• Journal of the Korean GEO-environmental Society
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• v.4 no.3
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• pp.69-77
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• 2003

This study was carried out to evaluate the WASP5 model parameters and to analyze the sensitivity of parameters in Daechung Reservoir. The values predicted by the model and tendency were very similar to the observed data at Daejeon intake, so it is possible to predict water quality of the Daejeon intake region in the future. Results from the sensitivity analysis showed that Chlorophyll-a was sensitive to variations in saturated growth rate of phytoplankton, endogenous respiration rate of phytoplankton, extinction coefficient and temperature. T-N was sensitive to mineralization rate of dissolved organic nitrogen and temperature. T-P was affected by T-P load, temperature, extinction coefficient, mineralization rate of dissolved organic phosphorus and saturated growth rate of phytoplankton. BOD was influenced by deoxygenation rate and temperature, and DO was influenced by temperature. Adequate input data was applied and assessed through the model sensitivity analysis. So it is possible to distinguish the input data which need careful attention when it has application to model.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 1999.03a
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• pp.11-15
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• 1999

The discrete state theory on emotion postulated that there existed discrete emotions, such as happiness, anger, fear, disgust, and so forth. Many investigators who emphasized discreteness of emotions have suggested that discrete emotions entailed their specific activities in the autonomic nervous system. The purposes of this study were to develop a model of emotion-specific physiological response patterns. The study postulated six emotions (i.e., happiness, sadness, anger, disgust, fear, and surprise) as the basic discrete emotions. Thirty eight college students participated in the present study. Twelve slides (2 for each emotion category) were presented to the subjects in random order. During resting period of 30 s prior to the presentation of each slide, four presentation of each slide, four physiological measures (EEG, ECG, EDA, and respiration) were recorded to establish a baseline. The same physiological measures were recorded while each slide was being presented for 60 s (producing an emotional sate). Then, the subjects were asked to rate the degree of emotion induced by the slide on semantic differential scales. This procedure was repeated for every slide. Based upon the results, a model of emotion-specific physiological response patterns was developed: four emotion (fear, disgust, sadness, and anger) were classified according to the characteristics of EEG and autonomic responses. However, emotions of happiness and surprise were not distinguished by any combination of the physiological measures employed in this study, suggesting another appropriate measure should be adopted for differentiation.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.36 no.6
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• pp.669-675
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• 2003

To increase fishery resources in coastal waters, juvenile fish and bivalves are artificially released every year in Korea. This study provides a methodology to estimate an optimal release quantity based on the carrying capacity of the receiving basins. Carrying capacity was defined by E.p. Odum's theory of ecosystem development as the upper limit of biomass, where total system respiration equals total primary production. The Ecopath trophic ecological model was used to determine carrying capacity in the surfzone ecosystem of Bangjukpo on the southern coast of Korea. Using a top-down control method, various biomasses of fish groups were given to the simulation, with primary production constant and no catch. The results showed that biomass of selected fish groups increased by two orders of magnitude, yielding a five-fold increase in overall consumer biomass. The resultant values are 10 times higher than those estimated in open seas. This can be explained by higher primary production in the Bangjukpo surfzone ecosystem. This method can be used for strategic releases and ecosystem management, particularly when based on an ecological background.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.173-173
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• 2017

지구표면에서 발생하는 물순환, 에너지순환, 탄소순환은 토지-대기-식생간의 물리화학적 관계에 의하여 발생하며 이를 모사하기 위해 지면 및 기후모델이 활용된다. 본 연구에서는 NCAR의 지면모형인 Community Land Model(CLM) v4.5를 동아시아에 적용하고자 한다. 동아시아 범위에서 Fluxtower가 설치되어 물, 에너지, 탄소 플럭스 자료가 관측된 지점에서 모형을 구동하고 결과를 평가하였다. CLM 결과에 따른 증발산(Evapotranspiration), 잠열(Latent heat), 헌혈(Sensible heat)과 같은 물 및 에너지 순환에 관한 결과 뿐 아니라 총 일차생산량(Gross primary production), 순생태계순환(Net ecosystem exchange), 생태계 호흡량(Ecosystem respiration)과 같은 탄소순환에 관한 결과를 비교, 분석하였다. 특히, 기초 결과 분석에 따라 지면 모형 내의 여러 모듈 중에서 화재 관련 모듈에 초점을 맞추어 CLM 모형을 개선하였다. 화재는 식생의 성장에 많은 영향을 미치는 모듈로서 탄소순환 모의에 중요한 역할을 한다. 전 지구 대상 모의를 기반으로 하는 CLM에서 삼림 및 초지 지역의 화재 발생는 국내총생산(Gross domestic product, GDP) 및 인구밀도에 따라 모수화되어 있으나, 이는 전 지구 혹은 지역 대상이 아닌 지점 수준의 모형적용을 위해 부적합하다. 이에 관련 모수들을 재산정하고 개선된 모형 결과를 정량화하기 위해 위에서 언급한 물순환, 에너지순환, 탄소순환 관련된 변수들의 모의값을 Fluxtower 관측값과 비교, 분석하였다.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.107-114
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• 1999

The large-scale distribution of crops Is usually determined by climate. We present the results of a climate-crop prediction based on spatial bio-physical process model approach, implemented in a GIS (Geographic Information System) environment using several regional and global agriculture-environmental databases. The model utilizes daily climate data like temperature, rainfall, solar radiation being generated stocastically by in-built model weather generator to determine the daily biomass and finally the crop yield. Crops are characterized by their specific growing period requirements, photosynthesis, respiration properties and harvesting index properties. Temperature and radiation during the growing period controls the development of each crop. The model simulates geographic/spatial distribution of climate by which a crop-growing belt can also be determined. The model takes both irrigated and non-irrigated area crop productivity into account and the potential increase in productivity by the technical means like mechanization is not considered. All the management input given at the base year 1995 was kept same for the next twenty-year changes until 2015. The simulated distributions of crops under current climatic conditions coincide largely with the current agricultural or specific crop growing regions. Simulation with assumed weather generated derived climate change scenario illustrate changes in the agricultural potential. There are large regional differences in the response across the country. The north-south and east-west regions responded differently with projected climate changes with increased and decreased productivity depending upon the crops and scenarios separately. When water was limiting or facilitating as non-irrigated and irrigated area crop-production effects of temperature rise and higher $CO_2$ levels were different depending on the crops and accordingly their production. Rise in temperature led to yield reduction in case of maize and rice whereas a gain was observed for wheat crop, doubled $CO_2$ concentration enhanced yield for all crops and their several combinations behaved differently with increase or decrease in yields. Finally, with this spatial modeling approach we succeeded in quantifying the crop productivity which may bring regional disparities under the different climatic scenarios where one region may become better off and the other may go worse off.

• Journal of the Korea Society of Computer and Information
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• v.28 no.11
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• pp.53-63
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• 2023

This study developed a cognitive impairment predictive model as one of the screening tests for preventing dementia in the elderly by using Automated Machine Learning(AutoML). We used 'Wearable lifelog data for high-risk dementia patients' of National Information Society Agency, then conducted using PyCaret 3.0.0 in the Google Colaboratory environment. This study analysis steps are as follows; first, selecting five models demonstrating excellent classification performance for the model development and lifelog data analysis. Next, using ensemble learning to integrate these models and assess their performance. It was found that Voting Classifier, Gradient Boosting Classifier, Extreme Gradient Boosting, Light Gradient Boosting Machine, Extra Trees Classifier, and Random Forest Classifier model showed high predictive performance in that order. This study findings, furthermore, emphasized on the the crucial importance of 'Average respiration per minute during sleep' and 'Average heart rate per minute during sleep' as the most critical feature variables for accurate predictions. Finally, these study results suggest that consideration of the possibility of using machine learning and lifelog as a means to more effectively manage and prevent cognitive impairment in the elderly.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.33 no.4
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• pp.353-359
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• 1988

Variations in photosynthetic capacities of leaves differing in thickness were explained on the basis of relationships between gas exchange and internal leaf structure. The relative importance of gas diffusion and of biochemical processes as limiting for leaf photosynthesis was also determined. Mesophyll cell surface was considered to be the limiting internal site for gas diffusion. and cell volume to be indicative of the sink capacity for CO$_2$ fixation. Increases in cell surface area were assumed to reduce proportionately mesophyll resistance to the liquid phase diffusion of CO$_2$. Increased cell volume was thought to account for a proportional increase in reaction rates for carboxylation, oxygenation. and dark respiration. This assumption was tested using chamber-grown Glycine max (L.) Merr. cv. Amsoy plants. Plants were grown under 200, 400, and 600 ${\mu}$mol photons m$\^$-2/ s$\^$-1/ of PAR to induce development of various leaf thickness. Photosynthetic CO$_2$ uptake rates were measured on the 3rd and 4th trifoliolate leaves under 1000 ${\mu}$mol photons m$\^$-2/ s$\^$-1/ of PAR and at the air temperature of 28 C. A pseudo -mechanistic photosynthesis model was modified to accommodate the concept of cell surface area as well as both cell volume and surface area. Both versions were used to simulate leaf photosynthesis. Computations based on volume and surface area showed slightly better agreement with experimental data than did those based on the surface area only. This implies that any single factor, whether it is photosynthetic model utilized in this study was suitable for relating leaf thickness to leaf productivity.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.149-149
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• 2019

Gross Primary production (GPP) and evapotranspiration (ET) are the two critical components of carbon and water cycle respectively, linking the terrestrial surface and ecosystem with the atmosphere. The ratio between GPP to ET is called ecosystem water use efficiency (EWUE) and its quantification at the forest site helps to understand the impact of climate change due to large scale anthropogenic activities such as deforestation and irrigation. This study was conducted at the FLUXNET forest site CN-Qia (2003-2005) using Community land model (CLM 5.0). We simulated carbon and water fluxes including GPP, ecosystem respiration (ER), and ET using climatic variables as forcing dataset for 30 years (1981-2010). Model results were validated with the FLUXNET tower observations. The correlation showed better performance with values of 0.65, 0.77, and 0.63 for GPP, ER, and ET, respectively. The model underestimated the results with minimum bias of -0.04, -1.67, and -0.40 for GPP, ER, and ET, respectively. Effect of climate 'CLIM' and '$CO_2$' were analyzed based on EWUE and its trend was evaluated in the study period. The positive trend of EWUE was observed in the whole period from 1981-2010, and the trend showed further increase when simulated with rising $CO_2$. The time period were divided into two parts, from 1981-2000 and from 2001 to 2010, to identify the warming effect on EWUE. The first period showed the similar increasing trend of EWUE, but the second period showed slightly decreasing trend. This might be associated with the increase in ET in the wet temperate forest site due to increase in climate warming. Water use efficiency defined by transpiration (TR) (TWUE), and inherent-TR based WUE (IT-WUE) were also discussed. This research provides the evidence to climate warming and emphasized the importance of long term planning for management of water resources and evaporative demand in irrigation, deforestation and other anthropogenic activities.

• Journal of Digital Convergence
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• v.19 no.9
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• pp.257-269
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• 2021

The purpose of this study is to collect biosignal data in a non-invasive and non-restrictive manner using a BCG (Ballistocardiogram) sensor, and utilize artificial intelligence machine learning algorithms in ICT and high-performance computing environments. And it is to present and study a method for developing and validating a data-based blood glucose prediction model. In the blood glucose level prediction model, the input nodes in the MLP architecture are data of heart rate, respiration rate, stroke volume, heart rate variability, SDNN, RMSSD, PNN50, age, and gender, and the hidden layer 7 were used. As a result of the experiment, the average MSE, MAE, and RMSE values of the learning data tested 5 times were 0.5226, 0.6328, and 0.7692, respectively, and the average values of the validation data were 0.5408, 0.6776, and 0.7968, respectively, and the coefficient of determination (R²) was 0.9997. If research to standardize a model for predicting blood sugar levels based on data and to verify data set collection and prediction accuracy continues, it is expected that it can be used for non-invasive blood sugar level management.

• Korean Journal of Medicinal Crop Science
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• v.26 no.6
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• pp.447-454
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• 2018

Background: The leaf temperature ($T_{LEAF}$) is one of the most important physical parameters governing water and carbon flux, including evapotranspiration, photosynthesis and respiration. Cnidium officinale is one of the important folk medicines for counteracting a variety of diseases, and is particularly used as a traditional medicinal crop in the treatment of female genital inflammatory diseases. In this study, we developed a model to estimate $T_{Leaf}$ of Cnidium officinale Makino based on black globe temperature ($T_{BGT}$). Methods and Results: This study was performed from April to July 2018 in field characterized by a valley and alluvial fan topography. Databases of $T_{LEAF}$ were curated by infrared thermometry, along with meteorological instruments, including a thermometer, a pyranometer, and an anemometer. Linear regression analysis and Student's t-test were performed to evaluate the performance of the model and significance of the parameters. The correlation coefficient between observed $T_{LEAF}$ and calculated $T_{BGT}$ obtained using an equation, developed to predict $T_{LEAF}$ based on $T_{BGT}$ was very high ($r^2=0.9500$, p < 0.0001). There was a positive relationship between $T_{BGT}$ and solar radiation ($r^2=0.8556$, p < 0.0001), but a negative relationship between $T_{BGT}$ and wind speed ($r^2=0.9707$, p < 0.0001). These results imply that heat exchange in leaves seems to be mainly controlled by solar radiation and wind speed. The correlation coefficient between actual and estimated $T_{BGT}$ was 0.9710 (p < 0.0001). Conclusions: The developed model can be used to accurately estimate the $T_{Leaf}$ of Cnidium officinale Makino and has the potential to become a practical alternative to assessing cold and heat stress.