• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.955-957
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• 2010

P2P VoD 스트리밍에서 비디오 서비스를 제공받는 사용자의 시청 패턴은 비디오의 시작부터 끝까지 순차시청 모델에 기초하고 있다. 최근 비디오 재생지점의 임의 변경을 허용하는 VCR 기능과 같은 사용자 interactivity 지원과 재생지점 변경 시 지연을 최소화 하는 연구가 활발하게 진행되어 왔다. 본 논문에서는 VCR 동작 시 오버레이 상에서 필요로 하는 컨텐츠를 소유한 피어를 신속하게 찾고 비디오 구간별 인기도에 근거하여 prefetching 을 지원하는 시스템을 제안한다. 비디오의 균등한 구간에서의 인기도를 측정하여 인기구간과 비인기구간에 대해서 동적으로 앵커 피어들을 링크드 리스트로 오버레이를 구성한다.

• Investigative Magnetic Resonance Imaging
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• v.26 no.1
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• pp.76-81
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• 2022

The number of women undergoing breast augmentation surgery with a prosthesis for cosmetic purposes or reconstruction after a mastectomy is steadily increasing. Hematoma is one of complications associated with breast augmentation surgery. It usually occurs early in the postoperative period. It rarely occurs late (after six months). However, chronic hematomas after prosthesis removal have not yet been reported in the radiological literature. We present a case of unilateral chronic organizing hematoma that developed late and grew persistently over long period after breast explantation, mimicking a soft tissue tumor of the chest wall clinically. Meanwhile, characteristic magnetic resonance imaging features of heterogeneous signal intensities on T1-weighted and T2-weighted images and dark signal intensity with a persistent enhancement of the peripheral wall of the lesion were found. These can be used for a differential diagnosis.

• Advances in aircraft and spacecraft science
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• v.10 no.6
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• pp.545-554
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• 2023

The widespread introduction of unmanned aircrafts has led to the understanding of the need to organize a common information space for manned and unmanned aircrafts, which is reflected in the Russian Unmanned aircraft system Traffic Management (RUTM) project. The present article deals with the issues of spatial information database (DB) organization, which is the core of RUTM and provides storage of various data types (spatial, aeronautical, topographical, meteorological, vector, etc.) required for flight safety management. Based on the analysis of functional capabilities and types of work which it needs to ensure, the architecture of spatial information DB, including the base of source information, base of display settings, base of vector objects, base of tile packages and also a number of special software packages was proposed. The issues of organization of these DB, types and formats of data and ways of their display are considered in detail. Based on the analysis it was concluded that the optimal construction of the spatial DB for RUTM system requires a combination of different model variants and ways of organizing data structures.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.09a
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• pp.818-819
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• 2005

• Proceedings of the Korea Water Resources Association Conference
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• 2005.09b
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• pp.1110-1111
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• 2005

• Proceedings of the Optical Society of Korea Conference
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• 1991.07a
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• pp.47-53
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• 1991

광굴절결정을 이용한 자기형성 시스템을 설명하고 특성 추출을 의한 패턴분류를 할 수 있음을 실험적으로 보였다.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.11a
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• pp.305-306
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• 2010

• Proceedings of the KWS Conference
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• 1992.10a
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• pp.96-99
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• 1992

No Abstract, See Full Text

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.2
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• pp.312-320
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• 2012

In this study, we introduce a new design methodology of a granular-oriented self-organizing polynomial neural networks (GoSOPNNs) that is based on multi-layer perceptron with Context-based Polynomial Neurons (CPNs) or Polynomial Neurons (PNs). In contrast to the typical architectures encountered in polynomial neural networks (PNN), our main objective is to develop a methodological design strategy of GoSOPNNs as follows : (a) The 1st layer of the proposed network consists of Context-based Polynomial Neuron (CPN). In here, CPN is fully reflective of the structure encountered in numeric data which are granulated with the aid of Context-based Fuzzy C-Means (C-FCM) clustering method. The context-based clustering supporting the design of information granules is completed in the space of the input data while the build of the clusters is guided by a collection of some predefined fuzzy sets (so-called contexts) defined in the output space. (b) The proposed design procedure being applied at each layer of GoSOPNN leads to the selection of preferred nodes of the network (CPNs or PNs) whose local characteristics (such as the number of contexts, the number of clusters, a collection of the specific subset of input variables, and the order of the polynomial) can be easily adjusted. These options contribute to the flexibility as well as simplicity and compactness of the resulting architecture of the network. For the evaluation of performance of the proposed GoSOPNN network, we describe a detailed characteristic of the proposed model using a well-known learning machine data(Automobile Miles Per Gallon Data, Boston Housing Data, Medical Image System Data).

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.1
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• pp.40-49
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• 2004

In this paper, we propose a new architecture of Genetic Algorithms(GAs)-based Self-Organizing Polynomial Neural Networks(SOPNN), discuss a comprehensive design methodology and carry out a series of numeric experiments. The conventional SOPNN is based on the extended Group Method of Data Handling(GMDH) method and utilized the polynomial order (viz. linear, quadratic, and modified quadratic) as well as the number of node inputs fixed (selected in advance by designer) at Polynomial Neurons (or nodes) located in each layer through a growth process of the network. Moreover it does not guarantee that the SOPNN generated through learning has the optimal network architecture. But the proposed GA-based SOPNN enable the architecture to be a structurally more optimized network, and to be much more flexible and preferable neural network than the conventional SOPNN. In order to generate the structurally optimized SOPNN, GA-based design procedure at each stage (layer) of SOPNN leads to the selection of preferred nodes (or PNs) with optimal parameters- such as the number of input variables, input variables, and the order of the polynomial-available within SOPNN. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization (predictive) abilities of the model. A detailed design procedure is discussed in detail. To evaluate the performance of the GA-based SOPNN, the model is experimented with using two time series data (gas furnace and NOx emission process data of gas turbine power plant). A comparative analysis shows that the proposed GA-based SOPNN is model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.