growth of urban vehicle and pedestrian flows has intensified congestion, delays, and safety concerns, underscoring the need for sustainable and intelligent traffic management in modern cities. Traditional centralized traffic signal control systems often face challenges of scalability, heterogeneity of traffic patterns, and limited real-time adaptability. To address these limitations, this study proposes a decentralized Multi-Agent Reinforcement Learning (MARL) framework for adaptive traffic signal control, where Deep Reinforcement Learning (DRL) agents are deployed at each intersection and trained on local conditions to enable real-time decision-making for both vehicles and pedestrians. A key innovation lies in the integration of Visible Light Communication (VLC), which leverages existing LED-based infrastructure in traffic lights, streetlights, and vehicles to provide high-capacity, low-latency, and energy-efficient data exchange, thereby enhancing each agent’s situational awareness while promoting infrastructure sustainability. The framework introduces a queue–request–response mechanism that dynamically adjusts signal phases, resolves conflicts between flows, and prioritizes urgent or emergency movements, ensuring equitable and safer mobility for all users. Validation through microscopic simulations in SUMO and preliminary real-world experiments demonstrates reductions in average waiting time, travel time, and queue lengths, along with improvements in pedestrian safety and energy efficiency. These results highlight the potential of MARL–VLC integration as a sustainable, resilient, and human-centered solution for next-generation urban traffic management.
- Decomposition-driven Mamba state space models with expert routing for wind curtailment forecastingPublication . Seman, Laio Oriel; Stefenon, Stefano Frizzo; Matos-Carvalho, João Pedro; Stefenon, Stefano Frizzo; ElsevierWind power curtailment, known in Brazil as constrained-off, has intensified with the rapid expansion of renew able generation, driven by transmission operational constraints. Accurate forecasting of curtailment is essential for grid planning, compensation mechanisms, and infrastructure optimization. However, constrained-off time series exhibit strong non-stationarity, multi-scale behavior, and long-range dependencies that challenge conventional forecasting models. This paper proposes a decomposition-driven Mamba state space framework with Mixture-of-Experts (MoE) routing for wind curtailment forecasting. The approach integrates Entropy-Guided Variational Mode Decomposition (EG-VMD) for adaptive trend and noise separation, Reversible Instance Normalization to mitigate distribution shift, multi-scale seasonal-trend modeling, and a sparse expert-based Mamba backbone with linear-time complexity. In benchmark comparisons against state-of-the-art forecasting models, the proposed MoE-Mamba reduces Mean Squared Error (MSE) by up to 27%, Root Mean Squared Error by 14.6%, and Mean Absolute Error by 16.1% relative to the strongest baselines, while improving upon a single-expert Mamba by 15.6% in MSE. The results highlight the effectiveness of decomposition-enhanced State Space Models with expert routing for scalable, long-horizon forecasting in renewable power systems.
- Decoupled Direct Power Compensators for a Dual-Inverter Based UPFCPublication . Monteiro, Joaquim; Silva, J. Fernando; Pinto, Sónia; Pires, V. Fernão; Monteiro, Joaquim; IEEEIn recent decades, Unified Power Flow Controllers (UPFCs) have become essential devices for enhancing the flexibility and controllability of modern electrical transmission networks. These devices facilitate the simultaneous control of local bus voltage and the optimization of power flow within electrical power transmission systems. The multilevel converter capability to operate with high voltage levels while generating low-distortion AC voltages, makes multilevel topologies suitable for UPFCs. Within this framework, this paper presents a novel multilevel converter topology and control strategy specifically designed for UPFC applications. The proposed solution employs a dual inverter topology driven by sliding mode direct power controllers, which are controlled via decoupled Proportional-Integral (PI) compensators. The characteristics, capabilities, and resulting flexibility of the proposed UPFC and control subsystem is validated through a series of simulation tests.
- Deep learning soft-decision GNSS multipath detection and mitigationPublication . Nunes, Fernando; Sousa, FernandoA technique is proposed to detect the presence of the multipath effect in Global Navigation Satellite Signal (GNSS) signals using a convolutional neural network (CNN) as the building block. The network is trained and validated, for a wide range of 𝐶/𝑁0 values, with a realistic dataset constituted by the synthetic noisy outputs of a 2D grid of correlators associated with different Doppler frequencies and code delays (time-domain dataset). Multipath-disturbed signals are generated in agreement with the various scenarios encompassed by the adopted multipath model. It was found that pre-processing the outputs of the correlators grid with the two-dimensional Discrete Fourier Transform (frequency-domain dataset) enables the CNN to improve the accuracy relative to the time-domain dataset. Depending on the kind of CNN outputs, two strategies can then be devised to solve the equation of navigation: either remove the disturbed signal from the equation (hard decision) or process the pseudoranges with a weighted least-squares algorithm, where the entries of the weighting matrix are computed using the analog outputs of the neural network (soft decision).
- Development and evaluation of a mobile application with augmented reality for guiding visitors on hiking trailsPublication . Silva, Rute; Jesus, Rui; Jorge, PedroTourism on the island of Santa Maria, Azores, has been increasing due to its characteristics in terms of biodiversity and geodiversity. This island has several hiking trails; the available information can be consulted in pamphlets and physical placards, whose maintenance and updating is difficult and expensive. Thus, the need to improve the visitors’ experience arises, in this case, by using the technological means currently available to everyone: a smartphone. This paper describes the development and evaluation of the user experience of a mobile application for guiding visitors on said hiking trails, as well as the design principles and main issues observed during this process. The application is based on an augmented reality interaction model providing visitors with an interactive and recreational experience through Augmented Reality in outdoor environments (without additional marks in the physical space and using georeferenced information), helping in navigation during the route and providing updated information with easy maintenance. For the design and evaluation of the application, two studies were carried out with users on-site (Santa Maria, Azores). The first had 77 participants, to analyze users and define the application’s characteristics, and the second had 10 participants to evaluate the user experience. The feedback from participants was obtained through questionnaires. In these questionnaires, an average SUS (System Usability Scale) score of 83 (excellent) and positive results in the UEQ (User Experience Questionnaire) were obtained.