• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.539-541
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• 2006

Biped locomotion is a popular research area in robotics due to the high adaptability of a walking robot in an unstructured environment. When attempting to automate the motion planning process for a biped walking robot, one of the main issues is assurance of dynamic stability of motion. This can be categorized into three general groups: body stability, body path stability, and gait stability. A zero moment point (ZMP), a point where the total forces and moments acting on the robot are zero, is usually employed as a basic component for dynamically stable motion. In this rarer, learning based neuro-fuzzy systems have been developed and applied to model ZMP trajectory of a biped walking robot. As a result, we can provide more improved insight into physical walking mechanisms.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.243-246
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• 1994

In this paper, a self-learning fuzzy controller is designed with a fuzzy approximation of an inverse model. The aim of an identification is to find an input command which is control of a system output. It is intuitional and easy to use a classical adaptive inverse modeling method for the identification, but it is difficult and complex to implement it. This problem can be solved with a fuzzy approximation of an inverse modeling. The fuzzy logic effectively represents the complex phenomena of the real world. Also fuzzy system could be represented by the neural network that is useful for a learning structure. The rule of a fuzzy inverse model is modified by the gradient descent method. The goal is to be obtained that makes the design of fuzzy controller less complex, and then this self-learning fuzz controller can be used for nonlinear dynamic system. We have applied this scheme to a nonlinear Ball and Beam system.

• Journal of the Korean Data and Information Science Society
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• v.27 no.3
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• pp.827-833
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• 2016

In this paper, we propose a deep least squares support vector machine (LS-SVM) for regression problems, which consists of the input layer and the hidden layer. In the hidden layer, LS-SVMs are trained with the original input variables and the perturbed responses. For the final output, the main LS-SVM is trained with the outputs from LS-SVMs of the hidden layer as input variables and the original responses. In contrast to the multilayer neural network (MNN), LS-SVMs in the deep LS-SVM are trained to minimize the penalized objective function. Thus, the learning dynamics of the deep LS-SVM are entirely different from MNN in which all weights and biases are trained to minimize one final error function. When compared to MNN approaches, the deep LS-SVM does not make use of any combination weights, but trains all LS-SVMs in the architecture. Experimental results from real datasets illustrate that the deep LS-SVM significantly outperforms state of the art machine learning methods on regression problems.

• Journal of Information Processing Systems
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• v.16 no.5
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• pp.1034-1047
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• 2020

The personal authentication technique is an essential tool in this complex and modern digital information society. Traditionally, the most general mechanism of personal authentication was using alphanumeric passwords. However, passwords that are hard to guess or to break, are often hard to remember. There are demands for a technology capable of replacing the text-based password system. Graphical passwords can be an alternative, but it is vulnerable to shoulder-surfing attacks. This paper looks through a number of recently developed graphical password systems and introduces a personal authentication system using a machine learning technique with electroencephalography (EEG) signals as a new type of personal authentication system which is easier for a person to use and more difficult for others to steal than other preexisting authentication systems.

• Journal of the Korean Institute of Rural Architecture
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• v.16 no.4
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• pp.67-74
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• 2014

Departmentalized Classroom System is new school operating system to apply social needs. Recent social needs are characterized as learning environment and self-learning system. The purpose of this study is to provide basic data for equal learning environment condition in middle school applying departmentalized classroom system. This study has progressed through analyzing on 11 remodelling case of middle school in Gangwon-Do. The method of this study is visiting middle schools that operate the system, grasping the condition for environment composition, and investigating and analyzing practical use of the environment. The results of this study are summarized as follows: 1) The space compositions for departmentalized classroom system are generally desirable, but some schools take irrational space composition, especially on home base-teacher laboratory, classroom-teacher laboratory. 2) The space area distributions are different in every school. This result is based on not taking standard criterion on space area distribution.

• English Language & Literature Teaching
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• v.8 no.1
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• pp.199-221
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• 2002

Multimedia-Assisted Language Learning(MALL) has recently been studied by many researchers. It has been reported that MALL is very effective in encouraging students' desire for learning, promoting their self-directed learning, and improving their communicative competence. Also, it has been evident that it depends on the quality of multimedia courseware whether MALL will be successful or not. However, many researchers have pointed out that most of multimedia coursewares coming into the market have little to do with our curriculum and they are not suitable for the use in the regular classroom. More multimedia coursewares that reflect our curriculum need to be developed. This paper first tries to explore the cognitive, constructivist, and psychological theories supportive of the development of multimedia courseware and then presents the overall procedure for designing and developing a multimedia courseware pursuant to the 7th English curriculum in the middle school. The multimedia courseware developed through this research is expected to enhance middle school students' communicative language skills in English and promote the development of multimedia coursewares of high quality.

• Journal of Electrical Engineering and Technology
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• v.13 no.4
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• pp.1715-1723
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• 2018

This work investigates the performance of fully complex- valued radial basis function network(FC-RBF) and complex extreme learning machine (CELM) based neural approaches for classification of power quality disturbances. This work engages the use of S-Transform to extract the features relating to single and combined power quality disturbances. The performance of the classifiers are compared with their real valued counterparts namely extreme learning machine(ELM) and support vector machine(SVM) in terms of convergence and classification ability. The results signify the suitability of complex valued classifiers for power quality disturbance classification.

• Journal of Sensor Science and Technology
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• v.30 no.1
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• pp.15-19
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• 2021

Image processing is one of the major techniques that are used for computer vision. Nowadays, researchers are using machine learning and deep learning for the aforementioned task. In recent years, digit recognition tasks, i.e., automatic meter recognition approach using electric or water meters, have been studied several times. However, two major issues arise when we talk about previous studies: first, the use of the deep learning technique, which includes a large number of parameters that increase the computational cost and consume more power; and second, recent studies are limited to the detection of digits and not storing or providing detected digits to a database or mobile applications. This paper proposes a system that can detect the digital number of meter readings using a lightweight deep neural network (DNN) for low power consumption and send those digits to an Android mobile application in real-time to store them and make life easy. The proposed lightweight DNN is computationally inexpensive and exhibits accuracy similar to those of conventional DNNs.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.4_2
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• pp.687-697
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• 2022

An automated system is needed for the effectiveness of non-destructive testing. In order to utilize the radiographic testing data accumulated in the film, the types of welding defects were classified into 9 and the shape of defects were analyzed. Data was preprocessed to use deep learning with high performance in image classification, and a combination of one-stage/two-stage method and convolutional neural networks/Transformer backbone was compared to confirm a model suitable for welding defect detection. The combination of two-stage, which can learn step-by-step, and deep-layered CNN backbone, showed the best performance with mean average precision 0.868.

• Educational Technology International
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• v.18 no.1
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• pp.27-47
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• 2017

Smart devices have a variety of affordances to foster meaningful learning in elementary school. For the design of smart learning environments, more research is needed to understand students' smart device usage and their perception of learning with smart devices. In order to capture smart device usage profiles among elementary school students in South Korea, this study carried out Latent Profile Analysis with three constructs: information search, communication, and study. Participants (n=253), who ranged from the fourth to the sixth grade students, were classified into three profiles of smart device usage: low-activity, communication, and high-activity groups. The smart device usage profiles varied depending on smartphone usage experience, and the profiles were significantly related with smart device addiction, not with smart device usage ability. Perceptions of smart education were also significantly associated with the profiles. The high-activity group showed more positive attitudes toward smart education than the others, but no significant difference was found in regard to negative attitudes. Based on the findings, this study discussed implications for the use of smart devices in elementary school.