• Journal of Industrial Technology
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• v.20 no.A
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• pp.131-138
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• 2000

A past monthly data is not faithful so much for a short term. But, the stochastic generation technique was provide of a long-term data. Thus this study is used a data which generated a monthly inflow amounts data by Thomas-Fiering model. This model is needed a certain process which determination of distribution, decision of continuous durability, etc. It was generated a inflow data every one month as Thomas-Fiering method. The generated inflow data was used input data for a monthly cumulative analysis. This analysis obtained a storage capacities which would be required during droughts having various return periods. It was presented a equation of fitting regression that was carried out regression analysis of 5, 10, 20, 50 years period.

• The Journal of Korea Robotics Society
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• v.15 no.4
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• pp.398-403
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• 2020

Robot's throwing control is difficult to accurately calculate because of air resistance and rotational inertia, etc. This complexity can be solved by using machine learning. Reinforcement learning using reward function puts limit on adapting to new environment for robots. Therefore, this paper applied deep reinforcement learning using neural network without reward function. Throwing is evaluated as a success or failure. AI network learns by taking the target position and control policy as input and yielding the evaluation as output. Then, the task is carried out by predicting the success probability according to the target location and control policy and searching the policy with the highest probability. Repeating this task can result in performance improvements as data accumulates. And this model can even predict tasks that were not previously attempted which means it is an universally applicable learning model for any new environment. According to the data results from 520 experiments, this learning model guarantees 75% success rate.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.6
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• pp.1399-1411
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• 1994

A model helicopter has more versatile flight capability than the fixed-wing aircraft and it can be used as an unmaned vehicle in hazardous area. A helicopter, similar to other aircrafts, is an unstable, multi-input multi-output nonlinear system exposed to strong disturbance. So it should be controlled by robust control theories that can be applied to multivariable systems. In this study, motion equations of hovering are established, linearized and transformed into a state equation form. Various parameters are measured and calculated in other to obtain the stability derivatives in the state equation. Hovering flight controller is designed using the digital LQG/LTR(Linear Quadratic Gaussian/Loop Transfer Recovery) control theory. The designed controller is tested by the nonlinear simulations and implemented on an IBM-PC/386. Experiments were carried out on a model helicopter attached to the 3-DOF gimbal. The designed controller showed satisfactory hovering capability to maintain the hovering for more than 40 seconds.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.11
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• pp.110-117
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• 1995

This study presents and algorithm and related techniques which could satisfy the important properties of check weighers and conveyor scales. The algorithm of Recursive Least Squares Regression is applied for the weighing system simulated as a dynamic model of the second order. Using the model and the algorithm, model parameters and then the mass being weighed can be determined from the step input. The performance of the algorithm was tested on a check weigher. Discussions were extended to the development of noise reduction techniques and to the lagged introduction of objects on the moving plate. It turns out that the algorithm shows several desirable features suitable for real-time signal processing with a microcomputer, which are high precision and stability in noisy environment.

• Journal of applied mathematics & informatics
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• v.12 no.1_2
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• pp.251-260
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• 2003

Generally, in deriving the solution of economic production quantity (EPQ) inventory model, we consider the demand rate and deterioration rate as constant quantity. But in case of real life problems, the demand rate and deterioration rate are not actually constant but slightly disturbed from their original crisp value. The motivation of this paper is to consider a more realistic EPQ inventory model with finite production rate, fuzzy demand rate and fuzzy deterioration rate. The effect of the loss in production quantity due to faulty/old machine have also been taken into consideration. The methodology to obtain the optimum value of the fuzzy total cost is derived and a numerical example is used to illustrate the computation procedure. A sensitivity analysis is also carried out to get the sensitiveness of the tolarance of different input parameters.

• 한국연소학회:학술대회논문집
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• 2006.04a
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• pp.167-171
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• 2006

The previous models for thermal behavior in the melting furnace were deterministic, composed of such a form that if the initial input conditions are determined, the results would have been come out by using the basic heat equilibrium equations. But making the experiment by trusting the analysis results, the melted slag is fortuitously set often, because temperature variation of the melted slag in the reaction process is not point function but path function. So in this study, a transient model was developed and verified by comparing with the experimental results.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.7 s.172
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• pp.94-101
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• 2005

This work proposes a new method for describing the hysteresis non-linearity of a piezoelectric actuator. The hysteresis behaviour of piezoelectric actuators, including the minor loop trajectory, are modeled by geometrical relationship between a reference major loop and its minor loops. This hysteresis model is transformed into inverse hysteresis model in order to output compensated voltage with regard to the given input displacement. A feedforward neural network, which is trained by a feedback PID control module, is incorporated to the inverse hysteresis model to compensate unknown dynamics of the piezoelectric system. To show the feasibility of the proposed feedforward-feedback controller, some experiments have been carried out and the tracking performance was compared to that of simple PTD controller.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.419-424
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• 1996

As track density increases, the effects of nonlinearity in pivot bearing of hard disk drive on the servo performance are becoming more important in considering the range of inertia force and the input torque during settling and tracking mode. Recently, an increasing attention is given to more precise experimental observations and modelings of pivot nonlinearity for achieving higher performance of servo control. In this paper, we propose a new model that shows an improved prediction of the pivot nonlinearity than existing preload-plus-two-slope model at matching simulations and experimental results in both time and frequency domains. Experimental measurements are carried out to validate and identify the specific nonlinearity presents in the pivot bearing when its in fine motion. Using the experimental results new model along with the existing one are characterized and compared for relevancies.

• Journal of the Korean Society for Heat Treatment
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• v.31 no.1
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• pp.1-5
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• 2018

This work demonstrated the application of an artificial neural network model for predicting the Jominy hardness curve by considering 13 alloying elements in low alloy steels. End-quench Jominy tests were carried out according to ASTM A255 standard method for 1197 samples. The hardness values of Jominy sample were measured at different points from the quenched end. The developed artificial neural network model predicted the Jominy curve with high accuracy ($R^2=0.9969$ for training and $R^2=0.9956$ for verification). In addition, the model was used to investigate the average sensitivity of input variables to hardness change.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.3
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• pp.246-252
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• 2005

Intelligent Space is a space where many sensors and intelligent devices are distributed. Mobile robots exist in this space as physical agents, which provide human with services. To realize this, human and mobile robots have to approach each other as much as possible. Moreover, it is necessary for them to perform interactions naturally. It is desirable for a mobile robot to carry out human affinitive movement. In this research, a mobile robot is controlled by the Intelligent Space through its resources. The mobile robot is controlled to follow walking human as stably and precisely as possible. In order to follow a human, control law is derived from the assumption that a human and a mobile robot are connected with a virtual spring model. Input velocity to a mobile robot is generated on the basis of the elastic force from the virtual spring in this model. And its performance is verified by the computer simulation and the experiment.