Abstract-Disasters create mass casualties and the number of casualties usually surpasses the capability of medical resources, hence, medical teams must attach paper triage to casualties for determining the priority of treatments based on the severity of their condition. However, since casualties' condition could change at anytime, the paper triage cannot provide the latest information of their health condition. Therefore, we have developed a wearable medical device that can continuously monitor the health condition of casualties. It is a lightweight and low-cost wearable electronic triage with sensing system that can monitor the vital sign of casualties and classify them into three levels of severe conditions, i.e., major, delayed, and minor status. The electronic triage is mainly built from a low-power 8-bit microcontroller unit, RF units, and sensors including pulse oximetry and thermocouple breath sensor. This electronic triage has been developed using low-cost electronic components that are available in developing countries such as Indonesia, so that, our electronic triage can be easily manufactured and maintained locally. Furthermore, we have also developed a simple android-based mobile application for data acquisition, priority classification, data storage and data transfer to medical record server in hospitals.
A method to identify the type of insects with accurate and precise results is of importance. Nowadays, an automatic object identification system with increased accuracy, improved speed, and less cost have been developed. Convolutional Neural Network (CNN) implementation for image identification or classification can be done by collecting large-scale datasets containing hundreds to millions of images to study the many parameters involved in the network. This research was conducted to develop and apply the CNN model to identify eight species of insects in the sweet corn field in Thailand. Those insects were Calomycterus sp., Rhopalosiphum
maidis, Frankliniella
williamsi, Spodoptera
frugiperda, Spodoptera
litura, Ostrinia
furnacalis, Mythimna
separata, and Helicoverpa
armigera. The CNN model in this research was built with four convolutional layers, which consist of Conv2D, batch normalization, max pooling, dropout sublayer, and a fully-connected layer. in total, 5568 images were trained with 10 trials and different train attempts for each trial, were then tested with 40 images. The result shows that the CNN model has succeeded in identifying images of sweet corn insects with 80% up to 95% prediction accuracy for images with no background.
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