• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2006년도 추계학술대회 학술발표 논문집 제16권 제2호
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• pp.305-309
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• 2006

In this paper, a face recognition algorithm system using Principle Component Analysis is proposed. The algorithm recognized a person by comparing characteristics (features) of the face to those of known individuals which is a face database of Intelligence Control Laboratory(ICONL). Experiments were simulated in order to demonstrate the performance of this algorithm due to face recognition which presented for the classification of face and non-face and the classification of known and unknown.

• 대기
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• 제15권3호
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• pp.155-165
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• 2005

The characteristics of six precipitation systems occurred around Cheongju in 2002 are analyzed after the convective/stratiform radar echo classification using radar reflectivity from the Meteorological Research Institute"s X-band Doppler weather radar. The Biggerstaff and Listemaa (2000) algorithm is applied for the classification and reveals a physical characteristics of the convective and stratiform rain diagnosed from the three-dimensional structure of the radar reflectivity. The area satisfying the vertical profile of radar reflectivity is well classified, while the area near the radar site and the topography-shielded area show a mis-classification. The seasonal characteristics of the precipitation system are also analyzed using the contoured frequency by altitude diagrams (CFADs). The heights of maximum reflectivity are 4 km and 5.5 km in spring and summer, respectively, and the vertical gradient of radar reflectivity from 1.5 km to the melting layer in spring is larger than in summer.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1992년도 하계학술대회 논문집 A
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• pp.449-452
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• 1992

Before performing an adaptive stereo matching using satellite images, classification is needed as a preprocessing step. This paper describes that classification of three land cover types : river, mountain, and agricultural fields. We proposed that classification algorithm using pyramid structure and texture features. Results of applying the proposed algorithm to satellite image improved classification accuracy.

• 대한전기학회논문지:시스템및제어부문D
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• 제54권6호
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• pp.383-389
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• 2005

This paper proposes a pattern classification algorithm for the defect distribution of Hard Disk Drive (HDD). In the HDD production, the defect pattern of defective HDD set is important information to diagnosis of defective HDD set. In this paper, 5 characteristics are determined for the classification to six standard defect pattern classes. A fuzzy inference system is proposed, the inputs of which are 5 characteristic values and the outputs are the possibilities that the input pattern is classified to standard patterns. Therefore, classification result is the pattern with maximum possibility. The proposed algorithm is implemented with the PC system for defective HDD sets and shows its effectiveness.

• KIEE International Transactions on Electrophysics and Applications
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• 제5C권3호
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• pp.138-142
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• 2005

In this paper, we compared recognition rates between NN(neural networks) and clustering method as a scheme of off-line PD(partial discharge) diagnosis which occurs at the stator coil of traction motor. To acquire PD data, three defective models are made. PD data for classification were acquired from PD detector. And then statistical distributions are calculated to classify model discharge sources. These statistical distributions were applied as input data of two classification tools, BP(Back propagation algorithm) and ANFIS(adaptive network based fuzzy inference system) pre-processed FCM(fuzzy c-means) clustering method. So, classification rate of BP were somewhat higher than ANFIS. But other items of ANFIS were better than BP; learning time, parameter number, simplicity of algorithm.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제24권3호
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• pp.321-330
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• 2020

Data classification and clustering is one of the most common applications of the machine learning. In this paper, we aim to provide the insight of the classification for Turing pattern image, which has high nonlinearity, with feature engineering using the machine learning without a multi-layered algorithm. For a given image data X whose fixel values are defined in [-1, 1], X - X³ and ∇X would be more meaningful feature than X to represent the interface and bulk region for a complex pattern image data. Therefore, we use X - X³ and ∇X in the neural network and clustering algorithm to classification. The results validate the feasibility of the proposed approach.

• International Journal of Contents
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• 제2권3호
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• pp.11-17
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• 2006

The Clustering is representative method of components classification. The previous clustering methods that use cohesion and coupling cannot be effective because design pattern has focused on relation between classes. In this paper, we classified design patterns with features of object-oriented relationship. The result is that classification by clustering showed higher precision than classification by facet. It is effective that design patterns are classified by automatic clustering algorithm. When patterns are retrieved in classification of design patterns, we can use to compare them because similar pattern is saved to same category. Also we can manage repository efficiently because of storing patterns with link information.

• 한국멀티미디어학회논문지
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• 제24권4호
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• pp.550-557
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• 2021

Image steganalysis is a technique for detecting images with steganographic algorithms applied, called stego images. With state-of-the-art CNN-based steganalysis methods, we can detect stego images with high accuracy, but it is not possible to know which steganographic algorithm is used. Identifying stego images is essential for extracting embedded data. In this paper, as the first step for extracting data from stego images, we propose a hierarchical CNN structure for senary classification of steganographic algorithms. The hierarchical CNN structure consists of multiple CNN networks which are trained to classify each steganographic algorithm and performs binary or ternary classification. Thus, it classifies multiple steganogrphic algorithms hierarchically and stepwise, rather than classifying them at the same time. In experiments of comparing with several conventional methods, including those of classifying multiple steganographic algorithms at the same time, it is verified that using the hierarchical CNN structure can greatly improve the classification accuracy.

• 한국통신학회논문지
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• 제31권2A호
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• pp.79-85
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• 2006

고성능 라우터, 스위치 및 네트워크 보안 장비에서 패킷 포워딩 기능을 고속으로 수행하기 위해서는 효과적인 패킷 분류 기술이 필수적인데, 최근에는 TCAM과 검색엔진 등, 고속의 컨텐트 기반 검색 하드웨어를 이용하는 방법들이 사용되고 있다. 패킷 분류 시에는 트래픽 차단, 트래픽 모니터링 등의 목적을 위해서 많은 규칙들이 사용될 수 있고, 삽입과 삭제가 시스템 운용 중에 발생할 수 있다. 특히, 고속의 네트워크 환경에서 패킷 포워딩의 성능을 저하시키지 않기 위해서는 동적으로 변화하는 규칙들을 효과적으로 갱신하는 방법이 필요하다. 본 논문에서는 TCAM을 이용한 패킷 분류시 효과적인 갱신이나 재배치를 위해서 순서화된 부분 정렬 알고리즘을 제안하고, 실험을 통하여 TCAM의 이용률이 70$\%$까지 높은 상황에서도 갱신으로 인한 재배치가 거의 일어나지 않도록 하여 재배치로 인한 패킷 처리의 지연을 줄일 수 있다는 결과를 보인다.

• 한국컴퓨터정보학회논문지
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• 제9권4호
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• pp.33-39
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• 2004

본 논문에서는 LVQ 네트워크의 분류성능을 향상시키기 위하여 F.C.P. Net.을 이용하여 LVQ 학습알고리즘을 설계하였다. F.C.P. Net.의 입력층과 부류층 사이의 연결강도는 SOM과 LVQ 알고리즘을 이용하여 초기 참조벡터의 설정 및 학습이 가능하게 하였다. 마지막으로 패턴벡터를 부류층의 뉴런에 의해 종속부류로 분류하고, F.C.P. Net.의 부류층과 출력층 사이의 연결강도는 분류된 종속부류를 부류로 지정하는 학습을 하게 된다. 또한 부류의 수가 결정되기만 하면 입력층, 부류층, 출력층의 뉴런의 수를 결정 할 수 있도록 하였다. 제안된 학습알고리즘의 성능을 검증하기 위하여 Fisher의 Iris 데이터를 학습벡터 및 시험 벡터로 사용하여 시뮬레이션 하였고, 제안된 학습방식의 분류 성능은 기존의 LVQ와 비교되어 기존의 학습방식보다 우수한 분류성공률을 확인하였다.