• 대한기계학회논문집
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• 제17권7호
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• pp.1783-1793
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• 1993

Pattern recognition technique using fuzzy c-means algorithm and multilayer perceptron was applied to classify tool wear states in turning. The tool wear states were categorized into the three regions 'Initial', 'Normal', 'Severe' wear. The root mean square(RMS) value of acoustic emission(AE) and current signal was used for the classification of tool wear states. The simulation results showed that a fuzzy c-means algorithm was better than the conventional pattern recognition techniques for classifying ambiguous informations. And normalized RMS signal can provide good results for classifying tool wear. In addition, a fuzzy c-means algorithm(success rate for tool wear classification : 87%) is more efficient than the multilayer perceptron(success rate for tool wear classification : 70%).

• Journal of information and communication convergence engineering
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• 제8권4호
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• pp.383-386
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• 2010

Pattern classification is one of the most important topics for machine learning research fields. However incomplete data appear frequently in real world problems and also show low learning rate in classification models. There have been many researches for handling such incomplete data, but most of the researches are focusing on training stages. In this paper, we proposed two classification methods for incomplete data using triangular shaped fuzzy membership functions. In the proposed methods, missing data in incomplete feature vectors are inferred, learned and applied to the proposed classifier using triangular shaped fuzzy membership functions. In the experiment, we verified that the proposed methods show higher classification rate than a conventional method.

• KIEE International Transactions on Electrophysics and Applications
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• 제5C권3호
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• pp.138-142
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• 2005

In this paper, we compared recognition rates between NN(neural networks) and clustering method as a scheme of off-line PD(partial discharge) diagnosis which occurs at the stator coil of traction motor. To acquire PD data, three defective models are made. PD data for classification were acquired from PD detector. And then statistical distributions are calculated to classify model discharge sources. These statistical distributions were applied as input data of two classification tools, BP(Back propagation algorithm) and ANFIS(adaptive network based fuzzy inference system) pre-processed FCM(fuzzy c-means) clustering method. So, classification rate of BP were somewhat higher than ANFIS. But other items of ANFIS were better than BP; learning time, parameter number, simplicity of algorithm.

• Industrial Engineering and Management Systems
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• 제4권1호
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• pp.102-108
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• 2005

This paper studies medical data classification methods, comparing decision tree and system reconstruction analysis as applied to heart disease medical data mining. The data we study is collected from patients with coronary heart disease. It has 1,723 records of 71 attributes each. We use the system-reconstruction method to weight it. We use decision tree algorithms, such as induction of decision trees (ID3), classification and regression tree (C4.5), classification and regression tree (CART), Chi-square automatic interaction detector (CHAID), and exhausted CHAID. We use the results to compare the correction rate, leaf number, and tree depth of different decision-tree algorithms. According to the experiments, we know that weighted data can improve the correction rate of coronary heart disease data but has little effect on the tree depth and leaf number.

• 한국컴퓨터정보학회논문지
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• 제20권8호
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• pp.15-21
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• 2015

This study proposed a Sasang constitution classification system that can increase the objectivity and reliability of Sasang constitution diagnosis using the image of frontal face, in order to solve problems in the subjective classification of Sasang constitution based on Sasang constitution specialists' experiences. For classification, characteristics indicating the shapes of the eyes, nose, mouth and chin were defined, and such characteristics were extracted using the morphological statistic analysis of face images. Then, Sasang constitution was classified through a SVM (Support Vector Machine) classifier using the extracted characteristics as its input, and according to the results of experiment, the proposed system showed a correct recognition rate of 93.33%. Different from existing systems that designate characteristic points directly, this system showed a high correct recognition rate and therefore it is expected to be useful as a more objective Sasang constitution classification system.

• 한국안전학회지
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• 제33권4호
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• pp.21-29
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• 2018

In many companies handling flammable liquids, explosion-proof electrical equipment have been installed according to the Korean Industrial Standards (KS C IEC 60079-10-1). In these standards, hazardous area for explosive gas atmospheres has to be classified by the evaluation of the evaporation rate of flammable liquid leakage. The evaporation rate is an important factor to determine the zones classification and hazardous area distance. However, there is no systematic method or rule for the estimation of evaporation rate in these standards and the first principle equations of a evaporation rate are very difficult. Thus, it is really hard for industrial workplaces to employ these equations. Thus, this problem can trigger inaccurate results for evaluating evaporation range. In this study, empirical models for estimating an evaporation rate of flammable liquid have been developed to tackle this problem. Throughout the sensitivity analysis of the first principle equations, it can be found that main factors for the evaporation rate are wind speed and temperature and empirical models have to be nonlinear. Polynomial regression is employed to build empirical models. Methanol, benzene, para-xylene and toluene are selected as case studies to verify the accuracy of empirical models.

• 한국지능시스템학회논문지
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• 제19권5호
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• pp.629-634
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• 2009

실시간 시스템 적용에 있어서, 수동 소나 표적의 식별을 위한 특징정보 추출 및 스코어링 알고리즘은 다음과 같은 문제점들을 가지고 있다. 즉, 주파수 스펙트럼으로부터 PSR(Propeller Shaft Rate) 및 BR(Blade rate) 등의 특징정보를 실시간으로 구별하는 것은 매우 어렵기 때문에 정확하고 효율적인 특징정보 추출(extraction)법을 요구한다. 또한, 추출된 특징정보들로 구성된 식별 DB(DataBase)는 잡음 및 불완전한 구성을 갖기 때문에 강인하고 효과적인 특징정보 스코어링(scoring)법을 요구한다. 나아가, 구조와 파라메터에 있어서 용이한 설계 절차를 요구한다. 이러한 문제들을 해결하기 위해서 진화 전략(ES : Evolution Strategy) 및 퍼지(fuzzy) 이론을 이용하는 지능형 특징정보 추출 및 스코어링 알고리즘이 제안되었다. 제안된 알고리즘의 성능을 검증하기 위해서는 수동 소나 표적의 실시간 식별이 수행되었다. 시뮬레이션 결과는 제안된 알고리즘이 실시간 시스템 적용에서 존재하는 문제점들을 효과적으로 해결할 수 있음을 보여준다.

• 대한두개안면성형외과학회지
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• 제23권3호
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• pp.111-118
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• 2022

Background: Although the zygomatic arch is an important structure determining facial prominence and width, no consensus exists regarding the classification of isolated zygomatic arch fractures, and the literature on this topic is scarce. To date, five papers have subdivided zygomatic arch fractures; however, only one of those proposed classifications includes the injury vector, although the injury vector is one of the most important factors to consider in fracture cases. Furthermore, the only classification that does include the injury vector is too complicated to be suitable for daily practice. In addition, the existing classifications are clinically limited because they do not consider greenstick fractures, nondisplaced fractures, or coronoid impingement. In the present study, we present a rearrangement of the previously published classifications and propose a modified classification of isolated zygomatic arch fractures that maximizes the advantages and overcomes the disadvantages of previous classification systems. Methods: The classification criteria for isolated zygomatic arch fractures described in five previous studies were analyzed, rearranged, and supplemented to generate a modified classification. The medical records, radiographs, and facial bone computed tomography findings of 134 patients with isolated zygomatic arch fractures who visited our hospital between January 2010 and December 2019 were also retrospectively analyzed. Results: We analyzed major classification criteria (displacement, the force vector of the injury, V-shaped fracture, and coronoid impingement) for isolated zygomatic arch fracture from the five previous studies and developed a modified classification by subdividing zygomatic arch fractures. We applied the modified classification to cases of isolated zygomatic arch fracture at our hospital. The surgery rate and injury severity differed significantly from fracture types I to VI. Conclusion: Using our modified classification, we could determine that both the injury force and the injury vector meaningfully influenced the surgery rate and the severity of the injuries.

• Journal of the Korean Data and Information Science Society
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• 제20권4호
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• pp.757-764
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• 2009

In discriminant analysis, we consider a special pattern which contains a block of missing observations. We assume that the two populations are equally likely and the costs of misclassification are equal. In this situation, we consider the bootstrap confidence intervals of the error rate in the circular models when the covariance matrices are equal and not equal.

• 품질경영학회지
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• 제23권2호
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• pp.53-67
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• 1995

The problem considered here is to find the optimal discriminant analysis method in growth curve model. It has been studied how to find correct prior probability for the effective classification in discriminant analysis. We use the balanced condition to calculate prior probability. From the informative simulation study, new classification rule for the growth curve model is suggested. The suggested classification rule has better classification result than the other previously suggested method in terms of error rate criterion.