• Communications for Statistical Applications and Methods
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• 제29권5호
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• pp.615-627
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• 2022

Principal Fitted Component (PFC) is a semi-parametric sufficient dimension reduction (SDR) method, which is originally proposed in Cook (2007). According to Cook (2007), the PFC has a connection with other usual non-parametric SDR methods. The connection is limited to sliced inverse regression (Li, 1991) and ordinary least squares. Since there is no direct comparison between the two approaches in various forward regressions up to date, a practical guidance between the two approaches is necessary for usual statistical practitioners. To fill this practical necessity, in this paper, we newly derive a connection of the PFC to covariance methods (Yin and Cook, 2002), which is one of the most popular SDR methods. Also, intensive numerical studies have done closely to examine and compare the estimation performances of the semi- and non-parametric SDR methods for various forward regressions. The founding from the numerical studies are confirmed in a real data example.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1994년도 Proceedings of the Korea Automatic Control Conference, 9th (KACC) ; Taejeon, Korea; 17-20 Oct. 1994
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• pp.13-20
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• 1994

The spectrum estimation methods of random processes are expressed in this paper. Beginning with the basic theory, non-parametric and parametric methods are overviewed. As to non-parametric method, numerical calculation method is also discussed. As to parametric method, AR model is a very famous and effective model representing random process. Estimation methods of AR parameters which have been proposed are mentioned here. Wavelet analysis is a recently interested technique in signal processing. An application of wavelet analysis is also shown.

• 융합신호처리학회논문지
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• 제20권4호
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• pp.193-198
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• 2019

인터넷상에서의 진짜 및 가짜 정보의 범람이 수많은 텍스트 분석에 대한 연구를 이끌었다. 문헌 표기 없이 타인의 저작물을 무단 복제 및 관련 없는 연구결과 조작 등이 한동안 세간의 주목을 이끌었다. 연구 분야에서 표절과 이의 대항 및 감소를 위해 다양한 도구들이 개발되었다. Pearson Spearman 본 연구에서는 코사인 유사성과 및 상관관계를 이용하는 파라미터 및 비 파라미터 방법을 이용하여 문장 유사성을 측정한다. Pearson 코사인 유사성과 상관관계는 가장 높은 유사성 계수를 얻었으나 Spearman 상관관계는 낮은 유사성 계수를 보여주었다. 본 논문에서는 정상성 가정과 편향성에 의존하는 파라미터 방법들에 반하도록 비정상성 가정으로 인한 문장 유사도를 측정하는 데 있어 비 파라미터 방법들을 사용하는 것을 제안한다.

• 한국멀티미디어학회논문지
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• 제19권9호
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• pp.1659-1668
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• 2016

Understanding 3D structure of scenes is of a great interest in various vision-related tasks. In this paper, we present a unified approach for estimating depth from a single monocular image. The key idea of our approach is to take advantages both of parametric learning and non-parametric sampling method. Using a parametric convolutional network, our approach learns the relation of various monocular cues, which make a coarse global prediction. We also leverage the local prediction to refine the global prediction. It is practically estimated in a non-parametric framework. The integration of local and global predictions is accomplished by concatenating the feature maps of the global prediction with those from local ones. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods both qualitatively and quantitatively.

• 한국CDE학회논문집
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• 제1권1호
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• pp.45-55
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• 1996

This paper presents a new approach to non-parametric modeling of cutter swept surface (CSS) for cutting simulation. Instead of explicitly modeling cutter swept volumes, silhouette curves of the cutter surface are utilized in computing the z-value of the CSS at a grid point on the x,y-plane. The non-parametric evaluation of the CSS constitutes the integral part of 3-axis cutting simulation. The proposed method is more efficient than the existing ones in the case of conventional cutters (i.e., ball-end mills and flat-end mills), and more importantly, it enables the non-parametric modeling of the CSS for the round-end mills which was not possible with the existing methods.

• Communications for Statistical Applications and Methods
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• 제22권2호
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• pp.181-199
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• 2015

In a short history, RNA-seq data have established a revolutionary tool to directly decode various scenarios occurring on whole genome-wide expression profiles in regards with differential expression at gene, transcript, isoform, and exon specific quantification, genetic and genomic mutations, and etc. RNA-seq technique has been rapidly replacing arrays with seq-based platform experimental settings by revealing a couple of advantages such as identification of alternative splicing and allelic specific expression. The remarkable characteristics of high-throughput large-scale expression profile in RNA-seq are lied on expression levels of read counts, structure of correlated samples and genes, larger number of genes compared to sample size, different sampling rates, inevitable systematic RNA-seq biases, and etc. In this study, we will comprehensively review how robust Bayesian and non-parametric methods have a better performance than classical statistical approaches by explicitly incorporating such intrinsic RNA-seq specific features with flexible and more appropriate assumptions and distributions in practice.

• 대한수의학회지
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• 제47권2호
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• pp.259-263
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• 2007

The choice of input distribution in quantitative risk assessments modeling is of great importance to get unbiased overall estimates, although it is difficult to characterize them in situations where data available are too sparse or small. The present study is particularly concerned with accommodation of uncertainties commonly encountered in the practice of modeling. The authors applied parametric and non-parametric bootstrap simulation methods which consist of re-sampling with replacement, in together with the classical Student-t statistics based on the normal distribution. The implications of these methods were demonstrated through an empirical analysis of trade volume from the amount of chicken and pork meat imported to Korea during the period of 1998-2005. The results of bootstrap method were comparable to the classical techniques, indicating that bootstrap can be an alternative approach in a specific context of trade volume. We also illustrated on what extent the bias corrected and accelerated non-parametric bootstrap method produces different estimate of interest, as compared by non-parametric bootstrap method.

• 산업진흥연구
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• 제7권4호
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• pp.1-8
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• 2022

우리 주위를 살펴보면 한 번 사용하고 버리는 일회용품이 많다. 폭죽이나 탄약과 같은 일회용품이 대표적인데 이들 일회용품은 제조 후 한 동안 저장되어 있다가 필요한 때 사용하고 나면 폐기처분하게 된다. 하지만 이런 일회용품은 일반 운영장비와 달리 신뢰성평가가 제대로 이루어지지 못했다. 이에 본 연구에서는 일회용품 중에서 탄약에 대한 저장탄약신뢰성프로그램을 통해 탄약의 경우 신뢰성 확보를 위해 정부에서 어떤 일을 하는지 먼저 살펴본다. 이어서 통계분석적인 측면에서 탄약과 같은 일회용품에 대한 신뢰성분석 방안으로 어떤 것이 있는지 알아본다. 구체적으로 통계학에서 로트의 품질수준을 파악하는 샘플링검사를 활용하여 일정한 시기에 생산된 탄약에 대한 신뢰성의 수준을 파악할 수 있다. 본 연구에서는 KS Q0001인 계수규준형 1회 샘플링검사표를 이용할 수 있음으로 보여준다. 다음으로 탄약의 저장신뢰도를 파악할 수 있는 방법으로 비모수적인 방법과 모수적인 방법을 소개한다. 비모수적인 방법중에서 특히 Kaplan-Meier 방법은 중도중단데이터가 포함된 경우에도 활용될 수 있다. 마지막으로 모수적인 방법 중에는 신뢰성분석에 많이 활용되는 와이블분포가 탄약의 저장신뢰도를 파악하는 데에도 활용될 수 있다.

• Journal of Communications and Networks
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• 제18권3호
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• pp.397-410
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• 2016

This paper proposes a novel cooperative localization method for distributed wireless networks in 3-dimensional (3D) global positioning system (GPS) denied environments. The proposed method, which is referred to as hybrid ellipsoidal variational algorithm (HEVA), combines the use of non-parametric belief propagation (NBP) and variational Bayes (VB) to benefit from both the use of the rich information in NBP and compact communication size of a parametric form. InHEVA, two novel filters are also employed. The first one mitigates non-line-of-sight (NLoS) time-of-arrival (ToA) messages, permitting it to work well in high noise environments with NLoS bias while the second one decreases the number of calculations. Simulation results illustrate that HEVA significantly outperforms traditional NBP methods in localization while requires only 50% of their complexity. The superiority of VB over other clustering techniques is also shown.

• International Journal of Contents
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• 제4권2호
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• pp.24-28
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• 2008

In this paper, we propose a new approach for content-based video retrieval using non-parametric based motion classification in the shot-based video indexing structure. Our system proposed in this paper has supported the real-time video retrieval using spatio-temporal feature comparison by measuring the similarity between visual features and between motion features, respectively, after extracting representative frame and non-parametric motion information from shot-based video clips segmented by scene change detection method. The extraction of non-parametric based motion features, after the normalized motion vectors are created from an MPEG-compressed stream, is effectively fulfilled by discretizing each normalized motion vector into various angle bins, and by considering the mean, variance, and direction of motion vectors in these bins. To obtain visual feature in representative frame, we use the edge-based spatial descriptor. Experimental results show that our approach is superior to conventional methods with regard to the performance for video indexing and retrieval.