• 한국통신학회논문지
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• 제38A권2호
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• pp.174-182
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• 2013

Most methods for fusion-based finger vein recognition were to fuse different features or matching scores from more than one trait to improve performance. To overcome the shortcomings of "the curse of dimensionality" and additional running time in feature extraction, in this paper, we propose a finger vein recognition technology based on matching score-level fusion of a single trait. To enhance the quality of finger vein image, the contrast-limited adaptive histogram equalization (CLAHE) method is utilized and it improves the local contrast of normalized image after ROI detection. Gabor features are then extracted from eight channels based on a bank of Gabor filters. Instead of using the features for the recognition directly, we analyze the contributions of Gabor feature from each channel and apply a weighted matching score-level fusion rule to get the final matching score, which will be used for the last recognition. Experimental results demonstrate the CLAHE method is effective to enhance the finger vein image quality and the proposed matching score-level fusion shows better recognition performance.

• 한국군사과학기술학회지
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• 제23권4호
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• pp.337-345
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• 2020

The Weapon-Target Assignment(WTA) problem can be formulated as an optimization problem that minimize the threat of targets. Existing methods consider the trade-off between optimality and execution time to meet the various mission objectives. We propose a multi-agent reinforcement learning algorithm for WTA based on mean field game to solve the problem in real-time with nearly optimal accuracy. Mean field game is a recent method introduced to relieve the curse of dimensionality in multi-agent learning algorithm. In addition, previous reinforcement learning models for WTA generally do not consider weapon interference, which may be critical in real world operations. Therefore, we modify the reward function to discourage the crossing of weapon trajectories. The feasibility of the proposed method was verified through simulation of a WTA problem with multiple targets in realtime and the proposed algorithm can assign the weapons to all targets without crossing trajectories of weapons.

• International Journal of Naval Architecture and Ocean Engineering
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• 제13권1호
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• pp.115-125
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• 2021

Simulation-based hull form optimization is a typical HEB (high-dimensional, expensive computationally, black-box) problem. Conventional optimization algorithms easily fall into the "curse of dimensionality" when dealing with HEB problems. A recently proposed Cross-Entropy (CE) optimization algorithm is an advanced stochastic optimization algorithm based on a probability model, which has the potential to deal with high-dimensional optimization problems. Currently, the CE algorithm is still in the theoretical research stage and rarely applied to actual engineering optimization. One reason is that the Monte Carlo (MC) method is used to estimate the high-dimensional integrals in parameter update, leading to a large sample size. This paper proposes an improved CE algorithm based on quasi-Monte Carlo (QMC) estimation using high-dimensional truncated Sobol subsequence, referred to as the QMC-CE algorithm. The optimization performance of the proposed algorithm is better than that of the original CE algorithm. With a set of identical control parameters, the tests on six standard test functions and a hull form optimization problem show that the proposed algorithm not only has faster convergence but can also apply to complex simulation optimization problems.

• 국제학술발표논문집
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• The 6th International Conference on Construction Engineering and Project Management
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• pp.559-563
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• 2015

Identification of energy use patterns in buildings has a great opportunity for energy saving. To find what energy use patterns exist, clustering analysis has been commonly used such as K-means and hierarchical clustering method. In case of high dimensional data such as energy use time-series, data reduction should be considered to avoid the curse of dimensionality. Principle Component Analysis, Autocorrelation Function, Discrete Fourier Transform and Discrete Wavelet Transform have been widely used to map the original data into the lower dimensional spaces. However, there still remains an ongoing issue since the performance of clustering analysis is dependent on data type, purpose and application. Therefore, we need to understand which data reduction techniques are suitable for energy use management. This research aims find the best clustering method using energy use data obtained from Seoul National University campus. The results of this research show that most experiments with data reduction techniques have a better performance. Also, the results obtained helps facility managers optimally control energy systems such as HVAC to reduce energy use in buildings.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.2617-2622
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• 2003

The pH neutralization process has long been taken as a representative benchmark problem of nonlinear chemical process control due to its nonlinearity and time-varying nature. For general nonlinear processes, it is difficult to control with a linear model-based control method so nonlinear controls must be considered. Among the numerous approaches suggested, the most rigorous approach is the dynamic optimization. However, as the size of the problem grows, the dynamic programming approach is suffered from the curse of dimensionality. In order to avoid this problem, the Neuro-Dynamic Programming (NDP) approach was proposed by Bertsekas and Tsitsiklis (1996). The NDP approach is to utilize all the data collected to generate an approximation of optimal cost-to-go function which was used to find the optimal input movement in real time control. The approximation could be any type of function such as polynomials, neural networks and etc. In this study, an algorithm using NDP approach was applied to a pH neutralization process to investigate the feasibility of the NDP algorithm and to deepen the understanding of the basic characteristics of this algorithm. As the global approximator, the neural network which requires training and k-nearest neighbor method which requires querying instead of training are investigated. The global approximator requires optimal control strategy. If the optimal control strategy is not available, suboptimal control strategy can be used even though the laborious Bellman iterations are necessary. For pH neutralization process it is rather easy to devise an optimal control strategy. Thus, we used an optimal control strategy and did not perform the Bellman iteration. Also, the effects of constraints on control moves are studied. From the simulations, the NDP method outperforms the conventional PID control.

• 한국융합학회논문지
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• 제7권4호
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• pp.1-7
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• 2016

구조적 MRI 영상은 여러 단 변량과 다변량 방법을 위해 그레이 메터 (GM), 화이트 메터 (WM), 뇌척수액 (CSF) 세션화 과정을 하고 난후 형태계측학적 특징을 추출하기 위해 사용한다. 새로운 접근 방법은 매우 가벼운 알츠하이머 병에서 가벼운 알츠하이머병의 진단을 위해 적용된다. 간이정신상태검사에 따른 형태계측학적 특징과 가우시안 복합 모델 파라미터를 결합하여 정상인으로부터 알츠하이머 병 환자로 분류하는 방법을 제안한다. 결합한 특징은 주성분 분석 기법을 이용한 고차원의 저주를 제거한 후 다중 커널 SVM 분류기에 공급한다. 제안한 진단 방법의 실험적 결과는 90%이상의 특성도와 고민감도에 따라 다중 커널 SVM을 가진 층화 정확도가 96%까지 최대 산출한다.

• 대한산업공학회지
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• 제37권1호
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• pp.64-73
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• 2011

Oil price prediction is an important issue for the regulators of the government and the related industries. When employing the time series techniques for prediction, however, it becomes difficult and challenging since the behavior of the series of oil prices is dominated by quantitatively unexplained irregular external factors, e.g., supply- or demand-side shocks, political conflicts specific to events in the Middle East, and direct or indirect influences from other global economical indices, etc. Identifying and quantifying the relationship between oil price and those external factors may provide more relevant prediction than attempting to unclose the underlying structure of the series itself. Technically, this implies the prediction is to be based on the vectoral data on the degrees of the relationship rather than the series data. This paper proposes a novel method for time series prediction of using Semi-Supervised Learning that was originally designed only for the vector types of data. First, several time series of oil prices and other economical indices are transformed into the multiple dimensional vectors by the various types of technical indicators and the diverse combination of the indicator-specific hyper-parameters. Then, to avoid the curse of dimensionality and redundancy among the dimensions, the wellknown feature extraction techniques, PCA and NLPCA, are employed. With the extracted features, a timepointspecific similarity matrix of oil prices and other economical indices is built and finally, Semi-Supervised Learning generates one-timepoint-ahead prediction. The series of crude oil prices of West Texas Intermediate (WTI) was used to verify the proposed method, and the experiments showed promising results : 0.86 of the average AUC.

• 한국전자통신학회논문지
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• 제8권6호
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• pp.899-905
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• 2013

본 논문은 복수카메라 기반 실시간 비행체 검출 및 추적하는 방법에 대해서 설명한다. 정밀하게 가공된 보정체를 필요로 하지 않는 복수카메라 자기보정 기법에 스케일을 추가하여, 간편하게 각 카메라 내부변수와 카메라 사이의 상대위치 관계를 구하는 복수카메라 보정기법을 제시한다. 비행체 검출 및 추적은 파티컬 필터링 기법을 적용하여 수행하는데, 적은수의 샘플로도 비행체 검출을 빠르고 정확하게 할 수 있도록 하는 Ray-based Importance Sampling을 고안했다. 3차원 공간을 일정한 크기의 격자구조로 나누고, 영상 특징점과 사영기하학을 이용하여 이 격자구조 위에 비행체의 이산적인 분포를 구한다. 이 분포에 따라 격자를 샘플링하고, 또 다시, 격자의 중심을 평균으로 하는 가우시안 분포로부터 비행체의 위치를 샘플링 한다. 이 두 단계의 샘플링을 통해 비행체가 있을 가능성이 높은 영역에 샘플을 집중적으로 분포시킬 수가 있다. 그리고, 복수의 카메라 영상으로부터 실시간으로 동기화된 영상 특징점을 검출하기 위하여 GPGPU를 이용한 병렬 영상처리 시스템을 구현하였다. 실험을 통해 제안한 방법의 유효성을 확인할 수 있었다.

• 한국컴퓨터산업학회논문지
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• 제4권12호
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• pp.887-900
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• 2003

데이터베이스의 많은 응용분야에서 대용량 고차원 데이터의 클러스터링을 요구하고 있다. 이에 따라 클러스터링 알고리즘에 대한 많은 연구가 이루어지고 있으나 기존의 알고리즘들은 “차원의 저주”에 기인하여 고차원 공간에서 효과적 및 효율적으로 수행하지 못하는 경향이 있다. 더욱이, 고차원 데이터는 상당한 양의 잡음 데이터를 포함하고 있으므로 알고리즘의 효과성 문제를 야기한다. 그러므로 고차원 데이터의 구조와 다양한 특성을 지원하는 적합한 클러스터링 알고리즘이 개발되어야 한다. 본 논문에서는 지금까지 연구된 고차원 클러스터링 기법을 조사한 후, 각 기법의 장단점과 적합한 응용 분야에 대한 비교 및 분석을 통하여 분류한다. 특히 본 논문에서는 최근의 연구를 통하여 개발한 점진적 프로젝션 기반의 클러스터링 알고리즘인 CLIP의 성능을 기존의 알고리즘과 비교 분석함으로써 그 효율성 및 효과성을 입증한다. 이러한 알리즘들의 소개 및 분류를 통하여 향후의 더욱 향상된 클러스터링 알고리즘 개발에 기반이 되고자 한다.

• 한국정보과학회논문지:데이타베이스
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• 제32권5호
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• pp.509-525
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• 2005

본 논문에서는 다차원 데이타의 유사도 검색을 효율적으로 지원하기 위한 벡터 근사 기반의 색인 구조를 제안한다. 제안하는 색인 구조는 공간 분할 방식으로 영역을 분할하고 실제 데이타들이 존재하는 영역에 대해 동적 비트를 할당하여 영역을 표현한다. 따라서, 분할된 영역들 사이에 겹침이 발생하지 않으며 하나의 중간 노드에 많은 영역 정보를 저장할 수 있어 트리의 깊이를 감소시킨다. 또한, 특정 영역에 군집화되어 있는 데이타에 대해서 효과적인 표현 기법을 제공하며 자식 노드의 영역 정보는 부모 노드의 영역 정보를 이용하여 상대적으로 표현함으로써 영역 표현에 대한 정확성을 보장한다. 이를 통해 검색성능 향상을 제공한다. 제안하는 색인 구조의 우수성을 보이기 위해 기존에 제안된 다차원 색인 구조와의 다양한 실험을 통하여 성능의 우수성을 입증한다. 성능 평가 결과를 통해 제안하는 색인 구조가 기존 색인 구조보다 $40\%$정도 검색 성능이 향상됨을 증명한다.