• International Journal of Safety
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• 제1권1호
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• pp.36-42
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• 2002

This paper is aimed at predicting the life of rubber insulating gloves under normal operating stresses from relatively rapid test performed at higher stresses. Specimens of rubber insulating gloves are subject to multiple stress conditions, i.e. combined electrical and thermal stresses. Two modes of electrical stress, step voltage stress and constant voltage stress are used in specimen aging. There are two types of test for electrical stress in this experiment: the one is Breakdown Voltage (BDV) test under step voltage stress and thermal stress and the other is lifetime test under constant voltage stress and temperature stress. The ac breakdown voltage defined as the break-down point of insulation that leakage current excesses a limit value, l0mA in this experiment, is determined. Because the very high variability of aging data requires the application of statistical model, Weibull distribution is used to represent the failure times as the straight line on Weibull probability paper. Weibull parameters are deter-mined by three statistical methods i.e. maximum likelihood method, graphical method and least squares method, which employ SAS package, Weibull probability paper and FORTRAN, respectively. Two chosen models for predicting the life under simultaneous electrical and thermal stresses are inverse power model and exponential model. And the constants of life equation for multistress aging are calculated using numerical method, such as Gauss Jordan method etc.. The completion of life equation enables to estimate the life at normal stress based on the data collected from accelerated aging test. Also the comparison of the calculated lifetimes between the inverse power model and the exponential model is carried out. And the lifetimes calculated by three statistical methods with lower voltage than test voltage are compared. The results obtained from the suggested experimental method are presented and discussed.

• 전기학회논문지
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• 제57권5호
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• pp.845-851
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• 2008

A simple RMRAC (Robust Model Reference Adaptive Control) scheme for the PMSM (Permanent Magnent Synchronous Motor) is proposed in the synchronous frame. A current control of PMSM is the most inner loop of electro-mechanical driving systems and it requires a fast and simple control law to play a foundation role in the control hierarchy. In the proposed synchronous current model, the input signal is composed of a calculated voltage by proposed adaptive laws and real system disturbance. The gains of feed-forward and feedback controllers are estimated by the proposed modified Gradient method respectively, where the system disturbances are assumed as filtered current tracking errors. After the estimation of the system disturbances from the tracking errors, the corresponding voltage is fed forward to control input voltage to compensate for the disturbances. The proposed method is robust against high frequency disturbance and has a fast dynamic response. It also shows a good real-time performance due to it's simplicity of control structure. Through the simulations and real experiments, efficiency of the proposed method is verified.

• 한국음향학회지
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• 제27권8호
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• pp.443-449
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• 2008

이동통신에서 배경잡음이 존재하는 실제 환경에서 음성신호처리의 가장 중요한 이슈중의 하나는 강인한 음성검출기를 설계하는 것이다. 상대적으로 간단하면서도 성능이 우수하여 대표적인 음성검출기로 사용되는 통계적모델기반 기법은 각 주파수 채널별 우도비를 이용하여 음성검출 검출식을 만들어내는 방식이다. 최근, 변별적 가중치 학습 (discriminative weight training)을 이용하여 주파수 체널별 가중치가 인가된 우도비를 이용한 음성검출 결정식을 갖는 음성검출기가 제안 되었으며 상대적으로 우수한 성능을 보였다. 본 연구에서는 기존의 변별적 가중치 학습의 입력벡터에 이전프레임의 결정식을 궤환구조형태를 바탕으로 추가하는 새로운 방식을 제안한다. 제안된 기법은 비정상 (non-staionary) 잡음 환경에서 객관적인 방법을 통해 상호비교 분석되었으며 결론적으로 우수한 성능을 보였다.

• KIEAE Journal
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• 제15권4호
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• pp.105-110
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• 2015

Purpose: This study aimed at finding the optimal input variables of the artificial neural network-based predictive model for the optimal controls of the indoor temperature environment. By applying the optimal input variables to the predictive model, the required time for restoring the current indoor temperature during the setback period to the normal setpoint temperature can be more precisely calculated for the cooling season. The precise prediction results will support the advanced operation of the cooling system to condition the indoor temperature comfortably in a more energy-efficient manner. Method: Two major steps employing the numerical computer simulation method were conducted for developing an ANN model and finding the optimal input variables. In the first process, the initial ANN model was intuitively determined to have input neurons that seemed to have a relationship with the output neuron. The second process was conducted for finding the statistical relationship between the initial input variables and output variable. Result: Based on the statistical analysis, the optimal input variables were determined.

• ETRI Journal
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• 제45권5호
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• pp.768-780
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• 2023

The application of unmanned aerial vehicles (UAVs) has recently attracted considerable interest in various areas. A three-dimensional multiple-input multiple-output concentric two-hemisphere model is proposed to characterize the scattering environment around a vehicle in an urban UAV-to-vehicle communication scenario. Multipath components of the model consisted of lineof-sight and single-bounced components. This study focused on the key parameters that determine the scatterer distribution. A time-variant process was used to analyze the nonstationarity of the proposed model. Vital statistical properties, such as the space-time-frequency correlation function, Doppler power spectral density, level-crossing rate, average fade duration, and channel capacity, were derived and analyzed. The results indicated that with an increase in the maximum scatter radius, the time correlation and level-crossing rate decreased, the frequency correlation function had a faster downward trend, and average fade duration increased. In addition, with the increase of concentration parameter, the time correlation, space correlation, and level-crossing rate increased, average fade duration decreased, and Doppler power spectral density became flatter. The proposed model was compared with current geometry-based stochastic models (GBSMs) and showed good consistency. In addition, we verified the nonstationarity in the temporal and spatial domains of the proposed model. These conclusions can be used as references in the design of more reasonable communication systems.

• Asian-Australasian Journal of Animal Sciences
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• 제13권8호
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• pp.1035-1039
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• 2000

This study developed a Poisson generalized linear mixed model and a procedure to estimate genetic parameters for count traits. The method derived from a frequentist perspective was based on hierarchical likelihood, and the maximum adjusted profile hierarchical likelihood was employed to estimate dispersion parameters of genetic random effects. Current approach is a generalization of Henderson's method to non-normal data, and was applied to simulated data. Underestimation was observed in the genetic variance component estimates for the data simulated with large heritability by using the Poisson generalized linear mixed model and the corresponding maximum adjusted profile hierarchical likelihood. However, the current method fitted the data generated with small heritability better than those generated with large heritability.

• International Journal of Ocean System Engineering
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• 제3권3호
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• pp.116-130
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• 2013

The prediction of ocean currents in real time over the warning times of a few hours or days is required in planning many operation-related activities in the ocean. Traditionally this is done through numerical models which are targeted toward producing spatially distributed information. This paper discusses a complementary method to do so when site-specific predictions are desired. It is based on the use of a recurrent type of neural network as well as the statistical tool of model tree. The measurements made at a site in Indian Ocean over a period of 4 years were used. The predictions were made over 72 time steps in advance. The models developed were found to be fairly accurate in terms of the selected error statistics. Among the two modeling techniques the model tree performed better showing the necessity of using distributed models for different sub-domains of data rather than a unique one over the entire input domain. Typically such predictions were associated with average errors of less than 2.0 cm/s. Although the prediction accuracy declined over longer intervals, it was still very satisfactory in terms of theselected error criteria. Similarly prediction of extreme values matched with that of the rest of predictions. Unlike past studies both east-west and north-south current components were predicted fairly well.

• 한국초전도ㆍ저온공학회논문지
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• 제17권2호
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• pp.41-44
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• 2015

Critical current of high-temperature superconducting (HTS) coil is influenced by its own self magnetic field. Direction and density distribution of the magnetic field around the coil are fixed after the shape of the coil is decided. If the entire part of the HTS tape has homogeneous $I_c$ distribution characteristic, quench would be initiated in fixed location on the coil. However, the actual HTS tape has inhomogeneous $I_c$ distribution along the length. If the $I_c$ distribution of the HTS tape is known, we can expect the spot within the HTS coil that has the highest probability to initiate the quench. In this paper, $I_c$ distribution within the HTS coil under self-field effect is simulated by MATLAB. In the simulation procedure, $I_c$ distribution of the entire part of the HTS tape is assume d to follow Gaussian-distribution by central limit theorem. The HTS coil model is divided into several segments, and the critical current of each segment is calculated based on the-generalized Kim model. Single pancake model is simulated and self-field of HTS coil is calculated by Biot-Savart's law. As a result of simulation, quench-initiating spot in the actual HTS coil can be predicted statistically. And that statistical analysis can help detect or protect the quench of the HTS coil.

• Journal of the Korean Data and Information Science Society
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• 제18권4호
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• pp.847-857
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• 2007

This paper provides a statistical algorithm which detects COD (chemical oxygen demand) measurement machine error on real-time. For this we propose to use regression model fitting and check its validity against the current observations. The main idea is that the normal regression relation between COD measurement and other parameters inside the machine will be violated when the machine is out of order.

• Journal of the Korean Statistical Society
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• 제25권4호
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• pp.567-576
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• 1996

The multiprocess dynamic survival model is proposed for the application of the regression model on the analysis of survival data with time-varying effects of covariates : where the survival data consists of numbers of deaths at certain time-points. The algorithm for the recursive estimation of a time-varying parameter vector is suggested. Also the algorithm of forecasting of numbers of deaths of each group in the next time interval based on the information gathered until the end of current time interval is suggested.