• 한국비블리아학회지
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• 제23권4호
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• pp.5-24
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• 2012

본 연구는 주제어기반 분류의 발전 과정 및 현황을 종합적으로 분석하고 그 기능과 유형을 명확히 함으로써 주제어기반 분류의 분류론적 개념을 정립하고, 향후 분류체계로써의 정착 및 발전을 위한 제안을 하고자 하는 목적을 가지고 있다. 이를 위하여 본 연구는 1937년부터 현재에 이르기까지 발표된 주제어기반 분류의 필요성에 대한 논의를 수집하여 분석하고, 주제어기반 분류에 해당하는 다양한 사례를 수집하여 그 명칭과 유형을 분석하였다. 또한 분류로써의 주요 기능 수행력을 기존의 문헌분류와 비교하고, 분류와 주제명표목과의 비교 분석을 통해 지식조직체계로써의 기능과 특성을 밝히고자 하였다. 이러한 분석의 결과, 주제어기반 분류는 구체적인 기능 정의, 유형, 사용되는 정보환경, 지식조직체계간의 관계성 등을 면밀히 고려함으로써 그 분류론적 개념과 기능을 명확히 정의할 수 있음을 밝혔고, 향후 분류체계로써의 정착과 발전을 위한 발전방안을 제시하였다.

• 한국기록관리학회지
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• 제18권1호
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• pp.155-175
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• 2018

이 연구의 목적은 함평사건희생자유족회의 소장 기록물에 대한 분류체계를 마련하는 데에 있다. 이에 따라 기록물의 맥락을 기능적 출처주의를 통해 구현하며, 기록물을 효과적으로 활용할 수 있도록 유형별 특성과 생산시기별 특성을 반영한 분류표를 제시하였다. 기능분류체계 개발 방법론인 DIRKS를 사용하여 함평사건희생자유족회의 업무분석을 수행함으로써, 업무기능-업무활동-처리행위로 이어지는 업무분류표를 도출한다. 함평사건희생자유족회 소장 기록물을 유형과 생산시기별 특성을 고려하여 그 범주를 결정한다. 기록물 맵핑은 업무분류표에 해당하는 업무분류체계에 1차적으로 실행하고, 2차적으로는 업무분류에 유형분류와 시대분류를 접목한 다중분류체계에 맵핑한다. 업무주제-업무활동-처리행위-유형-시대의 형태로 이어지는 기록물 분류표를 도출한다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 1999년도 Proceedings of International Symposium on Remote Sensing
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• pp.48-53
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• 1999

The paper presents an automated method for generating fuzzy rules and fuzzy membership functions for pattern classification from training sets of examples and an application to the land-cover classification. Initially, fuzzy subspaces are created from the partitions formed by the minimum and maximum of individual feature values of each class. The initial membership functions are determined according to the generated fuzzy partitions. The fuzzy subspaces are further iteratively partitioned if the user-specified classification performance has not been archived on the training set. Our classifier was trained and tested on patterns consisting of the DN of each band, (XS1, XS2, XS3), extracted from KITSAT-3 multispectral scene. The result represents that our classification method has higher generalization power.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2000년도 추계정기학술대회:지능형기술과 CRM
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• pp.417-426
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• 2000

This study suggests integrated neural network models for Interest rate forecasting using change-point detection, classifiers, and classification functions based on structural change. The proposed model is composed of three phases with tee-staged learning. The first phase is to detect successive and appropriate structural changes in interest rare dataset. The second phase is to forecast change-point group with classifiers (discriminant analysis, logistic regression, and backpropagation neural networks) and their. combined classification functions. The fecal phase is to forecast the interest rate with backpropagation neural networks. We propose some classification functions to overcome the problems of two-staged learning that cannot measure the performance of the first learning. Subsequently, we compare the structured models with a neural network model alone and, in addition, determine which of classifiers and classification functions can perform better. This article then examines the predictability of the proposed classification functions for interest rate forecasting using structural change.

• Journal of Electrical Engineering and Technology
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• 제11권2호
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• pp.309-319
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• 2016

This paper presents a new recognition and classification method for power quality (PQ) disturbances on the basis of pattern linguistic values. This method solves the difficulty of recognizing disturbances rapidly and accurately by using fuzzy logic. This method uses classification disturbance patterns to define the linguistic values of fuzzy input variables and used the input variables of corresponding disturbance pattern to set membership functions. This method also sets the fuzzy rules by analyzing the distribution regularities of the input variable values. One characteristic of this method is that the linguistic values of fuzzy input variables and the setting of membership functions are not only related to the input variables but also to the character of classification disturbance and the classification results. Furthermore, the number of fuzzy rules is equal to the number of disturbance patterns. By using this method for disturbance classification, the membership function and design of fuzzy rules are directly related to the objective of classification, thus effectively reducing the complexity of the design process and yielding accurate classification results. The classification results of the simulation and measured data verify the feasibility and effectiveness of this method.

• 비파괴검사학회지
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• 제15권4호
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• pp.531-539
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• 1996

본 연구에서는 초음파 신호형상인식법을 이용하여 용접부의 인공 결함을 분류하기 위한 연구를 실시하였다. 이를 위해 신호처리 및 특징 변수를 추출할 때에 많은 사용자 정의 변수를 가지는 신호 형상 인식 패키지를 개발하였으며 디지탈 신호처리, 특징 변수 추출, 특징 변수의 선택, 분류기 선정 등의 과정을 일괄적으로 처리하였다. 특히, 선형 분류기, 경험적 Bayesian 분류기 등의 통계적 분류기와 신경회로망 분류기를 함께 사용하여 비교, 검토하였다. 이에 관한 적용 연구로 노치와 구멍으로 이루어진 인공 결함을 분류하였다. 그 결과 인공결함 분류에서 높은 인식률을 얻었으며, 특히 적절히 학습 시켰을 경우 신경회로망 분류기가 통계적 분류기에 비하여 인식률 면에서 유리하였다.

• 한국멀티미디어학회논문지
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• 제23권9호
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• pp.1129-1138
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• 2020

The performance of convolutional deep learning networks is generally determined according to parameters of target dataset, structure of network, convolution kernel, activation function, and optimization algorithm. In this paper, a genetic algorithm is used to select the appropriate deep learning model and parameters for Alzheimer's classification and to compare the learning results with preliminary experiment. We compare and analyze the Alzheimer's disease classification performance of VGG-16, GoogLeNet, and ResNet to select an effective network for detecting AD and MCI. The simulation results show that the network structure is ResNet, the activation function is ReLU, the optimization algorithm is Adam, and the convolution kernel has a 3-dilated convolution filter for the accuracy of dementia medical images.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1995년도 추계학술대회 논문집
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• pp.393-396
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• 1995

In this syudy, the researches classifying the artificial and natural flaws in welding parts are performed using the smart pattern recognition technology. For this purpose the smart signal pattern recognition package including the user defined function was developed and the total procedure including the digital signal processing,feature extraction , feature selection and classifier selection is treated by bulk. Specially it is composed with and discussed using the statistical classifier such as the linear disciminant function classifier, the empirical Bayesian classifier. Also, the smart pattern recognition technology is applied to classification problem of natural flaw(i.e multiple classification problem-crack,lack of penetration,lack of fusion,porosity,and slag inclusion, the planar and volumetric flaw classification problem). According to this results, if appropriately learned the neural network classifier is better than ststistical classifier in the classification problem of natural flaw. And it is possible to acquire the recognition rate of 80% above through it is different a little according to domain extracting the feature and the classifier.

• 대한산업공학회지
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• 제27권1호
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• pp.47-53
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• 2001

In this paper, we compare the classification performances of both ensemble and clustering algorithms (Data Bagging, Variable Selection Bagging, Parameter Combining, Clustering) to logistic regression in consideration of various characteristics of input data. Four factors used to simulate the logistic model are (1) correlation among input variables (2) variance of observation (3) training data size and (4) input-output function. In view of the unknown relationship between input and output function, we use a Taguchi design to improve the practicality of our study results by letting it as a noise factor. Experimental study results indicate the following: When the level of the variance is medium, Bagging & Parameter Combining performs worse than Logistic Regression, Variable Selection Bagging and Clustering. However, classification performances of Logistic Regression, Variable Selection Bagging, Bagging and Clustering are not significantly different when the variance of input data is either small or large. When there is strong correlation in input variables, Variable Selection Bagging outperforms both Logistic Regression and Parameter combining. In general, Parameter Combining algorithm appears to be the worst at our disappointment.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제8권3호
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• pp.175-184
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• 2008

To construct user profiles automatically, an extraction method for representative keywords from a set of documents is needed. In our previous works, we suggested such a method and showed its usefulness. Here, we apply it to the classification problem and observe how much it contributes to performance improvement. The method can be used as a linear document classifier with few modifications. So, we first evaluate its performance for that case. The method is also applicable to some non-linear classification methods such as GIS (Generalized Instance Set). In GIS algorithm, generalized instances are built from training documents by a generalization function and then the K-NN algorithm is applied to them, where the method can be used as a generalization function. For comparative works, two famous linear classification methods, Rocchio and Widrow-Hoff algorithms, are also used. Experimental results show that our method is better than the others for the case that only positive documents are considered, but not when negative documents are considered together.