• Proceedings of the Korea Contents Association Conference
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• 2016.05a
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• pp.337-338
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• 2016

녹색기술정보포털 기사 정보와 회원정보를 이용하여 전체 녹색기술 분야에 대한 패스파인더 네트워크와 최근접 이웃 중심성을 도출하고 분야별 융합 동향을 분석하였다. 분석결과, 전체 녹색기술 분야 중에서 실리콘 태양전지 및 고효율 2차전지 등의 에너지 고효율화 분야에 대해 회원들의 관심이 높은 것으로 나타났으며, 실리콘 태양전지 분야에서 다른 녹색기술과의 융합이 가장 활발하게 이루어지는 것으로 분석되었다.

• The Journal of the Korea Contents Association
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• v.20 no.5
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• pp.1-10
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• 2020

In this paper, we propose a new pornographic content detection scheme using bi-directional relationships between neighboring auditory signals in order to accurately detect sound-centered obscene contents that are rapidly spreading via the Internet. To capture the bi-directional relationships between neighboring signals, we design a multilayered bi-directional dilated-causal convolution network by stacking several dilated-causal convolution blocks each of which performs bi-directional dilated-causal convolution operations. To verify the performance of the proposed scheme, we compare its accuracy to those of the previous two schemes each of which uses simple auditory feature vectors with a support vector machine and uses only the forward relationships in audio signals by a previous stack of dilated-causal convolution layers. As the results, the proposed scheme produces an accuracy of up to 84.38% that is superior performance up to 25.80% than other two comparison schemes.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.203-207
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• 2016

Drama CD는 One source multi use의 한 사례로써 다양한 콘텐츠 사업이 잘 연계되어 있는 일본에서는 이미 널리 활용되고 있는 콘텐츠이며 그에 따라 수익창출도 상당한 수준이다. 일본에 이웃한 한국에도 일본의 Drama CD가 영향을 미쳐, Drama CD가 발표되어왔고 일반 드라마 제작 이전의 테스트 베드로써 활용하는 수준까지 이르고 있다. 그러나 한국 콘텐츠 분야에서 Drama CD의 마켓 쉐어는 아직 그 입지가 크지 않으며, 또한 자체적인 잠재력에 비해 오락적 요소로서만 한정되어 활용되어 왔다. 본 논문에서는 Drama CD가 한국 시장에 One source multi use의 한 사례로써 Educational Content를 중심으로 가능성이 있는지에 대해 알아보고, 이와 함께 Drama CD를 활용할 수 있는 Educational Content 사업분야와 시장성에 대해 알아보고자 한다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.2
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• pp.249-256
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• 2022

Various studies are being actively conducted to show that the real-time visualization technology that combines BIM (Building Information Modeling) and AR (Augmented Reality) helps to increase construction management decision-making and processing efficiency. However, when large-capacity BIM data is projected into AR, there are various limitations such as data transmission and connection problems and the image cut-off issue. To improve the high efficiency of visualizing, a mesh optimization algorithm based on the k-nearest neighbors (KNN) classification framework to reconstruct BIM data is proposed in place of existing mesh optimization methods that are complicated and cannot adequately handle meshes with numerous boundaries of the 3D models. In the proposed algorithm, our target BIM model is optimized with the Unity C# code based on triangle centroid concepts and classified using the KNN. As a result, the algorithm can check the number of mesh vertices and triangles before and after optimization of the entire model and each structure. In addition, it is able to optimize the mesh vertices of the original model by approximately 56 % and the triangles by about 42 %. Moreover, compared to the original model, the optimized model shows no visual differences in the model elements and information, meaning that high-performance visualization can be expected when using AR devices.

• Korean Journal of Remote Sensing
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• v.30 no.6
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• pp.817-827
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• 2014

In the satellite remote sensing, the operational environment of the satellite sensor causes image degradation during the image acquisition. The degradation results in noise and blurring which badly affect identification and extraction of useful information in image data. This study proposes a maximum a posteriori (MAP) estimation using Point-Jacobian iteration to restore a degraded image. The proposed method assumes a Gaussian additive noise and Markov random field of spatial continuity. The proposed method employs a neighbor window of spoke type which is composed of 8 line windows at the 8 directions, and a boundary adjacency measure of Mahalanobis square distance between center and neighbor pixels. For the evaluation of the proposed method, a pixel-wise classification was used for simulation data using various patterns similar to the structure exhibited in high resolution imagery and an unsupervised segmentation for the remotely-sensed image data of 1 mspatial resolution observed over the north area of Anyang in Korean peninsula. The experimental results imply that it can improve analytical accuracy in the application of remote sensing high resolution imagery.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.4
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• pp.34-43
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• 2010

Texture information plays an important role in object recognition and classification. To perform an accurate classification, the texture feature used in the classification must be highly discriminative. This paper presents a novel texture descriptor for texture-based image retrieval and its application in Computer-Aided Diagnosis (CAD) system for Emphysema classification. The texture descriptor is based on the combination of local surrounding neighborhood difference and centralized neighborhood difference and is named as Combined Neighborhood Difference (CND). The local differences of surrounding neighborhood difference and centralized neighborhood difference between pixels are compared and converted into binary codewords. Then binomial factor is assigned to the codewords in order to convert them into high discriminative unique values. The distribution of these unique values is computed and used as the texture feature vectors. The texture classification accuracies using Outex and Brodatz dataset show that CND achieves an average of 92.5%, whereas LBP, LND and Gabor filter achieve 89.3%, 90.7% and 83.6%, respectively. The implementations of CND in the computer-aided diagnosis of Emphysema is also presented in this paper.

• Journal of Intelligence and Information Systems
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• v.15 no.1
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• pp.123-140
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• 2009

Collaborative filtering recommends products using customers' preferences, so it cannot recommend products to the new customer who has no preference information. This paper proposes a novel approach to new customer recommendations using the social network analysis which is used to search relationships among social entities such as genetics network, traffic network, organization network, etc. The proposed recommendation method identifies customers most likely to be neighbors to the new customer using the centrality theory in social network analysis and recommends products those customers have liked in the past. The procedure of our method is divided into four phases : purchase similarity analysis, social network construction, centrality-based neighborhood formation, and recommendation generation. To evaluate the effectiveness of our approach, we have conducted several experiments using a data set from a department store in Korea. Our method was compared with the best-seller-based method that uses the best-seller list to generate recommendations for the new customer. The experimental results show that our approach significantly outperforms the best-seller-based method as measured by F1-measure.

• Journal of the Korean Geographical Society
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• v.41 no.2 s.113
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• pp.188-194
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• 2006

Three-dimensional topological data is essential for 3D modeling and application such as emergency management and 3D network analysis. This paper reviewed current 3D topological data model and developed a method to construct 3D topological node-relation data structure from 2D computer aided design (CAD) data. The method needed two steps with medial axis-transformation and topological node-relation algorithms. Using a medial-axis transformation algorithm, the first step is to extract skeleton from wall data that was drawn polygon or double line in a CAD data. The second step is to build a topological node-relation structure by converting rooms to nodes and the relations between rooms to links. So, links represent adjacency and connectivity between nodes (rooms). As a result, with the conversion method 3D topological data for micro-level sub-unit of each building can be easily constructed from CAD data that are commonly used to design a building as a blueprint.

• The Journal of the Korea Contents Association
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• v.22 no.7
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• pp.46-54
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• 2022

The existing methods for determining influence in social networks do not accurately determine the influence of users because they do not delete or update existing relationships before they stop in the face of an increasing number of inactive users on social networks. In this paper, we propose a user influence detremination method using the temporal k-shell decomposition technique based on the creation date of users of social networks. To solve the problem of increasing influence of old users in social networks, we apply k-shell decomposition and age-specific order centrality as attenuation coefficients due to aging in neighbors. The age-decaying k-shell decomposition and age-specific order centrality are searched for influential users at the present time by applying the attenuation coefficient and age-dependent weights. Various performance evaluations are performed to show the superiority of the proposed method.

• Proceedings of the Korea Information Processing Society Conference
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• 2003.11b
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• pp.1165-1168
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• 2003

모바일 컴퓨팅 환경에서 이동성은 등록을 통해 이루어지고 있으며, 이음새 없는(seamless) 이동성을 제공하고자 많은 연구들이 진행되어 있으며, 그 중 방문하고자 하는 네트워크(Foreign Network)에 등록하는 시점을 언제로 하느냐에 따라 크게 다음의 2 가지 방법으로 대별된다. SIP-Registration은 핸드오프가 발생한 이후 등록(AAAF)을 함으로써 통화하는 중에 끊김(disruption)이나 지연(delay)이 발생한다. 이를 개선한 SIP-Shadow-Registration 방법은 핸드오프가 발생하기 이전에 이웃한 모든 노드들$(AAAF_n)$에게 모바일 노드(MN)의 관련 정보를 사전등록(Shadow Registration)하여 핸드오프 이후에 발생하는 끊김이나 지연을 방지하였다. 그러나 SIP-Shadow-Registration은 실제 사용하지 않는 n-2 개의 MN와 관련된 백본 네트워크에 불필요한 데이터 전달 및 관리라는 문제를 야기시킨다. 본 논문은 이러한 문제점들을 개선하고자 사전등록영역(SRR: Shadow Registration Region)을 구성하고 이의 알고리즘을 제안한다. 결과적으로 SIP 기반의 이동성이 필요할 때, 최소한의 사전등록영역을 통해 끊김이나 지연도 방지하고 추가적인 데이터 관리 문제도 해결할 수 있는 효과적인 방법이다.