INESCC-DL - Artigos em Revistas Internacionais
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- Autonomous Wireless Sensor with a Low Cost TEG for Application in Automobile VehiclesPublication . Costa, A.; Costa, D.; Morgado, J.; Santos, Helder; Ferreira, Carlos Daniel HenriquesThe present work consists in the development of an autonomous, low cost, reliable, energy scavenger sensor for automotive applications. Thermoelectric generators typically exhibit low efficiency but high reliability, making them suitable for autonomous, low average energy consumption, applications. A prototype sensor was developed for mounting in the engine exhaust pipe using a step-up voltage converter, a microcontroller, temperature and pressure sensing elements, conditioning electronics and a wireless transceiver, all powered by a low cost TEG (Peltier module TEC1-12706), through the scavenging of exhaust gases thermal energy. During the tests the prototype was able to sustain a regular signal transmission throughout the engine operation. The sensor was installed directly at the measuring point eliminating wired cables to hot and vibrating parts, thus, simplifying the installation of components and improving the reliability of the vehicle systems.
- Energy audit as an input for energy management and energy efficiency improvement in a gypsum manufacturing plantPublication . Bernardo, Hermano; Oliveira, Filipe Tadeu; Serrano, LuisThis paper aims at presenting the main results of an energy audit performed to a gypsum production plant, in Portugal, which due to the amount of energy consumed must comply with the Portuguese program SGCIE (Intensive Energy Consumption Management System). The program was created in 2008 to promote energy efficiency and energy consumption monitoring in intensive energy consuming facilities (energy consumption higher than 500 toe per year). Facilities operators are required to perform energy audits and take actions to draw up an action plan for energy efficiency, establishing targets for energy consumption reduction and greenhouse gases emissions indexes. An energy audit was carried out to identify potential energy conservation measures for improving energy efficiency, and also typical energy consumption patterns, sector/equipment load profiles and thermal equipment performance. This tool gives managers the information to support decision making on improving energy performance and reducing greenhouse gas emissions. A number of tangible targets and measures were devised and set to be implemented in the next few years. Results show that there is a considerable potential for reduction in the energy consumption and greenhouse gases emissions of gypsum manufacturing plants. Here, as elsewhere in the industrial sector, energy efficiency can only be achieved through a continuous energy monitoring and management system.
- High Sensitivity Micro-machined Piezoresistive Strain SensorPublication . Caseiro, D.; Santos, S.; Ferreira, Carlos Daniel Henriques; Neves, CarlosThis paper presents a micro-machined piezoresistive sensor capable of measuring very small strains. The sensor design, based on piezoresistive sensing technology, was optimized by the numerical method using Finite Element Method (FEM) to enhance sensibility. The high sensibility is achieved through a reduction of section and through the action of the bending moment. As a result, a sensor with a sensitivity of 569.4608 μV/V/με, which can be fabricated by the SensoNor MultiMEMS process, is proposed. Furthermore, practical essays with macro prototypes confirmed and validated the numerical analysis. Such a sensor can be a direct replacement for the strain gauges and its very high sensitivity opens the door to many other applications, that otherwise would not be possible.
- Synthetic image generation for effective deep learning model training for ceramic industry applicationsPublication . Gaspar, Fábio; Daniel Carreira; Rodrigues, Nuno; Miragaia, Rolando; Costa, Paulo; Pereira, António; Ribeiro, JoséIn the rapidly evolving field of machine learning engineering, access to large, high-quality, and well-balanced labeled datasets is indispensable for accurate product classification. This necessity holds particular significance in sectors such as the ceramics industry, in which effective production line activities are paramount and deep learning classification mechanisms are particularly relevant for streamlining processes; but real-world image samples are scarce and difficult to obtain, hindering dataset building and consequently model training and deployment. This paper presents a novel approach for dataset building in the context of the ceramic industry, which involves employing synthetic images for building or complementing datasets for image classification problems. The proposed methodology was implemented in CeramicFlow, an innovative computer graphics rendering pipeline designed to create synthetic images by employing computer-aided design models of ceramic objects and incorporating domain randomization techniques. As a result, a fully synthetic image dataset named Synthetic CeramicNet was created and validated in real-world ceramic classification problems. The results demonstrate that synthetic images provide an adequate basis for datasets and can significantly reduce reliance on real-world data when developing deep learning approaches for image classification problems in the ceramic industry. Furthermore, the proposed approach can potentially be applied to other industrial fields.
- Text Mining Applied to Electronic Medical RecordsPublication . Pereira, Luis; Rijo, Rui, Rui Pedro Charters Lopes; Silva, Catarina; Martinho, RicardoThe analysis of medical records is a major challenge, considering they are generally presented in plain text, have a very specific technical vocabulary and are nearly always unstructured. It is an interdisciplinary work that requires knowledge from several fields. The analysis may have several goals, such as assistance on clinical decision, classification of medical procedures, and to support hospital management decisions. This work presents the concepts involved, the relevant existent related work, and the main open issues for future research within the analysis of electronic medical records, using data and text mining techniques. It provides a comprehensive contextualization to all those who wish to perform an analytical work of medical records, enabling the identification of fruitful research fields. With the digitalization of medical records and the large amount of medical data available, this is an area of wide research potential.