• Journal of Nutrition and Health
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• v.48 no.1
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• pp.46-57
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• 2015

Purpose: The aim of this study was to determine an appropriate energy level of breakfast with less risk of chronic disease for Korean adults. Methods: Using data from the 2007~2009 Korean National Health & Nutrition Examination Survey, from a total of 12,238 adults aged 19~64, the final 7,769 subjects were analyzed except subjects who were undergoing treatment for cancer or metabolic disorder. According to the percent of breakfast energy intake versus their estimated energy requirement (EER), the subjects were divided into four groups: < 10% (very low, VL), 10~20% (low, L), 20~30% (moderate, M), ${\geq}30%$ (sufficient, S). All data were analyzed on the metabolic risk and nutritional state after application of weighted value and adjustment of sex, age, residential area, income, education, job or jobless, and energy intake using a general linear model or logistic regression. Results: The subjects of group S were 16.9% of total subjects, group M 39.2%, group L 37.6%, and group VL 6.3%. The VL group included more male subjects, younger-aged (19 to 40 years), urban residents, higher income, higher education, and fewer breakfasts eaters together with family members. Among the 4 groups, the VL group showed the highest waist circumference, while the S group showed the lowest waist circumference, body mass index, and serum total cholesterol. The groups of VL and L with lower intake of breakfast energy showed high percent of energy from protein and fat, and low percent of energy from carbohydrate. With the increase of breakfast energy level, intake of energy, most nutrients and food groups increased, and the percentage of subjects consuming nutrients below EAR decreased. The VL group showed relatively higher intake of snacks, sugar, meat and eggs, oil, and seasonings, and the lowest intake of vegetable. Risk of obesity by waist circumference was highest in the VL group by 1.90 times of the S group and the same trend was shown in obesity by BMI. Risk of dyslipidemia by serum total cholesterol was 1.84 times higher in the VL group compared to the S group. Risk of diabetes by Glu-FBS (fasting blood sugar) was 1.57 times higher in the VL group compared to the S group. Conclusion: The results indicate that higher breakfast energy level is positively related to lower metabolic risk and more desirable nutritional state in Korean adults. Therefore, breakfast energy intake more than 30% of their own EER would be highly recommended for Korean adults.

• Journal of Preventive Medicine and Public Health
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• v.24 no.3 s.35
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• pp.428-440
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• 1991

This study was conducted to estimate the prevalence rate of cervical cancer and to investigate its risk factors. 5,417 asymptomatic married women were screened from March, 1984 to December, 1990 in Taegu city. Of 5,417 examinees, 3,817 (70.46%) were normal, 1,542 (28.7%) showed inflammatory change, 51 (0.94%) were dysplasia and 7 (0.13%) were carcinoma in situ or invasive carcinomas. The prevalence of abnormal finding (dysplasia, carcinoma in situ or invasive carcinoma) was 1,070 per 100,000 population. The prevalence of dysplasia was 940 per 100,000 and that of carcinoma in situ or invasive carcinoma was 130 per 100,000. Age-adjusted prevalence rate for abnormal finding adjusted with standard population of Taegu city was estimated to be 850 per 100,000. The prevalence of cervical cancer was significantly increased with age (P<0.05). The prevalence of cervical cancer was significantly decreased with age at marriage and educational level (P<0.05). The history of induced abortion and the number of pregnancies were significantly associated with the prevalence of cervical cancer (p<0.05), whereas, the number of parity was not. Age at marriage was significantly associated with the prevalence of cervical cancer after stratification by age (p<0.05). However, the level of education, parity, induced abortion, number of pregnancies were not significant. Inflammation and human papiloma virus infection were associated with cervical cancer with odds ratio of 13.48 (95% confidence interval $7.80{\sim}23.40$) and 474.29 (95% confidence interval $196.80{\sim}1143.10$), respectively. In conclusion, for early detection of cervical cancer it should be recommended to perform mass cytological screening. In particular, regular and periodic cytologic screening, starting at age 25, for cervical cancer should be recommended for those women who have frequent cervical inflammation and for those women married before age of 20.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.1-27
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• 2017

It is known that the economic sentiment index and macroeconomic indicators are closely related because economic agent's judgment and forecast of the business conditions affect economic fluctuations. For this reason, consumer sentiment or confidence provides steady fodder for business and is treated as an important piece of economic information. In Korea, private consumption accounts and consumer sentiment index highly relevant for both, which is a very important economic indicator for evaluating and forecasting the domestic economic situation. However, despite offering relevant insights into private consumption and GDP, the traditional approach to measuring the consumer confidence based on the survey has several limits. One possible weakness is that it takes considerable time to research, collect, and aggregate the data. If certain urgent issues arise, timely information will not be announced until the end of each month. In addition, the survey only contains information derived from questionnaire items, which means it can be difficult to catch up to the direct effects of newly arising issues. The survey also faces potential declines in response rates and erroneous responses. Therefore, it is necessary to find a way to complement it. For this purpose, we construct and assess an index designed to measure consumer economic sentiment index using sentiment analysis. Unlike the survey-based measures, our index relies on textual analysis to extract sentiment from economic and financial news articles. In particular, text data such as news articles and SNS are timely and cover a wide range of issues; because such sources can quickly capture the economic impact of specific economic issues, they have great potential as economic indicators. There exist two main approaches to the automatic extraction of sentiment from a text, we apply the lexicon-based approach, using sentiment lexicon dictionaries of words annotated with the semantic orientations. In creating the sentiment lexicon dictionaries, we enter the semantic orientation of individual words manually, though we do not attempt a full linguistic analysis (one that involves analysis of word senses or argument structure); this is the limitation of our research and further work in that direction remains possible. In this study, we generate a time series index of economic sentiment in the news. The construction of the index consists of three broad steps: (1) Collecting a large corpus of economic news articles on the web, (2) Applying lexicon-based methods for sentiment analysis of each article to score the article in terms of sentiment orientation (positive, negative and neutral), and (3) Constructing an economic sentiment index of consumers by aggregating monthly time series for each sentiment word. In line with existing scholarly assessments of the relationship between the consumer confidence index and macroeconomic indicators, any new index should be assessed for its usefulness. We examine the new index's usefulness by comparing other economic indicators to the CSI. To check the usefulness of the newly index based on sentiment analysis, trend and cross - correlation analysis are carried out to analyze the relations and lagged structure. Finally, we analyze the forecasting power using the one step ahead of out of sample prediction. As a result, the news sentiment index correlates strongly with related contemporaneous key indicators in almost all experiments. We also find that news sentiment shocks predict future economic activity in most cases. In almost all experiments, the news sentiment index strongly correlates with related contemporaneous key indicators. Furthermore, in most cases, news sentiment shocks predict future economic activity; in head-to-head comparisons, the news sentiment measures outperform survey-based sentiment index as CSI. Policy makers want to understand consumer or public opinions about existing or proposed policies. Such opinions enable relevant government decision-makers to respond quickly to monitor various web media, SNS, or news articles. Textual data, such as news articles and social networks (Twitter, Facebook and blogs) are generated at high-speeds and cover a wide range of issues; because such sources can quickly capture the economic impact of specific economic issues, they have great potential as economic indicators. Although research using unstructured data in economic analysis is in its early stages, but the utilization of data is expected to greatly increase once its usefulness is confirmed.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.139-156
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• 2021

The biggest reason for using a deep learning model in image classification is that it is possible to consider the relationship between each region by extracting each region's features from the overall information of the image. However, the CNN model may not be suitable for emotional image data without the image's regional features. To solve the difficulty of classifying emotion images, many researchers each year propose a CNN-based architecture suitable for emotion images. Studies on the relationship between color and human emotion were also conducted, and results were derived that different emotions are induced according to color. In studies using deep learning, there have been studies that apply color information to image subtraction classification. The case where the image's color information is additionally used than the case where the classification model is trained with only the image improves the accuracy of classifying image emotions. This study proposes two ways to increase the accuracy by incorporating the result value after the model classifies an image's emotion. Both methods improve accuracy by modifying the result value based on statistics using the color of the picture. When performing the test by finding the two-color combinations most distributed for all training data, the two-color combinations most distributed for each test data image were found. The result values were corrected according to the color combination distribution. This method weights the result value obtained after the model classifies an image's emotion by creating an expression based on the log function and the exponential function. Emotion6, classified into six emotions, and Artphoto classified into eight categories were used for the image data. Densenet169, Mnasnet, Resnet101, Resnet152, and Vgg19 architectures were used for the CNN model, and the performance evaluation was compared before and after applying the two-stage learning to the CNN model. Inspired by color psychology, which deals with the relationship between colors and emotions, when creating a model that classifies an image's sentiment, we studied how to improve accuracy by modifying the result values based on color. Sixteen colors were used: red, orange, yellow, green, blue, indigo, purple, turquoise, pink, magenta, brown, gray, silver, gold, white, and black. It has meaning. Using Scikit-learn's Clustering, the seven colors that are primarily distributed in the image are checked. Then, the RGB coordinate values of the colors from the image are compared with the RGB coordinate values of the 16 colors presented in the above data. That is, it was converted to the closest color. Suppose three or more color combinations are selected. In that case, too many color combinations occur, resulting in a problem in which the distribution is scattered, so a situation fewer influences the result value. Therefore, to solve this problem, two-color combinations were found and weighted to the model. Before training, the most distributed color combinations were found for all training data images. The distribution of color combinations for each class was stored in a Python dictionary format to be used during testing. During the test, the two-color combinations that are most distributed for each test data image are found. After that, we checked how the color combinations were distributed in the training data and corrected the result. We devised several equations to weight the result value from the model based on the extracted color as described above. The data set was randomly divided by 80:20, and the model was verified using 20% of the data as a test set. After splitting the remaining 80% of the data into five divisions to perform 5-fold cross-validation, the model was trained five times using different verification datasets. Finally, the performance was checked using the test dataset that was previously separated. Adam was used as the activation function, and the learning rate was set to 0.01. The training was performed as much as 20 epochs, and if the validation loss value did not decrease during five epochs of learning, the experiment was stopped. Early tapping was set to load the model with the best validation loss value. The classification accuracy was better when the extracted information using color properties was used together than the case using only the CNN architecture.

• Korean Journal of Mineralogy and Petrology
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• v.34 no.1
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• pp.41-56
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• 2021

The characteristics of the six rock cleavages(R1~H2) in Jurassic Hapcheon granite were analyzed using the distribution of ① microcrack lengths(N=230), ② microcrack spacings(N=150) and ③ Brazilian tensile strengths(N=30). The 18 cumulative graphs for these three factors measured in the directions parallel to the six rock cleavages were mutually contrasted. The main results of the analysis are summarized as follows. First, the frequency ratio(%) of Brazilian tensile strength values(kg/㎠) divided into nine class intervals increases in the order of 60~70(3.3) < 140~150(6.7) < 100~110·110~120(10.0) < 90~100(13.3) < 80~90(16.7) < 120~130·130~140(20.0). The distribution curve of strength according to the frequency of each class interval shows a bimodal distribution. Second, the graphs for the length, spacing and tensile strength were arranged in the order of H2 < H1 < G2 < G1 < R2 < R1. Exponent difference(λS-λL, Δλ) between the two graphs for the spacing and length increases in the order of H2(-1.59) < H1(-0.02) < G2(0.25) < G1(0.63) < R2(1.59) < R1(1.96)(2 < 1). From the related chart, the six graphs for the tensile strength move gradually to the left direction with the increase of the above exponent difference. The negative slope(a) of the graphs for the tensile strength, suggesting a degree of uniformity of the texture, increases in the order of H((H1+H2)/2, 0.116) < G((G1+G2)/2, 0.125) < R((R1+R2)/2, 0.191). Third, the order of arrangement between the two graphs for the two directions that make up each rock cleavage(R1·R2(R), G1·G2(G), H1·H2(H)) were compared. The order of arrangement of the two graphs for the length and spacing is reverse order with each other. The two graphs for the spacing and tensile strength is mutually consistent in the order of arrangement. The exponent differences(ΔλL and ΔλS) for the length and spacing increase in the order of rift(R, -0.08) < grain(G, 0.14) < hardway(H, 0.75) and hardway(H, 0.16) < grain(G, 0.23) < rift(R, 0.45), respectively. Fourth, the general chart for the six graphs showing the distribution characteristics of the microcrack lengths, microcrack spacings and Brazilian tensile strengths were made. According to the range of length, the six graphs show orders of G2 < H2 < H1 < R2 < G1 < R1(< 7 mm) and G2 < H1 < H2 < R2 < G1 < R1(≦2.38 mm). The six graphs for the spacing intersect each other by forming a bottleneck near the point corresponding to the cumulative frequency of 12 and the spacing of 0.53 mm. Fifth, the six values of each parameter representing the six rock cleavages were arranged in the order of increasing and decreasing. Among the 8 parameters related to the length, the total length(Lt) and the graph(≦2.38 mm) are mutually congruent in order of arrangement. Among the 7 parameters related to the spacing, the frequency of spacing(N), the mean spacing(Sm) and the graph (≦5 mm) are mutually consistent in order of arrangement. In terms of order of arrangement, the values of the above three parameters for the spacing are consistent with the maximum tensile strengths belonging to group E. As shown in Table 8, the order of arrangement of these parameter values is useful for prior recognition of the six rock cleavages and the three quarrying planes.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.3
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• pp.1-40
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• 1965

To measure variations in some of the important agronomic characteristics of rice varieties under shifting of seedling dates, this study has been carried out at the Paddy Crop Division of Crop Experiment Station(then Agricultural Experiment Station) in Suwon for the period of three years 1958 to 1960. The varieties used in this study were Kwansan, Suwon ＃82, Mojo, Paltal and Chokwang, which have the different agronomic characteristics such as earliness and plant type. Seeds of each variety were sown at 14 different dates in 10-day interval starting on March 2. The seedlings were grown on seed bed for 30, 40, 50, 60, 70 and 80 days, respectively. The results of this study are as follows: A. Heading dates. 1. As the seeding date was delayed, the heading dates was almost proportionally delayed. The degree of delay was higher in early varieties and lower in late varieties and the longer the seedling stage, the more delayed the heading date. 2. Number of days to heading was proportionally lessened as seeding was delayed in all the varieties but the magnitude varied depending upon variety. In other words, the required period for heading in case of late planting was much shortened in late variety compared with early one. Within a variety, the number of days to heading was less shortened as the seedling stage was prolonged. Early variety reached earlier than late variety to the marginal date for the maximum shortening of days to heading and the longer the seeding stage, the limitted date came earlier. There was a certain limit in seeding date for shortening of days to heading as seeding was delayed, and days to heading were rather prolonged due to cold weather when seeded later than that date. 3. In linear regression equation, Y=a+bx obtained from the seeding dates and the number of days to heading, the coefficient b(shortening rate of days to heading) was closely correlated with the average number of days to heading. That is, the period from seeding to heading was more shortened in late variety than early one as seeding was delayed. 4. To the extent that the seedling stage is not so long and there is a linear relationship between delay of seeding and shortening of days to heading, it might be possible to predict heading date of a rice variety to be sown any date by using the linear regression obtained from variation of heading dates under the various seeding dates of the same variety. 5. It was found out that there was a close correlation between the numbers of days to heading in ordinary culture and the other ones. When a rice variety was planted during the period from the late part of March to the middle of June and the seedling ages were within 30 to 50 days, it could be possible to estimate heading date of the variety under late or early culture with the related data of ordinary culture. B. Maturing date. 6. Within (he marginal date for maturation of rice variety, maturing date was proportionally delayed as heading was delayed. Of course, the degree of delay depended upon varieties and seedling ages. The average air temperature (Y) during the ripening period of rice variety was getting lower as the heading date. (X) was delayed. Though there was a difference among varieties, in general, a linear regression equation(y=25.53-0.182X) could be obtained as far as heading date were within August 1 to September 13. 7. Depending upon earliness of a rice variety, the average air temperature during the ripening period were greatly different. Early variety underwent under 28$^{\circ}C$ in maximum while late variety matured under as low as 22$^{\circ}C$. 8. There was a highly significant correlation between the average air temperature (X) during the ripening period, and number of day (Y) for the maturation. And the relationship could be expressed as y=82.30-1.55X. When the average air temperature during the period was within the range of 18$^{\circ}C$ to 28$^{\circ}C$, the ripening period was shortened by 1.55 days with increase of 1$^{\circ}C$. Considering varieties, Kwansan was the highest in shortening the maturing period by 2.24 days and Suwon ＃82 was the lowest showing 0.78 days. It is certain that ripening of rice variety is accelerated at Suwon as the average air temperature increases within the range of 18$^{\circ}C$ to 28$^{\circ}C$. 9. Between number of days to heading (X) related to seeding dates and the accumulated average air temperature (Y) during the ripening period, a positive correlation was obtained. However, there was a little difference in the accumulated average air temperature during the ripening period even seeding dates were shifted to a certain extent. C. Culm- and ear-lengths. 10. In general all the varieties didn't show much variation in their culm-lengths in case of relatively early seeding but they trended to decrease the lengths as seeding was delayed. The magnitude of decreasing varied from young seedlings to old ones. Young seedlings which were seeded during May 21 to June 10 didn't decrease their culm-lengths, while seedlings old as 80 days decreased the length though under ordinary culture. 11. Variation in ear-length of rice varieties show the same trend as the culm-length subjected to the different seeding dates. When rice seedlings aged from 30 to 40 days, the ear-length remained constant but rice plants older than 40 days obviously decreased their ear-lengths. D. Number of panicles per hill. 12. The number of panicles per hill decreased up to a certain dates as seeding was delayed and then again increased the panicles due to the development of numerous tillers at the upper internodes. The seeding date to reach to the least number of panicles of rice variety depended upon the seedling ages. Thirty- to 40-day seedlings which were seeded during May 31 to June 10 developed the lowest number of panicles and 70- to 80-day seedlings sown for the period from April 11 to April 21 reached already to the minimum number of panicles. E. Number of rachillae. 13. To a certain seeding date, the number of rachillae didn't show any variation due to delay of seeding but it decreased remarkably when seeded later than the marginal date. 14. Variation in number of rachillae depended upon seedling ages. For example, 30- to 40-day old seedlings which, were originally seeded after May 31 started to decrease the rachillae. On the other hand, 80-day old seedlings which, were seeded on May 1 showed a tendency to decrease rachillae and the rice plant sown on May 31 could develop narrowly 3 or 4 panicles. F. Defective grain and 1.000-grain weights. 15. Under delay of the seeding dates, weight of the defective grains gradually increased till a certain date and then suddenly increased. These relationships could be expressed with two different linear regressions. 16. If it was assumed that the marginal date for ripening was the cross point of these two lines, the date seemed. closely related with seedling ages. The date was June 10- in 30- to 40-day old seedlings but that of 70- to 80-day old seedlings was May 1. Accordingly, the marginal date for ripening was getting earlier as the seedling stage was prolonged. 17. The 1.000-grain weight in ordinary culture was the heaviest and it decreased in both early and late cultures. G. Straw and rough rice weights. 18. Regardless of earliness of variety, rice plants under early culture which were seeded before March 22 or April 1 did not show much variation in straw weight due to seedling ages but in ordinary culture it gradually decreased and the degree was became greater in late culture. 19. Relationship between seeding dates (X) and grain weight related to varieties and seedling ages, could be expressed as a parabola analogous to a line (Y=77.28-7.44X$_1$-1.00lX$_2$). That is, grain yield didn't vary in early culture but it started to decrease when seeded later than a certain date, as seeding was delayed. The variation was much greater in cases of late planting and prolongation of seedling age. 20. Generally speaking, the relationship between grain yield (Y) and number of days to heading (X) was described with linear regression. However, the early varieties were the highest yielders within the range of 60 to 110, days to heading but the late variety greatly decreased its yield since it grows normally only under late culture. The grain yield, on the whole, didn't increase as number of days to heading exceeded more than 140 days.