The energy balance on the laser drawing process of poly (ethylene terephthalate) was analyzed by the on-line fiber temperature measurement. The energy converting process from the external work for drawing to the thermal energy was quantitatively investigated by comparing the measured temperature profiles with the estimated temperature profiles using energy balance equations. From the result, it was shown that 9-24% of the applied work was stored as the elastic energy into the system just after the neck drawing point, and that the stored elastic energy was released as the thermal energy with the progress of orientation-induced-crystallization. It was also shown that the ratio of stored energy to the applied work tend to increase with the increase of fiber speed.
Objective: To monitor the human respiration rate (RR) using infrared thermography (IRT) and artificial intelligence, in a completely non-invasive and automated manner. Approach: The human breathing signals (BS) were obtained using IRT. The RR was monitored under extreme conditions, by developing a deep learning (DL) based "Residual network 50+Facial landmark detection" (ResNet 50+FLD) model. This model was built and evaluated on 10,000 thermograms and is the first work that documents the use of a DL classifier on a large thermal dataset for nostril tracking. Further, the acquired BS were filtered using the Moving average filter (MAF), and the Butterworth filter (BF). The novel "Breathing signal characterization algorithm (BSCA)" was proposed to obtain the RR in an automated manner. This algorithm is the first work that identifies the breaths in the thermal BS as regular, prolonged, or rapid, using machine learning (ML). The "Exploratory data analysis" was performed to choose an appropriate ML algorithm for the BSCA. The performance of the "BSCA" was evaluated for both "Decision tree (DT)" and "Support vector machine(SVM)" models. Main results: The "ResNet 50+FLD model" had Validation and Testing accuracy, of 99.5 %, and 99.4 % respectively. The Precision, Sensitivity, Specificity, F-measure, and G- mean values were computed as well. The comparative analysis of the filters revealed that the BF performed better than the MAF. The "BSCA" performed better with the SVM classifier, than the DT classifier, with Validation accuracy, and Testing accuracy of 99.5%, and 98.83%, respectively. Significance: The ever-increasing number of critical cases and the limited availability of skilled medical attendants, advocates in favor of an automated and harmless health monitoring system. The proposed methodology eliminates the risk of infections that spread through contact. It can be used in darkness, and in remote areas as well, where there is a lack of medical attendants.
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