RCIPL
Repositório Institucional do Politécnico de Lisboa
Entradas recentes
Topology-aware neural networks for abnormal consumption detection and location in water distribuition networks
Publication . Caetano, João; Carriço, Nelson; Brentan, Bruno; Menapace, Andrea; Covas, Didia
This paper presents a topology-aware neural network approach for the detection, location, and quantification of abnormal consumptions in water distribution networks. The approach includes two main steps: the optimization of pressure sensor locations to maximize measurement sensitivity and the development of metamodels based on near real-time data. The metamodel is designed and trained to predict the consumptions at all nodes based on pressure measurements and users' consumption collected by smart meters. These nodal consumptions deduced from the actual measured consumption allow the location of potential abnormal uses in the network. The proposed methodology enables the development of two metamodels, each tailored to specific applications based on the training data. The Static Metamodel relies on pressure head measurements under the assumption of constant nodal consumption, whereas the Dynamic Metamodel accounts for daily consumption variations, enabling the detection and location of abnormal consumption in real-world scenarios. Both metamodels can detect the location of abnormal consumptions with reasonable accuracy, although this accuracy strongly depends on the number and spatial distribution of sensors, as well as the magnitude and location of the abnormal consumption. As water utilities implement advanced metering systems, the application of the proposed approach becomes more viable, enabling more effective and faster abnormal consumption detection.
Resisting to dystopias of bodily control: dance training and anorexia/bulimia
Publication . De Lima, Cecília; Performance Research
ABASTRACT - Dance training aims for a utopia of bodily control in order to develop an artistic expression through movement. The dancer seeks to control their body according to the values and aesthetics intrinsic to the training methodology. The urge of control has always been something very keen to humankind but also very sensitive and ambiguous. Different methodologies of such modes of control reflect not only different aesthetics, but also different values and visions of the living body. They are not innocuous physical training but transformative practices of the self in relation to the world. Therefore, such utopia raises critical questions: What is the nature of (self-)control envisioned within dance trainings? What aesthetic values form the horizon of a training process and how does its transformative power operate?
This paper departs from a personal experiential process to expose a critical perspective on the practice of bodily control developed by some dance trainings. Such perspective is conceived through an interrelation between traditional ballet training with the state of anorexia nervosa/bulimia, which is counterpointed by somatic dance training.
Grounded on an empirical understanding this is a practitioner narrative about utopian dance training and a manifesto against any practice of control that becomes a dystopia of oppression and annihilation of the fundamental knowledge intrinsic to the living body. Instead it cries out for a new perspective on the notion of control. Control needs to be perceived as a practice of deep understanding of the nature of the living body, as a condition of the body–world transformative process.
Differentiable neural search architecture with zero-cost metrics for insulator fault prediction
Publication . Seman, Laio Oriel; Buratto, William Gouvêa; Gonzalez, Gabriel Villarrubia; Leithardt, Valderi Reis Quietinho; Nied, Ademir; Stefenon, Stefano Frizzo
Reliable monitoring of high-voltage insulators is critical for maintaining the stability of electrical power systems, particularly under environmental contamination that can lead to flashover. Traditional inspection techniques struggle to anticipate degradation dynamics, while data-driven models often rely on fixed neural architectures that inadequately capture the complex temporal patterns in leakage current signals. This work proposes a Differentiable Neural Architecture Search (DARTS) framework, based on zero-cost metrics, tailored for time series forecasting in insulator monitoring. The method based on DARTS integrates a mixed encoder-decoder design with learnable selection over long short-term memory, gated recurrent units, and transformer components, coupled with a cross-attention bridge featuring temporal bias and gating mechanisms. To ensure efficient architecture exploration, the search leverages metrics such as SynFlow and Jacobian covariance for early candidate screening, followed by a bilevel optimization stage with entropy and diversity regularization. Experiments on real-world leakage current data demonstrate that the discovered architectures outperform manually designed baselines, offering improved forecasting performance.
Input attention, squeeze and excitation, and spatial transformer of YOLO for fault detection using UAV
Publication . Carvalho, João Pedro Matos; Stefenon, Stefano Frizzo; Leithardt, Valderi Reis Quietinho; Seman, Laio Oriel; Yow, Kin-Choong; Santana, Juan Francisco De Paz
The detection of faults in insulators is important to guarantee the continuous supply of electricity. To identify faults in these components, various object detection methods based on deep learning have been explored. This paper investigates architectural enhancements to the You Only Look Once (YOLO) framework for fault detection in electrical power grid insulators. Three structural variants are proposed: the Input Attention Transformer (IAT-YOLO) for spatial feature refinement, Squeeze-and-Excitation (SAE-YOLO) modules for channel recalibration, and Spatial Transformer Networks (STN-YOLO) for geometric alignment. Experiments were conducted on a publicly available insulator dataset from Unmanned Aerial Vehicles (UAVs), comprising seven defect categories, including pollution, breakage, and flashover damage. Results demonstrate that STN-YOLO and SAE-YOLO consistently improve generalization and robustness, achieving mAP values of up to 0.995 for specific classes. The findings highlight the effectiveness of integrating attention mechanisms and spatial transformations to enhance YOLO-based detection, contributing to improved automated inspection of the power grid.
Analysis of reinforced cork composite sandwich beams
Publication . Costa, Sérgio Filipe Marques Campos; Leite, Afonso Manuel da Costa de Sousa; Santos, Hugo Alexandre Freixial Argente dos
Abstract
Considering the continuous increase in the use of lightweight, biological, and renewable materials across all industries, cork—being a material that meets all these requirements while also offering exceptional mechanical characteristics such as high resistance to compression as well as to tension—generates significant interest for testing and studying its behavior in engineering applications. Thus, this study aims to carry out an experimental and numerical investigation of composite sandwich beams using cork agglomerate as the core, compare the results with previous studies and with other materials widely used today, and discuss their feasibility and limitations in potential applications. Several specimens were used, distributed into three types: T1, T2, and T3. The specimens are made of laminated composite, using epoxy resin and glass fiber fabric for the skins, and a cork agglomerate core. The different beam types also present different configurations of skin and core thicknesses and include fiber layers in the core region as well. Three-point bending tests were performed according to ASTM C393 to study their mechanical behavior, calculate their flexural stiffness, shear strength, and other relevant properties such as bending stress in the skins, critical shear stress of the core, and the shear stiffness of the sandwich structure. A numerical study based on finite element analysis of a 2D model developed in Ansys software was conducted to simulate the behavior of the specimens in the linear elastic regime and to verify the experimental results. The numerical results deviate from the experimental ones with respect to the deformed shape; however, there is a reasonable correlation in terms of the force–displacement curve in the linear elastic regime.