• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.61-64
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• 2006

SVDD(support vector data description)는 one-class 서포트 벡터 학습 방법론 중 하나로 비정상 물체에서 정상 데이터를 구분하기 위해서 특징 공간(feature space)에서 정의된 구를 이용하는 전략을 쓰는 방법론이다. 하지만 SVDD는 모든 데이터에 대해서 같은 중요도를 부가하는 단점을 가지고 있다. 최근에, 이런 문제점을 보완하기 위해 데이터의 밀도 분포에 따라서 중요도를 다르게 부가하는 D-SVDD(density-induced support vector data description) 방법론이 발표되었고, 아직도 많은 연구가 진행되고 있다. 본 논문에서는 D-SVDD를 이용해서 노이즈가 섞인 비정상 데이터를 노이즈가 제거된 정상 데이터로 복원하는 방법에 대해서 논한다. 특히, 본 논문에서 제안하는 방법론을 다른 방법론과 비교하여 본 논문의 방법론의 효용성에 대해서 다룬다.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.1
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• pp.25-31
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• 2005

In this paper, we present a face detection system using the Support Vector Domain Description (SVDD) in color images. Conventional face detection algorithms require a training procedure using both face and non-face images. In SVDD however we employ only face images for training. We can detect faces in color images from the radius and center pairs of SVDD. We also use Entropic Threshold for extracting the facial feature and sliding window for improved performance while saving processing time. The experimental results indicate the effectiveness and efficiency of the proposed algorithm compared to conventional PCA (Principal Component Analysis)-based methods.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.1
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• pp.1-6
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• 2007

The SVDD (support vector data description) is one of the most well-known one-class support vector learning methods, in which one tries the strategy of utilizing balls defined on the feature space in order to distinguish a set of normal data from all other possible abnormal objects. Recently, with the objective of generalizing the SVDD which treats all training data with equal importance, the so-called D-SVDD (density-induced support vector data description) was proposed incorporating the idea that the data in a higher density region are more significant than those in a lower density region. In this paper, we consider the problem of further improving the D-SVDD toward the use of a partial reference set for testing, and propose an LMI (linear matrix inequality)-based optimization approach to solve the improved version of the D-SVDD problems. Our approach utilizes a new class of density-induced distance measures based on the RSDE (reduced set density estimator) along with the LMI-based mathematical formulation in the form of the SDP (semi-definite programming) problems, which can be efficiently solved by interior point methods. The validity of the proposed approach is illustrated via numerical experiments using real data sets.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1913-1914
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• 2008

최근 유비쿼터스 네트워크에 대한 관심이 높아지고 있다. 하지만 유비쿼터스 네트워크는 무선으로 데이터를 전송함으로서 특성상 쉽게 침입자들로부터 침입을 당할 수 있는 보안 문제가 중요하게 대두되고 있다. 이에 따라 강력한 침입탐지 기술에 대한 요구가 증가되고 있다. 본 논문에서는 갈수록 늘어나는 새로운 변형 공격에 대한 탐지를 위하여 LBG-SVDD을 이용한 침입탐지 기법을 제안한다. LBG-SVDD은 새로운 변형 공격 침입 탐지가 발견되었을 때, 새로운 변형 공격 형태에 대한 빠른 학습 훈련을 통해 공격 침입 탐지를 할 수 있다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.20 no.1
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• pp.105-111
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• 2010

This paper proposes the algorithm which addresses the problem of detecting leak of defaced confidential documents from original confidential document. Generally, a confidential document is defaced into various forms by insiders and then they are trying to leak these defaced documents to outside. Traditional algorithms detecting leak of documents have low accuracy because they are based on similarity of two documents, which do not reflect various forms of defaced documents in detection. In order to overcome this problem, this paper proposes a novel v-SVDD algorithm which is based on SVDD, the novelty detection algorithm. The result of experiment shows that there is significant improvement m the accuracy of the v-SVDD in comparison with the traditional algorithms.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.90-92
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• 2006

기존의 Support Vector Data Description (SVDD) 방법은 학습 데이터의 개수가 증가함에 따라 학습 시간이 지수 함수적으로 증가하므로, 대량의 데이터를 학습하는 데에는 한계가 있었다. 본 논문에서는 학습 속도를 빠르게 하기 위해 K-means clustering 알고리즘을 이용하는 SVDD 알고리즘을 제안하고자 한다. 제안된 알고리즘은 기존의 decomposition 방법과 유사하게 K-means clustering 알고리즘을 이용하여 학습 데이터 영역을 sub-grouping한 후 각각의 sub-group들을 개별적으로 학습함으로써 계산량 감소 효과를 얻는다. 이러한 sub-grouping 과정은 hypersphere를 이용하여 학습 데이터를 둘러싸는 SVDD의 학습 특성을 훼손시키지 않으면서 중심점으로 모여진 작은 영역의 학습 데이터를 학습하도록 함으로써, 기존의 SVDD와 비교하여 학습 정확도의 차이 없이 빠른 학습을 가능하게 한다. 다양한 데이터들을 이용한 모의실험을 통하여 그 효과를 검증하도록 한다.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.21 no.11
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• pp.997-1004
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• 2011

A fault detection algorithm of a charge and discharge system to ensure the safe use of hybrid electric vehicle is proposed in this paper. This algorithm can be used as a complementary way to existing fault detection technique for a charge and discharge system. The proposed algorithm uses a SVDD technique, which additionally utilizes two methods for learning a large amount of data; one is to incrementally learn a large amount of data, the other one is to remove the data that does not affect the next learning using a new data reduction technique. Removal of data is selected by using lines connecting support vectors. In the proposed method, the data processing speed is drastically improved and the storage space used is remarkably reduced than the conventional methods using the SVDD technique only. A battery data and speed data of a commercial hybrid electrical vehicle are utilized in this study. A fault boundary is produced via SVDD techniques using the input and output in normal operation of the system without using mathematical modeling. A fault detection simulation is performed using both an artificial fault data and the obtained fault boundary via SVDD techniques. In the fault detection simulation, fault detection time via proposed algorithm is compared with that of the peak-peak method. Also the proposed algorithm is revealed to detect fault in the region where conventional peak-peak method is never able to do.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.3
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• pp.266-274
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• 2003

This paper considers an incremental support vector learning for the abnormality detection problems. One of the most well-known support vector learning methods for abnormality detection is the so-called SVDD(support vector data description), which seeks the strategy of utilizing balls defined on the kernel feature space in order to distinguish a set of normal data from all other possible abnormal objects. The major concern of this paper is to modify the SVDD into the direction of utilizing the relation between the optimal solution and incrementally given training data. After a thorough review about the original SVDD method, this paper establishes an incremental method for finding the optimal solution based on certain observations on the Lagrange dual problems. The applicability of the presented incremental method is illustrated via a design example.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.6
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• pp.183-190
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• 2011

In this paper, we propose a prototype system for abnormal sound detection and identification which detects and recognizes the abnormal situations by means of analyzing audio information coming in real time from CCTV cameras under surveillance environment. The proposed system is composed of two layers: The first layer is an one-class support vector machine, i.e., support vector data description (SVDD) that performs rapid detection of abnormal situations and alerts to the manager. The second layer classifies the detected abnormal sound into predefined class such as 'gun', 'scream', 'siren', 'crash', 'bomb' via a sparse representation classifier (SRC) to cope with emergency situations. The proposed system is designed in a hierarchical manner via a mixture of SVDD and SRC, which has desired characteristics as follows: 1) By fast detecting abnormal sound using SVDD trained with only normal sound, it does not perform the unnecessary classification for normal sound. 2) It ensures a reliable system performance via a SRC that has been successfully applied in the field of face recognition. 3) With the intrinsic incremental learning capability of SRC, it can actively adapt itself to the change of a sound database. The experimental results with the qualitative analysis illustrate the efficiency of the proposed method.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.4
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• pp.410-415
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• 2006

The SVDD(support vector data description) is one of the most important one-class support vector learning methods, which depends on the strategy of utilizing the balls defined on the feature space to discriminate the normal data from all other possible abnormal objects. This paper addresses on the extension of the SVDD method toward the problem of recovering the normal contents from the data contaminated with noises. The validity of the proposed de-noising method is shown via application to recovering the high-resolution images from the low-resolution images based on the high-resolution training data.