• Transactions of Materials Processing
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• v.10 no.5
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• pp.425-432
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• 2001

In order to investigate the influences of process parameters on the shape of lead frame, experimental study has been carried out. In the experiment, dimensional accuracy of the die sets, measurement accuracy has been managed carefully enough to simulate actual lead frame blanking process. With the blanking of square-shaped specimen, the effects of clearance, strip holding pressure and bridge width on the shape of blanked profile have been investigated. Experimental results show that the burnish ratio is increased as the clearance decreases. the strip holding pressure increases, and bridge width increases. Although the results seems to be similar to the ordinary blanking, the lead frame blanking shows a subtle different characteristics to the ordinary blanking due to the narrow bridge width.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.604-607
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• 2003

Most recommendation systems recommend the products or other information satisfying preferences of users on the basis of the users' previous profile information and other information related to product searches and purchase of users visiting web sites. This study aims to apply these application categories to e-mail more necessary to users. The E-Mail System has the strong personality so that there will be some problems even if e-mails are automatically classified by category through the learning on the basis of the personal rules. In consideration with this aspect, we need the semi-automatic system enabling both automatic classification and recommendation method to enhance the satisfaction of users. Accordingly, this paper uses two approaches as the solution against the misclassification that the users consider as the accuracy of classification itself using the dynamic threshold in Bayesian Learning Algorithm and the second one is the methodological approach using the recommendation agent enabling the users to make the final decision.

• Current Optics and Photonics
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• v.4 no.4
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• pp.277-285
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• 2020

Computer-generated holograms (CGH) are the core devices to solve the problem of freeform surface measurement. In view of the overlay error introduced in the manufacturing process of CGH, this paper proposes an evaluation model for analyzing the overlay error of CGH. The detection method of extracting CGH profile information by an ultra-depth of field micro-measurement system is presented. Furthermore, based on the detection method and technical scheme, the effect of overlay error on the wavefront accuracy of CGH can be evaluated.

• Transactions of Materials Processing
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• v.28 no.2
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• pp.83-88
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• 2019

This paper introduces a new concept - on-line FE model, as applied to metal rolling. The new technology allows for completion of process simulation within a tiny fraction of a second without loss of high-level prediction accuracy inherent to FEM. The three steps of an on-line FE model design namely, process metamorphosis, mesh design, and process variable design, are described in detail. The procedure is demonstrated step by step through designing actual on-line models for the prediction of the dog-bone profile in edge rolling. The validity and prediction accuracy of the on-line FE models are analyzed and discussed.

• Journal of Information Processing Systems
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• v.19 no.5
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• pp.652-662
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• 2023

Identifying highly discriminating genes is a critical step in tumor recognition tasks based on microarray gene expression profile data and machine learning. Gene selection based on tree models has been the subject of several studies. However, these methods are based on a single-tree model, often not robust to ultra-highdimensional microarray datasets, resulting in the loss of useful information and unsatisfactory classification accuracy. Motivated by the limitations of single-tree-based gene selection, in this study, ensemble gene selection methods based on multiple-tree models were studied to improve the classification performance of tumor identification. Specifically, we selected the three most representative tree models: ID3, random forest, and gradient boosting decision tree. Each tree model selects top-n genes from the microarray dataset based on its intrinsic mechanism. Subsequently, three ensemble gene selection methods were investigated, namely multipletree model intersection, multiple-tree module union, and multiple-tree module cross-union, were investigated. Experimental results on five benchmark public microarray gene expression datasets proved that the multiple tree module union is significantly superior to gene selection based on a single tree model and other competitive gene selection methods in classification accuracy.

• Journal of Mechanical Science and Technology
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• v.16 no.8
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• pp.1154-1164
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• 2002

Losses on the turbine consist of the mechanical loss, tip clearance loss, secondary flow loss and blade profile loss etc.,. More than 60 % of total losses on the turbine is generated by the two latter loss mechanisms. These losses are directly related with the reduction of turbine efficiency. In order to provide a new design methodology for reducing losses and increasing turbine efficiency, a two-dimensional axial-type turbine blade shape is modified by the optimization process with two-dimensional compressible flow analysis codes, which are validated by the experimental results on the VKI turbine blade. A turbine blade profile is selected at the mean radius of turbine rotor using on a heavy duty gas turbine, and optimized at the operating condition. Shape parameters, which are employed to change the blade shape, are applied as design variables in the optimization process. Aerodynamic, mechanical and geometric constraints are imposed to ensure that the optimized profile meets all engineering restrict conditions. The objective function is the pitchwise area averaged total pressure at the 30% axial chord downstream from the trailing edge. 13 design variables are chosen for blade shape modification. A 10.8 % reduction of total pressure loss on the turbine rotor is achieved by this process, which is same as a more than 1% total-to-total efficiency increase. The computed results are compared with those using 11 design variables, and show that optimized results depend heavily on the accuracy of blade design.

• Atmosphere
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• v.25 no.1
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• pp.117-127
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• 2015

The presences of clouds significantly influence the accuracy of ozone retrievals from satellite measurements. This study focuses on the influence of clouds on Ozone Monitoring instrument (OMI) ozone profile retrieval based on an optimal estimation. There are two operational OMI cloud products; OMCLDO2, based on absorption in $O_2-O_2$ at 477 nm, and OMCLDRR, based on filling in Fraunhofer lines by rotational Raman scattering (RRS) at 350 nm. Firstly, we characterize differences between $O_2-O_2$ and RRS effective cloud pressures using MODIS cloud optical thickness (COT), and then compare ozone profile retrievals with different cloud input data. $O_2-O_2$ cloud pressures are significantly smaller than RRS by ~200 hPa in thin clouds, which corresponds to either low COT or cloud fraction (CF). On the other hand, the effect of Optical centroid pressure (OCP) on ozone retrievals becomes significant at high CF. Tropospheric ozone retrievals could differ by up to ${\pm}10$ DU with the different cloud inputs. The layer column ozone below 300 hPa shows the cloud-induced ozone retrieval error of more than 20%. Finally, OMI total ozone is validated with respect to Brewer ground-based total ozone. A better agreement is observed when $O_2-O_2$ cloud data are used in OMI ozone profile retrieval algorithm. This is distinctly observed at low OCP and high CF.

• Bioinformatics and Biosystems
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• v.1 no.1
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• pp.82-87
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• 2006

The most popular protein structure prediction method is comparative modeling. To guarantee accurate comparative modeling, the sequence alignment between a query protein and a template should be accurate. Although choosing the best template based on the protein sequence alignments is most critical to perform more accurate fold-recognition in comparative modeling, even more critical is the sequence alignment quality. Contrast to a lot of attention to developing a method for choosing the best template, prediction of alignment accuracy has not gained much interest. Here, we develop a method for prediction of the shift score, a recently proposed measure for alignment quality. We apply support vector regression (SVR) to predict shift score. The alignment between a query protein and a template protein of length n in our own library is transformed into an input vector of length n +2. Structural alignments are assumed to be the best alignment, and SVR is trained to predict the shift score between structural alignment and profile-profile alignment of a query protein to a template protein. The performance is assessed by Pearson correlation coefficient. The trained SVR predicts shift score with the correlation between observed and predicted shift score of 0.80.

• Journal of the Korean Society of Safety
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• v.23 no.4
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• pp.13-18
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• 2008

This study deals with finish roll forming by forced displacement can be conceived as a method of eliminating errors in conventional form rolling under constant loads. This method produces a high-precision tooth profile by low-speed form rolling when a high rigid screw or cam is used at the pressurized section. Tooth profile is decided in the beginning of roll forming and ${\delta}_{max}$ mainly increases if the number of roll forming process is increased. Gear class is improved by one or two class after roll forming if the gear has convex type error and pressure angle error in KS 4 class. If the gear have concave type error and pressure angle error and pressure angle error, gear class is not improved in theory, but improved a little in practice. In the finishing roll forming, it inevitably yields both the concaving of tooth profile and plastic deflection of addendum of teeth. Experiments show that the concaving and the plastic deflection are successfully reduced, the accuracy of tooth profile reaches to KS 0 class.

• Journal of the Korea Society of Computer and Information
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• v.27 no.11
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• pp.47-55
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• 2022

Recently, researches on the prediction of sea water temperature using artificial intelligence models has been actively conducted in Korea. However, most researches in the sea around the Korean peninsula mainly focus on predicting sea surface temperatures. Unlike previous researches, this research predicted the vertical sea water temperature profile of the East Sea, which is very important in submarine operations and anti-submarine warfare, using XBT(eXpendable Bathythermograph) data and machine learning models(RandomForest, XGBoost, LightGBM). The model was trained using XBT data measured from sea surface to depth of 200m in a specific area of the East Sea, and the prediction accuracy was evaluated through MAE(Mean Absolute Error) and vertical sea water temperature profile graphs.