• 한국전산유체공학회:학술대회논문집
• /
• 2009.11a
• /
• pp.49-55
• /
• 2009

This paper evaluates performances of a recently developed divergence-free finite element method based on Hermite interpolated stream functions. Velocity bases are derived from Hermite interpolated stream functions to form divergence-free basis functions. These velocity basis functions constitute a solenoidal function space, and the simple gradient of the Hermite functions constitute an irrotational function space. The incompressible Navier-Stokes equation is orthogonally decomposed into a solenoidal and an irrotational parts, and the decoupled Navier-Stokes equations are projected onto their corresponding spaces to form proper variational formulations. To access accuracy and convergence of the present algorithm, three test problems are selected. They are lid-driven cavity flow, flow over a backward-facing step and buoyancy-driven flow within a square enclosure. Hermite interpolation functions from cubic to quintic are chosen to run the test problems. Numerical results are shown. In all cases it has shown that the present method has performed well in accuracies and convergences. Moreover, the present method does not require an upwinding or a stabilized term.

• International Journal of Fluid Machinery and Systems
• /
• v.2 no.1
• /
• pp.1-12
• /
• 2009

Surrogate modeling is applied to a compressor blade shape optimization to modify its stacking line and thickness to enhance adiabatic efficiency and total pressure ratio. Six design variables are defined by parametric curves and three objectives; efficiency, total pressure and a combined objective of efficiency and total pressure are considered to enhance the performance of compressor blade. Latin hypercube sampling of design of experiments is used to generate 55 designs within design space constituted by the lower and upper limits of variables. Optimum designs are found by formulating a PRESS (predicted error sum of squares) based averaging (PBA) surrogate model with the help of a gradient based optimization algorithm. The optimum designs using the current variables show that, to optimize the performance of turbomachinery blade, the adiabatic efficiency objective is improved substantially while total pressure ratio objective is increased a very small amount. The multi-objective optimization shows that the efficiency can be increased with the less compensation of total pressure reduction or both objectives can be increased simultaneously.

• Proceedings of the IEEK Conference
• /
• 1998.10a
• /
• pp.1273-1276
• /
• 1998

The HMM-Net is an architecture for a neural network that implements a hidden Markov model (HMM). The architecture is developed for the purpose of combining the discriminant power of neural networks with the time-domain modeling capability of HMMs. Criteria used for learning HMM-Net classifiers are maximum likelihood (ML), maximum mutual information (MMI), and minimization of mean squared error(MMSE). In this paper we propose an efficient learning method of HMM-Net classifiers using hybrid criteria, ML/MMSE and MMI/MMSE, and report the results of an experimental study comparing the performance of HMM-Net classifiers trained by the gradient descent algorithm with the above criteria. Experimental results for the isolated numeric digits from /0/ to /9/ show that the performance of the proposed method is better than the others in the respects of learning and recognition rates.

• Proceedings of the IEEK Conference
• /
• 2000.09a
• /
• pp.639-642
• /
• 2000

본 논문에서는 구름이나 연기와 같은 non-rigid object에 대한 영역 분할 방식에 대해 연구하였다. Non-rigid object의 효과적인 영역 분할을 위해서 object의 윤곽선을 정확히 파악해 낼 수 있는 장점을 가진 watershed 알고리즘을 사용하였다. 하지만 이 알고리즘은 object가 많은 영역으로 분할되는 oversegmentation 현상이 발생하여 본 논문에서는 pre, post-processing을 통해 이 oversegmentation 현상을 극복하고자 하였다. Pre-processing에서는 noise를 제거하고 영상을 단순화하면서 정확한 gradient magnitude를 구할 수 있는 방법에 대해서, post-processing에서는 통계적인 분석을 통한 region merging을 이용하여 object를 최적화 상태로 찾아줄 수 있는 방법에 대하여 연구하였다.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.33B no.3
• /
• pp.96-108
• /
• 1996

In this paper, we propose an object-oriented coding method in low bit-rate channels using block-based motion vectors and residual image compensation. First, we use a 2-stage algorithm for estimating motion parameters. In the first stage, coarse motion parameters are estimated by fitting block-based motion vectors and in the second stage, the estimated motion parametes are refined by the gradient method using an image reconstructed by motion vectors detected in the first stage. Local error of a 6-parameter model is compensted by blockwise motion parameter correction using residual image. Finally, model failure (MF) region is reconstructed by a fractal mapping method. Computer simulation resutls show that the proposed method gives better performance than the conventional ones in terms of th epeak signal to noise ratio (PSNR) and compression ratio (CR).

• Journal of the Korean Institute of Telematics and Electronics S
• /
• v.34S no.5
• /
• pp.52-65
• /
• 1997

For the degradation of severe noise and ill-conditioned blur the optimization function has the solution spaces which have many local optima around global solution. General restoration methods such as inverse filtering or gradient methods are mainly dependent on the properties of degradation model and tend to be isolated into a local optima because their convergences are determined in the convex space. Hence we introduce genetic algorithm as a searching method which will search solutions beyond the convex spaces including local solutins. In this paper we introudce improved evaluation square error) and fitness value for gray scaled images. Finally we also proposed the local fine tunign of window size and visit number for delicate searching mechanism in the vicinity of th global solution. Through the experiental results we verified the effectiveness of the proposed genetic operators and evaluation function on noise reduction over the conventional ones, as well as the improved performance of local fine tuning.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2005.11a
• /
• pp.238-241
• /
• 2005

최근 컴퓨터를 이용하여 효과적인 트레이드를 하려는 투자자들이 늘고 있다. 본 논문에서는 많은 인공지능 방법론 중에서 강화학습(reinforcement learning)을 이용하여 효과적으로 트레이딩하는 방법에 대해서 다루려한다. 특히 강화학습 중에서 natural policy gradient를 이용하여 actor의 파라미터를 업데이트하고, value function을 효과적으로 추정하기 위해 RLS(recursive least-squares) 기법으로 critic 부분을 업데이트하는 RLS 기반 natural actor-critic 알고리즘을 이용하여 트레이딩을 수행하는 전략에 대한 가능성을 살펴 보기로 한다.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.11 no.4
• /
• pp.267-274
• /
• 2011

Recently, actor-critic methods have drawn significant interests in the area of reinforcement learning, and several algorithms have been studied along the line of the actor-critic strategy. In this paper, we consider a new type of actor-critic algorithms employing the kernel methods, which have recently shown to be very effective tools in the various fields of machine learning, and have performed investigations on combining the actor-critic strategy together with kernel methods. More specifically, this paper studies actor-critic algorithms utilizing the kernel-based least-squares estimation and policy gradient, and in its critic's part, the study uses a sliding-window-based kernel least-squares method, which leads to a fast and efficient value-function-estimation in a nonparametric setting. The applicability of the considered algorithms is illustrated via a robot locomotion problem and a tunnel ventilation control problem.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2016.06a
• /
• pp.306-308
• /
• 2016

신호등 검출은 지능형 교통 시스템에서 매우 중요하며 최근 신호등 검출 관련한 연구가 활발히 진행 중이다. 하지만 기존의 신호등검출 알고리듬의 문제점은 조명의 변화에 민감하다는 문제점이 있다. 이러한 문제점을 해결하기 위하여 본 논문에서는 다음과 같은 신호등 검출 알고리듬을 제안한다. 먼저 제안하는 색상지도와 HSV(Hue-Saturation-Value)를 이용하여 신호등의 후보를 검출한다. 검출한 신호등의 후보로부터 HOG(Histogram of Oriented Gradient) 서술자를 이용하여 특징을 추출한 다음 최종적으로 선형 SVM(Support Vector Machine)을 이용하여 신호등을 검출하는 알고리듬을 제안한다.

• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.23 no.4
• /
• pp.372-378
• /
• 2013

An acoustical shape optimization problem is formulated for optimal design of a perforated reactive muffler with offset inlet/outlet. The mean transmission loss value in a target frequency range is maximized for an allowed pressure drop value between an inlet and an outlet. Partitions in the chamber are divided into several sub-partitions, whose lengths are selected as design variables. Each sub-partition has the same number of holes, whose sizes are equal. A finite element model is employed for acoustical and flow analyses. A gradient-based optimization algorithm is used to obtain an optimal muffler. The acoustical and fluidic characteristics of the optimal muffler are compared with those of a reference muffler. Validation experiment is carried out to support the effectiveness of our suggested method.