• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2021.06a
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• pp.183-186
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• 2021

인간 자세 추정 연구는 최근 크게 주목 받고 있는 연구 분야이다. 본 연구는 또한, 자기 지도 학습이라고 명명된 딥러닝 기법이 부상하면서 여러 문제가 해결되고 있다. 본 논문에서는, 이러한 문제를 해결하는 딥러닝 기반 인간 자세 추정 방법들을 유형별로 분류해본다. 그리고 각 분류별 설명과 함께 대표적인 방법들을 소개한다. 마지막으로, 결론에서는 본 연구가 앞으로 나아갈 방향에 대한 논의를 제시한다.

• Journal of the Korean Society for information Management
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• v.39 no.1
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• pp.91-117
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• 2022

The number of depressed patients in Korea and around the world is rapidly increasing every year. However, most of the mentally ill patients are not aware that they are suffering from the disease, so adequate treatment is not being performed. If depressive symptoms are neglected, it can lead to suicide, anxiety, and other psychological problems. Therefore, early detection and treatment of depression are very important in improving mental health. To improve this problem, this study presented a deep learning-based depression tendency model using Korean social media text. After collecting data from Naver KonwledgeiN, Naver Blog, Hidoc, and Twitter, DSM-5 major depressive disorder diagnosis criteria were used to classify and annotate classes according to the number of depressive symptoms. Afterwards, TF-IDF analysis and simultaneous word analysis were performed to examine the characteristics of each class of the corpus constructed. In addition, word embedding, dictionary-based sentiment analysis, and LDA topic modeling were performed to generate a depression tendency classification model using various text features. Through this, the embedded text, sentiment score, and topic number for each document were calculated and used as text features. As a result, it was confirmed that the highest accuracy rate of 83.28% was achieved when the depression tendency was classified based on the KorBERT algorithm by combining both the emotional score and the topic of the document with the embedded text. This study establishes a classification model for Korean depression trends with improved performance using various text features, and detects potential depressive patients early among Korean online community users, enabling rapid treatment and prevention, thereby enabling the mental health of Korean society. It is significant in that it can help in promotion.

• ETRI Journal
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• v.31 no.3
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• pp.282-291
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• 2009

An online mean-shift object tracking algorithm, which consists of a learning stage and an estimation stage, is proposed in this work. The learning stage selects the features for tracking, and the estimation stage composes a likelihood image and applies the mean shift algorithm to it to track an object. The tracking performance depends on the quality of the likelihood image. We propose two schemes to generate and integrate likelihood images: one based on the discrete AdaBoost (DAB) and the other based on the real AdaBoost (RAB). The DAB scheme uses tuned feature values, whereas RAB estimates class probabilities, to select the features and generate the likelihood images. Experiment results show that the proposed algorithm provides more accurate and reliable tracking results than the conventional mean shift tracking algorithms.

• Structural Engineering and Mechanics
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• v.42 no.5
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• pp.747-760
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• 2012

In today's online social structure, people with electronic devices or network have been closely related to whether any of the activities, work, school, etc., is related to electronic devices, intelligent robot, and network control. The best mobility and the first rich media of these products as smart phones, smart phones rise rapidly in recent years, high speed processing performance and high free way to install software, deeply loved by many business people. However, not only for smart phone business aspects of the use, but also can engage in education of the teachers or the students are learning a great help. This study construct OCR-assisted learning software written by the JAVA made, and the installation is provided by the Android mobile phone users.

• Journal of the Korea Society of Computer and Information
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• v.22 no.1
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• pp.135-141
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• 2017

This thesis progressed research on the improvements of NCS multimedia utilization for preliminary teachers. A national module has not been developed yet in terms of child education, so this thesis suggests a curriculum according to the courses taught for freshmen and sophomores of K University Child Education majors. To lessen the burden of tremendous work and classes, provide motivation and interest in learning and maximize the effect, this thesis provides NCS based curriculum. It expects to improve task performance of teachers and help them with better skills to make class materials using up-to-date multimedia, regardless of the computer literacy of preliminary teachers. This thesis does prior research on the abilities to make use of computers and understand the level of computer literacy. Then it suggests NCS based curriculum goals and its performance standards to utilize task-suitable software. It aims to enable efficient multimedia usage, and optimize the learning efficiency of education linked to Nuri precesses.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.224-230
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• 2021

YouTube is an OTT service that leads the home economy, which has emerged from the 2020 Corona Pandemic. With the growth of OTT-based individual media, creators are required to establish attractive storytelling strategies that can be preferred by viewers and elected for YouTube recommendation algorithms. In this study, we conducted a study on modeling that proposes a content storyline for creators. As the ability for Creators to create content that viewers prefer, we have presented the data literacy ability to find patterns in complex and massive data. We also studied the importance of compelling storytelling configurations that viewers prefer and can be selected for YouTube recommendation algorithms. This study is of great significance in that it deviated from the viewer-oriented recommendation system method and proposed a story suggestion model for individual creaters. As a result of incorporating this story proposal model into the production of the YouTube channel Tiger Love video, it showed a certain effectiveness. This story suggestion model is a machine learning text-based story suggestion system, excluding the application of photography or video.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2018.06a
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• pp.329-331
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• 2018

지속적인 딥러닝 기반의 영상처리 기술의 발전으로 객체분류나 객체검출 문제에 대해서 뛰어난 성능 보이고 있다. 하지만 객체추적 문제에서는 성능이 좋은 추적기는 실시간 동작이 불가능하고 딥러닝 기반의 객체추적도 단일 객체에만 고려한 기법이 많기 때문에 개선할 필요가 있다. 전처리로 검출된 객체영역과 kalman filter를 통해 예측된 추적영역 간의 embedding feature 비교를 통해 동일인물인지 판단하여 고유 ID를 부여하고 추적한다. 객체끼리 교차하거나 가려지는 상황에서 추적을 실패하게 되는데 이 후에 지속적인 추적을 위해 IoU 비교를 통해 후보 추적기로 남겨두는 과정을 거친다. 실험 결과 실시간 동작여부와 객체끼리 교차하거나 프레임 밖으로 나갔다가 다시 나타나는 경우에도 추적이 가능함을 확인하였다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• fall
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• pp.91-94
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• 2021

As deep learning technologies becoming developed, realistic fake videos synthesized by deep learning models called "Deepfake" videos became even more difficult to distinguish from original videos. As fake news or Deepfake blackmailing are causing confusion and serious problems, this paper suggests a novel model detecting Deepfake videos. We chose Residual Convolutional Neural Network (Resnet50) as an extraction model and Long Short-Term Memory (LSTM) which is a form of Recurrent Neural Network (RNN) as a classification model. We adopted cosine similarity with hinge loss to train our extraction model in embedding the features of Deepfake and original video. The result in this paper demonstrates that temporal features in the videos are essential for detecting Deepfake videos.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.06a
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• pp.1268-1271
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• 2022

Since it is common to have touch-screen devices, it is less challenging to draw sketches anywhere and save them in vector form. Current research on sketches considers coordinate sequence data and adopts sequential models for learning sketch representation in sketch understanding. In the sketch dataset, it has become customary that the dataset is in vector coordinate format. Moreover, the popular dataset does not consider real-life sketches, sketches from pencil, pen, and paper. Art psychology uses real-life sketches to analyze patients. ETRI presents a unique sketch dataset for sketch recognition of autism spectrum disorder in pixel format. We present a method to formulate the dataset for better generalization of sketch data. Through experiments, we show that pixel-based models can produce a good performance.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.34 no.12
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• pp.13-20
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• 2018

This research is to analyze the public cases of information facilities in terms of central circulations in multi level volumes such as atrium or court which provide visual intervention between different spaces and physical connections such as bridges. Hunt Library design balances the understood pre-existing needs with the University's emerging needs to create a forward-thinking learning environment. While clearly a contemporary structure within a traditional context of the NCSU campus, the Hunt Library provides a positive platform for influencing its surroundings. Both technical and programmatic innovations are celebrated as part of the learning experience and provide a versatile and stimulating environment for students. Public library as open spaces connecting to an interactive social domain over communities can provide variety of learning environments, or technology based labs. There are many cases of the public information spaces with dynamic networks where participants can play their roles in physical space as well as in the intellectual stimulation. In the research, new public projects provide typologies of information spaces with user oriented media. The research is to address a creative transition between the reading space and the experimental links of the integration of state-of-the-art technology is highly visible in the building's design. The user-friendly browsing system that replaces the traditional browsing with the virtual shelves classified and archived by their form, is to reduce the storage space of the public library and it is to allow more space for collaborative learning. In addition to the intelligent robot of information storages, innovative features is the large-scale visualization space that supports team experiments to carry out collaborative online works and therefore the public library's various programs is to provide visitors with more efficient participatory environment.