• Journal of Digital Contents Society
• /
• v.13 no.3
• /
• pp.271-278
• /
• 2012

Recently, infrared (IR)-based depth sensors have proliferated as consumer electronics thanks to decreased price, which led to various applications including gesture recognition in television virtual studios. However, the depth sensors fail to capture depth information correctly under strong light conditions emitting infrared light which are very common in television studios. This paper analyzes the mechanism of such interference between the depth sensors relying on certain IR frequencies and infrared light emitting sources, and provides methods to get correct depth information by applying filters. Also, it describes experiment methods and presents the results of applying multiple combinations of filters with different cut-off frequencies. Finally, it proves that the interference due to IR can be filtered out using proposed filtering method practically by experiment.

• Journal of Digital Contents Society
• /
• v.8 no.4
• /
• pp.521-529
• /
• 2007

In this paper, we introduce a music recommendation system that automatically recommends music according to users' musical tastes. The recommendation system uses a graph-based collaborating filtering in which similarities between musics are saved as a graph, and so it can perform fast recommendation based on the implicit preference information. It also has capability of recommending music according to users' dynamically changing preferences as well as users' static preferences. The recommendation server is implemented as an independent server using Java, and communicates with clients according to a specified protocol. A demo web site has been built by using the server and music download data from actual users, and the accuracy of recommendation has been measured through experiments.

• Korean Chemical Engineering Research
• /
• v.62 no.1
• /
• pp.87-98
• /
• 2024

This study was conducted to investigate the efficiency of the filtering sets composed of fiberboards, which were fabricated with lignocellulosic fiber and cork oak bark-based activated carbon (COA), as well as traditional Korean paper handmade from mulberry trees (KP) for the filtration of PM, TVOC and HCHO. Three-layers fiberboards (WRF) were fabricated with wood fiber in its surface layers and recycled fiber/COA in its core layer using a protein-based adhesive with the resin content of 8%. Filtering sets were composed of three WRF and one sheet of KP. Concentrations of PM, TVOC and HCHO generated with the combustion of a incense in a sealed laboratory hood were reduced efficiently with the operation of air-purifier installed the filtering sets. Except for the WRF fabricated with 4%/4% resin contents, other WRF were prepared with 5%/3% and 6%/2% resin contents in surface/core layers, and then the WRF were used with KP for the fabrication of filtering sets. Filtration efficiency of the filtering sets was improved as the core-layer resin content applied in the fabrication of WRF decreased. In addition, filtration efficiency of the WRF-based filtering set fabricated with KP of 25 g/m² basis weight was higher than that with KP of 45 g/m² basis weight. Filtering sets composed of three-layers fiberboards (RWF) that recycled fiber and wood fiber/COA were used in its surface and core layers, respectively, and KP-25g showed higher filtration efficiency than those of WRF-based filtering sets. Air-inhalation equipment installed the RWF-based, WRF-based filtering sets and without filtering set were operated in small indoor and large outdoor spaces. Efficiency for filtering PM and TVOC of the RWF-based filtering sets was higher than that of other filtering sets. It is concluded that fiberboard-based filtering sets composed of RWF and KP-25g can be used as a filter for reducing the concentrations of PM and TVOC existed in indoor and outdoor spaces.

• International Journal of Internet, Broadcasting and Communication
• /
• v.7 no.2
• /
• pp.56-64
• /
• 2015

The personal image information compiled through closed-circuit television (CCTV) will be open to the internet with the technology such as Long-Tail, Mash-Up, Collective Intelligence, Tagging, Open Application Programming Interface (Open-API), Syndication, Podcasting and Asynchronous JavaScript and XML (AJAX). The movie User Created Contents (UCC) connected to the internet with the skill of web 2.0 has the effects of abuse and threat without precedent. The purpose of this research is to develop the institutional and technological method to reduce these effects. As a result of this research, in terms of technology this paper suggests Privacy Zone Masking, IP Filtering, Intrusion-detection System (IDS), Secure Sockets Layer (SSL), public key infrastructure (PKI), Hash and PDF Socket. While in terms of management this paper suggests Privacy Commons and Privacy Zone. Based on CCTV-UCC linked to the above network, the research regarding personal image information security is expected to aid in realizing insight and practical personal image information as a specific device in the following research.

• The Journal of the Korea Contents Association
• /
• v.11 no.12
• /
• pp.52-59
• /
• 2011

In this paper, we propose an indexing method to support efficient similarity search for sequence databases. We present a new distance measurement called minimum DTW distance to enhance the filtering effects. The minimum DTW distance is to measure the minimum distance between a sequence data and the group of similar sequences. It enables similarity search through hierarchical index structure by filtering sequence databases. Finally, we show the superiority of our method through some experiments.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2019.01a
• /
• pp.437-438
• /
• 2019

스마트폰을 중심으로 한 모바일 기기의 보급과 온라인 소셜 네트워크 서비스의 이용자들이 증가하면서 사용자들은 많은 콘텐츠를 소비하고 공유한다. 이는 콘텐츠 사용자들의 개별적 기호에 맞지 않거나 만족도가 떨어지는 콘텐츠를 소비하게 한다. 이와 같은 문제를 해결하기 위해 소셜 네트워크 사용자에게 적합한 콘텐츠를 추천하기 위한 기법에 대한 연구가 활발하게 진행되고 있다. 본 논문에서는 온라인 상에 존재하는 다양한 정보 중에서 공연과 관련한 콘텐츠들을 중심으로 사용자 성향별로 추천을 해줄 수 있는 협업필터링 방법에 대하여 제안한다.

• Journal of KIISE:Software and Applications
• /
• v.32 no.9
• /
• pp.901-911
• /
• 2005

With the development of e-commerce and the proliferation of easily accessible information, information filtering has become a popular technique to prune large information spaces so that users are directed toward those items that best meet their needs and preferences. While many collaborative filtering systems have succeeded in capturing the similarities among users or items based on ratings to provide good recommendations, there are still some challenges for them to be more efficient, especially the user bias problem, non-transitive association problem and cold start problem. Those three problems impede us to capture more accurate similarities among users or items. In this paper, we provide probabilistic model approaches for UCHM and ICHM which are suggested to solve the addressed problems in hopes of achieving better performance. In this probabilistic model, objects (users or items) are classified into groups and predictions are made for users considering the Gaussian distribution of user ratings. Experiments on a real-word data set illustrate that our proposed approach is comparable with others.

• Journal of the Korean Society for information Management
• /
• v.23 no.3 s.61
• /
• pp.91-125
• /
• 2006

Recent advancements in information technology and the Internet have caused an explosive increase in the information available and the means to distribute it. However, such information overflow has made the efficient and accurate search of information a difficulty for most users. To solve this problem, an information retrieval and filtering system was developed as an important tool for users. Libraries and information centers have been in the forefront to provide customized services to satisfy the user's information needs under the changing information environment of today. The aim of this study is to propose an efficient information service for libraries and information centers to provide a personalized recommendation system to the user. The proposed method overcomes the weaknesses of existing systems, by providing a personalized hybrid recommendation method for multimedia contents that works in a large-scaled data and user environment. The system based on the proposed hybrid method uses an effective framework to combine Association Rule with Collaborative Filtering Method.

• Proceedings of the Korea Contents Association Conference
• /
• 2009.05a
• /
• pp.787-791
• /
• 2009

In this paper, we proposed the reservoir tank wireless integrated management using information filtering for improving the water quality and on-line managing efforts of reservoir tanks. Reservoir tank level sensor works the pump sending the data from reservoir tank control to the wireless control on sensing water level. At this time, every kind data which happens in the water tank transmits the line transmission modem. The data to be received from the line transmission modem is stored at the database after we record the logs by each hour. The proposed method defined the context and environment of the reservoir tank and predicted the profited service according to the pump motion, the solar battery, the chemicals, the water level, the line, and the modem using information filtering. we plan to conduct the proposed method to verify the adequacy and the validity of reservoir tank wireless integrated management using the information filtering.

• The Journal of Society for e-Business Studies
• /
• v.14 no.4
• /
• pp.47-57
• /
• 2009

Collaborative tagging systems allow users to attach tags to diverse sharable contents in social networks. These tags provide usefulness in reusing the contents for all community members as well as their creators. Three-dimensional data composed of users, items, and tags are used in the collaborative tag-based recommendation. They are generally more voluminous and sparse than two-dimensional data composed of users and items. Therefore, there are many difficulties in applying existing collaborative filtering methods directly to them. Latent factor models, which are also successful in the area of collaborative filtering recently, discover latent features(factors) for explaining observed values and solve problems based on the features. However, establishing the models require much time and efforts. In order to apply the latent factor models to three-dimensional collaborative filtering data, we have to overcome the difficulty of establishing them. This paper proposes various methods for determining preferences of users to items via establishing an intuitive model by assuming tags used for items as latent factors to users and items respectively. They are compared using real data for concluding desirable directions.