• Journal of Nutrition and Health
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• v.48 no.1
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• pp.30-45
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• 2015

Purpose: This study was planned to determine the characteristics of extremely obese patients during Bariatric surgery and to evaluate how the difference in the number of postsurgical personal nutritional educations they received affected the weight loss. Methods: This is a retrospective study on the basis of the medical records of extremely obese patients for 15 months after receiving gastric banding. A total of 60 people were selected as the study subjects and they were divided into the Less Educated Group and the More Educated Group according to the average number of personal nutritional educations they received. We investigated both groups to determine the general characteristic, health related lifestyle habits, obesity related complications and symptoms in possession, and eating habits before their surgery, the body composition measurement result, obesity determination indices at 1, 3, 6, 9, 12, and 15 months before and after their surgery, and the biochemical parameters at 6 months before and after their surgery. Results: Body fat and weight showed rapid reduction until 6 months after the surgery, but thereafter reduced slowly depending on the result of body composition measurement. Regarding body fat and weight, the More Educated Group, who received nutrition education more often, showed significantly lower levels than the Less Educated Group at 15 months after surgery. Regarding BMI and degree of obesity, the More Educated Group showed significantly lower levels than the Less Educated Group at 15 months after surgery. Here, we were assured that BMI is reversely proportional to the number of personal nutritional educations at 15 months, which is more outstanding after surgery than before surgery. Conclusion: Long-term nutritional education is a key factor for the extremely obese patient in maintaining the effects of Bariatric surgery on weight and body fat reduction onwards. In the next stage, considering the characteristics of the study subjects, adoption of individual nutrition education is recommended for postsurgical prospective arbitration of obesity in order to monitor blood pressure, obesity related complications, symptoms in possession, and how eating habits and health related life habits change, and to judge the actual effect of the nutritional education method at the same time.

• Journal of the Korea Society of Computer and Information
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• v.24 no.12
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• pp.51-57
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• 2019

In this paper, we analyzed the performance of IoT based smart wearable mine detection device. There are various mine detection methods currently used by the military. Still, in the general field, mine detection is performed by visual detection, probe detection, detector detection, and other detection methods. The detection method by the detector is using a GPR sensor on the detector, which is possible to detect metals, but it is difficult to identify non-metals. It is hard to distinguish whether the area where the detection was performed or not. Also, there is a problem that a lot of human resources and time are wasted, and if the user does not move the sensor at a constant speed or moves too fast, it is difficult to detect landmines accurately. Therefore, we studied the smart wearable mine detection device composed of human body antenna, main microprocessor, smart glasses, body-mounted LCD monitor, wireless data transmission, belt type power supply, black box camera, which is to improve the problem of the error of mine detection using unidirectional ultrasonic sensing signal. Based on the results of this study, we will conduct an experiment to confirm the possibility of detecting underground mines based on the Internet of Things (IoT). This paper consists of an introduction, experimental environment composition, simulation analysis, and conclusion. Introduction introduces the research contents such as mines, mine detectors, and research progress. It consists of large anti-personnel mine, M16A1 fragmented anti-mine, M15 and M19 antitank mines, plastic bottles similar to mines and aluminum cans. Simulation analysis is conducted by using MATLAB to analyze the mine detection device implementation performance, generating and transmitting IoT signals, and analyzing each received signal to verify the detection performance of landmines. Then we will measure the performance through the simulation of IoT-based mine detection algorithm so that we will prove the possibility of IoT-based detection landmine.