• The Korean Journal of Applied Statistics
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• v.24 no.3
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• pp.515-521
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• 2011

In this paper we consider failure detection equipment that which find failures in repairable systems and enable repair operations. In practical situations, failure detection equipment may come across troubles that can cause the omissions in detecting system failures and have a serious effect on system reliability. We analyze this effect through the appropriate modeling of Markov processes.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.05a
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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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.12
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• pp.3023-3029
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• 2013

This is an example we generally have a variety of equipment that can detect and track the targets and track them quickly and accurately through the information exchange among each piece of equipment. These equipment have similar detection areas (FOV), but some are different due to the limit of the resolution of the equipment. In this paper, we studied the method of reducing detection time and tracking the targets automatically.

• Korean Journal of Clinical Laboratory Science
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• v.36 no.2
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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.

• International conference on construction engineering and project management
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• 2022.06a
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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.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.4
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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.

• The Korean Journal of Applied Statistics
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• v.26 no.1
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• pp.111-118
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• 2013

In this paper we study the effect of failure detection equipment(FDE)s on system availability. A new repair scheme is considered for the step of repairing FDE which becomes out of order in the course of repairing the main system(MS). We compute and compare the availability of MS.

• Journal of the Semiconductor & Display Technology
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• v.19 no.4
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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.

• Journal of Power System Engineering
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• v.20 no.2
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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.

• Journal of the Korean Society of Safety
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• v.37 no.3
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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.