• The Plant Pathology Journal
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• v.32 no.6
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• pp.508-518
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• 2016

Early blight of tomato caused by Alternaria solani, is responsible for severe yield losses in tomato. The conidia survive on soil surface and old dry lower leaves of the plant and spread when suitable climatic conditions are available. Macroclimatic study reveals that highest inoculum concentration of Alternaria spores appeared in May 2012 to 2013 and lowest concentration during January 2012 to 2013. High night temperature positively correlated and significantly (P < 0.01) involved in conidial spore dispersal and low relative humidity (RH) displayed significant (P < 0.05) but negative correlation with conidial dispersal. The objective of the study was to modify microclimatic conditions of tomato crop canopy which may hamper conidial dispersal and reduce disease severity. We evaluated effect of marigold intercropping and plastic mulching singly and in consortia on A. solani conidial density, tomato leaf damage and microclimatic parameters as compar to tomato alone (T). Tomato-marigold intercropping-plastic mulching treatment (T + M + P) showed 35-39% reduction in disease intensity as compared to tomato alone. When intercropped with tomato, marigold served as barrier to conidial movement and plastic mulching prevented evapotranspiration and reduced the canopy RH that resulted in less germination of A. solani spores. Marigold intercropping and plastic mulching served successfully as physical barrier against conidial dissemination to diminish significantly the tomato foliar damage produced by A. solani.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.282-285
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• 2018

The purpose of this study is to investigate the effect of personal factors and community factors on the quality of life based on the presence of chronic diseases based on the Big Data Platform. The research methodology was the matching of the 2017 Community Health Survey data and the National Statistical Office data to the health center units. In the study, The higher the age, the higher the education level, the higher the monthly household income, the economic activity, the spouse, the higher the quality of life. In the case of community factors, the lower the population density, the lower the elderly population ratio, the more doctors engaged in medical institutions, the higher the financial independence, the higher the quality of life.

• Health Policy and Management
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• v.32 no.1
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• pp.45-52
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• 2022

Background: There are regional variations in the incidence of coronavirus disease 2019 (COVID-19), which means that some regions are more exposed to the risk of COVID-19 than others. Therefore, this study aims to investigate regional variations in the incidence of COVID-19 in Korea and identify risk factors associated with the incidence of COVID-19 using community-level data. Methods: This study was conducted at the districts (si·gun·gu) level in Korea. Data of COVID-19 incidence by districts were collected from the official website of each province. Data was also obtained from the Korean Statistical Information Service and the Community Health Survey; socio-demographic factor, transmission pathway, healthcare resource, and factor in response to COVID-19. Community risk factors that drive the incidence of COVID-19 were selected using a least absolute shrinkage and selection operator regression. Results: As of June 2021, the incidence of COVID-19 differed by more than 80 times between districts. Among the candidate factors, sex ratio, population aged 20-29, local financial independence, population density, diabetes prevalence, and failure to comply with the quarantine rules were significantly associated with COVID-19 incidence. Conclusion: This study suggests setting COVID-19 quarantine policy and allocating resources, considering the community risk factors. Protecting vulnerable groups should be a high priority for these policies.

• Structural Monitoring and Maintenance
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• v.9 no.1
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• pp.59-80
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• 2022

In the present study, the objective is to detect the structural damages using the responses obtained from the sensors at the optimal location under uncertainty conditions. Reducing the error rate in damage detection process due to responses' noise is an important goal in this study. In the proposed algorithm for optimal sensor placement, the noise of responses recorded from the sensors is initially reduced using the principal component analysis. Afterward, the optimal sensor placement is obtained by the damage detection equation based sensitivity analysis. The sensors are placed on degrees of freedom corresponding to the minimum error rate in structural damage detection through this procedure. The efficiency of the proposed method is studied on a truss bridge, a space dome, a double-layer grid as well as a three-story experimental frame structure and the results are compared. Moreover, the performance of the suggested method is compared with three other algorithms of Average Driving Point Residue (ADPR), Effective Independence (EI) method, and a mass weighting version of EI. In the examples, young's modulus, density, and cross-sectional areas of the elements are considered as uncertainty parameters. Ultimately, the results have demonstrated that the presented algorithm under uncertainty conditions represents a high accuracy to obtain the optimal sensor placement in the structures.

• Journal of the Korea Fashion and Costume Design Association
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• v.24 no.4
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• pp.87-99
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• 2022

The purpose of this study is to develop machine embroidery textile designs for each technique that can be expressed using a single-headed computer embroidery sewing machine through a machine embroidery CAD system. For research, embroidery CAD utilized the Artistic digitizer, and the guillotine computer-mechanical magnetization machine used ELNA. The design concept was limited to portraits and relics of independence activists in six memorial halls built in Korea. The results of this study are as follows. First, it was found that the machine embroidery texture, which could only be produced by industries in the past, can be expand in the infinite creative embroidery design area by enabling the digitalization of motif images and the simulation of machine embroidery techniques through various layout options. Second, in the development of machine embroidery textures, it was found that the setting of the width, height, axis ratio, stitch, object, path, length, density, layer order, etc. in embroidery CAD is a very important part of determining the completeness of the embroidery results. Third, mechanical embroidery textile designs, which can be represented by single-head computer machine embroidery machine were able to show colorful embroidery results that differs from the original image by using seven main techniques and five deep technique alone or in combination, according to the designer's intention.

• Communications for Statistical Applications and Methods
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• v.30 no.2
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• pp.163-178
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• 2023

GARCH-X(1, 1) model specifies that conditional variance follows an AR(1) process and includes a past exogenous variable. This study proposes a new class from that model by allowing a more general (non-linear) variance function to follow an AR(1) process. The functions applied to the variance equation include exponential, Tukey's ladder, and Yeo-Johnson transformations. In the framework of normal and student-t distributions for return errors, the empirical analysis focuses on two stock indices data in developed countries (FTSE100 and SP500) over the daily period from January 2000 to December 2020. This study uses 10-minute realized volatility as the exogenous component. The parameters of considered models are estimated using the adaptive random walk metropolis method in the Monte Carlo Markov chain algorithm and implemented in the Matlab program. The 95% highest posterior density intervals show that the three transformations are significant for the GARCHX(1, 1) model. In general, based on the Akaike information criterion, the GARCH-X(1, 1) model that has return errors with student-t distribution and variance transformed by Tukey's ladder function provides the best data fit. In forecasting value-at-risk with the 95% confidence level, the Christoffersen's independence test suggest that non-linear models is the most suitable for modeling return data, especially model with the Tukey's ladder transformation.

• Korean Journal of Social Welfare Studies
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• no.36
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• pp.55-80
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• 2008

Research on the welfare state or income inequality has been concerned with variations in inequality between societies or families. These studies tend to view the family as a unit of shared interests where incomes are pooled and distributed equally. This study makes a theoretical and empirical case for why it is important to look at economic dependency within the family in comparative welfare state research. Using the Luxembourg Income Study data this study examined married women's dependency on their husbands' earnings in 16 western industrialized countries. The constructed measure for married women's level of economic dependency followed the procedure of Sørensen & McLanahan(1987), which stated : "her dependency is measured by the extent to which a woman's standard of living(as determined by her share of income) is derived from a transfer from her husband." The finding suggested that married women's economic dependence was lowest in Scandinavian countries. On the contrary, in Southern Europe countries most married women were dependent on husbands' earnings. In Netherlands, Austria, Germany where the share of part-time work among married women was high, married women's economic dependence was also high. This showed the women's labor force participation did not mean that the majority of couples were equal with respect to earnings, nor that a major shift in the sexual division of labour has taken place. This paper analysed the causal relationship between the married women's economic independence and the welfare state by using Ragin(2000)'s Fuzzy-Set Qualitative Comparative Analysis. This analysis considered the various conditions of the welfare state : namely, left power, union mobilization density, women's mobilization, public service sector employment and generous support on the family. The result showed that powerful union, high level of women's mobilization and the generous support on the family were necessary conditions for 'relatively high' level of married women's economic independence.

• School Mathematics
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• v.17 no.2
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• pp.241-255
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• 2015

The purpose of this study is to analyze teachers' understanding on generating random number in Monte Carlo simulation and to provide educational implications in school practice. The results showed that the 70% of the teachers selected wrong ideas from three types for random-number as strategies for problem solving a probability problem and also they make some errors to justify their opinion. The first kind of the errors was that the probability of a point or boundary was equal to the value of the probability density function in the continuous probability distribution. The second kind of the errors was that the teachers failed to recognize that the sample space has been changed by conditional probability. The third kind of the errors was that when two random variables X, Y are independence of each other, then only, joint probability distribution is satisfied $P(X=x,\;Y=y)=p(X=x){\times}P(Y=y{\mid}X=x)$.

• KIEAE Journal
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• v.1 no.2
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• pp.5-12
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• 2001

Through the harmony of natural and artificial systems a city is composed of, the ecology-oriented urban planning seeks for qualitative improvements of a city on which our life is based. To enhance the ecology-oriented urban planning, the followings are suggested by a comparative analysis of Korea with Germany regarding the development process, the instruments, and the establishment of indicators for the planning. Firstly, though our national land development plan is closely connected with B-plan, it has little to do with the natural environment. Moreover, the natural environment plan of the Ministry of Environment is almost impossible to carry out in terms of urban construction work. For this reason, the instrument for dealing with the development and environment plan systems together as well as the completion of the current plan system is needed for the ecologically acceptable urban development in the long term. Secondly, in order to realize what is mentioned above in the concrete it seems to be desirable for the system and the instrument to be devised at the extent of B-plan. The regulations of the plan should have strong legal binding force and practicality as well. The element of ecology-oriented urban planning are (1) degree of independence and appropriate density, (2) conservation of natural elements such as soil, water, animals and plants etc., (3) energy saving in land use, (4) activation of B-plan and inducement of active participation of residents. Thirdly, it will be useful to develop various kinds of indicators for the environment plan provided in advance so that the ecology-oriented urban developments may be under control. It also should be taken into consideration that the indicators are supposed to be comprehensive, representative, and practical enough to make the most of at the early stage of drawing up a plan. The kinds of indicators which can be used in the ecology-oriented urban development include (1) soil, (2) water, (3) vegetation and plants, (4) animals, (5) climate, and (6) transportation.

• Journal of Digital Convergence
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• v.11 no.11
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• pp.409-414
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• 2013

Naive Bayesian classifiers are a powerful and well-known type of classifiers that can be easily induced from a dataset of sample cases. However, the strong conditional independence assumptions can sometimes lead to weak classification performance. Normally, naive Bayesian classifiers use Gaussian distributions to handle continuous attributes and to represent the likelihood of the features conditioned on the classes. The probability density of attributes, however, is not always well fitted by a Gaussian distribution. Another eminent type of classifier is the neuro-fuzzy classifier, which can learn fuzzy rules and fuzzy sets using supervised learning. Since there are specific structural similarities between a neuro-fuzzy classifier and a naive Bayesian classifier, the purpose of this study is to apply learning distribution graphs constructed by a neuro-fuzzy network to naive Bayesian classifiers. We compare the Gaussian distribution graphs with the fuzzy distribution graphs for the naive Bayesian classifier. We applied these two types of distribution graphs to classify leukemia and colon DNA microarray data sets. The results demonstrate that a naive Bayesian classifier with fuzzy distribution graphs is more reliable than that with Gaussian distribution graphs.