• Korean journal of applied entomology
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• v.50 no.4
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• pp.343-352
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• 2011

The developmental period of Laodelphax striatellus Fallen, a vector of rice stripe virus (RSV), was investigated at ten constant temperatures from 12.5 to $35{\pm}1^{\circ}C$ at 30 to 40% RH, and a photoperiod of 14:10 (L:D) h. Eggs developed successfully at each temperature tested and their developmental time decreased as temperature increased. Egg development was fasted at $35^{\circ}C$(5.8 days), and slowest at $12.5^{\circ}C$ (44.5 days). Nymphs could not develop to the adult stage at 32.5 or $35^{\circ}C$. The mean total developmental time of nymphal stages at 12.5, 15, 17.5, 20, 22.5, 25, 27.5 and $30^{\circ}C$ were 132.7, 55.9, 37.7, 26.9, 20.2, 15.8, 14.9 and 17.4 days, respectively. One linear model and four nonlinear models (Briere 1, Lactin 2, Logan 6 and Poikilotherm rate) were used to determine the response of developmental rate to temperature. The lower threshold temperatures of egg and total nymphal stage of L. striatellus were $10.2^{\circ}C$ and $10.7^{\circ}C$, respectively. The thermal constants (degree-days) for eggs and nymphs were 122.0 and 238.1DD, respectively. Among the four nonlinear models, the Poikilotherm rate model had the best fit for all developmental stages ($r^2$=0.98~0.99). The distribution of completion of each development stage was well described by the two-parameter Weibull function ($r^2$=0.84~0.94). The emergence rate of L. striatellus adults using DYMEX$^{(R)}$ was predicted under the assumption that the physiological age of over-wintered nymphs was 0.2 and that the Poikilotherm rate model was applied to describe temperature-dependent development. The result presented higher predictability than other conditions.

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.1041-1053
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• 2018

In this study, a real-time storage level and capacity monitoring and forecasting system for Yongdam Dam watershed was developed using high resolution satellite image. The drought indices such as Standardized Precipitation Index (SPI) from satellite data were used for storage level monitoring in case of drought. Moreover, to predict storage volume we used a statistical method based on Principle Component Analysis (PCA) of Singular Spectrum Analysis (SSA). According to this study, correlation coefficient between storage level and SPI (3) was highly calculated with CC=0.78, and the monitoring and predictability of storage level was diagnosed using the drought index calculated from satellite data. As a result of analysis of principal component analysis by SSA, correlation between SPI (3) and each Reconstructed Components (RCs) data were highly correlated with CC=0.87 to 0.99. And also, the correlations of RC data with Normalized Water Surface Level (N-W.S.L.) were confirmed that has highly correlated with CC=0.83 to 0.97. In terms of high resolution satellite image we developed a water detection algorithm by applying an exponential method to monitor the change of storage level by using Multi-Spectral Instrument (MSI) sensor of Sentinel-2 satellite. The materials of satellite image for water surface area detection in Yongdam dam watershed was considered from 2016 to 2018, respectively. Based on this, we proposed the possibility of real-time drought monitoring system using high resolution water surface area detection by Sentinel-2 satellite image. The results of this study can be applied to estimate of the reservoir volume calculated from various satellite observations, which can be used for monitoring and estimating hydrological droughts in an unmeasured area.

• Korean Journal of Soil Science and Fertilizer
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• v.25 no.1
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• pp.26-37
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• 1992

Fifty-five field experiments were conducted in order to find out some useful indices for the prediction of N-supplying capability(NSC) of soils under cultivation of corn in Pennsylvania over 3 years from 1986. Contents of $NO_3-N$, absorbance at 200 nm of the extract from soil with 0.01M $NaHCO_3$ were identified to be used as indices before planting. Methods for the estimation of organic nitrogen available later in the growing season(KCLA-N, PBBA-N, UV260 nm absorbance of $NaHCO_3$ extract) were not to be used as good indices individually, but when those are combined together with inorganic $NO_3-N$ showed a highly significant correlationship with the NSC. The year of an even distribution of rainfall, 1987, gave the highest significant correlationship between NSC and the indices. For soils of the same texture with slightly different physical properties, combined indices obtained from physico-chemical factors improved the degree of predictability when the grades of soil slope, depth of Ap were considered at the same time. More futher researches such as this need to be done before any conclusive result can be drawn.

• Journal of Nutrition and Health
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• v.42 no.2
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• pp.119-127
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• 2009

Malnutrition of hospitalized patients can adversely affect clinical outcomes and cost. Several nutritional screening tools have been developed to identify patients with malnutrition risk. However, many of those possess practical pitfalls of requiring much time and labor to administer and may not be highly applicable to a Korean population. This study sought to develop and evaluate a Nutrition Risk Screening Tool (NRST) which is simple and quick to administer and widely applicable to Korean hospitalized patients with various diseases. The study was also designed to generate a screening tool predictable of various clinical outcomes and to validate it against the Nutritional Risk Screening 2002 (NRS 2002). Electronic medical records of 424 patients hospitalized at a general hospital in Seoul during a 14-month period were abstracted for anthropometric, medical, biochemical, and clinical outcome variables. The study employed a 4-step process consisting of selecting NRST components, searching a scoring scheme, validating against a reference tool, and confirming clinical outcome predictability. NRST components were selected by stepwise multiple regression analysis of each clinical outcome (i.e., hospitalization period, complication, disease progress, and death) on several readily available patient characteristics. Age and serum levels of albumin, hematocrit (Hct), and total lymphocyte count (TLC) remained in the last model for any of 4 dependent variables were decided as NRST components. Odds ratios of malnutrition risk based on NRS 2002 according to levels of the selected components were utilized to frame a scoring scheme of NRST. A NRST score higher than 3.5 was set as a cut-off score for malnutrition risk based on sensitivity and specificity levels against NRS 2002. Lastly differences in clinical outcomes by patients' NRST results were examined. The results showed that the NRST can significantly predict the in-hospital clinical outcomes. It is concluded that the NRST can be useful to simply and quickly screen patients at high-nutritional risk in relation to prospective clinical outcomes.

• Childhood Kidney Diseases
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• v.12 no.1
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• pp.62-69
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• 2008

Purpose: The most concerning issue in children with urinary tract infection(UTI) is the probability of underlying genitourinary anomalies and vesicoureteral reflux (VUR), which is frequently associated with renal scarring and eventually end-stage renal disease. Therefore, voiding cystourethrography(VCUG) is usually recommended at the earliest convenient time for children with UTI. However, VCUG is an invasive procedure that requires catheterization and exposure to X-ray. In this study, we aimed to determine the predictability of clinical, laboratory and imaging parameters for VUR in children with UTI. Methods: Data of children with bacteriologically proven UTI who underwent VCUG were evaluated retrospectively for clinical(age, gender, fever), laboratory(leukocytosis, ESR, CRP, pyuria, blood urea nitrogen, serum creatinine) and imaging(renal ultrasound and DMSA renal scan) findings. First, children with UTI were divided into two groups according to the presence of VUR as non-VUR group and with VUR group, and clinical, laboratory variables were compared between these groups. Second, patients who had VUR were reclassified as low-grade VUR(grade I-II) group and high-grade(grade III-V) VUR group according to grading of VUR, and clinical, laboratory and imaging variables were compared between these groups. Results: Among 410 children with UTI, 137 had VUR and 78 high-grade VUR. Fever, leukocytosis, ESR, CRP, pyuria were associated with VUR. In addition, abnormal findings of ultrasonography and DMSA renal scan were closely related to VUR. However, these clinical and laboratory variable in patients with high grade VUR were not different significantly, compared to those with low-grade VUR group. Conclusion: Fever, leukocytosis, ESR, CRP seems to be potentially useful predictors of VUR in pediatric patients with UTI. In addition, renal ultrasonography and DMSA renal scan findings supported the presence of VUR. Further study of these findings could limit unnecessary VCUG in patients with UTI.

• Nuclear Medicine and Molecular Imaging
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• v.41 no.4
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• pp.299-308
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• 2007

Purpose: The aim of this study was to develop a bioinformatics software and to test it in serum samples of papillary thyroid cancer using mass spectrometry (SELDI-TOF-MS). Materials and Methods: Development of 'Protein analysis' software performing decision tree analysis was done by customizing C4.5. Sixty-one serum samples from 27 papillary thyroid cancer, 17 autoimmune thyroiditis, 17 controls were applied to 2 types of protein chips, CM10 (weak cation exchange) and IMAC3 (metal binding - Cu). Mass spectrometry was performed to reveal the protein expression profiles. Decision trees were generated using 'Protein analysis' software, and automatically detected biomarker candidates. Validation analysis was performed for CM10 chip by random sampling. Results: Decision tree software, which can perform training and validation from profiling data, was developed. For CM10 and IMAC3 chips, 23 of 113 and 8 of 41 protein peaks were significantly different among 3 groups (p<0.05), respectively. Decision tree correctly classified 3 groups with an error rate of 3.3% for CM10 and 2.0% for IMAC3, and 4 and 7 biomarker candidates were detected respectively. In 2 group comparisons, all cancer samples were correctly discriminated from non-cancer samples (error rate = 0%) for CM10 by single node and for IMAC3 by multiple nodes. Validation results from 5 test sets revealed SELDI-TOF-MS and decision tree correctly differentiated cancers from non-cancers (54/55, 98%), while predictability was moderate in 3 group classification (36/55, 65%). Conclusion: Our in-house software was able to successfully build decision trees and detect biomarker candidates, therefore it could be useful for biomarker discovery and clinical follow up of papillary thyroid cancer.

• Korean Journal of Agricultural and Forest Meteorology
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• v.11 no.4
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• pp.174-184
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• 2009

Evapotranspiration (ET) is one of the major hydrologic processes in terrestrial ecosystems. A reliable estimation of spatially representavtive ET is necessary for deriving regional water budget, primary productivity of vegetation, and feedbacks of land surface to regional climate. Moderate resolution imaging spectroradiometer (MODIS) provides an opportunity to monitor ET for wide area at daily time scale. In this study, we applied a MODIS-based ET algorithm and tested its reliability for nine flux tower sites in East Asia. This is a stand-alone MODIS algorithm based on the Penman-Monteith equation and uses input data derived from MODIS. Instantaneous ET was estimated and scaled up to daily ET. For six flux sites, the MODIS-derived instantaneous ET showed a good agreement with the measured data ($r^2=0.38$ to 0.73, ME = -44 to $+31W\;m^{-2}$, RMSE =48 to $111W\;m^{-2}$). However, for the other three sites, a poor agreement was observed. The predictability of MODIS ET was improved when the up-scaled daily ET was used ($r^2\;=\;0.48$ to 0.89, ME = -0.7 to $-0.6\;mm\;day^{-1}$, $RMSE=\;0.5{\sim}1.1\;mm\;day^{-1}$). Errors in the canopy conductance were identified as a primary factor of uncertainty in MODIS-derived ET and hence, a more reliable estimation of canopy conductance is necessary to increase the accuracy of MODIS ET.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1031-1042
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• 2023

In this study, a deep learning model was developed to predict the yield of cabbage and radish, one of the five major supply and demand management vegetables, using satellite images of Landsat 8. To predict the yield of cabbage and radish in Gangwon-do from 2015 to 2020, satellite images from June to September, the growing period of cabbage and radish, were used. Normalized difference vegetation index, enhanced vegetation index, lead area index, and land surface temperature were employed in this study as input data for the yield model. Crop yields can be effectively predicted using satellite images because satellites collect continuous spatiotemporal data on the global environment. Based on the model developed previous study, a model designed for input data was proposed in this study. Using time series satellite images, convolutional neural network, a deep learning model, was used to predict crop yield. Landsat 8 provides images every 16 days, but it is difficult to acquire images especially in summer due to the influence of weather such as clouds. As a result, yield prediction was conducted by splitting June to July into one part and August to September into two. Yield prediction was performed using a machine learning approach and reference models , and modeling performance was compared. The model's performance and early predictability were assessed using year-by-year cross-validation and early prediction. The findings of this study could be applied as basic studies to predict the yield of field crops in Korea.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.63-83
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• 2019

Investors prefer to look for trading points based on the graph shown in the chart rather than complex analysis, such as corporate intrinsic value analysis and technical auxiliary index analysis. However, the pattern analysis technique is difficult and computerized less than the needs of users. In recent years, there have been many cases of studying stock price patterns using various machine learning techniques including neural networks in the field of artificial intelligence(AI). In particular, the development of IT technology has made it easier to analyze a huge number of chart data to find patterns that can predict stock prices. Although short-term forecasting power of prices has increased in terms of performance so far, long-term forecasting power is limited and is used in short-term trading rather than long-term investment. Other studies have focused on mechanically and accurately identifying patterns that were not recognized by past technology, but it can be vulnerable in practical areas because it is a separate matter whether the patterns found are suitable for trading. When they find a meaningful pattern, they find a point that matches the pattern. They then measure their performance after n days, assuming that they have bought at that point in time. Since this approach is to calculate virtual revenues, there can be many disparities with reality. The existing research method tries to find a pattern with stock price prediction power, but this study proposes to define the patterns first and to trade when the pattern with high success probability appears. The M & W wave pattern published by Merrill(1980) is simple because we can distinguish it by five turning points. Despite the report that some patterns have price predictability, there were no performance reports used in the actual market. The simplicity of a pattern consisting of five turning points has the advantage of reducing the cost of increasing pattern recognition accuracy. In this study, 16 patterns of up conversion and 16 patterns of down conversion are reclassified into ten groups so that they can be easily implemented by the system. Only one pattern with high success rate per group is selected for trading. Patterns that had a high probability of success in the past are likely to succeed in the future. So we trade when such a pattern occurs. It is a real situation because it is measured assuming that both the buy and sell have been executed. We tested three ways to calculate the turning point. The first method, the minimum change rate zig-zag method, removes price movements below a certain percentage and calculates the vertex. In the second method, high-low line zig-zag, the high price that meets the n-day high price line is calculated at the peak price, and the low price that meets the n-day low price line is calculated at the valley price. In the third method, the swing wave method, the high price in the center higher than n high prices on the left and right is calculated as the peak price. If the central low price is lower than the n low price on the left and right, it is calculated as valley price. The swing wave method was superior to the other methods in the test results. It is interpreted that the transaction after checking the completion of the pattern is more effective than the transaction in the unfinished state of the pattern. Genetic algorithms(GA) were the most suitable solution, although it was virtually impossible to find patterns with high success rates because the number of cases was too large in this simulation. We also performed the simulation using the Walk-forward Analysis(WFA) method, which tests the test section and the application section separately. So we were able to respond appropriately to market changes. In this study, we optimize the stock portfolio because there is a risk of over-optimized if we implement the variable optimality for each individual stock. Therefore, we selected the number of constituent stocks as 20 to increase the effect of diversified investment while avoiding optimization. We tested the KOSPI market by dividing it into six categories. In the results, the portfolio of small cap stock was the most successful and the high vol stock portfolio was the second best. This shows that patterns need to have some price volatility in order for patterns to be shaped, but volatility is not the best.