• Journal of the Korean Institute of Telematics and Electronics
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• v.16 no.4
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• pp.1-12
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• 1979

This article is a sequel to " Korean Character Processing: Part I. Theoretical Foundation " and deals with the practical and historical aspects of the same subject. We discuss , in the first half, the functional design of Korean I/O terminals, Korean character generators based on the conversion algorithm and dot matrix fonts, input keyboard configuration ( trade -offs between a key set and the number of key -strokes ), and the conditions to be considered for binary code design. The second half of the article is devoted to the history of Korean Character processing which is seen from the personal viewpoints. The recorded works are classified into 4 groups according to their maj or contents. Then we bring up each problematic issue to give a critical review of articles . Issues related to output (conversion process) and input ( character recognition) are separated. The bibliography is given in a chronological order.cal order.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.47 no.12
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• pp.31-38
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• 2010

In tins paper, a new DA(Distributed Arithmetic) tilter implementation technique has been proposed. Contrary to vertical directional calculation of input sample bit format in the conventional DA implementation technique, proposed implementation technique utilizes horizontal directional calculation of input sample bit format. Since proposed technique calculates in horizontal direction, it does not need ROM and utilizes the Modified Booth algorithm. Furthermore proposed technique can be applied to implement the variable coefficients filters in addition to the fixed coefficients filters. Using conventional and proposed techniques, a 20 tap filter is implemented by Verilog-HDL coding. Through Synopsis synthesis tool, it has been shown that 41.6% area reduction can be achieved.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.7
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• pp.424-433
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• 2003

Evolutionary design related to the optimal design of Polynomial Neural Networks (PNNs) structure for model identification of complex and nonlinear system is studied in this paper. The PNN structure is consisted of layers and nodes like conventional neural networks but is not fixed and can be changable according to the system environments. three types of polynomials such as linear, quadratic, and modified quadratic is used in each node that is connected with various kinds of multi-variable inputs. Inputs and order of polynomials in each node are very important element for the performance of model. In most cases these factors are decided by the background information and trial and error of designer. For the high reliability and good performance of the PNN, the factors must be decided according to a logical and systematic way. In the paper evolutionary algorithm is applied to choose the optimal input variables and order. Evolutionary (genetic) algorithm is a random search optimization technique. The evolved PNN with optimally chosen input variables and order is not fixed in advance but becomes fully optimized automatically during the identification process. Gas furnace and pH neutralization processes are used in conventional PNN version are modeled. It shows that the designed PNN architecture with evolutionary structure optimization can produce the model with higher accuracy than previous PNN and other works.

• Earthquakes and Structures
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• v.8 no.4
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• pp.843-857
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• 2015

System identification is regarded as the most basic technique for structural health monitoring to evaluate structural integrity. Although many system identification techniques extracting mode information (e.g., mode frequency and mode shape) have been proposed so far, it is also desired to identify physical parameters (e.g., stiffness and damping). As for high-rise buildings subjected to long-period ground motions, system identification for evaluating only the shear stiffness based on a shear model does not seem to be an appropriate solution to the system identification problem due to the influence of overall bending response. In this paper, a system identification algorithm using a shear-bending model developed in the previous paper is revised to identify both shear and bending stiffnesses. In this algorithm, an ARX (Auto-Regressive eXogenous) model corresponding to the transfer function for interstory accelerations is applied for identifying physical parameters. For the experimental verification of the proposed system identification framework, vibration tests for a 3-story steel mini-structure are conducted. The test structure is specifically designed to measure horizontal accelerations including both shear and bending responses. In order to obtain reliable results, system identification theories for two different inputs are investigated; (a) base input motion by a modal shaker, (b) unknown forced input on the top floor.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.1
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• pp.1-8
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• 2018

Software testing is important to determine the reliability of the system, a task that requires a lot of effort and cost. Model-based testing has been proposed as a way to reduce these costs by automating test designs from models that regularly represent system requirements. For each path of model to generate an input value to perform a test, meta-heuristic technique is used to find the test data. In this paper, we propose an automatic test data generation method using a slicing method and a priority policy, and suppress unnecessary computation by excluding variables not related to target path. And then, experimental results show that the proposed method generates test data more effectively than conventional method.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.52 no.7
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• pp.424-424
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• 2003

Evolutionary design related to the optimal design of Polynomial Neural Networks (PNNs) structure for model identification of complex and nonlinear system is studied in this paper. The PNN structure is consisted of layers and nodes like conventional neural networks but is not fixed and can be changable according to the system environments. three types of polynomials such as linear, quadratic, and modified quadratic is used in each node that is connected with various kinds of multi-variable inputs. Inputs and order of polynomials in each node are very important element for the performance of model. In most cases these factors are decided by the background information and trial and error of designer. For the high reliability and good performance of the PNN, the factors must be decided according to a logical and systematic way. In the paper evolutionary algorithm is applied to choose the optimal input variables and order. Evolutionary (genetic) algorithm is a random search optimization technique. The evolved PNN with optimally chosen input variables and order is not fixed in advance but becomes fully optimized automatically during the identification process. Gas furnace and pH neutralization processes are used in conventional PNN version are modeled. It shows that the designed PNN architecture with evolutionary structure optimization can produce the model with higher accuracy than previous PNN and other works.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.6
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• pp.609-616
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• 2017

In this paper, we propose an algorithm to obtain more stable and high recognition rate by using left and right rotation information of input image in order to obtain a stable recognition rate in face recognition. The proposed algorithm uses the facial image as the input information in the web camera environment to reduce the size of the image and normalize the information about the brightness and color to obtain the improved recognition rate. We apply Principal Component Analysis (PCA) to the detected candidate regions to obtain feature vectors and classify faces. Also, In order to reduce the error rate range of the recognition rate, a set of data with the left and right $45^{\circ}$ rotation information is constructed considering the directionality of the input face image, and each feature vector is obtained with PCA. In order to obtain a stable recognition rate with the obtained feature vector, it is after scattered in the eigenspace and the final face is recognized by comparing euclidean distant distances to each feature. The PCA-based feature vector is low-dimensional data, but there is no problem in expressing the face, and the recognition speed can be fast because of the small amount of calculation. The method proposed in this paper can improve the safety and accuracy of recognition and recognition rate faster than other algorithms, and can be used for real-time recognition system.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.39 no.8
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• pp.14-25
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• 2002

In this paper, we propose a modified AFC algorithm which is suitable for the implementation of WCDMA reverse link receiver modem. To reduce the complexity, the modified CP-FDD algorithm named 'Normalized CP-FDD' is applied to the AFC loop. The proposed FDD algorithm overcomes the conventional CP-FDD's sensitivity to the variance of input signal amplitude and increases the linear range of S -curve. Therefore, offset frequency estimation using the proposed scheme can be more stable than the conventional method. Unlike IS-95, since pilot symbol in WCDMA is not transmitted continuously, we introduce a moving average filter at the FDD input to increase the number of cross-product. So, tracking speed and stability are improved. For more rapid frequency acquisition and tracking, we adopt a multi-stage tracking mode. Using NCO having ROM table structure, the frequency offset is compensated. We applied the proposed algorithm in the implementation of WCDMA base station modem successfully.

• The KIPS Transactions:PartD
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• v.10D no.3
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• pp.417-426
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• 2003

The problem of finding clusters in high dimensional data is well known in the field of data mining for its importance, because cluster analysis has been widely used in numerous applications, including pattern recognition, data analysis, and market analysis. Recently, a new framework, projected clustering, to solve the problem was suggested, which first select subdimensions of each candidate cluster and then each input point is assigned to the nearest cluster according to a distance function based on the chosen subdimensions of the clusters. We propose a new algorithm for subdimensional clustering of high dimensional data, each of the three major steps of which partitions the input points into several candidate clutters with proper numbers of points, filters the clusters that can not be useful in the next steps, and then merges the remaining clusters into the predefined number of clusters using a closeness function, respectively. The result of extensive experiments shows that the proposed algorithm exhibits better performance than the other existent clustering algorithms.

• The KIPS Transactions:PartB
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• v.17B no.5
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• pp.333-344
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• 2010

This paper addresses the way to compose paronamic images from images taken the same objects. With the spread of digital camera, the panoramic image has been studied to generate with its interest. In this paper, we propose a panoramic image generation method using scaling and rotation invariant features. First, feature points are extracted from input images and matched with a RANSAC algorithm. Then, after the perspective model is estimated, the input image is registered with this model. Since the SURF feature extraction algorithm is adapted, the proposed method is robust against geometric distortions such as scaling and rotation. Also, the improvement of computational cost is achieved. In the experiment, the SURF feature in the proposed method is compared with features from Harris corner detector or the SIFT algorithm. The proposed method is tested by generating panoramic images using $640{\times}480$ images. Results show that it takes 0.4 second in average for computation and is more efficient than other schemes.