• Nuclear Engineering and Technology
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• v.51 no.4
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• pp.954-962
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• 2019

It is well known that the variance of tally is biased in a Monte Carlo calculation based on the power iteration method. Several studies have been conducted to estimate the real variance. Among them, the batch method, which was proposed by Gelbard and Prael, has been utilized actively in many Monte Carlo codes because the method is straightforward, and it is easy to implement the method in the codes. However, there is a problem when utilizing the batch method because the estimated variance varies depending on batch size. Often, the appropriate batch size is not realized before the completion of several Monte Carlo calculations. This study recognizes this shortcoming and addresses it by permitting selection of an appropriate batch size.

• Journal of the Korea Society for Simulation
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• v.5 no.2
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• pp.59-72
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• 1996

The purpose of this study is to make an improvement to the batch-means method, which is a procedure to construct a confidence interval(c.i.) for the steady-state process mean of a stationary simulation output process. In the batch-means method, the data in the output process are grouped into batches. The sequence of means of the data included in individual batches is called a batch-menas process and can be treated as an independently and identically distributed set of variables if each batch includes sufficiently large number of observations. The traditional batch-means method, therefore, uses a batch size as large as possible in order to. destroy the autocovariance remaining in the batch-means process. The c.i. prodedure developed and empirically tested in this study uses a small batch size which can be well fitted by a simple ARMA model, and then utilizes the dependence structure in the fitted model to correct for bias in the variance estimator of the sample mean.

• Analytical Science and Technology
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• v.12 no.3
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• pp.196-202
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• 1999

Brown algae, Sargassum horneri, was used as an adsorbent to remove heavy metal Cd(II) and Pb(II) ions. Sargassum horneri picked in East Coast was formulated into the grain with the size of 40-60 mesh after wind dry. Batch method was used in order to investigate the adsorption rate by measuring the adsorption amounts with shaking time. In the column method, the adsorption amounts were measured by flowing metal solutions into the algae-packed column at the rate of 1 ml/min. Adsorption amounts in both batch method and column method were in the following order : pH 10.5>7.0>3.5. It was found that Pb(II) was more adsorbed on the algae grain than Cd(II). It was also revealed that the adsorption amounts reached the maximum within 5 minutes irrespective of pH condition in the batch method. It was concluded that the batch method was more effective than the column method in terms of recovery rate.

• Journal of the Korea Institute of Building Construction
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• v.13 no.6
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• pp.565-578
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• 2013

Batch production is common in repetitive construction projects, and it is not unusual for different batch sizes to be used by contractors in one project. While several scheduling methods, such as the Linear Scheduling Method (LSM) and the Repetitive Scheduling Method (RSM) have been proposed and used, no mathematical method for repetitive construction projects has been developed, and it is difficult to consider different batch sizes with the existing methods. An original mathematical algorithm for scheduling repetitive projects with different batch sizes is proposed in this study. This algorithm is illustrated with assumptions of resource continuity and single path in a project and introduces new terms, control batch and critical batch. The algorithm logics and mathematical equations are validated by comparison with the outcomes from a graphical scheduling approach through a simple and practical hypothetic project. As a result, it is expected that the proposed algorithm can be easily adapted and extended to computer software for scheduling, and can be a starting point for research on batch size management in repetitive construction projects.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.187-194
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• 2020

In today's process-oriented industries, such as semiconductor and petrochemical processes, autocorrelation exists between observed data. As a management method for the process where autocorrelation exists, a method of using the observations is to construct a batch so that the batch mean approaches to independence, or to apply the EWMA (Exponentially Weighted Moving Average) statistic of the observed value to the EWMA control chart. In this paper, we propose a method to determine the batch size of UBM (Unweighted Batch Mean), which is commonly used as a management method for observations, and a method to determine the optimal batch size based on ARL (Average Run Length) We propose a method to estimate the standard deviation of the process. We propose an improved control chart for processes in which autocorrelation exists.

• Progress in Superconductivity and Cryogenics
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• v.7 no.3
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• pp.9-12
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• 2005

[ $Y_1Ba_2Cu_3O_{7-8}$ ] (YBCO) coated conductor has been fabricated by batch-type process using oxide-precursor-based metal-organic deposition (MOD) method. The batch-type process can be scaled up more simply to Produce long-length YBCO conductor than the reel-to-reel process. Also, it has less handling problems and is adequate to the ambient gas environment. In this work, YBCO oride powder was used as a starting precursor for MOD method. After reel-to-reel dip coating process, me ter-long-buffered metal tape was wound around a cylinder and underwent calcination and annealing processes. Annealed YBCO films showed good c-axis alignment and dense surface morphology with no cracks, but exhibited very low critical current density of $10^5\;A/cm^2$.

• Journal of Korean Institute of Industrial Engineers
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• v.33 no.2
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• pp.246-253
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• 2007

All of the machines in a production line can be classified into bottleneck and non-bottleneck machines. A bottleneck is a resource whose capacity limits the throughput of the whole production facility. This paper addresses a batch sizing problem at the bottleneck machine. Traditionally, most batch sizing decisions have been made based on the EOQ (economic order quantity) model where setup and inventory costs are considered while throughput rate is assumed to be given. However, since batch size affects the capacity of the bottleneck machine, the throughput rate may not be constant. As the batch size increases, the frequency of the setup decreases. The saved setup time can be transferred to processing time, which results in higher throughput. But, the larger batch size may also result in longer lead time and larger WIP inventory level. This paper presents an alternative method to determine batch size at the bottleneck machine in a manufacturing line. A linear search algorithm is introduced to find optimal throughput rate and batch size at the same time. Numerical examples are provided to see how the proposed method works and to investigate the effects of some parameters.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.3
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• pp.285-294
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• 2012

Using bearing measurement only, target motion state is not directly obtained so that TMA (Target Motion Analysis) is needed for this situation. TMA is a nonlinear estimation technique used in passive SONAR systems. Also it is the one of important techniques for underwater combat management systems. TMA can be divided to two parts: batch estimation and sequential estimation. It is preferable to use sequential estimation for reducing computational load as well as adaptively to target maneuvers, batch estimation is still required to attain target initial state vector for convergence of sequential estimation. Selection of batch time interval which depends on observability is critical in TMA performance. Batch estimation in general utilizes predetermined batch time interval. In this paper, we propose a new method called the BTIS (Batch Time Interval and Initial State Estimation). The proposed BTIS estimates target initial status and determines the batch time interval sequentially by using a bank of GMM-TS (Gaussian Mixture Measurement-Track Splitting) filters. The performance of the proposal method is verified by a Monte Carlo simulation study.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.10
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• pp.833-841
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• 2002

We develope a model that can manipulate the reflux ratio of a batch distillation process in real time for optimal operations. Firstly, reflux ratio decision model for batch distillation unit was developed using the simple short-cut method. Secondly, more detailed rigorous method was applied to improve the accuracy of the model. Based on these models, operational strategies for the optimal reflux ratio was proposed. The results are illustrated with suitable examples and compared with the results using commercial simulator.

• Journal of Internet Computing and Services
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• v.19 no.3
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• pp.35-42
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• 2018

Deep learning is known as a method of high accuracy among several methods for image classification. In this paper, we propose a method of enhancing the accuracy of image classification using CNN with a batch normalization method for classification of images using deep CNN (Convolutional Neural Network). In this paper, we propose a method to add a batch normalization layer to existing neural networks to enhance the accuracy of image classification. Batch normalization is a method to calculate and move the average and variance of each batch for reducing the deflection in each layer. In order to prove the superiority of the proposed method, Accuracy and mAP are measured by image classification experiments using five image data sets SHREC13, MNIST, SVHN, CIFAR-10, and CIFAR-100. Experimental results showed that the CNN with batch normalization is better classification accuracy and mAP rather than using the conventional CNN.