• 반도체디스플레이기술학회지
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• 제18권1호
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• pp.97-101
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• 2019

Understanding and classification of the human's emotion play an important tasks in interacting with human and machine communication systems. This paper proposes a novel emotion recognition method by extracting facial keypoints, which is able to understand and classify the human emotion, using active Appearance Model and the proposed classification model of the facial features. The existing appearance model scheme takes an expression of variations, which is calculated by the proposed classification model according to the change of human facial expression. The proposed method classifies four basic emotions (normal, happy, sad and angry). To evaluate the performance of the proposed method, we assess the ratio of success with common datasets, and we achieve the best 93% accuracy, average 82.2% in facial emotion recognition. The results show that the proposed method effectively performed well over the emotion recognition, compared to the existing schemes.

• International Journal of Computer Science & Network Security
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• 제22권1호
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• pp.93-96
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• 2022

Natural language processing (NLP) is utilized to understand a natural text. Text analysis systems use natural language algorithms to find the meaning of large amounts of text. Text classification represents a basic task of NLP with a wide range of applications such as topic labeling, sentiment analysis, spam detection, and intent detection. The algorithm can transform user's unstructured thoughts into more structured data. In this work, a text classifier has been developed that uses academic admission and registration texts as input, analyzes its content, and then automatically assigns relevant tags such as admission, graduate school, and registration. In this work, the well-known algorithms support vector machine SVM and K-nearest neighbor (kNN) algorithms are used to develop the above-mentioned classifier. The obtained results showed that the SVM classifier outperformed the kNN classifier with an overall accuracy of 98.9%. in addition, the mean absolute error of SVM was 0.0064 while it was 0.0098 for kNN classifier. Based on the obtained results, the SVM is used to implement the academic text classification in this work.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2020년도 가을 학술논문 발표대회
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• pp.173-174
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• 2020

In modular construction, safety management is required to prevent the occurrence of risk factors such as lifting work and high place work due to the characteristics of the construction method. Therefore, this study is a basic study for safety management of modular construction, and the purpose of this study is to compare and analyze the classification system of modular construction derived by analyzing the construction process of the existing case. Based on the results of this study, the safety management classification system will be used in future research to derive safety management factors in modular construction.

• 한국농촌건축학회논문집
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• 제17권1호
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• pp.45-54
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• 2015

This study is about the Type classification of architectural assets in Seosungro (one of the 4 Roads of Rampart in Junggu, Daegu), the main stronghold of Urban Regeneration projects according to the recent Urban Regeneration law. The purpose of this study is to suggest the basic data about Operation management method of Original Downtown modern buildings and valuable hanok, and Conservational Regeneration of Architectural property of Urban Environmental Improvement and Architectural assets. By researching, analysing the feature and classifying the type of the buildings in Seosungro, The type classified Conservation plan can be suggested. The Types of the Architectural assets will be the basic data of the application plan of modern buildings which is for the urban regeneration, and this can predict the quantity and the demand of the building for effective urban regeneration, and also can be an effective Urban regeneration policy data.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.354-354
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• 2000

In this paper, we would study the applicability of neural networks to the recognition process of Korean stenographic character image, applying the classification function, which is the greatest merit of those of neural networks applied to the various pans so far, to the stenographic character recognition, relatively simple classification work. Korean stenographic recognition algorithms, which recognize the characters by using some methods, have a quantitative problem that despite the simplicity of the structure, a lot of basic characters are impossible to classify into a type. They also have qualitative one that it is not easy to classify characters for the delicacy of the character forms. Even though this is the result of experiment under the limited environment of the basic characters, this shows the possibility that the stenographic characters can be recognized effectively by neural network system. In this system, we got 90.86% recognition rate as an average.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2001년도 하계종합학술대회 논문집(5)
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• pp.61-64
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• 2001

This paper describes a basic study for the classification of cognitive mental states as a basic research of a human-computer interface technique. To recognize the mental states, we obtained 22 subjects’brain waves in course of two types of experiments. One of the experiments is to choose an answer among yes, no or reject buttons, to underlying questions and the other is to select an icon displayed in a monitor screen. After acquiring the brain wave signals, we construct a feature set with the percent power increase for a given segment with respect to that of the reference period. The linear discriminative algorithm is used to classify the cognitive yes/no mental states.

• KIEE International Transaction on Systems and Control
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• 제12D권1호
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• pp.27-32
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• 2002

In this paper, we would study the applicability of neural networks to the recognition process of Korean stenographic character image, applying the classification function, which is the greatest merit of those of neural networks applied to the various parts so far, to the stenographic character recognition, relatively simple classification work. Korean stenographic recognition algorithms, which recognize the characters by using some methods, have a quantitative problem that despite the simplicity of the structure, a lot of basic characters are impossible to classify into a type. They also have qualitative one that It Is not easy to classify characters fur the delicacy of the character farms. Even though this is the result of experiment under the limited environment of the basic characters, this shows the possibility that the stenographic characters can be recolonized effectively by neural network system. In this system, we got 90.86% recognition rate as an average.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2023년도 봄 학술논문 발표대회
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• pp.331-332
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• 2023

With the aging of buildings, the number and importance of regular inspections of buildings are increasing. The current safety inspection goes through a procedure in which a skilled technician visits an old building, visually checks it, takes a photo, and finally organizes and judges it at the office. For this, field personnel and analysis and review personnel are required. Since the inspection procedure includes taking pictures, a huge amount of data has been accumulated from the time digital photos were used to the present. When a model that can check cracks outside a building is developed using these data, manpower and time required can be greatly reduced. Therefore, this study aims to create a model for classifying cracks that occur outside the building through the artificial intelligence method. The created model can be used as a basic model for determining cracks only by external photography in the future, and furthermore, it can be used as basic data for calculating the size and width of cracks.

• 한국도서관정보학회지
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• 제34권1호
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• pp.239-260
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• 2003

이 연구는 농학분야의 정보자료를 효율적으로 관리하기 위한 새로운 문헌분류표의 모형을 제시하기 위하여 시도된 것이다. 이를 위해 먼저 농학분야의 학문적 정의와 범위, 체계에 대하여 고찰하였고, 현재 사용되고 있는 KDC, DDC, UDC, NDC 등의 문헌분류법에서 농학분야 주제를 전개하고 있는 강목표에 대하여 비교 분석하였고 NAL의 AGRICOLA SCC를 살펴보았다. 그리고 이를 토대로 농학분야의 새로운 문헌분류표의 강목을 설정하여 전개하였다. 새로운 강목분류표의 전개는 농업과 관련한 인문사회학, 식물관련 농업, 동물관련 농업, 인간과의 관계성, 농업관련 보조분야 순으로 전개하였고, 강목표는 23개의 항목으로 설정되었다.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1998년도 추계학술대회
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• pp.255-256
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• 1998

In this paper, we propose the multi-axial classification system using parallel coding method that is systemic and flexible properties for representing dental clinical behavior. The methodology and organization of this thesis as follows. First, an analysis of other classification systems. Second, the domain of medical behavior and axises using selected elements was were determined. Third, the new code system is constructed of these common factors in properties of prediction of hierarchy, brevity, simplicity, flexibility and mnemonic usage. Finally, the framework of classification system for dental was made using multi-axial code system. The result of the this study, the eight bases axis of multi-axial code system is composed and can be basic information of research for construction of classification system of all medical domain.