• 품질경영학회지
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• 제50권1호
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• pp.77-89
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• 2022

Purpose: In order to improve the audit quality of a company, an in-depth analysis is required to categorize the audit report in the form of a text document containing the details of the external audit. This study introduces a systematic methodology to extract keywords for each group that determines the differences between groups such as 'audit plan' and 'interim audit' using audit reports collected in the form of text documents. Methods: The first step of the proposed methodology is to preprocess the document through text mining. In the second step, the documents are classified into groups using machine learning techniques and based on this, important vocabularies that have a dominant influence on the performance of classification are extracted. In the third step, the association rules for each group's documents are found. In the last step, the final keywords for each group representing the characteristics of each group are extracted by comparing the important vocabulary for classification with the important vocabulary representing the association rules of each group. Results: This study quantitatively calculates the importance value of the vocabulary used in the audit report based on machine learning rather than the qualitative research method such as the existing literature search, expert evaluation, and Delphi technique. From the case study of this study, it was found that the extracted keywords describe the characteristics of each group well. Conclusion: This study is meaningful in that it has laid the foundation for quantitatively conducting follow-up studies related to key vocabulary in each stage of auditing.

• 한국복식학회:학술대회논문집
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• 한국복식학회 2003년도 International Costume Conference
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• pp.68-68
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• 2003

Recently, street fashion has been regularly introduced in many fashion media because both consumers and marketers began to recognize the importance of street fashion as a meaningful and objective fashion information source. As the globalization proceeds in fashion field,' the street fashion informations in major cities which has led world fashion trends become more influential on domestic fashion, but little concern has been paid on it. This study classified women's street fashion in 4 major world fashion cities such as Paris, London, New York, Tokyo and identified style characteristics of each group. For data collection, 795 front-view photos were selected from the two fashion trade publication 'STREET' and 'VIEW' which has introduced street fashion photos in those cities from 1996. Classification process went on three stages: sorting, naming and grouping. 49 undergraduate students were divided into 12 teams and about 80 photos were given to each team to sort into several sub-groups by overall images or common style characteristics. Then each sub-group was named according to common images or characteristics. Final groups came out after grouping each sub-group with a similar or same title together. For each group, common style characteristics were analyzed.

• Smart Structures and Systems
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• 제13권5호
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• pp.849-868
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• 2014

An indirect approach is explored for structural health bridge monitoring allowing for wide, yet cost-effective, bridge stock coverage. The detection capability of the approach is tested in a laboratory setting for three different reversible proxy types of damage scenarios: changes in the support conditions (rotational restraint), additional damping, and an added mass at the midspan. A set of frequency features is used in conjunction with a support vector machine classifier on data measured from a passing vehicle at the wheel and suspension levels, and directly from the bridge structure for comparison. For each type of damage, four levels of severity were explored. The results show that for each damage type, the classification accuracy based on data measured from the passing vehicle is, on average, as good as or better than the classification accuracy based on data measured from the bridge. Classification accuracy showed a steady trend for low (1-1.75 m/s) and high vehicle speeds (2-2.75 m/s), with a decrease of about 7% for the latter. These results show promise towards a highly mobile structural health bridge monitoring system for wide and cost-effective bridge stock coverage.

• 제어로봇시스템학회논문지
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• 제18권9호
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• pp.887-893
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• 2012

In this paper, we propose a new approach to extract directional features based on directional patterns of each region in fingerprint images. The proposed approach computes the center of gravity to extract features from fingerprint images of various shapes. According to it, we divide a fingerprint image into four regions and compute the directional values of each region. To extract directional features of each region from a fingerprint image, we spilt direction values of ridges in a region into 18 classes and compute frequency distribution of each region. Through the result of our experiment using FVC2002 DB database acquired by electronic devices, we show that directional features are effectively extracted from various fingerprint images of exceptional inputs which lost all or part of singularities. To verify the performance of the proposed approach, we explained the process to model Arch, Left, Right and Whorl class using the extracted directional features of four regions and analyzed the classification result.

• 한국운동역학회지
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• 제26권1호
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• pp.51-69
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• 2016

Objective: The purpose of this study is to suggest new terminology for the ninety-five hand techniques based on the significance of their angular momentum, determined by analyzing each technique's influence or impact on the compartmentalized angular momentum of the trunk, upper arm, and forearm in the Taekwondo Poomsae. Method: An athlete who won the 2014 World Taekwondo Poomsae championship was selected and agreed to participate in the data collection phase of our investigation. The video data was collected using eight infrared cameras (Oqus 300, Qualysis, Sweden) and the Qualisys Track Manager software (Qualisys, Sweden). The angular momentum of each movement was then calculated using the Matlab R2009a software (The Mathworks, Inc., USA). Results: The classification of the ninety-five hand techniques in the Taekwondo Poomsae based on the significance of each segment's momentum is as follows. Makgi (blocking) is classified into fourteen categories, jireugi (punching) is classified into three categories, chigi (hitting) was classified into six categories, palgupchigi (elbow hitting) was classified into four categories, and jjireugi (thrusting) was classified two categories. Conclusion: This study offers a new approach, based on a biomechanical method, to the classification of the hand techniques that reflect kinesthetic motions in the Taekwondo Poomsae.

• Steel and Composite Structures
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• 제37권2호
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• pp.193-209
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• 2020

In this paper, the efficiency of five Machine Learning (ML) methods consisting of Deep Learning (DL), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and Gradient Tree Booting (GTB) for regression and classification of the Ultimate Load Factor (ULF) of nonlinear inelastic steel frames is compared. For this purpose, a two-story, a six-story, and a twenty-story space frame are considered. An advanced nonlinear inelastic analysis is carried out for the steel frames to generate datasets for the training of the considered ML methods. In each dataset, the input variables are the geometric features of W-sections and the output variable is the ULF of the frame. The comparison between the five ML methods is made in terms of the mean-squared-error (MSE) for the regression models and the accuracy for the classification models, respectively. Moreover, the ULF distribution curve is calculated for each frame and the strength failure probability is estimated. It is found that the GTB method has the best efficiency in both regression and classification of ULF regardless of the number of training samples and the space frames considered.

• 센서학회지
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• 제17권6호
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• pp.406-413
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• 2008

We have been implemented simple statistical pattern recognition methods for harmful gases classification using gas sensors array fabricated by MEMS (Micro Electro Mechanical System) technology. The performance of pattern recognition method as a gas classifier is highly dependent on the choice of pre-processing techniques for sensor and sensors array signals and optimal classification algorithms among the various classification techniques. We carried out pre-processing for each sensor's signal as well as sensors array signals to extract features for each gas. We adapted simple statistical pattern recognition algorithms, which were PCA (Principal Component Analysis) for visualization of patterns clustering and MLR (Multi-Linear Regression) for real-time system implementation, to classify harmful gases. Experimental results of adapted pattern recognition methods with pre-processing techniques have been shown good clustering performance and expected easy implementation for real-time sensing system.

• 기록학연구
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• 제9호
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• pp.41-73
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• 2004

This article provides the base in relation to the classification system of public records of Japan and Tiwan which the original order of the classification system of public records of Government-General of Chosun is reconstructed and the efficient classification system is prepared. The classification system of public records at the period of Meiji(明治) in Japan was classified two forms, one is function-based classification, the other is organization-based classification. Each ministry(省) was fundamentally based In function-based classification and organization-based classification, adopted them in changed forms as its condition and situation had been changed. Government-General of Tiwan adopted Japan's archival management system and put its classification system and life schedule In operation. The classification system of Government-General of Tiwan adopted function-based classification of the ministry of foreign affairs in Japan, changed its forms as the organization and business activity were transformed. As a result of arrangement and analysis of examples for the classification of public records of Government-General of Chosun from 1910' to the middle area of 1930', the classification of public records of Government-General of Chosun was constructed on level order; 'organization of ministry(部) or department(局)--business activity of ministry or department--low function of business activity of ministry or department'. But this classification system had two sides, flexible and unstable in that the classification system had exeptional parts and the breadth of items was changed greatly. The classification system of Government-General of Chosun, which had adopted organization-based classification of the ministry of home affairs in Japan, result in expanding the breadth of items and causing great change of items for the organization and business activity were vast and its change was very great.

• 대한원격탐사학회지
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• 제19권4호
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• pp.329-339
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• 2003

본 연구에서는 무감독 영상분류를 위하여 특성이 다른 센서로 수집된 영상들에 대한 의사결정 수준의 영상 융합기법을 제안하였다. 제안된 기법은 공간 확장 분할에 근거한 무감독 계층군집 영상분류기법을 개개의 센서에서 수집된 영상에 독립적으로 적용한 후 그 결과로 생성되는 분할지역의 퍼지 클래스 벡터(fuzzy class vector)를 이용하여 각 센서의 분류 결과를 융합한다. 퍼지 클래스벡터는 분할지역이 각 클래스에 속할 확률을 표시하는 지시(indicator) 벡터로 간주되며 기대 최대화 (EM: Expected Maximization) 추정 법에 의해 관련 변수의 최대 우도 추정치가 반복적으로 계산되어진다. 본 연구에서는 같은 특성의 센서 혹은 밴드 별로 분할과 분류를 수행한 후 분할지역의 분류결과를 퍼지 클래스 벡터를 이용하여 합성하는 접근법을 사용하고 있으므로 일반적으로 다중센서의 영상의 분류기법에 사용하는 화소수준의 영상융합기법에서처럼 서로 다른 센서로부터 수집된 영상의 화소간의 공간적 일치에 대한 높은 정확도를 요구하지 않는다. 본 연구는 한반도 전라북도 북서지역에서 관측된 다중분광 SPOT 영상자료와 AIRSAR 영상자료에 적용한 결과 제안된 영상 융합기법에 의한 피복 분류는 확장 벡터의 접근법에 의한 영상 융합보다 서로 다른 센서로부터 얻어지는 정보를 더욱 적합하게 융합한다는 것을 보여주고 있다.

• 대한원격탐사학회지
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• 제15권1호
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• pp.39-50
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• 1999

위성에서 관측된 다 대역 위성영상 데이터를 이용목적에 따라 분류하기 위해서는 복잡한 처리과정과 많은 시간을 필요로 하며, 감독분류시 훈련 데이터의 선택과 고려되는 다양한 특징 값들은 분류 정확도를 좌우할 만큼 민감한 특성을 나타내고 있다. 따라서 본 논문에서는 훈련데이터의 선택과 다양한 특징 값들 중 실제 영상분류에 기여도가 높은 특징을 추출하기 위하여 퍼지 기반의 $\gamma$모델을 이용한 분류네트웍을 구성하였다. 훈련집합 선택시 분류하고자 하는 지역의 밝기 분포도, 텍스쳐 특징 그리고 NDVI(Normalized Difference Vegetation Index)를 분류에 사용될 특징으로 선택하였고, 분류네트웍 출력 값의 오류가 최소화 되도록 Gradient Desoent 방법을 이용하여 각 노드의 $\gamma$파라미터를 훈련시키는 과정을 채택하였다. 이러한 훈련을 통하여 얻어진 파라미터를 이용하면 각 노드의 연결특성을 알 수 있으며, 다양한 입력 노드의 특징들 중 영상분류에 기여도가 적은 특징들을 추출하여 제거할 수 있다.