• 응용통계연구
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• 제24권3호
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• pp.515-521
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• 2011

이 논문에서는 수리가능한 시스템의 고장을 감지해서 수리 개시를 가능하게 하는 고장감지장치를 고려한 시스템 신뢰도 산출을 연구한다. 실제 상황에서 고장감지장치의 고장이 가능할 수 있는데 이는 시스템 고장의 미발견을 초래할 수 있고 시스템 신뢰도에 큰 영향을 주게 된다. 적절한 마코프 확률과정을 이용하여 감지장치의 고장이 가져오는 시스템 신뢰도에 대한 영향을 분석한다.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2023년도 봄 학술논문 발표대회
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• pp.173-174
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• 2023

In this paper, we introduce a model for detecting Personal Protective Equipment (PPE) using YOLO (You Only Look Once), an object detection neural network. PPE is used to maintain a safe working environment, and proper use of PPE protects workers' safety and health. However, failure to wear PPE or wearing it improperly can cause serious safety issues. Therefore, a PPE detection system is crucial in industrial settings.

• 한국정보통신학회논문지
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• 제17권12호
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• pp.3023-3029
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• 2013

일반적으로 함정은 표적을 탐지하고 추적하는 기능을 하는 다양한 장비를 보유하고 있으며 각 장비들 간의 정보교류를 통해 보다 정확하고 신속하게 대상 표적을 추적하고 있다. 이런 장비들은 대체로 유사한 표적탐색영역(FOV)을 보유하지만 일부는 해당 장비의 오차범위(Resolution) 한계로 인해 장비간의 차이가 발생하기도 한다. 본 논문에서는 전자광학추적장비(Electro Optic Tracking System)와 레이더 시스템 간의 표적탐색영역(FOV) 차이를 보상하기 위해 사용된 전자광학추적장비 표적탐색 방식을 랜덤한 표적정보를 기준으로 다양한 방법을 통해 탐색시간을 단축하고, 자동으로 표적을 탐지/추적할 수 있는 방법에 대해 연구하였다.

• 대한임상검사과학회지
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• 제36권2호
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• pp.98-109
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• 2004

The lower limit of detection, precision, setting method of target value, reportable range determination, recovery, linearity, and comparison study with another equipment was evaluated for the Toshiba-120FR chemistry autoanalyzer which was newly introduced at the Daejeon Veteran Hospital in Dec. 2003. Nineteen kinds of test for AST, ALT, ALP, LDH, GGT, TP, ALB, GLU, T-cho, T-bil, TG, UA, CAL, IP, AMY, HDL-C, LDL-C, Cre and BUN were performed to evaluate the lower limit of detection, precision, setting method of target value, reportable range determination, recovery, linearity, and comparison study with other equipment according to the NCCLS guidelines(EP5-A, EP6-P, EP9-A). The Toshiba-120FR autochemical analyzer showed good precision for all tested items. The data concerning the lower limit of detection, precision(total CV 0.47%~3.65%), setting method of target value, reportable range determination, recovery(93%~111%), linearity($R^2=0.997{\sim}0.999$), and comparison study(r=0.977~0.999) with other equipment was acceptable for all tested items. The results of evaluation for the Toshiba-120FR autochemical analyzer showed that this equipment could be used as an alternative to other equipment.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.736-743
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• 2022

Construction workers experience a high rate of fatal incidents from mobile equipment in the industry. One of the major causes is the decline in the acoustic condition of workers due to the constant exposure to construction noise. Previous studies have proposed various ways in which audio sensing and machine learning techniques can be used to track equipment's movement on the construction site but not on the audibility of safety signals. This study develops a novel framework to help automate safety surveillance in the construction site. This is done by detecting the audio sound at a different signal-to-noise ratio of -10db, -5db, 0db, 5db, and 10db to notify the worker of imminent dangers of mobile equipment. The scope of this study is focused on developing a signal processing model to help improve the audible sense of mobile equipment for workers. This study includes three-phase: (a) collect audio data of construction equipment, (b) develop a novel audio-based machine learning model for automated detection of collision hazards to be integrated into intelligent hearing protection devices, and (c) conduct field experiments to investigate the system' efficiency and latency. The outcomes showed that the proposed model detects equipment correctly and can timely notify the workers of hazardous situations.

• 제어로봇시스템학회논문지
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• 제21권4호
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• pp.315-320
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• 2015

This paper describes the implemenation of localization and visualization scheme to find out an ultrasonic source caused by defects of a power distribution line equipment. To increase the fault detection performance, $2{\times}4$ sensor array is configured with MEMS ultrasonic sensors, and from the sensor signals aquired, the azimuth and elevation angles of the ultrasonic source is estimated based on the delay-sum beam forming method. Also, to visualize the estimated location, it is marked on the background image. Experimental results show applicability of the present technique.

• 응용통계연구
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• 제26권1호
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• pp.111-118
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• 2013

이 논문에서는 고장감지장치(FDE)가 시스템 가용도에 미치는 영향을 연구한다. 주시스템(MS) 수리 도중에 고장이 발생한 FDE를 수리하기 위한 새로운 수리 정책을 고려한다. 이 논문의 주요 목적은 MS의 가용도를 계산하고 비교하는 데에 있다.

• 반도체디스플레이기술학회지
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• 제19권4호
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• pp.121-125
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• 2020

With miniaturization of semiconductor, the manufacturing process become more complex, and undetected small changes in the state of the equipment have unexpectedly changed the process results. Fault detection classification (FDC) system that conducts more active data analysis is feasible to achieve more precise manufacturing process control with advanced machine learning method. However, applying machine learning, especially in supervised learning criteria, requires an arduous data labeling process for the construction of machine learning data. In this paper, we propose a semi-supervised learning to minimize the data labeling work for the data preprocessing. We employed equipment status variable identification (SVID) data and optical emission spectroscopy data (OES) in silicon etch with SF6/O2/Ar gas mixture, and the result shows as high as 95.2% of labeling accuracy with the suggested semi-supervised learning algorithm.

• 동력기계공학회지
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• 제20권2호
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• pp.66-72
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• 2016

As a method to improve the equipment reliability, early warning researches that can be detected fault symptom of an equipment at an early stage are being performed out among developed countries. In this paper, when abnormal signal is input to actual normal signal of a pump, early detection studies on pump's fault symptom were carried out with auto-associative kernel regression as an advanced pattern recognition algorithm. From analysis, correlations among power of motor driving pump, discharge flow of pump, power output of pump, and discharge pressure of pump are exited. When the abnormal signal is input to one of those normal signals, the other expected values are changed due to the influence of the abnormal signal. Therefore, the fault symptom of pump through the early-warning index is able to detect at an early stage.

• 한국안전학회지
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• 제37권3호
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• pp.16-23
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• 2022

This study aims to provide a fundamental material for identifying fire and no-fire signals using the detection signal characteristics of IoT-based fire-detection systems. Unlike analog automatic fire-detection equipment, IoT-based fire-detection systems employ wireless digital communication and are connected to a server. If a detection signal exceeds a threshold value, the measured values are saved to a server within seconds. This study was conducted with the detection data saved from seven fire accidents that took place in traditional markets from 2020 to 2021, in addition to 233 fire alarm data that have been saved in the K institute from 2016 to 2020. The saved values demonstrated variable and continuous VC-Signals. Additionally, we discovered that the detection signals of two fire accidents in the K institution had a VC-Signal. In the 233 fire alarms that took place over the span of 5 years, 31% of smoke alarms and 30% of temperature alarms demonstrated a VC-Signal. Therefore, if we selectively recognize VC-Signals as fire signals, we can reduce about 70% of false alarms.