• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.241-244
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• 2014

In this paper, we apply the learning of the mobile robot insect augmented reality Digilog Book. In the era of electronic, book written in paper space just have moved to virtual reality space. The virtual reality, constraints spatial and physical, in the real world, it is a technique that enables to experience indirectly situation not experienced directly as user immersive experience type interface. Applied to the learning robot Digilog Book that allows the fusion of paper analog and digital content, using the augmented reality technology, to experience various interactions. Apply critical elements moving, three-dimensional images and animation to enrich the learning, for easier block assembly, designed to grasp more easily rank order between the blocks. Anywhere at any time, is capable of learning of the robot in Digilog Book to be executed by the mobile phone in particular.

• Journal of the Semiconductor & Display Technology
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• v.22 no.4
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• pp.113-118
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• 2023

Maintenance of semiconductor equipment processes is crucial for the continuous growth of the semiconductor market. The process must always be upheld in optimal condition to ensure a smooth supply of numerous parts. Additionally, it is imperative to monitor the status of the robots that play a central role in the process. Just as many senses of organs judge a person's body condition, robots also have numerous sensors that play a role, and like human joints, they can detect the condition first in the joints, which are the driving parts of the robot. Therefore, a normal state test bed and an abnormal state test bed using an aging reducer were constructed by simulating the joint, which is the driving part of the robot. Various sensors such as vibration, torque, encoder, and temperature were attached to accurately diagnose the robot's failure, and the test bed was built with an integrated system to collect and control data simultaneously in real-time. After configuring the user screen and building a database based on the collected data, the characteristic values of normal and abnormal data were analyzed, and machine learning was performed using the KNN (K-Nearest Neighbors) machine learning algorithm. This approach yielded an impressive 94% accuracy in failure diagnosis, underscoring the reliability of both the test bed and the data it produced.

• The Journal of the Korea Contents Association
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• v.10 no.10
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• pp.54-67
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• 2010

Applying the conventional machine-learning method which has been frequently used in health-care area has several fundamental problems for modern U-health service analysis. First of all, we are still lack of application examples of the traditional method for our modern U-health environment because of its short term history of U-health study. Second, it is difficult to apply the machine-learning method to our U-health service environment which requires real-time management of disease because the method spends a lot of time in the process of learning. Third, we cannot implement a personalized U-health diagnosis system using the conventional method because there is no way to assign weights on the disease-related variables although various kinds of machine-learning schemes have been proposed. In this paper, a novel diagnosis scheme PCADP is proposed to overcome the problems mentioned above. PCADP scheme is a personalized diagnosis method and it makes the bio-data analysis just a 'process' in the U-health service system. In addition, we offer a semantics modeling of the U-health ontology framework in order to describe U-health data and service specifications as meaningful representations based on this PCADP. The PCADP scheme is a kind of statistical diagnosis method which has characteristics of flexible structure, real-time processing, continuous improvement, and easy monitoring of decision process. Upto the best of authors' knowledge, the PCADP scheme and ontology framework proposed in this paper reveals one of the best characteristics of flexible structure, real-time processing, continuous improvement, and easy monitoring among recently developed U-health schemes.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.3B
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• pp.279-289
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• 2006

The reconstruction of low dimension nonlinear behavior from the hydrologic time series has been an active area of research in the last decade. In this study, we present the applications of a powerful state space reconstruction methodology using the method of Support Vector Machines (SVM) to the Great Salt Lake (GSL) volume. SVMs are machine learning systems that use a hypothesis space of linear functions in a Kernel induced higher dimensional feature space. SVMs are optimized by minimizing a bound on a generalized error (risk) measure, rather than just the mean square error over a training set. The utility of this SVM regression approach is demonstrated through applications to the short term forecasts of the biweekly GSL volume. The SVM based reconstruction is used to develop time series forecasts for multiple lead times ranging from the period of two weeks to several months. The reliability of the algorithm in learning and forecasting the dynamics is tested using split sample sensitivity analyses, with a particular interest in forecasting extreme states. Unlike previously reported methodologies, SVMs are able to extract the dynamics using only a few past observed data points (Support Vectors, SV) out of the training examples. Considering statistical measures, the prediction model based on SVM demonstrated encouraging and promising results in a short-term prediction. Thus, the SVM method presented in this study suggests a competitive methodology for the forecast of hydrologic time series.

• Proceedings of the KIPE Conference
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• 1999.07a
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• pp.526-530
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• 1999

This paper presents a newly developed speed sensorless drive using Neural Network algorithm. Neural Network algorithm can be divided into three categories. In the first one, a Back Propagation-based NN algorithm is well-known to gradient descent method. In the second scheme, a Extended Kalman Filter-based NN algorithm has just the time varying learning rate. In the last scheme, a Recursive Least Square-based NN algorithm is faster and more stable than the classical back-propagation algorithm for training multilayer perceptrons. The number of iterations required to converge and the mean-squared error between the desired and actual outputs is compared with respect to each method. The theoretical analysis and experimental results are discussed.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.3
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• pp.169-174
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• 2000

In this paper, Haar wavelet-based neural network is described for the identification and control of discrete-time nonlinear dynamical systems. Wavelets are suited to depict functions with local nonlinearities and fast variations because of their intrinsic properties of finite support and self-similarity. Due to the orthonormal properties of Haar wavelet functions, wavelet neural networks result in a greatly simplified training problem. This wavelet-based scheme performs adaptively both the identification of nonlinear functions and the control of the overall system, while the multilayer neural network is applied to the control system just after its sufficient learning of the unknown functions. Simulation shows that the wavelet network can be a good alternative to a multilayer neural network with backpropagation.

• The Journal of Economics, Marketing and Management
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• v.4 no.4
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• pp.25-28
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• 2016

In this paper we expand on the notion of "integration" in terms of the variety of ways in which it would manifest itself in business education. Our main argument is that "integration" is multidimensional and has been manifest in pedagogy, research and service dimensions of university programs for a long time. However, assessments of "integration" efforts have been spotty thus far and only recently are being formalized. We present several examples in business curriculum and with increased focus on formal assessments of "integration" efforts, business education will become more pragmatic. The goal of this paper is to unpack the broad construct of "integration," and discuss its historical and current manifestations in business education. Ultimately, we conclude that while the process of integrative thinking is well underway for a long time in business education, the assessment of outcomes of integrative thinking is just taking root through formal ETS tests. We believe that integrative thinking in business education is an ultimate indicator of the effectiveness of the business curriculum, as students skilled in this area will be best prepared for the real-life jobs in the market place.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.01a
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• pp.103-104
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• 2018

이 연구는 디지털 네트워크 세상으로 인한 새로운 학습이론인 커넥티비즘을 알아보고 SW 교육이 나가야할 방향을 알아보았다. 커넥티비즘에서 학습은 지식이나 정보를 연결하는 과정으로 정의하고 학습자가 기존에 알고 있는 지식을 바탕으로 새로운 지식을 끊임없이 연결하거나 단절시키는 과정이 반복된다고 말한다. 커넥티비즘에 따른 SW 교육의 새로운 방향을 제시하면, 1)SW 학습 목표는 지식의 연결과정으로 다양성과 가변성을 내포하여 설정하고, 2)현재 정보 교과서 개발 체재의 변화와, 3)협업과 네트워킹이 강조되는 도구를 활용한 SW 교육 활동을 지향하며, 4)교육 비용 절감을 위한 적시학습(just-in-time learning) 지향 및 5)SW 교육 목적에 합리적 의사결정을 통한 연결 지식 배양 역량을 추가하고, 6)문제 해결학습 보다는 문제발견학습을 중시하도록 설정해야 한다.

• The Journal of Korean Association of Computer Education
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• v.4 no.1
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• pp.127-134
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• 2001

Many teachers at vocational high schools have had difficulties overcoming the gap between what students know and what students have to achieve in many topics. Mathematics is toughest of all, since most of textbooks in electronics are assuming student's basic knowledge in math. Considering that many students with very low achievements are entering vocational high schools, reality is far from such assumption. Inevitably, we have to face two difficult questions; do we have enough time to teach these kids all the math that they need in two years? If not, what alternatives we should adopt? We just do not have enough time and therefore find out a way to cope with harsh reality. According to our preliminary study, we suggest that multimedia-based CAI may be the best way to attack this problem. From hardware point of view, fortunately, many of vocational high schools are reasonably equipped for multimedia-based education. However there have been hardly any effort to develop courseware for vocational education in Korea. In this paper, a CAI title for learning basic characteristics of alternating current has been designed and implemented. The developed multimedia-based CAI title has been applied with respect to first grade students at local vocational high schools. A survey after classes shows that CAI could help student feel much comfortable with Basic Electricity course and grasp physical understanding much easily. Accordingly we conclude that classes adopting CAI would be of great help to put education in vocational high schools on the right track.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.54.2-54.2
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• 2019

Removing noise which occurs inevitably when taking image data has been a big concern. There is a way to raise signal-to-noise ratio and it is regarded as the only way, image stacking. Image stacking is averaging or just adding all pixel values of multiple pictures taken of a specific area. Its performance and reliability are unquestioned, but its weaknesses are also evident. Object with fast proper motion can be vanished, and most of all, it takes too long time. So if we can handle single shot image well and achieve similar performance, we can overcome those weaknesses. Recent developments in deep learning have enabled things that were not possible with former algorithm-based programming. One of the things is generating data with more information from data with less information. As a part of that, we reproduced stacked image from single shot image using a kind of deep learning, conditional generative adversarial network (cGAN). r-band camcol2 south data were used from SDSS Stripe 82 data. From all fields, image data which is stacked with only 22 individual images and, as a pair of stacked image, single pass data which were included in all stacked image were used. All used fields are cut in $128{\times}128$ pixel size, so total number of image is 17930. 14234 pairs of all images were used for training cGAN and 3696 pairs were used for verify the result. As a result, RMS error of pixel values between generated data from the best condition and target data were $7.67{\times}10^{-4}$ compared to original input data, $1.24{\times}10^{-3}$. We also applied to a few test galaxy images and generated images were similar to stacked images qualitatively compared to other de-noising methods. In addition, with photometry, The number count of stacked-cGAN matched sources is larger than that of single pass-stacked one, especially for fainter objects. Also, magnitude completeness became better in fainter objects. With this work, it is possible to observe reliably 1 magnitude fainter object.