• Journal of Broadcast Engineering
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• v.9 no.3
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• pp.196-206
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• 2004

This paper presents an efficient joint disparity-motion estimation algorithm for stereo sequence CODEC. Disparity vectors are estimated by the left and right motion vectors and previous disparity vectors for every frame. In order to obtain more accurate disparity vectors. we include a spatial prediction Process after the feint estimation. From joint estimation and spatial prediction, we can obtain accurate disparity vectors and then Increase coding efficiency. Finally, we proposed the backward quadtree decomposition. which helps the encoder to have a more detailed disparity vector map without transmitting additional coding bits for quadtree information. We confirmed superior performance of the proposed method through computer simulation.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.4
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• pp.349-354
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• 2014

PIN(Personal Identification Number)-based identification method has been used to identify the user of smart cards. However, this type of identification method has several problems. Firstly, PIN can be forgotten by owners of the card. Secondly, PIN can be used by others illegally. Furthermore, the possibility of hacking PIN can be high because this PIN type matching process is performed on terminal. Thus, in this paper we suggest a new identification method which is performed on smart card using face feature information. The proposed identification method uses low-sized face feature vectors and simple matching algorithm in order to get around the limits in computing capability and memory size of smart card.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.3
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• pp.226-231
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• 2013

In The matching algorithm for automatic packaging of drugs is essential to determine whether the canister can exactly refill the suitable medicine. In this paper, we propose a hierarchical neural network with the upper and lower layers which can perform real-time processing and classification of many types of medicine bottles to prevent accidental medicine disaster. A few number of low-dimensional feature vector are extracted from the label images presenting medicine-bottle information. By using the extracted feature vectors, the lower layer of MLP(Multi-layer Perceptron) neural networks is learned. Then, the output of the learned middle layer of the MLP is used as the input to the upper layer of the MLP learning. The proposed hierarchical neural network shows good classification performance and real- time operation in the test of up to 30 degrees rotated to the left and right images of 100 different medicine bottles.

• Composites Research
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• v.24 no.2
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• pp.38-45
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• 2011

In this paper, we present an optimization method for the single Dallenbach-layer type microwave absorbers composed of E-glass fabric/epoxy composite laminates. The composite prepreg containing carbon nanotubes (CNT) was used to control the electrical property of the composites laminates. The design technology using the genetic algorithm was used to get the optimal thicknesses of the laminates and the filler contents at various center frequencies, for which the numerical model of the complex permittivity of the composite laminate was incorporated. In the optimal design results, the content of CNT increased in proportion to the center frequency, but, on the contrary, the thickness of the microwave absorbers decreased. The permittivity and reflection loss are measured using vector network analyzer and 7 mm coaxial airline. The influence of the mismatches in between measurement and prediction of the thickness and the complex permittivity caused the shift of the center frequency, blunting of the peak at the center frequency and the reduction of the absorbing bandwidth.

• Food Science of Animal Resources
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• v.38 no.2
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• pp.362-375
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• 2018

This study proposed a rapid microscopic examination method for pork freshness evaluation by using the self-assembled hyperspectral microscopic imaging (HMI) system with the help of feature extraction algorithm and pattern recognition methods. Pork samples were stored for different days ranging from 0 to 5 days and the freshness of samples was divided into three levels which were determined by total volatile basic nitrogen (TVB-N) content. Meanwhile, hyperspectral microscopic images of samples were acquired by HMI system and processed by the following steps for the further analysis. Firstly, characteristic hyperspectral microscopic images were extracted by using principal component analysis (PCA) and then texture features were selected based on the gray level co-occurrence matrix (GLCM). Next, features data were reduced dimensionality by fisher discriminant analysis (FDA) for further building classification model. Finally, compared with linear discriminant analysis (LDA) model and support vector machine (SVM) model, good back propagation artificial neural network (BP-ANN) model obtained the best freshness classification with a 100 % accuracy rating based on the extracted data. The results confirm that the fabricated HMI system combined with multivariate algorithms has ability to evaluate the fresh degree of pork accurately in the microscopic level, which plays an important role in animal food quality control.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.26 no.2
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• pp.149-156
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• 2008

In this paper, we propose a method aiming at updating the building information of the digital maps using single high resolution satellite image and digital map. Firstly we produced a digital orthoimage through the automatic co-registration of QuickBird image and 1:1,000 digital map. Secondly we extracted building height information through the template matching of digital map's building vector data and the image's edges obtained by Canny operator. Finally we refined the shape of some buildings by using the result from template matching as the seed polygon of the greedy snake algorithm. In order to evaluate the proposed method's effectiveness, we estimated accuracy of the extracted building information using LiDAR DSM and 1:1,000 digital map. The evaluation results showed the proposed method has a good potential for extraction and revision of building information.

• Water for future
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• v.28 no.5
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• pp.219-233
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• 1995

A non-stationary multivariate model is selected in which the mean and variance of rainfall are not temporally or spatially constant. And the rainfall prediction system is constructed which uses the recursive estimation algorithm, Kalman filter, to estimate system states and parameters of rainfall model simulataneously. The on-line, real-time, multivariate short-term, rainfall prediction for multi-stations and lead-times is carried out through the estimation of non-stationary mean and variance by the storm counter method, the normalized residual covariance and rainfall speed. The results of rainfall prediction system model agree with those generated by non-stationary multivariate model. The longer the lead time is, the larger the root mean square error becomes and the further the model efficiency decreases form 1. Thus, the accuracy of the rainfall prediction decreases as the lead time gets longer. Also it shows that the mean obtained by storm counter method constitutes the most significant part of the rainfall structure.

• Journal of Korea Multimedia Society
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• v.9 no.7
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• pp.864-871
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• 2006

With the advancement of the digital broadcasting and popularity of Internet, there is an increasing demand for digital data. Recently, several studies have been made on the digital watermarking for copyright protection of digital data. We propose a blind video watermarking using minimum modification of motion vectors. Conventional methods based on motion vectors do watermarking using modification of motion vectors. However, change of motion vectors results in the degradation of video quality. Thus, our proposed algorithm minimizes modification of the original motion vectors to avoid degradation of video quality using simple embedded conditions. Besides, our scheme guarantees the amount of embedded watermark data using the adaptive threshold considering the human visual characteristic. In addition, this is compatible with current video compression standards without changing the bitstream. Experimental result shows that the proposed scheme obtains better video quality than other previous algorithms by about $0.5{\sim}1.0\;dB$.

• Journal of Intelligence and Information Systems
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• v.9 no.1
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• pp.139-155
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• 2003

This paper presents the new methodology of analyzing and classifying patterns of customers in mobile telecommunication market to enhance the performance of predicting the credit information based on the decision tree and neural network. With the application of variance selection process from decision tree, the systemic process of defining input vector's value and the rule generation were developed. In point of customer management, this research analyzes current customers and produces the patterns of them so that the company can maintain good customer relationship and makes special management on the customer who has huh potential of getting out of contract in advance. The real implementation of proposed method shows that the predicted accuracy is higher than existing methods such as decision tree(CART, C4.5), regression, neural network and combined model(CART and NN).

• Journal of Korea Multimedia Society
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• v.13 no.4
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• pp.575-581
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• 2010

With widespread use of the Internet and improvements in streaming media and compression technology, digital music, video, and image can be distributed instantaneously across the Internet to end-users. However, most conventional Digital Right Management are often not secure and not fast enough to process the vast amount of data generated by the multimedia applications to meet the real-time constraints. The SVC offers temporal, spatial, and SNR scalability to varying network bandwidth and different application needs. Meanwhile, for many multimedia services, security is an important component to restrict unauthorized content access and distribution. This suggests the need for new cryptography system implementations that can operate at SVC. In this paper, we propose a new scrambling encryption for reserving the characteristic of scalability in MPEG4-SVC. In the base layer, the proposed algorithm is applied and performed the selective scambling. And it encrypts various MVS and intra-mode scrambling in the enhancement layer. In the decryption, it decrypts each encrypted layers by using another encrypted keys. Throughout the experimental results, the proposed algorithms have low complexity in encryption and the robustness of communication errors.