• Journal of Internet Computing and Services
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• v.8 no.1
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• pp.33-45
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• 2007

Using switches and sensors informing the current on or off state, this paper suggests a sleeping control and remote monitoring system that not only can recognize the sleeping situations but also can control for keeping an appropriate sleeping situation remotely, And we show an example that this system is applied to the healthcare sleeping mat, Our system comprises the following 3 parts: a part for detecting the sleeping situations, a part for extracting sensing data and sending/receiving the relating situated data, and a part controlling and monitoring the all of sleeping situations. In details, in order to develop our system, we used the touch and pressure-sensitive sensors with On/Off functions for a purpose of the first part, The second part consists of the self-developed embedded board with the socket based communication as well as extracting real-time sensing data. And the third part is implemented by service modules for providing controlling and monitoring functions previously described. Finally, these service modules are implemented by the TMO scheme, one of real-time object-oriented programming models and the communications among them is supported using the TMOSM of distributed real-time middleware.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.1-12
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• 2022

Traditional methods of measuring corneal ulcers were difficult to present objective basis for diagnosis because of the subjective judgment of the medical staff through photographs taken with special equipment. In this paper, we propose a method to detect the ulcer area on a pixel basis in corneal ulcer images using a semantic segmentation model. In order to solve this problem, we performed the experiment to detect the ulcer area based on the DeepLab model which has the highest performance in semantic segmentation model. For the experiment, the training and test data were selected and the backbone network of DeepLab model which set as Xception and ResNet, respectively were evaluated and compared the performances. We used Dice similarity coefficient and IoU value as an indicator to evaluate the performances. Experimental results show that when 'crop & resized' images are added to the dataset, it segment the ulcer area with an average accuracy about 93% of Dice similarity coefficient on the DeepLab model with ResNet101 as the backbone network. This study shows that the semantic segmentation model used for object detection also has an ability to make significant results when classifying objects with irregular shapes such as corneal ulcers. Ultimately, we will perform the extension of datasets and experiment with adaptive learning methods through future studies so that they can be implemented in real medical diagnosis environment.