• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.291-294
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• 2006

This paper presents a decentralized input-output feedback linearizing controller for a multi-machine power system. Firstly, the controller is designed using input-output feedback linearization for modified outputs. Then we present a guideline for selecting gains of the controller and parameters in the modified outputs. Simulations illustrate the effectiveness of the proposed control scheme and the selection guideline.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.5
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• pp.361-366
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• 2013

Prediction of process parameters is very important in parameter design. If predictions are fairly accurate, the quality improvement process will be useful to save time and reduce cost. The concept of dual response approach based on response surface methodology has widely been investigated. Dual response approach may take advantages of optimization modeling for finding optimum setting of input factor by separately modeling mean and variance responses. This study proposes an alternative dual response approach based on machine learning techniques instead of statistical analysis tools. A hybrid neural network-genetic algorithm has been proposed for the purpose of parameter design. A neural network is first constructed to model the relationship between responses and input factors. Mean and variance responses correspond to output nodes while input factors are used for input nodes. Using empirical process data, process parameters can be predicted without performing real experimentations. A genetic algorithm is then applied to find the optimum settings of input factors, where the neural network is used to evaluate the mean and variance response. A drug formulation example from pharmaceutical industry has been studied to demonstrate the procedures and applicability of the proposed approach.

• Journal of Power System Engineering
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• v.21 no.4
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• pp.42-50
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• 2017

A velocity controller using a modified model reference adaptive controller (M-MRAC) and a projection operator for a fish sorting belt conveyor system with uncertainty parameters, input saturation and bounded disturbances is proposed in this paper. To improve the tracking performance and robustness of the proposed controller in the presence of bounded disturbances, the followings are done. Firstly, the reference model for the conventional model reference adaptive controller (CMRAC) is replaced by a modified reference model for a M-MRAC to reduce unexpected high frequency oscillation in control input signal when the adaptation rate is increased. Secondly, estimated parameters in an adaptive law are varied smoothly under bounded external disturbances and a projection operator is utilized in an adaptive law for the proposed M-MRAC controller to be robust. Thirdly, an auxiliary error vector is introduced for compensating the error dynamics of the system when the saturation input occurs. Finally, the experimental results are shown to verify the better effectiveness and performance of the proposed controller under the bounded disturbance and saturated input than that of a CMRAC.

• Fire Science and Engineering
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• v.31 no.2
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• pp.44-51
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• 2017

Accurate predictions of the activation time for smoke detectors using a fire simulation is are required to ensure the reliability of the RSET (Required Safe Egress Time) calculation in the process of PBD (Performance-Based Design). The objective of this study was to enhance the accuracy of input parameters for the numerical models of smoke detector based on the FDS. To this end, a Fire Detector Evaluator (FDE) developed in previous studies was improved. The uniformities of flow and smoke inside the FDE were improved and accurate measurements of the obscuration per meter (OPM) related to detector operation were also performed through a decrease in the forward scattering of smoke particles. The input parameters using the improved FDE showed a significant difference from the previous FDE quantitatively. In particular, a larger difference was found in a photoelectric detector compared to an ionization detector. Considering that the operating conditions of smoke detectors are affected by the detector type, combustibles, smoke particulars, and color, the database (DB) on the input parameters for various detectors and combustibles should be built to improve the reliability of PBD in future studies.

• Journal of Environmental Science International
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• v.20 no.4
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• pp.501-509
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• 2011

Unlike many laboratory-scale studies on absorption of organic compounds (VOCs), limited pilot-scale studies have been reported. Accordingly, the present study was carried out to examine operation parameters for the effective control of a hydrophilic VOC (methyl ethyl ketone, MEK) by applying a circular pilot-scale packed-absorption system (inside diameter 37 cm ${\times}$ height 167 cm). The absorption efficiencies of MEK were investigated for three major operation parameters: input concentration, water flow rate, and ratio of gas flow-rate to washing water amount (water-to-gas ratio). The experimental set-up comprised of the flow control system, generation system, recirculation system, packed-absorption system, and outlet system. For three MEK input concentrations (300, 350, and 750 ppm), absorption efficiencies approached near 95% and then, decreased gradually as the operation time increased, thereby suggesting a non-steady state condition. Under these conditions, higher absorption efficiencies were shown for lower input concentration conditions, which were consistent with those of laboratory-scale studies. However, a steady state condition occurred for two input concentration conditions (100 and 200 ppm), and the difference in absorption efficiencies between these two conditions were insignificant. As supported by an established gas-liquid absorption theory, a higher water flow rate exhibited a greater absorption efficiency. Moreover, as same with the laboratory-scale studies, the absorption efficiencies increased as water-to-gas ratios increased. Meanwhile, regardless of water flow rates or water-to-gas ratios, as the operation time of the absorption became longer, the pH of water increased, but the elevation extent was not substantial (maximum pH difference, 1.1).

• Journal of Korea Water Resources Association
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• v.44 no.3
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• pp.209-220
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• 2011

Input uncertainty is one of the major sources of uncertainty in hydrologic modeling. In this paper, first, three alternate rainfall inputs generated by different interpolation schemes were used to see the impact on a distributed watershed model. Later, the residuals of precipitation interpolations were tested as a source of ensemble streamflow generation in two river basins in the U.S. Using the Monte Carlo parameter search, the relationship between input and parameter uncertainty was also categorized to see sensitivity of the parameters to input differences. This analysis is useful not only to find the parameters that need more attention but also to transfer parameters calibrated for station measurement to the simulation using different inputs such as downscaled data from weather generator outputs. Input ensembles that preserves local statistical characteristics are used to generate streamflow ensembles hindcast, and showed that the ensemble sets are capturing the observed steamflow properly. This procedure is especially important to consider input uncertainties in the simulation of streamflow forecast.

• Geotechnical Engineering
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• v.6 no.2
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• pp.5-20
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• 1990

In the seismic analysis of structures, where the dynamic soil-structure interaction (DSSI) is considred, earthquake input motions as well as dynamic soil properties are random in nature. To take into account the random nature of both the input motions and the dynamic soil properties systematically, a probabilistic analysis of the DSSI subjected to seismic loading is proposed in this paper, The complex response method formulized by the elastic half space theory, the random vibration theory, and the Rosenblueth's two-point estimate method are combined for the proposed probabilistic analysis. The conclusions drawn from this study are as follows ' 1) The uncertainty bands of the earthquake input motions proposed by Kanai-Tajimi as well as those of the dynamic properties are large the coefecients of variation of those parameters tinge from 0.4 to 0.6. 2) The uncertainties of the dynamic soil properties are more sensitive to the structural responses than those of the input motion parameters. 3) The effect of correlations between the input motion parameters and the dynamic soil properties is negligible.

• Journal of the Korean Society of Safety
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• v.34 no.5
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• pp.46-54
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• 2019

Even though the fire performance-based design concept has been introduced for various structures and buildings, which have their own specific fire performance level, the uncertainties of input parameters always exist and, then, could reduce significantly the reliability of the fire modeling. Sensitivity analysis was performed with three limited input parameters, HRRPUA, type of combustible materials, and mesh size, which are significantly important for fire modeling. The output variables are limited to the maximum HRR, the time reaching the reference temperature($60^{\circ}C$), and that to reach limited visible distance(5 m). In addition, correlation coefficient analysis was attempted to analyze qualitatively and quantitatively the degree of relation between input and output variables above. Finally, the relationship among the three variables is also analyzed by the principal component analysis (PCA) to systematically analyze the input data bias. Sensitivity analysis showed that the type of combustible materials is more sensitive to maximum HRR than the ignition source and mesh size. However, the heat release parameter of the ignition source(HRR) is shown to be much more sensitive than the combustible material types and mesh size to both time to reach the reference temperature and that to reach the critical visible distance. Since the derived results can not exclude the possibility that there is a dependency on the fire model applied in this study, it is necessary to generalize and standardize the results of this study for the fire models such as various buildings and structures.

• The Journal of the Korea Contents Association
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• v.11 no.3
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• pp.48-55
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• 2011

In this paper, we analyze the input-output characteristics of fuzzy inference systems according to the division of entire input spaces and the fuzzy reasoning methods to identify the fuzzy model for nonlinear process. And fuzzy model is expressed by identifying the structure and parameters of the system by means of input variables, fuzzy partition of input spaces, and consequence polynomial functions. In the premise part of the rules Min-Max method using the minimum and maximum values of input data set and C-Means clustering algorithm forming input data into the hard clusters are used for identification of fuzzy model and membership function is used as a series of triangular membership function. In the consequence part of the rules fuzzy reasoning is conducted by two types of inferences. The identification of the consequence parameters, namely polynomial coefficients, of the rules are carried out by the standard least square method. And lastly, we use gas furnace process which is widely used in nonlinear process and we evaluate the performance for this nonlinear process.

• Proceedings of the IEEK Conference
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• 2001.06d
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• pp.249-252
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• 2001

The most basic means of communication among humans is a voice. Without speaking of voice technologies, we found it is important and convenient to use a voice in everyday life. But. in consideration to speech recognition systems, we can't always desire a normal voice input as input signal to the system. Generally speaking. a pathological voice as against a normal which is a voice with a problem in the larynx. could be also special case of input voice. Of course, but the distortion of a speech signal by environmental effects i.e., noise or transmission channel was a raised problem. we will take up a pathological voices with laryngeal disease which is essential distortion factor in voice. Also, we are to find out the difference of acoustic parameters distribution between normal and pathological voice by a statistical method in our research.