• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.4
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• pp.256-266
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• 2022

Empirical models including the Angstrom-Prescott (AP) model have been used to estimate solar radiation at sites, which would support a wide use of crop models. The objective of this study was to estimate two sets of solar radiation estimates using the AP coefficients derived for climate zone (AP_Frere) and specific site (AP_Choi), respectively. The daily solar radiation was estimated at 18 sites in Korea where long-term measurements of solar radiation were available. In the present study, daily solar radiation and sunshine duration were collected for the period from 2012 to 2021. Daily weather data including maximum and minimum temperatures and rainfall were also obtained to prepare input data to a process-based crop model, CERES-Rice model included in Decision Support System for Agrotechnology Transfer (DSSAT). It was found that the daily estimates of solar radiation using the climate zone specific coefficient, SFrere, had significantly less error than those using site-specific coefficients S_Choi (p<0.05). The cumulative values of S_Frere for the period from march to September also had less error at 55% of study sites than those of S_Choi. Still, the use of S_Frere and S_Choi as inputs to the CERES-Rice model resulted in slight differences between the outcomes of crop growth simulations, which had no significant difference between these outputs. These results suggested that the AP coefficients for the temperate climate zone would be preferable for the estimation of solar radiation. This merits further evaluation studies to compare the AP model with other sophisticated approaches such as models based on satellite data.

• Journal of Korea Water Resources Association
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• v.55 no.11
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• pp.931-939
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• 2022

In Korea, about two-thirds of the precipitation is concentrated in the summer season, so the problem of turbidity in the summer flood season varies from year to year. Concentrated rainfall due to abnormal rainfall and extreme weather is on the rise. The inflow of turbidity caused a sudden increase in turbidity in the water, causing a problem of turbidity in the dam reservoir. In particular, in Korea, where rivers and dam reservoirs are used for most of the annual average water consumption, if turbidity problems are prolonged, social and environmental problems such as agriculture, industry, and aquatic ecosystems in downstream areas will occur. In order to cope with such turbidity prediction, research on turbidity modeling is being actively conducted. Flow rate, water temperature, and SS data are required to model turbid water. To this end, the national measurement network measures turbidity by measuring SS in rivers and dam reservoirs, but there is a limitation in that the data resolution is low due to insufficient facilities. However, there is an unmeasured period depending on each dam and weather conditions. As a sensor for measuring turbidity, there are Optical Backscatter Sensor (OBS) and YSI, and a sensor for measuring SS uses equipment such as Laser In-Situ Scattering and Transmissometry (LISST). However, in the case of such a high-tech sensor, there is a limit due to the stability of the equipment. Therefore, there is an unmeasured period through analysis based on the acquired flow rate, water temperature, SS, and turbidity data, so it is necessary to develop a relational expression to calculate the SS used for the input data. In this study, the AEM3D model used in the Water Resources Corporation SURIAN system was used to improve the accuracy of prediction of turbidity through the turbidity-SS relationship developed based on the measurement data near the dam outlet.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.1
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• pp.28-36
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• 2023

Solar radiation that is measured at relatively small number of weather stations is one of key inputs to crop models for estimation of crop productivity. Solar radiation products derived from GK-2A and Himawari 8 satellite data have become available, which would allow for preparation of input data to crop models, especially for assessment of crop productivity under an agrivoltaic system where crop and power can be produced at the same time. The objective of this study was to compare the degree of agreement between the solar radiation products obtained from those satellite data. The sub hourly products for solar radiation were collected to prepare their daily summary for the period from May to October in 2020 during which both satellite products for solar radiation were available. Root mean square error (RMSE) and its normalized error (NRMSE) were determined for daily sum of solar radiation. The cumulative values of solar radiation for the study period were also compared to represent the impact of the errors for those products on crop growth simulations. It was found that the data product from the Himawari 8 satellite tended to have smaller values of RMSE and NRMSE than that from the GK-2A satellite. The Himawari 8 satellite product had smaller errors at a large number of weather stations when the cumulative solar radiation was compared with the measurements. This suggests that the use of Himawari 8 satellite products would cause less uncertainty than that of GK2-A products for estimation of crop yield. This merits further studies to apply the Himawari 8 satellites to estimation of solar power generation as well as crop yield under an agrivoltaic system.

• The Journal of Korean Society for Radiation Therapy
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• v.35
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• pp.41-51
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• 2023

Purpose: This study compares and analyzes the image quality of 3D-CBCT(Cone Beam Computed-Tomography) and Gated CBCT according to baseline changes during SBRT(Stereotactic Body RadioTherapy) in lung cancer patients to find a useful CBCT method for correcting movement due to breathing Materials and methods : Insert a solid tumor material with a diameter of 3 cm into the QUASAR^TM phantom. 4-Dimentional Computed-Tomography(4DCT) images were taken with a speed of the phantom at period 3 sec and a maximum amplitude of 20 mm. Using the contouring menu of the computerized treatment planning system Eclipse^TM Gross Tumor Volume was outlined on solid tumor material. Set-up the same as when acquiring a 4DCT image using Truebeam STx^TM, breathing patterns with baseline changes of 1 mm, 3 mm, and 5 mm were input into the phantom to obtain 3D-CBCT (Spotlight, Full) and Gated-CBCT (Spotlight, Full) images five times repeatedly. The acquired images were compared with the Signal-to-Noise Ratio(SNR), Contrast-to-Noise Ratio(CNR), Tumor Volume Length, and Motion Blurring Ratio(MBR) based on the 4DCT image. Results: The average Signal-to-Noise Ratio, Contrast-to-Noise Ratio, Tumor Volume Length and Motion Blurring Ratio of Spotlight Gated CBCT images were 13.30±0.10%, 7.78±0.16%, 3.55±0.17%, 1.18±0.06%. As a result, Spotlight Gated-CBCT images according to baseline change showed better values than Spotligtht 3D-CBCT images. Also, the average Signal-to-Noise Ratio, Contrast-to-Noise Ratio, Tumor Volume Length and Motion Blurring Ratio of Full Gated CBCT images were 12.80±0.11%, 7.60±0.11%, 3.54±0.16%, 1.18±0.05%. As a result Full GatedCBCT images according to baseline change showed better values than Full 3D-CBCT images. Conclusion : Compared to 3D-CBCT images, Gated-CBCT images had better image quality according to the baseline change, and the effect of Motion Blurring Artifacts caused by breathing was small. Therefore, it is considered useful to image guided using Gated-CBCT when a baseline change occurs due to difficulty in regular breathing during SBRT that exposes high doses in a short period of time

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.119-133
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• 2020

Fama asserted that in an efficient market, we can't make a trading rule that consistently outperforms the average stock market returns. This study aims to suggest a machine learning algorithm to improve the trading performance of an intraday short volatility strategy applying asymmetric volatility spillover effect, and analyze its trading performance improvement. Generally stock market volatility has a negative relation with stock market return and the Korean stock market volatility is influenced by the US stock market volatility. This volatility spillover effect is asymmetric. The asymmetric volatility spillover effect refers to the phenomenon that the US stock market volatility up and down differently influence the next day's volatility of the Korean stock market. We collected the S&P 500 index, VIX, KOSPI 200 index, and V-KOSPI 200 from 2008 to 2018. We found the negative relation between the S&P 500 and VIX, and the KOSPI 200 and V-KOSPI 200. We also documented the strong volatility spillover effect from the VIX to the V-KOSPI 200. Interestingly, the asymmetric volatility spillover was also found. Whereas the VIX up is fully reflected in the opening volatility of the V-KOSPI 200, the VIX down influences partially in the opening volatility and its influence lasts to the Korean market close. If the stock market is efficient, there is no reason why there exists the asymmetric volatility spillover effect. It is a counter example of the efficient market hypothesis. To utilize this type of anomalous volatility spillover pattern, we analyzed the intraday volatility selling strategy. This strategy sells short the Korean volatility market in the morning after the US stock market volatility closes down and takes no position in the volatility market after the VIX closes up. It produced profit every year between 2008 and 2018 and the percent profitable is 68%. The trading performance showed the higher average annual return of 129% relative to the benchmark average annual return of 33%. The maximum draw down, MDD, is -41%, which is lower than that of benchmark -101%. The Sharpe ratio 0.32 of SVS strategy is much greater than the Sharpe ratio 0.08 of the Benchmark strategy. The Sharpe ratio simultaneously considers return and risk and is calculated as return divided by risk. Therefore, high Sharpe ratio means high performance when comparing different strategies with different risk and return structure. Real world trading gives rise to the trading costs including brokerage cost and slippage cost. When the trading cost is considered, the performance difference between 76% and -10% average annual returns becomes clear. To improve the performance of the suggested volatility trading strategy, we used the well-known SVM algorithm. Input variables include the VIX close to close return at day t-1, the VIX open to close return at day t-1, the VK open return at day t, and output is the up and down classification of the VK open to close return at day t. The training period is from 2008 to 2014 and the testing period is from 2015 to 2018. The kernel functions are linear function, radial basis function, and polynomial function. We suggested the modified-short volatility strategy that sells the VK in the morning when the SVM output is Down and takes no position when the SVM output is Up. The trading performance was remarkably improved. The 5-year testing period trading results of the m-SVS strategy showed very high profit and low risk relative to the benchmark SVS strategy. The annual return of the m-SVS strategy is 123% and it is higher than that of SVS strategy. The risk factor, MDD, was also significantly improved from -41% to -29%.

• Journal of Animal Environmental Science
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• v.10 no.1
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• pp.47-58
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• 2004

This work was carried out to investigate the effects of rice hull continuously utilized and/or replenished on the composting properties and to obtain the fundamental data between an unsupported wall and a soil supported wall during the period of composting with pig slurry in winter season. There were no the temperature holding effects in soil supported wall. New compost facility design for the temperature holding effects from a soil supported wall was required. The results were as follows; 1. Composting 1㎥ of pig slurry caused to save on 0.31㎥ of bulking agent in the unsupported wall in comparison with a soil supported wall in the rice hull single addition, and 0.45㎥ in the rice hull gradual addition. 2. The pile in the rice hull single addition had a high temperature in 4 days of composting indicating $71^{\circ}C$ and had a tendency in repeating periodically between $40^{\circ}C$ and $65^{\circ}C$ till 43 days of composting. And also the temperature of the pile was maintained between $48^{\circ}C$ and $28^{\circ}C$ after 50 days of composting. The pile of a rice hull gradual addition had the lower point of the temperature high increasingly according to adding up rice hull during the 35 days of composting. 3. The pH recorded in the rice hull single addition was higher(8.35∼10.02) compared to the rice hull gradual addition(8.6∼9.8). The pile of a rice hull single addition had a tendency in abruptly decreasing pH of the unsupported wall during the period of between 0.363$\textrm m^3$ and 0.537$\textrm m^3$ as a unit of pig slurry per rice hull. EC depending upon the way in adding rice hull was changed between 1.10 mS/$\textrm {cm}^3$ and 1.87 mS/$\textrm {cm}^3$. 4. The organic matter in an unsupported wall of the hull single addition was maintained the level of 55% during the period between 0.119㎥ and 0.363㎥ as a unit of pig slurry per rice hull while in the soil supported wall between 48 and 70. Water soluble C:N ratio was maintained between 1 and 2 in the rice hull single addition, while between 1 and 3 in the rice hull gradual addition. 5. Fertilizer constituents were detected higher level in the unsupported wall than in the soil supported wall in all treatments. This was dependant upon the input of pig slurry.

• Korean Journal of Agricultural and Forest Meteorology
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• v.14 no.4
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• pp.207-221
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• 2012

Air temperature in Korea has increased by $1.5^{\circ}C$ over the last 100 years, which is nearly twice the global average rate during the same period. Moreover, it is projected that such change in temperature will continue in the 21st century. The objective of this study was to evaluate the potential impacts of future climate change on the rice production and adaptation methods in Korea. Climate data for the baseline (1971~2000) and the three future climate (2011~2040, 2041~2070, and 2071~2100) at fifty six sites in South Korea under IPCC SRES A1B scenario were used as the input to the rice crop model ORYZA2000. Six experimental schemes were carried out to evaluate the combined effects of climatic warming, $CO_2$ fertilization, and cropping season on rice production. We found that the average production in 2071~2100 would decrease by 23%, 27%, and 29% for early, middle, and middle-late rice maturing type, respectively, when cropping seasons were fixed. In contrast, predicted yield reduction was ~0%, 6%, and 7%, for early, middle, and middle-late rice maturing type, respectively, when cropping seasons were changed. Analysis of variation suggested that climatic warming, $CO_2$ fertilization, cropping season, and rice maturing type contributed 60, 10, 12, and 2% of rice yield, respectively. In addition, regression analysis suggested 14~46 and 53~86% of variations in rice yield were explained by grain number and filled grain ratio, respectively, when cropping season was fixed. On the other hand, 46~78 and 22~53% of variations were explained respectively with changing cropping season. It was projected that sterility caused by high temperature would have no effect on rice yield. As a result, rice yield reduction in the future climate in Korea would resulted from low filled grain ratio due to high growing temperature during grain-filling period because the $CO_2$ fertilization was insufficient to negate the negative effect of climatic warming. However, adjusting cropping seasons to future climate change may alleviate the rice production reduction by minimizing negative effect of climatic warming without altering positive effect of $CO_2$ fertilization, which improves weather condition during the grain-filling period.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.59-77
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• 2012

This study focuses on predicting which firms will increase capital by issuing new stocks in the near future. Many stakeholders, including banks, credit rating agencies and investors, performs a variety of analyses for firms' growth, profitability, stability, activity, productivity, etc., and regularly report the firms' financial analysis indices. In the paper, we develop predictive models for rights issues using these financial analysis indices and data mining techniques. This study approaches to building the predictive models from the perspective of two different analyses. The first is the analysis period. We divide the analysis period into before and after the IMF financial crisis, and examine whether there is the difference between the two periods. The second is the prediction time. In order to predict when firms increase capital by issuing new stocks, the prediction time is categorized as one year, two years and three years later. Therefore Total six prediction models are developed and analyzed. In this paper, we employ the decision tree technique to build the prediction models for rights issues. The decision tree is the most widely used prediction method which builds decision trees to label or categorize cases into a set of known classes. In contrast to neural networks, logistic regression and SVM, decision tree techniques are well suited for high-dimensional applications and have strong explanation capabilities. There are well-known decision tree induction algorithms such as CHAID, CART, QUEST, C5.0, etc. Among them, we use C5.0 algorithm which is the most recently developed algorithm and yields performance better than other algorithms. We obtained data for the rights issue and financial analysis from TS2000 of Korea Listed Companies Association. A record of financial analysis data is consisted of 89 variables which include 9 growth indices, 30 profitability indices, 23 stability indices, 6 activity indices and 8 productivity indices. For the model building and test, we used 10,925 financial analysis data of total 658 listed firms. PASW Modeler 13 was used to build C5.0 decision trees for the six prediction models. Total 84 variables among financial analysis data are selected as the input variables of each model, and the rights issue status (issued or not issued) is defined as the output variable. To develop prediction models using C5.0 node (Node Options: Output type = Rule set, Use boosting = false, Cross-validate = false, Mode = Simple, Favor = Generality), we used 60% of data for model building and 40% of data for model test. The results of experimental analysis show that the prediction accuracies of data after the IMF financial crisis (59.04% to 60.43%) are about 10 percent higher than ones before IMF financial crisis (68.78% to 71.41%). These results indicate that since the IMF financial crisis, the reliability of financial analysis indices has increased and the firm intention of rights issue has been more obvious. The experiment results also show that the stability-related indices have a major impact on conducting rights issue in the case of short-term prediction. On the other hand, the long-term prediction of conducting rights issue is affected by financial analysis indices on profitability, stability, activity and productivity. All the prediction models include the industry code as one of significant variables. This means that companies in different types of industries show their different types of patterns for rights issue. We conclude that it is desirable for stakeholders to take into account stability-related indices and more various financial analysis indices for short-term prediction and long-term prediction, respectively. The current study has several limitations. First, we need to compare the differences in accuracy by using different data mining techniques such as neural networks, logistic regression and SVM. Second, we are required to develop and to evaluate new prediction models including variables which research in the theory of capital structure has mentioned about the relevance to rights issue.

• Journal of Korean Academy of Nursing
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• v.12 no.2
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• pp.1-26
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• 1982

The main purpose of this study was to test the effect of the structured information on the sleep amount of the patients undergoing open heart surgery. This study has specifically addressed to the Following two basic research questions: (1) Would the structed in formation influence in the reduction of sleep disturbance related to anxiety and Physical stress before and after the operation? and (2) that would be the effects of the structured information on the level of preoperative state anxiety, the hormonal change, and the degree of behavioral change in the patients undergoing an open heart surgery? A Quasi-experimental research was designed to answer these questions with one experimental group and one control group. Subjects in both groups were matched as closely as possible to avoid the effect of the differences inherent to the group characteristics, Baseline data were also. collected on both groups for 7 days prior to the experiment and found that subjects in both groups had comparable sleep patterns, trait anxiety, hormonal levels and behavioral level. A structured information as an experimental input was given to the subjects in the experimental group only. Data were collected and compared between the experimental group and the control group on the sleep amount of the consecutive pre and post operative days, on preoperative state anxiety level, and on hormonal and behavioral changes. To test the effectiveness of the structured information, two main hypotheses and three sub-hypotheses were formulated as follows; Main hypothesis 1: Experimental group which received structured information will have more sleep amount than control group without structured information in the night before the open heart surgery. Main hypothesis 2: Experimental group with structured information will have more sleep, amount than control group without structured information during the week following the open heart surgery Sub-hypothesis 1: Experimental group with structured information will be lower in the level of State anxiety than control group without structured information in the night before the open heart surgery. Sub-hypothesis 2 : Experimental group with structured information will have lower hormonal level than control group without stuctured information on the 5th day after the open heart surgery Sub-hypothesis 3: Experimental group with structured information will be lower in the behavioral change level than control group without structured information during the week after the open heart surgery. The research was conducted in a national university hospital in Seoul, Korea. The 53 Subjects who participated in the study were systematically divided into experimental group and control group which was decided by random sampling method. Among 53 subjects, 26 were placed in the experimental group and 27 in the control group. Instruments; (1) Structed information: Structured information as an independent variable was constructed by the researcher on the basis of Roy's adaptation model consisting of physiologic needs, self-concept, role function and interdependence needs as related to the sleep and of operational procedures. (2) Sleep amount measure: Sleep amount as main dependent variable was measured by trained nurses through observation on the basis of the established criteria, such as closed or open eyes, regular or irregular respiration, body movement, posture, responses to the light and question, facial expressions and self report after sleep. (3) State anxiety measure: State Anxiety as a sub-dependent variable was measured by Spi-elberger's STAI Anxiety scale, (4) Hormornal change measure: Hormone as a sub-dependent variable was measured by the cortisol level in plasma. (5) Behavior change measure: Behavior as a sub-dependent variable was measured by the Behavior and Mood Rating Scale by Wyatt. The data were collected over a period of four months, from June to October 1981, after the pretest period of two months. For the analysis of the data and test for the hypotheses, the t-test with mean differences and analysis of covariance was used. The result of the test for instruments show as follows: (1) STAI measurement for trait and state anxiety as analyzed by Cronbachs alpha coefficient analysis for item analysis and reliability showed the reliability level at r= .90 r= .91 respectively. (2) Behavior and Mood Rating Scale measurement was analyzed by means of Principal Component Analysis technique. Seven factors retained were anger, anxiety, hyperactivity, depression, bizarre behavior, suspicious behavior and emotional withdrawal. Cumulative percentage of each factor was 71.3%. The result of the test for hypotheses show as follows; (1) Main hypothesis, was not supported. The experimental group has 282 minutes of sleep as compared to the 255 minutes of sleep by the control group. Thus the sleep amount was higher in experimental group than in control group, however, the difference was not statistically significant at .05 level. (2) Main hypothesis 2 was not supported. The mean sleep amount of the experimental group and control group were 297 minutes and 278 minutes respectively Therefore, the experimental group had more sleep amount as compared to the control group, however, the difference was not statistically significant at .05 level. Thus, the main hypothesis 2 was not supported. (3) Sub-hypothesis 1 was not supported. The mean state anxiety of the experimental group and control group were 42.3, 43.9 in scores. Thus, the experimental group had slightly lower state anxiety level than control group, howe-ver, the difference was not statistically significant at .05 level. (4) Sub-hypothesis 2 was not supported. . The mean hormonal level of the experimental group and control group were 338 ㎍ and 440 ㎍ respectively. Thus, the experimental group showed decreased hormonal level than the control group, however, the difference was not statistically significant at .05 level. (5) Sub-hypothesis 3 was supported. The mean behavioral level of the experimental group and control group were 29.60 and 32.00 respectively in score. Thus, the experimental group showed lower behavioral change level than the control group. The difference was statistically significant at .05 level. In summary, the structured information did not influence the sleep amount, state anxiety or hormonal level of the subjects undergoing an open heart surgery at a statistically significant level, however, it showed a definite trends in their relationships, not least to mention its significant effect shown on behavioral change level. It can further be speculated that a great degree of individual differences in the variables such as sleep amount, state anxiety and fluctuation in hormonal level may partly be responsible for the statistical insensitivity to the experimentation.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.127-146
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• 2017

Services using artificial intelligence have begun to emerge in daily life. Artificial intelligence is applied to products in consumer electronics and communications such as artificial intelligence refrigerators and speakers. In the financial sector, using Kensho's artificial intelligence technology, the process of the stock trading system in Goldman Sachs was improved. For example, two stock traders could handle the work of 600 stock traders and the analytical work for 15 people for 4weeks could be processed in 5 minutes. Especially, big data analysis through machine learning among artificial intelligence fields is actively applied throughout the financial industry. The stock market analysis and investment modeling through machine learning theory are also actively studied. The limits of linearity problem existing in financial time series studies are overcome by using machine learning theory such as artificial intelligence prediction model. The study of quantitative financial data based on the past stock market-related numerical data is widely performed using artificial intelligence to forecast future movements of stock price or indices. Various other studies have been conducted to predict the future direction of the market or the stock price of companies by learning based on a large amount of text data such as various news and comments related to the stock market. Investing on commodity asset, one of alternative assets, is usually used for enhancing the stability and safety of traditional stock and bond asset portfolio. There are relatively few researches on the investment model about commodity asset than mainstream assets like equity and bond. Recently machine learning techniques are widely applied on financial world, especially on stock and bond investment model and it makes better trading model on this field and makes the change on the whole financial area. In this study we made investment model using Support Vector Machine among the machine learning models. There are some researches on commodity asset focusing on the price prediction of the specific commodity but it is hard to find the researches about investment model of commodity as asset allocation using machine learning model. We propose a method of forecasting four major commodity indices, portfolio made of commodity futures, and individual commodity futures, using SVM model. The four major commodity indices are Goldman Sachs Commodity Index(GSCI), Dow Jones UBS Commodity Index(DJUI), Thomson Reuters/Core Commodity CRB Index(TRCI), and Rogers International Commodity Index(RI). We selected each two individual futures among three sectors as energy, agriculture, and metals that are actively traded on CME market and have enough liquidity. They are Crude Oil, Natural Gas, Corn, Wheat, Gold and Silver Futures. We made the equally weighted portfolio with six commodity futures for comparing with other commodity indices. We set the 19 macroeconomic indicators including stock market indices, exports & imports trade data, labor market data, and composite leading indicators as the input data of the model because commodity asset is very closely related with the macroeconomic activities. They are 14 US economic indicators, two Chinese economic indicators and two Korean economic indicators. Data period is from January 1990 to May 2017. We set the former 195 monthly data as training data and the latter 125 monthly data as test data. In this study, we verified that the performance of the equally weighted commodity futures portfolio rebalanced by the SVM model is better than that of other commodity indices. The prediction accuracy of the model for the commodity indices does not exceed 50% regardless of the SVM kernel function. On the other hand, the prediction accuracy of equally weighted commodity futures portfolio is 53%. The prediction accuracy of the individual commodity futures model is better than that of commodity indices model especially in agriculture and metal sectors. The individual commodity futures portfolio excluding the energy sector has outperformed the three sectors covered by individual commodity futures portfolio. In order to verify the validity of the model, it is judged that the analysis results should be similar despite variations in data period. So we also examined the odd numbered year data as training data and the even numbered year data as test data and we confirmed that the analysis results are similar. As a result, when we allocate commodity assets to traditional portfolio composed of stock, bond, and cash, we can get more effective investment performance not by investing commodity indices but by investing commodity futures. Especially we can get better performance by rebalanced commodity futures portfolio designed by SVM model.