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Publicações

2021

Design of CAN Bus Communication Interfaces for Forestry Machines

Autores
Spencer, G; Mateus, F; Torres, P; Dionisio, R; Martins, R;

Publicação
COMPUTERS

Abstract
This paper presents the initial developments of new hardware devices targeted for CAN (Controller Area Network) bus communications in forest machines. CAN bus is a widely used protocol for communications in the automobile area. It is also applied in industrial vehicles and machines due to its robustness, simplicity, and operating flexibility. It is ideal for forestry machinery producers who need to couple their equipment to a machine that allows the transportation industry to recognize the importance of standardizing communications between tools and machines. One of the problems that producers sometimes face is a lack of flexibility in commercialized hardware modules; for example, in interfaces for sensors and actuators that guarantee scalability depending on the new functionalities required. The hardware device presented in this work is designed to overcome these limitations and provide the flexibility to standardize communications while allowing scalability in the development of new products and features. The work is being developed within the scope of the research project "SMARTCUT-Remote Diagnosis, Maintenance and Simulators for Operation Training and Maintenance of Forest Machines ", to incorporate innovative technologies in forest machines produced by the CUTPLANT S.A. It consists of an experimental system based on the PIC18F26K83 microcontroller to form a CAN node to transmit and receive digital and analog messages via CAN bus, tested and validated by the communication between different nodes. The main contribution of the paper focuses on the presentation of the development of new CAN bus electronic control units designed to enable remote communication between sensors and actuators, and the main controller of forest machines.

2021

Three-Phase Optimal Power Flow based on Affine Arithmetic

Autores
Moran, JP; Lopez, JC; Feltrin, AP;

Publicação
2021 IEEE PES Innovative Smart Grid Technologies Conference - Latin America (ISGT Latin America)

Abstract

2021

Um modelo para a gestão da informação organizacional

Autores
Pinto, Maria Manuela Gomes de Azevedo; Brandão, Marta; Oliveira, Rosário;

Publicação

Abstract

2021

Struggling for Survival

Autores
Castro, RL; Costa, J;

Publicação
Cases on Small Business Economics and Development During Economic Crises - Advances in Business Strategy and Competitive Advantage

Abstract
Keywords: family business; internationalization; SMEs; family SMEs; international expansion; family ownership

2021

Colossal Enhancement of Strain Sensitivity Using the Push-Pull Deformation Method

Autores
Robalinho, P; Gomes, A; Frazao, O;

Publicação
IEEE SENSORS JOURNAL

Abstract
In this work, a colossal enhancement of strain sensitivities through the push-pull deformation method in interferometry is reported for the first time. For the demonstration of the new method, two cascaded interferometers in a fiber loop mirror are used. Usually, strain is applied at the fiber end of the interferometers. In this work, we propose applying strain at the middle of the two cascaded interferometers whereas the fiber ends of the sensor are fixed. Strain is then applied in the fusion region between the two-cascaded interferometers in a push-pull configuration, thus ensuring simultaneously the extension of one interferometer and the compression of the other. Although the carrier signal is maintained constant, the proposed technique induces a colossal enhancement of sensitivity in the envelope signal. Strain sensitivities up to 10000 pm/ $\mu \varepsilon $ are achieved.

2021

Incremental Learning for Dermatological Imaging Modality Classification

Autores
Morgado, AC; Andrade, C; Teixeira, LF; Vasconcelos, MJM;

Publicação
JOURNAL OF IMAGING

Abstract
With the increasing adoption of teledermatology, there is a need to improve the automatic organization of medical records, being dermatological image modality a key filter in this process. Although there has been considerable effort in the classification of medical imaging modalities, this has not been in the field of dermatology. Moreover, as various devices are used in teledermatological consultations, image acquisition conditions may differ. In this work, two models (VGG-16 and MobileNetV2) were used to classify dermatological images from the Portuguese National Health System according to their modality. Afterwards, four incremental learning strategies were applied to these models, namely naive, elastic weight consolidation, averaged gradient episodic memory, and experience replay, enabling their adaptation to new conditions while preserving previously acquired knowledge. The evaluation considered catastrophic forgetting, accuracy, and computational cost. The MobileNetV2 trained with the experience replay strategy, with 500 images in memory, achieved a global accuracy of 86.04% with only 0.0344 of forgetting, which is 6.98% less than the second-best strategy. Regarding efficiency, this strategy took 56 s per epoch longer than the baseline and required, on average, 4554 megabytes of RAM during training. Promising results were achieved, proving the effectiveness of the proposed approach.

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