• Proceedings of the Korea Contents Association Conference
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• 2019.05a
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• pp.11-12
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• 2019

IoT 및 SNS의 발달로 인해 관계를 표현하는 그래프 모델링 기법이 활용되고 있다. 실시간 스트림 그래프에서 유사한 모형의 그래프를 탐색하기 위한 근사 Top-k 서브 그래프 매칭에 대한 요구가 증가하고 있다. 본 논문에서는 그래프 스트림에서 간선의 유형 및 구조적 차이를 고려한 효율적인 근사 Top-k 서브 그래프 매칭 기법을 제안한다. 임계값 기반의 필터링과 스트림 환경에 맞는 연속 서브 그래프 매칭 구조를 제안함으로써 그래프 스트림에 적합한 질의 처리를 수행한다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2008.11a
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• pp.139-142
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• 2008

본 논문에서는 근사 인코딩 기법을 이용한 H.264/AVC 비디오 코덱에서의 비트율 제어 알고리즘을 제안한다. H.264는 기존의 동영상 압축 표준보다 월등한 압축 성능을 나타내지만, 구조적 복잡성으로 인해 비트율 제어 측면에서는 과거에 제안된 H.264를 위한 비트율 제어 알고리즘들의 성능은 기대에 미치지 못하였다. 제안된 알고리즘은 근사 인코딩 기법을 사용하여 실제 H.264 인코딩이 이루어지기 이전에 향후 발생될 인코딩 비트를 미리 예측할 수 있고, 비트율 제어에서 매우 높은 중요성을 차지하는 프레임의 복잡도 예측에서 우수한 성능을 나타낸다. 알고리즘의 연산량 측면에서도 제안된 근사 인코딩 기법은 간단한 구조로 이루어져 있어 장점을 나타낸다. 본 논문에서는 DCT 영역에서의 각 프레임의 zero의 개수를 분석하여 얻어낸 영상의 특성을 비트율 제어에 활용한다. 실험결과는 제안된 알고리즘이 H.264 레퍼런스 소프트웨어의 가장 최신 버전인 JM12.2 환경에서 기존의 알고리즘에 비해 우수한 성능을 나타낸다는 것을 알 수 있다.

• The Korean Journal of Applied Statistics
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• v.37 no.5
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• pp.663-673
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• 2024

The multi-armed bandits (MAB) problem, involves selecting actions to maximize rewards within dynamic environments. This study explores the application of Thompson sampling, a robust MAB algorithm, within the context of big data analytics and statistical learning theory. By leveraging large-scale banner click data from recommendation systems, we evaluate Thompson sampling's performance across various simulated scenarios, employing advanced approximation techniques. Our findings demonstrate that Thompson sampling, particularly with Langevin Monte Carlo approximation, maintains robust performance and scalability in big data environments. This underscores its practical significance and adaptability, aligning with contemporary challenges in statistical learning.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.6
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• pp.557-563
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• 2010

This paper addresses the issue of adaptive global optimization using Kriging metamodel known as EGO(Efficient Global Optimization). The algorithm adaptively chooses where to generate subsequent samples based on an explicit trade-off between reduction of global uncertainty and exploration of the region of the interest. A strategy that saves the computational cost by using expectations derived from probabilistic nature of approximate model is proposed. At every iteration, a candidate test point that seems to be feasible/inactive or has little possibility to improve for minimum is identified and excluded from updating approximate models. By doing that the computational cost is saved without loss of accuracy.

• The Journal of the Korea Contents Association
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• v.20 no.8
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• pp.42-53
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• 2020

With the development of social network services, graph structures have been utilized to represent relationships among objects in various applications. Recently, a demand of subgraph matching in real-time graph streams has been increased. Therefore, an efficient approximate Top-k subgraph matching scheme for low latency in real-time graph streams is required. In this paper, we propose an approximate Top-k subgraph matching scheme considering data reuse in graph stream environments. The proposed scheme utilizes the distributed stream processing platform, called Storm to handle a large amount of stream data. We also utilize an existing data reuse scheme to decrease stream processing costs. We propose a distance based summary indexing technique to generate Top-k subgraph matching results. The proposed summary indexing technique costs very low since it only stores distances among vertices that are selected in advance. Finally, we provide k subgraph matching results to users by performing an approximate Top-k matching on the summary indexing. In order to show the superiority of the proposed scheme, we conduct various performance evaluations in diverse real world datasets.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.3
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• pp.259-266
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• 2011

We present a new dual sequential approximate optimization (SAO) algorithm called SD-TDQAO (sequential dual two-point diagonal quadratic approximate optimization). This algorithm solves engineering optimization problems with a nonlinear objective and nonlinear inequality constraints. The two-point diagonal quadratic approximation (TDQA) was originally non-convex and inseparable quadratic approximation in the primal design variable space. To use the dual method, SD-TDQAO uses diagonal quadratic explicit separable approximation; this can easily ensure convexity and separability. An important feature is that the second-derivative terms of the quadratic approximation are approximated by TDQA, which uses only information on the function and the derivative values at two consecutive iteration points. The algorithm will be illustrated using mathematical and topological test problems, and its performance will be compared with that of the MMA algorithm.

• The Journal of the Acoustical Society of Korea
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• v.27 no.3
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• pp.149-153
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• 2008

General underwater target detection methods adopt short-time FFT for estimate target doppler. This paper proposes the efficient target detection method, instead of conventional FFT, using approximate FFT for distributed sensor network target detection, which requires lighter computations. In the proposed method, we decrease computational rate of FFT by the quantization of received signal. For validation of the proposed method, experiment result which is applied to FFT based active sonar detector and real oceanic data is presented.

• The Journal of the Acoustical Society of Korea
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• v.21 no.5
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• pp.494-502
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• 2002

In this paper, the Low Rank Approximation (LRA) method to suppress the interference of signals from temporal fluctuations is applied. The reverberation signals and temporally fluctuating signals are separated from the measured data using the Ink. The Singular value decomposition (SVD) method is applied to extract the low rank and the temporally stable reverberation was extracted using the LRA. The reverberation suppression is performed on the LRA residual value obtained by removing the approximate reverberation signals. In overall, the method can be applied to the suppression of reververation in active sonar system as well as to the modeling of reverberation.

• Proceedings of the KSME Conference
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• 2005.11a
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• pp.146.1-146.1
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• 2005

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.6
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• pp.3661-3666
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• 2015

The paper describes the integrated design system using MDO and approximation technique. In MDO related research, final target is an integrated and automated MDO framework systems. However, in order to construct the integrated design system, the prerequisite condition is how much save computational cost because of iterative process in optimization design and lots of data information in CAD/CAE integration. Therefore, this paper presents that an efficient approximation method, Adaptive approximation, is a competent strategy via MDO framework systems.