• Journal of The Korean Association For Science Education
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• v.39 no.6
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• pp.799-807
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• 2019

The study was conducted to identify if the first-year program of science-focused school improved students' scientific core competency and science learning motivation. The first-year program of the science-focused school consisted of basic education in scientific inquiry, investigation, advanced experiment, and basic education in small research. There were a total of 262 participants in the program, and 169 students took three survey tests. Through the analysis of a group-based trajectory modeling, students were differentiated based on similarity of score changes. This study showed that the first-year program of the science-focused school significantly improved students' scientific core competency and science learning motivation. A group-based trajectory modeling found that about 40~60% of students saw the effects of the program. The students who chose the humanity track showed effects, while some students who chose the science-focused track did not show effects. A group-based trajectory modeling showed the methodological effects of identifying the change process of individual students. This study identified the positive effects of science-focused school policy statistically and is a meaningful example for analyzing the effectiveness of science-focused school programs.

• Korean Journal of Applied Biomechanics
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• v.17 no.1
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• pp.81-90
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• 2007

The effort identifying positioning information of the moving object in real time has been a issue not only in sport biomechanics but also other academic areas. In order to solve this issue, this study tried to track the movement of a pitched ball that might provide an easier prediction because of a clear focus and simple movement of the object. Machine learning has been leading the research of extracting information from continuous images such as object tracking. Though the rule-based methods in artificial intelligence prevailed for decades, it has evolved into the methods of statistical approach that finds the maximum a posterior location in the image. The development of machine learning, accompanied by the development of recording technology and computational power of computer, made it possible to extract the trajectory of pitched baseball from recorded images. We present a method of baseball tracking, based on object tracking methods in machine learning. We introduce three state-of-the-art researches regarding the object tracking and show how we can combine these researches to yield a novel engine that finds trajectory from continuous pitching images. The first research is about mean shift method which finds the mode of a supposed continuous distribution from a set of data. The second research is about the research that explains how we can find the mode and object region effectively when we are given the previous image's location of object and the region. The third is about the research of representing data into features that we can deal with. From those features, we can establish a distribution to generate a set of data for mean shift. In this paper, we combine three works to track baseball's location in the continuous image frames. From the information of locations from two sets of images, we can reconstruct the real 3-D trajectory of pitched ball. We show how this works in real pitching images.

• Proceedings of the KIEE Conference
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• 1989.07a
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• pp.112-115
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• 1989

This paper proposes a learning control algorithm for trajectory tracking of large scale system. The controller using only localized informations is composed of stabilizing controller and iterative learning controller. Stabilization and convergence of each subsystem is assured under some conditions which are inequalities of inter-connection terms and learning controller gain.

• Journal of Educational Research in Mathematics
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• v.27 no.2
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• pp.207-225
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• 2017

With a significant role of textbooks in shaping students' opportunities to learn, textbook analysis is essential to reveal these opportunities to learn the concept of area and volume. This research aims to show how the Korean textbooks pace students' learning of area and volume across grades by scrutinizing the textbooks with students' developmental sequences, called learning trajectories. Tasks about area and volume in all Korean elementary textbooks (grade 1 to 6) are coded with the specific developmental stages suggested in learning trajectories. As a result, we find considerable misalignment between the textbooks and the learning trajectories. The textbooks provide opportunities to experience developmental progressions of area and volume later than ages suggested in the learning trajectories. In addition, learning opportunities are significantly concentrated in grade 5 for area and grade 6 for volume with heavy emphases on applying formulas of area or volume. The findings from this research provides important implications concerning design of textbooks as well as improving students' opportunities in the mathematics classrooms.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.40 no.2
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• pp.19-25
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• 2003

For a class of multi-input multi-output nonlinear systems which perform a given task repetitively, an extended type of a direct leaning control (DLC) is proposed using the information on the (vector) relative degree of a multi-input multi-output system. Existing DLC methods are observed to be applied to a limited class of systems with the relative degree one and a new DLC law is suggested which can be applied to systems having higher relative degree. Using the proposed control law, the control input corresponding to the new desired output trajectory is synthesized directly based on the control inputs obtained from the learning process for other output trajectories. To show the validity and the performance of the proposed DLC, simulations are performed for trajectory tracking control of a two-axis SCARA robot.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.5
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• pp.634-642
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• 1993

In this paper a new trajectory control method is proposed for a robot manipulator using a time delay neural network(TDNN) as a feedforward controller with an algorithm to learn inverse dynamics of the manipulator. The TDNN structure has so favorable characteristics that neurons can extract more dynamic information from both present and past input signals and perform more efficient learning. The TDNN neural network receives two normalized inputs, one of which is the reference trajectory signal and the other of which is the error signals from the PD controller. It is proved that the normalized inputs to the TDNN neural network can enhance the learning efficiency of the neural network. The proposed scheme was investigated for the planar robot manipulator with two joints by computer simulation.

• Journal of Educational Research in Mathematics
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• v.20 no.1
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• pp.85-102
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• 2010

Linear programming(LP) is useful for finding the best way in a given condition for some list of requirements represented as linear equations. This study analysed LP in mathematics contexts and LP in school mathematics contexts, considered learning process of LP from an epistemological point of view, and explored a hypothetical learning trajectory of LP. The differences between mathematics contexts and school mathematics contexts are whether they considered that the convex polytope $\Omega$ is feasible/infeasible or bounded/unbounded or not, and whether they prove the theorem that the optimum is always attained at a vertex of the polyhedronor not. And there is a possibility that students could not understand what is maximum and minimum of a linear function when the domain of the function is limited. By considering these three aspects, we constructed hypothetical learning trajectory consisted of 4 steps. The first step is to see a given linear expression as linear function, the second step is to partition a given domain by straight lines, the third step is to construct the conception of y-intercept by relating lines and the range of k, and the forth step is to identify whether there exists the optimum in a given domain or not.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.12-17
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• 1991

In this paper, a variable structure system control method is proposed to the trajectory control of robot arm. A proposed method uses nonlinear switching function and saturation function. Furthermore, learning control method uses to decrease of the following error. The computer simulation results show that the chattering and the following error decrease and is improved the control the performance by a proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.144-148
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• 1989

We solve the inverse kinematics problems in robotics by employing a neural network. In the practical situation. it is not easy to obtain the exact inverse kinematics solution, since there are many unforeseen errors such as the shift of a robot base the link's bending, et c. Hence difficulties follow in the trajectory planning. With the neural network, it is possible to train the robot motion so that the robot follows the desired trajectory without errors even under the situation where the unexpected errors are involved. In this work, Back-Propagation rule is used as a learning method.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.540-543
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• 1997

In this paper, a precise trajectory tracking method for mobile robot using a vision system is presented. In solving the problem of precise trajectory tracking, a hierarchical control structure is used which is composed of the path planer, vision system, and dynamic controller. When designing the dynamic controller, non-ideal conditions such as parameter variation, frictional force, and external disturbance are considered. The proposed controller can learn bounded control input for repetitive or periodic dynamics compensation which provides robust and adaptive learning capability. Moreover, the usage of vision system makes mobile robot compensate the cumulative location error which exists when relative sensor like encoder is used to locate the position of mobile robot. The effectiveness of the proposed control scheme is shown through computer simulation.