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.

The modular assembly system can make it possible for the variety of products to be assembled in a short lead time. In this system, necessary components are assembled to optional components tailor to customers’ orders. Budget for inventory investments composed of inventory and purchasing costs are practically limited and the purchasing cost is often paid when an order is arrived. Service cost is assumed to be proportional to service level and it is included in budget constraint. We develop a heuristic procedure to find a good solution for a continuous review inventory system of the modular assembly system with a budget constraint. A regression analysis using a quadratic function based on the exponential function is applied to the cumulative density function of a normal distribution. With the regression result, an efficient heuristics is proposed by using an approximation for some complex functions that are composed of exponential functions only. A simple problem is introduced to illustrate the proposed heuristics.

In this paper, we investigate how the power consumption of a heat pump dryer depends on various factors in the drying process by analyzing variables that affect the power consumption. Since there are in general many variables that affect the power consumption, for a feasible analysis, we utilize the principal component analysis to reduce the number of variables (or dimensionality) to two or three. We find that the first component is correlated positively to the entrance temperature of various devices such as compressor, expander, evaporator, and the second, negatively to condenser. We then model the power consumption as a multiple regression with two and/or three transformed variables of the selected principal components. We find that fitted value from the multiple regression explains 80~90% of the observed value of the power consumption. This results can be applied to a more elaborate control of the power consumption in the heat pump dryer.

Currently, many nations in the world make a strong effort to exploit the new and renewable energy. Tide and Tidal Current Energy are the constant and regular power sources with higher and more stable quality compared to other renewable sources. The present paper investigate the status of Tide and Tidal Current energy analyzing its latest technology and development.

In this paper, we investigate the statistical correlation of the time series for temperature measured at the heat box in the automobile drying process. We show, in terms of the sample variance, that a significant non-linear correlation exists in the time series that consist of absolute temperature changes. To investigate further the non-linear correlation, we utilize the volatility, an important concept in the financial market, and induce volatility time series from absolute temperature changes. We analyze the time series of volatilities in terms of the de-trended fluctuation analysis (DFA), a method especially suitable for testing the long-range correlation of non-stationary data, from the correlation perspective. We uncover that the volatility exhibits a long-range correlation regardless of the window size. We also analyze the cross correlation between two (inlet and outlet) volatility time series to characterize any correlation between the two, and disclose the dependence of the correlation strength on the time lag. These results can contribute as important factors to the modeling of forecasting and management of the heat box’s temperature.

We develop an optimization algorithm for a periodic review inventory system under a stochastic budget constraint. While most conventional studies on the periodic review inventory system consider a simple budget limit in terms of the inventory investment being less than a fixed budget, this study adopts more realistic assumption in that purchasing costs are paid at the time an order is arrived. Therefore, probability is employed to express the budget constraint. That is, the probability of total inventory investment to be less than budget must be greater than a certain value assuming that purchasing costs are paid at the time an order is arrived. We express the budget constraint in terms of the Lagrange multiplier and suggest a numerical method to obtain optional values of the cycle time and the safety factor to the system. We also perform the sensitivity analysis in order to investigate the dependence of important quantities on the budget constraint. We find that, as the amount of budget increases, the cycle time and the average inventory level increase, whereas the Lagrange multiplier decreases. In addition, as budget increases, the safety factor increases and reaches to a certain level. In particular, we derive the condition for the maximum safety factor.

In this paper, we develop an efficient approach to solve a multiple-item budget-constraint newsboy problem with a reservation policy. A conventional approach for solving such problem utilizes an approximation for the evaluation of an inverse of a Gaussian cumulative density function when the argument of the function is small, and a heuristic method for finding an optimal Lagrangian multiplier. In contrast to the conventional approach, this paper proposes more accurate method of evaluating the function by using the normalization and an effective numerical integration method. We also propose an efficient way to find an optimal Lagrangian multiplier by proving that the equation for the budget-constraint is in fact a monotonically increasing function in the Lagrangian multiplier. Numerical examples are tested to show the performance of the proposed approach with emphases on the behaviors of the inverse of a Gaussian cumulative density function and the Lagrangian multiplier. By using sensitivity analysis of different budget constraints, we show that the reservation policy indeed provides greater expected profit than the classical model of not having the reservation policy.

In this paper, we develop an efficient approach to solve a continuous review inventory system with a budget constraint when the semi-finished product and optional components are required to be assembled. We are, in particular, interested in a budget constraint that includes a service level. The service cost, such as labor and facility costs, tends to increase as the service level increase, and it makes the problem difficult to solve. Assuming that the reorder point for a semi-finished product is given, we show that the order quantity for the semi-finished product and the order quantity and reorder point for optional components can be determined by minimizing the total cost that includes setup cost, inventory holding cost, and shortage cost. The performance of the proposed approach is tested by numerical examples. By using sensitivity analysis, we conclude that, as the reorder point for semi-finished product increases, the order quantity for semi-finished product increases, whereas the order quantity and reorder point of optional components decreases.

If service level is increased, then service cost such as labor cost and facility cost will be increased. This service cost is included in the budget constraint in this paper. This service cost makes the problem difficult to solve. The purpose of this research is to develop an efficient approach for a continuous review inventory system with budget constraint when the semi-finished product and optional components are required to be assembled. Assuming that the reorder point for semi-finished product is given, order quantity for semi-finished product and order quantity and reorder point will be determined to minimize total cost that includes setup cost, inventory holding cost, and shortage cost. The performance of the proposed approach is checked by using an example.

In this paper, we proposed a new method for the determination of strategies that are required in a continuous optimization algorithm based on the fictitious play theory. In order to apply the fictitious play theory to continuous optimization problems, it is necessary to express continuous values of a variable in terms of discrete strategies. In this paper, we proposed a method in which all strategies contain an equal number of selected real values that are sorted in their magnitudes. For comparative analysis of the characteristics and performance of the proposed method of representing strategies with respect to the conventional method, we applied the method to the two types of benchmarking functions: separable and inseparable functions. From the ex- perimental results, we can infer that, in the case of the separable functions, the proposed method not only outperforms but is more stable. In the case of inseparable functions, on the contrary, the performance of the optimization depends on the benchmarking functions. In particular, there is a rather strong correlation between the performance and stability regardless of the benchmarking functions.

Since the non-metric Multidimensional scaling (nMDS), a data visualization technique, provides with insights about engineering, economic, and scientific applications, it is widely used for analyzing large non-metric multidimensional data sets. The nMDS re

In this study, we propose a new method for generating candidate solutions based on both the Cauchy and the Gaussian probability distributions in order to use the merit of the solutions generated by these distributions. The Cauchy probability distribution

In this study, we propose a new method for generating candidate solutions based on both the Cauchy and the Gaussian probability distributions in order to use the merit of the solutions generated by these distributions. The Cauchy probability distribution has larger probability in the tail region than the Gaussian distribution. Thus, the Cauchy distribution can yield higher probabilities of generating candidate solutions of large-varied variables, which in turn has an advantage of searching wider area of variable space. On the contrary, the Gaussian distribution can yield higher probabilities of generating candidate solutions of small-varied variables, which in turn has an advantage of searching deeply smaller area of variable space. In order to compare and analyze the performance of the proposed method against the conventional method, we carried out experiments using benchmarking problems of real valued functions. From the result of the experiment, we found that the proposed method based on the Cauchy and the Gaussian distributions outperformed the conventional one for most of benchmarking problems, and verified its superiority by the statistical hypothesis test.

  The facility location in designing a supply chain network is an important decision problem that gives form, structure, and shape to the entire supply chain system. Location problems involve determining the location, number, and size of the facilities to

본 연구에서는 점탄성감쇠기가 설치된 비비례 감쇠 구조물의 바람에 대한 확률적 응답을 진동수영역에서 구하였다. 복소수 고유치 및 고유백터를 바탕으로 모드중첩법을 이용하여 응답의 RMS 값을 구하고 그것을 근사적인 방법인 모드 변형에너지법에서 얻은 결과와 비교하였다. 또한, 가력 진동수에 따라서 변하는 점탄성감쇠기의 강성 및 감쇠 계수를 상수로 모형화하였을 때의 풍응답 해석 결과의 정확성을 진동수영역에서 검증하였다. 해석결과에 의하면 감쇠기의 진동수 의존 특성은 구조물의 1차 고유 진동수에 의해서 비교적 정확하게 표현되었고, 모드 변형에너지법은 대체로 정확한 결과를 도출하였지만, 가속도 응답을 구할 때에는 다소 큰 오차를 유발하였다.

본 논문은 강섬유 보강 콘크리트 부재에 수직으로 기계적 정착된 고강도 확대머리철근의 정착성능을 평가하기 위하여 실시한 기초 인발실험결과를 정리한 것이다. 주요 실험변수는 강섬유의 혼입률, 콘크리트 강도, 정착길이, 확대머리철근 항복강도 , 전단보강유무 등이다. 실험체의 좌우 순피복두께는 확대머리철근 직경의 두 배로 계획하였다. 확대머리철근을 중심으로 1.5ldt,0.7ldt가 되는 위치에 힌지 지점을 두고, 확대머리 철근을 직접 인발하였다. 인발실험결과, 실험변수에 따라 콘크리트 파괴 및 철근 인장파단이 나타났다. 강섬유를 보강한 실험체가 동일한 변수의 강섬유를 보강하지 않은 실험체에 비히여 콘크리트 압축강도는 2.7~5.4% 크게 나타난 반면 인발강도는 20.9~63.1% 크게 나타나 정착성능 향상에 큰 기여를 하는 것으로 평가되었다. 강섬유 보강 콘크리트에 대해 확대머리철근과 평행한 전단보강근의 배근은 1.7~7.7% 인발강도를 증가시켰으나, 콘크리트 강도 증가를 고려할 때 정착성능 향상에 미치는 영향이 크지 않았다. 본 실험체 상세와 같이 강섬유보강 콘크리트 부재에 수직정착된 SD600의 확대머리철근 정착설계는 KCI2017, KCI2012의 정착길이 설계식을 그대로 사용할 수 있을 것 으로 사료된다.

확대머리 SD600 고강도 인장철근으로 단부 정착된 SFRC 깊은보의 전단성능을 평가하기 위해 전단 실험을 수행하였다. 실험 변수는 주인장 철근의 단부 정착방법(확대머리 철근, 일자형 철근), 단부 정착길이, 전단보강근 유무 등이다. 전단경간비는 1을 가지는 실험체에 대한 전단실험결과, 모든 실험체는 초기 휨 균열이 발생한 후 경사균열이 진행되면서 최종적으로 압축전단파괴되었다. 확대머리 철근으로 기계적 정착된 실험체들이 일자형 철근 정착에 비하여 5.6∼22.4% 더 큰 전단강도를 나타내었다. 확대머리 철근으로 기계적 정착된 실험체들에 대하여 최대하중의 75%까지는 지압응력이 전체 정착응력의 0.9~17.2%에 도달하였으나, 최대하중 시점에서 지압응력이 전체 정착응력의 22.4%~46%에 도달하여 큰 응력 부담률을 나타내었다. 이를 통하여 확대머리 지압응력에 의한 정착응력 증가가 전단강도에 큰 영향을 미침을 알 수 있다. 실험 전단강도가 실용식에 의한 전단강도의 2.68~4.65 배로 평가되어, 실용식이 전단내력을 안전측으로 평가하였다.

In this paper, anchorage behavior of high-strength headed rebar with minimum edge distance was evaluated. The experimental parameters are the compressive strengths of concrete, the strengths and the anchorage lengths of headed rebar. The maximum pullout strengths and final failure modes were evaluated through the experiment.