• KSBB Journal
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• v.10 no.2
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• pp.119-125
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• 1995

The morphology of fungal growth, which is an important variable for separability and rheological property of fermented medium, was quantified with fractal geometry Fractal dimensions were determined for submerged growth of two industrially important fungi, Aspergillus niger and Aspergillus oruzae. The tendency of pellet formation was related to the fractal dimension of fungi.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.186-188
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• 2005

Hand region detection in images is an important process in many computer vision applications. It is a process that usually starts at a pixel-level, and that involves a pre-process of color space transformation followed by a classification process. A color space transformation is assumed to increase separability between skin classes for hands and non-skin classes for other parts, to increase similarity among different skin tones, and to bring a robust performance under varying illumination conditions, without any sound reasonings. In this work, we examine if the color space transformation does bring those benefits to the problem of hand region detection on a dataset of images with different hand postures, backgrounds, people, and illuminations. Results indicate that best of the color space is the normalized RGB.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.9
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• pp.1363-1368
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• 2015

Recently, computer form were smaller than before because of computing technique's development and many wearable device are formed. So, computer's cognition of human emotion has importantly considered, thus researches on analyzing the state of emotion are increasing. Human voice includes many information of human emotion. This paper proposes a discriminative feature vector selection for emotion classification based on speech. For this, we extract some feature vectors like Pitch, MFCC, LPC, LPCC from voice signals are divided into four emotion parts on happy, normal, sad, angry and compare a separability of the extracted feature vectors using Bhattacharyya distance. So more effective feature vectors are recommended for emotion classification.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.31 no.1 s.256
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• pp.1-10
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• 2007

This paper presents a design guidance of jig/fixture for flexible manufacturing system based on the verification of a base assembly motion instability. In flexible assembly system, the base assembly needs to be maintained in its assembled state without being taken apart. This requires stability in motion while the base assembly is handled or tilted. Therefore, the instability of the base assembly motion should be considered when determining the guide line of designing jig/fixture by evaluating a degree of the motion instability of the base assembly. To derive the instability, first we inference collision free assembly directions by extracting separable directions for the mating parts and calculate the separability which gives informations as to how the parts can be easily separated. Using these results, we determine the instability evaluated by summing all the modified separabilities of each component part within base assembly.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2003.04a
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• pp.151-156
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• 2003

본 연구에서는 2000년 LANDSAT ETM+ 수도권 영상을 이용하여 도시지역 10개소, 식생지역 10개소를 선정해서 각각에 대해 표준화 주성분 분석을 적용하여 두 지역간의 고유벡터 매트릭스를 비교ㆍ분석해보았다. 도시 지역과 식생 지역각각에 대해 총 6개의 주성분이 생성되었으며 PC-2와 고유벡터 부호가 변한 밴드(band2, band7)를 RGB로 조합하여 수원지역을 대상으로 분류(Classification)한 결과의 정확도를 분광서명 분별 분석(Signature Separability Analysis)통해 얻은 밴드조합(band1, band3, band5) 영상의 분류결과와 비교해 보았다. 수원지역 2000년 IKONOS 영상의 다중분광 밴드(4×4m)와 전정색 밴드(1x1m)를 융합한 영상이 분류 정확도를 판단하는 기준으로 사용되었다. 비교결과 분류 전체 정확도는 각각 87.7%, 77.29% Khat 지수는 0.83, 0.68로 나타나 PC-2, 밴드2, 밴드7을 이용했을 때 분류 정확도를 높일 수 있다는 결과를 얻었다.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.460-463
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• 1999

We present an efficient face recognition method that is robust to illumination changes. We named the proposed method as SKKUfaces. We first compute eigenfaces from training images and then apply fisher discriminant analysis using the obtained eigenfaces that exclude eigenfaces correponding to first few largest eigenvalues. This way, SKKUfaces can achieve the maximum class separability without considering eigenfaces that are responsible for illumination changes, facial expressions and eyewear. In addition, we have developed a method that efficiently computes beween-scatter and within-scatter matrices in terms of memory space and computation time. We have tested the performance of SKKUfaces on the YALE and the SKKU face databases. Initial Experimental results show that SKKUfaces performs greatly better over Fisherfaces on the input images of large variations in lighting and eyewear.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.111-114
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• 2008

The existence of noise bands may deform the typical shape of the spectrum, making the accuracy of clustering degraded. This paper proposes a statistical approach to remove noise bands in hyperspectral data using the correlation coefficient of bands as an indicator. Considering each band as a random variable, two adjacent signal bands in hyperspectral data are highly correlative. On the contrary, existence of a noise band will produce a low correlation. For clustering, the unsupervised ${\kappa}$-nearest neighbor clustering method is implemented in accordance with three well-accepted spectral matching measures, namely ED, SAM and SID. Furthermore, this paper proposes a hierarchical scheme of combining those measures. Finally, a separability assessment based on the between-class and the within-class scatter matrices is followed to evaluate the applicability of the proposed noise band removal method. Also, the paper brings out a comparison for spectral matching measures.

• Journal of Korean Institute of Industrial Engineers
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• v.32 no.2
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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.

• Journal of Electrical Engineering and Technology
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• v.9 no.1
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• pp.170-177
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• 2014

This paper presents a new method of classification of the induction machine faults using Time Frequency Representation, Particle Swarm Optimization and artificial neural network. The essence of the feature extraction is to project from faulty machine to a low size signal time-frequency representation (TFR), which is deliberately designed for maximizing the separability between classes, a distinct TFR is designed for each class. The feature vectors size is optimized using Particle Swarm Optimization method (PSO). The classifier is designed using an artificial neural network. This method allows an accurate classification independently of load level. The introduction of the PSO in the classification procedure has given good results using the reduced size of the feature vectors obtained by the optimization process. These results are validated on a 5.5-kW induction motor test bench.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.370-375
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• 1998

Spectral reflectance signatures of upland crops at OSMI bands were collected and evaluated for the feasibility of crop discrimination knowledge-based on crop calendar. Effective bands and their ratio values for discriminating corn from two other legumes were defined with OSMI equivalent bands and their ratio values. June 22 among measurements dates was the best date for corn discrimination from two other legumes, peanut and soybean, because all OSMI equivalent bands and their ratio values in June 22 were highly significant for corn separability. Phenological growth stage of a silage corn (rs510) could be estimated as a function of spectral reflectance signatures in vegetative stage. Five growth stage prediction models were generated by the SAS procedures REG and STEPWISE with OSMI equivalent bands and their ratio values in vegetative stage.