• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.5
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• pp.791-800
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• 2022

CCN, which has emerged to overcome the limitations of existing network structures, enables efficient networking by changing the IP Address-based network method to the Content-based network method. At this time, the contents are stored on each node(router) rather than on the top server, and considering the limitation of storage capacity, it is very important to determine which contents to store and release through the cache policy, and there are several cache policies that have been studied so far. In this paper, two of the existing cache policies, producer popularity-based and distance-based, were mixed. In addition, the mixing ratio was set differently to experiment, and we proved which experiement was the most efficient one.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.6
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• pp.1113-1120
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• 2021

Content Central Network (CCN) appeared to improve network efficiency by transforming IP-based network into content name-based network structures. Each router performs caching mechanism to improve network efficiency in the CCN. And the cache replacement policy applied to the CCN router is an important factor that determines the overall performance of the CCN. Therefore various studies has been done relating to cache replacement policy of the CCN. In this paper, we proposed a cache replacement policy that improves the limitations of the LFU policy. The proposal algorithm applies real-time producer popularity-based variables. And through experiments, we proved that the proposed policy shows a better cache hit ratio than existing policies.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1095-1102
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• 2023

Content Centric Network (CCN) is a network that emerged to improve the existing network structure and communicates based on content names instead of addresses. It utilises caches to distribute traffic and reduce response time by delivering content from intermediate nodes. In this paper, we propose a popularity-based caching policy to efficiently utilise the limited CS space in CCN environment. The performance of CCNs can vary significantly depending on which content is prioritised to be stored and released. To achieve the most efficient cache replacement, we propose a real-time content popularity-based efficient cache replacement policy that calculates and prioritises content popularity based on constructor popularity, constructor distance, and content hits, and demonstrate the effectiveness of the new policy through experiments.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.07a
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• pp.36-39
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• 2020

최근 딥 러닝을 이용한 방법들이 이미지 분류에서 뛰어난 성능을 보임에 따라, 복잡한 특징을 담고 있는 얼굴 이미지에 대해 이를 적용하려는 시도가 늘어나고 있다. 특히, 이미지로부터 주요한 특징들을 추출하여 간결하게 이미지를 대표할 수 있는 이미지 기술자 (Image descriptor)를 딥 러닝을 통해 생성하는 연구가 인기를 끌고 있다. 이는 딥 러닝 끝 단에 있는 Fully-connected layer 의 출력으로 얻을 수 있으며 이미지의 의미론적 상관관계를 이용하여 학습된다. 구체적으로, 이미지 기술자는 실수형 벡터 데이터로서, 한 장의 이미지를 수치화 하여 비슷한 이미지 사이에는 벡터 거리가 가깝게, 서로 다른 이미지 사이에는 벡터 거리가 멀게 구성된다. 본 연구에서는 미리 학습된 인공 신경망을 통과시켜 얻은 얼굴 이미지 기술자를 활용하여 멤버 분류를 위한 두 개의 인공 신경망을 학습하는 것을 목표로 한다. 제안된 방법을 검증하기 위해 얼굴 인식에 널리 사용되는 벤치 마크 데이터셋을 활용하였고, 그 결과 제안된 방법이 높은 정확도로 멤버를 분류할 수 있다는 것을 확인하였다.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2007.11a
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• pp.440-447
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• 2007

사진은 시각화되어 있으면서도 사용자가 생성하기 쉽다는 이유로 가장 인기 있는 컨텐트 중 하나이며, 사진을 찍은 위치 정보는 해당 장소가 갖는 특징 혹은 상징성으로, 상품 및 서비스와 연관성을 갖고 있기 때문에 상거래의 매개체가 될 가능성을 내포하고 있다. 그러나 현재의 상거래 환경에서는 사진의 위치정보를 자동화, 체계화하여 저장하고 활용하는데 한계를 가지고 있어서 사진 자체가 상거래에 활용되는 모델을 찾아보기 힘들다. 본 연구에서 제시하는 U-Photo 비즈니스 모델은 사용자가 찍은 사진의 배경에 해당하는 장소를 그 장소를 통해 광고를 하고자 하는 광고주와 연계하고, 그 사진을 클릭했을 때 광고주의 사이트가 로딩 되도록 하는, 컨텐트 생성자, 컨텐트 소비자, 광고주 연계 비즈니스 모델이다. 본 논문은 유비쿼터스 컴퓨팅 환경에서 사진을 활용한 비즈니스 모델을 제안하고, 본 비즈니스 모델이 어떤 함의를 지니고 있는지를 분석하며 시장에서 실제 작동할 조건을 예측하여 본 비즈니스 모델을 평가한다.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.6
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• pp.526-533
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• 2016

The researches protecting user's location in location-based services(LBS) have received much attention. Especially k-anonymity is the most popular privacy preservation method. k-anonymization means that it selects k-1 other dummies or clients to make the cloaking region. This reduced the probability of the query issuer's location being exposed to untrusted parties to 1/k. But query's location may expose to adversary when k-1 dummies are concentrated in query's location or there is dummy in where query can not exist. Therefore, we proposed the dummy system model and algorithm taking the real environment into account to protect user's location privacy. And we proved the efficiency of our method in terms of experiment result.

• Journal of KIISE
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• v.41 no.12
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• pp.1136-1144
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• 2014

Twitter is a social network service that generates about 140 million contents a day. Contents of Twitter contain a variety of information and many researchers research those in various fields. In this research, we propose a method for evaluating the importance of content based on characteristics of Twitter. We have found that number of follower means user's popularity and Re-tweet that means the popularity of content. We perform experiments about proposed method using real Twitter data for proving effectiveness of proposed method. Also, we found information providers in Twitter are public user who represent a company or a representative of a specific group.

• Korean Journal of Child Education & Care
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• v.18 no.2
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• pp.33-48
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• 2018

The purpose of this study was to explore five-year-old popular child's behavior patterns through participant observation and to analyze the collected data with qualitative method. One popular child was selected from D child care center of P city in Kyeonggi province. Peer-nomination method was used to select the popular child. Participant observation was conducted from April to November in 2017 through 36 observations in free choice activities. As well as observation records, formal and informal interview records with parents and teachers, parent counseling journals, observation journals, and child's personal records were used to understand the popular child. Three steps, including recording, coding, and making themes, were conducted to analyze the data. The main results of the research were as follows: The behavior patterns of popular child in this study presented both positive and negative sides. The positive behavior patterns included consideration, responsibility, high concentration, and a sense of humor. The negative behavior patterns showed control and exclusion. This study has implications for development of educational program and environment to enhance positive peer relationship.

• Journal of The Korean Association of Information Education
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• v.26 no.3
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• pp.197-207
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• 2022

In this paper, we develop a machine learning based block code generation and recommendation model for the purpose of reducing cognitive load of learners during coding education that learns the learner's block that has been made in the block programming environment using natural processing model and fine-tuning and then generates and recommends the selectable blocks for the next step. To develop the model, the training dataset was produced by pre-processing 50 block codes that were on the popular block programming language web site 'Entry'. Also, after dividing the pre-processed blocks into training dataset, verification dataset and test dataset, we developed a model that generates block codes based on LSTM, Seq2Seq, and GPT-2 model. In the results of the performance evaluation of the developed model, GPT-2 showed a higher performance than the LSTM and Seq2Seq model in the BLEU and ROUGE scores which measure sentence similarity. The data results generated through the GPT-2 model, show that the performance was relatively similar in the BLEU and ROUGE scores except for the case where the number of blocks was 1 or 17.

• Journal of Digital Contents Society
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• v.13 no.1
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• pp.101-110
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• 2012

Recently, social network sites are very popular with the enhancement of mobile device function and distribution. This gives rise to the registrations of the people on the social network sites and the usage of services on the social sites is also getting active. However, social network sites' venders do not provide services enough compared to the demand of users' to share contents from diverse roots by users effectively. In addition, the personal information can be revealed improperly in processes sharing policies and it is obvious that it raises a privacy invasion problem when users access the contents created from diverse devices according to the relationship by policies. However, the existing methods for the integration management of social network are weak to solve this problem. Thus, we propose a model to preserve user privacy, categorize contents efficiently, and give the access control permissions at the same time. In this paper, we encrypt policies and the trusted third party classifies the encrypted policies when the social network sites share the generated contents by users. In addition, the proposed model uses the RCBAC model to manage the contents generated by various devices and measures the similarity between relationships after encrypting when the user policies are shared. So, this paper can contribute to preserve user policies and contents from malicious attackers.