• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.1
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• pp.64-72
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• 2016

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.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.664-667
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• 1997

For higher component density per chip, it is necessary to identify and control the semiconductor manufacturing process more stringently. Recently, neural networks have been identified as one of the most promising techniques for modeling and control of complicated processes such as plasma etching process. Since wafer states after each run using identical recipe may differ from each other, conventional neural network models utilizing input factors only cannot represent the actual state of process and equipment. In this paper, in addition to the input factors of the recipe, real-time tool data are utilized for modeling of 64M DRAM s-poly plasma etching process to reflect the actual state of process and equipment. For real-time tool data, we collect optical emission spectroscopy (OES) data. Through principal component analysis (PCA), we extract principal components from entire OES data. And then these principal components are included to input parameters of neural network model. Finally neural network model is trained using feed forward error back propagation (FFEBP) algorithm. As a results, simulation results exhibit good wafer state prediction capability after plasma etching process.

• Transactions of Materials Processing
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• v.14 no.7 s.79
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• pp.601-606
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• 2005

A hole expansion process is an important process in producing a hub-hole in a wheel disc of a vehicle. In this process, the main parameter is the formability of a material that is expressed as the hole expansion ratio. In the process, a crack is occurred in the upper edge of a hole as the hole is expanded. Since prediction of the forming limit by hole expansion experiment needs tremendous time and effort, an appropriate fracture criterion has to be developed for finite element analysis to define forming limit of the material. In this paper, the hole expansion process of a hub-hole is studied by finite element analysis with ABAQUS/standard considering several ductile fracture criteria. The fracture mode and hole expansion ratio are compared with respect to the various fracture criteria. These criteria do not predict its fracture mode or hole expansion ratio adequately and show deviation from experimental results of hole expansion. A modified ductile fracture criterion is newly proposed to consider the deformation characteristics of a material accurately in a hole expansion process. A fracture propagation analysis at the hub-hole edge is also performed for high accuracy of prediction using the new fracture criterion proposed.

• Journal of Korean Institute of Industrial Engineers
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• v.33 no.1
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• pp.1-10
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• 2007

The capabilities ofBusiness Process Management Systems (BPMS's) are continuously extended to increase theeffectiveness of the management and enactment of business processes. This paper identifies the challenge ofcase prediction, which for a specific case under the control of a BPMS deals with the estimation of the remaining time until it is completed. An accurate case prediction facility is a valuable tool for the operationalcontrol of business processes, as it enables the pre-active monitoring of time violations. Little research has beencarried out in this area and few commercial tools support case prediction. This paper lists the requirements onsuch a facility and sketches sonae directions to reach a solution. To illustrate the depth of the problem, a smallaspect of the problem is treated in more detail. It involves the complex relations between tasks and resources inbusiness processes, which makes an exact analytical approach mfeasible.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.163-165
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• 2009

Automatic Vision Inspection(AVI) systems automatically detect defect features and measure their sizes via camera vision. It is important to predict the performance of an AVI to meet customer's specification in advance. In this paper, we propose a statistical method for prediction of false alarm rate regarding inconsistency of defect size measuremet process. We only need are a simple experimental trial for repeated defect size measurement test. The statistical features from the experiement are utilized in the prediction process. Therefore, the proposed method is swift and easy to implement and use. The experiment shows a close prediction compared to manual inspection results.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.6
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• pp.1810-1818
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• 1996

A new finite elemetn approach is introduced for direct prediction of bland shapes and strain distributions from desired final shapes in sheet metal forming. The approach deals with the geometric compatibility of finite elements, plastic deformation theory, minimization of plastic work with constraints, and a proper initial guess. The algorithm developed is applied to cylindrical cup drawing, square cup drawing, and fron fender forming to confirm its validity by demonstratin reasonable accurate numerical results of each problems. Rapid calculation with this algorithm enables easy determination of various process variables for design of sheet metal forming process.

• Transactions of Materials Processing
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• v.28 no.2
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• pp.89-97
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• 2019

In this paper, we examine the application of a new concept - on-line FE model in various metal rolling processes. This technology allows for completion of process simulation within a tiny fraction of a second without losing the high level of prediction accuracy inherent to FEM. The procedure is systematically demonstrated through the design of actual on-line models for the prediction of the width spread in horizontal rolling of the slab using a dog bone profile and horizontal rolling of the strip with a strip profile. The validity and the prediction accuracy of the on-line FE models were analyzed and discussed.

• Korean Journal of Remote Sensing
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• v.38 no.4
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• pp.327-341
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• 2022

Cloud removal is often required to construct time-series sets of optical images for environmental monitoring. In regression-based cloud removal, the selection of an appropriate regression model and the impact analysis of the input images significantly affect the prediction performance. This study evaluates the potential of Gaussian process (GP) regression for cloud removal and also analyzes the effects of cloud-free optical images and spectral bands on prediction performance. Unlike other machine learning-based regression models, GP regression provides uncertainty information and automatically optimizes hyperparameters. An experiment using Sentinel-2 multi-spectral images was conducted for cloud removal in the two agricultural regions. The prediction performance of GP regression was compared with that of random forest (RF) regression. Various combinations of input images and multi-spectral bands were considered for quantitative evaluations. The experimental results showed that using multi-temporal images with multi-spectral bands as inputs achieved the best prediction accuracy. Highly correlated adjacent multi-spectral bands and temporally correlated multi-temporal images resulted in an improved prediction accuracy. The prediction performance of GP regression was significantly improved in predicting the near-infrared band compared to that of RF regression. Estimating the distribution function of input data in GP regression could reflect the variations in the considered spectral band with a broader range. In particular, GP regression was superior to RF regression for reproducing structural patterns at both sites in terms of structural similarity. In addition, uncertainty information provided by GP regression showed a reasonable similarity to prediction errors for some sub-areas, indicating that uncertainty estimates may be used to measure the prediction result quality. These findings suggest that GP regression could be beneficial for cloud removal and optical image reconstruction. In addition, the impact analysis results of the input images provide guidelines for selecting optimal images for regression-based cloud removal.

• Proceedings of the Korean Biophysical Society Conference
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• 2003.06a
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• pp.65-65
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• 2003

After many genome projects, algorithms and software to process explosively growing biological information have been developed. To process huge amount of biological information, high performance computing equipments are essential. If we use the remote resources such as computing power, storages etc., through a Grid to share the resources in the Internet environment, we will be able to obtain great efficiency to process data at a low cost. Here we present the performance improvement of the protein secondary structure prediction (PSIPred) by using the Grid platform, distributing protein sequence data on the Grid where each computer node analyzes its own part of protein sequence data to speed up the structure prediction. On the Grid, genome scale secondary structure prediction for Mycoplasma genitalium, Escherichia coli, Helicobacter pylori, Saccharomyces cerevisiae and Caenorhabditis slogans were performed and analyzed by a statistical way to show the protein structural deviation and comparison between the genomes. Experimental results show that the Grid is a viable platform to speed up the protein structure prediction and from the predicted structures.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.4
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• pp.871-877
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• 2008

It is important to investigate the relationship between weld process parameters and weld bead geometry for adaptive arc robot welding. Howeve, it is difficult to predict an exact back-bead owing to gap in process of butt welding. In this paper, the quantitative prediction system to specify the relationship external weld conditions and weld bead geometry was developed to get suitable back-bead in butt welding which is widely applied on industrial field. Multiple regression analysis for the prediction of process parameters was used as the research method. And, the results of the prediction method were compared and analyzed.