In this paper, we utilize a Gaussian process to predict the power consumption in the air-conditioning system. As the power consumption in the air-conditioning system takes a form of a time-series and the prediction of the power consumption becomes very important from the perspective of the efficient energy management, it is worth to investigate the time-series model for the prediction of the power consumption. To this end, we apply the Gaussian process to predict the power consumption, in which the Gaussian process provides a prior probability to every possible function and higher probabilities are given to functions that are more likely consistent with the empirical data. We also discuss how to estimate the hyper-parameters, which are parameters in the covariance function of the Gaussian process model. We estimated the hyper-parameters with two different methods (marginal likelihood and leave-one-out cross validation) and obtained a model that pertinently describes the data and the results are more or less independent of the estimation method of hyper-parameters. We validated the prediction results by the error analysis of the mean relative error and the mean absolute error. The mean relative error analysis showed that about 3.4% of the predicted value came from the error, and the mean absolute error analysis confirmed that the error in within the standard deviation of the predicted value. We also adopt the non-parametric Wilcoxon’s sign-rank test to assess the fitness of the proposed model and found that the null hypothesis of uniformity was accepted under the significance level of 5%. These results can be applied to a more elaborate control of the power consumption in the air-conditioning system.

Decisions on reliability screening rules and burn-in policies are determined based on the estimated reliability. The variability in a semiconductor manufacturing process does not only causes quality problems but it also makes reliability estimation more complicated. This study investigates the nonuniformity characteristics of integrated circuit reliability according to defect density distribution within a wafer and between wafers then develops optimal burn-in policy based on the estimated reliability. New reliability estimation model based on yield information is developed using a spatial stochastic process. Spatial defect density variation is reflected in the reliability estimation, and the defect densities of each die location are considered as input variables of the burn-in optimization. Reliability screening and optimal burn-in policy subject to the burn-in cost minimization is examined, and numerical experiments are conducted.

본 연구에서는 부분적으로 정상상태 확률과정으로 모델링할 수 있는 가진입력에 대하여 확률적으로 정의된 구조물의 최대응답에 대한 구속조건을 만족시키면서 제어력을 최소화 할 수 있는 최적설계 방법을 제안한다. 최적화 과정에서 안정성의 확보를 위해 제어기를 전상태 피드백 LQR제어기의 형태로 한정하였으며 가중치 행렬을 설계변수로 하고 Riccati 행렬을 매개변수로 하여 목적함수와 구속조건 함수 및 그 기울기를 계산한다. 제안된 방법을 통해 설계된 전상태 피드백 LQR제어기는 목표 응답성능을 만족시킬 수 있었고 이에 필요한 최대 제어력을 확률적으로 정량화하여 제어금기의 제작에 유용한 자료가 될 수 있도록 하였다. 상태변수 추정을 위해 독립적으로 설계된 Kalman 필터와 최적화된 LQR 제어기가 결합된 LQG 제어기 및 그 차수를 축소시킨 제어기는 모두 큰 성능의 저하가 없었으며 따라서 제안된 설계방법을 이용하여 구조물의 최대응답에 관한 구속조건을 만족시키는 출력 피드백 제어기 설계가 충분히 가능함을 확인하였다.