• Natural Product Sciences
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• 제24권3호
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• pp.164-170
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• 2018

Rapid geographical classification of Bupleuri Radix is important in quality control. In this study, near infrared spectroscopy (NIRS) combined with supervised pattern recognition was attempted to classify Bupleuri Radix according to geographical origins. Three supervised pattern recognitions methods, partial least square discriminant analysis (PLS-DA), quadratic discriminant analysis (QDA) and radial basis function support vector machine (RBF-SVM), were performed to establish the classification models. The QDA and RBF-SVM models were performed based on principal component analysis (PCA). The number of principal components (PCs) was optimized by cross-validation in the model. The results showed that the performance of the QDA model is the optimum among the three models. The optimized QDA model was obtained when 7 PCs were used; the classification rates of the QDA model in the training and test sets are 97.8% and 95.2% respectively. The overall results showed that NIRS combined with supervised pattern recognition could be applied to classify Bupleuri Radix according to geographical origin.

• 한국한의학연구원논문집
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• 제14권2호
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• pp.41-46
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• 2008

The study was to investigate the agreement rate between the statistical diagnosis of pattern identification by discriminant analysis and the clinical diagnosis of pattern identification by medical specialist in obese patients with BMI$\geqq$23. The agreement rate of deficiency of the spleen, phlegm-retention, deficiency of Yang, retention of undigested food, stagnation of liver Gi, and blood stagnation are 0.40, 0.33, 0.52, 0.76, 0.71, and 0.66, respectively and accuracy rate and prediction rate using linear discriminant function are 0.59 and 0.61, respectively. Therefore, the complementary management in CRF questionnaires and/or consultation from experts will improve the accuracy and prediction rate, which will be helpful for pattern identification of obesity by clinical experts.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume II
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• pp.706-709
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• 2006

Cancer is one of the major causes of death; however, the survival rate can be increased if discovered at an early stage for timely treatment. According to the statistics of the World Health Organization of 2002, breast cancer was the most prevalent cancer for all cancers occurring in women worldwide, and it account for 16.8% of entire cancers inflicting Korean women today. In order to classify the type of breast cancer whether it is benign or malignant, this study was conducted with the use of the discriminant analysis and the decision tree of data mining with the breast cancer data disclosed on the web. The discriminant analysis is a statistical method to seek certain discriminant criteria and discriminant function to separate the population groups on the basis of observation values obtained from two or more population groups, and use the values obtained to allow the existing observation value to the population group thereto. The decision tree analyzes the record of data collected in the part to show it with the pattern existing in between them, namely, the combination of attribute for the characteristics of each class and make the classification model tree. Through this type of analysis, it may obtain the systematic information on the factors that cause the breast cancer in advance and prevent the risk of recurrence after the surgery.

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 2000년도 THE THIRD INTERNATIONAL CONFERENCE ON AGRICULTURAL MACHINERY ENGINEERING. V.II
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• pp.270-278
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• 2000

A machine vision system using charge coupled device(CCD) camera for the weed detection in a radish farm was developed. Shape features were analyzed with the binary images obtained from color images of radish and weeds. Aspect, Elongation and PTB were selected as significant variables for discriminant models using the STEPDISC option. The selected variables were used in the DISCRIM procedure to compute a discriminant function for classifying images into one of the two classes. Using discriminant analysis, the successful recognition rate was 92% for radish and 98% for weeds. To recognize radish and weeds more effectively than the discriminant analysis, an artificial neural network(ANN) was used. The developed ANN model distinguished the radish from the weeds with 100%. The performance of ANNs was improved to prevent overfitting and to generalize well using a regularization method. The successful recognition rate in the farms was 93.3% for radish and 93.8% for weeds. As a whole, the machine vision system using CCD camera with the artificial neural network was useful to detect weeds in the radish farms.

• 스마트미디어저널
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• 제5권1호
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• pp.30-37
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• 2016

In recent decades, the study of human brain function has dramatically increased thanks to the advent of Functional Magnetic Resonance Imaging. This is a powerful tool which provides a deep view of the activities of the brain. From fMRI data, the neuroscientists analyze which parts of the brain have responsibility for a particular action and finding the common pattern representing each state involved in these tasks. This is one of the most challenges in neuroscience area because of noisy, sparsity of data as well as the differences of anatomical brain structure of each person. In this paper, we propose the use of appropriate discriminant methods, such as Fisher Discriminant Ratio and hypothesis testing, together with strong boosting ability of Adaboost classifier. We prove that discriminant methods are effective in classifying cognitive states. The experiment results show significant better accuracy than previous works. We also show that it is possible to train a successful classifier without prior anatomical knowledge and use only a small number of features.

• 한국통신학회논문지
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• 제39A권2호
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• pp.92-99
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• 2014

본 논문은 결정 트리(Decision tree) 구조를 기반으로 한 표정 인식 방법을 제안한다. ASM(Active Shape Model)과 LBP(Local Binary Pattern)를 통해, 표정 영상들의 국소 특징들을 추출한다. 국소 특징들로부터 표정들을 잘 분류할 수 있는 판별 특징(Discriminant feature)들을 추출하고, 그 판별 특징들은 모든 조합의 각 두 가지 표정들을 분류시킨다. 분류를 통해 얻어진 정인식의 합을 통해, 정인식 최대화 기반 국소 영역과 표정 조합을 결정한다. 이 가지 분류들을 종합하여, 결정 트리를 생성한다. 이 결정 트리 기반 표정 인식률은 약 84.7%로, 결정 트리를 고려하지 않은 방법보다, 더 좋은 인식 성능을 보였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권3호
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• pp.1390-1403
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• 2016

Facial expression recognition (FER) plays a very significant role in computer vision, pattern recognition, and image processing applications such as human computer interaction as it provides sufficient information about emotions of people. For video-based facial expression recognition, depth cameras can be better candidates over RGB cameras as a person's face cannot be easily recognized from distance-based depth videos hence depth cameras also resolve some privacy issues that can arise using RGB faces. A good FER system is very much reliant on the extraction of robust features as well as recognition engine. In this work, an efficient novel approach is proposed to recognize some facial expressions from time-sequential depth videos. First of all, efficient Local Binary Pattern (LBP) features are obtained from the time-sequential depth faces that are further classified by Generalized Discriminant Analysis (GDA) to make the features more robust and finally, the LBP-GDA features are fed into Hidden Markov Models (HMMs) to train and recognize different facial expressions successfully. The depth information-based proposed facial expression recognition approach is compared to the conventional approaches such as Principal Component Analysis (PCA), Independent Component Analysis (ICA), and Linear Discriminant Analysis (LDA) where the proposed one outperforms others by obtaining better recognition rates.

• 대한전자공학회논문지SP
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• 제43권2호
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• pp.82-86
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• 2006

패턴인식 시스템은 일반적으로 데이터의 전처리, 특징 추출, 학습단계의 과정을 거쳐서 개발되어 진다. 그중에서도 특징 추출 과정은 다차원 공간을 가진 입력 데이터의 복잡도를 줄여서 다음 단계인 학습단계에서 계산 복잡도와 인식률을 향상시키는 역할을 한다. 패턴인식에서 특징 추출 기법으로써 principal component analysis, factor analysis, linear discriminant analysis 같은 방법들이 널리 사용되어져 왔다. 이 논문에서는 singular value decomposition (SVD) 방법이 패턴인식 시스템의 특징 추출과정에 유용하게 사용될 수 있음을 보인다. 특징 추출단계에서 SVD 기법의 유용성을 검증하기 위하여 원격탐사 응용에 적용하였는데, 실험결과는 널리 쓰이는 PCA에 비해 약 25%의 인식률의 향상을 가져온다는 것을 알 수 있다.

• 한국의류학회지
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• 제27권7호
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• pp.758-769
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• 2003

In this research, I classified adult women's back types through anthropometric measurement and photographic measurement to present a judging individual body size according to the type. Also, Ⅰ calculated regression fomula by types and presented the basic bodice pattern. The results were as follows: 1. The result of factor analysis indicated that 5 factors were extracted and those factors comprised 75.89% of total variance. 2. According to the cluster analysis, Ⅰclassified the back types into 6 types. Type 1 was passive posture in the upper and the lower parts of the back. Type 2 was active posture in the upper and the lower parts of the back. Type 3 had the lowest protrusion of the back. Type 4 had the upper part of the back which is mostly bent downward. Type 5 was the most suitable shape. Type 6 had the lower part of the back which was turned over the most. 3. Ⅰconducted a discriminant analysis to judge the body types of individuals. 4. For the calculation of measurements necessary for the basic bodice pattern, Ⅰpresented regression formulas by each type. 5. By conducting the wearing experiments, Ⅰsuccessfully made the final basic bodice patterns by types. As a result of comparative experiments between the basic bodice patterns and comparison bodice pattern, the suitability of basic bodice patterns were more highly assessed.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1996년도 추계학술대회 논문집
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• pp.212-216
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• 1996

Recently, with the development of NC and CNC machine tools and the high labor wage, the cutting process requires the high speed and automatic system which uses industrial robots and the flexible manufacturing system(FMS) that combines several machine tools. In this system, the whole system can be influenced by just one of the machin tools. So it needs to detect a problem and to solve it immediately In in-process state. The monitoring system through measuring the motor current with current sensor has been attracting the attention of lots of researchers view of its low cost and flexibility. By using the pattern discriminant with the detected three-phase-current signal, that is, $I_{RMS}$, a system which can monitor and analyze abnormal machining process condition of the workpiece during the machining will be able to be developed in this research.h.