• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.5
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• pp.401-410
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• 2013

Due to the increasing need for spatial information, there have been a lot of research related with monitoring and revision of changed regions for the acquisition of the accurate and latest information. In this paper, the optimal monitoring management plan for changed regions with respect to the revision periods was proposed. For this purpose, the representative monitoring methods, which are based on database, professional manpower and crowdsourcing of continuous revision, and aerial imagery, satellite imagery and LiDAR of cyclic revision, were investigated. Then, the properties and application status of monitoring systems in Korea were illustrated according to the methods. Finally, the optimal monitoring management plan for continuous and cyclic revisions was suggested through the comparison of properties and revisionable objects of each method. From the result, it was shown to be appropriate for the optimal monitoring management plan of continuous revision as using Internet-Architectural Information System (e-AIS) database cooperated with professional manpower and crowdsourcing, and cyclic revision as using domestic high-resolution satellite images and LiDAR data processed semi-automatically.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.122-122
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• 2017

Recently, environmental problems have become an area of growing interests. In-situ monitoring of water quality is fundamental to most environmental applications. The accurate measurement of nitrate concentrations is fundamental to understanding biogeochemistry in aquatic ecosystems. Several studies have reported that one of the most feasible methods to measure nitrate concentration is the use of Ion Selective-electrodes (ISEs). The ISE application to water monitoring has several advantages, such as direct measurement methodology, high sensitivity, wide measurement range, low cost, and portability. However, the ISE methods may yield inconsistent results where there was a difference in temperature between the calibration and measurement solutions, which is associated with the temperature dependence of ionic activity coefficients in solution. In this study, to investigate the potential of using the combination of a temperature sensor and nitrate ISEs for minimizing the effect of temperature on real-time nitrate sensing in natural water, a prototype of on-site water monitoring system was built, mainly consisting of a sensor chamber, an array of 3 ISEs, an waterproof temperature sensor, an automatic sampling system, and an arduino MCU board. The analog signals of ISEs were obtained using the second-order Sallen-key filter for performing voltage following, differential amplification, and low pass filtering. The performance test of the developed water nitrate sensing system was conducted in a monitoring station of drinking water located in Jeongseon, Kangwon. A temperature compensation method based on two-point normalization was proposed, which incorporated the determination of temperature coefficient values using regression equations relating solution temperature and electrode signal determined in our previous studies.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.4
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• pp.155-159
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• 2018

In this paper, we designed and built a system that is site manager can be monitored remotely to the growth of the fishery environment. To this end, modular distribution system and environmental monitoring of live fishery products. Simulation results, were building a purification system and the oxygen supply to the fishery living inside the container was to build a system that can be monitored in real time. As a result, it was able to raise the necessary environment for the storage and distribution of fisheries products. We constructed a monitoring system with database and communication environment and created the optimal environment suitable for growth with sensor. As a result, it was possible to monitor the environment for the storage and distribution of fishery products and to build a suitable fishery products management system.

• Journal of Preventive Medicine and Public Health
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• v.39 no.2
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• pp.171-176
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• 2006

Objectives : The objective of this study is to assess the factors influencing biological monitoring of textile coating factory workers exposed to N,N-dimethylformamide(DMF). Methods : We studied 35 workers who were occupationally exposed to DMF from 9 textile coating factories. The study was carried out in two phases; summer and winter. While air concentration of DMF, temperature and humidity were assessed in order to monitor the atmospheric conditions, biological monitoring was done to determine the internal dose by analyzing the N-methylformamide(NMF) collected from urine at the beginning and end of the shift. Questionnaires and medical surveillance were also obtained during the two phases. Results : Median air concentrations of DMF in winter and summer were 1.85 ppm and 2.78 ppm respectively. Also the difference between the urinary NMF concentration at the beginning and end of the shift $({\Delta}NMF)$ was always significant in each season (P < 0.001). The correlations between log DMF in air, log end-of-shift urinary NMF (r=0.555, P < 0.001) and log ${\Delta}NMF$ (r = 0.444, P < 0.001) was statistically significant in summer. The temperature, humidity, a shift system and different styles of clothing worn were significantly different during the two phases. In a multivariate analysis, temperature and the concentration of DMF in the air were the main factors influencing biological monitoring of textile coating factory workers. Conclusions : Concerning more comprehensive prevention measures to reduce exposure for those workers occupationally exposed to DMF, dermal exposure conditions such as temperature and humidity together with the air concentration of DMF should be assessed and biological monitoring is necessary to reduce adverse health effects, especially during the summer.

• Journal of Environmental Science International
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• v.21 no.9
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• pp.1149-1162
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• 2012

The impact of a considerable increase in traffic volume on the emission and concentrations of air pollutants was investigated at three beaches (Haeundae (HB), Gwanganri (GB), and Songjeong (SB)) in Busan during beach opening period (BOP) in 2011. During the BOP, passenger car was the major vehicle type, followed by taxi, and van. CO was the major contributor of total air pollutant emissions followed by NOx, VOC, and $PM_{10}$. For the temporal variation of the emission of air pollutants during the BOP, it was generally the highest in the afternoon followed by the evening and morning, except for SB. For the spatial variation of their emission, it was the highest at GB followed by SB and HB. The emissions of air pollutants during the BOP were generally higher than those during the Non-BOP, except for HB. In contrast, the significant impact of the traffic volume increase on the concentrations of air pollutants at monitoring sites near the three beaches during the BOP were not found compared to the Non-BOP due to the significant distances between monitoring sites of air pollutants and monitoring sites of traffic volume at the beaches.

• Asian Journal of Atmospheric Environment
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• v.7 no.2
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• pp.105-113
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• 2013

We constructed and tested an airborne peroxyacetyl nitrate (PAN) monitoring system based on luminol chemiluminescence detection with fast gas chromatography. This system allowed for simultaneous measurement of PAN and nitrogen dioxide ($NO_2$) with a time resolution of <2 min. Actual sample masses within the fixed volume sample loop at various altitudes and temperatures were adjusted to standard atmosphere, using measured pressures and temperatures. The airborne PAN measurement system was evaluated during two field studies above the southern Korean Peninsula in August and October 2009. The detection limit based on the ISO approach was 0.035 ppbv PAN, well below the observed concentrations of 0.185-1.49 ppbv during these studies. Under these conditions, the PAN mixing ratios were positively correlated with $O_x$ ($O_x=O_3+NO_2$), with slopes varying between 0.014 and 0.033 and intercepts between 22.6 and 55.1 ppbv $O_x$. The intercepts corresponded roughly to background $O_x$ mixing ratios in central Europe; however, the slopes were above the range of slopes reported in other studies. We also enhanced the durability, safety, and ease of maintenance of the PAN monitoring system by redesigning the structure of the conventional luminol cell.

• Smart Structures and Systems
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• v.22 no.4
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• pp.399-411
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• 2018

The difficulty in modeling complex nonlinear structures lies in the presence of significant sources of uncertainties mainly attributed to sudden changes in the structure's behavior caused by regular aging factors or extreme events. Quantifying these uncertainties and accurately representing them within the complex mathematical framework of Structural Health Monitoring (SHM) are significantly essential for system identification and damage detection purposes. This study highlights the importance of uncertainty quantification in SHM frameworks, and presents a comparative analysis between intrusive and non-intrusive techniques in quantifying uncertainties for SHM purposes through two different variations of the Kalman Filter (KF) method, the Ensemble Kalman filter (EnKF) and the Polynomial Chaos Kalman Filter (PCKF). The comparative analysis is based on a numerical example that consists of a four degrees-of-freedom (DOF) system, comprising Bouc-Wen hysteretic behavior and subjected to El-Centro earthquake excitation. The comparison is based on the ability of each technique to quantify the different sources of uncertainty for SHM purposes and to accurately approximate the system state and parameters when compared to the true state with the least computational burden. While the results show that both filters are able to locate the damage in space and time and to accurately estimate the system responses and unknown parameters, the computational cost of PCKF is shown to be less than that of EnKF for a similar level of numerical accuracy.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.178-180
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• 2021

In this paper, we design a system to monitor environmental data in fish farms in real-time and provide machine learning-based prediction services to prevent damage on fish farms caused by changes in the sea environment. The proposed system will install an IoT device module consisting of sensors that can measure hydrogen concentration, salinity, dissolved oxygen, and water temperature, which can be transferred to Cloud DB using LTE or LoRa communication technology and then monitor the real-time condition through a web or mobile application. In addition, it has a function to prepare for changes within the environment of fish farms by applying machine learning-based prediction technology using collected data.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 1996.06a
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• pp.135-137
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• 1996

• Smart Structures and Systems
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• v.5 no.4
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• pp.469-482
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• 2009

The emerging sensor-based structural health monitoring (SHM) technology has a potential for cost-effective maintenance of aging civil infrastructure systems. The author proposes to integrate continuous and global monitoring using on-structure sensors with targeted local non-destructive evaluation (NDE). Significant technical challenges arise, however, from the lack of cost-effective sensors for monitoring spatially large structures, as well as reliable methods for interpreting sensor data into structural health conditions. This paper reviews recent efforts and advances made in addressing these challenges, with example sensor hardware and health monitoring software developed in the author's research center. The hardware includes a novel fiber optic accelerometer, a vision-based displacement sensor, a distributed strain sensor, and a microwave imaging NDE device. The health monitoring software includes a number of system identification methods such as the neural networks, extended Kalman filter, and nonlinear damping identificaiton based on structural dynamic response measurement. These methods have been experimentally validated through seismic shaking table tests of a realistic bridge model and tested in a number of instrumented bridges and buildings.