• Journal of the Institute of Convergence Signal Processing
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• v.23 no.3
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• pp.173-178
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• 2022

Recently, various computing technologies such as artificial intelligence, big data, and IoT are developing. In particular, artificial intelligence-based deepfake technology is being used in various fields such as the content and medical industry. Deepfake technology is a combination of deep learning and fake, and is a technology that synthesizes a person's face or body through deep learning, which is a core technology of AI, to imitate accents and voices. This paper uses deepfake technology to study the creation of virtual characters through the synthesis of animation models and real person photos. Through this, it is possible to minimize various cost losses occurring in the animation production process and support writers' work. In addition, as deepfake open source spreads on the Internet, many problems emerge, and crimes that abuse deepfake technology are prevalent. Through this study, we propose a new perspective on this technology by applying the deepfake technology to children's material rather than adult material.

• Journal of Korean Home Economics Education Association
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• v.31 no.1
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• pp.77-88
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• 2019

This study was to execute the flipped learning as a learner-centered teaching and learning method in the course on family living culture for home economics education students in a college of education, and to investigate its effect. Flipped learning was designed in three stages(Pre class/In class/After class), and a questionnaire survey was distributed to 40 students to measure the class satisfaction. In addition, class worksheets and reflection journals that students wrote after every class were analyzed. Students positively evaluated flipped learning because they could take non-competition class with questions and discussion, etc. escaping from a one-way lecture. This study found that the level of class satisfaction was high due to high learning effect as the dual learning was available in case of prerequisite learning or individual learning. In particular, the class using Visual Thinking was considered interesting and useful in understanding and summarizing the learning contents. This study has shown that the willingness to take other flipped learning class in their major was high. To conclude, this study has found positive learning effects in the learner-centered teaching and learning method or flipped learning for the course concerning family living culture. This researcher expects that flipped learning may be utilized in the secondary education in the future as an effective learner-centered teaching and learning method for the purpose of fostering talents for the future in the era of the fourth industrial revolution.

• Information Systems Review
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• v.22 no.4
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• pp.21-39
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• 2020

Recently, deep learning has shown high performance in various applications such as pattern analysis and image classification. Especially known as a difficult task in the field of machine learning research, stock market forecasting is an area where the effectiveness of deep learning techniques is being verified by many researchers. This study proposed a deep learning Convolutional Neural Network (CNN) model to predict the direction of stock prices. We then used the feature selection method to improve the performance of the model. We compared the performance of machine learning classifiers against CNN. The classifiers used in this study are as follows: Logistic Regression, Decision Tree, Neural Network, Support Vector Machine, Adaboost, Bagging, and Random Forest. The results of this study confirmed that the CNN showed higher performancecompared with other classifiers in the case of feature selection. The results show that the CNN model effectively predicted the stock price direction by analyzing the embedded values of the financial data

• Information Systems Review
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• v.21 no.1
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• pp.23-38
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• 2019

The social network service (SNS) is one of the important marketing channels, so many companies actively exploit SNSs by posting SNS messages with appropriate content and style for their customers. In this paper, we focused on the psychological distances embedded in the SNS messages and developed a method to measure the psychological distance in SNS message by mixing a traditional content analysis, natural language processing (NLP), and machine learning. Through a traditional content analysis by human coding, the psychological distance was extracted from the SNS message, and these coding results were used for input data for NLP and machine learning. With NLP, word embedding was executed and Bag of Word was created. The Support Vector Machine, one of machine learning techniques was performed to train and test the psychological distance in SNS message. As a result, sensitivity and precision of SVM prediction were significantly low because of the extreme skewness of dataset. We improved the performance of SVM by balancing the ratio of data by upsampling technique and using data coded with the same value in first content analysis. All performance index was more than 70%, which showed that psychological distance can be measured well.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.2
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• pp.99-110
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• 2023

This study proposed a classification of malicious network traffic using the cyber threat framework(Mitre ATT&CK) and machine learning to solve the real-time traffic detection problems faced by current security monitoring systems. We applied a network traffic dataset called UNSW-NB15 to the Mitre ATT&CK framework to transform the label and generate the final dataset through rare class processing. After learning several boosting-based ensemble models using the generated final dataset, we demonstrated how these ensemble models classify network traffic using various performance metrics. Based on the F-1 score, we showed that XGBoost with no rare class processing is the best in the multi-class traffic environment. We recognized that machine learning ensemble models through Mitre ATT&CK label conversion and oversampling processing have differences over existing studies, but have limitations due to (1) the inability to match perfectly when converting between existing datasets and Mitre ATT&CK labels and (2) the presence of excessive sparse classes. Nevertheless, Catboost with B-SMOTE achieved the classification accuracy of 0.9526, which is expected to be able to automatically detect normal/abnormal network traffic.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.463-463
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• 2023

국내 수도관 보급률이 증가하면서 기존 노후화된 수도관들과 추가로 노후화된 수도관들이 증가하고 있다. 경과년수가 오래된 시설이 증가하는 것은 잠재적인 사고발생 위험을 증가시킨다. 실제 노후화된 상수도 시설물로 인해 단수, 누수, 수질오염, 지반함몰 발생이 증가하는 추세이다. 이러한 현상들은 시민들의 생활과 안전, 경제활동에 직접적인 영향을 끼치기 때문에 이에 대한 대책 마련이 시급한 상태이다. 본 연구에서는 AI를 기반으로 상수도관의 노후도 및 위험도를 예측하는 모델을 설계하고자 하였다. 대상지역을 인천광역시 서구로 선정하여 유량과 수질의 실시간 계측데이터를 수집하였다. 딥러닝 기법 중 하나인 LSTM(Long Short-Term Memory)을 이용하여 데이터를 예측하였고, 결정계수(R²)와 RMSE(Root Mean Square Error)로 학습데이터와 검증데이터의 비율을 정하여 예측도를 평가하였다. 유량과 수질 데이터 중 80%는 학습데이터로 20%는 검증 데이터로 분리하였고, LSTM의 셀과레이어 수를 해석에 적합한 범위로 설정한 결과, 실제값과 예측값이 높은 상관성을 보이는 것으로 나타났다. 예측된 유량 및 수질의 결과는 상수도 관리에 중요한 정보를 제공하며, 사고 위험도 평가와 관 노후화에 따른 대응력을 향상시키는 데 도움이 될 것으로 판단된다.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.209-209
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• 2023

본 연구에서는 강우 시 발생하는 음향 및 진동 신호를 기반으로 강우강도를 산정하기 위한 방법론을 제안하였다. 먼저, Raspberry Pi, 콘덴서 마이크 및 가속도 센서로 구성된 관측 기기로부터 실제 비가 내리는 환경에서의 음향 및 진동 신호를 수집하였다. 가속도 센서로부터 계측된 진동 신호를 활용하여 강우 유무에 대한 이진 분류를 수행하고, 강우가 발생한 것으로 판단된 기간에 해당하는 음향 신호에 Short-Time Fourier Transform 기술을 적용하여 주파수 영역에서 나타나는 magnitude의 평균과 표준 편차, 최고 주파수 등의 특징을 기반으로 강우강도를 산정하였다. 이를 위해 앙상블 기반의 머신러닝 학습 모델인 XGBoost 알고리즘을 사용하였으며, 광학 우적계를 통해 관측한 강우강도와 산정 결과를 비교·평가하였다. 강우강도 산정 과정에서 사용된 음향 신호의 길이를 1초, 10초, 1분으로 구분하였으며, 무강우 기간 내 음향 정보로부터 배경 음향에 의한 노이즈를 제거하고자 하였다. 최종적으로 강우 유무 이진 분류 과정의 선행 여부, 음향 신호의 길이 및 노이즈 제거 방법에 따른 강우강도 산정 결과들에 대한 성능 비교를 통해 본 연구에서 제안하고자 하는 방법론의 실효성을 평가하였다.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.27-65
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• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.

• Therapeutic Science for Rehabilitation
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• v.11 no.4
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• pp.23-39
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• 2022

Objective : To summarize clinical and demographic variables and machine learning uses for predicting functional outcomes of patients with stroke. Methods : We searched PubMed, CINAHL and Web of Science to identify published articles from 2010 to 2021. The search terms were "machine learning OR data mining AND stroke AND function OR prediction OR/AND rehabilitation". Articles exclusively using brain imaging techniques, deep learning method and articles without available full text were excluded in this study. Results : Nine articles were selected for this study. Support vector machines (19.05%) and random forests (19.05%) were two most frequently used machine learning models. Five articles (55.56%) demonstrated that the impact of patient initial and/or discharge assessment scores such as modified ranking scale (mRS) or functional independence measure (FIM) on stroke patients' functional outcomes was higher than their clinical characteristics. Conclusions : This study showed that patient initial and/or discharge assessment scores such as mRS or FIM could influence their functional outcomes more than their clinical characteristics. Evaluating and reviewing initial and or discharge functional outcomes of patients with stroke might be required to develop the optimal therapeutic interventions to enhance functional outcomes of patients with stroke.

• Journal of Korean Home Economics Education Association
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• v.34 no.1
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• pp.151-165
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• 2022

The aim of this study is to examine the effectiveness of non-face-to-face classes conducted due to the COVID-19 crisis. In order to achieve this goal, a non-face-to-face class with flipped learning was applied in one subject of clothing major held at 'S' University in Cheongju, Korea. In addition, this study tried to analyze the differences between pre- and post-non-face-to-face classes in problem analysis ability, resource/information/technology literacy, convergent thinking ability as core competencies, and overall learning satisfaction. As a result, after participating in the non-face-to-face class in which the flipped learning was applied, the students recognized that their abilities improved in the three problem-solving competency sub-areas, and their overall learning satisfaction also increased. The effectiveness of non-face-to-face classes in the field of clothing and fashion has been mainly measured in fashion design and clothing construction courses. However, based on the results of this study, it can be suggested that non-face-to-face classes in a theory-oriented lecture-type class can be effective methods for improving students' core competencies such as problem-solving skills if teaching-learning methods such as flipped learning are applied. Therefore, the results of this study will be useful data for designing differentiated non-face-to-face class strategies in a theory-oriented lecture-type class to improve the core competencies of college students.