• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.18-18
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• 2011

Climate change is impacting and will increasingly impact both the quantity and quality of the world's water resources in a variety of ways. In some areas warming climate results in increased rainfall, surface runoff, and groundwater recharge while in others there may be declines in all of these. Water quality is described by a number of variables. Some are directly impacted by climate change. Temperature is an obvious example. Notably, increased atmospheric concentrations of $CO_2$ triggering climate change increase the $CO_2$ dissolving into water. This has manifold consequences including decreased pH and increased alkalinity, with resultant increases in dissolved concentrations of the minerals in geologic materials contacted by such water. Climate change is also expected to increase the number and intensity of extreme climate events, with related hydrologic changes. A simple framework has been developed in New Zealand for assessing and predicting climate change impacts on water resources. Assessment is largely based on trend analysis of historic data using the non-parametric Mann-Kendall method. Trend analysis requires long-term, regular monitoring data for both climate and hydrologic variables. Data quality is of primary importance and data gaps must be avoided. Quantitative prediction of climate change impacts on the quantity of water resources can be accomplished by computer modelling. This requires the serial coupling of various models. For example, regional downscaling of results from a world-wide general circulation model (GCM) can be used to forecast temperatures and precipitation for various emissions scenarios in specific catchments. Mechanistic or artificial intelligence modelling can then be used with these inputs to simulate climate change impacts over time, such as changes in streamflow, groundwater-surface water interactions, and changes in groundwater levels. The Waimea Plains catchment in New Zealand was selected for a test application of these assessment and prediction methods. This catchment is predicted to undergo relatively minor impacts due to climate change. All available climate and hydrologic databases were obtained and analyzed. These included climate (temperature, precipitation, solar radiation and sunshine hours, evapotranspiration, humidity, and cloud cover) and hydrologic (streamflow and quality and groundwater levels and quality) records. Results varied but there were indications of atmospheric temperature increasing, rainfall decreasing, streamflow decreasing, and groundwater level decreasing trends. Artificial intelligence modelling was applied to predict water usage, rainfall recharge of groundwater, and upstream flow for two regionally downscaled climate change scenarios (A1B and A2). The AI methods used were multi-layer perceptron (MLP) with extended Kalman filtering (EKF), genetic programming (GP), and a dynamic neuro-fuzzy local modelling system (DNFLMS), respectively. These were then used as inputs to a mechanistic groundwater flow-surface water interaction model (MODFLOW). A DNFLMS was also used to simulate downstream flow and groundwater levels for comparison with MODFLOW outputs. MODFLOW and DNFLMS outputs were consistent. They indicated declines in streamflow on the order of 21 to 23% for MODFLOW and DNFLMS (A1B scenario), respectively, and 27% in both cases for the A2 scenario under severe drought conditions by 2058-2059, with little if any change in groundwater levels.

• Research in Plant Disease
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• v.15 no.3
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• pp.193-197
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• 2009

Pseudocercospora leaf spot was major disease of grape cultivar 'Campbell Early' in Korea. Leaf spot first appeared in early June and rapidly dispersed to other leaves through rainy season. Disease progress of leaf spot by Pseudocercospora vitis in plastic green house, in the two provinces (Gimje and Gimcheon), were investigated in 2007. Differences of Infected leaves (%) between cultivation systems were observed in field and plastic green house, but there was no difference between provinces. Micro environmental factors, such as temperature and relative humidity, were correlated with infected leaves by PROC REG procedure of SAS (Statistical Analysis System). As a result, regression model best described ($R^2=0.95^{**}$) the infected leaves as a function of the interaction of cumulated temperatures; Y (Infected leaves)=-7.0101+0.0496$\times$20Hcum (Cumulated hour above 20 degree)+0.0208$\times$20cum (Cumulated temperature above 20 degree)-0.2781$\times$25Hcum (Cumulated hour above 25 degree). A statistics model was shown that cumulated hour and temperature above specific degree were critical factor for Pseudocercospora leaf spot on the grapevine leaves in plastic green house.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.27 no.3
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• pp.245-256
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• 2017

Objectives: The present study is aimed at performing real-time measurement of fibrous materials using an F-1 fiber monitor, investigating the correlations between the measurements and environmental conditions, and assessing the feasibility of the use of the monitor in actual exposure assessments based on the accuracy and reliability of the device. Methods: Asbestos specimens with a fixed asbestos content were dispersed in a chamber and collected with a particle measuring test device. Measurements obtained by the existing PCM method, and with the F-1 fiber monitor were compared. In addition, concentrations of asbestos fibers obtained by the PCM method, the TEM method, and the F-1 fiber monitor were compared with that of specific ABS scenarios in NOA regions. Correlations of asbestos contents in soil and weather conditions with each method of measurement were analyzed. Results: Laboratory results showed that levels of asbestos fibers measured with each method increased as fiber contents in soil increased. In the accuracy and reproducibility assessment, no significant differences were found between the different methods of measurement. On-site assessment results showed positive correlations among the methods, and these correlations were less significant compared with what was shown by the laboratory results. Levels of asbestos fibers increased as asbestos contents in soil increased, and as temperature increased. Levels of asbestos fibers decreased as humidity increased, and wind speed did not significantly affect the extent to which asbestos fibers were scattered. Conclusions: While it would be premature to replace existing methods with the use of F-1 fiber monitors in real sites based on the results of this study, the monitor may be useful in the screening of the sites, which assesses hazard levels in different regions. Replacement of existing methods with the use of F-1 fiber monitors may be possible after the limitations identified in this study are overcome, and additional assessment data are obtained and reviewed under different conditions to confirm the reliability of the monitor in future research. Obtained assessment results may be used as basic data for the assessment of asbestos hazard in NOA regions.

• Korean Journal of Food Science and Technology
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• v.26 no.3
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• pp.305-309
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• 1994

Precooked powder of redbean porridge (RP) was prepared by the series of process extrusion, drying, milling and blending with a mixture of whole redbean flour and corn starch and others. The optimum process and quality of products for RP were investigated. After extrusion under the moisture content 24 to 26%, twin screw speed 350 rpm, extrusion temperature 150 to $155^{\circ}C$ and feed rate 60 kg/hr, the product had a higher quality with its natural redbean flavor/color. During the extrusion process, extrusion temperature and specific mechanical energy increased from 150 to $198^{\circ}C$ and from 134 to 144 kwh/ton respectively, as the amount of addition water decreased from 6 to 2 kg/hr. By the hot air drying of redbean extrudate (RE). it could be dried below to 4% moisture content, of which level considered as an optimal moisture content for anti-caking of the powdered product, at $80^{\circ}C$ for 4hrs and at $100^{\circ}C$ for 1.5 hrs respectively. In the sieve analysis of extrudate powder, when the product milled through a mesh size of 0.5 mm or 1.0 mm, about 80% or 65% of the feed was passed a 65 mesh screen respectively. Moisture absorption of final blended products was formed a cake under 100% of relative humidity after 13 hrs of storage. As the amount of RE powder reduced, the flavor score of products decreased by sensory evaluation of products prepared by the different ratio of RE powder, corn starch and sugar.

• Journal of Environmental Science International
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• v.23 no.10
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• pp.1719-1729
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• 2014

This study is carried out in order to bridge the gap to understand the relationships between South Asian and East Asian monsoon systems by comparing the summer (June-September) precipitation of Nepal and South Korea. Summer monsoon precipitation data from Nepal and South Korea during 30 years (1981-2010) are used in this research to investigate the association. NCEP/NCAR reanalysis data are also used to see the nature of large scale phenomena. Statistical applications are used to analyze these data. The analyzed results show that summer monsoon precipitation is higher over Nepal ($1513.98{\pm}159.29mm\;y^{-1}$) than that of South Korea ($907.80{\pm}204.71mm\;y^{-1}$) and the wettest period in both the countries is July. However, the coefficient of variation shows that amplitude of interannual variation of summer monsoon over South Korea (22.55%) is larger in comparison to that of Nepal (10.52%). Summer monsoon precipitation of Nepal is found to be significantly correlated to that of South Korea with a correlation coefficient of 0.52 (99% confidence level). Large-scale circulations are studied to further investigate the relationship between the two countries. wind and specific humidity at 850 hPa show a strong westerly from Arabian Sea to BOB and from BOB, wind moves towards Nepal in a northwestward direction during the positive rainfall years. In case of East Asia, strong northward displacement of wind can be observed from Pacific to South Korea and strong anticyclone over the northwestern Pacific Ocean. However, during the negative rainfall years, in the South Asian region we can find weak westerly from the Arabian Sea to BOB, wind is blowing in a southerly direction from Nepal and Bangladesh to BOB.

• Korean Journal of Agricultural Science
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• v.28 no.1
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• pp.18-26
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• 2001

Application of the Asian ladybird (Harmonia axyridis) to control several species of aphids in the plastic green houses in mind, control effects of aphid populations regulated by the Asian ladybird were observed. The green peach aphid, the turnip aphid, and the cotton aphid were present on mustard plants, Angelia utlis, ornamental kales, and egg plants at greenhouses in spring, summer, and winter. Adults and larvae of the Asian ladybird used in experiments were collected from aggregated sites at Taejon in the autumn and reared on the cotton aphid in the laboratory. In winter, more number of adults and larvae of ladybirds than in other seasons were needed to control aphid population in successively double plastic greenhouses with supplied subterranean water for keeping warmth. In spring and summer, it was possible to keep the aphid populations low when necessary by manipulating ladybird populations according to the density of aphids. On the other hand, the innate increasing rate of aphid, the aphid population density at the time of applying ladybird, the predacious ability of ladybird at specific developmental stages, and needed periods should be taken into account to control aphids. In addition, the environmental factors, for example, optimum temperature and humidity should be considered to be biologically effective when ladybirds are released to greenhouses.

• Korean Journal of Agricultural Science
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• v.10 no.1
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• pp.118-128
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• 1983

Simulation model of natural air grain drying was discussed and modified to predict the changes of grain moisture content and dry matter loss of rough rice drying. The modified simulation model was then validated using actual test data. A series of simulated drying tests using official weather data for 15 years from Beaumont, Texas, was taken to make minimum airflow rate and maximum bed depth of rough rice drying by natural air, under different conditions of initial moisture content of rough rice, airflow rate and harvest date.

• Journal of the Korean earth science society
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• v.30 no.4
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• pp.464-477
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• 2009

In this study, we examined the patterns of local circulation over the Daechung lake area through the numerical experiment designed to investigate the impact of lake on the local circulation. The results of numerical experiment showed that the surface temperature predicted by WRF model was lower than the observation, while the wind speed was stronger than the observation. The local circulation over the lake area was characterized by a lake breeze induced by a horizontal thermal contrast between the lake surface and the Surrounding land. At Daecheong Lake, a lake breeze formed at 09 LST and dissipated at 18 LST, with maximum intensity at 15 LST. The vertical extent of the simulated circulation was about 1,200 m. The specific humidity increased as the humid air above the lake moved landward due to the daytime circulation of the lake breeze. The numerical experiments of sensitivity to existence of the lake showed that the simulated surface temperature decreased in the experiment with the lake. Wind speed was more intense around the lake area when the actual land use was substituted by grassland land use. The results of numerical experiments suggest that the lake-induced lake breeze circulation has an effect on the meteorology of planetary boundary layer around the lake.

• Korean Journal of Agricultural and Forest Meteorology
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• v.14 no.2
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• pp.53-62
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• 2012

Demand for high resolution weather data grows in the agriculture and forestry fields. Local Analysis and Prediction System (LAPS) can be used to analyze the local weather at high spatial and temporal resolution, utilizing the data from various sources including numerical weather prediction models, wind or temperature profilers, Automated Weather Station (AWS) networks, radars, and satellites. LAPS has been set to analyze weather elements such as air temperature, relative humidity, wind speed, and wind direction every hour at the spatial resolution of $100m{\times}100m$ for Gyeonggi-do on near real-time basis. The AWS data were revised by adding the agricultural field AWS data (33 stations) in addition to the KMA data. The analysis periods were from 1 to 31 August 2009 and from 15 to 21 February 2010. The comparison of the LAPS output showed the smaller errors when using the agricultural AWS observation data together with the KMA data as its input data than using only either the agricultural or KMA AWS data. The accuracy of the current system needs improvement by further optimization of analyzing options of the system. However, the system is highly applicable to various fields in agriculture and forestry because it can provide site specific data with reasonable time intervals.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2060-2077
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• 2019

Recently, mobile healthcare services have attracted significant attention because of the emerging development and supply of diverse wearable devices. Smartwatches and health bands are the most common type of mobile-based wearable devices and their market size is increasing considerably. However, simple value comparisons based on accumulated data have revealed certain problems, such as the standardized nature of health management and the lack of personalized health management service models. The convergence of information technology (IT) and biotechnology (BT) has shifted the medical paradigm from continuous health management and disease prevention to the development of a system that can be used to provide ground-based medical services regardless of the user's location. Moreover, the IT-BT convergence has necessitated the development of lifestyle improvement models and services that utilize big data analysis and machine learning to provide mobile healthcare-based personal health management and disease prevention information. Users' health data, which are specific as they change over time, are collected by different means according to the users' lifestyle and surrounding circumstances. In this paper, we propose a prediction model of user physical activity that uses data characteristics-based long short-term memory (DC-LSTM) recurrent neural networks (RNNs). To provide personalized services, the characteristics and surrounding circumstances of data collectable from mobile host devices were considered in the selection of variables for the model. The data characteristics considered were ease of collection, which represents whether or not variables are collectable, and frequency of occurrence, which represents whether or not changes made to input values constitute significant variables in terms of activity. The variables selected for providing personalized services were activity, weather, temperature, mean daily temperature, humidity, UV, fine dust, asthma and lung disease probability index, skin disease probability index, cadence, travel distance, mean heart rate, and sleep hours. The selected variables were classified according to the data characteristics. To predict activity, an LSTM RNN was built that uses the classified variables as input data and learns the dynamic characteristics of time series data. LSTM RNNs resolve the vanishing gradient problem that occurs in existing RNNs. They are classified into three different types according to data characteristics and constructed through connections among the LSTMs. The constructed neural network learns training data and predicts user activity. To evaluate the proposed model, the root mean square error (RMSE) was used in the performance evaluation of the user physical activity prediction method for which an autoregressive integrated moving average (ARIMA) model, a convolutional neural network (CNN), and an RNN were used. The results show that the proposed DC-LSTM RNN method yields an excellent mean RMSE value of 0.616. The proposed method is used for predicting significant activity considering the surrounding circumstances and user status utilizing the existing standardized activity prediction services. It can also be used to predict user physical activity and provide personalized healthcare based on the data collectable from mobile host devices.