• Proceedings of the Korean Society for Quality Management Conference
• /
• 2004.04a
• /
• pp.148-151
• /
• 2004

This study presents a new intelligent yield management methodology which can forecast the yield level of a production unit based on features' behaviors. In this proposed methodology, we identify the existing features using C5.0 that are combination of nodes (i.e., variables) in the decision tree generated by C5.0, use SOM(Self-Organizing Map) neural networks in oder to extract the feature's patterns and classify, and then make features' control rules using C5.0.

• Journal of Sensor Science and Technology
• /
• v.10 no.6
• /
• pp.286-294
• /
• 2001

In this study, 3-D underwater object recognition using the self-made 3-3 type composite ultrasonic transducer and modified SOFM(Self Organizing Feature Map) neural network are investigated. Properties of the self-made 3-3 type composite specimens are satisfied considerably with requirements as an underwater ultrasonic transducer's materials. 3-D underwater all object's recognition rates obtained from both the training data and testing data in different objects, such as a rectangular block, regular triangular block, square block and cylinderical block, were 100% and 94.0%, respectively. All object's recognition rates are obtained by utilizing the self-made 3-3 type composite transducer and SOFM neural network. From the object recognition rates, it could be seen that an ultrasonic transducer fabricated with the self-made 3-3 type composite resonator will be able to have application for the underwater object recognition.

• Journal of the Korean Institute of Telematics and Electronics S
• /
• v.36S no.8
• /
• pp.101-112
• /
• 1999

In this paper, the codebook design method of VQ (vector quantization) is proposed an method to extract feature data of image for multimedia information searching. Conventional VQ codebook design methods are unsuitable to extract the feature data of images because they have too much computation time, memory for vector decoding and blocking effects like DCT (discrete cosine transform). The proposed design method is consists of the feature extraction by WT (wavelet transform) and the data group divide method by PCA (principal component analysis). WT is introduced to remove the blocking effect of an image with high compressing ratio. Computer simulations show that the proposed method has the better performance in processing speed than the VQ design method using SOM (self-organizing map).

• Journal of Institute of Control, Robotics and Systems
• /
• v.5 no.7
• /
• pp.821-826
• /
• 1999

This paper proposes a face recognition method using feature spaces and image vectors in the image plane. We obtain the 2-D feature space using the self-organizing map which has two inputs from the axis of the given image. The image vector consists of its weights and the average gray levels in the feature space. Also, we can reconstruct an normalized face by using the image vector having no connection with the size of the given face image. In the proposed method, each face is recognized with the best match of the feature spaces and the maximum match of the normally retrieval face images, respectively. For enhancing recognition rates, our method combines the two recognition methods by the feature spaces and the retrieval images. Simulations are conducted on the ORL(Olivetti Research laboratory) images of 40 persons, in which each person has 10 facial images, and the result shows 100% recognition and 14.5% rejection rates for the 20$\times$20 feature sizes and the 24$\times$28 retrieval image size.

• Annual Conference on Human and Language Technology
• /
• 1995.10a
• /
• pp.137-142
• /
• 1995

코호넨의 SOFM(Self-Organizing Feature Map)온 빠른 검증 학습이 가능하여 다층 퍼셉트론의 단점을 보완할 수 있는 패턴분류기로 부각되고 있다. 그러나 기본적으로 고정된 크기와 구조의 네트워크를 사용하기 때문에 실재 문제에 적용하기가 쉽지 않다는 문제가 있다. 본 논문에서는 패턴에 대한 사전 정보없이 복잡한 패턴공간을 적응적으로 분할하기 위해 구조적응되는 자기조직화 신경망을 소개하고 이를 인쇄체 한글 문자의 인식에 적용한 결과를 보여준다. 여기에서 제안하는 신경망은 SOFM의 각 셀이 좀더 자세한 SOFM으로 확장될 수 있도록하며, 확률분포가 0인 셀을 제거함으로써 패턴 공간에 보다 근사한 분류를 가능하게 한다. 실제로 이러한 방식이 한글과 같은 복잡한 분류 문제에서 어떻게 작동하는지 설명하고, 한글 완성형 2350자에 대해 실험한 결과를 보여준다.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.19-19
• /
• 2000

This paper introduces a new scheme for neural network classifier which can describe the shape of patterns in clustered group by using a self-organizing teeming algorithm. The prototype based neural network classifier can not describe the shape of group and it has low classification performance when the data groups are complex. To improve above-mentioned problem, new neural scheme is introduced. This proposed neural network algorithm can be regarded as the extension of self-organizing feature map which can describe The experimental results shows that the proposed algorithm can describe the shape of pattern successfully.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.32 no.9C
• /
• pp.861-866
• /
• 2007

A clustering algorithm for Gaussian Probability Distribution Function (GPDF) data called Centroid Neural Network with a Bhattacharyya Kernel (BK-CNN) is proposed in this paper. The proposed BK-CNN is based on the unsupervised competitive Centroid Neural Network (CNN) and employs a kernel method for data projection. The kernel method adopted in the proposed BK-CNN is used to project data from the low dimensional input feature space into higher dimensional feature space so as the nonlinear problems associated with input space can be solved linearly in the feature space. In order to cluster the GPDF data, the Bhattacharyya kernel is used to measure the distance between two probability distributions for data projection. With the incorporation of the kernel method, the proposed BK-CNN is capable of dealing with nonlinear separation boundaries and can successfully allocate more code vector in the region that GPDF data are densely distributed. When applied to GPDF data in an image classification probleml, the experiment results show that the proposed BK-CNN algorithm gives 1.7%-4.3% improvements in average classification accuracy over other conventional algorithm such as k-means, Self-Organizing Map (SOM) and CNN algorithms with a Bhattacharyya distance, classed as Bk-Means, B-SOM, B-CNN algorithms.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2002.11a
• /
• pp.445-453
• /
• 2002

Past and current customer behavior is the best predicator of future customer behavior. This paper introduces a procedure on personalized defection detection and prevention for an online game site. The basic idea for our defection detection and prevention is adopted from the observation that potential defectors have a tendency to take a couple of months or weeks to gradually change their behavior (i.e. trim-out their usage volume) before their eventual withdrawal. For this purpose, we suggest a SOM (Self-Organizing Map) based procedure to determine the possible states of customer behavior from past behavior data. Based on this representation of the state of behavior, potential defectors are detected by comparing their monitored trajectories of behavior states with frequent and confident trajectories of past defectors. The key feature of this study includes a defection prevention procedure which recommends the desirable behavior state for the ext period so as to lower the likelihood of defection. The defection prevention procedure can be used to design a marketing campaign on an individual basis because it provides desirable behavior patterns for the next period. The experiments demonstrate that our approach is effective for defection prevention and efficient for defection detection because it predicts potential defectors without deterioration of prediction accuracy compared to that of the MLP (Multi-Layer Perceptron) neural network.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.52 no.2
• /
• pp.102-106
• /
• 2003

This paper describes a new pattern classifier neural network to extract the feature from a letter. The proposed pattern classifier is based on relative distance, which is measure between an input datum and the center of cluster group. So, the proposed classifier neural network is called relative neural network(RNN). According to definitions of the distance and the learning rule, the structure of RNN is designed and the pseudo code of the algorithm is described. In feature extraction of letter, RNN, in spite of deletion of learning rate, resulted in the identical performance with those of winner-take-all(WTA), and self-organizing-map(SOM) neural network. Thus, it is shown that RNN is suitable to extract the feature of a letter.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.5
• /
• pp.1856-1869
• /
• 2015

This paper addresses the issues of 3D human activity detection, tracking and recognition from RGB-D video sequences using a feature structured framework. During human tracking and activity recognition, initially, dense depth images are captured using depth camera. In order to track human silhouettes, we considered spatial/temporal continuity, constraints of human motion information and compute centroids of each activity based on chain coding mechanism and centroids point extraction. In body skin joints features, we estimate human body skin color to identify human body parts (i.e., head, hands, and feet) likely to extract joint points information. These joints points are further processed as feature extraction process including distance position features and centroid distance features. Lastly, self-organized maps are used to recognize different activities. Experimental results demonstrate that the proposed method is reliable and efficient in recognizing human poses at different realistic scenes. The proposed system should be applicable to different consumer application systems such as healthcare system, video surveillance system and indoor monitoring systems which track and recognize different activities of multiple users.