• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.3
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• pp.129-141
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• 2023

Crop models have been used to predict yield under diverse environmental and cultivation conditions, which can be used to support decisions on the management of forage crop. Cultivar parameters are one of required inputs to crop models in order to represent genetic properties for a given forage cultivar. The objectives of this study were to compare calibration and ensemble approaches in order to minimize the uncertainty of crop yield estimates using the SIMPLE crop model. Cultivar parameters were calibrated using Log-likelihood (LL) and Generic Composite Similarity Measure (GCSM) as an objective function for Metropolis-Hastings (MH) algorithm. In total, 20 sets of cultivar parameters were generated for each method. Two types of ensemble approach. First type of ensemble approach was the average of model outputs (E_em), using individual parameters. The second ensemble approach was model output (E_pm) of cultivar parameter obtained by averaging given 20 sets of parameters. Comparison was done for each cultivar and for each error calculation methods. 'Jowoo' and 'Yeongwoo', which are forage rice cultivars used in Korea, were subject to the parameter calibration. Yield data were obtained from experiment fields at Suwon, Jeonju, Naju and I ksan. Data for 2013, 2014 and 2016 were used for parameter calibration. For validation, yield data reported from 2016 to 2018 at Suwon was used. Initial calibration indicated that genetic coefficients obtained by LL were distributed in a narrower range than coefficients obtained by GCSM. A two-sample t-test was performed to compare between different methods of ensemble approaches and no significant difference was found between them. Uncertainty of GCSM can be neutralized by adjusting the acceptance probability. The other ensemble method (E_pm) indicates that the uncertainty can be reduced with less computation using ensemble approach.

• Journal of Korean Dental Hygiene Science
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• v.6 no.2
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• pp.79-89
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• 2023

Background and Objectives: The prevalence of dementia is steadily increasing each year, and preceding studies continue to explore the association between dementia and oral health. Dental hygienists require specialized competencies to provide appropriate dental healthcare services, necessitating the development of a tool for the objective measurement of their knowledge levels. This study aimed to develop a knowledge assessment tool for dental hygienists concerning considerations for dental care for patients with dementia. Methods: The study constructed preliminary items based on a literature review and then conducted expert validation, a pilot survey, and the main survey. The main survey was conducted among 220 dental hygienists. Validity and reliability analyses were conducted with the collected data to select the final items, and the correctness rates for each selected item were verified. Results: As a result of the analysis of the collected data, 18 items were eliminated out of a total of 40 preliminary items, leaving a total of 6 factors and 22 items. The Cronbach's α value for the selected items was 0.791. The six factors are as follows: 'Considerations during dental treatment for dementia patients' (5 items), 'medication side effects in dementia patients' (4 items), 'oral care methods for dementia patients' (4 items), 'communication with dementia patients' (4 items), 'psychological reactions of dementia patients' (3 items), and 'guidance for dementia patients' (2 items). The item with the highest correctness rate was item 2 of the 'guidance for dementia patients' category at 98.6%, while the item with the lowest correctness rate was item 2 of the 'psychological reactions of dementia patients' category at 5.9%. Conclusion: This study validated the reliability and validity of the knowledge assessment tool, which lays the foundation for future research on dental hygienists and dementia. It contributes essential data for ongoing education, development of educational programs, and establishing operational guidelines in healthcare institutions.

• Journal of Service Research and Studies
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• v.14 no.1
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• pp.63-76
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• 2024

Multi-family housing plays a crucial role as a living and experiencing space, and its environment has a direct impact on the well-being and stability of its residents. Therefore, Crime Prevention Design (CPTED) for multi-family housing is of utmost importance. However, crime-related data in China is not disclosed to the public because of its specificity, making it difficult for researchers to conduct further in-depth studies based on accurate crime data. As a result, the establishment and application of CPTED theory in terms of crime prevention is limited and delayed. This study aims to explore three aspects of CPTED in multi-family housing as perceived by home-buying consumers. It investigated consumer perception of the CPTED, the importance of each element and ways to increase awareness of CPTED in multifamily housing in order to effectively improve multifamily crime prevention design principles and further enhance public safety. This study examined the current state and future trends of CPTED in China by analyzing relevant research reports and literature, aiming to gain insights into the crime prevention awareness of Chinese homeowners. In addition, a survey was conducted on Chinese consumers to unravel the importance of CPTED and increase awareness of its various elements in multifamily-family. This study used a Likert scale and SPSS reliability analysis to determine the cognitive status of multi-family CPTED, the importance of each element, and proposed an improvement plan based on the analysis results. As this study was limited by the difficulty of implementation and the lack of validation of its practical effectiveness, it is recommended that future research needs to validate the effectiveness of crime prevention designs and produce more practical results. Furthermore, it is crucial to utilize this study to inform the implementation of security solutions that are tailored to the unique characteristics of each district. Additionally, it is important to offer guidance on how to enhance community safety by increasing residents' awareness of security through education and information dissemination. The author hopes that the representative multi-family CPTED awareness, the importance of each element, and plans for improvement shall be summarized from this study, and provide foundational data for the future development of CPTED based on the Chinese region.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.29-45
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• 2012

Bond rating is regarded as an important event for measuring financial risk of companies and for determining the investment returns of investors. As a result, it has been a popular research topic for researchers to predict companies' credit ratings by applying statistical and machine learning techniques. The statistical techniques, including multiple regression, multiple discriminant analysis (MDA), logistic models (LOGIT), and probit analysis, have been traditionally used in bond rating. However, one major drawback is that it should be based on strict assumptions. Such strict assumptions include linearity, normality, independence among predictor variables and pre-existing functional forms relating the criterion variablesand the predictor variables. Those strict assumptions of traditional statistics have limited their application to the real world. Machine learning techniques also used in bond rating prediction models include decision trees (DT), neural networks (NN), and Support Vector Machine (SVM). Especially, SVM is recognized as a new and promising classification and regression analysis method. SVM learns a separating hyperplane that can maximize the margin between two categories. SVM is simple enough to be analyzed mathematical, and leads to high performance in practical applications. SVM implements the structuralrisk minimization principle and searches to minimize an upper bound of the generalization error. In addition, the solution of SVM may be a global optimum and thus, overfitting is unlikely to occur with SVM. In addition, SVM does not require too many data sample for training since it builds prediction models by only using some representative sample near the boundaries called support vectors. A number of experimental researches have indicated that SVM has been successfully applied in a variety of pattern recognition fields. However, there are three major drawbacks that can be potential causes for degrading SVM's performance. First, SVM is originally proposed for solving binary-class classification problems. Methods for combining SVMs for multi-class classification such as One-Against-One, One-Against-All have been proposed, but they do not improve the performance in multi-class classification problem as much as SVM for binary-class classification. Second, approximation algorithms (e.g. decomposition methods, sequential minimal optimization algorithm) could be used for effective multi-class computation to reduce computation time, but it could deteriorate classification performance. Third, the difficulty in multi-class prediction problems is in data imbalance problem that can occur when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed boundary and thus the reduction in the classification accuracy of such a classifier. SVM ensemble learning is one of machine learning methods to cope with the above drawbacks. Ensemble learning is a method for improving the performance of classification and prediction algorithms. AdaBoost is one of the widely used ensemble learning techniques. It constructs a composite classifier by sequentially training classifiers while increasing weight on the misclassified observations through iterations. The observations that are incorrectly predicted by previous classifiers are chosen more often than examples that are correctly predicted. Thus Boosting attempts to produce new classifiers that are better able to predict examples for which the current ensemble's performance is poor. In this way, it can reinforce the training of the misclassified observations of the minority class. This paper proposes a multiclass Geometric Mean-based Boosting (MGM-Boost) to resolve multiclass prediction problem. Since MGM-Boost introduces the notion of geometric mean into AdaBoost, it can perform learning process considering the geometric mean-based accuracy and errors of multiclass. This study applies MGM-Boost to the real-world bond rating case for Korean companies to examine the feasibility of MGM-Boost. 10-fold cross validations for threetimes with different random seeds are performed in order to ensure that the comparison among three different classifiers does not happen by chance. For each of 10-fold cross validation, the entire data set is first partitioned into tenequal-sized sets, and then each set is in turn used as the test set while the classifier trains on the other nine sets. That is, cross-validated folds have been tested independently of each algorithm. Through these steps, we have obtained the results for classifiers on each of the 30 experiments. In the comparison of arithmetic mean-based prediction accuracy between individual classifiers, MGM-Boost (52.95%) shows higher prediction accuracy than both AdaBoost (51.69%) and SVM (49.47%). MGM-Boost (28.12%) also shows the higher prediction accuracy than AdaBoost (24.65%) and SVM (15.42%)in terms of geometric mean-based prediction accuracy. T-test is used to examine whether the performance of each classifiers for 30 folds is significantly different. The results indicate that performance of MGM-Boost is significantly different from AdaBoost and SVM classifiers at 1% level. These results mean that MGM-Boost can provide robust and stable solutions to multi-classproblems such as bond rating.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.23-46
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• 2017

Although there have been cases of evaluating the value of specific companies or projects which have centralized on developed countries in North America and Europe from the early 2000s, the system and methodology for estimating the economic value of individual technologies or patents has been activated on and on. Of course, there exist several online systems that qualitatively evaluate the technology's grade or the patent rating of the technology to be evaluated, as in 'KTRS' of the KIBO and 'SMART 3.1' of the Korea Invention Promotion Association. However, a web-based technology valuation system, referred to as 'STAR-Value system' that calculates the quantitative values of the subject technology for various purposes such as business feasibility analysis, investment attraction, tax/litigation, etc., has been officially opened and recently spreading. In this study, we introduce the type of methodology and evaluation model, reference information supporting these theories, and how database associated are utilized, focusing various modules and frameworks embedded in STAR-Value system. In particular, there are six valuation methods, including the discounted cash flow method (DCF), which is a representative one based on the income approach that anticipates future economic income to be valued at present, and the relief-from-royalty method, which calculates the present value of royalties' where we consider the contribution of the subject technology towards the business value created as the royalty rate. We look at how models and related support information (technology life, corporate (business) financial information, discount rate, industrial technology factors, etc.) can be used and linked in a intelligent manner. Based on the classification of information such as International Patent Classification (IPC) or Korea Standard Industry Classification (KSIC) for technology to be evaluated, the STAR-Value system automatically returns meta data such as technology cycle time (TCT), sales growth rate and profitability data of similar company or industry sector, weighted average cost of capital (WACC), indices of industrial technology factors, etc., and apply adjustment factors to them, so that the result of technology value calculation has high reliability and objectivity. Furthermore, if the information on the potential market size of the target technology and the market share of the commercialization subject refers to data-driven information, or if the estimated value range of similar technologies by industry sector is provided from the evaluation cases which are already completed and accumulated in database, the STAR-Value is anticipated that it will enable to present highly accurate value range in real time by intelligently linking various support modules. Including the explanation of the various valuation models and relevant primary variables as presented in this paper, the STAR-Value system intends to utilize more systematically and in a data-driven way by supporting the optimal model selection guideline module, intelligent technology value range reasoning module, and similar company selection based market share prediction module, etc. In addition, the research on the development and intelligence of the web-based STAR-Value system is significant in that it widely spread the web-based system that can be used in the validation and application to practices of the theoretical feasibility of the technology valuation field, and it is expected that it could be utilized in various fields of technology commercialization.

• The Korean Journal of Nuclear Medicine Technology
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• v.14 no.1
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• pp.67-72
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• 2010

Purpose: In Asan Medical Center we perform myocardial perfusion SPECT to evaluate cardiac event risk level for non-cardiac surgery patients. In case of patients with cancer, we check tumor metastasis using whole body bone scan and whole body PET scan and then perform myocardial perfusion SPECT to reduce unnecessary exam. In case of short term in patients, we perform $^{201}Tl$ myocardial perfusion SPECT after whole body bone scan a minimum 16 hours in order to reduce hospitalization period but it is still the actual condition in which the evaluation about the affect of the crosstalk contamination due to the each other dissimilar isotope administration doesn't properly realize. So in our experiments, we try to evaluate crosstalk contamination influence on $^{201}Tl$ myocardial perfusion SPECT using anthropomorphic torso phantom and patient's data. Materials and Methods: From 2009 August to September, we analyzed 87 patients with $^{201}Tl$ myocardial perfusion SPECT. According to $^{201}Tl$ myocardial perfusion SPECT yesterday whole body bone scan possibility of carrying out, a patient was classified. The image data are obtained by using the dual energy window in $^{201}Tl$ myocardial perfusion SPECT. We analyzed $^{201}Tl$ and $^{99m}Tc$ counts ratio in each patients groups obtained image data. We utilized anthropomorphic torso phantom in our experiment and administrated $^{201}Tl$ 14.8 MBq (0.4 mCi) at myocardium and $^{99m}Tc$ 44.4 MBq (1.2 mCi) at extracardiac region. We obtained image by $^{201}Tl$ myocardial perfusion SPECT without gate method application and analyzed spatial resolution using Xeleris ver 2.0551. Results: In case of $^{201}Tl$ window and the counts rate comparison result yesterday whole body bone scan of being counted in $^{99m}Tc$ window, the difference in which a rate to 24 hours exponential-functionally notes in 1:0.114 with Ventri (GE Healthcare, Wisconsin, USA), 1:0.249 after the bone tracer injection in 12 hours in 1:0.411 with 1:0.79 with Infinia (GE healthcare, Wisconsin, USA) according to a reduction a time-out was shown (Ventri p=0.001, Infinia p=0.001). Moreover, the rate of the case in which it doesn't perform the whole body bone scan showed up as the average 1:$0.067{\pm}0.6$ of Ventri, and 1:$0.063{\pm}0.7$ of Infinia. According to the phantom after experiment spatial resolution measurement result, and an addition or no and time-out of $^{99m}Tc$ administrated, it doesn't note any change of FWHM (p=0.134). Conclusion: Through the experiments using anthropomorphic torso phantom and patients data, we found that $^{201}Tl$ myocardium perfusion SPECT image later carried out after the bone tracer injection with 16 hours this confirmed that it doesn't receive notable influence in spatial resolution by $^{99m}Tc$. But this investigation is only aimed to image quality, so it needs more investigation in patient's radiation dose and exam accuracy and precision. The exact guideline presentation about the exam interval should be made of the validation test which is exact and in which it is standardized about the affect of the crosstalk contamination according to the isotope use in which it is different later on.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.127-147
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• 2020

The world is suffering from numerous human and economic losses due to the novel coronavirus infection (COVID-19). The Korean government established a strategy to overcome the national infectious disease crisis through research and development. It is difficult to find distinctive features and changes in a specific R&D field when using the existing technical classification or science and technology standard classification. Recently, a few studies have been conducted to establish a classification system to provide information about the investment research areas of infectious diseases in Korea through a comparative analysis of Korea government-funded research projects. However, these studies did not provide the necessary information for establishing cooperative research strategies among countries in the infectious diseases, which is required as an execution plan to achieve the goals of national health security and fostering new growth industries. Therefore, it is inevitable to study information services based on the classification system and classification model for establishing a national collaborative R&D strategy. Seven classification - Diagnosis_biomarker, Drug_discovery, Epidemiology, Evaluation_validation, Mechanism_signaling pathway, Prediction, and Vaccine_therapeutic antibody - systems were derived through reviewing infectious diseases-related national-funded research projects of South Korea. A classification system model was trained by combining Scopus data with a bidirectional RNN model. The classification performance of the final model secured robustness with an accuracy of over 90%. In order to conduct the empirical study, an infectious disease classification system was applied to the coronavirus-related research and development projects of major countries such as the STAR Metrics (National Institutes of Health) and NSF (National Science Foundation) of the United States(US), the CORDIS (Community Research & Development Information Service)of the European Union(EU), and the KAKEN (Database of Grants-in-Aid for Scientific Research) of Japan. It can be seen that the research and development trends of infectious diseases (coronavirus) in major countries are mostly concentrated in the prediction that deals with predicting success for clinical trials at the new drug development stage or predicting toxicity that causes side effects. The intriguing result is that for all of these nations, the portion of national investment in the vaccine_therapeutic antibody, which is recognized as an area of research and development aimed at the development of vaccines and treatments, was also very small (5.1%). It indirectly explained the reason of the poor development of vaccines and treatments. Based on the result of examining the investment status of coronavirus-related research projects through comparative analysis by country, it was found that the US and Japan are relatively evenly investing in all infectious diseases-related research areas, while Europe has relatively large investments in specific research areas such as diagnosis_biomarker. Moreover, the information on major coronavirus-related research organizations in major countries was provided by the classification system, thereby allowing establishing an international collaborative R&D projects.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.117-140
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• 2019

As the aging phenomenon accelerates and various social problems related to the elderly of the vulnerable are raised, the need for effective elderly care solutions to protect the health and safety of the elderly generation is growing. Recently, more and more people are using Smart Toys equipped with ICT technology for care for elderly. In particular, log data collected through smart toys is highly valuable to be used as a quantitative and objective indicator in areas such as policy-making and service planning. However, research related to smart toys is limited, such as the development of smart toys and the validation of smart toy effectiveness. In other words, there is a dearth of research to derive insights based on log data collected through smart toys and to use them for decision making. This study will analyze log data collected from smart toy and derive effective insights to improve the quality of life for elderly users. Specifically, the user profiling-based analysis and elicitation of a change in quality of life mechanism based on behavior were performed. First, in the user profiling analysis, two important dimensions of classifying the type of elderly group from five factors of elderly user's living management were derived: 'Routine Activities' and 'Work-out Activities'. Based on the dimensions derived, a hierarchical cluster analysis and K-Means clustering were performed to classify the entire elderly user into three groups. Through a profiling analysis, the demographic characteristics of each group of elderlies and the behavior of using smart toy were identified. Second, stepwise regression was performed in eliciting the mechanism of change in quality of life. The effects of interaction, content usage, and indoor activity have been identified on the improvement of depression and lifestyle for the elderly. In addition, it identified the role of user performance evaluation and satisfaction with smart toy as a parameter that mediated the relationship between usage behavior and quality of life change. Specific mechanisms are as follows. First, the interaction between smart toy and elderly was found to have an effect of improving the depression by mediating attitudes to smart toy. The 'Satisfaction toward Smart Toy,' a variable that affects the improvement of the elderly's depression, changes how users evaluate smart toy performance. At this time, it has been identified that it is the interaction with smart toy that has a positive effect on smart toy These results can be interpreted as an elderly with a desire to meet emotional stability interact actively with smart toy, and a positive assessment of smart toy, greatly appreciating the effectiveness of smart toy. Second, the content usage has been confirmed to have a direct effect on improving lifestyle without going through other variables. Elderly who use a lot of the content provided by smart toy have improved their lifestyle. However, this effect has occurred regardless of the attitude the user has toward smart toy. Third, log data show that a high degree of indoor activity improves both the lifestyle and depression of the elderly. The more indoor activity, the better the lifestyle of the elderly, and these effects occur regardless of the user's attitude toward smart toy. In addition, elderly with a high degree of indoor activity are satisfied with smart toys, which cause improvement in the elderly's depression. However, it can be interpreted that elderly who prefer outdoor activities than indoor activities, or those who are less active due to health problems, are hard to satisfied with smart toys, and are not able to get the effects of improving depression. In summary, based on the activities of the elderly, three groups of elderly were identified and the important characteristics of each type were identified. In addition, this study sought to identify the mechanism by which the behavior of the elderly on smart toy affects the lives of the actual elderly, and to derive user needs and insights.

• Korean Journal of Agricultural and Forest Meteorology
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• v.15 no.2
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• pp.76-84
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• 2013

An adequate downscaling of the official forecasts of Korea Meteorological Administration (KMA) is a prerequisite to improving the value and utility of agrometeorological information in rural areas, where complex terrain and small farms constitute major features of the landscape. In this study, we suggest a simple correction scheme for scaling down the KMA temperature forecasts from mesoscale (5 km by 5 km) to the local scale (30 m by 30 m) across a rural catchment, especially under temperature inversion conditions. The study area is a rural catchment of $50km^2$ area with complex terrain and located on a southern slope of Mountain Jiri National Park. Temperature forecasts for 0600 LST on 62 days with temperature inversion were selected from the fall 2011-spring 2012 KMA data archive. A geospatial correction scheme which can simulate both cold air drainage and the so-called 'thermal belt' was used to derive the site-specific temperature deviation across the study area at a 30 m by 30 m resolution from the original 5 km by 5 km forecast grids. The observed temperature data at 12 validation sites within the study area showed a substantial reduction in forecast error: from ${\pm}2^{\circ}C$ to ${\pm}1^{\circ}C$ in the mean error range and from $1.9^{\circ}C$ to $1.6^{\circ}C$ in the root mean square error. Improvement was most remarkable at low lying locations showing frequent cold pooling events. Temperature prediction error was less than $2^{\circ}C$ for more than 80% of the observed inversion cases and less than $1^{\circ}C$ for half of the cases. Temperature forecasts corrected by this scheme may accelerate implementation of the freeze and frost early warning service for major fruits growing regions in Korea.

• Korean Journal of Clinical Laboratory Science
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• v.49 no.2
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• pp.114-120
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• 2017

There have been many studies to develop methods for predicting diabetes and to prevent diabetes. The validity of glycated hemoglobin (HbA1c), one of the commonly known tools in predicting diabetes, has been verified by many previous studies. In this study, we examined the cutoff value of HbA1c for diabetes and impaired fasting glucose (IFG). Based on this study, we proposed a proper clinical guideline and evaluated the validation of the guideline. Excluding those without blood glucose and HbA1c data, we used the data of 5,161 subjects (2,281 men and 2,880 women) over the age of 20 years from the 2015 Korean National Health and Nutrition Examination Survey. The correlation efficient of fasting plasma glucose (FPG) and HbA1c was 0.79, indicating a strong relationship. Howeve, the correlation efficient of FPG and HbA1c was low, showing 0.27 in non-diabetes, 0.39 in IFG, and 0.66 in diabetes, showing a strong relationship. The cutoff value of HbA1c for predicting diabetes using ROC curve was 6.05% (sensitivity 84.6%, and specificity 92.0%), and AUC was 0.941 (0.937 in men, and 0.946 in women). The cutoff value of HbA1c for predicting IFG using ROC curve was 5.55% (sensitivity 64.5%, and specificity 70.0%), and AUC was 0.733 (0.708 in men, and 0.764 in women). Therefore, it may not be appropriate to apply the guidelines for diagnosing IFG since sensitivity and specificity were below 70%. For future studies retarding the cutoff value of HbA1c in predicting IFG, high sensitivity and specificity are expected if we segment the reference range of IFG.