• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.4
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• pp.64-80
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• 2018

In Korea, citizens can only know general information about crime. Thus it is difficult to know how much they are exposed to crime. If the police can predict the crime risky area, it will be possible to cope with the crime efficiently even though insufficient police and enforcement resources. However, there is no prediction system in Korea and the related researches are very much poor. From these backgrounds, the final goal of this study is to develop an automated crime prediction system. However, for the first step, we build a big data set which consists of local real crime information and urban physical or non-physical data. Then, we developed a crime prediction model through machine learning method. Finally, we assumed several possible scenarios and calculated the probability of crime and visualized the results in a map so as to increase the people's understanding. Among the factors affecting the crime occurrence revealed in previous and case studies, data was processed in the form of a big data for machine learning: real crime information, weather information (temperature, rainfall, wind speed, humidity, sunshine, insolation, snowfall, cloud cover) and local information (average building coverage, average floor area ratio, average building height, number of buildings, average appraised land value, average area of residential building, average number of ground floor). Among the supervised machine learning algorithms, the decision tree model, the random forest model, and the SVM model, which are known to be powerful and accurate in various fields were utilized to construct crime prevention model. As a result, decision tree model with the lowest RMSE was selected as an optimal prediction model. Based on this model, several scenarios were set for theft and violence cases which are the most frequent in the case city J, and the probability of crime was estimated by $250{\times}250m$ grid. As a result, we could find that the high crime risky area is occurring in three patterns in case city J. The probability of crime was divided into three classes and visualized in map by $250{\times}250m$ grid. Finally, we could develop a crime prediction model using machine learning algorithm and visualized the crime risky areas in a map which can recalculate the model and visualize the result simultaneously as time and urban conditions change.

• Korean Journal of Food Science and Technology
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• v.31 no.1
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• pp.128-137
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• 1999

In this study, shiitake mushrooms were dried by hot air, vacuum and freeze drying methods and theire physical properties were compared. Since the pore size affects the amount of absorption, the characteristics of water sorption were investigated at various humidities and temperatures. Results showed that the freeze dried product had the greatest pore area and the highest absorption capacity. However, all the dried samples showed similar quality. The browning degrees were severely changed with increased relative humidities and temperatures. Among these drying methods, the freeze drying gave the greatest change in browning degree. The GAB monolayer moisture contents of the dried shiitake mushroom were $5.3{\sim}7.7%$. The prediction model was also provided using parameters such as relative humidity, temperature and pore area.

• Journal of Advanced Navigation Technology
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• v.23 no.6
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• pp.597-604
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• 2019

Artificial neural networks are algorithms that simulate learning through interaction and experience in neurons in the brain and that are a method that can be used to produce accurate results through learning that reflects the characteristics of data. In this study, a model using deep neural network was presented to improve the predicted wind speed values in the meteorological dynamic model. The wind speed prediction improvement model using the deep neural network presented in the study constructed a model to recalibrate the predicted values of the meteorological dynamics model and carried out the verification and testing process and Separate data confirm that the accuracy of the predictions can be increased. In order to improve the prediction of wind speed, an in-depth neural network was established using the predicted values of general weather data such as time, temperature, air pressure, humidity, atmospheric conditions, and wind speed. Some of the data in the entire data were divided into data for checking the adequacy of the model, and the separate accuracy was checked rather than being used for model building and learning to confirm the suitability of the methods presented in the study.

• The Korean Journal of Applied Statistics
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• v.35 no.4
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• pp.543-552
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• 2022

When frost occurs, crops are directly damaged. When crops come into contact with low temperatures, tissues freeze, which hardens and destroys the cell membranes or chloroplasts, or dry cells to death. In July 2020, a sudden sub-zero weather and frost hit the Minas Gerais state of Brazil, the world's largest coffee producer, damaging about 30% of local coffee trees. As a result, coffee prices have risen significantly due to the damage, and farmers with severe damage can produce coffee only after three years for crops to recover, which is expected to cause long-term damage. In this paper, we tried to predict frost using frost generation data and weather observation data provided by the Korea Meteorological Administration to prevent severe frost. A model was constructed by reflecting weather factors such as wind speed, temperature, humidity, precipitation, and cloudiness. Using XGB(eXtreme Gradient Boosting), SVM(Support Vector Machine), Random Forest, and MLP(Multi Layer perceptron) models, various hyper parameters were applied as training data to select the best model for each model. Finally, the results were evaluated as accuracy(acc) and CSI(Critical Success Index) in test data. XGB was the best model compared to other models with 90.4% ac and 64.4% CSI, followed by SVM with 89.7% ac and 61.2% CSI. Random Forest and MLP showed similar performance with about 89% ac and about 60% CSI.

• The Journal of the Korea Contents Association
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• v.21 no.6
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• pp.35-43
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• 2021

Citrus quality is generally determined by its sugar content and acidity. In particular, sugar content is a very important factor because it determines the taste of citrus. Currently, the most commonly used method of measuring citrus sugar content in farms is a portable juiced sugar meter and a non-destructive sugar meter. This method can be easily measured by individuals, but the accuracy of the sugar content is inferior to that of the citrus NongHyup official machine. In particular, there is an error difference of 0.5 Brix or more, which is still insufficient for use in the field. Therefore, in this paper, we propose an AI model that predicts the citrus sugar content of unmeasured days within the error range of 0.5 Brix or less based on the previously collected citrus sugar content and meteorological data (average temperature, humidity, rainfall, solar radiation, and average wind speed). In addition, it was confirmed that the prediction model proposed through performance evaluation had an mean absolute error of 0.1154 for Seongsan area and 0.1983 for the Hawon area in Jeju Island. Lastly, the proposed model supports an error difference of less than 0.5 Brix and is a technology that supports predictive measurement, so it is expected that its usability will be highly progressive.

• Food Science and Preservation
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• v.24 no.2
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• pp.181-186
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• 2017

This study was conducted to investigate the weight loss, firmness, external color and vitamin C (VC) content of tomatoes (Lycopersicon esculentum) using non-destructive method to measure identical tomato samples during 15 days storage at low temperature and high humidity. Tomatoes were harvested at the light red stage, sorted, box packed and then stored in thermo-hygrostat ($10{\pm}1^{\circ}C$, $90{\pm}10%RH$). The quality changes in weight loss, firmness and external color were measured every 3 day interval. Weight loss was increased by $1.13{\pm}0.15%$, but it may not be considered to affect quality. Surface color of fruit was changed, especially in lightness and hue angle value. The color values were analyzed by analysis of variance (ANOVA), and the results were significant (p<0.001). Firmness of fruit declined during storage, but it did not decrease in direct proportion. On the storage of day 15, firmness was decreased to 40% of initial state. At last, all the experiment data are summarized and the relationship between firmness and weight loss is analyzed to construct a linear regression mathematical model that can predict the weight loss with the firmness value measured by non-destructive method. This research result could be useful in helping tomato exporters and suppliers to get real-time quality factor by using proposed method and regression model.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.9
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• pp.261-268
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• 2018

The use of electric cable was limited due to the installation time and large space as the increase of power demand and load quantity in side line. In order to solve these problems, the application of busway system which can supply the large current was increasing. But it was lack of methods of performance tests to evaluate the reliability and results of test for busway system. In this paper, we presented items to evaluate the reliability test for epoxy coated busway system with reference to IEC 61349-6. In addition, we proposed items to evaluate the reliability and long term life cycle test for the epoxy coated busway system. The combined acceleration deterioration test that reflects actual conditions of the survey as much as possible was conducted considering both thermal and electrical stresses. The deterioration condition was selected to satisfy fifty years life expectation and the insulation performance verification test of the busway system confirmed the long term life prediction. Furthermore, as test items for reliability assessment of compliance with the environment for the use of temperature, humidity and load current where busway system was installed, thermal overload test, water immersion test, cold shock temperature test and thermal cycle test were performed. And we examined changes in characteristics and abnormality after tests. From results, the test items presented to evaluate performance and reliability of the epoxy insulated busway system were confirmed to be appropriate in this paper, and the performance of the product was also confirmed to be excellent for reliability tests.

• Korean Journal of Agricultural and Forest Meteorology
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• v.4 no.1
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• pp.1-11
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• 2002

This study was conducted to reveal the effects of local climatic conditions on the early growth of Korean white pine progeny test stands. For this, stand variables such as mean DBH, mean height, basal area per hectare, and volume per hectare by stand age and locality were first measured and summarized for each stand. Based on these statistics, annual increments for 10 years from stand age 10 to 20 were calculated for each of stand variables. The effects of local climatic conditions as one of environmental factors on the growth were then analyzed by both a topoclimatological method and a spatial statistical technique. From yearly climatic estimates,30 climatic indices which affect the tree growth were computed for each of the progeny test stand. The annual increments were then correlated with and regressed on the climatic indices to examine effects of local climatic conditions on the growth. Gapyung area provided the best conditions for the early growth of Korean white pine and Kwangju area ranked second. On the other hand, the growth pattern in Youngdong ranked last overall as expected. It is also found that the local growth patterns of Korean white pine in juvenile stage were affected by typical weather conditions. The conditions such as low temperature and high relative humidity provide favor environment for the early growth of Korean white pine. Especially, it was concluded that the low temperature is a main factor influencing the early growth of Korean white pine based on the results of correlation analysis and regression equations developed far the prediction of annual increments of stand variables.

• Journal of Energy Engineering
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• v.15 no.4 s.48
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• pp.243-249
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• 2006

An experimental study on the refrigerant-side pressure drop of slit fin an tube heat exchanger has been carried out. A comparison was made between the predictions of previously proposed empirical correlations and experimental data for the pressure drop on design conditions of condenser in micro-fin tube for R22 and Rl34a. Experiments were carried out under the conditions of inlet refrigerant temperature of $60^{\circ}C$ and mass fluxes varying from $150\;to\;250\;kg/m^{2}s$ for R22 and Rl34a. The inlet air conditions are dry bulb temperature of $35^{\circ}C$, relative humidity of 40% and air velocity varying from 0.68 to 1.43 m/s. Experiments show that pressure drop for R134a was $22{\sim}22.6%$ higher than R22 for the degree of subcooling $5^{\circ}C$ For the mass fluxes of $200{\sim}250\;kg/m^{2}s$, the deviation between the experimental and predicted values for the pressure drop was less than ${\pm}20%$ for R22 and Rl34a.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.374-390
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• 2021

Growth and yield of field vegetable crops would be affected by climate conditions, which cause a relatively large fluctuation in crop production and consumer price over years. The yield prediction system for these crops would support decision-making on policies to manage supply and demands. The objectives of this study were to compile literatures related to onion and garlic and to perform data-mining analysis, which would shed lights on the development of crop models for these major field vegetable crops in Korea. The literatures on crop growth and yield were collected from the databases operated by Research Information Sharing Service, National Science & Technology Information Service and SCOPUS. The keywords were chosen to retrieve research outcomes related to crop growth and yield of onion and garlic. These literatures were analyzed using text mining approaches including word cloud and semantic networks. It was found that the number of publications was considerably less for the field vegetable crops compared with rice. Still, specific patterns between previous research outcomes were identified using the text mining methods. For example, climate change and remote sensing were major topics of interest for growth and yield of onion and garlic. The impact of temperature and irrigation on crop growth was also assessed in the previous studies. It was also found that yield of onion and garlic would be affected by both environment and crop management conditions including sowing time, variety, seed treatment method, irrigation interval, fertilization amount and fertilizer composition. For meteorological conditions, temperature, precipitation, solar radiation and humidity were found to be the major factors in the literatures. These indicate that crop models need to take into account both environmental and crop management practices for reliable prediction of crop yield.