• Journal of the Semiconductor & Display Technology
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• v.19 no.3
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• pp.77-81
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• 2020

In this paper, we create scaffold defect classification models using machine learning based data. We extract the characteristic from collected scaffold external images using USB camera. SVM, KNN, MLP algorithm of machine learning was using extracted features. Classification models of three type learned using train dataset. We created scaffold defect classification models using test dataset. We quantified the performance of defect classification models. We have confirmed that the SVM accuracy is 95%. So the best performance model is using SVM.

• Proceedings of the Safety Management and Science Conference
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• 2010.11a
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• pp.551-557
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• 2010

The research proposes the hypergeometric, binomial and Poisson yield models for defective and defect. The paper also presents the hypothesis test, confidence interval and control charts for DPU(Defect Per Unit) and DPO(Defect Per Opportunity). Especially the study considers the analysis of compound Poisson yield models using various DOU density distributions.

• Smart Structures and Systems
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• v.30 no.3
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• pp.303-315
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• 2022

The degradation of wheel tread may result in serious hazards in the railway operation system. Therefore, timely wheel defect diagnosis of in-service trains to avoid tragic events is of particular importance. The focus of this study is to develop a novel wheel defect detection approach based on the relevance vector machine (RVM) which enables online detection of potentially defective wheels with trackside monitoring data acquired under different running-speed conditions. With the dynamic strain responses collected by a trackside monitoring system, the cumulative Fourier amplitudes (CFA) characterizing the effect of individual wheels are extracted to formulate multiple probabilistic regression models (MPRMs) in terms of multi-kernel RVM, which accommodate both variables of vibration frequency and running speed. Compared with the general single-kernel RVM-based model, the proposed multi-kernel MPRM approach bears better local and global representation ability and generalization performance, which are prerequisite for reliable wheel defect detection by means of data acquired under different running-speed conditions. After formulating the MPRMs, we adopt a Bayesian null hypothesis indicator for wheel defect identification and quantification, and the proposed method is demonstrated by utilizing real-world monitoring data acquired by an FBG-based trackside monitoring system deployed on a high-speed trial railway. The results testify the validity of the proposed method for wheel defect detection under different running-speed conditions.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2009.05a
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• pp.94-97
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• 2009

Cast defect in large steel ingots are estimated in quality and compared each other cast conditions on simulation results by now. The cast defects, micro-crack, shrinkage, pin hole which are predictable in simulation with a reasonable accuracy. In this study, 15 ton steel ingot casting was simulated for solidification model and cast defect prediction. And the real cast was carried out in a foundry for the compeer to the simulation results, the cast defect prediction. Also, the quantity of predicted defect was tried to measuring with the defect mach counting for the various simulated cast conditions. The defect quantity work was used to find the optimized cast condition in DOE(design of experiment) procedure.

• Journal of the Korean Institute of Telematics and Electronics D
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• v.34D no.5
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• pp.32-44
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• 1997

A three-dimensional (3D) full-dynamic damage model for ion implantation in crystalline silicon was proposed to calculate more accurately point defect distributions and ion-implanted concentration profiles during ion implantation process. The developed model was based on the physical monte carlo approach. This model was applied to simulate B and BF2 implantation. We compared our results for damage distributions with those of the analytical kinchin-pease approach. In our result, the point defect distributions obtained by our new model are less than those of kinchin-pease approach, and the vacancy distributions differ from the interstitial distributions. The vacancy concentrations are higher than the interstitial ones before 0.8 . Rp to the silicon surface, and after the 0.8 . Rp to the silicon bulk, the interstitial concentrations are revesrsely higher than the vacancy ones.The fully-dynamic damage model for the accumulative damage during ion implantation follows all of the trajectories of both ions and recoiled silicons and, concurrently, the cumulative damage effect on the ions and the recoiled silicons are considered dynamically by introducing the distributon probability of the point defect. In addition, the self-annealing effect of the vacancy-interstitial recombination during ion implantation at room temperature is considered, which resulted in the saturation level for the damage distribution.

• International Journal of Precision Engineering and Manufacturing
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• v.4 no.6
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• pp.12-19
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• 2003

This paper presents the effect of boundary conditions in various failure pressure models published for the estimation of failure pressure. Furthermore, this approach is extended to the failure prediction with the aid of a failure probability model. The first order Taylor series expansion of the limit state function is used in order to estimate the probability of failure associated with each corrosion defect in buried pipelines for long exposure period with unit of years. A failure probability model based on the von-Mises failure criterion is adapted. The log-normal and standard normal probability functions for varying random variables are adapted. The effects of random variables such as defect depth, pipe diameter, defect length, fluid pressure, corrosion rate, material yield stress, material ultimate tensile strength and pipe thickness on the failure probability of the buried pipelines are systematically investigated for the corrosion pipeline by using an adapted failure probability model and varying failure pressure model.

• Journal of the Semiconductor & Display Technology
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• v.19 no.4
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• pp.59-64
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• 2020

In this paper, we found the solution using data based machine learning regression method to check the pore shape, to solve the problem of the experiment quantity occurring when producing scaffold with the 3d printer. Through experiments, we learned secured each print condition and pore shape. We have produced the scaffold from scaffold pore shape defect prediction model using multiple linear regression method. We predicted scaffold pore shapes of unsecured print condition using the manufactured scaffold pore shape defect prediction model. We randomly selected 20 print conditions from various predicted print conditions. We print scaffold five times under same print condition. We measured the pore shape of scaffold. We compared printed average pore shape with predicted pore shape. We have confirmed the prediction model precision is 99 %.

• Journal of the Semiconductor & Display Technology
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• v.22 no.1
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• pp.54-58
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• 2023

In this study, we compared the detection accuracy of the parameters of the scaffold failure detection model. A detection algorithm based on convolutional neural network was used to construct a failure detection model for scaffold. The parameter properties of the model were changed and the results were quantitatively verified. The detection accuracy of the model for each parameter was compared and the parameter with the highest accuracy was identified. We found that the activation function has a significant impact on the detection accuracy, which is 98% for softmax.

• Journal of Software Assessment and Valuation
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• v.15 no.1
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• pp.79-86
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• 2019

Software dimension reduction identifies the commonality of elements and extracts important feature elements. So it reduces complexity by simplify and solves multi-collinearity problems. And it reduces redundancy by performing redundancy and noise detection. In this study, we proposed defect severity-based dimension reduction model. Proposed model is applied defect severity-based NASA dataset. And it is verified the number of dimensions in the column that affect the severity of the defect. Then it is compares and analyzes the dimensions of the data before and after reduction. In this study experiment result, the number of dimensions of PC4's dataset is 2 to 3. It was possible to reduce the dimension.

• The Transactions of the Korea Information Processing Society
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• v.13 no.5
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• pp.236-242
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• 2024

Software Defect Prediction (SDP) enhances the efficiency of software development by proactively identifying modules likely to contain errors. A major challenge in SDP is improving prediction performance. Recent research has applied deep learning techniques to the field of SDP, with the SAINT model particularly gaining attention for its outstanding performance in analyzing structured data. This study compares the SAINT model with other leading models (XGBoost, Random Forest, CatBoost) and investigates the latest deep learning techniques applicable to SDP. SAINT consistently demonstrated superior performance, proving effective in improving defect prediction accuracy. These findings highlight the potential of the SAINT model to advance defect prediction methodologies in practical software development scenarios, and were achieved through a rigorous methodology including cross-validation, feature scaling, and comparative analysis.