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이어 잭 포트 유선통신 기법을 이용한 피부 수분 측정 스마트 제어 프로덕트 개발
양상모(Sang Mo Yang) 대한기계학회 2021 大韓機械學會論文集A Vol.45 No.7
최근 스마트폰, 스마트 패드 등의 스마트 디바이스와 연결되어 새로운 서비스를 제공하는 다양한 외부 장치가 개발되고 있다. 이러한 외부 장치는 스마트 프로덕트(smart product) 혹은 앱세서리(appcessory)로 불리고 있다. 본 논문에서는 스마트 디바이스의 이어폰 포트와 연결되어 피부 수분을 측정할 수 있는 외부 제어 장치를 개발하였다. 피부 수분 측정을 위해 임피던스 측정법을 사용하였으며, 신호 변조 회로를 통해 측정 결과를 스마트 디바이스로 전송하는 자동화 시스템을 구성하였다. 본 연구의 개발 결과를 검증하기 피부 수분 정밀 측정 기기인 C+K사의 Corneometer CM825를 이용하여 임상실험 결과의 상관성 분석을 하였으며, 그 결과 두 측정 기기 간의 상관성이 비교적 높음을 알 수 있었다. The development of external devices for smartphones and smart pads to provide new services has recently become a problem. These external devices are called smart products or appcessories. This paper discusses the development of skin humidity sensing devices related to the earphone port in smartphones. Segmental bioelectrical impedance analysis (SIBA) was used for skin humidity sensing, and a signal transmit system configuration was developed on a modulation circuit. To examine the study results, we performed correlation analysis between a Corneometer CM825 and a skin humidity sensing device. Notably, high correlations were shown between both measuring devices.
딥 러닝에 의한 전동기 기계적 고장 수준 결정을 위한 전동기 진동/전류 데이터 특성 분석 연구
한지훈(Ji-Hoon Han),박상욱(Sang-Uk Park),홍선기(Sun-Ki Hong) 대한전기학회 2021 전기학회논문지 Vol.70 No.10
In the classic motor fault diagnosis system, a method of analyzing the differences between the normal and collected state signals of the motor to be diagnosed was used and the method can diagnose only the limited situations because the diagnosis is based on the frequency of the mechanical failure. In order to compensate for this, some studies on a system that performs more specialized fault diagnosis through deep learning algorithms were carried out. However, the level of failure cannot be determined because these studies consider only the signals that have a great influence on motor operation. To solve this problem, the characteristics of vibration and current signals are analyzed to develop a deep learning algorithm suitable for fault level determination. The characterized signals are used for fault diagnosis and prediction. Fault diagnosis based on vibration signal is carried out through DT-CNN (Decision Tree Convolutional Neural Network). In addition, it is checked whether the current signal in the initial failure state, which is relatively insensitive to failure, can be classified through a deep learning algorithm. The proposed data utilization performance was evaluated through an induction motor and the analyzed signal-based fault diagnosis system is expected to enable a more precise diagnosis compared to the existing system.
그린함수를 이용한 가동중원전의 피로손상 평가프로그램 개발
김익중(Ik-Joong Kim),이정민(Jung-Min Lee),양상모(Sang-Mo Yang),김영진(Young-Jin Kim),최재붕(Jae-Boong Choi),김홍기(Hong-Gi Kim),최영환(Young-Hwan Choi) 대한기계학회 2010 대한기계학회 춘추학술대회 Vol.2010 No.11
A couple of years ago, in Korea, Periodic Safety Review (PSR) was adopted in order to assure the continued safe operation of nuclear power plants. The PSR considering various aging effects is being performed every ten-years in general and, for this, complicated procedures are required such as inspection, structure analysis, failure assessment and combination of them. A web-based regulatory aging monitor Program is proposed in this paper, which manages key operating data efficiently and assists effective evaluation of major components of nuclear power plant. By using the proposed program, experts can measure real-time data and to detect damages of major components of nuclear power plant.