• Journal of The Korean Association of Information Education
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• v.3 no.1
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• pp.134-142
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• 1999

The demand for recognizing the color as well as the object shape is increasing to use the detailed information, because-the expense of color input/output devices become cheap. The research on the color correction should be researched for the exact color presentation and color reproduction of color input/output systems. In this paper, we researched on the color correction of color scanner. The characterization of color scanner is a two step process of gray-balancing and color transformation. The decoupling of the gray-balancing from the color transformation enables the portability of the scanner characterization. We used the least square methods for the line fitting and the Neural Network for the storage space and computation speed. The output of Neural Network is similar to the target value in three-dimensional tristimulus space. The proposed color correction method can be used for all scanners of a manufacturer's model because of the portability.

• Journal of KIISE
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• v.45 no.1
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• pp.15-21
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• 2018

Low-Rank Representation (LRR) based methods are widely used in many practical applications, such as face clustering and object detection, because they can guarantee high prediction accuracy when used to constructing graphs in graph - based semi-supervised learning. However, in order to solve the LRR problem, it is necessary to perform singular value decomposition on the square matrix of the number of data points for each iteration of the algorithm; hence the calculation is inefficient. To solve this problem, we propose an improved and faster LRR method based on the recently published Fast LRR (FaLRR) and suggests ways to introduce and optimize additional constraints on the underlying optimization goals in order to address the fact that the FaLRR is fast but actually poor in classification problems. Our experiments confirm that the proposed method finds a better solution than LRR does. We also propose Fast MLRR (FaMLRR), which shows better results when the goal of minimizing is added.

• The Korean Journal of Applied Statistics
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• v.30 no.6
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• pp.1015-1025
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• 2017

Bayesian personalized ranking (BPR) is a state-of-the-art recommendation system techniques for implicit feedback data. Unfortunately, there might be a loss of information because the BPR model considers only the binary transformation of implicit feedback that is non-binary data in most cases. We propose a modified BPR method using a level of confidence based on the size or strength of implicit feedback to overcome this limitation. The proposed method is useful because it still has a structure of interpretable models for underlying personalized ranking i.e., personal pairwise preferences as in the BPR and that it is capable to reflect a numerical size or the strength of implicit feedback. We propose a computation algorithm based on stochastic gradient descent for the numerical implementation of our proposal. Furthermore, we also show the usefulness of our proposed method compared to ordinary BPR via an analysis of steam video games data.

• The Journal of the Korea Contents Association
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• v.18 no.1
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• pp.64-75
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• 2018

With the development of location-aware technologies and the activation of smart phones, location based social networks(LBSN) have been activated to allow people to easily share their location. In particular, studies on recommending the location of user interests by using the user check-in function in LBSN have been actively conducted. In this paper, we propose a location recommendation scheme considering time and user activities in LBSN. The proposed scheme considers user preference changes over time, local experts, and user interest in rare places. In other words, it uses the check-in history over time and distinguishes the user activity area to identify local experts. It also considers a rare place to give a weight to the user preferred place. It is shown through various performance evaluations that the proposed scheme outperforms the existing schemes.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.47 no.6
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• pp.1-9
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• 2010

In this paper, we propose a hi-speed/low-power Extended QRD-RLS(QR-Decomposition Recursive Least Squares) equalizer with systolic array structure. In the conventional systolic array structure, vector mode CORDIC on the boundary cell calculates angle of input vector, and the rotation mode CORDIC on the internal cell rotates vector. But, in the proposed structure, it is shown that implementation complexity can be reduced using the rotation direction of vector mode CORDIC and rotation mode CORDIC. Furthermore, calculation time can be reduced by 1/2 since vector mode and rotation mode CORDIC operate at the same time. Through HDL coding and chip implementation, it is shown that implementation area is reduced by 23.8% compared with one of conventional structure.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.26 no.4
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• pp.424-434
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• 2015

Adaptive beamforming algorithms have been widely used to remove interference and jamming in the phased array radar system. Advances in the field programmable gate array(FPGA) technology now make possible the real time processing of adaptive beamforming (ABF) algorithm. In this paper, the FPGA based real-time implementation method of adaptive beamforming system(beamformer) in the pre-processor module for active electronically scanned array(AESA) radar is proposed. A compact FPGA-based adaptive beamformer is developed using commercial off the shelf(COTS) FPGA board with communication via OpenVPX(Virtual Path Cross-connect) backplane. This beamformer comprises a number of high speed complex processing including QR decomposition & back substitution for matrix inversion and complex vector/matrix calculations. The implemented result shows that the adaptive beamforming patterns through FPGA correspond with results of simulation through Matlab. And also confirms the possibility of application in AESA radar due to the real time processing of ABF algorithm through FPGA.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.23 no.8
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• pp.2072-2079
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• 1998

In this paper, we propose a new decorrelating detector called pseudo-inverse-filtering type decorrelating detector for asynchronous CDMA channels. We first show that the matched filtering and decorrelating operations of the existing decorrelating detectors can be replaced with the pseudo-inverse filtering operations in synchronous channels, and using this fact we show that the decorrelating detector has the largest SNR among the linear detectors that can eliminate MAI. Then we introduce asynchronous pseudo-inverse filtering type decorrelating detector by extending this result for asynchronous channels, and discuss implementation methods of the proposed decorrelating detectors. Since the proposed scheme employs a decentralized structure for updating coefficients, it has the flexibility to add/remove users. Finally we analyze the performance of the proposed decorrelating detector in terms of the bit error rate, and examine its performance improvements over the conventional detectors through computer simulations.

• The Journal of the Acoustical Society of Korea
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• v.36 no.1
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• pp.1-11
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• 2017

In this study, novel approximate form of the square-root operator of three dimensional acoustic Parabolic Equation (3D PE) is proposed using a rational approximant for two variables. This form has two advantages in comparison with existing approximation studies of the square-root operator. One is the wide-angle capability. The proposed form has wider angle accuracy to the inclination angle of ${\pm}62^{\circ}$ from the range axis of 3D PE at the bearing angle of $45^{\circ}$, which is approximately three times the angle limit of the existing 3D PE algorithm. Another is that the denominator of our approximate form can be expressed into the product of one-dimensional operators for depth and cross-range. Such a splitting form is very preferable in the numerical analysis in that the 3D PE can be easily transformed into the tridiagonal matrix equation. To confirm the capability of the proposed approximate form, comparative study of other approximation methods is conducted based on the phase error analysis, and the proposed method shows best performance.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.49 no.4
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• pp.23-35
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• 2012

This paper presents a 3-D pose (position and orientation) estimation method for an elliptic object in 3-D space. It is difficult to resolve the problem of determining 3-D pose parameters with respect to an elliptic feature in 3-D space by interpretation of its projected feature onto an image plane. As an alternative, we propose a two points-based pose estimation algorithm to seek the 3-D information of an elliptic feature. The proposed algorithm determines a homogeneous transformation uniquely for a given correspondence set of an ellipse and two coplanar points that are defined on model and image plane, respectively. For each plane, two triangular features are extracted from an ellipse and two points based on the polarity in 2-D projection space. A planar homography is first estimated by the triangular feature correspondences, then decomposed into 3-D pose parameters. The proposed method is evaluated through a series of experiments for analyzing the errors of 3-D pose estimation and the sensitivity with respect to point locations.

• Journal of the Korean Society for information Management
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• v.38 no.2
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• pp.113-127
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• 2021

The purpose of this study is to propose a personalized book recommendation system to promote the use of university libraries. In particular, unlike many recommended services that are based on existing users' preferences, this study proposes a method that derive evaluation metrics using individual users' book rental history and tendencies, which can be an effective alternative when users' preferences are not available. This study suggests models using two matrix decomposition methods: Singular Value Decomposition(SVD) and Stochastic Gradient Descent(SGD) that recommend books to users in a way that yields an expected preference score for books that have not yet been read by them. In addition, the model was implemented using a user-based collaborative filtering algorithm by referring to book rental history of other users that have high similarities with the target user. Finally, user evaluation was conducted for the three models using the derived evaluation metrics. Each of the three models recommended five books to users who can either accept or reject the recommendations as the way to evaluate the models.