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This paper proposes a new method to develop a thermal model of an insulated gate bipolar transistor (IGBT) employing an optical fiber sensor mounted on the chip structure. Some features of the sensor such as electromagnetic immunity, small size and fast response time, allow the identification of temperature changes generated by the energy loss during device operation through direct measurement. In fact, this measurement method is considered impossible with conventional sensors. The online monitoring of the junction temperature enables identify the thermal characteristics of the IGBT. The results are used to develop an accurate model to simulate the heat generated during the device conduction and switching processes. The model showed a difference of only 0.3% between measured and simulated results, besides allowing evaluate separately the heat generated by each turn-ON/OFF process.

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Pattern classification of ingestive behavior in grazing animals has extreme importance in studies related to animal nutrition, growth and health. In this paper, a system to classify chewing patterns of ruminants in in vivo experiments is developed. The proposal is based on data collected by optical fiber Bragg grating sensors (FBG) that are processed by machine learning techniques. The FBG sensors measure the biomechanical strain during jaw movements, and a decision tree is responsible for the classification of the associated chewing pattern. In this study, patterns associated with food intake of dietary supplement, hay and ryegrass were considered. Additionally, two other important events for ingestive behavior were monitored: rumination and idleness. Experimental results show that the proposed approach for pattern classification is capable of differentiating the five patterns involved in the chewing process with an overall accuracy of 94%.