• The Korean Journal of Applied Statistics
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• v.22 no.6
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• pp.1277-1287
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• 2009

For stratifying skewed populations, the Lavall$\acute{e}$e-Hidiroglou(LH) algorithm is often considered to have a take-all stratum with the largest units and some take-some strata with the middle-size and small units. Related to its iterative nature have been reported some numerical difficulties such as the dependency of the ultimate stratum boundaries to a choice of initial boundaries and the slow convergence to locally-optimum boundaries. The geometric stratification has been recently proposed to provide initial boundaries that can avoid such numerical difficulties in implementing the LH algorithm. Since the geometric stratification does not pursuit the optimization but the equalization of the stratum CVs, the corresponding stratum boundaries may not be (near) optimal. This paper revisits these issues concerning convergence and near-optimality of optimal stratification algorithms using artificial numerical examples. We also discuss the formation of the strata and the sample allocation under the optimization process and some aspects related to discontinuity arisen from the finiteness of both population and sample as well.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.334-336
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• 2005

According as quality of life improves, pursuit of agreeable iife became realistic problem. Specially, noise had been appraised to element that infiuence in human life directly and indirectly Therefore, necessity of study about noise control is increased for better labor conditions and agreeable habitat. In this paper, implementation of active mufflers using recursive LMS algorithms is presented. Analyze exhaust pipe noise of a gasoline and Diesel car and use adaptation IIR filter algorithm that stability is solidified and controled exhaust pipe noise of a car. computer simulation is performed to show the effectiveness of a proposed algorithm.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.40 no.11
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• pp.979-987
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• 2012

This paper implements and integrates algorithms for terrain following of UCAVs (Unmanned Combat Aerial Vehicles): trajectory generation, guidance, and navigation. Terrain following is very important for UCAVs because they perform very dangerous missions such as Suppression of Enemy Air Defences while the terrain following can improve the survivability of UCAVs against from the air defence systems of the enemy. To deal with the GPS jamming, terrain referenced navigation based on nonlinear filter is chosen. For the trajectory generation, Voronoi diagram is adopted to generate horizontal plane path to avoid the air defense system. Cubic spline method is used to generate vertical plane path to prevent collisions with ground while flying sufficiently close to surface. Follow-the-Carrot and pure pursuit tracking methods, which are look-ahead point based guidance algorithms, are applied for the guidance. Numerical simulation is performed to verify the performance of the integrated terrain following algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.7
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• pp.424-430
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• 2014

In this paper, we propose a compressed sensing-based signal recovery technique for an uplink multi-user spatial modulation (MU-SM) system. In the MU-SM system, only one antenna among $N_t$ antennas of each user becomes active by nature. Thus, this characteristics is exploited for signal recovery at a base station. We modify the conventional orthogonal matching pursuit (OMP) algorithm which has been widely used for sparse signal recovery in literature for the MU-SM system, which is called MU-OMP. We also propose a parallel OMP algorithm for the MU-SM system, which is called MU-POMP. Specifically, in the proposed algorithms, antenna indices of a specific user who was selected in the previous iteration are excluded in the next iteration of the OMP algorithm. Simulation results show that the proposed algorithms outperform the conventional OMP algorithm in the MU-SM system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.12
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• pp.756-763
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• 2014

In this paper, we introduce a new sparse signal recovery algorithm referred to as the matching pursuit with greedy tree search (GTMP). The tree search in our proposed method is implemented to minimize the cost function to improve the recovery performance of sparse signals. In addition, a pruning strategy is employed to each node of the tree for efficient implementation. In our performance guarantee analysis, we provide the condition that ensures the exact identification of the nonzero locations. Through empirical simulations, we show that GTMP is effective for sparse signal reconstruction and outperforms conventional sparse recovery algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.5
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• pp.2346-2362
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• 2016

As one of the most important issues in computer vision and image processing, online object tracking plays a key role in numerous areas of research and in many real applications. In this study, we present a novel tracking method based on the proposed structured sparse representation model, in which the tracked object is assumed to be sparsely represented by a set of object and background templates. The contributions of this work are threefold. First, the structure information of all the candidate samples is utilized by a joint sparse representation model, where the representation coefficients of these candidates are promoted to share the same sparse patterns. This representation model can be effectively solved by the simultaneous orthogonal matching pursuit method. In addition, we develop a tracking algorithm based on the proposed representation model, a discriminative candidate selection scheme, and a simple model updating method. Finally, we conduct numerous experiments on several challenging video clips to evaluate the proposed tracker in comparison with various state-of-the-art tracking algorithms. Both qualitative and quantitative evaluations on a number of challenging video clips show that our tracker achieves better performance than the other state-of-the-art methods.

• The Journal of the Acoustical Society of Korea
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• v.37 no.1
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• pp.39-45
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• 2018

Compressive sensing allows recovering an original signal which has a small dimension of the signal compared to the dimension of the entire signal in a short period of time through a small number of observations. In this paper, we proposed a method for detecting tonal signal which caused by the machinery component of a vessel such as an engine, gearbox, and support elements. The tonal signal can be modeled as the sparse signal in the frequency domain when it compares to whole spectrum range. Thus, the target tonal signal can be estimated by S-OMP (Simultaneous-Orthogonal Matching Pursuit) which is one of the sparse signal recovery algorithms. In simulation section, we showed that S-OMP algorithm estimated more precise frequencies than the conventional FFT (Fast Fourier Transform) thresholding algorithm in low SNR (Signal to Noise Ratio) region.

• Journal of the Korea Society for Simulation
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• v.19 no.4
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• pp.209-218
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• 2010

The aim of the study on the tactical object representation techniques in synthetic environment is on acquiring fundamental techniques for detection and tracking of tactical objects, and evaluating the strategic situation in the virtual ground. In order to acquire these techniques, there need the tactical objects' position tracking and evaluation, and an inter-sharing technique between tactical models. In this paper, we study the algorithms on the sensor data fusion and coordinate conversion, proportional navigation guidance(PNG), and pursuit-evasion technique for engineering and higher level models. Additionally, we simulate the position evaluation of tractical objects using the pursuit and evasion maneuvers between a submarine and a torpedo.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.5A
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• pp.717-725
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• 2000

Within the last several years, there has been a large number of algorithms developed for face recognition. The majority of these algorithms have been view- and projection-based algorithms. Our definition of projection is not restricted to projecting the image onto an orthogonal basis the definition is expansive and includes a general class of linear transformation of the image pixel values. The class includes correlation, principal component analysis, clustering, gray scale projection, and matching pursuit filters. In this paper, we perform a detailed analysis of this class of algorithms by evaluating them on the FERET database of facial images. In our experiments, a projection-based algorithms consists of three steps. The first step is done off-line and determines the new basis for the images. The bases is either set by the algorithm designer or is learned from a training set. The last two steps are on-line and perform the recognition. The second step projects an image onto the new basis and the third step recognizes a face in an with a nearest neighbor classifier. The classification is performed in the projection space. Most evaluation methods report algorithm performance on a single gallery. This does not fully capture algorithm performance. In our study, we construct set of independent galleries. This allows us to see how individual algorithm performance varies over different galleries. In addition, we report on the relative performance of the algorithms over the different galleries.

• Communications for Statistical Applications and Methods
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• v.3 no.3
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• pp.113-123
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• 1996

Data-dependent (adaptive) choice of asymptotically efficient score functions for rank estimators and M-estimators of regression parameters in a linear regression model with left-truncated and right-censored data are developed herein. The locally adaptive smoothing techniques of Muller and Wang (1990) and Uzunogullari and Wang (1992) provide good estimates of the hazard function h and its derivative h' from left-truncated and right-censored data. However, since we need to estimate h'/h for the asymptotically optimal choice of score functions, the naive estimator, which is just a ratio of estimated h' and h, turns out to have a few drawbacks. An altermative method to overcome these shortcomings and also to speed up the algorithms is developed. In particular, we use a subroutine of the PPR (Projection Pursuit Regression) method coded by Friedman and Stuetzle (1981) to find the nonparametric derivative of log(h) for the problem of estimating h'/h.