• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1353-1366
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• 2015

These days Shewhart control chart for evaluating stability of the process is widely used in various field. But it must follow strict assumption of distribution. In real-life problems, this assumption is often violated when many quality characteristics follow non-normal distribution. Moreover, it is more serious in multivariate quality characteristics. To overcome this problem, many researchers have studied the non-parametric control charts. Recently, SVDD (Support Vector Data Description) control chart based on RBF (Radial Basis Function) Kernel, which is called K-chart, determines description of data region on in-control process and is used in various field. But it is important to select kernel parameter or etc. in order to apply the K-chart and they must be predetermined. For this, many researchers use grid search for optimizing parameters. But it has some problems such as selecting search range, calculating cost and time, etc. In this paper, we research the efficiency of selecting parameter regions as data structure vary via simulation study and propose a new method for determining parameters so that it can be easily used and discuss a robust choice of parameters for various data structures. In addition, we apply it on the real example and evaluate its performance.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.592-595
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• 2010

본 논문에서는 감시카메라 환경에서 취득한 오디오 데이터를 입력으로 하여, 비정상 상황을 인식하는 시스템을 제안한다. 제안된 시스템은 단일클래스 SVM의 대표적인 모델인 SVDD와 최근 얼굴 인식 분야에서 성공적인 업적을 보여주고 있는 신호 처리 분야의 SRC를 계층적으로 결합한 구조로써, 첫 번째 계층에서는 SVDD로 비정상 소리를 신속하게 탐지하여 관리자에게 알람 경고하고, 두 번째 계층의 SRC는 탐지된 비정상 소리를 유형별로 세분화 식별하여 관리자에게 비상 상황을 보고함으로써 관리자의 위기 상황 대처를 돕는다. 제안된 시스템은 실시간 처리가 가능하며, 점증적 갱신의 학습 능력으로 인하여 비정상 오디오 데이터베이스의 변화에도 능동적으로 적응할 수 있다. 실험을 통하여 제안된 시스템의 성능을 검증한다.

• The Korean Journal of Applied Statistics
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• v.26 no.6
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• pp.903-913
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• 2013

Social network services generate a considerable amount of social data every day on personal feelings or thoughts. This social data provides changing patterns of information production and consumption but are also a tool that reflects social phenomenon. We analyze negative emotional words from daily blogs to detect the change in blooger sentiment using multivariate control charts. We used the all the blogs produced between 1 January 2008 and 31 December 2009. Hotelling's T-square control chart control chart is commonly used to monitor multivariate quality characteristics; however, it assumes that quality characteristics follow multivariate normal distribution. The performance of a multivariate control chart is affected by this assumption; consequently, we introduce the support vector data description and its extension (K-control chart) suggested by Sun and Tsung (2003) and they are applied to detect the chage in blogger sentiment.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.935-936
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• 2006

In this paper, we have applied a real-time face processor includes detection, recognition, and learning to a intelligent store management service robot. We use the Haar classifier and adaboost learning algorithm for face detection. For face recognition and learning, a PCA algorithm and a SVDD algorithm is used. We have developed a store management service robot and applied these algorithms to verify the performance.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.1
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• pp.7-14
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• 2010

In this paper, we introduce a hierarchical internet application traffic classification system based on SVM as an alternative overcoming the uppermost limit of the conventional methodology which is using the port number or payload information. After selecting an optimal attribute subset of the bidirectional traffic flow data collected from the campus, the proposed system classifies the internet application traffic hierarchically. The system is composed of three layers: the first layer quickly determines P2P traffic and non-P2P traffic using a SVM, the second layer classifies P2P traffics into file-sharing, messenger, and TV, based on three SVDDs. The third layer makes specific classification of the entire 16 application traffics. By classifying the internet application traffic finely or coarsely, the proposed system can guarantee an efficient system resource management, a stable network environment, a seamless bandwidth, and an appropriate QoS. Also, even a new application traffic is added, it is possible to have a system incremental updating and scalability by training only a new SVDD without retraining the whole system. We validate the performance of our approach with computer experiments.