• 한국컴퓨터정보학회논문지
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• 제28권2호
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• pp.9-17
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• 2023

본 논문에서는 베어링의 결함 진단을 위한 단일 클래스 분류의 진동 이상 탐지 시스템을 제안한다. 베어링 고장으로 인해 발생하는 경제적 및 시간적 손실을 줄이기 위해 정확한 결함 진단시스템은 필수적이며 문제 해결을 위해 딥러닝 기반의 결함 진단 시스템들이 널리 연구되고 있다. 그러나 딥러닝 학습을 위한 실제 데이터 채집 환경에서 비정상 데이터 확보에 어려움이 있으며 이는 데이터 편향을 초래한다. 이에 정상 데이터만 활용하는 단일 클래스 분류 방법을 활용한다. 일반적인 방법으로는 AutoEncoder를 통한 압축과 복원 과정을 학습하여 진동 데이터의 특성을 추출한다. 추출된 특성으로 단일 클래스 분류기를 학습하여 이상 탐지를 실시한다. 하지만 이와 같은 방법은 진동 데이터의 주파수 특성을 고려하지 않아서 진동 데이터의 특성을 효율적 추출할 수 없다. 이러한 문제를 해결하기 위해 진동 데이터의 주파수 특성을 고려한 AutoEncoder 모델을 제안한다. 분류 성능은 accuracy 0.910, precision 1.0, recall 0.820, f1-score 0.901이 나왔다. 주파수 특성을 고려한 네트워크 설계로 기존 방법들보다 우수한 성능을 확인하였다.

• 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.

• 한국멀티미디어학회논문지
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• 제22권11호
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• pp.1251-1258
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• 2019

Intelligent tutoring system enables users to effectively learn by utilizing various artificial intelligence techniques. For instance, it can recommend a proper curriculum or learning method to individual users based on their learning history. To do this effectively, user's characteristics need to be analyzed and classified based on various aspects such as interest, learning ability, and personality. Even though data labeled by the characteristics are required for more accurate classification, it is not easy to acquire enough amount of labeled data due to the labeling cost. On the other hand, unlabeled data should not need labeling process to make a large number of unlabeled data be collected and utilized. In this paper, we propose a semi-supervised learning method based on feedback variational auto-encoder(FVAE), which uses both labeled data and unlabeled data. FVAE is a variation of variational auto-encoder(VAE), where a multi-layer perceptron is added for giving feedback. Using unlabeled data, we train FVAE and fetch the encoder of FVAE. And then, we extract features from labeled data by using the encoder and train classifiers with the extracted features. In the experiments, we proved that FVAE-based semi-supervised learning was superior to VAE-based method in terms with accuracy and F1 score.

• 한국언어정보학회:학술대회논문집
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• 한국언어정보학회 2007년도 정기학술대회
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• pp.146-154
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• 2007

AutoCor is a method for the automatic acquisition and classification of corpora of documents in closely-related languages. It is an extension and enhancement of CorpusBuilder, a system that automatically builds specific minority language corpora from a closed corpus, since some Tagalog documents retrieved by CorpusBuilder are actually documents in other closely-related Philippine languages. AutoCor used the query generation method odds ratio, and introduced the concept of common word pruning to differentiate between documents of closely-related Philippine languages and Tagalog. The performance of the system using with and without pruning are compared, and common word pruning was found to improve the precision of the system.

• 방송공학회논문지
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• 제10권2호
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• pp.156-165
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• 2005

This paper presents a new auto brighoess control algorithm fur adaptive background subtraction. The algorithm is designed to cope with the problem of auto-brightness adjustment feature of consumer-type cameras. The experimental results show the proposed method improves performance of the classification. This will be beneficial to many computer vision applications in term of reducing the cost of implementation and making them more available to the mass consumer market.

• 대한세포병리학회지
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• 제9권2호
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• pp.139-146
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• 1998

The AutoPap 300 QC System is an automated device for the analysis and classification of conventional cervical cytology slides for quality control purpose. These studies evaluated the sensitivity of the AutoPap 300 QC System, and estimated morphologic features other than epithelial abnormality to identify a high quality control(QC) score with the AutoPap 300 QC System. The sensitivity of the AutoPap 300 QC System at 10% review rate for 210 cases of cervicovaginal cytology with low grade squamous intraepithelial lesion(LSIL) and higher grade lesion was assessed, and compared with a 10% random rescreening. The morphologic features, such as presence of endocervical component, dirty background, atrophy, abnormal ceil size, and celluiarity of single atypical cells were estimated in 45 cases of no review and 30 cases of QC review cases. The AutoPap 300 QC System identified 119(56.7%) out of 210 cases with LSIL and higher grade lesion at 10% review rate. It was more sensitive to squamous cell lesions$(50{\sim}62%)$ than to glandular lesions(10%). The dirty background and the scanty cellularity of single atypical cells were significantly related to low QC score. Conclusively, AutoPap 300 QC System is superior to human random rescreen for the identification of false negative smears. The upgrading of this device is required to enhance the defection of glandular lesion and certain Inadequate conditions of the slides.

• 시스템엔지니어링학술지
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• 제16권1호
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• pp.51-57
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• 2020

The fighter pilot uses radar mounted on the fighter to obtain high-resolution SAR (Synthetic Aperture Radar) images for a specific area of distance, and then the pilot visually classifies targets within the image. However, the target configuration captured in the SAR image is relatively small in size, and distortion of that type occurs depending on the depression angle, making it difficult for pilot to classify the type of target. Also, being present with various types of clutters, there should be errors in target classification and pilots should be even worse if tasks such as navigation and situational awareness are carried out simultaneously. In this paper, the concept of operation and functional structure of radar system for fighter jets were presented to transfer the SAR image target classification task of fighter pilots to radar system, and the method of target classification with high accuracy was studied using the CNN ensemble model to archive higher classification accuracy than single CNN model.

• 통상정보연구
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• 제7권4호
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• pp.351-365
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• 2005

Korean auto mobile industry has been contributed to development on national economy for last 30 years. Especially, The fact is that latest increasement of Korean automobile selling is worthy of notice in U.S. market which is the biggest automobile market of the world. But development of automobile industry unattainable nothing of helping of auto parts industry. So, when we discuss about growth of automobile industry, we also have to consider role of auto parts industry at the same time. The purpose of this study was to analyze exporting competition of Korean auto parts in U.S. market by using Index of Export Bias and Market Comparative Advantage Index. For attaining the purpose of study, we classified the Korean auto parts which exported to U.S. market and the world by using the six units classification of the Harmonized System(HS). Also we measured Index of Export Bias and Market Comparative Advantage Index. Analyzing period was 1998-2004. The results of Index of Export Bias indicated that HS Code No. 8708.50, 8708.91 represented over 3 numerical value and 8708.92, 8708.60, 8708.39, 8708.29 represented over 2 numerical value. Additional results indicated that the Korean auto parts which gained exporting competition in the U.S. market were HS Code No. 8708.70, 8708.93, 8708.92. The products which will have exporting competition in the U.S. market would be HS Code No. 8708.99,

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제8권12호
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• pp.483-490
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• 2019

네트워크 침입 탐지 시스템(NIDS)에서 분류의 기능은 상당히 중요하며 탐지 성능은 다양한 특징에 따라 달라진다. 최근 딥러닝에 대한 연구가 많이 이루어지고 있으나 네트워크 침입탐지 시스템에서는 많은 수의 트래픽과 고차원의 특징으로 인하여 속도가 느려지는 문제점이 있다. 따라서 딥러닝을 분류에 사용하는 것이 아니라 특징 추출을 위한 전처리 과정으로 사용하며 추출한 특징을 기반으로 분류하는 연구 방법을 제안한다. 딥러닝의 대표적인 비지도 학습인 Stacked AutoEncoder를 사용하여 특징을 추출하고 Random Forest 분류 알고리즘을 사용하여 분류한 결과 분류 성능과 탐지 속도의 향상을 확인하였다. IOT 환경에서 수집한 데이터를 이용하여 정상 및 공격트래픽을 멀티클래스로 분류하였을 때 99% 이상의 성능을 보였으며, AE-RF, Single-RF와 같은 다른 모델과 비교하였을 때도 성능 및 탐지속도가 우수한 것으로 나타났다.

• 통상정보연구
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• 제8권3호
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• pp.299-321
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• 2006

The purpose of this study was to analyze export competitiveness of Korean auto parts and Chinese auto parts in U.S. market by using Index of Export Bias and Market Comparative Advantage Index. For attaining the purpose of study, we classified the auto parts which exported to U.S. market and the imported products by using the six units classification of the Harmonized System(HS). Analyzing period was 1998-2005. The analysis of Korean results of MCA indicated that the Korean auto parts which gained export competitiveness in the U.S. market were HS Code No. 8708.94, 8708.99, 8708.92. The products which will have export competitiveness in the U.S. market would be HS Code No. 8708.93, 8708.39, 8708.60 respectively. On the other hand, the results indicated that the Chinese auto parts which gained export competitiveness in the U.S. market were HS Code No. 8708.70, 8708.31, 8708.91, 8708.60, 8708.39. From this study, we find the following strategies for successful advancing into the U.S. and world market. i) Linking strategy through working cooperation with local auto firms, government and academic world. ii) Advance strategy of auto firm accompany by module working and system auto parts firm. iii) Retention strategy of large technical institution established by auto parts firms and taking cooperation of auto firms iv) Settlement strategy for having weaken competitive article and production field. v) Cost-cutting strategy through strengthening logistics cooperation system between auto parts firms and auto firms. vi) Active invitation strategy of foreign investment under quickly cooperating of government.