• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2170-2172
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• 2003

This paper presents the position tracking control of an inverted pendulum on an inclined railway. In general, inclining the railway leads to errors in the pendulum angle even though the pendulum is stabilized, which results in errors in the cart position. To solve this problem, a linear quadratic regulation (LQR) controller with an integrator is used for compensating the resulting error in the cart position. The proposed method is evaluated by comparing LQR controllers with and without an integrator. Experimental results show that the LQR controller with an integrator is better in performance than one without an integrator.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.11
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• pp.204-212
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• 1999

In this paper, the vibrational motion of a flexible beam clamped on a translating base and carrying a moving mass is investigated. The equations of motion which describe the total dynamics of the beam-mass-cart system are derived and the coupled dynamic equations are solved by unconstrained modal analysis. In modal analysis, the exact normal mode solutions corresponding to the eigenfrequencies for the position of the moving mass and the ratios of the mass of the flexible beam, the moving mass and the base cart are used. Proper transformations of the time solutions between the normal modes for a position and those for the next position of the moving mass are also adopted. Numerical simulations are carried out to obtain the open-loop responses of the system in tracking the pre-designed path of the moving mass.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.66.4-66
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• 2001

In this paper we have fabricated the two-dimensional Rod Balancing System which expands conventional one-dimensional inverted pendulum control system and designed its controller. The X-axis cart and Y-axis bar of the Rod Balancing System, which is composed of X-Y table, are actuated through timing belt by each of two geared DC motors, and the rod mounted on a X-axis cart can be brought to the desired position and maintained in a vertical position by motor-control. For the control of the Rod Balancing System, we used a fuzzy logic controller that is an approach to systems control when the exact mathematical model of the plant is unknown or the mathematical model is too complex to understand.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.155-156
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• 2019

Traditional machine learning based emotion analysis methods such as Classification and Regression Tree (CART), Support Vector Machine (SVM), and k-nearest neighbor classification (kNN) are less accurate. In this paper, we propose an improved kNN classification method. Improved methods and data normalization achieve the goal of improving accuracy. Then, three classification algorithms and an improved algorithm were compared based on experimental data.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.227-233
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• 1996

The Inverted Pendulum has been one of most popular nonlinear dynamic systems for the exploration of control techniques. This paper presents a new linear optimal control techniques and nonlinear neural network learning methods. The multiayered neural networks are used to add nonlinear effects on the linear optimal regulator(LQR). The new regulator can compensate nonlinear system uncertainties that are not considered in the LQR design, and can tolerated a wider range of uncertainties than the LQR alone. The new regulator has two neural networks for modeling and control. The neural network for modeling is used to obtain a more accurate model than the given mathematical equations. The neural network for control is used to overcome deficiencies by adding corrections to the linear coefficients of the LQR and by adding nonlinear effects on the LQR. Computer simulations are performed to show the applicability and a more robust regulator than the LQR alone.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.01a
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• pp.271-272
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• 2020

Data mining is known useful in medical area when no availability of evidence favoring a particular treatment option is found. Huge volume of structured/unstructured data is collected by the healthcare field in order to find unknown information or knowledge for effective diagnosis and clinical decision making. The data of 5,179 records considered for analysis has been collected from Korean National Health and Nutrition Examination Survey(KHANES) during 2-years. Data splitting, referred as the training and test sets, was applied to predict to fit the model. We analyzed to predict chronic kidney disease (CKD) using data mining method such as naive Bayes, logistic regression, CART and artificial neural network(ANN). This result present to select significant features and data mining techniques for the lifestyle factors related CKD.

• Annual Conference of KIPS
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• 2021.11a
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• pp.1136-1139
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• 2021

쇼핑카트 부착 모듈형 로봇 'Cart-Rider'는 어드미턴스 제어를 통한 사용자의 힘 보조 기능, 딥러닝을 활용한 네비게이션 기능, GPS 를 활용한 도난 방지 기능을 제공하는 로봇으로 대형 마트에서 발생하는 안전사고 및 쇼핑카트 도난을 예방하는 동시에 사용자에게 편의성을 제공하는 로봇이다. 또한 여러 대를 겹쳐서 보관하는 기존의 카트 시스템을 유지하고 탈부착이 용이하도록 하드웨어를 제작하여 환경에 영향을 주지 않고 유지 및 보수가 용이하도록 제작했다.

• Review of Korea Contents Association
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• v.13 no.3
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• pp.23-27
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• 2015

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.05a
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• pp.648-661
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• 2006

• Proceedings of the Korean Nuclear Society Conference
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• 2005.05a
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• pp.147-148
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• 2005