Structural health monitoring of rotary aerospace structures is investigated in this research. A monitoring system is proposed based on the electromechanical impedance spectrum of piezoelectric transducers and a portable transceiver. To investigate the applicability and preliminary results of this method, a turbomachine prototype (laboratory device) is developed, and integrated composite piezoelectric films are deposited on the blades. Next, a self-diagnostic characterization is initially implemented to the piezo-films. Transceiver functionality and accuracy is verified using an Ivium impedance analyzer. The verified measuring path was used in structural health monitoring of pristine and damaged blades at rotational speed of 0 and 1000 r/min. The effects of damage formation and rotational speed on the impedance signature are discussed based on the variations in mechanical impedance using a two-dimensional model. Once damage occurs in a blade at each speed, it results in a frequency shift of the impedance signature at antiresonance peaks compared to the corresponding baseline. The results show a clear frequency shift of existing peaks and the appearance of new peaks as damage grows to a secure minimal detectable size. This achievement confirms the applicability of this method for incipient damage detection on rotary structures prior to any failure.

Reducing maintenance costs while increasing the safety and reliability, especially in moving structures, needs a reliable nondestructive analyzer. The aim of this research is to provide a practical solution for this problem, which works based on high-frequency excitation of stationary and moving structures by propagating standing Lamb waves in the range of 1 to 1000 kHz. The proposed solution comprises of a controlled frequency swept signal source, a number of piezoelectric sensors, a portable analyzer, and a rotary mechanism. Measuring the accurate electromechanical impedance (EMI) is made possible by monitoring the applied voltages, currents, and phase differences, accurately. In this study, design and implementation of a low-cost, compact and portable transceiver is explored for periodic structural health monitoring of a proposed rotary structure using EMI technique. The compactness of the proposed system is an essential requirement for rotary structures as compared to bulky, heavy and expensive laboratory-type impedance analyzers. Challenges in design and development of such a system are discussed in this paper, together with mitigations to make the system functional and practical. An experimental study is carried out directly in frequency domain to measure the real and imaginary parts of impedance spectrum of piezo-transducers. Stationary experiments are first carried out on an aluminum plate 5083. Having ensured of the transceiver accuracy, a turbomachine prototype which is able to work at up to 3000 rpm and 300 °C is introduced to perform tests on a rotary structure. Damage detection is investigated for pristine and damaged superalloy blades considering the sensible change in mechanical stiffness of the blades. The proposed transceiver is calibrated with a standard impedance analyzer. The results show that the portable transceiver has the capability to detect structural incipient damages before any catastrophic failure, thus avoiding undesirable shut down during the operation.

This work focuses on optimizing the scheduling of virtual power plants (VPPs)—as implemented in the Portuguese national project New Generation Storage (NGS)—to maximize social welfare and enhance energy trading efficiency within modern energy grids. By integrating distributed energy resources (DERs), including renewable energy sources and energy storage systems, VPPs represent a pivotal element of sustainable urban energy systems. The scheduling problem is formulated as a Mixed-Integer Linear Programming (MILP) task and addressed by using a parallelized simulated annealing (SA) algorithm implemented on high-performance computing (HPC) infrastructure. This parallelization accelerates solution space exploration, enabling the system to efficiently manage the complexity of larger DER networks and more sophisticated scheduling scenarios. The approach demonstrates its capability to align with the objectives of smart cities by ensuring adaptive and efficient energy distribution, integrating dynamic pricing mechanisms, and extending the operational lifespan of critical energy assets such as batteries. Rigorous simulations highlight the method’s ability to reduce optimization time, maintain solution quality, and scale efficiently, facilitating real-time decision making in energy markets. Moreover, the optimized coordination of DERs supports grid stability, enhances market responsiveness, and contributes to developing resilient, low-carbon urban environments. This study underscores the transformative role of computational infrastructure in addressing the challenges of modern energy systems, showcasing how advanced algorithms and HPC can enable scalable, adaptive, and sustainable energy optimization in smart cities. The findings demonstrate a pathway to achieving socially and environmentally responsible energy systems that align with the priorities of urban resilience and sustainable development.

Multi-human detection and tracking in indoor surveillance is a challenging task due to various factors such as occlusions, illumination changes, and complex human-human and human-object interactions. In this study, we address these challenges by exploring the benefits of a low-level sensor fusion approach that combines grayscale and neuromorphic vision sensor (NVS) data. We first generate a custom dataset using an NVS camera in an indoor environment. We then conduct a comprehensive study by experimenting with different image features and deep learning networks, followed by a multi-input fusion strategy to optimize our experiments with respect to overfitting. Our primary goal is to determine the best input feature types for multi-human motion detection using statistical analysis. We find that there is a significant difference between the input features of optimized backbones, with the best strategy depending on the amount of available data. Specifically, under a low-data regime, event-based frames seem to be the preferred input feature type, while higher data availability benefits the combined use of grayscale and optical flow features. Our results demonstrate the potential of sensor fusion and deep learning techniques for multi-human tracking in indoor surveillance, although it is acknowledged that further studies are needed to confirm our findings.

In the context of Shared Autonomous Vehicles, the need to monitor the environment inside the car will be crucial. This article focuses on the application of deep learning algorithms to present a fusion monitoring solution which was three different algorithms: a violent action detection system, which recognizes violent behaviors between passengers, a violent object detection system, and a lost items detection system. Public datasets were used for object detection algorithms (COCO and TAO) to train state-of-the-art algorithms such as YOLOv5. For violent action detection, the MoLa InCar dataset was used to train on state-of-the-art algorithms such as I3D, R(2+1)D, SlowFast, TSN, and TSM. Finally, an embedded automotive solution was used to demonstrate that both methods are running in real-time.