• Journal of Elementary Mathematics Education in Korea
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• v.13 no.1
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• pp.1-15
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• 2009

This research is to provide a useful reference for the future revision of textbook by comparative analysis with the textbook in the 4th grade of elementary school in Japan. The results from this research is same as follows: First, Korean curriculum is emphasizing the reasonable problem-solving ability developed on the base of the mathematical knowledge and skill. Meantime, Japanese puts much value on the is focusing on discretion and the capability in life so that they emphasize each person's learning and raising the power of self-learning and thinking. The ratio on mathematics in both company are high, but Japanese ensures much more hours than Korean. Second, the chapter of Korean textbook is composed of 8 units and the title of the chapter is shown as key word, then the next objects are describes as 'Shall we do$\sim$' type. Hence, the chapter composition of Japanese textbook is different among the chapter and the title of the chapter is described as 'Let's do$\sim$'. Moreover, Korean textbook is arranged focusing on present study, however Japanese is composed with each independent segments in the present study subject to the study contents. Third, Japanese makes students understand the decimal as the extension of the decimal system with measuring unit($\ell$, km, kg) then, learn the operation by algorithm. In Korea, students learn fraction earlier than decimal, but, in Japan students learn decimal earlier than fraction. For the diagram, in Korea, making angle with vertex and side comes after the concept of angle, vertex and side is explained. Hence, in Japan, they show side and vertex to present angle.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.1
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• pp.51-58
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• 2023

In this paper, we propose an organ segmentation technique for the automatic extraction of medical diagnostic indicators from X-ray images. In order to calculate diagnostic indicators of heart disease and spinal disease such as VHS(vertebral heart scale) and Cobb angle, it is necessary to accurately segment the thoracic spine, carina, and heart in a chest X-ray image. A deep neural network model in which the high-resolution representation of the image for each layer and the structure converted into a low-resolution feature map are connected in parallel was adopted. This structure enables the relative position information in the image to be effectively reflected in the segmentation process. It is shown that learning performance can be improved by combining the OCR module, in which pixel information and object information are mutually interacted in a multi-step process, and the channel attention module, which allows each channel of the network to be reflected as different weight values. In addition, a method of augmenting learning data is presented in order to provide robust performance against changes in the position, shape, and size of the subject in the X-ray image. The effectiveness of the proposed theory was evaluated through an experiment using 145 human chest X-ray images and 118 animal X-ray images.

• Journal of the Korean School Mathematics Society
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• v.6 no.1
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• pp.101-113
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• 2003

The purpose of this study is to analyze students misunderstood types relate to trigonometric function and to devise its teaching method using GSP. To do this, we performed several steps as followings: First, we performed questionnaire survey to 70 students belong to second year at high school to find students comprehension degree about radian angle representation and trigonometric function graph. Second, we devised the teaching-learning materials relate to trigonometric function graph using GSP. And then, we used them in the class of 35 students who are at the time to learn trigonometric function in the first year at high school. Third, we conducted Questionnaire survey to students studied through teaching and learning materials using GSP. As a result of doing the survey, we found that general students were interested in the class using GSP and they could also operate computer without difficulty.

• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.4
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• pp.81-90
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• 2017

This paper presents a smart seat for correction of driver posture while driving. We introduce good postures with seat height, seat angle, head height, back of knees, distances of foot pedals, tilt of seat, etc. There have been some studies on correction of good posture while driving, effects of driving environment on driver's posture, sitting strategies based on seating pressure distribution, estimation of driver's standard postures, and others. However, there are a few studies on guide of good postures while driving for problem of driver's posture using machine leaning. Therefore, we suggest a smart seat for correction of driver's posture based on machine leaning, 1) developed the system to get postures by 10 piezoelectric effect element, 2) collect piezoelectric values from 37 drivers and 28 types of cars, 3) suggest 4 types of good postures while driving, 4) analyze test postures by kNN. As the results, we can guide good postures for bad or problems of postures while driving.

• Steel and Composite Structures
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• v.28 no.4
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• pp.509-516
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• 2018

The differential quadrature (DQ) and teaching-learning based optimization (TLBO) methods are coupled to introduce a hybrid numerical method for maximizing fundamental natural frequency of laminated composite skew plates. The fiber(s) orientations are selected as design variable(s). The first-order shear deformation theory (FSDT) is used to obtain the governing equations of the plate. The equations of motion and the related boundary conditions are discretized in space domain by employing the DQ method. The discretized equations are transferred from the time domain into the frequency domain to obtain the fundamental natural frequency. Then, the DQ solution is coupled with the TLBO method to find the maximum frequency of the plate and its related optimum stacking sequences of the laminate. Convergence and applicability of the proposed method are shown and the optimum fundamental frequency parameter of the plates with different skew angle, boundary conditions, number of layers and aspect ratio are obtained. The obtained results can be used as a benchmark for further studies.

• Journal of the Korean Institute of Navigation
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• v.21 no.3
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• pp.55-66
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• 1997

In this paper, applications of neural networks to vibration control of flexible single link robot manipulator are ocnsidered. The architecture of neural networks is a hidden layer, which is comprised of self-recurrent one. Tow neural networks are utilized in a control system ; one as an identifier is called neuro identifier and the othe ra s a controller is called neuro controller. The neural networks can be used to approximate any continuous function to any desired degree of accuracy and the weights are updated by dynamic error-backpropagation algorithm(DEA). To guarantee concegence and to get faster learning, an approach that uses adaptive learning rates is developed by introducing a Lyapunov function. When a flexible manipulator is ratated by a motor through the fixed end, transverse vibration may occur. The motor torque should be controlle dinsuch as way, that the motor is rotated by a specified angle. while simulataneously stabilizing vibration of the flexible manipulators so that it is arrested as soon as possible at the end of rotation. Accurate vibration control of lightweight manipulator during the large body motions, as well as the flexural vibrations. Therefore, dynamic models for a flexible single link manipulator is derived, and LQR controller and nerual networks controller are composed. The effectiveness of the proposed nerual networks control system is confirmed by experiments.

• The Journal of Korean Society for Neurotherapy
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• v.22 no.3
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• pp.25-29
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• 2018

Purpose In this study, 45 students were divided into task - oriented group, task - oriented intermittent group, and continuous group, and the effect of each variable on learning was changed to angle of the arms' we checked. Methods This study was conducted for 45 days from March 13 to 18, 2017 for five days in the 20 generals living in Changwon city. Dart and goniometer were used for the measurement. In the exercise program, darts were placed on a smooth, hard surface of 1.5M height and the distance to the subject was set 1.5m away. In the dart throw practice, all three groups were marked with an elbow flexion $30^{\circ}$ on the wall, and the subjects were instructed to throw the dart at that point. Results Comparisons between groups showed significant differences between the continuous task-oriented training group and the intermittent task-oriented training group and the control group at the end of training (*** P <0.001). Conclusion This study showed that the intermittent task-oriented training method showed the highest effect (*** P <0.001), which proved that the intermittent method is the most effective exercise method among the three groups.

• Advances in nano research
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• v.12 no.2
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• pp.223-239
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• 2022

Object detection has always been to pursue objects with particular properties or representations and to predict details on objects including the positions, sizes and angle of rotation in the current picture. This was a very important subject of computer vision science. While vision-based object tracking strategies for the analysis of competitive videos have been developed, it is still difficult to accurately identify and position a speedy small ball. In this study, deep learning (DP) network was developed to face these obstacles in the study of tennis motion tracking from a complex perspective to understand the performance of athletes. This research has used CNN-VGG 16 to tracking the tennis ball from broadcasting videos while their images are distorted, thin and often invisible not only to identify the image of the ball from a single frame, but also to learn patterns from consecutive frames, then VGG 16 takes images with 640 to 360 sizes to locate the ball and obtain high accuracy in public videos. VGG 16 tests 99.6%, 96.63%, and 99.5%, respectively, of accuracy. In order to avoid overfitting, 9 additional videos and a subset of the previous dataset are partly labelled for the 10-fold cross-validation. The results show that CNN-VGG 16 outperforms the standard approach by a wide margin and provides excellent ball tracking performance.

• Current Optics and Photonics
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• v.8 no.3
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• pp.246-258
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• 2024

In aerospace and energy engineering, the reconstruction of three-dimensional (3D) temperature distributions is crucial. Traditional methods like algebraic iterative reconstruction and filtered back-projection depend on voxel division for resolution. Our algorithm, blending deep learning with computer graphics rendering, converts 2D projections into light rays for uniform sampling, using a fully connected neural network to depict the 3D temperature field. Although effective in capturing internal details, it demands multiple cameras for varied angle projections, increasing cost and computational needs. We assess the impact of camera number on reconstruction accuracy and efficiency, conducting butane-flame simulations with different camera setups (6 to 18 cameras). The results show improved accuracy with more cameras, with 12 cameras achieving optimal computational efficiency (1.263) and low error rates. Verification experiments with 9, 12, and 15 cameras, using thermocouples, confirm that the 12-camera setup as the best, balancing efficiency and accuracy. This offers a feasible, cost-effective solution for real-world applications like engine testing and environmental monitoring, improving accuracy and resource management in temperature measurement.

• Journal of the Korean Society of Physical Medicine
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• v.5 no.3
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• pp.405-412
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• 2010

Purpose : The purpose of this study was to assess the effects of the trunk muscle activity on bridging exercise according to the knee joint angle. Methods : Twenty-five healthy adults volunteered to participate in this study. Subjects were required to complete following four bridging exercises; knee joint flexion $120^{\circ}$, $90^{\circ}$, $60^{\circ}$, $45^{\circ}$. Surface electromyography from selected trunk muscles was normalized to maximum voluntary isometric contraction. Muscle activity was measured by QEMG-4 system(LXM 3204, Laxtha Korea). A repeated measures of one-way ANOVA with post-hoc Bonferroni's correction was used to determine the influence of bridging exercise on muscle activity for each muscle and descriptive statistics was used to determine local/global muscle ratio. Results : The internal oblique of bridging exercises $120^{\circ}$, $90^{\circ}$ showed significantly(p<.05). The erctor spinae of all bridging exercises showed significant excepted between $60^{\circ}$ and $45^{\circ}$(p<.05). Median of internal oblique/rectus abdominis ratio of $120^{\circ}$ was 4.41, $90^{\circ}$ was 3.94, $60^{\circ}$ was 3.58, $45^{\circ}$ was 3.39. Median of internal oblique/external oblique ratio of $120^{\circ}$ was 2.66, $90^{\circ}$ was 2.43, $60^{\circ}$ was 2.87, $45^{\circ}$ was 2.64. Conclusion : Angular motion decreasing with knee joint flexion made erector spinae activation increase. on the other hand, as decreasing abdomen muscle activation, the more performing motor learning is required for abdomen muscle strength and co-contraction for the trunk stabilization.