• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.1
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• pp.140-145
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• 2010

In this work, to theoretically analyze the nonlinear charging characteristic, a Radial Basis Function Neural Network (RBFNN) is adopted. Based on the RBFNN, an charging characteristic tendency of a Linear Type Magnetic Flux Pump (LTMFP) is analyzed. In the paper, we developed the LTMFP that generates stable and controllable charging current and also experimentally investigated its charging characteristic in the cryogenic system. From these experimental results, the charging current of the LTMFP was also found to be frequency dependent with nonlinear quality due to the nonlinear magnetic behaviour of superconducting Nb foil. On the whole, in the case of essentially cryogenic experiment, since cooling costs loomed large in the cryogenic environment, it is difficult to carry out various experiments. Consequentially, in this paper, we estimated the nonlinear characteristic of charging current as well as realized the intelligent model via the design of RBFNN based on the experimental data. In this paper, we view RBF neural networks as predominantly data driven constructs whose processing is based upon an effective usage of experimental data through a prudent process of Fuzzy C-Means clustering method. Also, the receptive fields of the proposed RBF neural network are formed by the FCM clustering.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.424-424
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• 2018

인공신경망(Artificial Neural Network; ANN)은 뇌에 존재하는 생물학적 신경세포와 이들의 신호처리 과정을 수학적으로 묘사하여 뇌가 나타내는 지능적 형태의 반응을 구현한 것이다. 인공신경망은 학습(training)을 통해 입력과 출력으로 구성되는 하나의 시스템을 병렬적이고 비선형적으로 구축할 수 있으며, 유연한 모델링 특성으로 인하여 시스템 예측, 패턴인식, 분류 및 공정제어 등의 다양한 분야에서 활용되고 있다. 인공신경망에 대한 최초의 이론은 Muculloch and Pitts(1943)가 제안한 Perceptron에서 시작 되었으며, 기본적인 학습기법인 오차역전파 기법(back-propagation Algorithm) 이 1980년대에 들어 수학적으로 정립된 이후 여러 분야에서 활용되기 시작하였다). 본 연구에서는 하도추적, 구체적으로는 상류단의 복수의 수위관측을 이용하여 하류단의 수위를 예측하기 위하여 인공신경망 모델을 구성하였다. 대상하도는 금강유역의 용담댐과 대청댐 사이의 본류이며, 상류단 입력자료로써 본류에 있는 수통, 호탄 관측소 관측수위와 지류인 송천 관측소 관측수위를 고려하였다. 출력 값으로는 하류단의 옥천 관측소 수위를 3시간 및 6시간의 선행시간으로 예측하도록 인공신경망 모형을 구성하였다. 인공신경망의 학습(testing), 시험(testing), 검증(validation)을 위해 2000년부터 2012년까지 13년간의 시수위자료를 이용하여 학습을 진행하였으며, 2013년부터 2014년의 2년간의 수위자료를 이용한 시험을 통해 최적의 모형을 선정하였다. 또한 선정된 최적의 모형을 이용하여 2015년부터 2016년까지의 수위예측을 수행하였다.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.33 no.2
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• pp.84-91
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• 2005

For long time, the takeoff and landing control of airship was worked by human handling. With the development of the autonomous control system, the exact controls during the takeoff and landing were required and lots of methods and algorithms were suggested. This paper presents the result of airship take-off and landing by buoyancy control using air ballonet volume change and performance control of pitch angle for stable flight within the desired altitude. For the complexity of airship's dynamics, firstly, simple PID controller was applied. Due to the various atmospheric conditions, this controller didn't give satisfactory results. Therefore, new control method was designed to reduce rapidly the error between designed trajectory and actual trajectory by learning algorithm using an artificial neural network. Generally, ANN has various weaknesses such as large training time, selection of neuron and hidden layer numbers required to deal with complex problem. To overcome these drawbacks, in this paper, the RBFN (radial basis function network) controller developed. The weight value of RBFN is acquired by learning which to reduce the error between desired input output through and airship dynamics to impress the disturbance. As a result of simulation, the controller using the RBFN is superior to PID controller which maximum error is 15M.

• The Journal of Korea Robotics Society
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• v.15 no.3
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• pp.277-285
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• 2020

A differential drive wheeled robot is a kind of mobile robot suitable for indoor navigation. Model predictive control is an optimal control technique with various advantages and can achieve excellent performance. One of the main advantages of model predictive control is that it can easily handle constraints. Therefore, it deals with realistic constraints of the mobile robot and achieves admirable performance for trajectory tracking. In addition, the intention of the robot can be properly realized by adjusting the weight of the cost function component. This control technique is applied to the local planner of the navigation component so that the mobile robot can operate in real environment. Using the Robot Operating System (ROS), which has transcendent advantages in robot development, we have ensured that the algorithm works in the simulation and real experiment.

• Journal of Advanced Navigation Technology
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• v.18 no.3
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• pp.215-222
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• 2014

In this paper, parafoil airdrop system has been designed and analyzed. 6-degrees of freedom (6-DOF) model of the parafoil system is set up. Nonlinear model predictive control (NMPC) and Proportion integration differentiation (PID) methods were separately applied to adjust the flap yaw angle. Compared the results of setting time and overshoot time of yaw angle, it is found that the of yaw angle is more stable by using PID method. Then, trajectory following controller was designed based on the simulation results of trajectory following effects, which was carried out by using MATLAB. The lateral offset error of parafoil trajectory can be eliminated by its lateral deviation control. The later offset deviation reference was obtained by the interpolation of the current planning path. Moreover, using the designed trajectory, the trajectory following system was simulated by adding the wind disturbances. It is found that the simulation result is highly agreed with the designed trajectory, which means that wind disturbances have been eliminated with the change of yaw angle controlled by PID method.

• Journal of the Society of Naval Architects of Korea
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• v.60 no.3
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• pp.155-164
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• 2023

About 75% of vessel collision accidents are caused by human error, which causes enormous economic loss, environmental pollution, and human casualties, thus research on automatic collision avoidance of vessels is being actively conducted. In addition, vessels must comply with the COLREGs rules stipulated by IMO when performing collision avoidance with other vessels in motion. In this study, the collision risk was calculated by estimating the position and velocity of other vessels through the Probabilistic Data Association Filter (PDAF) algorithm based on RADAR sensor data. When a collision risk is detected, we propose an event-triggered Nonlinear Model Predict Control (NMPC) algorithm that geometrically creates waypoints that satisfy COLREGs and follows them. To verify the proposed algorithm, simulations through MATLAB are performed.

• Korean Chemical Engineering Research
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• v.46 no.3
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• pp.486-491
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• 2008

This paper describes a model identification method that has been applied to a commercial 12-inch RTP (rapid thermal processing) equipment with an ultimate aim to develop a high-performance advanced controller. Seven thermocouples are attached on the wafer surface and twelve tungsten-halogen lamp groups are used to heat up the wafer. To obtain a MIMO balanced state space model, multiple SIMO (single-input multiple-output) identification with highorder ARX models have been conducted and the resulting models have been combined, transformed and reduced to a MIMO balanced state space model through a balanced truncation technique. The identification experiments were designed to minimize the wafer warpage and an output linearization block has been proposed for compensation of the nonlinearity from the radiation-dominant heat transfer. As a result from the identification at around 600, 700, and $800^{\circ}C$, respectively, it was found that $y=T(K)^2$ and the state dimension of 80-100 are most desirable. With this choice the root-mean-square value of the one-step-ahead temperature prediction error was found to be in the range of 0.125-0.135 K.

• Convergence Security Journal
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• v.12 no.6
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• pp.85-92
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• 2012

There are many software reliability models that are based on the times of occurrences of errors in the debugging of software. It is shown that it is possible to do asymptotic likelihood inference for software reliability models based on infinite failure model and non-homogeneous Poisson Processes (NHPP). For someone making a decision about when to market software, the conditional failure rate is an important variables. The finite failure model are used in a wide variety of practical situations. Their use in characterization problems, detection of outliers, linear estimation, study of system reliability, life-testing, survival analysis, data compression and many other fields can be seen from the many study. Statistical Process Control (SPC) can monitor the forecasting of software failure and there by contribute significantly to the improvement of software reliability. Control charts are widely used for software process control in the software industry. In this paper, we proposed a control mechanism based on NHPP using mean value function of log Poission, log-linear and Parto distribution.

• Proceedings of the Korea Society for Simulation Conference
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• 1999.10a
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• pp.169-169
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• 1999

최근 산업 플랜트의 공정제어 시스템은 복잡하고 대규모화되어 고장 발생시 경제적 손실과 위험성이 증폭되어 규정된 안정서와 신뢰성 확보가 필수적이라 할 수 있다. 고장검출 및 진단기법은 시스템의 신뢰성을 높이기 위한 효과적인 방안을 연구하는 것으로 현대에 들어서 많은 학자들의 관심을 끌고 있으며 실제 계통에 점차적으로 응용되고 있다. 현재까지 개발된 고장검출 및 진단기법은 사용된 프로세스 모델의 형태, 고장검출 진단 알고리즘에 따라 다양하게 분류 될 수 있으며 일반적으로 사용된 모델에 따라 크게 1) 정량적 모델에 근거한 해석적 기법, 2) 정성적 모델에 근거한 기법, 3) 지식기반 진단 기법으로 구분 할 수 있다. 이중 정량적 모델 기법은 대상계통의 수학적 모델에 근거하여 운전 데이터를 분석함으로서 고장검출 진단을 수행하는 해석적 기법으로서 근본적으로 계통의 정확한 수학적 모델을 요구하므로 불확실성을 포함한 계통 및 비선형성이 강한 계통등에는 적용이 곤란하다. 정성적 모델 및 지식기반 기법은 정량적 진단 기법과는 달리 대상 프로세스에 대한 수학적 모델 대신에 운전자의 경험과 프로세스 변수간의 상호 작용 및 고장의 전파과정, 고장원인과 증상과의 직접적인 관계에 대한 구조적 지식에 근거한 것으로 고장원인에 대한 계통의 동작을 추론 할 수 있으며, 상황 변화에 따른 영향을 예측할 수 있다. 본 논문에서는 정성적 모델 및 지식기반 기법에 근거한 고장검출 및 진단 기술을 화력 발전소 보일로 프로세스에 적용하여 정성적 시뮬레이션에 의한 설비의 고장을 조기에 발견하여 고장 파급으로 인한 발전 정지 및 설비의 손상 확대를 방지하고 고장 발생시 신속한 원인 규명 및 후속 조치관련 정보들을 운전원에게 제공할 목적으로 현재 전력원에서 개발중인 지능형 경보시스템에 대하여 기술하고자 한다.음과 같이 설명하였다. 서로 상반되는 것들이 다음과 같이 설명하였다. 서로 상반되는 것들이 부딛힘이 없이 공존하고 일상의 논리가 무시된다. 부정, 의심이 없고 확실한 것이 없다. 한 대상에 가졌던 생각이 다른 대상에 옮겨간다(displacement). 한 대상이 여러 대상이 갖고 있는 의미를 함축하고 있다(condensation). 시각적인 순서가 무시된다. 마음속의 생각과 외부의 실제적인 일을 구분하지 못한다. 시간 상의 순서가 있다가 없다가 한다. 차례로 일어나야 할 일이 동시에 한꺼번에 일어난다. 대상들이 서로 비슷해지고 동시에 있을 수 없는 대상들이 함께 나타난다. 사고의 정상적인 구조가 와해된다. Matte-Blance는 무의식에서는 여러 독립된 대상들간의 구분을 없애며, 주체와 객체를 하나로 보려는 대칭화(symmetrization)의 경향이 있기 때문에 이런 변화가 생긴다고 하였다. 또 대칭화가 진행되면 무한대의 느낌을 갖게 되어, 전지(moniscience), 전능(omnipotence), 무력감(impotence), 이상화(idealization)가 나타난다. 그러나 무의식에 대칭화만 있는 것은 아니며, 의식의 사고양식인 비대칭도 어느 정도 나타나며, 대칭화의 정도에 따라, 대상들이 잘 구분되어 있는 단계, 의식수준의 감정단계, 집단 내에서의 대칭화 단계, 집단간에서의 대칭화 단계, 구분이 없어지는 단계로 구분하였다.systems. We believe that this taxonomy is a significant contribution because it adds clarity, completeness, and "global perspective" to workflow architectural discussions. The vocabulary suggested here

• Journal of the Korean Geotechnical Society
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• v.28 no.12
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• pp.53-64
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• 2012

In this study, the modified cyclic triaxial tests with Joomunjin standard sand are performed for dynamic deformation characteristics, such as Young's moduli and damping ratio. The cyclic triaxial test is equipped with Local Displacement Transducer (LDT) on the outside of a cell which has a range from $10^{-4}$ to $10^{-1}$ of shear strains, ${\gamma}$ (%), instead of conventional cyclic triaxial test which has linear variable displacement transducer (LVDT) with low precision. With the small strain control, tests were carried out at various loading rates, void ratios, and effective confining pressures. Based on the test results, such as dynamic deformation characteristics, shear modulus, and damping ratio, it is found that the test can measure more range of medium strains (0.02-0.2%) than results obtained from conventional test (resonant column test). For the medium strain range, dynamic deformation characteristics investigated by the cyclic triaxial test are also different from those predicted by nonlinear model in conventional test.