• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.94-94
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• 2021

현재 전 지구적으로 일어나고 있는 극단적 기후현상과 이로 인한 자연재해의 원인은 복합적이다. 기후변화로 인한 영향과 동시에 도시화 또한 하나의 원인으로 작용하고 있다. 이러한 영향을 완화하기 위한 방안으로 도시의 그린인프라와 저영향개발은 최근 지속가능한 발전을 위해 꼭 필요한 요소로 자리잡고 있다. 그린인프라 유형 중 하나인 옥상녹화는 많은 이점을 제공한다. 도시에 위치한 건물의 상층부, 내부 및 주변 온도을 낮춤으로써 얻을 수 있는 에너지절감 효과와 강우 시 상당량의 빗물을 저류함으로써 기대되는 우수유출 저감효과, 그리고 도시 공간 내 식물의 적극적인 도입으로 인한 이산화탄소 배출 저감 효과 등을 기대할 수 있다. 본 연구에서는 도시지역의 열섬현상완화와 유출저감의 방안 중 하나인 옥상녹화(Green roof)의 효과를 정량적으로 평가하기 위해 콘크리트로 이루어진 동일한 제원의 실험동을 구축하고, 실험동 내외부의 연직방향 온도, 습도, 강우, 풍속, 일사량 등의 기상자료를 측정할 수 있는 센서를 설치하였다. 각 실험동에서 측정된 기상자료를 Flux Profile Method를 적용하여 무강우기간과 강우발생기간 동안의 연직 방향의 현열속, 잠열속, 토양열속(H, LE, G) 을 산정하였다. 에너지 평형에 따라 산정된 각 실험동의 열속과 지표면 복사량 관측자료을 정량적으로 비교하여 적용성을 평가하였다. 실험의 대조군인 일반 코팅재로 마감된 콘크리트 지붕의 무강우 기간 중 최대 현열속 693.82 W/m² 잠열속은 330.15 W/m² 으로 나타났으며, 실험군인 옥상녹화가 조성된 지붕의 최대 현열속 436.27 W/m², 잠열속 949.20 W/m² 으로 나타났으며, 산정치와 관측치 시계열의 NSE는 0.81 으로 Flux Profile Method를 통해 산정된 열속의 정확도는 비교적 높은 것으로 나타났다. 이와 같은 방법으로 옥상녹화의 정량적 평가가 가능해짐으로써 향후 기후변화 대응방안 및 전략 수립 시 옥상녹화의 온도저감효과 분석에 적극 활용할 수 있을 것으로 판단된다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.28-30
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• 2022

Recently, the number of confirmed cases has increased again with the new variant of COVID-19. Quarantine is recommended, especially to prevent the rapidly increasing spread, as environmental controls, such as minimizing contact with others, can increase safety. In addition, there are often cases in which the patient's condition cannot be confirmed from the standpoint of a guardian, such as visitation being prohibited under certain conditions. At this time, the sensor data values of oxygen, carbon dioxide concentrations, temperature and humidity, and alcohol, which are medical gases used in hospitals, are collected remotely using ZigBee wireless communication technology. Design a system that can be stored and monitored in a database. We propose an environmental monitoring system, which is a visualization system designed to allow hospitals to check and feedback data on the managed environment, and to give reliability to parents.

• Journal of Bio-Environment Control
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• v.31 no.3
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• pp.221-229
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• 2022

This study was aimed to investigate spatial and vertical characteristics of greenhouse environments according to the location of the environmental sensors, and to investigate the correlations between temperature, light intensity, and carbon dioxide (CO₂) concentration according to the type of greenhouse. Temperature, relative humidity (RH), CO₂, and light sensors were installed in the four-different vertical positions of the whole canopy as well as ground and roof space at the five spatial locations of the Venlo greenhouse. Also, correlations between temperature, light intensity, and CO₂ concentration in Venlo and semi-closed greenhouses were analyzed using the Curve Expert Professional program. The deviations among the spatial locations were larger in the CO₂ concentration than other environmental factors in the Venlo greenhouse. The average CO₂ concentration ranged from 465 to 761 µmol·mol^-1 with the highest value (646 µmol·mol^-1) at the Middle End (4ME) close to the main pipe (50Ø) of the liquefied CO₂ gas supply and lowest (436 µmol·mol^-1) at the Left Middle (5LM). The deviation among the vertical positions was greater in temperature and relative humidity than other environments. The time zone with the largest deviation in average temperature was 2 p.m. with the highest temperature (26.51℃) at the Upper Air (UA) and the lowest temperature (25.62℃) at the Lower Canopy (LC). The time zone with the largest deviation in average RH was 1 p.m. with the highest RH (76.90%) at the LC and the lowest RH (71.74%) at the UA. The highest average CO₂ concentration at each hour was Roof Air (RF) and Ground (GD). The coefficient of correlations between temperature, light intensity, and CO₂ concentration were 0.07 for semi-closed greenhouse and 0.66 for Venlo greenhouse. All the results indicate that while the CO₂ concentration in the greenhouse needs to be analyzed in the spatial locations, temperature and humidity needs to be analyzed in the vertical positions of canopy. The target CO₂ fertilization concentration for the semi-closed greenhouse with low ventilation rate should be different from that of general greenhouses.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.21 no.1
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• pp.1-10
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• 2016

Currently available surface seawater partial pressure carbon dioxide ($pCO_2$) data sets in the East Sea are not enough to quantify statistically the carbon dioxide flux through the air-sea interface. To complement the scarcity of the $pCO_2$ measurements, we construct a neural network (NN) model based on satellite data to map $pCO_2$ for the areas, which were not observed. The NN model is constructed for the Ulleung Basin, where $pCO_2$ data are best available, to map and estimate the variability of $pCO_2$ based on in situ $pCO_2$ for the years from 2003 to 2012, and the sea surface temperature (SST) and chlorophyll data from the MODIS (Moderate-resolution Imaging Spectroradiometer) sensor of the Aqua satellite along with geographic information. The NN model was trained to achieve higher than 95% of a correlation between in situ and predicted $pCO_2$ values. The RMSE (root mean square error) of the NN model output was $19.2{\mu}atm$ and much less than the variability of in situ $pCO_2$. The variability of $pCO_2$ with respect to SST and chlorophyll shows a strong negative correlation with SST than chlorophyll. As SST decreases the variability of $pCO_2$ increases. When SST is lower than $15^{\circ}C$, $pCO_2$ variability is clearly affected by both SST and chlorophyll. In contrast when SST is higher than $15^{\circ}C$, the variability of $pCO_2$ is less sensitive to changes in SST and chlorophyll. The mean rate of the annual $pCO_2$ increase estimated by the NN model output in the Ulleung Basin is $0.8{\mu}atm\;yr^{-1}$ from 2003 to 2014. As NN model can successfully map $pCO_2$ data for the whole study area with a higher resolution and less RMSE compared to the previous studies, the NN model can be a potentially useful tool for the understanding of the carbon cycle in the East Sea, where accessibility is limited by the international affairs.

• Journal of Mushroom
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• v.16 no.3
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• pp.155-161
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• 2018

In this study, we analyze the growth environment using smart farm technology in order to develop the optimal growth model for the precision cultivation of the bottle-grown oyster mushroom 'Suhan'. Experimental farmers used $88m^2$ of bed area, 2 rows and 5 columns of shelf shape, 5 hp refrigerator, 100T of sandwich panel for insulation, 2 ultrasonic humidifiers, 12 kW of heating, and 5,000 bottles for cultivation. Data on parameters such as temperature, humidity, carbon dioxide concentration, and illumination, which directly affect mushroom growth, were collected from the environmental sensor part installed at the oyster mushroom cultivator and analyzed. It was found that the initial temperature at the time of granulation was $22^{\circ}C$ after the scraping, and the mushroom was produced and maintained at about $25^{\circ}C$ until the bottle was flipped. On fruiting body formation, mushrooms were harvested while maintaining the temperature between $13^{\circ}C$ and $15^{\circ}C$. Humidity was approximately 100% throughout the growth stage. Carbon dioxide concentration gradually increased until 3 days after the beginning of cultivation, and then increased rapidly to approximately 2,600 ppm. From the 6th day, $CO_2$ concentration was gradually decreased through ventilation and maintained at 1,000 ppm during the harvest. Light was not provided at the initial stage of oyster mushroom cultivation. On the $3^{rd}$ and $4^{th}$ day, mushrooms were irradiated by 17 lux light. Subsequently, the light intensity was increased to 115-120 lux as the growth progressed. Fruiting body characteristics of 'Suhan' cultivated in a farmhouse were as follows: Pileus diameter was 30.9 mm and thickness was 4.5 mm; stipe thickness was 11.0 mm and length was 76.0 mm; stipe and pileus hardness was 0.8 g/mm and 2.8 g/mm, respectively; L values of the stipe and pileus were 79.9 and 52.3, respectively. The fruiting body yield was 160.2 g/850 ml, and the individual weight was 12.8 g/10 unit.

• Journal of Mushroom
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• v.17 no.3
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• pp.119-125
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• 2019

This study aims to report the results for the analysis of the growth environment by applying smart farm technology to "Chunchu No 2" farmers in order to develop an optimal growth model for precision cultivation of bottle-grown oyster mushrooms. The temperature, humidity, carbon dioxide concentration, and illumination data were collected and analyzed using an environmental sensor installed to obtain growth environment data from the oyster mushroom cultivator. Analysis of the collected temperature data revealed that the temperature at the time of granulation was $19.5^{\circ}C$ after scraping, and the mushroom was generated and maintained at about $21^{\circ}C$ until the bottle was flipped. When the fruiting body grew and approached harvest time, mushrooms were harvested while maintaining the temperature between $14^{\circ}C$ and $18^{\circ}C$. The humidity was maintained at almost 100% during the complete growth stage. Carbon dioxide concentration gradually increased until 3 days after the beginning of cultivation, and then increased rapidly to almost 5,500 ppm. From the 6th day, carbon dioxide concentration was gradually decreased through ventilation and was maintained at 1,600 ppm during harvest. Light intensity of 8 lux was irradiated up to day 6 after seeding, and growth was then continued while periodically irradiating 4 lux light. The fruiting body characteristics of "Chunchu No 2" cultivated in the farmhouse were as follows: pileus diameter of 26.5 mm and thickness of 4.9 mm, stipe thickness of 8.9 mm, and length of 68.7 mm. The fruiting body yield was 166.8 g/850 ml, and the individual weight was 12.8 g/10 units.

• Journal of Mushroom
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• v.15 no.3
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• pp.118-123
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• 2017

This study was carried out to develop the technology to manage the growth of mushrooms, which were cultivated based on long-term information obtained from quantified data. In this study, hardware that monitored and controlled the growth environment of the mushroom cultivation house was developed. An algorithm was also developed to grow mushrooms automatically. Environmental management for the growth of mushrooms was carried out using cultivation sites, computers, and smart phones. To manage the environment of the mushroom cultivation house, the environmental management data from farmers cultivating the highest quality mushrooms in Korea were collected and a growth management database was created. On the basis of the database value, the management environment for the test cultivar (hukthali) was controlled at $0.5^{\circ}C$ with 3-7% relative humidity and 10% carbon dioxide concentration. As a result, it was possible to produce mushrooms that were almost similar to those cultivated in farms with the best available technology.

• Journal of Animal Environmental Science
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• v.7 no.3
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• pp.155-164
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• 2001

Environmental factors such as $NH_3,\;H_2S,\;CO_2$, dust, temperature, and humidity in the animal house are a potential health hazard to humans and animals. Until now, most of measurement methods can only provide periodic results with low accuracy. A data acquisition system which can measure continuously and simultaneously $NH_3,\;H_2S,\;CO_2$, temperature, and humidity was developed and installed in two pig houses. Daily changes of environment for the pig-houses were investigated by the data acquisition system. In order to evaluate NH$_3$sensor, gas samples were obtained and NH$_3$concentrations were measured at nine positions; combinations of three positions(inlet, middle, and outlet) and three heights(0 cm, 40 cm, 150 cm). Ammonia concentration of 14.0 ~37.1 ppm for slurry pig-house is higher than that of 8.4~29.7 ppm for scraper pig-house, and there were no statistical differences among the positions. However, the concentration of $NH_3$at 150 cm was higher than thats of 0 cm and 40 cm.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.4
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• pp.223-233
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• 2021

Systematic and continuous monitoring and management of the energy consumption of buildings are important for estimating building energy efficiency, and ultimately aim to cope with climate change and establish effective policies for environment, and energy supply and demand policies. Globally, buildings consume 36% of total energy and account for 39% of carbon dioxide emissions. The purpose of this study is to generate three-dimensional thermo-spatial building models with photogrammetric technique using drone TIR (Thermal Infrared) images to measure the temperature emitted from a building, that is essential for the building energy rating system. The aerial triangulation was performed with both optical and TIR images taken from the sensor mounted on the drone, and the accuracy of the models was analyzed. In addition, the thermo-spatial models of temperature distribution of the buildings in three-dimension were visualized. Although shape of the objects 3D building modeling is relatively inaccurate as the spatial and radiometric resolution of the TIR images are lower than that of optical images, TIR imagery could be used effectively to measure the thermal energy of the buildings based on spatial information. This paper could be meaningful to present extension of photogrammetry to various application. The energy consumption could be quantitatively estimated using the temperature emitted from the individual buildings that eventually would be uses as essential information for building energy efficiency rating system.

• Journal of Korean Society of Forest Science
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• v.108 no.1
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• pp.40-49
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• 2019

Coarse woody debris (CWD), which is a component of the forest ecosystem, plays a major role in forest energy flow and nutrient cycling. In particular, CWD isolates carbon for a long time and is important in terms of slowing the rate of carbon released from the forest to the atmosphere. Therefore, this study measured the physiochemical characteristics and respiration rate ($R_{CWD}$) of CWD for Larix kaempferi and Pinus rigida in temperate forests in central Korea. In summer 2018, CWD samples from decay class (DC) I to IV were collected in the 14 forest stands. $R_{CWD}$ and physiochemical characteristics were measured using a closed chamber with a portable carbon dioxide sensor in the laboratory. In both species, as CWD decomposition progressed, the density ($D_{CWD}$) of the CWD decreased while the water content ($WC_{CWD}$) increased. Furthermore, the carbon concentrations did not significantly differ by DC, whereas the nitrogen concentration significantly increased and the C/N ratio decreased. The respiration rate of L. kaempferi CWD increased significantly up to DC IV, but for P. rigida it increased to DC II and then unchanged for DC II-IV. Accordingly, except for carbon concentration, all the measured characteristics showed a significant correlation with $R_{CWD}$. Multiple linear regression showed that $WC_{CWD}$ was the most influential factor on $R_{CWD}$. $WC_{CWD}$ affects $R_{CWD}$ by increasing microbial activity and is closely related to complex environmental factors such as temperature and light conditions. Therefore, it is necessary to study their correlation and estimate the time-series pattern of CWD moisture.