• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.05a
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• pp.585-589
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• 2002

In this paper, hierarchical retrieval system based on efficient feature extraction is proposed. In order to retrieval the image with robustness for geometrical transformation such as translation, scaling, and rotation. After performing the 2-level wavelet transform on image, We extract moment in low-level subband which was subdivided into subimages and texture feature, contrast of GLCM(Gray Level Co-occurrence Matrix). At first we retrieve the candidate images in database by the ones of image. To perform a more accurate image retrieval, the edge information on the high-level subband was subdivided horizontally, vertically and diagonally. And then, the energy rate of edge per direction was determined and used to compare the energy rate of edge between images for higher accuracy.

• Current Optics and Photonics
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• v.3 no.2
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• pp.154-163
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• 2019

In this study, we developed an optical sighting system capable of shooting at a long-distance target by operating a digital gyro mirror composed of a gyro sensor and an FSM. The optical sighting system consists of a reticle part, a digital gyro mirror (FSM), a parallax correction lens, a reticle-ray reflection mirror, and a partial reflection window. In order to obtain the optimal volume and to calculate the leading angle range according to the driving angle of the FSM, a calculation program using Euler rotation angles and a three-dimensional reflection matrix was developed. With this program we have confirmed that the horizontal leading angle of the developed optical sighting system can be implemented under about ${\pm}8^{\circ}$ for the maximum horizontal driving angle (${\beta}={\pm}12.5^{\circ}$) of the current FSM. Also, if the ${\beta}$ horizontal driving angle of the FSM is under about ${\pm}15.5^{\circ}$, it can be confirmed that the horizontal direction leading angle can be under ${\pm}10.0^{\circ}$. If diagonal leading angles are allowed, we confirmed that we can achieve a diagonal leading angle of ${\pm}10.0^{\circ}$ with a vertical driving angle ${\alpha}$ of ${\pm}7.1^{\circ}$ and horizontal driving angle ${\beta}$ of ${\pm}12.5^{\circ}$.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.4
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• pp.940-947
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• 2015

This paper presents the region of interest pattern image extraction method on a display printed matrix pattern. Proposed method can not use conventional method such as laser, ultrasonic waves and touch sensor. It searches feature point and rotation angle using luminance and pattern reliable feature points of input image, and then it extracts region of interest. In order to extract region of interest, we simulate proposed method using pattern image written various angles on display panel. The proposed method makes progress using the OpenCV and the window program, and was designed using Verilog-HDL and was verified through the FPGA Board(xc6vlx760) of Xilinx.

• Journal of IKEEE
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• v.18 no.1
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• pp.165-171
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• 2014

This paper presents a low complexity QR decomposition (QRD) architecture for MIMO detector. In the proposed approach, various CORDIC-based QRD algorithms are efficiently combined together to reduce the computational complexity of the QRD hardware. Based on the computational complexity analysis on various QRD algorithms, a low complexity approach is selected at each stage of QRD process. The proposed QRD architecture can be applied to any arbitrary dimension of channel matrix, and the complexity reduction grows with the increasing matrix dimension. Our QR decomposition hardware was implemented using Samsung $0.13{\mu}m$ technology. The numerical results show that the proposed architecture achieves 47% increase in the QAR (QRD Rate/Gate count) with 28.1% power savings over the conventional Householder CORDIC-based architecture for the $4{\times}4$ matrix decomposition.

• Journal of Korean Society for Geospatial Information Science
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• v.19 no.4
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• pp.139-144
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• 2011

This study proposed a classification method of LIDAR data by using simultaneously the color information (R, G, B) and reflection intensity information (I) obtained from terrestrial LIDAR and by analyzing the association between these data through the use of statistical classification methods. To this end, first, the factors that maximize variance were calculated using the variables, R, G, B, and I, whereby the factor matrix between the principal factor and each variable was calculated. However, although the factor matrix shows basic data by reducing them, it is difficult to know clearly which variables become highly associated by which factors; therefore, Varimax method from orthogonal rotation was used to obtain the factor matrix and then the factor scores were calculated. And, by using a non-hierarchical clustering method, K-mean method, a cluster analysis was performed on the factor scores obtained via K-mean method as factor analysis, and afterwards the classification accuracy of the terrestrial LiDAR data was evaluated.

• The Journal of the Acoustical Society of Korea
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• v.14 no.2E
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• pp.5-11
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• 1995

In this paper, a method using a neural network was applied for the purpose of urilizing spatial features. The adopted model of neural network the three-layered architecture, and the training algorithm is the back-propagation algorithm. Co-occurrence matrix which is generated from original imge was used for imput pattern to the neural network in order to tolerate variations of patterns like rotation of displacement. Co-occurrence matrix is explained in appendix. To evaluate this method, classification was executed with this method and texture features method over the city area and sand area, which cannot be separated with the conventional method mentioned aboved. In the results of this method and texture features proposed by Haralick the method using texture features was separation rate of 67%~89%. On the contrary, the method using neural network proposed in this research was stable and high separation rate of 80%~98%.

• Journal of Broadcast Engineering
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• v.24 no.4
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• pp.580-591
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• 2019

This paper focuses on to improving object detection performance using the camera and LiDAR on autonomous vehicle platforms by fusing detected objects from individual sensors through a late fusion approach. In the case of object detection using camera sensor, YOLOv3 model was employed as a one-stage detection process. Furthermore, the distance estimation of the detected objects is based on the formulations of Perspective matrix. On the other hand, the object detection using LiDAR is based on K-means clustering method. The camera and LiDAR calibration was carried out by PnP-Ransac in order to calculate the rotation and translation matrix between two sensors. For Sensor fusion, intersection over union(IoU) on the image plane with respective to the distance and angle on world coordinate were estimated. Additionally, all the three attributes i.e; IoU, distance and angle were fused using logistic regression. The performance evaluation in the sensor fusion scenario has shown an effective 5% improvement in object detection performance compared to the usage of single sensor.

• Journal of Welding and Joining
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• v.34 no.3
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• pp.23-30
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• 2016

This paper discusses the optimization of friction stir spot welding (FSSW) process parameters for joining Aluminum alloy (AA6061-T6) with Magnesium alloy (AZ31B) sheets. Prior to optimization an empirical relationship was developed to predict the Tensile Shear Fracture Load (TSFL) incorporating the four most important FSSW parameters, i.e., tool rotational speed, plunge rate, dwell time and tool diameter ratio, using response surface methodology (RSM). The experiments were conducted based on four factor, five levels central composite rotatable design (CCD) matrix. The maximum TSFL obtained was 3.61kN, with the tool rotation of 1000 rpm, plunge rate of 16 mm/min, dwell time of 5 sec and tool diameter ratio of 2.5.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.42 no.8 s.338
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• pp.1-10
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• 2005

In this paper, the explicit low density codes construction from the generalized permutation matrices related to algebra theory is investigated, and we design several Jacket inverse block matrices on the recursive formula and permutation matrices. The results show that the proposed scheme is a simple and fast way to obtain the low density codes, and we also Proved that the structured low density parity check (LDPC) codes, such as the $\pi-rotation$ LDPC codes are the low density Jacket inverse block matrices too.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.11 no.2
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• pp.1-8
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• 2002

Cutting force is obtained as a sum of chip removing force and ploughing force. Chip removing force is estimated by multiplying specific cutting pressure by cutting area. Since ploughing force is caused from dullness of a tool, its magnitude is constant if depth of cut is bigger than a certain value. Using the linearity of chip removing force to cutting area and the constancy of ploughing force regardless of depth of cut which is over a certain limit each force is separated from measured cutting force and used to establish cutting force model. New rotation matrix to convert the measured cutting force in reference axes into the forces in cutter axes is obtained by considering that tool angles are projected angles from cutter axes to reference axes.. Spindle tilt is also considered far the model. The predicted cutting force estimated from the model is in good agreement with the measured force.