• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.1838-1842
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• 2004

Gain and phase margins of a first order plus delayed time (FOPDT) process controlled by generalized predictive controller (GPC) are related to the control parameters ${\lambda}$ (control move suppression parameter) and ${\alpha}$ (smoothing filter coefficient) and the normalized delay of the process. Variation ranges of gain and phase margins are determined. It is shown that the margins cannot be assigned independently for a wide range of variation and the range is narrowing by increase of the normalized delay of the process. And finally curves are given to use for adjustment of the controller parameters in order to obtain a specific pair of gain and phase margins.

• 전자공학회논문지B
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• 제32B권3호
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• pp.503-511
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• 1995

A batch-mode algorithm is proposed to increase the speed of learning in the error backpropagation algorithm with variable learning rate and variable momentum parameters in classification problems. The objective function is normalized with respect to the number of patterns and output nodes. Also the gradient of the objective function is normalized in updating the connection weights to increase the effect of its backpropagated error. The learning rate and momentum parameters are determined from a function of the gradient norm and the number of weights. The learning rate depends on the square rott of the gradient norm while the momentum parameters depend on the gradient norm. In the two typical classification problems, simulation results demonstrate the effectiveness of the proposed algorithm.

• 전자공학회논문지B
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• 제30B권8호
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• pp.1-7
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• 1993

This paper proposed an algorithm that extracts the optimum characteristics parameters to identify the signatures from the spectrum using 2-D FFT. The signature image input through a scanner is normalized into 250*128 pixels in the prepocessor. Normalized image is divided into block segments and each segment is transformed into space spectrum by 2-d FFT. There are several methods extracting the signature characteristic parameters from that spectrum. The result of experimentations which use the characteristic parameters extracted between $0^{\circ}and\;90^{\circ}$ in (0, 0), (63, 0) corners from 64$\times$64 block spectrum shows that the signature identification rate using that method gives 92.5% of successful achievement for 100 signatures, higher than the others.

• 말소리와 음성과학
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• 제10권3호
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• pp.41-48
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• 2018

This study examined the acoustic characteristics of Korean alveolar fricatives of normal children. Developing children aged 3 and 7, typically produced 2 types of nonsense syllables containing alveolar fricative /sV/ and /VsV/ sequences where V was any one of three corner vowels (/i, a, and u/). Stimuli containing the speech materials used in a production experiment were presented randomly to 12 speech language pathologists (SLPs) for a perception test. The SLPs responded by selecting one of seven alternative sounds. Acoustic measures such as duration of frication noise, normalized intensity, skewness, and center of gravity were examined. There was significant difference in acoustic measures when comparing vowels. Comparison of syllable structures indicated statistically significant differences in duration of frication noise and normalized intensity. Acoustic parameters could account for the perceptual data. Relating the acoustic and perception data by means of logistic regression suggests that duration of frication noise and normalized intensity are the primary cues to perceiving Korean fricatives.

• 전기전자학회논문지
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• 제25권4호
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• pp.741-749
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• 2021

본 논문에서 제안한 전류형 LLC AC to DC 고주파 공진 컨버터는 스위치 양단에 병렬로 공진 커패시터(C₁, C₂)를 연결함으로써 ZVS(Zero Voltage Switching)동작 뿐만 아니라 2차측 Diode의 ZCS(Zero Current Switching) 동작이 가능하므로 스위칭 소자의 턴-온 및 2차측 다이오드의 턴-오프 손실을 저감시킬 수 있다. 본 논문에서 제안한 LLC AC to DC 고주파 공진 컨버터의 회로 해석은 무차원화 제어 파라메타를 도입하여 범용성 있게 기술하였다. 또한 제안한 LLC AC to DC 고주파 공진 컨버터의 운전 특성은 무파원화 제어 주파수(μ), 무차원화 저항(λ) 등의 무차원화 제어 파라메타를 이용하여 특성 평가를 수행하였다. 특성 평가를 통한 특성값을 기초한 LLC AC to DC 고주파 공진 컨버터 설계 기법의 일예를 제시하였으며, 실험 및 PSIM 시뮬레이션을 통해 이론 해석의 정당성을 입증하였다.

• 한국수자원학회논문집
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• 제39권8호
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• pp.677-690
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• 2006

본 연구에서는 LH-OAT (Latin Hypercube Ore factor At a Time) 민감도분석 방법과 SCE-UA (Shuffled Complex Evolution at University of Arizona) 최적화 기법을 적용하여 보청천 유역에서 SWAT모형에 대한 자동보정 방법을 제시하였다. LH-OAT 방법은 전역 민감도분석과 부분 민감도 분석의 장점을 조합하여 가용매개변수 공간에 대하여 효율적으로 매개변수의 민감도 분석이 가능하게 하였다. LH-OAT민감도 분석으로부터 결정된 매개변수의 민감도 등급은 SWAT 모형의 자동보정 과정에서 요구되는 보정대상 매개변수의 선택에 유용하게 적용될 수 있다. SCE-UA 방법을 적용한 SWAT모형의 자동보정 해석결과는 보정자료, 보정매개변수, 통계적 오차의 선택에 따라서 모형의 성능이 좌우되었다. 보정기간과 보정매개변수가 증가함에 따라 검증기간에 대한 RMSE (Root Mean Square Error), NSEF (Nash-Sutcliffe Model Efficiency), RMAE (Relative Mean Absolute Error), NMSE (Normalized Mean Square Error) 등의 모형오차는 감소하였지만, NAE (Normalized Average Error) 및 SDR(Standard Deviation Ratio)은 개선되지 않았다. SWAT모형의 보정에 적용되는 보정자료, 보정매개변수 및 모형평가를 위한 통계적 오차 선택이 해석결과에 미치는 복잡한 영향을 이해하기 위하여 다양한 대표유역을 대상으로 추가적인 연구가 필요하다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.84-89
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• 2003

Fuzzy controller is one of the succeed controller used in the process control in case of model uncertainties. But it my be difficult to fuzzy controller to articulate the accumulated knowledge to encompass all circumstance. Hence, it is essential to provide a tuning capability. There are many parameters in fuzzy controller can be adapted, scale factor tuning of normalized fuzzy controller is one of the adaptation parameter. Two adaptation methods are implemented in this work on an experimental thermal process, which simulate heating process in liquefied petroleum gases (LPG) recovery process in one of petrochemical industries: Gradient decent (GD) adaptation method; supervisory fuzzy controller. A comparison between the two methods is discussed.

• 음성과학
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• 제11권1호
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• pp.21-29
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• 2004

In this paper we examined the effect of normalization on discriminating the pathological voice into normal and abnormal classes using artificial neural network. Average values per each parameter were used to normalize each set of parameter values. Artificial neural networks were used as classifiers. And the effect of normalization was evaluated by comparing the discrimination results between original and normalized parameter sets.

• 음성과학
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• 제4권2호
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• pp.17-24
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• 1998

In this paper a method to diagnose pathological voices using wavelet transform is sug gested. Pathological voices are collected from hospital and analyzed by the suggested method. Normal voices are collected separately and analyzed. Then the results are compared to find the differences in their characteristics. Three level wavelet transform is used. Normalized energy ratios between the levels and normalized peak-to-peak values are used as parameters. As a result, it was possible to distinguish between normal and pathological voices.

• 대한의용생체공학회:의공학회지
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• 제22권3호
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• pp.293-301
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• 2001

A patient-specific musculoskeletal model, whose parameters can be identified noninvasively, was developed for the automatic generation of patient-specific stimulation pattern in FES. The musculotendon system was modeled as a torque-generator and all the passive systems of the musculotendon working at the same joint were included in the skeletal model. Through this, it became possible that the whole model to be identified by using the experimental joint torque or the joint angle trajectories. The model parameters were grouped as recruitment of muscle fibers, passive skeletal system, static and dynamic musculotendon systems, which were identified later in sequence. The parameters in each group were successfully estimated and the maximum normalized RMS errors in all the estimation process was 8%. The model predictions with estimated parameter values were in a good agreement with the experimental results for the sinusoidal, triangular and sawlike stimulation, where the normalized RMS error was less than 17%, Above results show that the suggested musculoskeletal model and its parameter estimation method is reliable.