• Agricultural and Biosystems Engineering
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• 제5권1호
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• pp.25-29
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• 2004

Fluorescence spectroscopy was used to characterize chicken carcass diseases. Spectral signatures of three different disease categories of poultry carcasses (airsacculitis, cadaver and septicemia) were obtained from fluorescence emission measurements in the wavelength range of 360 to 600 nm with 330 nm excitation. Principal Component Analysis (PCA) was used to select the most significant wavelengths for the classification of poultry carcasses. These wavelengths were analyzed for pathologic correlation of poultry diseases. Using a Soft Independent Modeling of Class Analogy (SIMCA) of principal components with a Mahalanobis distance metric, poultry carcasses were individually classified into different classes with $97.9\%$ accuracy.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2011년도 하계학술대회
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• pp.261-263
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• 2011

이 논문에서는 쿼리-바이-싱잉/허밍 (Query-by-singing/humming, QbSH) 시스템에서의 거리 함수를 다양화하면서 그 성능 변화를 살펴본다. QbSH는 디지털 음악의 사용이 보편화되면서 음악 검색의 방법으로 많은 연구가 진행되어 왔으며, 많은 경우, dynamic time warping (DTW) 방법으로 사용해서 정합하고 있다. 그러나, DTW에서 사용하는 거리에 대해서는 특별한 관심을 가지지 않았으며, 일반적으로 절대적 차이값이나 그것의 제곱값을 많이 사용해 왔다. 이 논문에서는 여러 가지 거리에 대해서 성능을 측정하였다. 성능측정은 특정한 시스템에서 이루어진 것이기 때문에 일반성을 가지지 않을 수 있으나, DTW에서 사용하는 거리를 기존의 것과 다른 것으로 변화시켜서 성능을 향상시킬 가능성이 있음을 이 논문에서는 밝힌다. 본 논문에서는 10-12초 길이의 1000번의 쿼리 (Query)에 대해서 28시간 정도의 데이터베이스에서 실험한 결과, 논문에서 제안하는 거리가 기존의 절대적 차이값을 사용한 것보다 제1후보 검출 정확도가 10% 가량 상승함을 확인할 수 있었다.

• Journal of Multimedia Information System
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• 제4권4호
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• pp.189-194
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• 2017

In this study, we propose a method which is based on "Image segmentation by histogram thresholding using hierarchical cluster analysis"/HCA/ and "A nonparametric approach for histogram segmentation"/NHS/. HCA method uses that all histogram bins are one cluster then it reduces cluster numbers by using distance metric. Because this method has too many clusters, it is more computation. In order to eliminate disadvantages of "HCA" method, we used "NHS" method. NHS method finds all local minima of histogram. To reduce cluster number, we use NHS method which is fast. In our approach, we combine those two methods to eliminate disadvantages of Arifin method. The proposed method is not only less computational than "HCA" method because combined method has few clusters but also it uses local minima of histogram which is computed by "NHS".

• 한국CDE학회논문집
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• 제4권2호
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• pp.127-138
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• 1999

For reverse engineering, curves and surfaces are modeled for new products by interpolating the digitized data points. But there are many measuring or deviation errors. Therefore, it is important to handle errors during the curve or surface modeling. If the errors are ignored, designer could get undesirable results. For this reason, fairing procedure with the aesthetics criteria is necessary in computer modeling. This paper presents methods of 3D NURBS curve fairing. The techniques are based on automatic repositioning of the digitized dat points or the NURBS curve control points by a constrained nonlinear optimization algorithm. The objective function is derived variously by derived curved. Constraints are distance measures between the original and the modified digitized data points. Changes I curve shape are analyzed by illustrations of curve shapes, and continuous plotting of curvature and torsion.

• 대한임베디드공학회논문지
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• 제15권5호
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• pp.251-259
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• 2020

Ad hoc On-demand Distance Vector (AODV) protocol and its variants employ two important timers, hello and hold timer to keep track of topology changes. Moreover, hold timer is computed by multiplying constant value to hello timer. But, this configuration leads to inaccurate settings of hold timer. To solve this problem, in this paper, we propose a new dynamic adjustment of hello and hold timer scheme by removing dependency between them. A new metric to measure mobility is applied into hello timer, while expected link lifetime does holder timer. Simulation results show a significant reduction in the number of messages, a fact suggesting that it is possible to maintain and in some cases improve the performance of AODV with a minimum amount of messages released into the network.

• 산업경영시스템학회지
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• 제32권1호
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• pp.20-25
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• 2009

Mahalanobis Taguchi System (MTS) is a pattern information technology, which has been used in different diagnostic applications to make quantitative decisions by constructing a multivariate measurement scale using data analytic methods without any assumption regarding statistical distribution. The MTS performs Taguchi's fractional factorial design based on the Mahahlanobis Distance (MS) as a performance metric. In this work, MTS is used for analyzing Wisconsin Breast Cancer data which has ten attributes. Ten different tests are conducted for the data to determine if the patient has cancer or not. Also, MTS is used for reducing the number of test to define the relationship between each attribute and diagnosis result. The accuracy of diagnosis is compare with two different previous research.

• 한국원자력학회:학술대회논문집
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• 한국원자력학회 1996년도 추계학술발표회논문집(1)
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• pp.415-420
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• 1996

The main objective of the present study is to propose a new measure of uncertainty importance based on distributional sensitivity analysis. The new measure is developed to utilize a metric distance obtained from cumulative distribution functions (cdfs). The measure is evaluated for two cases: one is a cdf given by a known analytical distribution and the other given by an empirical distribution generated by a crude Monte Carlo simulation. To study its applicability, the present measure has been applied to two different cases. The results are compared with those of existing three methods. The present approach is a useful measure of uncertainty importance which is based on cdfs. This method is simple and easy to calculate uncertainty importance without any complex process. On the basis of the results obtained in the present work, the present method is recommended to be used as a tool for the analysis of uncertainty importance.

• 대한산업공학회지
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• 제32권2호
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• pp.74-81
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• 2006

In this paper, we deal with the separation of data by concurrently determined, piecewise nonlinear discriminant functions. Toward the end, we develop a new $l_1$-distance norm error metric and cast the problem as a mixed 0-1 integer and linear programming (MILP) model. Given a finite number of discriminant functions as an input, the proposed model considers the synergy as well as the individual role of the functions involved and implements a simplest nonlinear decision surface that best separates the data on hand. Hence, exploiting powerful MILP solvers, the model efficiently analyzes any given data set for its piecewise nonlinear separability. The classification of four sets of artificial data demonstrates the aforementioned strength of the proposed model. Classification results on five machine learning benchmark databases prove that the data separation via the proposed MILP model is an effective supervised learning methodology that compares quite favorably to well-established learning methodologies.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2013년도 추계학술대회
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• pp.36-37
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• 2013

음악 장르 분류 분야에서는 다양한 특징을 모아서 사용하는 방법과 support vector machine (SVM) 분류기가 주로 사용되고 있다. 이 논문에서는 거리 함수 학습를 음악 장르 분류에 적용하여 성능 향상을 꾀한다. 여러 거리 함수 학습 방법 중 하나의 방법을 선택하고, 일반적으로 많이 사용되는 특징 셋을 활용하여 다양한 특징 셋에 대해서 적용하였을 때, 실제 성능 향상이 있는지를 알아본다. 세 종류의 특징 셋을 사용하여 실험한 결과 두 가지 특징이 같이 있는 특징 셋에 대해서만 성능 향상이 있었으며, SVM보다 높은 성능을 보이지 못 했다.

• 한국기계가공학회지
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• 제16권1호
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• pp.1-8
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• 2017

Warpage is a major defect frequently found in the injection molding process, and the reduction of warpage is a very challenging problem because of the uncontrollable factors, such as variations in the process parameters. Without any countermeasure against these noises, attempts to reduce the defects often lead to failure. In this research, a new robust design methodology, based on the Mahalanobis Taguchi System (MTS) to reduce warpage, is presented. The MTS performs the orthogonal array experiments and uses the signal-to-noise (SN) ratio of the Mahalanobis distance as a performance metric. The validity of the proposed method is illustrated through an optimal design of the injection molding process of a CPU base plate.