• Journal of agriculture & life science
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• v.50 no.2
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• pp.45-52
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• 2016

In order to select excellent cross parents for development new cultivar of low temperature tolerant, combining ability was conducted by 24 cross combinations obtained from crosses between 'Dahong', 'Gamhong', 'Maehyang', 'Seolhyang' as seed parents and 'Dahong', 'Gamhong', 'Maehyang', 'Sugyeong', 'Sunhong', 'Wongyo 3111' as pollen parents. The results showed that two cultivars of 'Dahong', 'Gamhong' were not suitable for seed parents. Because average fruit weight in case of 'Dahong' as a seed parent was the tendency to become the smallest in total cross combinations, survival ratio in case of 'Gamhong' as a seed parent was the lowest in total cross combinations. And fruit hardness in case of 'Sunhong' as a pollen parent was the tendency to low, incidence of malformed fruit in case of 'Sugyeong' as a pollen parent was the tendency to be increased. Therefore, two cultivars of 'Sunhong', 'Sugyeong' was also not suitable for pollen parents. But In case of 'Maehyang' and 'Seolhyang' as seed parents, germination percent and survival rate were relatively higher than other seed parents, fruit quality and yield had also excellent. Therefore, there could be selected to 'Maehyang' and 'Seolhyang' as seed parents and 'Maehyang', 'Seolhyang', 'Wongyo3111' as pollen parents. Especially, the crosses 'Maehyang' × 'Seolhyang', 'Seolhyang' × 'Maehyang', 'Seolhyang' × 'Wongyo3111' were suitable for cross combination for development new cultivar of low temperature tolerant to be demonstrated the excellence as cross parents in sugar contents, fruit weight, fruit hardness and yield.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.3
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• pp.357-365
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• 2018

In the 21st century, as the emergence of the age of creative economy is expected, interest in the cultivation of creative talents required in society around the world is newly rising. Sustainable development education should not be limited to school education, but should be promoted and supported at all social education sites for the purpose of lifelong education. Therefore, the purpose of this paper is to consider the relationship between the possibility of formal and informal learning and the development of capacity in higher education. Exploratory and qualitative research based on intensive groups was designed using several groups of formal and informal learning settings. In China, the creation of a creative economy is set as a major national policy direction for the new government. What is required for talented people in the creative economy era and how to educate them is becoming a major policy issue. The development of core competencies requires multiple contexts based on cognitive non-cognitive disposition. By combining the formal and informal learning environment within higher education for the purpose of a new learning culture, it can provide a variety of situations and improve competency development. While this study can identify aspects of formal and informal learning settings, the interdependencies between them are still difficult to grasp. However, practical implications can be seen clearly. In other words, based on the results, you can point out key aspects of competency acquisition that can be a key element in the higher education environment. As a result, this study analyzed implications for formal and informal learning environments for new ways of developing core competencies in higher education.

• Journal of the Korea Computer Graphics Society
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• v.29 no.5
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• pp.11-20
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• 2023

In this paper, we propose a novel deep learning-based motion reconstruction approach that facilitates the generation of full-body motions, including finger motions, while also enabling the online adjustment of motion generation delays. The proposed method combines the Vive Tracker with a deep learning method to achieve more accurate motion reconstruction while effectively mitigating foot skating issues through the use of an Inverse Kinematics (IK) solver. The proposed method utilizes a trained AutoEncoder to reconstruct character body motions using tracker data in real-time while offering the flexibility to adjust motion generation delays as needed. To generate hand motions suitable for the reconstructed body motion, we employ a Fully Connected Network (FCN). By combining the reconstructed body motion from the AutoEncoder with the hand motions generated by the FCN, we can generate full-body motions of characters that include hand movements. In order to alleviate foot skating issues in motions generated by deep learning-based methods, we use an IK solver. By setting the trackers located near the character's feet as end-effectors for the IK solver, our method precisely controls and corrects the character's foot movements, thereby enhancing the overall accuracy of the generated motions. Through experiments, we validate the accuracy of motion generation in the proposed deep learning-based motion reconstruction scheme, as well as the ability to adjust latency based on user input. Additionally, we assess the correction performance by comparing motions with the IK solver applied to those without it, focusing particularly on how it addresses the foot skating issue in the generated full-body motions.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.237-244
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• 2023

Today, AI is being applied to various industrial fields, leading to a paradigm shift in the overall industry. In the fashion industry, AI is also used to predict trends and provide various services for consumers, and in particular, AI image creation tools have the potential as a tool for fashion design development. This study investigated the possibilities and limitations of using Midjourny for fashion design development by creating images using Midjourney among AI image creation tools and identifying its characteristics. The characteristics of images created in Midjourney are as follows. First, it has the intuitiveness to create images by intuitively applying or combining images corresponding to commands. Second, there is randomness in which different images are generated when the same command is entered at different times. Third, when using existing images and commands together, the image created in Midjourney is more dependent on the existing image than the command. In conclusion, Midjourny's various image creation functions and the ability to change images according to commands can be helpful in developing original fashion designs. However, it is important to note that fashion designs that cannot be worn or made are sometimes presented. It is expected that the results of this study will serve as basic data for the use of AI image creation tools for fashion design development.

• Information Systems Review
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• v.24 no.4
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• pp.23-40
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• 2022

Although Support Vector Machine(SVM) has been used in various fields such as bankruptcy prediction model, the hyperplane learned by SVM in class imbalance problem can be severely skewed toward minority class and has a negative impact on performance because the area of majority class is expanded while the area of minority class is invaded. This study proposed optimized uneven margin SVM(OPT-UMSVM) combining threshold moving or post scaling method with UMSVM to cope with the limitation of the traditional even margin SVM(EMSVM) in class imbalance problem. OPT-UMSVM readjusted the skewed hyperplane to the majority class and had better generation ability than EMSVM improving the sensitivity of minority class and calculating the optimized performance. To validate OPT-UMSVM, 10-fold cross validations were performed on five sub-datasets with different imbalance ratio values. Empirical results showed two main findings. First, UMSVM had a weak effect on improving the performance of EMSVM in balanced datasets, but it greatly outperformed EMSVM in severely imbalanced datasets. Second, compared to EMSVM and conventional UMSVM, OPT-UMSVM had better performance in both balanced and imbalanced datasets and showed a significant difference performance especially in severely imbalanced datasets.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.89-95
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• 2024

Nowadays, the competitiveness of a company depends on the ability of all organizational members to share and utilize the organizational knowledge accumulated by the organization. As if to prove this, the world is now focusing on ChetGPT service using generative AI technology based on LLM (Large Language Model). However, it is still difficult to apply the ChetGPT service to work because there are many hallucinogenic problems. To solve this problem, sLLM (Lightweight Large Language Model) technology is being proposed as an alternative. In order to construct sLLM, corporate data is essential. Corporate data is the organization's ERP data and the company's office document knowledge data preserved by the organization. ERP Data can be used by directly connecting to sLLM, but office documents are stored in file format and must be converted to data format to be used by connecting to sLLM. In addition, there are too many technical limitations to utilize office documents stored in file format as organizational knowledge information. This study proposes a method of storing office documents in DB format rather than file format, allowing companies to utilize already accumulated office documents as an organizational knowledge system, and providing office documents in data form to the company's SLLM. We aim to contribute to improving corporate competitiveness by combining AI technology.

• Korean Journal of Radiology
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• v.23 no.4
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• pp.466-478
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• 2022

Objective: ¹⁸F-fluorodeoxyglucose (FDG) PET/CT is often used for detecting malignancy in patients with newly diagnosed hemophagocytic lymphohistiocytosis (HLH), with acceptable sensitivity but relatively low specificity. The aim of this study was to improve the diagnostic ability of ¹⁸F-FDG PET/CT in identifying malignancy in patients with HLH by combining ¹⁸F-FDG PET/CT and clinical parameters. Materials and Methods: Ninety-seven patients (age ≥ 14 years) with secondary HLH were retrospectively reviewed and divided into the derivation (n = 71) and validation (n = 26) cohorts according to admission time. In the derivation cohort, 22 patients had malignancy-associated HLH (M-HLH) and 49 patients had non-malignancy-associated HLH (NM-HLH). Data on pretreatment ¹⁸F-FDG PET/CT and laboratory results were collected. The variables were analyzed using the Mann-Whitney U test or Pearson's chi-square test, and a nomogram for predicting M-HLH was constructed using multivariable binary logistic regression. The predictors were also ranked using decision-tree analysis. The nomogram and decision tree were validated in the validation cohort (10 patients with M-HLH and 16 patients with NM-HLH). Results: The ratio of the maximal standardized uptake value (SUVmax) of the lymph nodes to that of the mediastinum, the ratio of the SUVmax of bone lesions or bone marrow to that of the mediastinum, and age were selected for constructing the model. The nomogram showed good performance in predicting M-HLH in the validation cohort, with an area under the receiver operating characteristic curve of 0.875 (95% confidence interval, 0.686-0.971). At an appropriate cutoff value, the sensitivity and specificity for identifying M-HLH were 90% (9/10) and 68.8% (11/16), respectively. The decision tree integrating the same variables showed 70% (7/10) sensitivity and 93.8% (15/16) specificity for identifying M-HLH. In comparison, visual analysis of ¹⁸F-FDG PET/CT images demonstrated 100% (10/10) sensitivity and 12.5% (2/16) specificity. Conclusion: ¹⁸F-FDG PET/CT may be a practical technique for identifying M-HLH. The model constructed using ¹⁸F-FDG PET/CT features and age was able to detect malignancy with better accuracy than visual analysis of ¹⁸F-FDG PET/CT images.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.139-157
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• 2016

Bankruptcy involves considerable costs, so it can have significant effects on a country's economy. Thus, bankruptcy prediction is an important issue. Over the past several decades, many researchers have addressed topics associated with bankruptcy prediction. Early research on bankruptcy prediction employed conventional statistical methods such as univariate analysis, discriminant analysis, multiple regression, and logistic regression. Later on, many studies began utilizing artificial intelligence techniques such as inductive learning, neural networks, and case-based reasoning. Currently, ensemble models are being utilized to enhance the accuracy of bankruptcy prediction. Ensemble classification involves combining multiple classifiers to obtain more accurate predictions than those obtained using individual models. Ensemble learning techniques are known to be very useful for improving the generalization ability of the classifier. Base classifiers in the ensemble must be as accurate and diverse as possible in order to enhance the generalization ability of an ensemble model. Commonly used methods for constructing ensemble classifiers include bagging, boosting, and random subspace. The random subspace method selects a random feature subset for each classifier from the original feature space to diversify the base classifiers of an ensemble. Each ensemble member is trained by a randomly chosen feature subspace from the original feature set, and predictions from each ensemble member are combined by an aggregation method. The k-nearest neighbors (KNN) classifier is robust with respect to variations in the dataset but is very sensitive to changes in the feature space. For this reason, KNN is a good classifier for the random subspace method. The KNN random subspace ensemble model has been shown to be very effective for improving an individual KNN model. The k parameter of KNN base classifiers and selected feature subsets for base classifiers play an important role in determining the performance of the KNN ensemble model. However, few studies have focused on optimizing the k parameter and feature subsets of base classifiers in the ensemble. This study proposed a new ensemble method that improves upon the performance KNN ensemble model by optimizing both k parameters and feature subsets of base classifiers. A genetic algorithm was used to optimize the KNN ensemble model and improve the prediction accuracy of the ensemble model. The proposed model was applied to a bankruptcy prediction problem by using a real dataset from Korean companies. The research data included 1800 externally non-audited firms that filed for bankruptcy (900 cases) or non-bankruptcy (900 cases). Initially, the dataset consisted of 134 financial ratios. Prior to the experiments, 75 financial ratios were selected based on an independent sample t-test of each financial ratio as an input variable and bankruptcy or non-bankruptcy as an output variable. Of these, 24 financial ratios were selected by using a logistic regression backward feature selection method. The complete dataset was separated into two parts: training and validation. The training dataset was further divided into two portions: one for the training model and the other to avoid overfitting. The prediction accuracy against this dataset was used to determine the fitness value in order to avoid overfitting. The validation dataset was used to evaluate the effectiveness of the final model. A 10-fold cross-validation was implemented to compare the performances of the proposed model and other models. To evaluate the effectiveness of the proposed model, the classification accuracy of the proposed model was compared with that of other models. The Q-statistic values and average classification accuracies of base classifiers were investigated. The experimental results showed that the proposed model outperformed other models, such as the single model and random subspace ensemble model.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.177-192
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• 2016

Machine learning is a field of artificial intelligence. It refers to an area of computer science related to providing machines the ability to perform their own data analysis, decision making and forecasting. For example, one of the representative machine learning models is artificial neural network, which is a statistical learning algorithm inspired by the neural network structure of biology. In addition, there are other machine learning models such as decision tree model, naive bayes model and SVM(support vector machine) model. Among the machine learning models, we use SVM model in this study because it is mainly used for classification and regression analysis that fits well to our study. The core principle of SVM is to find a reasonable hyperplane that distinguishes different group in the data space. Given information about the data in any two groups, the SVM model judges to which group the new data belongs based on the hyperplane obtained from the given data set. Thus, the more the amount of meaningful data, the better the machine learning ability. In recent years, many financial experts have focused on machine learning, seeing the possibility of combining with machine learning and the financial field where vast amounts of financial data exist. Machine learning techniques have been proved to be powerful in describing the non-stationary and chaotic stock price dynamics. A lot of researches have been successfully conducted on forecasting of stock prices using machine learning algorithms. Recently, financial companies have begun to provide Robo-Advisor service, a compound word of Robot and Advisor, which can perform various financial tasks through advanced algorithms using rapidly changing huge amount of data. Robo-Adviser's main task is to advise the investors about the investor's personal investment propensity and to provide the service to manage the portfolio automatically. In this study, we propose a method of forecasting the Korean volatility index, VKOSPI, using the SVM model, which is one of the machine learning methods, and applying it to real option trading to increase the trading performance. VKOSPI is a measure of the future volatility of the KOSPI 200 index based on KOSPI 200 index option prices. VKOSPI is similar to the VIX index, which is based on S&P 500 option price in the United States. The Korea Exchange(KRX) calculates and announce the real-time VKOSPI index. VKOSPI is the same as the usual volatility and affects the option prices. The direction of VKOSPI and option prices show positive relation regardless of the option type (call and put options with various striking prices). If the volatility increases, all of the call and put option premium increases because the probability of the option's exercise possibility increases. The investor can know the rising value of the option price with respect to the volatility rising value in real time through Vega, a Black-Scholes's measurement index of an option's sensitivity to changes in the volatility. Therefore, accurate forecasting of VKOSPI movements is one of the important factors that can generate profit in option trading. In this study, we verified through real option data that the accurate forecast of VKOSPI is able to make a big profit in real option trading. To the best of our knowledge, there have been no studies on the idea of predicting the direction of VKOSPI based on machine learning and introducing the idea of applying it to actual option trading. In this study predicted daily VKOSPI changes through SVM model and then made intraday option strangle position, which gives profit as option prices reduce, only when VKOSPI is expected to decline during daytime. We analyzed the results and tested whether it is applicable to real option trading based on SVM's prediction. The results showed the prediction accuracy of VKOSPI was 57.83% on average, and the number of position entry times was 43.2 times, which is less than half of the benchmark (100 times). A small number of trading is an indicator of trading efficiency. In addition, the experiment proved that the trading performance was significantly higher than the benchmark.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.21 no.2
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• pp.281-313
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• 1976

To obtain fundamental informations on cotton breeding efficiences for Korea, individual genetic relationships and interrelationships between the agronomic characteristics of Upland cotton were investigated. These experiments were couducted at the Mokpo Branch Station $(34^{\circ}48'N, $ $126^{\circ}23'E$ and altitude of 10m above sea level) from 1969 through 1972. Heterosis, combining ability, dominance and recessive gene action, genetic variance, and phenotypic and genotypic correlation were investigated by $F_1'S$ from an 11-parent partial diallel cross and the segregating $F_2$ and $F_3$ populations of the cross Paymaster times Heujueusseo Trice. The following points resulted from this study, 1. Heteroses for number of bolls per plant and lint yield were significant at 27, 84% and 37.26%, respectively. No other character had significant heteroses. 2. The GCA estimates for all studied characteristics were higher than the SCA estimates. Varieties with high GCA effects were Suwon 1 for earliness, Paymaster and Arijona for high lint percent, and Arijona for long fiber, etc, 3. SCA estimates for lint yield varied widely in crosses with Mokpo 4, Mokpo 6 and Heujueusseo Trice. Those crosses with the highest SCA effects were combinations with large characteristics differences, Example of these crosses are Mokpo 4 times Acala 1517W, Mokpo 4 times D. P. L. and Heujueusseo Trice aud Paymaster. 4. Early-maturing varieties were completely dominant to late-maturing varieties in some combinations while other crosses gave intermediate phenotypes. These results suggest additive genetic action by multi-genes. Heujueusseo Trice, Mokpo 6, and Suwon 1 showed highest degree of dominance for earliness. 5. There were no significant trends for inheritance of weight of boll and 100 seeds weight. 6. Long staple was partially to completely dominant to short staple. Though there were single gene ratios the rate of dominance decreased in the $F_2$ and $F_3$ populations in the cross between the long staple variety Paymaster and the short staple variety Heujueusseo Trice. Diallel cross $F_1$ hybrids showed complicated allelic gene action for staple length. Various dominance degree were shown by varieties. 7. Number of bolls per plant indicated strong over-dominance and small non-allelic additive gene action. 8. Lint Yield was characterized by over-dominance and by multiple non-allelic-gene action. High-yielding varieties were dominant to low-yielding ones. However, the low-yielding variety Heujueusseo Trice showed over-dominance, indicating different reactions according to the varieties and combinations. 9. Broad sense heritability for days to flowering was 34-39% while narrow sense heritability was 11%. Large variations of individual plants caused by Korean climatic conditions cause this situation. Heritability estimates for weight of boll was 30% for broad sense and 22% for narrow sense. 10. Heritability estimates for staple length and lint percent were very high suggesting strong selection effects. 11. Narrow sense heritability estimates for number of bolls per plant was 30% in the diallel cross $F_1$ hybrids and 36% in the $F_2$ population of the special cross. Broad sense heritability was estimated at 67% suggesting that. 12. Heritability estimates for lint yield was low due to high over-dominance in the diallel cross $F_1$ hybrids. Heritability estimates for yield was low in the $F_1$ hybrids but high in the $F_2$ and $F_3$ populations. 13. Phenotypic and genotypic correlations between lint percent and days to flowering and between staple length and days to flowering were high in the $F_1, $ $F_2$ and $F_3$ populations. Late-maturing varieties and individuals had long staple and high lint percent in general. As the correlation between days to flowering and lint yield was extremely low, the two traits were considered independent of each other. Days to flowering and number of bolls per plant were negatively correlated in the $F_3$ population, indicating early-maturing individual plants with many bolls may be readily selected. 14. Phenotypic and genotypic correlations between lint percent and staple length were high in $F_1, $ $F_2$ and $F_3$ populations. Accordingly, long staple varieties were high in lint percent. It was recognized that lint yield and lint percent were positively correlated in the diallel cross $F_1$ hybrids, and lint percent and staple length were positively correlated in the $F_2$ population, indicating that lint percent and staple length affect lint yield. 15. Lint yield was significantly and positively phenotypically correlated with number of bolls per plant in $F_1, $ $F_2$ and $F_3$ populations. A high genotypic correlation was also noted indicating a close genetic relationship. The selection efficiencies for a high-yielding variety can be increased when individual plants with many bolls are selected in later generations. The selection efficiencies for good fiber quality can be enhanced when individuals with long staple and high lint percent are selected in early generations.