• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.05a
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• pp.40-41
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• 2021

The construction industry has the highest occupational accidents/injuries among all industries. Korean government installed surveillance camera systems at construction sites to reduce occupational accident rates. Construction safety managers are monitoring potential hazards at the sites through surveillance system; however, the human capability of monitoring surveillance system with their own eyes has critical issues. Therefore, this study proposed to build a deep learning-based safety monitoring system that can obtain information on the recognition, location, identification of workers and heavy equipment in the construction sites by applying multiple-object tracking with instance segmentation. To evaluate the system's performance, we utilized the MS COCO and MOT challenge metrics. These results present that it is optimal for efficiently automating monitoring surveillance system task at construction sites.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.05a
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• pp.305-306
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• 2012

• Journal of Welding and Joining
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• v.21 no.7
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• pp.34-41
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• 2003

Weld quality monitoring and seam tracking along the butt-joint lengthwise to the tube axis are studied. The material of tube is 60kg/$\textrm{mm}^2$ grade steel sheet, and the longitudinal butt-joint is shaped by 2 roll bending machine. The tube with a thickness of 1.5mm, diameter of 105.4mm and length of 2000mm is successfully obtained by the $CO_2$ laser welding system equipped with a seam tracker and plasma sensor. Experimental results show that the developed welding system can be used for the precision seam tracking and the real-time monitoring of weld quality, and the laser welded tube can be used for car body md component after tubular hydroforming.

• Journal of Korea Multimedia Society
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• v.21 no.5
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• pp.545-557
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• 2018

The Global Positioning System (GPS) has become popular and widely used in many fields from military to civilian applications. However, GPS signals are suffered from interference due to its weak signal over wireless channel. There are many types of interference, such as jamming, blocking multipath, and spoofing, which can mislead the operation of GPS receiver. In this paper, vector-based tracking loop model with integrity check is proposed to detect and mitigate the harmful effect of interference on GPS receiver operation. The suggested methods are implemented in the tracking loop of GPS receiver. As a first method, integrity check with carrier-to-noise ratio (C/No) monitoring technique is applied to detect the presence of interference and prevent contaminated channels out of tracking channels to calculate position. As a second method, a vector-based tracking loop using Extended Kalman Filter with adaptive noise covariance according to C/No monitoring results. The proposed methods have been implemented on simulated dataset. The results demonstrates that the suggested methods significantly mitigate interference of Additive White Gaussian Noise (AWGN) and improve position calculation by 44%.

• Journal of Ocean Engineering and Technology
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• v.11 no.4
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• pp.189-196
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• 1997

An oil tracking system that monitors the spilt oil trajectory by using GPS was developed. The system consists of a tracking buoy deployed on the oil spilt area and an onshore(or onboard) monitoring station. The tracking buoy is equipped with GPS, signal converter, handy radio and battery while the monitoring station includes a station radio, signal converter, antennas and PC. The hull shape of buoy is designed to effectively simulate the spilt oil movement at sea surface. Radio sets for HAM are used as a data transmitter and a data receiving station, and signal converter is also for amateur use. A field experiment was conducted and it was shown that the integrated system is relable and robust. The developed oil tracking system reveals reatively good performance at reasonable cost. In favorable environment the system may communicate in the distance more than 50km.

• Korean Journal of Remote Sensing
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• v.38 no.1
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• pp.45-56
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• 2022

The Ieodo Ocean Research Station (IORS) lies between the exclusive economic zone (EEZ) boundaries of Korea, Japan, and China. The geographical positioning of the IORS makes it ideal for monitoring ships in the area. In this study, we introduce ship monitoring results by Automatic Identification System (AIS) and the Broadband 3G^TM radar, which has been developed for use in small ships using the Frequency Modulated Continuous Wave (FMCW) technique. AIS and FMCW radar data were collected at IORS from November 23th to 30th, 2013. The acquired FMCW radar data was converted to 2-D binary image format over pre-processing, including the internal and external noise filtering. The ship positions detected by FMCW radar images were passed into a tracking algorithm. We then compared the detection and tracking results from FMCW radar with AIS information and found that they were relatively well matched. Tracking performance is especially good when ships are across from each other. The results also show good monitoring capability for small fishing ships, even those not equipped with AIS or with a dysfunctional AIS.

• Journal of the Korea Institute of Building Construction
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• v.21 no.5
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• pp.397-408
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• 2021

The construction industry has the highest occupational accidents/injuries and has experienced the most fatalities among entire industries. Korean government installed surveillance camera systems at construction sites to reduce occupational accident rates. Construction safety managers are monitoring potential hazards at the sites through surveillance system; however, the human capability of monitoring surveillance system with their own eyes has critical issues. A long-time monitoring surveillance system causes high physical fatigue and has limitations in grasping all accidents in real-time. Therefore, this study aims to build a deep learning-based safety monitoring system that can obtain information on the recognition, location, identification of workers and heavy equipment in the construction sites by applying multiple object tracking with instance segmentation. To evaluate the system's performance, we utilized the Microsoft common objects in context and the multiple object tracking challenge metrics. These results prove that it is optimal for efficiently automating monitoring surveillance system task at construction sites.

• Journal of IKEEE
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• v.24 no.2
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• pp.392-402
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• 2020

This paper proposes a solar tracking system without photo-sensors. The system can be classified into four modules: Solar Tracking, MPPT, ESS, and Real-Time Monitoring. Nine solar panels, as a basic unit, are adopted with grid structures of different heights to reduce wind influence and to enable solar tracking without photo-sensors. The low-cost MCU implements MPPT method which generates PWM switching signal for boost converter. The unit of ESS consists of three-series and four-parallel lithium-ion batteries in order to enable monitoring for abnormalities in temperature and electrical characteristics of battery. Four MCUs used in the system consists of two AVR Atmega128, and two Raspberry PI, and they exchanges operation informations. Experimental results of the proposed system show the solar tracking performance, the possibility of on-site and remote monitoring and the convenience of maintenance based on IoT technology.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.26 no.1
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• pp.38-43
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• 2012

For safe and stable operations of power plants, it is essential to monitor closely crucial measurement values related to power plant trips. In this paper, an intelligent power plant operating state monitoring technique enabling the operating crew member to monitor conveniently the status of the important measurement values and to perceive almost instantly the significance of the implications of those measurement values is developed. The proposed technique is called a "POST(Plant Operating State Tracking) Chart" technique and provides the foundations in developing an intelligent and integrated power plant operating state monitoring support system called the "P-OASIS"(Plant Operation Assessment and Support Intelligent System). The P-OASIS is applied to a thermal power plant of 500[MW] capacity and exhibited impressive performances.

• Journal of Welding and Joining
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• v.9 no.3
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• pp.34-40
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• 1991

An automatic welding system was constructed with a personal computer to capture the welding data in addition to vision seam tracking for the gas metal arc welding process. The monitoring of welding signals is composed of the acquisition of welding voltage and current, obtained by using two differential amplifiers and A/D converters, and processing of the measured data. Using interrupt handing circuit for time sharing, two jobs of seam tracking and monitoring were performed at the required sampling time. Relations between welding signals and various welding circumstances were analyzed from the experimental results.