• Journal of Korean Society of Forest Science
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• v.110 no.4
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• pp.610-621
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• 2021

The soil creep, primarily caused by earthquakes and torrential rainfall events, has widely occurred across the country. The Korea Forest Service attempted to quantify the soil creep susceptible areas using a discriminant value table to prevent or mitigate casualties and/or property damages in advance. With the advent of advanced computer technologies, machine learning-based classification models have been employed for managing mountainous disasters, such as landslides and debris flows. This study aims to quantify the soil creep susceptibility using several classifiers, namely the k-Nearest Neighbor (k-NN), Naive Bayes (NB), Random Forest (RF), and Support Vector Machine (SVM) models. To develop the classification models, we downscaled 292 data from 4,618 field survey data. About 70% of the selected data were used for training, with the remaining 30% used for model testing. The developed models have the classification accuracy of 0.727 for k-NN, 0.750 for NB, 0.807 for RF, and 0.750 for SVM against test datasets representing 30% of the total data. Furthermore, we estimated Cohen's Kappa index as 0.534, 0.580, 0.673, and 0.585, with AUC values of 0.872, 0.912, 0.943, and 0.834, respectively. The machine learning-based classifications for soil creep susceptibility were RF, NB, SVM, and k-NN in that order. Our findings indicate that the machine learning classifiers can provide valuable information in establishing and implementing natural disaster management plans in mountainous areas.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.8
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• pp.139-148
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• 2019

Fostering science-gifted individuals are very important for the future of the nation, and it is especially important to cultivate information-gifted individuals in the age of the fourth industry. There is no standardized curriculum for each gifted education center of the University. Therefore, in this study, we analyzed how effective the curriculum developed on the basis of computing thinking is to affect the characteristics of the information-gifted individuals. The curriculum developed on the components of computing thinking was applied to the information-gifted students of K University. In order to verify the effectiveness of the curriculum, we developed a creative personality test and an intrinsic motivation test, and conducted tests before and after the training. We compared pre-post test results by t-test with R program. The creative personality test consisted of 36 items with 6 factors: risk-taking, self - acceptance, curiosity, humor, dominance, and autonomy. The intrinsic motivation test consisted of 20 items with 5 items: curiosity and interest oriented tendency, challenging learning task preference orientation, independent judgment dependency propensity, independent mastery propensity, and internal criterion propensity. The effect of the curriculum on the creative personality of the experimental group was significant (0.009, 0.05). The significance level of the intrinsic motivation was 0.056 and was not significant at the 0.05 level of significance.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.3
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• pp.9-15
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• 2019

Large global online shopping malls, such as Amazon, offer services in English or in the language of a country when their products are sold. Since many customers purchase products based on the product reviews, the shopping malls actively utilize the sentimental analysis technique in judging preference of each product using the large amount of review data that the customer has written. And the result of such analysis can be used for the marketing to look the potential shoppers. However, it is difficult to apply this English-based semantic analysis system to different languages used around the world. In this study, more than 500,000 data from Amazon fine food reviews was used for training a deep learning based system. First, sentiment analysis evaluation experiments were carried out with three models of English test data. Secondly, the same data was translated into seven languages (Korean, Japanese, Chinese, Vietnamese, French, German and English) and then the similar experiments were done. The result suggests that although the accuracy of the sentimental analysis was 2.77% lower than the average of the seven countries (91.59%) compared to the English (94.35%), it is believed that the results of the experiment can be used for practical applications.

• Journal of Physiology & Pathology in Korean Medicine
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• v.32 no.6
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• pp.370-374
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• 2018

Oriental Diagnosis System(ODS) is an artificial intelligence program that utilize entered diagnosis knowledge, determine patient's disease and decide right medicine. The purpose of this study is to find a correlation between pattern Identification in Korean medicine and each sasang types(Tae-Eum and So-Yang) by analyzing ODS diagnosis result. Eventually our study secure availability of using ODS program at clinical training or developing diagnosis program. Subject of this study is 50 patients who was performed Sasang constitution diagnosis (28 patients were Tae-Eum and 22 patients were So-Yang). We analyize patient's diagnosis records by using ODS program and obtained result about pattern Identification. We used SPSS statistics 23 in analyzing the differences of the scores of Eight Principle Pattern Identification, Qi-Blood Pattern Identification, and Bing-xie Pattern Identification in each Sasang types (Tae-Eum, So-Yang). The Heat and Heat-moisture scores were significantly different(p<0.05) and Qi-Blood Pattern Identification scores were not different in each Sasang types(p>0.05). And Weight was significantly different in each Sasang types(p<0.05). It is hard to generalize the result because subject of this study was not enough and had sample speciality(tinnitus patients). However, we explained correlation between pattern Identification in korean medicine and each sasang types based on quantifiable and objective evidence system. it can be used at education of korean medicine and evidence of practice diagnosis. Futhermore, there have been no studies about anaylizing correlation between pattern Identification in Korean medicine and each sasang types using ODS program. So it is worthy of being utilized at clinical evidence data of ODS program.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.7
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• pp.61-67
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• 2019

The purpose of this study is to apply self-leadership program using films to nursing students and to confirm the effects of self-leadership, empowerment, self-directed learning and happiness. The study participants were 60 nursing students, the data was collected from March 7, to June 13, 2017. The data was analyzed by frequency, percentage, mean, standard deviation, and paired t-test using SPSS WIN 24.0 computer program. The self-leadership program consisted of 13 sessions, and 14 films were used for self-management, self-training, and self-branding. The self-leadership of nursing students was significantly increased after education(t=-4.38, p<.001). In details, behavior-focused strategies, natural-reward strategies, and constructive thought pattern strategies were all significant. Empowerment also increased significantly after education(t=-5.83, p<.001), and personal skills, collective recognition, and self-determination were all significant. Self-directed learning were high after education(t=-3.31, p=.002), and learning plans and learning practices were significant. In addition, the happiness of nursing college students was significantly higher after education(t=-4.49, p<.001). As a result of this study, self-leadership program using movies can improve self-leadership, empowerment, self-directed learning and happiness of nursing students and It will be possible to apply as educational intervention in the future.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.10
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• pp.285-290
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• 2021

In the medical field, disease diagnosis and prediction research using artificial intelligence technology is being actively conducted. It is being released as a variety of products for disease diagnosis and prediction, which are most widely used in the application of artificial intelligence technology based on medical images. Artificial intelligence is being applied to diagnose diseases, to classify diseases into benign and malignant, and to separate disease regions for use in identification or reading according to the risk of disease. Recently, in connection with cloud technology, its utility as a service product is increasing. Among the diseases dealt with in this paper, liver disease is a disease with very high risk because it is difficult to diagnose early due to the lack of pain. Artificial intelligence technology was introduced based on medical images as a non-invasive diagnostic method for diagnosing these diseases. We describe the development of a web service to help the most meaningful clinical reading of liver cirrhosis patients. Then, it shows the web service process and shows the operation screen of each process and the final result screen. It is expected that the proposed service will be able to diagnose liver cirrhosis at an early stage and help patients recover through rapid treatment.

• Journal of Convergence for Information Technology
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• v.11 no.6
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• pp.1-6
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• 2021

In the field of speech recognition, as the DNN is applied, the use of speech recognition is increasing, but the amount of calculation for parallel training needs to be larger than that of the conventional GMM, and if the amount of data is small, overfitting occurs. To solve this problem, we propose an efficient method for robust voice feature extraction and voice signal noise removal even when the amount of data is small. Speech feature extraction efficiently extracts speech energy by applying the difference in frame energy for speech and the zero-crossing ratio and level-crossing ratio that are affected by the speech signal. In addition, in order to remove noise, the noise of the speech signal is removed by removing the noise of the speech signal with an average predictive improved LMS filter with little loss of speech information while maintaining the intrinsic characteristics of speech in detection of the speech signal. The improved LMS filter uses a method of processing noise on the input speech signal by adjusting the active parameter threshold for the input signal. As a result of comparing the method proposed in this paper with the conventional frame energy method, it was confirmed that the error rate at the start point of speech is 7% and the error rate at the end point is improved by 11%.

• Journal of The Korean Association of Information Education
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• v.24 no.5
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• pp.483-494
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• 2020

With the advent of the 4th Industrial Revolution, interest in AI education is increasing. In order to cultivate talented people with AI competencies who will lead the future, AI education must be conducted in a sound manner at the school site. Although AI education is being conducted at home and abroad, it was determined that the role of the AI education platform is important to implement better AI education, so this study investigated the perception of experts on the AI education platform. A perception survey was conducted based on five criteria: teaching and learning management, educational contents, accessibility, performance of AI education platform, and level suitability of elementary school students. As a results, the number of 103 educational experts selected 'Entry' as the most proper platform among the eight platforms - 'Machine learning for Kids', 'Teachable Machine', 'AI Oceans(code.org)', 'Entry', 'Genie Block', 'Elice', 'mBlock' and etc. Analysis shows that this is because 'Entry' provides quality educational content, has convenient accessibility, is easy to manage teaching and learning, as well as an AI education platform suitable for the level of elementary school. In order to apply various AI education platforms to the school field, it is necessary to train teachers in AI-related training to train them as AI education experts, and to continuously provide opportunities to experience AI education platforms. In this study, there are limitations to what is called 'a population perception survey'. because only 103 people were surveyed, and most of the experts are working in a specific area(Gyeonggi-do). In the future, it is judged that research targeting experts at the national level should be conducted to supplement these limitations.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.417-425
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• 2020

Recently, the development of weapons systems in conjunction with the Fourth Industrial Revolution has increased the number of weapon systems that integrate individual commercial systems by actively incorporating the superior fields of private commercial technologies. In the case of such commercial product-based weapons systems, however, the development of ILS elements across all areas, such as technical manual, military field maintenance support equipment, interactive electronic technical manual (IETM), and Computer Based Training (CBT) development, and weapons system Reliability, Availability, Maintainability(RAM) target values, and logistics support analysis (LSA) was requested to expand the level of development by developers. The commercial product-based System Integration (SI) weapon system will be able to complete the comprehensive military balance for complementing and effective maintenance of the military and developers only when the ILS development, which is limited to the essential elements of the required group, Request For Proposal (RFP) and Post-Logistics Support (PLS) directions were applied in the framework of outsourcing maintenance implementation. As a result, by checking the operation status of current weapons systems, the logistics support of commercial product-based weapon systems selected outsourcing as the basic policy that is decided based on the development requirements, focusing on maintaining operation through a rational decision-making process, and presented a plan for applying the development plan to the RFP for the determined core elements and PLS.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.27 no.2
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• pp.49-70
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• 2022

Sea Surface Temperature (SST), one of the ocean features, has a significant impact on climate, marine ecosystem and human activities. Therefore, SST prediction has been always an important issue. Recently, deep learning has drawn much attentions, since it can predict SST by training past SST patterns. Compared to the numerical simulations, deep learning model is highly efficient, since it can estimate nonlinear relationships between input data. With the recent development of Graphics Processing Unit (GPU) in computer, large amounts of data can be calculated repeatedly and rapidly. In this study, Short-term SST will be predicted through Convolutional Neural Network (CNN)-based U-Net that can handle spatiotemporal data concurrently and overcome the drawbacks of previously existing deep learning-based models. The SST prediction performance depends on the seasonal and interannual SST variabilities around the southern coast of Korea. The predicted SST has a wide range of variance during spring and summer, while it has small range of variance during fall and winter. A wide range of variance also has a significant correlation with the change of the Pacific Decadal Oscillation (PDO) index. These results are found to be affected by the intensity of the seasonal and PDO-related interannual SST fronts and their intensity variations along the southern Korean seas. This study implies that the SST prediction performance using the developed deep learning model can be significantly varied by seasonal and interannual variabilities in SST.