• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.2
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• pp.87-95
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• 2002

In this paper, we proposed a fast multi-resolution motion estimation(MRME) algorithm. This algorithm exploits the half-pixel accuracy motion estimation(HPAME) for exact motion vectors in the baseband and block classification for the reduction of bit amounts and computational loads. Generally, as the motion vector in the baseband are used as initial motion vector in the high frequency subbands, it has crucial effect on quality of the motion compensated image. For this reason, we exploit HPAME in the motion estimation for the baseband. But HPAME requires additional bit and computational loads so that we use block classification for the selective motion estimation in the high frequency subbands to compensate these problems. In result, we could reduce the bit rate and computational load at the similar image quality with conventional MRME. The superiority of the proposed algorithm was confirmed by the computer simulation.

• The Korean Journal of Applied Statistics
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• v.34 no.6
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• pp.957-968
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• 2021

Inverse censoring probability weighting (ICPW) is a popular technique in survival data analysis. In applications of the ICPW technique such as the censored regression, it is crucial to accurately estimate the censoring probability. A simulation study is undertaken in this article to see how censoring probability estimate influences model performance in censored regression using the ICPW scheme. We compare three censoring probability estimators, including Kaplan-Meier (KM) estimator, Cox proportional hazard model estimator, and local KM estimator. For the local KM estimator, we propose to reduce the predictor dimension to avoid the curse of dimensionality and consider two popular dimension reduction tools: principal component analysis and sliced inverse regression. Finally, we found that the Cox proportional hazard model estimator shows the best performance as a censoring probability estimator in both mean and median censored regressions.

• The Korean Journal of Applied Statistics
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• v.36 no.4
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• pp.323-335
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• 2023

In causal analysis of high dimensional data, it is important to reduce the dimension of covariates and transform them appropriately to control confounders that affect treatment and potential outcomes. The augmented inverse probability weighting (AIPW) method is mainly used for estimation of average treatment effect (ATE). AIPW estimator can be obtained by using estimated propensity score and outcome model. ATE estimator can be inconsistent or have large asymptotic variance when using estimated propensity score and outcome model obtained by parametric methods that includes all covariates, especially for high dimensional data. For this reason, an ATE estimation using an appropriate dimension reduction method and semiparametric model for high dimensional data is attracting attention. Semiparametric method or sparse sufficient dimensionality reduction method can be uesd for dimension reduction for the estimation of propensity score and outcome model. Recently, another method has been proposed that does not use propensity score and outcome regression. After reducing dimension of covariates, ATE estimation can be performed using matching. Among the studies on ATE estimation methods for high dimensional data, four recently proposed studies will be introduced, and how to interpret the estimated ATE will be discussed.

• The Journal of the Acoustical Society of Korea
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• v.21 no.6
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• pp.552-558
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• 2002

Recently, based on the human speech recognition (HSR) model of Fletcher, the multi-band speech recognition has been intensively studied by many researchers. As a new automatic speech recognition (ASR) technique, the multi-band speech recognition splits the frequency domain into several sub-bands and recognizes each sub-band independently. The likelihood scores of sub-bands are weighted according to reliabilities of sub-bands and re-combined to make a final decision. This approach is known to be robust under noisy environments. When the noise is stationary a sub-band SNR can be estimated using the noise information in non-speech interval. However, if the noise is non-stationary it is not feasible to obtain the sub-band SNR. This paper proposes the inverse sub-band distance (ISD) weighting, where a distance of each sub-band is calculated by a stochastic matching of input feature vectors and hidden Markov models. The inverse distance is used as a sub-band weight. Experiments on 1500∼1800㎐ band-limited white noise and classical guitar sound revealed that the proposed method could represent the sub-band reliability effectively and improve the performance under both stationary and non-stationary band-limited noise environments.

• Journal of the Institute of Convergence Signal Processing
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• v.4 no.3
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• pp.1-6
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• 2003

It is difficult to detect and to track the moving targets like tanks and diesel vehicles due to the variety of terrain and moving of targets. It is possible to be happened the aliasing conditions as the difficulty of antenna deployment in the complex environment like the battle fields. In this paper, we study the problem of detecting and tracking of moving targets which are emitting wideband signals under severe spatial aliasing conditions because of the sparse arrays. We developed a direction of arrival(DOA) estimation algorithm based on subband MUSIC(Multiple Signal Classification) method which produces high-resolution estimation. In this algorithm, the true bearings are invariant regardless of changes of frequency bands while the aliased false bearings vary. As a result, the proposed algorithm overcomes the aliasing effects and improves the localization performance in sparse passive arrays.

• The Proceeding of the Korean Institute of Electromagnetic Engineering and Science
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• v.27 no.6
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• pp.13-19
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• 2016

• Transactions of the Korean Society of Mechanical Engineers B
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• v.34 no.7
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• pp.679-688
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• 2010

The major objective of the present study is to extend the applications of inverse analysis to more realistic engineering fields with a complex combustion process rather than the traditional simple heat-transfer problems. In order to do this, the unknown initial mass fractions of $CH_4/O_2$ are estimated from the temperature measurement data by inverse analysis in the practical diffusion-controlled turbulent combustion problem. In order to ensure efficient inverse analysis, the repulsive particle swarm optimization (RPSO) method, which belongs to the class of stochastic evolutionary global optimization methods, is implemented as an inverse solver. Based on this study, it is expected that useful information can be obtained when inverse analysis is used in the diagnosis, design, or optimization of real combustion systems involving unknown parameters.

• Proceedings of the KSR Conference
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• 2007.11a
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• pp.734-743
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• 2007

The KTX station is one of special generators that produce a lot of trips caused by special land use such as university, airport, and super shopping mall. Special generators need special attention in developing travel demand models since the standard trip generation and distribution model in the conventional four-step approach do not provide reliable estimates of their travel patterns. New modeling approach, activity-based model, considering travel behavior of person, seem to be more appropriate for those special generators. Thus, this study introduces a spatial-temporal activity-based approach and how activity-based approach can be applied to estimation of travel demand at KTX stations.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.38 no.9
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• pp.747-755
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• 2014

The heat transfer mechanism for radiation is directly related to the emission of photons and electromagnetic waves. Depending on the participation of the medium, the radiation can be classified into two forms: surface and gas radiation. In the present study, unknown radiation properties were estimated using an inverse boundary analysis of surface radiation in an axisymmetric cylindrical enclosure. For efficiency, a repulsive particle swarm optimization (RPSO) algorithm, which is a relatively recent heuristic search method, was used as inverse solver. By comparing the convergence rates and accuracies with the results of a genetic algorithm (GA), the performances of the proposed RPSO algorithm as an inverse solver was verified when applied to the inverse analysis of the surface radiation problem.

• Proceedings of the Korean Statistical Society Conference
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• 2002.05a
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• pp.7-12
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• 2002

경제활동인구조사에서 시군구의 실업자를 추정하는데 소지역 추정법을 이용하는 방안에 대한 연구는 관심의 대상이 되고 있다. 본 연구에서는 합성 추정법과 복합 추정법을 이용한 시군구 실업통계 작성법을 소개하였고 추정량이 편향이므로 잭나이프 방법을 이용한 추정량의 정도를 계산하는 절차를 설명하였으며, 광주광역시의 구별 실업통계작성을 사례로 제시하였다.