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Publicações

2020

Intensive summer course in robotics – Robotcraft

Autores
Fonseca Ferreira, NM; Araujo, A; Couceiro, MS; Portugal, D;

Publicação
Applied Computing and Informatics

Abstract

2020

Evaluation of Lightweight Convolutional Neural Networks for Real-Time Electrical Assets Detection

Autores
Barbosa, J; Dias, A; Almeida, J; Silva, E;

Publicação
Advances in Intelligent Systems and Computing

Abstract
The big growth of electrical demand by the countries required larger and more complex power systems, which have led to a greater need for monitoring and maintenance of these systems. To overcome this problem, UAVs equipped with appropriated sensors have emerged, allowing the reduction of the costs and risks when compared with traditional methods. The development of UAVs together with the great advance of the deep learning technologies, more precisely in the detection of objects, allowed to increase the level of automation in the process of inspection. This work presents an electrical assets monitoring system for detection of insulators and structures (poles and pylons) from images captured through a UAV. The proposed detection system is based on lightweight Convolutional Neural Networks and it is able to run on a portable device, aiming for a low cost, accurate and modular system, capable of running in real time. © 2020, Springer Nature Switzerland AG.

2020

Usability and Sense of Presence in Virtual Worlds for Distance Education: A Case Study with Virtual Reality Experts

Autores
Krassmann, AL; Rocha Mazzuco, AEd; Melo, M; Bessa, M; Bercht, M;

Publicação
Proceedings of the 12th International Conference on Computer Supported Education

Abstract

2020

Black-box inter-application traffic monitoring for adaptive container placement

Autores
Neves, F; Vilaça, R; Pereira, J;

Publicação
Proceedings of the 35th Annual ACM Symposium on Applied Computing

Abstract

2020

Wireless Sensor Network for Ignitions Detection: An IoT approach

Autores
Brito, T; Pereira, AI; Lima, J; Valente, A;

Publicação
Electronics

Abstract
Wireless Sensor Networks (WSN) can be used to acquire environmental variables useful for decision-making, such as agriculture and forestry. Installing a WSN on the forest will allow the acquisition of ecological variables of high importance on risk analysis and fire detection. The presented paper addresses two types of WSN developed modules that can be used on the forest to detect fire ignitions using LoRaWAN to establish the communication between the nodes and a central system. The collaboration between these modules generate a heterogeneous WSN; for this reason, both are designed to complement each other. The first module, the HTW, has sensors that acquire data on a wide scale in the target region, such as air temperature and humidity, solar radiation, barometric pressure, among others (can be expanded). The second, the 5FTH, has a set of sensors with point data acquisition, such as flame ignition, humidity, and temperature. To test HTW and 5FTH, a LoRaWAN communication based on the Lorix One gateway is used, demonstrating the acquisition and transmission of forest data (simulation and real cases). Even in internal or external environments, these results allow validating the developed modules. Therefore, they can assist authorities in fighting wildfire and forest surveillance systems in decision-making.

2020

Evaluation of Hunting-Based Optimizers for a Quadrotor Sliding Mode Flight Controller

Autores
Oliveira, J; Oliveira, PM; Boaventura Cunha, J; Pinho, T;

Publicação
Robotics

Abstract
The design of Multi-Input Multi-Output nonlinear control systems for a quadrotor can be a difficult task. Nature inspired optimization techniques can greatly improve the design of non-linear control systems. Two recently proposed hunting-based swarm intelligence inspired techniques are the Grey Wolf Optimizer (GWO) and the Ant Lion Optimizer (ALO). This paper proposes the use of both GWO and ALO techniques to design a Sliding Mode Control (SMC) flight system for tracking improvement of altitude and attitude in a quadrotor dynamic model. SMC is a nonlinear technique which requires that its strictly coupled parameters related to continuous and discontinuous components be correctly adjusted for proper operation. This requires minimizing the tracking error while keeping the chattering effect and control signal magnitude within suitable limits. The performance achieved with both GWO and ALO, considering realistic disturbed flight scenarios are presented and compared to the classical Particle Swarm Optimization (PSO) algorithm. Simulated results are presented showing that GWO and ALO outperformed PSO in terms of precise tracking, for ideal and disturbed conditions. It is shown that the higher stochastic nature of these hunting-based algorithms provided more confidence in local optima avoidance, suggesting feasibility of getting a more precise tracking for practical use.

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