• Journal of The Korean Association For Science Education
• /
• v.37 no.4
• /
• pp.577-586
• /
• 2017

The purpose of this study is to try to structuralize the perception of the mothers of science-gifted elementary students using the concept mapping approach. The mothers who participated in this research had children who were 5th and 6th graders selected as science-gifted by a regional education office, a science high school and two national universities in a city. One of the authors interviewed 26 mothers, and extracted 50 general statements of their perceptions about the career path of their children. Ten mothers who participated in interviews sorted a shuffled pack of statement cards. The categorization of the statements into the dissimilarity matrix was carried out by SPSS multidimensional scaling analysis and hierarchical cluster analysis to generate a conceptual diagram. After that 140 mothers rated each statement using a Likert-type response scale from one to five. The result showed six clusters of parental views such as were 'Burden of private education, grades and going to the next grade,' 'Thinking about career guidance in gifted education and school,' 'Parental roles in child career education,' 'Difficulties in career guidance at home,' 'Demand for strengthening the parental capacity for career guidance,' and 'Demand for social support.' 'Demand for social support' obtained the highest sympathy from mothers of elementary science gifted.

• Journal of Korean Society of Coastal and Ocean Engineers
• /
• v.8 no.2
• /
• pp.123-136
• /
• 1996

A three-dimensional Galerkin-FEM model which can handle the temporal and spatial variation of density is presented. The hydrostatic approximation is used and density effects are included by means of conservation equation of heat and the equation of state. The finite difference grids are used in the horizontal plane and a set of linear-shape functions is used for the vertical expansion. The similarity transform is introduced to solve resultant matrix equations. The proposed model was first applied to the density-driven circulation in an idealized basin in the presence of the heat exchange between the air and the sea. The advection terms in the momentum equation were ignored, while the convection terms were retained in the heat equation. Coefficients of the vertical eddy viscosity and diffusivity were fixed to be constant. Calculation in a non-rotating idealized basin shows that the difference in heat capacity with depth gives rise to the horizontal gradient of temperature. Consequently, there is a steady new in the upper layer in the direction of increasing depth with compensatory counter flow .in the lower layer. With Coriolis force, geostrophic flow was predominant due to the balance between the pressure gradient and the Coriolis force. As a test in region of irregular topography, the model is applied to the Yellow Sea. Although the resultant flow was very complex, the character of the flow Showed to be geostrophic on the whole.

• Journal of Broadcast Engineering
• /
• v.13 no.1
• /
• pp.174-184
• /
• 2008

Complementary color filter array (CCFA) is widely used in consumer-level digital video cameras, since it not only has high sensitivity and good signal-to-noise ratio in low-light condition but also is compatible with the interlaced scanning used in broadcast systems. However, the full-color images obtained from CCFA suffer from the color artifacts such as false color and zipper effects. These artifacts can be removed with edge-adaptive demosaicking (ECD) approaches which are generally used in rrimary color filter array (PCFA). Unfortunately, the unique array pattern of CCFA makes it difficult that CCFA adopts ECD approaches. Therefore, to apply ECD approaches suitable for CCFA to demosaicking is one of the major issues to reconstruct the full-color images. In this paper, we propose a new ECD algorithm for CCFA. To estimate an edge direction precisely and enhance the quality of the reconstructed image, a function of spatial variances is used as a weight, and new color conversion matrices are presented for considering various edge directions. Experimental results indicate that the proposed algorithm outperforms the conventional method with respect to both objective and subjective criteria.

• Korean Journal of Remote Sensing
• /
• v.35 no.5_1
• /
• pp.705-714
• /
• 2019

Precision positioning is necessary for various use of high-resolution UAV images. Basically, GCP is used for this purpose, but in case of emergency situations or difficulty in selecting GCPs, the data shall be obtained without GCPs. This study proposed a method of improving positional accuracy for x, y coordinate of UAV based 3 dimensional point cloud data generated without GCPs. Road vector file by the public data (Open Data Portal) was used as reference data for improving location accuracy. The geometric correction of the 2 dimensional ortho-mosaic image was first performed and the transform matrix produced in this process was adopted to apply to the 3 dimensional point cloud data. The straight distance difference of 34.54 m before the correction was reduced to 1.21 m after the correction. By confirming that it is possible to improve the location accuracy of UAV images acquired without GCPs, it is expected to expand the scope of use of 3 dimensional spatial objects generated from point cloud by enabling connection and compatibility with other spatial information data.

• Journal of Internet Computing and Services
• /
• v.22 no.2
• /
• pp.29-39
• /
• 2021

As damages to individuals, private sectors, and businesses increase due to newly occurring cyber attacks, the underlying network security problem has emerged as a major problem in computer systems. Therefore, NIDS using machine learning and deep learning is being studied to improve the limitations that occur in the existing Network Intrusion Detection System. In this study, a deep learning-based NIDS model study is conducted using the Convolution Neural Network (CNN) algorithm. For the image classification-based CNN algorithm learning, a discrete algorithm for continuity variables was added in the preprocessing stage used previously, and the predicted variables were expressed in a linear relationship and converted into easy-to-interpret data. Finally, the network packet processed through the above process is mapped to a square matrix structure and converted into a pixel image. For the performance evaluation of the proposed model, NSL-KDD, a representative network packet data, was used, and accuracy, precision, recall, and f1-score were used as performance indicators. As a result of the experiment, the proposed model showed the highest performance with an accuracy of 85%, and the harmonic mean (F1-Score) of the R2L class with a small number of training samples was 71%, showing very good performance compared to other models.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.20 no.3
• /
• pp.16-22
• /
• 2019

Off-line programming and robot calibration through simulation are essential when setting up a robot in a robot automation production line. In this study, we developed a new robot calibration method to match the CAD data of the production line with the measurement data on the site using 3D scanner. The proposed method calibrates the robot using 3D point cloud data through Iterative Closest Point algorithm. Registration is performed in three steps. First, vertices connected by three planes are extracted from CAD data as feature points for registration. Three planes are reconstructed from the scan point data located around the extracted feature points to generate corresponding feature points. Finally, the transformation matrix is calculated by minimizing the distance between the feature points extracted through the ICP algorithm. As a result of applying the software to the automobile welding robot installation, the proposed method can calibrate the required accuracy to within 1.5mm and effectively shorten the set-up time, which took 5 hours per robot unit, to within 40 minutes. By using the developed system, it is possible to shorten the OLP working time of the car body assembly line, shorten the precision teaching time of the robot, improve the quality of the produced product and minimize the defect rate.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.19 no.11
• /
• pp.205-212
• /
• 2018

In order to overcome shortcomings of commercial analysis program for solving certain geotechnical problems, finite element method adopting isoparametric quadrilateral element was selected as a tool for analyzing soil behavior and calculating process was programmed. Two examples were considered in order to verify reliability of the developed program. One of the two examples is the case of acting isotropic confining pressure on finite element and the other is the case of acting shear stress on the sides of the finite element. Isoparametric quadrilateral element was considered as the finite element and displacements in the element can be expressed by node displacements and shape functions in the considered element. Calculating process for determining strain which is defined by derivatives using global coordinates was coded using the Jacobian and the natural coordinates. Four point Gauss rule was adopted to convert double integral which defines stiffness of the element into numerical integration. As a result of executing analysis of the finite element under isotropic confining pressure, calculated stress corresponding to four Gauss points and center of the element were equal to the confining pressure. In addition, according to the analyzed results for the element under shear stress, horizontal stresses and vertical stresses were varied with positions in the element and the magnitudes and distribution pattern of the stresses were thought to be rational.

• Journal of Broadcast Engineering
• /
• v.26 no.3
• /
• pp.247-257
• /
• 2021

This paper proposes a new technique for calibrating a multi-view RGB-D camera using a 3D (dimensional) skeleton. In order to calibrate a multi-view camera, consistent feature points are required. In addition, it is necessary to acquire accurate feature points in order to obtain a high-accuracy calibration result. We use the human skeleton as a feature point to calibrate a multi-view camera. The human skeleton can be easily obtained using state-of-the-art pose estimation algorithms. We propose an RGB-D-based calibration algorithm that uses the joint coordinates of the 3D skeleton obtained through the posture estimation algorithm as a feature point. Since the human body information captured by the multi-view camera may be incomplete, the skeleton predicted based on the image information acquired through it may be incomplete. After efficiently integrating a large number of incomplete skeletons into one skeleton, multi-view cameras can be calibrated by using the integrated skeleton to obtain a camera transformation matrix. In order to increase the accuracy of the calibration, multiple skeletons are used for optimization through temporal iterations. We demonstrate through experiments that a multi-view camera can be calibrated using a large number of incomplete skeletons.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.21 no.3
• /
• pp.183-191
• /
• 2021

Non-intrusive load monitoring is a technology that can be used for predicting and classifying the type of appliances through real-time monitoring of user power consumption, and it has recently got interested as a means of energy-saving. In this paper, we propose a system for classifying appliances from user consumption data by combining GAF(Gramian angular field) technique that can be used for converting one-dimensional data to the two-dimensional matrix with convolutional neural networks. We use REDD(residential energy disaggregation dataset) that is the public appliances power data and confirm the classification accuracy of the GASF(Gramian angular summation field) and GADF(Gramian angular difference field). Simulation results show that both models showed 94% accuracy on appliances with binary-state(on/off) and that GASF showed 93.5% accuracy that is 3% higher than GADF on appliances with multi-state. In later studies, we plan to increase the dataset and optimize the model to improve accuracy and speed.

• Progress in Medical Physics
• /
• v.14 no.4
• /
• pp.211-217
• /
• 2003

The Principal component analysis (PCA) is a well-known data analysis method that is useful in linear feature extraction and data compression. The PCA is a linear transformation that applies an orthogonal rotation to the original data, so as to maximize the retained variance. PCA is a classical technique for obtaining an optimal overall mapping of linearly dependent patterns of correlation between variables (e.g. neurons). PCA provides, in the mean-squared error sense, an optimal linear mapping of the signals which are spread across a group of variables. These signals are concentrated into the first few components, while the noise, i.e. variance which is uncorrelated across variables, is sequestered in the remaining components. PCA has been used extensively to resolve temporal patterns in neurophysiological recordings. Because the retinal signal is stochastic process, PCA can be used to identify the retinal spikes. With excised rabbit eye, retina was isolated. A piece of retina was attached with the ganglion cell side to the surface of the microelectrode array (MEA). The MEA consisted of glass plate with 60 substrate integrated and insulated golden connection lanes terminating in an 8${\times}$8 array (spacing 200 $\mu$m, electrode diameter 30 $\mu$m) in the center of the plate. The MEA 60 system was used for the recording of retinal ganglion cell activity. The action potentials of each channel were sorted by off­line analysis tool. Spikes were detected with a threshold criterion and sorted according to their principal component composition. The first (PC1) and second principal component values (PC2) were calculated using all the waveforms of the each channel and all n time points in the waveform, where several clusters could be separated clearly in two dimension. We verified that PCA-based waveform detection was effective as an initial approach for spike sorting method.