• Korean Institute of Interior Design Journal
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• v.22 no.6
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• pp.3-10
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• 2013

This study intends to clarify the key elements of designing low energy residential building construction by planning out residential construction in nature oriented designing method utilizing nearby environment and nature oriented energy from designing stage instead of construction of low energy residential building. Development of building technology is proportional to the development of technology that lasts already. However, what is no less important than the advancement of technology, it is the study of fundamental phenomena energy use in response to climate, reduction, such as recycling. It is possible in such a purpose, it is assumed that there is a need to study elements implementation plan in accordance with the climatic characteristics of the study. Method for controlling the condition solar radiation, sunshine, depending on the characteristics of the weather, by utilizing the convection phenomenon of nature, to maintain the air comfort in the interior space is the essence of eco-friendly construction and passive Property This is an important architectural elements to be aim. For through the analysis of this case, corresponding to the phenomenon of the features of the macro climate and micro climate due to climate change, a combination building blocks of classification placement of each, shape, structure, elevation, space, of the material appeared in various it was possible to know the construction characteristics were. As shown in each case, construction method to address climate change has been found to apply to a comprehensive analysis climatic characteristics of each region, in response to this, the construction of element each corresponding.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.9
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• pp.5877-5884
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• 2014

Recently, abnormally cold weather has been reported more frequently in winter due to the climate change and abnormal weather changes. On the other hand, the heating capacity of a railcar may be not enough to warm the cabin under severe cold climatic conditions, which is one of the reasons for the passengers' complaints about heating. In this study, the effects of ambient temperature and heater power on the cabin temperature was investigated to obtain the minimum ambient temperature for the tested railcar. The test railcar was placed in a large-climatic chamber, and various ambient temperature conditions were simulated. The effects of the heater output were investigated by monitoring the cabin temperature under a range of heater output conditions. The mean cabin temperature was $14.0^{\circ}C$, which was far lower than the required minimum temperature of $18^{\circ}C$, under a $-10^{\circ}C$ ambient temperature condition with the maximum heat power. When the ambient temperature was set to $0^{\circ}C$ and $10^{\circ}C$, the maximum achievable cabin temperature was $26.1^{\circ}C$ and $34.0^{\circ}C$. Through calculations using the interpolation method, the minimum ambient temperature to maintain an $18^{\circ}C$ cabin temperature was $-6.7^{\circ}C$ for this car. The vertical temperature difference was higher with a higher power output and higher ambient temperature. The maximum vertical temperature difference was higher than $10^{\circ}C$ in some cases. However, the horizontal temperature difference vs. low temperature (< $2^{\circ}C$) was independent of the power output and ambient temperature. As a result, it is very important to reduce the vertical temperature difference to achieve good heating performance.

• Journal of the Korean Solar Energy Society
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• v.39 no.4
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• pp.55-67
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• 2019

Recently, as the amount of particulate matter blowing from China increases, the domestic air environment is rapidly deteriorating. This pollution of the atmosphere greatly affects the light energy reaching the ground. Particularly, since the light enters the solar cell module in various forms, the amount of input energy of the solar power generation system may be changed depending on the ratio of direct beam irradiation and diffused horizontal irradiation. In this paper, we analyze how the ratio of direct beam component and diffused component on global horizontal irradiation varies with the atmospheric conditions. In addition, the reliability of the regression equation, designed to decompose the global horizontal irradiation into horizontal direct beam irradiation and diffused horizontal irradiation, was verified according to the level of air pollution. So, we derive the most suitable decomposition model for use in domestic climatic conditions in Korea by comparing the ratio of direct and diffuse component on the horizontal which is calculated with Perez model and Watanabe model using the meteorological weather data observed for 14 months. Finally, to reduce the error of the transposition result, we verified the reliability of the decomposition which depends on the atmospheric environment.

• Journal of Biosystems Engineering
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• v.43 no.4
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• pp.426-439
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• 2018

Purpose: To continuously monitor soil and climatic properties, a data acquisition system (DAQ) was developed and tested in plum farms (Gyewol-ri and Haechang-ri, Suncheon, Korea). Methods: The DAQ consisted of a Raspberry-Pi processor, a modem, and an ADC board with multiple sensors (soil moisture content (SEN0193), soil temperature (DS18B20), climatic temperature and humidity (DHT22), and rainfall gauge (TR-525M)). In the laboratory, various tests were conducted to calibrate SEN0193 at different soil moistures, soil temperatures, depths, and bulk densities. For performance comparison of the SEN0193 sensor, two commercial moisture sensors (SMS-BTA and WT-1000B) were tested in the field. The collected field data in Raspberry-Pi were transmitted and stored on a web server database through a commercial communications wireless network. Results: In laboratory tests, it was found that the SEN0193 sensor voltage reading increased significantly with an increase in soil bulk density. A linear calibration equation was developed between voltage and soil moisture content depending on the farm soil bulk density. In field tests, the SEN0193 sensor showed linearity (R = 0.76 and 0.73) between output voltage and moisture content; however, the other two sensors showed no linearity, indicating that site-specific calibration is important for accurate sensing. In the long-term monitoring results, it was observed that the measured climate temperature was almost the same as website information. Soil temperature information was higher than the values measured by DS18B20 during spring and summer. However, the local rainfall measured using TR 525M was significantly different from the values on the website. Conclusion: Based on the test results obtained using the developed monitoring system, it is thought that the measurement of various parameters using one device would be helpful in monitoring plum growth. Field data from the local farm monitoring system can be coupled with website information from the weather station and used more efficiently.

• Journal of the Korean Association of Geographic Information Studies
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• v.17 no.2
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• pp.32-43
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• 2014

To identify major causes of the global environment changes arising from extreme and unusual weather patterns occurring these days, and to foresee future environmental changes, it is highly important to shed light on the correlation between climate changes and global environment system. To investigate the correlation between climate changes and global environment system, it calls for establishing an integrated climate-environment DB for analyzing comparatively the data on climatic changes and global environment system. In the preceding studies, we researched an XML-based integrated climate-environment DB and developed a management system for the DB. However, the existing integrated climate-environment DB, designed and installed only for individual PCs, does not allow multiple users 'simultaneous access. Accordingly, it fails to systematically update and sharing data which is being generated continuingly. Hence, this study aims to develop an easy-to-use GIS-based integrated DB management system by improving the existing integrated climate-environment DB through the adoption of the client/server model. For this, this study collected and analyzed climate and environment data prior to designing and building a DBMS-based integrated DB. In addition, in order for multidisciplinary researchers to easily get access and apply the integrated DB, this study designed and developed a GIS-based integrated DB management system using a client/server model which facilitates connections with multiple PCs. The GIS-based integrated climate-environment DB management system makes it easier to efficiently manage and locate scattered climate-environment data. It is also expected that the DB system will bring the effects in saving time and cost by avoiding the overlapping generation of data in the areas of integrated climate-environment research.

• Korean Journal of Agricultural and Forest Meteorology
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• v.12 no.1
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• pp.1-10
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• 2010

A regional climate model (RCM) can be a powerful tool to enhance spatial resolution of climate and weather information (IPCC, 2001). In this study we conducted dynamical downscaling using Weather Research and Forecasting Model (WRF) as a RCM in order to obtain high resolution regional agroclimate indices over the Korean Peninsula. For the purpose of obtaining detailed high resolution agroclimate indices, we first reproduced regional weather for the period of March to June, 2002-2008 with dynamic downscaling method under given lateral boundary conditions from NCEP/NCAR (National Centers for Environmental Prediction/National Center for Atmospheric Research) reanalysis data. Normally, numerical model results have shown biases against observational results due to the uncertainties in the modelis initial conditions, physical parameterizations and our physical understanding on nature. Hence in this study, by employing a statistical method, the systematic bias in the modelis results was estimated and corrected for better reproduction of climate on high resolution. As a result of the correction, the systematic bias of the model was properly corrected and the overall spatial patterns in the simulation were well reproduced, resulting in more fine-resolution climatic structures. Based on these results, the fine-resolution agro-climate indices were estimated and presented. Compared with the indices derived from observation, the simulated indices reproduced the major and detailed spatial distributions. Our research shows a possibility to simulate regional climate on high resolution and agro-climate indices by using a proper downscaling method with a dynamical weather forecast model and a statistical correction method to minimize the model bias.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.5
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• pp.480-488
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• 2018

The development of information and communication technology has been carried out actively in the field of agriculture to generate valuable information from large amounts of data and apply big data technology to utilize it. Crops and their varieties are determined by the influence of the natural environment such as temperature, precipitation, and sunshine hours. This paper derives the climatic factors affecting the production of crops using the garlic growth process and daily meteorological variables. A prediction model was also developed for the production of garlic per unit area. A big data analysis technique considering the growth stage of garlic was used. In the exploratory data analysis process, various agricultural production data, such as the production volume, wholesale market load, and growth data were provided from the National Statistical Office, the Rural Development Administration, and Korea Rural Economic Institute. Various meteorological data, such as AWS, ASOS, and special status data, were collected and utilized from the Korea Meteorological Agency. The correlation analysis process was designed by comparing the prediction power of the models and fitness of models derived from the variable selection, candidate model derivation, model diagnosis, and scenario prediction. Numerous weather factor variables were selected as descriptive variables by factor analysis to reduce the dimensions. Using this method, it was possible to effectively control the multicollinearity and low degree of freedom that can occur in regression analysis and improve the fitness and predictive power of regression analysis.

• Korean Journal of Agricultural and Forest Meteorology
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• v.10 no.1
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• pp.9-16
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• 2008

In this study we investigated the effects of the microclimatic conditions on tree growth in different site types for natural deciduous forests in Korea. First, we classified all the sites into 36 types according to their aspect (east, west, south, and north), elevation (higher than 1,000 m, 700$\sim$1,000 m, and lower than 700 m), and topographical conditions (ridge, slope, and valley). For each site type, we measured diameter growth with increment borer, and then estimated periodic annual increment of diameter, height and volume. We applied a topoclimatological technique for estimating microclimatic conditions, and produced monthly climatic estimates from which 17 weather variables (including indices of warmth, coldness, and aridity) were computed for each site type. The periodic annual increments of diameter, height, and volume were then correlated by regression analysis with those weather variables to examine effects of microclimate on tree growth by site type. We found that the correlation of diameter growth by site type was significantly correlated with most weather variables except daily photoperiod. Water condition was the most important factor for the height growth. For volume growth, on the other hand, the conditions such as relatively high temperature and low humidity provided favorable environment. Our regression analysis shows that aridity index is a good predictor for tree growth including diameter, height and volume increments.

• ETRI Journal
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• v.44 no.4
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• pp.573-587
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• 2022

The agricultural sector is completely different from other sectors since it completely relies on various natural and climatic factors. Climate changes have many effects, including lack of annual rainfall and pests, heat waves, changes in sea level, and global ozone/atmospheric CO₂ fluctuation, on land and agriculture in similar ways. Climate change also affects the environment. Based on these factors, farmers chose their crops to increase productivity in their fields. Many existing agricultural ontologies are either domain-specific or have been created with minimal vocabulary and no proper evaluation framework has been implemented. A new agricultural ontology focused on subdomains is designed to assist farmers using Jaccard relative extractor (JRE) and Naïve Bayes algorithm. The JRE is used to find the similarity between two sentences and words in the agricultural documents and the relationship between two terms is identified via the Naïve Bayes algorithm. In the proposed method, the preprocessing of data is carried out through natural language processing techniques and the tags whose dimensions are reduced are subjected to rule-based formal concept analysis and mapping. The subdomain ontologies of weather, pest, and soil are built separately, and the overall agricultural ontology are built around them. The gold standard for the lexical layer is used to evaluate the proposed technique, and its performance is analyzed by comparing it with different state-of-the-art systems. Precision, recall, F-measure, Matthews correlation coefficient, receiver operating characteristic curve area, and precision-recall curve area are the performance metrics used to analyze the performance. The proposed methodology gives a precision score of 94.40% when compared with the decision tree(83.94%) and K-nearest neighbor algorithm(86.89%) for agricultural ontology construction.

• Environmental Analysis Health and Toxicology
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• v.31
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• pp.3.1-3.11
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• 2016

Objectives This study was conducted to describe the regional malaria incidence in relation to the geographic and climatic conditions and describe the effect of altitude on the expansion of malaria over the last decade in Papua New Guinea. Methods Malaria incidence was estimated in five provinces from 1996 to 2008 using national health surveillance data. Time trend of malaria incidence was compared with rainfall and minimum/maximum temperature. In the Eastern Highland Province, time trend of malaria incidence over the study period was stratified by altitude. Spatio-temporal pattern of malaria was analyzed. Results Nationwide, malaria incidence was stationary. Regionally, the incidence increased markedly in the highland region (292.0/100000/yr, p =0.021), and remained stationary in the other regions. Seasonality of the malaria incidence was related with rainfall. Decreasing incidence of malaria was associated with decreasing rainfall in the southern coastal region, whereas it was not evident in the northern coastal region. In the Eastern Highland Province, malaria incidence increased in areas below 1700 m, with the rate of increase being steeper at higher altitudes. Conclusions Increasing trend of malaria incidence was prominent in the highland region of Papua New Guinea, while long-term trend was dependent upon baseline level of rainfall in coastal regions.