• Journal of Bio-Environment Control
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• v.1 no.2
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• pp.162-168
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• 1992

As the automation of nutrient solution management proceeds in the field of hydroponics, effective supporting systems to manage the nutrient solution by computer become needed. This study was attempt to predict the EC of nutrient solution using the neural networks. The multilayer perceptron consisting of 3 layers with the back propagation learning algorithm was selected for EC prediction, of which nine variables in the input layer were the concentrations of each ion and one variable in the output layer the EC of nutrient solution. The meq unit in ion concentration was selected fir input variable in the input layer. After the 10,000 learning sweeps with 108 sample data, the comparison of predicted and measured ECs for 72 test data showed good agreements with the correlation coefficient of 0.998. In addition, the predicted ECs by neural network showed relatively equal or closer to the measured ones than those by current complicated models.

• Journal of Welding and Joining
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• v.32 no.2
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• pp.43-47
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• 2014

Famous artificial neural network (ANN) is applied to predict proper process window of arc welding. Target weldment is variously combined lap joint fillet welding of automotive steel plates. ANN's system variable such as number of hidden layers, perceptrons and transfer function are carefully selected through case by case test. Input variables are welding condition and steel plate combination, for example, welding machine type, shield gas composition, current, speed and strength, thickness of base material. The number of each input variable referred in welding experiment is counted and provided to make it possible to presume the qualitative precision and limit of prediction. One of experimental process windows is excluded for predictability estimation and the rest are applied for neural network training. As expected from basic ANN theory, experimental condition composed of frequently referred input variables showed relatively more precise prediction while rarely referred set showed poorer result. As conclusion, application of ANN to arc welding process window derivation showed comparatively practical feasibility while it still needs more training for higher precision.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2000.10a
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• pp.623-628
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• 2000

This study were conducted to evaluate the mass balance of phosphorus and nitrogen with cropping in experimental pot where the mass of input concentration, and fertilization were variable. Four treatments include CSWNF, TWCF, SWNF. And these cases were compared to the control case of tap water irrigation with conventional fertilization (CONTROL). Fertilization were following conventional fertilization , N : P : K = 11kg : 7kg : 8kg. Input water loading was CSWNF (N:48.7g ,P:3.6g), TWCF(7.8g, 0.6g), SWHF(38.8g ,2.9g), TWNF(38.8g, 2.9g ) and CONTROL(0g ,0g) The result is nitrogen decrease rate; TWCF(19.2%), SWHF(14.9%), CSWNF(9.2%) and SWCF(5.6%). phosphorous decrease rate ; TWCF (10%), SWHF(3.7%), SWCF(0.9%) and CSWNF(0.3%).

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.3187-3188
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• 2000

Based on fuzzy control algorithm this paper constructed fuzzy controller for automated vehicles. For passenger's convenience especially comfortability controller need to reduce the frequency of input variable's changing. So we established membership functions for comfortability as well as speed following. It made possible to control comfortability directly. To demonstration the efficiency of fuzzy controller, we carried out simulation with a automobile's transfer function. Also we compared the difference of input variable. By comparing two controller's response, we can confirm the merit of fuzzy controller about comfortability. Fuzzy controller can reduce input changing frequency.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.791-794
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• 1995

This paper introduces a new approach to adaptive control using a combination of direct, indirect and variable structrure method. A new variable structrue input is derived to counteract the effects of disturbances. Direct input is used to have fast response and indirect input improves the transient behavior of the system. Computer simulation results illustrate the very satisfactory performance of the proposed algorithm.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.481-482
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• 2007

This paper demonstrates OFDM with adaptive modulation applied to Multiple-Input Multiple-Output (MIMO) systems. We apply an optimization algorithm to obtain a bit and power allocation for each subcarrier assuming instantaneous channel knowledge. The analysis and simulation is considered in two stages. The first stage involves the application of a variable-rate variable-power MQAM technique for a Single-Input Single-Output(SISO) OFDM system. This is compared with the performance of fixed OFDM transmission where a constant rate is applied to each subcarrier. The second stage applies adaptive modulation to a general MIMO system by making use of the Singular Value Decomposition to separate the MIMO channel into parallel subchannels. For a two-input antenna, two-output antenna system, the performance is compared with the performance of a system using selection diversity at the transmitter and maximal ratio combining at the receiver.

• Journal of Power Electronics
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• v.2 no.2
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• pp.130-138
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• 2002

The windup phenomenon appears and degrades control performance when a controller with integrating action is used and plant input is limited. An anti-windup integal-proportional(IP) controller is proposed for the variable-speed moter drives and it is experimentally applied to the speed control of a vector-controlled induction moter driven by a pulse width modulated (PWM) voltage source inverter (VSI). The consistency range of the IP controller is firstly derived and the intergal state is controlled to salisfy always the consistency range according to whether the the controller output is saturated or not. Although the operating condition like moter load or speed command is changed under the limited plant input, It is expermentally verified that the speed response has much improved performance, such as no overshoot and fast settling time, and the maximmum plant input is also effectively utilized.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.3117-3119
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• 1999

In this paper, a new phase detector which can detect phase difference of variable input frequency and represent as a DC voltage is designed. The proposed phase detector has detection range from $-180^{\circ}$ to $180^{\circ}$. It is implemented by digital electronic circuit. It operates from 125 kHz to 4 MHz frequency of input signal and it's maximum phase error is $360/256^{\circ}$.

• Computers and Concrete
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• v.29 no.3
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• pp.145-159
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• 2022

In this study, the extreme learning machine and deep learning models were devised to estimate the bond strength of corroded reinforcement in concrete. The six inputs and one output were used in this study. The compressive strength, concrete cover, bond length, steel type, diameter of steel bar, and corrosion level were selected as the input variables. The results of bond strength were used as the output variable. Moreover, the Analysis of variance (Anova) was used to find the effect of input variables on the bond strength of corroded reinforcement in concrete. The prediction results were compared to the experimental results and each other. The extreme learning machine and the deep learning models estimated the bond strength by 99.81% and 99.99% accuracy, respectively. This study found that the deep learning model can be estimated the bond strength of corroded reinforcement with higher accuracy than the extreme learning machine model. The Anova results found that the corrosion level was found to be the input variable that most affects the bond strength of corroded reinforcement in concrete.

• Journal of the Korean Institute of Intelligent Systems
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• v.27 no.1
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• pp.50-58
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• 2017

For nonlinear control system design, there are many studies based on TSK fuzzy model. However, TSK fuzzy modelling needs nonlinear dynamic equations of the object system or a data set fully distributed in input-output space. This paper proposes an modelling technique using only step input response data. The technique uses also the genetic algorithm. The object systems in this paper are nonlinear to control input variable or output variable. In the case of nonlinear to control input, response data obtained with several step input values are used. In the case of nonlinear to output, step input response data and zero input response data are used. This paper also presents a fuzzy controller design technique from TSK fuzzy model. The effectiveness of the proposed techniques is verified with numerical examples.