• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.933-948
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• 2023

Atmospheric aerosols not only have adverse effects on human health but also exert direct and indirect impacts on the climate system. Consequently, it is imperative to comprehend the characteristics and spatiotemporal distribution of aerosols. Numerous research endeavors have been undertaken to monitor aerosols, predominantly through the retrieval of aerosol optical depth (AOD) via satellite-based observations. Nonetheless, this approach primarily relies on a look-up table-based inversion algorithm, characterized by computationally intensive operations and associated uncertainties. In this study, a novel high-resolution AOD direct retrieval algorithm, leveraging machine learning, was developed using top-of-atmosphere reflectance data derived from the Geostationary Ocean Color Imager-II (GOCI-II), in conjunction with their differences from the past 30-day minimum reflectance, and meteorological variables from numerical models. The Light Gradient Boosting Machine (LGBM) technique was harnessed, and the resultant estimates underwent rigorous validation encompassing random, temporal, and spatial N-fold cross-validation (CV) using ground-based observation data from Aerosol Robotic Network (AERONET) AOD. The three CV results consistently demonstrated robust performance, yielding R²=0.70-0.80, RMSE=0.08-0.09, and within the expected error (EE) of 75.2-85.1%. The Shapley Additive exPlanations(SHAP) analysis confirmed the substantial influence of reflectance-related variables on AOD estimation. A comprehensive examination of the spatiotemporal distribution of AOD in Seoul and Ulsan revealed that the developed LGBM model yielded results that are in close concordance with AERONET AOD over time, thereby confirming its suitability for AOD retrieval at high spatiotemporal resolution (i.e., hourly, 250 m). Furthermore, upon comparing data coverage, it was ascertained that the LGBM model enhanced data retrieval frequency by approximately 8.8% in comparison to the GOCI-II L2 AOD products, ameliorating issues associated with excessive masking over very illuminated surfaces that are often encountered in physics-based AOD retrieval processes.

• The Korean Journal of Nuclear Medicine
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• v.37 no.2
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• pp.73-82
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• 2003

Objectives: A new software (Cardiac SPECT Analyzer: CSA) was developed for quantification of volumes and election fraction on gated myocardial SPECT. Volumes and ejection fraction by CSA were validated by comparing with those quantified by Quantitative Gated SPECT (QGS) software. Materials and Methods: Gated myocardial SPECT was peformed in 40 patients with ejection fraction from 15% to 85%. In 26 patients, gated myocardial SPECT was acquired again with the patients in situ. A cylinder model was used to eliminate noise semi-automatically and profile data was extracted using Gaussian fitting after smoothing. The boundary points of endo- and epicardium were found using an iterative learning algorithm. Enddiastolic (EDV) and endsystolic volumes (ESV) and election fraction (EF) were calculated. These values were compared with those calculated by QGS and the same gated SPECT data was repeatedly quantified by CSA and variation of the values on sequential measurements of the same patients on the repeated acquisition. Results: From the 40 patient data, EF, EDV and ESV by CSA were correlated with those by QGS with the correlation coefficients of 0.97, 0.92, 0.96. Two standard deviation (SD) of EF on Bland Altman plot was 10.1%. Repeated measurements of EF, EDV, and ESV by CSA were correlated with each other with the coefficients of 0.96, 0.99, and 0.99 for EF, EDV and ESV respectively. On repeated acquisition, reproducibility was also excellent with correlation coefficients of 0.89, 0.97, 0.98, and coefficient of variation of 8.2%, 5.4mL, 8.5mL and 2SD of 10.6%, 21.2mL, and 16.4mL on Bland Altman plot for EF, EDV and ESV. Conclusion: We developed the software of CSA for quantification of volumes and ejection fraction on gated myocardial SPECT. Volumes and ejection fraction quantified using this software was found valid for its correctness and precision.

• Journal of Korean Home Economics Education Association
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• v.29 no.1
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• pp.15-33
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• 2017

The purpose of this study was to develop and evaluate a practical problem-based teaching learning process plan for safety in residential environment to raise safety awareness of middle school students. The plan consisting of 4 lessons has been developed and implemented according to the ADDIE model. Various activity materials (26 student's activity sheets and 8 reading texts, and 8 teacher's reading texts) and visual materials (4 sets of pictures & photos and 8 moving pictures) as well as questionnaire were developed for the 4-session lessons. The plans were implemented by the researcher to 4 classes 121 freshmen of M boy's middle school in Kyeongbuk during December 21st to 29th, 2015. Students were highly enjoyed and satisfied with the whole 4-lessons in the aspects such as the level of participation in the lesson, understanding of the contents, adequacy of materials and activities, and usefulness in own's daily life. Students also reported that they were highly aware to practice the contents learned from the lessons in daily family life at home with one's family and recommended to teach the lessons to other schools, too. It can be concluded that the teaching learning process plan for safety in residential environment would raise safety awareness of middle school students through the Home Economics subject.

• 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.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.4
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• pp.295-304
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• 2022

The purpose of this study is to detect the sorghum panicle using YOLOv5 based on RGB images acquired by a unmanned aerial vehicle (UAV) system. The high-resolution images acquired using the RGB camera mounted in the UAV on September 2, 2022 were split into 512×512 size for YOLOv5 analysis. Sorghum panicles were labeled as bounding boxes in the split image. 2,000images of 512×512 size were divided at a ratio of 6:2:2 and used to train, validate, and test the YOLOv5 model, respectively. When learning with YOLOv5s, which has the fewest parameters among YOLOv5 models, sorghum panicles were detected with mAP@50=0.845. In YOLOv5m with more parameters, sorghum panicles could be detected with mAP@50=0.844. Although the performance of the two models is similar, YOLOv5s ( 4 hours 35 minutes) has a faster training time than YOLOv5m (5 hours 15 minutes). Therefore, in terms of time cost, developing the YOLOv5s model was considered more efficient for detecting sorghum panicles. As an important step in predicting sorghum yield, a technique for detecting sorghum panicles using high-resolution RGB images and the YOLOv5 model was presented.

• Korean Journal of Radiology
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• v.23 no.2
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• pp.226-236
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• 2022

Objective: This study aimed to explore the myelin volume change in patients with mild traumatic brain injury (mTBI) with post-concussion syndrome (PCS) using a multidynamic multiecho (MDME) sequence and automatic whole-brain segmentation. Materials and Methods: Forty-one consecutive mTBI patients with PCS and 29 controls, who had undergone MRI including the MDME sequence between October 2016 and April 2018, were included. Myelin volume fraction (MVF) maps were derived from the MDME sequence. After three dimensional T1-based brain segmentation, the average MVF was analyzed at the bilateral cerebral white matter (WM), bilateral cerebral gray matter (GM), corpus callosum, and brainstem. The Mann-Whitney U-test was performed to compare MVF and myelin volume between patients with mTBI and controls. Myelin volume was correlated with neuropsychological test scores using the Spearman rank correlation test. Results: The average MVF at the bilateral cerebral WM was lower in mTBI patients with PCS (median [interquartile range], 25.2% [22.6%-26.4%]) than that in controls (26.8% [25.6%-27.8%]) (p = 0.004). The region-of-interest myelin volume was lower in mTBI patients with PCS than that in controls at the corpus callosum (1.87 cm³ [1.70-2.05 cm³] vs. 2.21 cm³ [1.86-3.46 cm³]; p = 0.003) and brainstem (9.98 cm³ [9.45-11.00 cm³] vs. 11.05 cm³ [10.10-11.53 cm³]; p = 0.015). The total myelin volume was lower in mTBI patients with PCS than that in controls at the corpus callosum (0.45 cm³ [0.39-0.48 cm³] vs. 0.48 cm³ [0.45-0.54 cm³]; p = 0.004) and brainstem (1.45 cm³ [1.28-1.59 cm³] vs. 1.54 cm³ [1.42-1.67 cm³]; p = 0.042). No significant correlation was observed between myelin volume parameters and neuropsychological test scores, except for the total myelin volume at the bilateral cerebral WM and verbal learning test (delayed recall) (r = 0.425; p = 0.048). Conclusion: MVF quantified from the MDME sequence was decreased at the bilateral cerebral WM in mTBI patients with PCS. The total myelin volumes at the corpus callosum and brainstem were decreased in mTBI patients with PCS due to atrophic changes.

• Preventive Nutrition and Food Science
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• v.7 no.3
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• pp.265-272
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• 2002

In this study, we examined the effects of dietary fatty acids on the fatty acid composition of phospholipid fractions in regions of the brain and on behavioral development in rats. The Sprague Dawley rats were fed the experimental diets 3~4 wks prior to the conception. Experimental diets consisted of 10% fat(wt/wt) which were from either safflower oil (SO, poor in $\omega$3 fatty acids), mixed oil MO, P/M/S ratio : 1:1.4:1, $\omega$6/$\omega$3 ratio = 6.3), or mixed oil supplemented with vitamin E (+500 mg/kg diet). At 3 and 9 weeks of age, frontal cortex (FC), corpus striatum (CS), hippocampus (H), and cerebellum (CB) were dissected from the whole brain. The fatty acid content was determined in the different phospholipid fractions: phosphatidylcholine (PC), phosphatidyl-serine (PS), and phosphatidylethanolamine (PE) in the rat brain regions. In the visual discrimination test, the order of the cumulative errors made in Y-water maze test were SO > MO > ME. This suggested that the balanced diet supplemented with vitamin I had the most beneficial effect on learning ability. The overall characteristics of correlation between fatty acids and behavior development were that the frequency of cumulative errors were negatively correlated significantly with monounsaturated fatty acids (MUFAs), ie., 18:1 $\omega$9 and 22:1 $\omega$9. Docosa-hexaenoic acid (22:6 $\omega$3) of PS in frontal cortex (FC) was negatively correlated with the number of errors made in the Y-water maze test.22:5 $\omega$6 PS in hippocampus (H), PC and PE in corpus striatum (CS), PC in cerebellum (CB) were positively correlated with cumulative errors. And these errors were negatively correlated with 20:4 $\omega$ 6 of PE in corpus striatum (CS) and PC in cerebellum (CB). Especially, O1eic acid (18:1 u 9) in all phospholipid fractions (PC, PS, PE) of hippocampus was negatively correlated with the number of errors. These findings demonstrate that the MUFAs were might be essential for proper brain development, especially in hippocampus which is generally thought to be the regions of memory and learning.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.11 no.1
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• pp.53-62
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• 2017

In this paper, we present the score classification accuracy of BBS(Berg Balance Scale) which is the most commonly used balance evaluation tool using machine learning. Data acquisition was performed using the Noraxon system and an inertial sensor of Noraxon system was attached to the body in 8 locations (left and right ankle, left and right upper buttocks, left and right wrists, back, forehead). Based on the 3-axis accelerometer of the inertial sensor, the feature vector STFT(Short Time Fourier Transform) and SAM(Signal Area Magnitude) were extracted. Then, the items of the BBS were divided into static movement and dynamic movement depending on the operation characteristics, and the feature vectors were selected according to the sensor attachment positions which affect the score for each item of the BBS. Feature vectors selected for each item of BBS were classified using GMM(Gaussian Mixture Model). As a result of the accuracy calculation for 40 subjects, 55.5%, 72.2%, 87.5%, 50%, 35.1%, 62.5%, 43.3%, 58.6%, 60.7%, 33.3%, 44.8%, 89.2%, 51.8%, 85.1%, respectively.

• Journal of Korean Home Economics Education Association
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• v.11 no.2
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• pp.89-110
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• 1999

The purpose of this study was to investigate the perception and eating behaviors of middle school students in Kwangju area about food and nutrition section of Home Economics course. The subjects were 480 middle school students. The major results were as follows： On the perception f food and nutrition section, the 1st graders learned ‘how to settle the problems related to food and nutrition and food choices’. The 2nd graders learned ‘the knowledge and principle of food and nutrition for improving dietary life’. The 3rd graders learned ‘actual dietary life functions like arranging menu and preparing meals’. Consequently, there was a significant difference in each grade and all boy and girl students have the highest response to the item ‘they learn how to settle the nutrition problem and foods choices’. On the interests in food and nutrition section, lower graders and girl students have higher interest than hgiher graders and boy students. The area which was considered as most interesting in food and nutrition sections was ‘cooking’ in all students. The perception of the concern on dietary life after learning food and nutrition section was higher in the 1st graders. On the perception of the area where was a great change in their own dietary life after learning food and nutrition section, the 1st graders responded there was a great change in ‘balanced dietary life’, the 2nd graders in ‘eating habit and nutrition of adolescence’and the 3rd graders in ‘cooking’. Especially, girl students showed more changes. On the perception of degree which food and nutrition section gives a help to actual life, students over 50% responded that it was helpful to their actual life. This showed that the lower the grades, the more help it gave to those students. On the perception of mother’s job, the students with working mother considered that they learned ‘actual dietary life functions like functions like arranging menu and preparing meals’from the food and nutrition section. In addition, they had high interests in the area of ‘cooking’. It was recognized that as the family size was greater, the food and nutrition section gave more helps to actual life. On eating behaviors of breakfast, the higher the grade, the more frequent they don’t have breakfast, boy students have more frequencies of having breakfast than girl students and the primary reason of not having breakfast is poor appetite. On whether going without a meal affects study or not, most of the 1st, 2nd and 3rd graders whether they are boys and girls or not responded it affected their study. On eating behaviors of lunch, most of boy and girl students in the 1st, 2nd 3rd grade have lunch box. On eating behaviors of supper, eating time is usually between 7：00 and 8：00 p.m and eating hour is mostly 10 or 15 minutes. On eating behaviors between meals, frequency of eating between meals is mostly once or twice a day and girl students have more frequencies of eating between meals, and the cause was hunger commonly in boy and girl students and ‘killing time’in girl students. The focus in getting the foods is ‘taste’and favorite food is fruit regardless of the grade.

• Journal of the Korea Convergence Society
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• v.9 no.10
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• pp.303-313
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• 2018

In order to provide quality services across international airports, airline personnel must rapidly and effectively develop and share knowledge. Combining components of adaptive structuration theory (AST) and media synchronicity theory (MST), a research framework was developed to convey three distinct stages of knowledge sharing. We use the grounded theory research method for the qualitative data collected from audio transcripts of employees learning how to use and work with company issued smartphones with push-to-talk functionalities. Data was collected from 33 operations personnel. The results of the content analysis are recorded for the elements of each of the three concepts of our research framework. During the social interaction stage, the content of the audio conversations shifts mainly from conflict management to task management; for media synchronicity, from quality to quantity; for productive outcomes, from efficiency to commitment. New insights are uncovered from our analysis of data from the field as users advance from learning how to use the mobile devices, to using the devices for managing knowledge for their work in the airline industry.