• Journal of Broadcast Engineering
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• v.27 no.4
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• pp.527-537
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• 2022

As multimedia contents demand and supply increase, internet traffic around the world increases. Several standardization groups are striving to establish more efficient compression standards to mitigate the problem. In particular, research to introduce deep learning technology into compression standards is actively underway. Despite the fact that deep learning-based technologies show high performance, they suffer from the domain gap problem when test video sequences have different characteristics of training video sequences. To this end, several methods have been made to introduce content-adaptive deep video compression. In this paper, we will look into these methods by three aspects: codec information-aware methods, model selection methods, and information signaling methods.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.79-88
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• 2024

The use of the social media has become part of our daily life activities. The social web channels provide the content generation facility to its users who can share their views, opinions and experiences towards certain topics. The researchers are using the social media content for various research areas. Sentiment analysis, one of the most active research areas in last decade, is the process to extract reviews, opinions and sentiments of people. Sentiment analysis is applied in diverse sub-areas such as subjectivity analysis, polarity detection, and emotion detection. Stance classification has emerged as a new and interesting research area as it aims to determine whether the content writer is in favor, against or neutral towards the target topic or issue. Stance classification is significant as it has many research applications like rumor stance classifications, stance classification towards public forums, claim stance classification, neural attention stance classification, online debate stance classification, dialogic properties stance classification etc. This research study explores different feature sets such as lexical, sentiment-specific, dialog-based which have been extracted using the standard datasets in the relevant area. Supervised learning approaches of generative algorithms such as Naïve Bayes and discriminative machine learning algorithms such as Support Vector Machine, Naïve Bayes, Decision Tree and k-Nearest Neighbor have been applied and then ensemble-based algorithms like Random Forest and AdaBoost have been applied. The empirical based results have been evaluated using the standard performance measures of Accuracy, Precision, Recall, and F-measures.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06d
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• pp.105-108
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• 2007

Bayesian learning network is employed for diverse applications. This paper discusses the Bayesian learning network algorithm structure which can be applied in the wireless sensor network environment for various online applications. First, this paper discusses Bayesian parameter learning, Bayesian DAG structure learning, characteristics of wireless sensor network, and data gathering in the wireless sensor network. Second, this paper discusses the important considerations about the online Bayesian learning network and the conceptual structure of the learning network algorithm.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.3
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• pp.209-215
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• 2014

Sequence tagging is the task of predicting frame-wise labels for a given input sequence and has important applications to diverse domains. Conventional methods such as maximum likelihood (ML) learning matches global features in empirical and model distributions, rather than local features, which directly translates into frame-wise prediction errors. Recent probabilistic sequence models such as conditional random fields (CRFs) have achieved great success in a variety of situations. In this paper, we introduce a novel discriminative CRF learning algorithm to minimize local feature mismatches. Unlike overall data fitting originating from global feature matching in ML learning, our approach reduces the total error over all frames in a sequence. We also provide an efficient gradient-based learning method via gradient forward-backward recursion, which requires the same computational complexity as ML learning. For several real-world sequence tagging problems, we empirically demonstrate that the proposed learning algorithm achieves significantly more accurate prediction performance than standard estimators.

• Journal of the Korea Society of Computer and Information
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• v.23 no.1
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• pp.139-145
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• 2018

This paper recommends flipped learning as a method to improve the learning abilities and the level of software utilization when it comes to using computers in children education institutes. Flipped learning enables a class fully making use of the up-to-date multimedia-related technology. Especially, flipped learning leads a participation-oriented class rather than lecture-based ones. Young child's teachers can, not only improve their capabilities to utilize multimedia, but also manage classes that follow the trend of the fourth industrial revolution. Therefore, this paper introduces the importance of media education when it comes to training preliminary teachers and suggests a flipped learning curriculum. This paper finds significance in future efficient education for raising creative and integrated thinking children.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.11a
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• pp.1532-1533
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• 2011

본 연구에서는 SQL를 배우고자 하는 개발자들에게 SQL 문법을 학습할 수 있는 교육용 Tool을 개발한다. 개발자가 예제와 설명을 통하여 SQL 문법을 배우고 ER-Diagram을 보면서 논리적인 DB의 개념을 이용하여 쉽게 학습할 수 있다. 예제는 초급과 중급으로 나누어져 있어 사용자의 수준에 맞는 학습이 선택가능하다. TPC-H 데이터는 DSS 환경에서 사용되는 표준 데이터 모델로 Database Generater를 통해 생성하며 본 연구에서 사용자가 데이터량의 조정이 가능하도록 구성하였다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• fall
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• pp.297-298
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• 2021

최근 한국의 자살률은 10만 명 당 25.7명으로 높은 수치를 기록하고 있으며 한국 사회의 큰 문제로 자리 잡고 있다. 특히 한강 교량 내 투신자살 시도를 하는 경우가 매우 많다. 본 논문에서는 한강 교량 내 투신자살 시도를 예방하기 위해 자세 인식의 정확도를 향상하기 위해 딥러닝 기반의 교량에서의 자살 방식 시스템을 개발하였으며, 국내의 자살 예방률이 높아지기를 기대한다.

• Korean Medical Education Review
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• v.11 no.1
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• pp.21-33
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• 2009

The purpose of this study is to review a variety of e-learning use in medical education, and to analyze the e-learning related research in medical education, finally to discuss possibilities and limitations of e-learning in future. Subjects of this research are 46 papers published in Korean Medical Database, PubMed, MEDLIS, RISS4U. Content analysis of 46 papers have been conducted based on the period of research, research methods, research subjects, study personnel, effectiveness. The results are as follows. First, various e-learning, such as hyper-media, simulation-based medical education (SBME), game-based learning, web-based learning, computer-based test (CBT) are implemented in medical education. Second, 35 research (76.1%) has verified the positive effect of e-learning. Third, in the case of Korean studies, experimental studies (46.2%) in a short period (46.2%) of 50-100 people (42.3%) to take the most. As a result, it is reported a lack of theoretical discussion and insight on e-learning compared to foreign research. Educational paradigms are currently shifting from off-line to on-line, from traditional classroom lecture to e-learning. But e-learning is not a substitution to traditional teaching, but a matter of choice. The choice is up to medical professors and students.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.1
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• pp.21-31
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• 2016

The changes to Ubiquitous Network Environment leads existing learning environment to Smart Learning Environment. Expecially, Smart Learning Environment is in changing paradigm existing teacher centered environment and learner centered environment, recently the demand of Smart Learning Environment for learners are growing up. This study analyzed Learning Environments for Smart Learning Environment focused on the learners through analyzing Ubiquitous Network Environment that is concentrated on the physical aspects and the non-physical aspects. Also, we suggested learning several ways that can be effectively applied based on the environmental characteristics of Smart Learning.

• Journal of Korea Game Society
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• v.18 no.4
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• pp.99-106
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• 2018

Board games have many game characters and many state spaces. Therefore, games must be long learning. This paper used reinforcement learning algorithm. But, there is weakness with reinforcement learning. At the beginning of learning, reinforcement learning has the drawback of slow learning speed. Therefore, we tried to improve the learning speed by using the heuristic using the knowledge of the problem domain considering the game tree when there is the same best value during learning. In order to compare the existing character the improved one. I produced a board game. So I compete with one-sided attacking character. Improved character attacked the opponent's one considering the game tree. As a result of experiment, improved character's capability was improved on learning speed.