• 환경영향평가
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• 제15권2호
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• pp.101-109
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• 2006

Water quality simulation models include the difference between the measured and estimated values as an inevitable consequence because they represent the complicated natural phenomena as simplified mathematical equations. The major reason of the difference occurrence is due to the use of the imprecise values of the model parameters, but the parameter values are currently determined by the try and error method directly performed by humans. However, the use of this method requires many time and endeavor of humans, and generally does not obtain the most suitable parameter values. A method for deciding model parameter values is, therefore, developed in this study. The method minimizes the difference between the measured and estimated values and also distributes uniformly the measured values on the upper and lower sides of the line representing the estimated values. A user interface based on this method is also developed by using the Visual Basic 6.0 of Microsoft, and it can be operated in the environment of Windows 98/2000. In this study, the method for deciding model parameter values is applied for estimating the water quality of the stream Ko-heung. The results of the application show that the method, including its computer program, can effectively obtain the most suitable parameter values and also save many working time in comparison with the existing method directly performed by humans.

• 한국진공학회지
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• 제5권2호
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• pp.99-106
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• 1996

Two types of regularization method (singular system and HMP approaches) for generating depth-concentration profiles from angle-resolved XPS data were evaluated. Both approaches showed qualitatively similar results although they employed different numerical algorithms. The application of the regularization method to simulated data demonhstrates its excellent utility for the complex depth profile system . It includes the stable restoration of depth-concentration profiles from the data with considerable random error and the self choice of smoothing parameter that is imperative for the successful application of the regularization method. The self choice of smoothing parameter is based on generalized cross-validation method which lets the data themselves choose the optimal value of the parameter.

• 천문학회보
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• 제38권2호
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• pp.57.1-57.1
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• 2013

We investigate the method to measure both the present value of the matter energy density contrast and the Hubble parameter directly from the measurement of the linear growth rate which is obtained from the large scale structure of the Universe. From this method, one can obtain the value of the nuisance cosmological parameter $\Omo$ (the present value of the matter energy density contrast) within 3% error if the growth rate measurement can be reached $z >3.5$. One can also investigate the evolution of the Hubble parameter without any prior on the value of $H_0$ (the current value of the Hubble parameter). Especially, estimating the Hubble parameter are insensitive to the errors on the measurement of the normalized growth rate $f \sigma_8$. However, this method requires the high $z$ ($z >3.5$) measurement of the growth rate in order to get the less than 5% errors on the measurements of $H(z)$ at $z \leq 1.2$ with the redshift bin $\Delta z = 0.2$. Thus, this will be suitable for the next generation large scale structure galaxy surveys like WFMOS and LSST.

• Industrial Engineering and Management Systems
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• 제6권2호
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• pp.119-124
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• 2007

The most difficult and time-intensive issue in the successful implementation of genetic algorithms is to find good parameter setting, one of the most popular subjects of current research in genetic algorithms. In this study, we present a new efficient experimental design method for parameter optimization in a genetic algorithm for general job shop scheduling problem using the Taguchi method. Four genetic parameters including the population size, the crossover rate, the mutation rate, and the stopping condition are treated as design factors. For the performance characteristic, makespan is adopted. The number of jobs, the number of operations required to be processed in each job, and the number of machines are considered as noise factors in generating various job shop environments. A robust design experiment with inner and outer orthogonal arrays is conducted by computer simulation, and the optimal parameter setting is presented which consists of a combination of the level of each design factor. The validity of the optimal parameter setting is investigated by comparing its SN ratios with those obtained by an experiment with full factorial designs.

• 한국통신학회논문지
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• 제16권7호
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• pp.628-639
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• 1991

GaAs MESFET 모델의 정확한 매채변수 값을 구하기 위하여 바이어스 의존성을 바탕으로 3가지 바이어스 변화에 대한 S-,파라미터 측정만으로 모델 매개변수를 추출할 수 있는 새로운 계산 방법을 제시한다. Weighted Broyden update방법의 최적화 과정에서 얻어지는 오차 함수에 대한 매개 변수의 검토를 이용하여 전형 및 비전형 매개 변수의 유일해를 결정한다. 제안된 방법을 적용하기 위해 Marterka & Kacprzak 모델을 사용하였으며 추출한 매개변수 값의 정당성을 측정치와 비교함으로서 입증하였다.

• Journal of Power Electronics
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• 제8권4호
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• pp.354-362
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• 2008

This paper presents neural load torque compensation method which is composed of a deadbeat load torque observer and gains compensation by a parameter estimator. As a result, the response of the PMSM (permanent magnet synchronous motor) obtains better precision position control. To reduce the noise effect, the post-filter is implemented by a MA (moving average) process. The parameter compensator with an RLSM (recursive least square method) parameter estimator is adopted to increase the performance of the load torque observer and main controller. The parameter estimator is combined with a high performance neural load torque observer to resolve problems. The neural network is trained in online phases and it is composed by a feed forward recall and error back-propagation training. During normal operation, the input-output response is sampled and the weighting value is trained multi-times by the error back-propagation method at each sample period to accommodate the possible variations in the parameters or load torque. As a result, the proposed control system has a robust and precise system against load torque and parameter variation. Stability and usefulness are verified by computer simulation and experiment.

• 대한인간공학회:학술대회논문집
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• 대한인간공학회 1993년도 추계학술대회논문집
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• pp.124-132
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• 1993

This study compared two prediction methods-regression and artificial neural network (ANN) on the visual search performance when monitoring a multi-parameter screen with different occurrence frequencies. Under the highlighting condition for the highest occurrence frequency parameter as a search cue, it was found from the requression analysis that variations of mean search time (MST) could be expained almost by three factors such as the number of parameters, the target occurrence frequency of a highlighted parameter, and the highlighted parameter size. In this study, prediction performance of ANN was evaluated as an alternative to regression method. Backpropagation method which was commonly used as a pattern associator was employed to learn a search behavior of subjects. For the case of increased number of parameters and incresed target occurrence frequency of a highlighted parameter, ANN predicted MST's moreaccurately than the regression method (p<0.000). Only the MST's predicted by ANN did not statistically differ from the true MST's. For the case of increased highlighted parameter size. both methods failed to predict MST's accurately, but the differences from the true MST were smaller when predicted by ANN than by regression model (p=0.0005). This study shows that ANN is a good predictor of a visual search performance and can substitute the regression method under certain circumstances.

• 대한전기학회논문지
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• 제36권8호
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• pp.594-602
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• 1987

This paper proposes a new method of reducing the processing time and the size of consummimg memories in Hough transform. In this method, only the functional patterns are considered. The candidate points which are accumulated into the parameter space are computed in a many-to-one fashion and the parameter space is quantized dynamically to maintain a fine precision where it is needed. And a coarse-to-fine extraction method is used to reduce the processing time. The many-to-one fashional computation results in a relatively high-densed accumulation of candidate points around the parameter points corresponding to the image patterns in the image space. So, the dynamic quantization procedure can be simplified and the local maxima can be determined easily. And more effective reduction can be obtained as the dimension of parameter space is increased.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 추계종합학술대회 논문집(2)
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• pp.245-248
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• 2000

In this paper, we present a high-performance SiGe HBT's RF input impedance parameter extraction method. The SiGe HBT has emitter width of 0.5${\mu}{\textrm}{m}$ and length of 6${\mu}{\textrm}{m}$. S-parameter has been measured with the collector current of 1~3㎃ using on-wafer RF measuring system . The pre-calculation method was used in order to overcome the local minimum problem. This method enabled us to extract a RF(1~10㎓) input impedance parameter.

• 전자공학회논문지B
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• 제31B권2호
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• pp.28-34
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• 1994

In this paper, a new time-domain parameter extraction method for speech recognition is proposed. The suggested emthod is based on the fact that the local peak-to-peak interval, i.e., the interval between maxima and minima of speech waveform is closely related to the frequency component of the speech signal. The parameterization is achieved by a sort of filter bank technique in the time domain. To test the proposed parameter extraction emthod, an isolated word recognizer based on Vector Quantization and Hidden Markov Model was constructed. As a test material, 22 words spoken by ten males were used and the recognition rate of 92.9% was obtained. This result leads to the conclusion that the new parameter extraction method can be used for speech recognition system. Since the proposed method is processed in the time domain, the real-time parameter extraction can be implemented in the class of personal computer equipped onlu with an A/D converter without any DSP board.