• 정보관리연구
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• 제27권2호
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• pp.33-64
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• 1996

본 연구는 데이터베이스 기술분류의 표준시안을 제시하기 위하여 1차년도(1994년) 연구 결과에 대한 관점을 체계화하고 구체화시켜 수정, 보완하는 형식으로 이루어졌다. 분류관점을 정보와 이를 지원하는 시스템 측면으로 크게 나누어, 데이터베이스 일반, 정보유통, 정보검색, 데이터베이스 시스템, 주변 관련주제를 분류기준으로 하는 표준 시안의 모형이 제시되었다.

• 한국도서관정보학회지
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• 제32권4호
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• pp.99-117
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• 2001

본 연구는 분류자동화를 위해 이미 연구된 바 있는 농학 및 의학분야의 AutoBC 시스템에 대한 계속적인 연구의 일환으로 식물학분야의 문헌에 대해 분류자동화가 가능한지의 여부를 CC의 원리를 응용하여 실험 및 검증한 것이다. 분류자동화를 위한 데이터베이스는 원통형과 행렬식의 원리에 의해 설계되었으며, 문헌의 표제나 키워드를 입력하여 자동적인 주제인지 및 분류기호가 생성될 수 있는 윈도우용 자동분류시스템을 새로이 개발하여 실험하였다.

• 한국측량학회지
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• 제17권2호
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• pp.189-196
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• 1999

지금까지 원격탐사 영상의 분류 결과를 GIS에 하나의 레이어 또는 속성항목으로 이용하기 위한 다양한 연구가 진행되어오고 있으나 퍼지분류결과를 GIS에 이용하려는 시도는 그리 많지 않았던 것이 사실이다. 그러므로, 이 연구에서는 기존에 많이 이용되고 있는 원격탐사 영상의 분류방법에 비해 정확도 면에서 보다 신뢰할 수 있고 분류항목별 분류결과를 독립적으로 추출할 수 있는 퍼지감독분류 결과를 GIS에 적용해보고자 하는 의도에서 시작되었다. 이 연구의 진행과정에서 퍼지분류 결과를 GIS 데이터베이스의 그리드 데이터로 변환하였으며, Membership Grade Value 파일들은 지형정보체계의 테이블 데이터로 변환하여 포인터 레이어를 매개로 그리드의 각 셀에 대한 Membership Grade Value를 확인할 수 있도록 함으로써 퍼지 분류 영상을 GIS 데이터베이스로 이용할 수 있도록 하였다.

• Journal of Information Processing Systems
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• 제14권3호
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• pp.709-726
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• 2018

Database classification is an important preprocessing step for the multi-database mining (MDM). In fact, when a multi-branch company needs to explore its distributed data for decision making, it is imperative to classify these multiple databases into similar clusters before analyzing the data. To search for the best classification of a set of n databases, existing algorithms generate from 1 to ($n^2-n$)/2 candidate classifications. Although each candidate classification is included in the next one (i.e., clusters in the current classification are subsets of clusters in the next classification), existing algorithms generate each classification independently, that is, without taking into account the use of clusters from the previous classification. Consequently, existing algorithms are time consuming, especially when the number of candidate classifications increases. To overcome the latter problem, we propose in this paper an efficient approach that represents the problem of classifying the multiple databases as a problem of identifying the connected components of an undirected weighted graph. Theoretical analysis and experiments on public databases confirm the efficiency of our algorithm against existing works and that it overcomes the problem of increase in the execution time.

• 대한임베디드공학회논문지
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• 제10권3호
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• pp.179-188
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• 2015

We propose obstacle classification method based on 2D LIDAR(Light Detecting and Ranging) database. The existing obstacle classification method based on 2D LIDAR, has an advantage in terms of accuracy and shorter calculation time. However, it was difficult to classifier the type of obstacle and therefore accurate path planning was not possible. In order to overcome this problem, a method of classifying obstacle type based on width data of obstacle was proposed. However, width data was not sufficient to improve accuracy. In this paper, database was established by width, intensity, variance of range, variance of intensity data. The first classification was processed by the width data, and the second classification was processed by the intensity data, and the third classification was processed by the variance of range, intensity data. The classification was processed by comparing to database, and the result of obstacle classification was determined by finding the one with highest similarity values. An experiment using an actual autonomous vehicle under real environment shows that calculation time declined in comparison to 3D LIDAR and it was possible to classify obstacle using single 2D LIDAR.

• 한국도서관정보학회지
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• 제23권
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• pp.329-362
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• 1995

Newspaper Article Information is an important source of information on social phenomenon with historical value. The development of computer and technology of information communication enables the construction of Newspaper Article Database by CTS and service through computer communication. It made it possible for the peoples to utilize the Newspaper Article Information easily. However, it is very difficult to utilize the currently prevailing system. There are differences in classification system of Newspaper Article Database and the Data Display Format. This survey aims to review the characteristics of Newspaper Article Database and current domestic computer communication service system. By comparing the classification system of Retrieval Menu and Data Display Format, I intended to suggest the standardized way of utilization which enables the users utilize them more easily and conveniently. The results of this survey is as follows : 1. More sub-divided distinction of classification item is required. 2. Separate classification item should be established for the distinction of article form which is very difficult to classify the subject. 3. Data Display Format should be equi n.0, pped with standardized format and signal which enables the users recognize it easily.

• 한국문헌정보학회지
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• 제23권
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• pp.207-261
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• 1992

The purpose of this study is (1) to design and test a database which can be automatically classified, and (2) to generate automatic classification number by processing the keywords in titles using the code combination method of Colon Classification(CC) as well as an automatic recognition of subjects in order to develop an automatic classification system (Auto BC System) based on CC which can be applied to any research library. To conduct this study, 1,510 words in the fields of agricultrue and medicine were selected, analized in terms of [P], [M], [E], [S], [T] employed in CC, and included in a database for classification. For the above-mentioned subject fields, the principle of an automatic classification was specified in order to generate automatic classification codes as well as to perform an automatic subject recognition of the titles included. Whenever necessary, editing, deleting, appending and reindexing of a database can be made in this automatic classification system. Appendix 1 shows the result of the automatic classification of books in the fields of agriculture and medicine. The results of the study are summarized below. 1. The classification number for the title of a book can be automatically generated by using the facet principles of Colon Classification. 2. The automatic subject recognition of a book is achieved by designing a database making use of a globe-principle, and by specifying the subject field for each word. 3. The automatic subject-recognition of input data is achieved by measuring the number of searched words by each subject field. 4. The combination of classification numbers is achieved by flowcharting of classification formular of each subject field. 5. The efficient control of classification numbers is achieved by designing control codes on the database for classification. 6. The automatic classification by means of Auto BC has been proved to be successful in the research library concentrating on a Single field. The general library may have some problem in employing this system. The automatic classification through Auto BC has the following advantages: 1. Speed of the classification process can be improve. 2. The revision or updating of classification schemes can be facilitated. 3. Multiple concepts can be expressed in a single classification code. 4. The consistency of classification can be achieved with the classification formular rather than the classifier's subjective judgement. 5. A user's retrieving process can be made after combining the classification numbers through keywords relating to the material to be searched. 6. The materials can be classified by a librarian without subject backgrounds. 7. The large body of materials can be quickly classified by means of a machine processing. 8. This automatic classification is expected to make a good contribution to design of the total system for library operations. 9. The information flow among libraries can be promoted owing to the use of the same program for the automatic classification.

• 융합보안논문지
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• 제1권1호
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• pp.37-45
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• 2001

In this paper, separated and optimized pattern database model is proposed. In order to improve efficiency of Network-based IDS, pattern database is classified by proper basis. Classification basis is decided by the specific Intrusions validity on specific target. Using this model, IDS searches only valid patterns in pattern database on each captured packets. In result, IDS can reduce system resources for searching pattern database. So, IDS can analyze more packets on the network. In this paper, proper classification basis is proposed and pattern database classified by that basis is formed. And its performance is verified by experimental results.

• Journal of Electrical Engineering and Technology
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• 제13권1호
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• pp.485-492
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• 2018

In this paper, we propose facial expression recognition using CNN (Convolutional Neural Network), one of the deep learning technologies. The proposed structure has general classification performance for any environment or subject. For this purpose, we collect a variety of databases and organize the database into six expression classes such as 'expressionless', 'happy', 'sad', 'angry', 'surprised' and 'disgusted'. Pre-processing and data augmentation techniques are applied to improve training efficiency and classification performance. In the existing CNN structure, the optimal structure that best expresses the features of six facial expressions is found by adjusting the number of feature maps of the convolutional layer and the number of nodes of fully-connected layer. The experimental results show good classification performance compared to the state-of-the-arts in experiments of the cross validation and the cross database. Also, compared to other conventional models, it is confirmed that the proposed structure is superior in classification performance with less execution time.

• Journal of Information Processing Systems
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• 제9권1호
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• pp.31-40
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• 2013

In this paper, a novel method is proposed to build an ensemble of classifiers by using a feature selection schema. The feature selection schema identifies the best feature sets that affect the arrhythmia classification. Firstly, a number of feature subsets are extracted by applying the feature selection schema to the original dataset. Then classification models are built by using the each feature subset. Finally, we combine the classification models by adopting a voting approach to form a classification ensemble. The voting approach in our method involves both classification error rate and feature selection rate to calculate the score of the each classifier in the ensemble. In our method, the feature selection rate depends on the extracting order of the feature subsets. In the experiment, we applied our method to arrhythmia dataset and generated three top disjointed feature sets. We then built three classifiers based on the top-three feature subsets and formed the classifier ensemble by using the voting approach. Our method can improve the classification accuracy in high dimensional dataset. The performance of each classifier and the performance of their ensemble were higher than the performance of the classifier that was based on whole feature space of the dataset. The classification performance was improved and a more stable classification model could be constructed with the proposed approach.