• The Journal of Korean Association of Computer Education
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• v.16 no.4
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• pp.13-21
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• 2013

Recently, programming education is being actively performed in education field with development of educational programming language and teaching and learning methods for elementary students. However, programming education have limit to apply to the overall computer science curriculum, because it is performed by more than 5th grade and focused on the utilization of programming tools than problem-solving process. It is necessary to expand the range of students and educational content considered with problem-solving process for encouraging programming education in computer science. In this study, we suggest the easy-to-use programming tool for lower grade(1st grade) and robot programming task based on improvement of student's thinking ability. We use Tangible User Interface(TUI) for elementary student's(1st grade) convenience of programming and developed the robot programming task for improvement of logical thinking. As a result of this experiment, tangible programming tool can be used easily in elementary students(1st grade) and developed robot programming task is effective in improvement of logical thinking.

• Journal of Internet Computing and Services
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• v.17 no.3
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• pp.67-73
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• 2016

With the rapid development of mobile services and the prevalence of education robots, robots are being developed to become a part of our lives and they can be utilized to assist teachers in giving education or learning to students. This standard has been proposed to define the degree of autonomy for education robot. The autonomy is an ability to perform a given work based on current state and sensor value without human intervention. The degree of autonomy is a scale indicating the extent of autonomy and it is determined in between 1 and 10 by considering the level of work and human intervention. It has been adapted as per standard and education robots can be utilized in teaching the students autonomously. Education robots can be beneficial in education and it is expected to contribute in assisting the teacher's education.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.24 no.8
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• pp.119-125
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• 2010

This paper proposes a new solution about physical problem of autonomous mobile robots system using ultrasonic sensors. An mobile robot uses several sensors for recognition of its circumstance. However, such sensor data are not accurate all the time. A means of removing the noise that sensor data contains constantly, It is possible for simulation to estimate its circumstance based on ultrasonic sensor data by learning algorithm SVR(Support Vector Regression). To use SVR, it is being selected parameter and kernel which are the component of SVR. Selecting the component of SVR, the most accurate parameter data was selected through the tests because it is not existed determined data. In addition, choosing the kernel uses RBF(Radial Basis Function) kernel which is the most generalized. This paper proposes SVR based algorithm to compensate for the above demerits of ultrasonic sensor through the experimentation under three different environments.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.10
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• pp.2180-2188
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• 2009

This paper is study on real-time hand gesture recognition system based on vision for intelligent robot control. We are proposed a recognition system using PCA and BP algorithm. Recognition of hand gestures consists of two steps which are preprocessing step using PCA algorithm and classification step using BP algorithm. The PCA algorithm is a technique used to reduce multidimensional data sets to lower dimensions for effective analysis. In our simulation, the PCA is applied to calculate feature projection vectors for the image of a given hand. The BP algorithm is capable of doing parallel distributed processing and expedite processing since it take parallel structure. The BP algorithm recognized in real time hand gestures by self learning of trained eigen hand gesture. The proposed PCA and BP algorithm show improvement on the recognition compared to PCA algorithm.

• The Mathematical Education
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• v.53 no.3
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• pp.413-434
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• 2014

Logic lays the foundation of Mathematics and the development of Mathematics is dependent on critical thinking. So it is important that school mathematics helps students develop their logical and critical thinking ability for both mathematics learning and problem solving in general. MINDSTORMS, a LEGO based programming activity kit, is an effective teaching and learning tool that can be used to enhance logical and critical thinking in students. This study focused on measuring the growth of students' ability to think logically and critically when they used MINDSTORMS activities to learn programming. In addition, we investigated how the students' logical and critical thinking changed from the MINDSTORMS learning experience. The study confirmed that the programming activities using MINDSTORMS help to enhance logical and critical thinking in students. The students attitude about logical and critical thinking became more positive and the activities helped to engage students to think logically and critically. This type of programming activities should be valuable in mathematics education and it should be included in a general mathematics curriculum.

• The Journal of Korea Robotics Society
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• v.18 no.2
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• pp.182-188
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• 2023

Due to the acceleration of an aging society, the need for lower limb exoskeletons to assist gait is increasing. And for use in daily life, it is essential to have technology that can accurately estimate gait phase even in the walking environment and walking speed of the wearer that changes frequently. In this paper, we implement an LSTM-based gait phase estimation learning model by collecting gait data according to changes in gait speed in outdoor level ground and stair environments. In addition, the results of the gait phase estimation error for each walking environment were compared after learning for both max hip extension (MHE) and max hip flexion (MHF), which are ground truth criteria in gait phase divided in previous studies. As a result, the average error rate of all walking environments using MHF reference data and MHE reference data was 2.97% and 4.36%, respectively, and the result of using MHF reference data was 1.39% lower than the result of using MHE reference data.

• Wind and Structures
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• v.36 no.6
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• pp.405-421
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• 2023

Strong wind is the main factors of wind-damage of high-rise buildings, which often creates largely economical losses and casualties. Wind pressure plays a critical role in wind effects on buildings. To obtain the high-resolution wind pressure field, it often requires massive pressure taps. In this study, two traditional methods, including bilinear and bicubic interpolation, and two deep learning techniques including Residual Networks (ResNet) and Generative Adversarial Networks (GANs), are employed to reconstruct wind pressure filed from limited pressure taps on the surface of an ideal building from TPU database. It was found that the GANs model exhibits the best performance in reconstructing the wind pressure field. Meanwhile, it was confirmed that k-means clustering based retained pressure taps as model input can significantly improve the reconstruction ability of GANs model. Finally, the generalization ability of k-means clustering based GANs model in reconstructing wind pressure field is verified by an actual engineering structure. Importantly, the k-means clustering based GANs model can achieve satisfactory reconstruction in wind pressure field under the inputs processing by k-means clustering, even the 20% of pressure taps. Therefore, it is expected to save a huge number of pressure taps under the field reconstruction and achieve timely and accurately reconstruction of wind pressure field under k-means clustering based GANs model.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.2
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• pp.209-214
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• 1998

In this paper, we are dealing with the problem of controlling unknown nonlinear dynamical system by using neural networks. A novel error self-recurrent(ESR) neural model is presented to perform black-box identification. Through the various outcome of the experiment, a new neural network is seen to be considerably faster than the BP algorithm and has advantages of being less affected by poor initial weights and learning rate. These characteristics make it flexible to design the controller in real-time based on neural networks model. In addition, we design an adaptive PID controller that Keyser suggested by using ESR neural networks, and present a method on the implementation of adaptive controller based on neural network for practical applications. We obtained good results in the case of robot manipulator experiment.

• Annual Conference of KIPS
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• 2021.11a
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• pp.783-786
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• 2021

In recent years, the use of LiDAR sensors is increasing as autonomous driving, robot control, and drones are considered more. Contrary to ordinary cameras, LiDAR sensors make it possible to handle challenging problems by calculating the distance between objects. This crucial characteristic makes more active research on deep learning models dealing with point clouds which are data of LiDAR. In this paper, among the schemes of using the point cloud, the Point-based approach is mainly discussed. Furthermore, future streams and insights can be considered by looking at solving methods and the limitations.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.1
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• pp.1-8
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• 2024

Robotic arms have been widely utilized in various labor-intensive industries such as manufacturing, agriculture, and food services, contributing to increasing productivity. In the development of industrial robotic arms, camera sensors have many advantages due to their cost-effectiveness and small sizes. However, estimating object positions is a challenging problem, and it critically affects to the robustness of object manipulation functions. This paper proposes a method for estimating the 3D positions of objects, and it is applied to a pick-and-place task. A deep learning model is utilized to detect 2D bounding boxes in the image plane, and the pinhole camera model is employed to compute the object positions. To improve the robustness of measuring the 3D positions of objects, we analyze the effect of lens distortion and introduce a false positive filtering process. Experiments were conducted on a real-world scenario for moving medicine bottles by using a camera-based manipulator. Experimental results demonstrated that the distortion removal and false positive filtering are effective to improve the position estimation precision and the manipulation success rate.