• 대한전자공학회논문지
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• 제21권6호
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• pp.7-16
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• 1984

본 논문에서는 디지탈 자동 음성 응답장치의 개발에 관하여 전반적인 사항을 기술하였다. 개발된 디지탈 음성 응답 장치는 전화국에서 가입자가 전화번호를 문의하였을 때 자동 응답할 구 있도록 특별히 구성된 시스템이다. 본 시스템의 구현 algorithm으로는 pitch predictive loop(PPL)을 가지는 RELP(residual excited linear pediction)방식을 사용하였는데 system memory는 비교적 적은 반면 음질은 아주 좋은 것이 개발된 자동 응답기의 큰 장점이라 하겠다. Hardware는 bit-slice microprocessor를 사용한 음성 합성기와 controller 및 I/O로 이루어져 있는데 이들은 실시간 신호처리와 시스템의 적응성 및 신뢰성을 고려하여 설계하였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.357-357
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• 2000

In this study, we consider a multi-input multi-output styrene polymerization reactor system for which the monomer conversion and the weight average molecular weight are controlled by manipulating the jacket inlet temperature and the feed flow rate. The reactor system is identified by using a linear subspace identification method and then the output feedback model predictive controller is constructed on the basis of the identified model. Here we use the Best Linear Unbiased Estimation (BLUE) filter as a stochastic estimator instead of the Kalman filter. The BLUE filter observes the state successfully without any a priori information of initial states. In contrast to the Kalman filter, the BLUE filter eliminates the offset by observing the state of the augmented system regardless of a priori information of the initial state for an integral white noise augmented system. A BLUE filter has a finite impulse response (FIR) structure which utilizes finite measurements and inputs on the most recent time interval [i-N, i] in order to avoid long processing times.

• 대한화학회지
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• 제60권4호
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• pp.225-234
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• 2016

The activity of 34 sulfonamide derivatives has been estimated by means of multiple linear regression (MLR), artificial neural network (ANN), simulated annealing (SA) and genetic algorithm (GA) techniques. These models were also utilized to select the most efficient subsets of descriptors in a cross-validation procedure for non-linear -log (IC50) prediction. The results obtained using GA-ANN were compared with MLR-MLR, MLR-ANN, SA-ANN and GA-ANN approaches. A high predictive ability was observed for the MLR-MLR, MLR-ANN, SA-ANN and MLR-GA models, with root mean sum square errors (RMSE) of 0.3958, 0.1006, 0.0359, 0.0326 and 0.0282 in gas phase and 0.2871, 0.0475, 0.0268, 0.0376 and 0.0097 in solvent, respectively (N=34). The results obtained using the GA-ANN method indicated that the activity of derivatives of sulfonamides depends on different parameters including DP03, BID, AAC, RDF035v, JGI9, TIE, R7e+, BELM6 descriptors in gas phase and Mor 32u, ESpm03d, RDF070v, ATS8m, MATS2e and R4p, L1u and R3m in solvent. In conclusion, the comparison of the quality of the ANN with different MLR models showed that ANN has a better predictive ability.

• 한국음향학회지
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• 제26권6호
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• pp.243-249
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• 2007

본 논문은 Group Delay(GD)를 이용한 음성신호 기반의 효과적인 성별인식 시스템을 제안한다. 일반적인 음성 인식과 관련된 시스템에서 사용되는 특징들은 위상에 관한 정보를 제거한 크기만의 정보를 이용하여 구성한다. 본 연구에서는 위상에 관한 정보를 토대로 유도되어 지는 GD의 성별에 따른 특징을 알아보고, 보다 향상된 성별인식을 위해 MFCC(Mel-frequency cepstral coefficient), LPC(linear predictive coding) 계수, 반사계수(reflection coefficient) 그리고 포만트(formant)등과 같은 크기 정보와 GD를 이용한 결합 특징 벡터를 적용하였다. 실험을 통해 성별에 따른 GD의 특징을 확인할 수 있었고, 이를 이용한 제안된 특징 벡터를 사용했을 때 우수한 인식 성능을 얻을 수 있었다.

• 한국정보통신학회논문지
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• 제12권1호
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• pp.164-171
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• 2008

디지털 통신에서는 전송 채널의 왜곡을 보상해 주는 적응 등화기가 필수적이다. 이러한 적응 등화기는 요구되는 비트 오율 (BER: Bit Error Rate)을 얻기 위해서 이동통신 시스템의 특성에 적합하고 최적의 성능을 가지는 적응 알고리즘이 필요하게 된다. 본 논문에서는 무선 이동 채널에서 성능이 우수한 잡음 예측 결정 궤환 등화기 (NPDFE: Noise-Predictive Decision Feedback Equalizer)를 제안한다. 제안된 NPDFE는 직교 위상 변조 (QPSK; Quadrature Phase Shift Keying) 방식을 사용하는 시스템에 대해 가산성 백색 가우스 잡음 (AWGN: Additive White Gaussian Noise)이 발생한다는 기본 가정하에 라이시안 페이딩, 유럽 표준 (ETSI: European Telecommunications Standards Institute) 페이딩, 그리고 레일리 페이딩 채널에서의 성능을 분석한다. 시뮬레이션에서 사용되는 등화기 구조는 선형 등화기(LE: Linear Equalizer), 결정 궤환 등화기 (DFE: Decision Feedback Equalizer), 그리고 제안된 NPDFE이다. 각 등화 알고리즘을 사용하는 QPSK 변조 방식의 성능 비교는 BER을 통하여 이루어진다.

• Corrosion Science and Technology
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• 제18권2호
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• pp.61-71
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• 2019

Flow-Accelerated Corrosion (FAC) is a phenomenon in which a protective coating on a metal surface is dissolved by a flow of fluid in a metal pipe, leading to continuous wall-thinning. Recently, many countries have developed computer codes to manage FAC in power plants, and the FAC prediction model in these computer codes plays an important role in predictive performance. Herein, the FAC prediction model was developed by applying a machine learning method and the conventional nonlinear regression method. The random forest, a widely used machine learning technique in predictive modeling led to easy calculation of FAC tendency for five input variables: flow rate, temperature, pH, Cr content, and dissolved oxygen concentration. However, the model showed significant errors in some input conditions, and it was difficult to obtain proper regression results without using additional data points. In contrast, nonlinear regression analysis predicted robust estimation even with relatively insufficient data by assuming an empirical equation and the model showed better predictive power when the interaction between DO and pH was considered. The comparative analysis of this study is believed to provide important insights for developing a more sophisticated FAC prediction model.

• 대한원격탐사학회지
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• 제33권1호
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• pp.25-35
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• 2017

Spatial downscaling with fine resolution auxiliary variables has been widely applied to predict precipitation at fine resolution from coarse resolution satellite-based precipitation products. The spatial downscaling framework is usually based on the decomposition of precipitation values into trend and residual components. The fine resolution auxiliary variables contribute to the estimation of the trend components. The main focus of this study is on quantitative analysis of impacts of trend component estimates on predictive performance in spatial downscaling. Two regression models were considered to estimate the trend components: multiple linear regression (MLR) and geographically weighted regression (GWR). After estimating the trend components using the two models,residual components were predicted at fine resolution grids using area-to-point kriging. Finally, the sum of the trend and residual components were considered as downscaling results. From the downscaling experiments with time-series Tropical Rainfall Measuring Mission (TRMM) 3B43 precipitation data, MLR-based downscaling showed the similar or even better predictive performance, compared with GWR-based downscaling with very high explanatory power. Despite very high explanatory power of GWR, the relationships quantified from TRMM precipitation data with errors and the auxiliary variables at coarse resolution may exaggerate the errors in the trend components at fine resolution. As a result, the errors attached to the trend estimates greatly affected the predictive performance. These results indicate that any regression model with high explanatory power does not always improve predictive performance due to intrinsic errors of the input coarse resolution data. Thus, it is suggested that the explanatory power of trend estimation models alone cannot be always used for the selection of an optimal model in spatial downscaling with fine resolution auxiliary variables.

• 한국항행학회논문지
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• 제23권5호
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• pp.424-429
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• 2019

5G의 상용화 등 네트워킹 기술의 발전은 다양한 시스템들이 네트워크를 통해서 정보를 교환하고 제어할 수 있는 기반을 제공하고 있다. 또한, 네트워크에서 발생하는 많은 현상들은 정보의 지연과 관련되기 때문에 지연된 정보를 갖는 시스템의 제어의 중요성이 증가하고 있다. 본 논문에서는 최근들어 지연이 있을 때에 저복잡도 제어기 설계에 많이 활용되는 예측 제어를 도입하여, 지연된 다중 입력을 갖는 시스템에서 지연의 크기와 입력의 수에 상관없이 거의 일정한 복잡도를 갖는 예측 제어기를 제시한다. 또한, 출력 궤환 구조를 갖는 예측 제어기가 점근적 수렴이 보장됨을 증명한다. 모의 실험을 통해서 제안된 방식이 상태 벡터를 확장한 전통적인 방식이나, 다른 예측 기반 제어 방식에 비해 적은 복잡도를 가지면서 안정성을 보장하는 제어기 설계 성공이 높게 발생함을 확인하였다.

• Structural Engineering and Mechanics
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• 제18권2호
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• pp.211-229
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• 2004

The main purpose of this paper is the numerical analysis of the non-linear seismic response of a RC building mock-up. The mock-up is subjected to different synthetic horizontal seismic excitations. The numerical approach is based on a 3D-model involving multilayered shell elements. These elements are composed of several single-layer membranes with various eccentricities. Bending effects are included through these eccentricities. Basic equations are first written for a single membrane element with its own eccentricity and then generalised to the multilayered shell element by superposition. The multilayered shell is considered as a classical shell element : all information about non-linear constitutive relations are investigated at the local scale of each layer, whereas balance and kinematics are checked afterwards at global scale. The non-linear dynamic response of the building is computed with Newmark algorithm. The numerical dynamic results (blind simulations) are considered in the linear and non linear cases and compared with experimental results from shaking table tests. Multilayered shell elements are found to be a promising tool for predictive computations of RC structures behaviour under 3D seismic loadings. This study was part of the CAMUS International Benchmark.

• Corrosion Science and Technology
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• 제19권6호
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• pp.302-309
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• 2020

This study aimed to predict the pitting corrosion characteristics of AL-6XN super-austenitic steel using multiple linear regression. The variables used in the model are degree of sensitization, temperature, and pH. Experiments were designed and cyclic polarization curve tests were conducted accordingly. The data obtained from the cyclic polarization curve tests were used as training data for the multiple linear regression model. The significance of each factor in the response (critical pitting potential, repassivation potential) was analyzed. The multiple linear regression model was validated using experimental conditions that were not included in the training data. As a result, the degree of sensitization showed a greater effect than the other variables. Multiple linear regression showed poor performance for prediction of repassivation potential. On the other hand, the model showed a considerable degree of predictive performance for critical pitting potential. The coefficient of determination (R2) was 0.7745. The possibility for pitting potential prediction was confirmed using multiple linear regression.