• 품질경영학회지
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• 제42권4호
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• pp.607-616
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• 2014

Purpose: This paper reviews popular measurement system indices and proposes a procedure for assessing a measurement system using two parameters with intraclass correlation and a factor for process capability. Methods: Gage Repeatability and Reproducibility(GR&R), precision-to-tolerance ratio(PTR), number of distinct categories, producer's and consumer's risks are employed to assess the measurement capabilities and discuss the relationships between measurement system metrics. Results: Two-dimensional plot by two parameters is presented to assess adequacy of the measurement system and process capability. A numerical example and previously studied case study are provided for illustration. Conclusion: The procedure proposed in this paper using two-dimension parameters provides a valuable procedure and helpful guidelines to quality and production managers in assessing the capabilities of a measurement system and choosing the needed actions to be the most benefit.

• Archives of Plastic Surgery
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• 제39권6호
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• pp.637-642
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• 2012

Background Fingertip injuries involving subtotal or total loss of the digital pulp are common types of hand injuries and require reconstruction that is able to provide stable padding and sensory recovery. There are various techniques used for reconstruction of fingertip injuries, but the most effective method is functionally and aesthetically controversial. Despite some disadvantages, cross-finger pulp flap is a relatively simple procedure without significant complications or requiring special techniques. Methods This study included 90 patients with fingertip defects who underwent cross-finger pulp flap between September 1998 and March 2010. In 69 cases, neurorrhaphy was performed between the pulp branch from the proper digital nerve and the recipient's sensory nerve for good sensibility of the injured fingertip. In order to evaluate the outcome of our surgical method, we observed two-point discrimination in the early (3 months) and late (12 to 40 months) postoperative periods. Results Most of the cases had cosmetically and functionally acceptable outcomes. The average defect size was $1.7{\times}1.5$ cm. Sensory return began 3 months after flap application. The two-point discrimination was measured at 4.6 mm (range, 3 to 6 mm) in our method and 7.2 mm (range, 4 to 9 mm) in non-innervated cross-finger pulp flaps. Conclusions The innervated cross-finger pulp flap is a safe and reliable procedure for lateral oblique, volar oblique, and transverse fingertip amputations. Our procedure is simple to perform under local anesthesia, and is able to provide both mechanical stability and sensory recovery. We recommend this method for reconstruction of fingertip injuries.

• 대한원격탐사학회지
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• 제20권4호
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• pp.235-244
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• 2004

본 연구는 SPOT-4 위성의 VEGETATION-1 센서의 가시 채널, 근적외 채널, 단파 적외채널 자료를 이용하여 눈과 구름을 구별하기 위해 새롭게 제시된 알고리즘을 평가하기 위한 것이다. 눈과 구름의 마스크를 위해 전통적으로 이용되고 있는 임계치 방법들은 본 연구에서 좋은 결과를 보여 주지 못하였다 따라서 K-means 군집화 방법이 이러한 임계치 방법 대신 본 연구에서 사용되었다. 군집화에서는 두 임계치 알고리즘을 통합하여 적설과 구름을 그룹화 시켜 동시에 추출한 화소들을 적용하였다. 이것은 전체 영상을 군집화에 적용시킬 때와 비교해 군집화의 과정을 단순화시키고 나아가 정확도를 향상시킬 수 있다. 본 연구는 이러한 과정을 통해 얻어진 결과를 임계치 방법이 적용되었을 때의 결과와 비교함과 동시에 VEGETATION 자료의 분별능력을 평가하였다. 본 연구에서 제시한 방법을 이용하였을 때, 구름과 눈의 분별 능력은 상당히 향상되었다. 분별 오차는 임계치 방법을 사용하였을 때 보다 구름에 대해 19.4% 적설에 대해 9.7% 정도 감소하였다.

• Journal of the Korean Statistical Society
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• 제23권1호
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• pp.63-78
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• 1994

For discrimination with binary variables, reformulated full and first order Bahadur model with incomplete observations are presented. This allows prior probabilities associated with multiple population to be estimated for the sample-based classification rule. The EM algorithm is adopted to provided the maximum likelihood estimates of the parameters of interest. Some experiences with the models are evaluated and discussed.

• Journal of the Korean Data and Information Science Society
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• 제15권3호
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• pp.555-563
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• 2004

In classification and discrimination, we often face with incomplete group variable arising typically from many missing values and/or incredible cases. This paper suggests the use of K-means clustering for pre-adjusting incompleteness and in turn classification based on generalized statistical distance is performed. For illustrating the proposed procedure, simulation study is conducted comparatively with CART in data mining and traditional techniques which are ignoring incompleteness of group variable. Simulation study manifests that our methodology out-performs.

• 한국지진공학회:학술대회논문집
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• 한국지진공학회 2003년도 춘계 학술발표회논문집
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• pp.12-15
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• 2003

Korea Institute of Geoscience and Mineral Resources(KIGAM) is operating Wonju Korea Seismic Research Station(KSRS), 29 regional seismic research stations and 5 Korea-China joint seismic stations in China. Also KIGAM is operating Korea Earthquake Monitoring System (KEMS) to archive the real-time data stream and to determine event parameters (epicenter, origin time, and magnitude) by the automatic processing and analyst review. To do this, KEMS used KIGAM's regional seismic network and other institute's network in a near real-time base. From Dec. 1, 2001 to Nov. 30, 2002, 3,827 seismic events were analyzed in a automatic processing procedure and finally 3,437 events were analyzed by analyst and archived. But problem is this event catalog includes not only natural earthquake, but also artificial events produced by the blast. More than 80 % events were concentrated in daytime and many events were concentrated in the confirmed blast sites, Pyeongyang, Pocheon, Yeongjong-do, Donghae city, etc. Because these artificial events are a major potential cause of error when estimating the seismicity of a specific region, discrimination procedure has to be developed in the first place.

• Communications for Statistical Applications and Methods
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• 제22권6호
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• pp.575-587
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• 2015

The development in data collection techniques results in high dimensional data sets, where discrimination is an important and commonly encountered problem that are crucial to resolve when high dimensional data is heterogeneous (non-common variance covariance structure for classes). An example of this is to classify microbial habitat preferences based on codon/bi-codon usage. Habitat preference is important to study for evolutionary genetic relationships and may help industry produce specific enzymes. Most classification procedures assume homogeneity (common variance covariance structure for all classes), which is not guaranteed in most high dimensional data sets. We have introduced regularized elimination in partial least square coupled with QDA (rePLS-QDA) for the parsimonious variable selection and classification of high dimensional heterogeneous data sets based on recently introduced regularized elimination for variable selection in partial least square (rePLS) and heterogeneous classification procedure quadratic discriminant analysis (QDA). A comparison of proposed and existing methods is conducted over the simulated data set; in addition, the proposed procedure is implemented to classify microbial habitat preferences by their codon/bi-codon usage. Five bacterial habitats (Aquatic, Host Associated, Multiple, Specialized and Terrestrial) are modeled. The classification accuracy of each habitat is satisfactory and ranges from 89.1% to 100% on test data. Interesting codon/bi-codons usage, their mutual interactions influential for respective habitat preference are identified. The proposed method also produced results that concurred with known biological characteristics that will help researchers better understand divergence of species.

• 대한의용생체공학회:의공학회지
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• 제30권2호
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• pp.169-178
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• 2009

In this study, we suggest Korean nonlinear fitting formula (KNFF) to maximize speech intelligibility for digital hearing aids based on NAL-NL1 (NAL-nonlinear, version 1). KNFF was derived from the same procedure which is used for deriving NAL-NL1. KNFF consider the long-term average speech spectrum of Korean instead of English because the frequency characteristic of Korean is different from that of English. New insertion gains of KNFF were derived using the SII (speech intelligibility index) program provided by ANSI. In addition, the insertion gains were modified to maximize the intelligibility of high frequency words. To verify effect of the new fitting gain, we performed speech discrimination test (SDT) and preference test using the hearing loss simulator from NOISH. In the SDT, a word set as test material consists of 50 1-syllable word generally used in hearing clinic. As a result of the test, in case of moderate hearing loss with severe loss on high frequency, the SDT scores of KNFF was more improved about 3.2% than NAL-NLl and about 6% in case of the sever hearing loss. Finally we have obtained the result that it was the effective way to increase gain of mid-high frequency bands and to decrease gain of low frequency bands in order to maximize speech intelligibility of Korean.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권3호
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• pp.1243-1263
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• 2018

The two-stage linear discrimination analysis (TSLDA) is a feature extraction technique to solve the small size sample problem in the field of image recognition. The TSLDA has retained all subspace information of the between-class scatter and within-class scatter. However, the feature information in the four subspaces may not be entirely beneficial for classification, and the regularization procedure for eliminating singular metrics in TSLDA has higher time complexity. In order to address these drawbacks, this paper proposes an improved two-stage linear discriminant analysis (Improved TSLDA). The Improved TSLDA proposes a selection and compression method to extract superior feature information from the four subspaces to constitute optimal projection space, where it defines a single Fisher criterion to measure the importance of single feature vector. Meanwhile, Improved TSLDA also applies an approximation matrix method to eliminate the singular matrices and reduce its time complexity. This paper presents comparative experiments on five face databases and one handwritten digit database to validate the effectiveness of the Improved TSLDA.

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 1996년도 International Conference on Agricultural Machinery Engineering Proceedings
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• pp.823-833
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• 1996

An ultimate purpose of this study is to develop an automatic brown rice quality inspection system using image processing technique. In this study emphasis was put on developing an algorithm for discriminating the brown rice kernels depending on their external quality with a color image processing system equipped with an adaptor for magnifying the input image and optical fiber for oblique illumination. Primarily , geometrical and optical features of sample images were analyzed with unhulled paddy and various brown rice kernel samples such as sound, cracked, green-transparent , green-opaque, colored, white-opaque and brokens. Secondary, an algorithm for discrimination of the rice kernels in static state was developed on the basis of the geometrical and optical parameters screened by a statistical analysis(STEPWISE and DISCRIM Procedure, SAS ver.6). Brown rice samples could be discriminated by the algorithm developed in this study with an accuracy of 90% to 96% for the sound , cracked, colored, broken and unhulled , about 81% for the green-transparent and the white-opaque and about 75% for the green-opaque, respectively. A total computing time required for classification was about 100 seconds/1000 kernels with the PC 80486-DX2, 66MHz.