• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.614-620
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• 2005

Ensemble에서 feature selection은 각 classifier의 학습할 데이터의 변수를 다르게 하여 diversity를 높이며, 이것은 일반적인 성능향상을 가져온다. Feature selection을 할 때 쓰는 방법 중의 하나가 Genetic Algorithm (GA)이며, GA-SVM은 GA를 기본으로 한 wrapper based feature selection mechanism으로 response model과 keystroke dynamics identity verification model을 만들 때 좋은 성능을 보였다. 하지만 population 안의 후보들간의 diversity를 보장해주지 못한다는 단점 때문에 classifier들의 accuracy와 diversity의 균형을 맞추기 위한 heuristic parameter setting이 존재하며 이를 조정해야만 하였다. 우리는 GA-SVM 알고리즘을 바탕으로, population안 후보들의 fitness를 측정할 때 accuracy와 diversity 둘 다 고려하는 fitness function을 도입하여 추가적인 classifier 선택 작업을 제거하면서 성능을 유지시키는 방안을 연구하였으며 결과적으로 알고리즘의 복잡성을 줄이면서도 모델의 성능을 유지시켰다.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.4
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• pp.327-332
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• 2002

The main problems of computational complexity in object tracking are definition of objects, segmentations and identifications in non-structured environments with erratic movements and collisions of objects. The object's information as a region that corresponds to objects without discriminating among objects are considered. This paper describes the algorithm that, automatically and efficiently, recognizes and keeps tracks of interest-regions selected by users in video or camera image sequences. The block-based feature matching method is used for the region tracking. This matching process considers only dominant feature points such as corners and curved-edges without requiring a pre-defined model of objects. Experimental results show that the proposed method provides above 96% precision for correct region matching and real-time process even when the objects undergo scaling and 3-dimen-sional movements In successive image sequences.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.11
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• pp.1132-1142
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• 1991

This paper proposed a method of feature parameter extraction for shape information analysis of moving object. In the 2-D plane, moving object are extracted by the difference method. Feature parameters of moving object are chosen area, perimeter, a/p ratio, vertex, x/y ratio. We changed brightness variation from the range of 600Lux to the 1400Lux and then determined Permissible Error range of feature parameter due to the brightness variation. So as to verify the validity of proposed method, experiment are performed with a toy car and it's results showed that decision error was less than 6%.

• Journal of Electrical Engineering and Technology
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• v.10 no.5
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• pp.2149-2154
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• 2015

This paper proposes a novel watermarking method to discover the tampers and localize it in digital image. The image which is to be used to generate a watermark is first wavelet decomposed and the edge feature from the sub bands of high frequency coefficients are retrieved to generate a watermark (Edge Feature Image) and which is to be embed on the cover image. Before embedding the watermark, the pixels of cover image are disordered through the Arnold Transform and this helps to upgrade the security of the watermark. The embedding of generated edge feature image is done only on the Least Significant Bit (LSB) of the cover image. The invisibleness and robustness of the proposed method is computed using Peak-Signal to Noise Ratio (PSNR) and Normalized Correlation (NC) and it proves that the proposed method delivers good results and the proposed method also detects and localizes the tampers efficiently. The invisibleness of proposed method is compared with the existing method and it proves that the proposed method is better.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1550-1552
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• 2005

In this paper, we have designed a Fingerprint Recognition System based on the Embedded LINUX. The fingerprint is captured using the AS-S2 semiconductor sensor. To extract a feature vector we transform the image of the fingerprint into a column vector. The image is row-wise filtered with the low-pass filter of the Haar wavelet. The feature vectors of the different fingerprints are compared by computing with the probabilistic neural network the distance between the target feature vector and the stored feature vectors in advance. The system implemented consists of a server PC based on the LINUX and a client based on the Embedded LINUX. The client is a Tynux box-x board using a PXA-255 CPU. The algorithm is simple and fast in computing and comparing the fingerprints.

• Journal of the Korean Data and Information Science Society
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• v.18 no.2
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• pp.315-326
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• 2007

The rapid proliferation of wireless networks and mobile computing applications has changed the landscape of network security. Anomaly detection is a pattern recognition task whose goal is to report the occurrence of abnormal or unknown behavior in a given system being monitored. This paper presents a dynamic anomaly detection scheme that can effectively identify a group of especially harmful internal masqueraders in cellular mobile networks. Our scheme uses the trace data of wireless application layer by a user as feature value. Based on the feature values, the use pattern of a mobile's user can be captured by rough sets, and the abnormal behavior of the mobile can be also detected effectively by applying a roughness membership function with both the age of the user profile and weighted feature values. The performance of our scheme is evaluated by a simulation. Simulation results demonstrate that the anomalies are well detected by the proposed dynamic scheme that considers the age of user profiles.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.204-206
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• 2005

In this paper, we have designed a Fingerprint Recognition System based on the Embedded LINUX. The fingerprint is captured using the AS-S2 semiconductor sensor. To extract a feature vector we transform the image of t10he fingerprint into a column vector. The image is row-wise filtered with the low-pass filter of the Haar wavelet. The feature vectors of the different fingerprints are compared by computing with the probabilistic neural network the distance between the target feature vector and the stored feature vectors in advance. The system implemented consists of a server PC based on the LINUX and a client based on the Embedded LINUX. The client is a Tynux box-x board using a PXA-255 CPU. The algorithm is simple and fast in computing and comparing the fingerprints.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.400-404
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• 1997

This paper presents spatial relationshaps and engineering features for the feature based modeling of Sheet Metal Weld Assemblies (SMWA) that are made of sheet metal components through are welding processes. Spatial relationships in ProMod-S, a sheet metal product modeler,are further extended for the SMWA modeling. Some spatial relationships for special weld joint types are newly introduced. The geometrical and topological relations between spatial reationship, mating features, and assembly features are defined. Finally, assembly data stucturess for the product modeling of SMWA are proposed. They are an engineering relation to represent the constraints between component features, and a mating bond to integrate component design information.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.2
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• pp.75-81
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• 2006

The purpose of this paper is to recognize the feature points of ECG and human pulse -which signal shows the electric and physical characteristics of heart respectively- using wavelet transform. Wavelet transform is proper method to analyze a signal in time-frequency domain. In the process of wavelet decomposition and reconstruction of ECG and human pulse signal, we removed the noises of signal and recognized the feature points of signal using some of decomposed component of signal. We obtained the result of recognition rate that is estimated about 95.45$\%$ in case of QRS complex, 98.08$\%$ in case of S point and P point and 92.81$\%$ in case of C point. And we computed diagnosis parameters such as RRI, U-time and E-time.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.2
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• pp.157-163
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• 2005

In this paper, we present the multimodal system based on the fusion of two user-friendly biometric modalities: Iris and Face. In order to reach robust identification and verification we are going to combine two different biometric features. we specifically apply 2-D discrete wavelet transform to extract the feature sets of low dimensionality from iris and face. And then to obtain Reduced Joint Feature Vector(RJFV) from these feature sets, Direct Linear Discriminant Analysis (DLDA) is used in our multimodal system. In addition, the Synergetic Neural Network(SNN) is used to obtain matching score of the preprocessed data. This system can operate in two modes: to identify a particular person or to verify a person's claimed identity. Our results for both cases show that the proposed method leads to a reliable person authentication system.