• Transactions of Materials Processing
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• v.28 no.5
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• pp.275-280
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• 2019

The aim of this study is to fabricate an ultra-fine pure rhodium wire using multi-pass wire drawing process. To manufacture $30{\mu}m$ ultra-fine rhodium wire from the initial $50{\mu}m$ wire, a multi-pass wire drawing process was designed based on the uniform reduction ratio theory. The elastic-plastic finite element analysis was then conducted to validate the efficacy of the designed process. The drawing load, drawing stress, and the distribution of the effective strain were evaluated using the finite element analysis. Finally, the wire drawing experiment was performed to validate the designed wire drawing process. From the results of the experiment, the diameter of the final drawn wire was found to be $29.85{\mu}m$.

• Journal of Applied Reliability
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• v.18 no.1
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• pp.20-32
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• 2018

Purpose: The purpose of this study is to set up an anomaly detection criteria for sensor data coming from a motorcycle. Methods: Five sensor values for accelerator pedal, engine rpm, transmission rpm, gear and speed are obtained every 0.02 second from a motorcycle. Exploratory data analysis is used to find any pattern in the data. Traditional process control methods such as X control chart and time series models are fitted to find any anomaly behavior in the data. Finally unsupervised learning algorithm such as k-means clustering is used to find any anomaly spot in the sensor data. Results: According to exploratory data analysis, the distribution of accelerator pedal sensor values is very much skewed to the left. The motorcycle seemed to have been driven in a city at speed less than 45 kilometers per hour. Traditional process control charts such as X control chart fail due to severe autocorrelation in each sensor data. However, ARIMA model found three abnormal points where they are beyond 2 sigma limits in the control chart. We applied a copula based Markov chain to perform statistical process control for correlated observations. Copula based Markov model found anomaly behavior in the similar places as ARIMA model. In an unsupervised learning algorithm, large sensor values get subdivided into two, three, and four disjoint regions. So extreme sensor values are the ones that need to be tracked down for any sign of anomaly behavior in the sensor values. Conclusion: Exploratory data analysis is useful to find any pattern in the sensor data. Process control chart using ARIMA and Joe's copula based Markov model also give warnings near similar places in the data. Unsupervised learning algorithm shows us that the extreme sensor values are the ones that need to be tracked down for any sign of anomaly behavior.

• Transactions of Materials Processing
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• v.27 no.4
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• pp.244-249
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• 2018

Rhodium is a representative platinum group material. Rhodium is used in several industrial fields including jewelry, chemical reaction catalyst, electric component etc. In recently, ultra-fine rhodium wire has been applied to the pins of probe card used to test a semiconductor. In this study, in order to produce a fine rhodium wire with the diameter of $50{\mu}m$, a fine rhodium wire drawing process was designed. After design of the fine wire drawing process by using a uniform reduction ratio theory, finite element analysis was performed. Finally, fine wire drawing experiment was performed to verify the effectiveness of the designed process.

• Journal of information and communication convergence engineering
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• v.10 no.3
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• pp.295-299
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• 2012

Defect generation during organic bottom anti-reflective coating (BARC) in the photo lithography process is closely related to humidity control in the BARC coating unit. Defects are related to the water component due to the humidity and act as a blocking material for the etching process, resulting in an extreme pattern bridging in the subsequent BARC etching process of the poly etch step. In this paper, the lower limit for the humidity that should be stringently controlled for to prevent defect generation during BARC coating is proposed. Various images of defects are inspected using various inspection tools utilizing optical and electron beams. The mechanism for defect generation only in the specific BARC coating step is analyzed and explained. The BARC defect-induced gate pattern bridging mechanism in the lithography process is also well explained in this paper.

• Journal of the Korean Mathematical Society
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• v.32 no.4
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• pp.739-751
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• 1995

Let ${X(t) : 0 \leq t < \infty}$ be an almost surely continuous Gaussian process with X(0) = 0, E{X(t)} = 0 and stationary increments $E{X(t) - X(s)}^2 = \sigma^2($\mid$t - s$\mid$)$, where $\sigma(y)$ is a function of $y \geq 0(e.g., if {X(t);0 \leq t < \infty}$ is a standard Wiener process, then $\sigma(t) = \sqrt{t})$. Assume that $\sigma(t), t > 0$, is a nondecreasing continuous, regularly varying function at infinity with exponent $\gamma$ for some $0 < \gamma < 1$.

• Journal of Korea Water Resources Association
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• v.55 no.7
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• pp.545-556
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• 2022

It is recommended to use long-term hydrometeorological data for more than the service life of the hydraulic structures and water resource planning. For the purpose of expanding rainfall data, stochastic simulation models, such as Modified Bartlett-Lewis Rectangular Pulse (BLRP) and Neyman-Scott Rectangular Pulse (NSRP) models, have been widely used. The optimal parameters of the model can be estimated by repeatedly comparing the statistical moments defined through a combination of parameters of the probability distribution in the optimization context. However, parameter estimation using relatively small observed rainfall statistics corresponds to an ill-posed problem, leading to an increase in uncertainty in the parameter estimation process. In addition, as shown in previous studies, extreme values are underestimated because objective functions are typically defined by the first and second statistical moments (i.e., mean and variance). In this regard, this study estimated the parameters of the NSRP model using the objective function with the third moment and compared it with the existing approach based on the first and second moments in terms of estimation of extreme rainfall. It was found that the first and second moments did not show a significant difference depending on whether or not the skewness was considered in the objective function. However, the proposed model showed significantly improved performance in terms of estimation of design rainfalls.

• Proceedings of the Korean Vacuum Society Conference
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• 2010.02a
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• pp.458-458
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• 2010

The process window for the etch selectivity of silicon nitride ($Si_3N_4$) layers to extreme ultra-violet (EUV) resist and variation of line edge roughness (LER) of EUV resist were investigated durin getching of $Si_3N_4$/EUV resist structure in a dual-frequency superimposed capacitive coupled plasma (DFS-CCP) etcher by varying the process parameters, such as the $CH_2F_2$ and $N_2$ gas flow rate in $CH_2F_2/N_2$/Ar plasma. The $CH_2F_2$ and $N_2$ flow rate was found to play a critical role in determining the process window for infinite etch selectivity of $Si_3N_4$/EUV resist, due to disproportionate changes in the degree of polymerization on $Si_3N_4$ and EUV resist surfaces. The preferential chemical reaction between hydrogen and carbon in the hydrofluorocarbon ($CH_xF_y$) polymer layer and the nitrogen and oxygen on the $Si_3N_4$, presumably leading to the formation of HCN, CO, and $CO_2$ etch by-products, results in a smaller steady-state hydrofluorocarbon thickness on $Si_3N_4$ and, in turn, in continuous $Si_3N_4$ etching due to enhanced $SiF_4$ formation, while the $CH_xF_y$ layer is deposited on the EUV resist surface. Also critical dimension (and line edge roughness) tend to decrease with increasing $N_2$ flow rate due to decreased degree of polymerization.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.236-236
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• 2015

Flood is one of the major natural disasters affecting millions of people. Thailand also, frequently faces with this type of disaster. Especially, 2011 mega flood in Central Thailand, inundated highway severely attributed to the failure of national economic and risk to life. Lesson learned from such an extreme event caused flood monitoring and warning becomes one of the sound mitigations. The highway flood hazard mapping accomplished in this research is one of the strategies. This is due to highway flood is the potential risk to life and limb, and potential damage to property. Monitoring and warning therefore help reducing live and property losses. In this study, degree of highway flood hazard was assessed by weighting factors for each cause of the highway flood using Multi Criteria Analysis (MCA) based Analytic Hierarchy Process (AHP). These weighting factors are the essential information to classify the degree of highway flood hazard to enable pinpoint on flood monitoring and flood warning in hazard areas. The highway flood causes were then investigated. It was found that three major factors influence to the highway flood are namely the highway characteristics, the hydrological characteristics and the land topography characteristics. The weight of importance for each cause of the highway flood in the whole country was assessed by weighting 3 major factors influence to the highway flood. According to the result of MCA analysis, the highway, the hydrological and the land topography characteristics were respectively weighted as 35, 35 and 30 percent influence to the cause of highway flood. These weighting factors were further utilized to classify the degree of highway flood hazard. The Weight Linear Combination (WLC) method was used to compute the total score of all highways according to each factor. This score was later used to categorize highway flood as high, moderate and low degree of hazard levels. Highway flood hazard map accomplished in this research study is applicable to serve as the handy tool for highway flood warning. However, to complete the whole warning process, flood water level monitoring system for example the camera gauge should be installed in the hazard highway. This is expected to serve as a simple flood monitor as part of the warning system during such extreme or critical event.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.36.3-36.3
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• 2019

Feedback process is one of the most important topics in the study of AGNs since it plays a key role in linking the SMBHs and their host galaxies. In order to further understand the co-evolution of SMBHs and their host galaxies, we probe the feedback process in local type-2 AGNs with a series of integral-field-spectroscopy observations. In the first part of my talk, I will introduce our GMOS observations of luminous type-2 AGNs at z < 0.1, which are selected using the integrated [O III] kinematics. Based on the dedicated emission-line diagnostics and kinematic studies, we identify the signatures of AGN-driven outflows and quantify the outflow size in the targets with extreme [O III] kinematics. For the targets without extreme [O III] kinematics, we find the presence of weak AGN-driven outflows, which are indicated by the significant differences between the kinematics of gas and stars. Then, I will present our recent study of 40 type-2 AGNs based on the SNIFS IFU. By comparing the radial profile of velocity dispersion of gas and stars, we measure the size of AGN-driven outflows in these targets and extend the outflow size-AGN luminosity relation in our previous GMOS studies. We also discuss the feedback effect of AGN-driven outflows by connecting the outflow velocity and host galaxy properties. These results highlight the importance of spatially-resolved observation in investigating gas kinematics and identifying the signatures of AGN-driven outflows.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.12-22
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• 2021

This article suggests the machine learning model, i.e., classifier, for predicting the production quality of free-machining 303-series stainless steel(STS303) small rolling wire rods according to the operating condition of the manufacturing process. For the development of the classifier, manufacturing data for 37 operating variables were collected from the manufacturing execution system(MES) of Company S, and the 12 types of derived variables were generated based on literature review and interviews with field experts. This research was performed with data preprocessing, exploratory data analysis, feature selection, machine learning modeling, and the evaluation of alternative models. In the preprocessing stage, missing values and outliers are removed, and oversampling using SMOTE(Synthetic oversampling technique) to resolve data imbalance. Features are selected by variable importance of LASSO(Least absolute shrinkage and selection operator) regression, extreme gradient boosting(XGBoost), and random forest models. Finally, logistic regression, support vector machine(SVM), random forest, and XGBoost are developed as a classifier to predict the adequate or defective products with new operating conditions. The optimal hyper-parameters for each model are investigated by the grid search and random search methods based on k-fold cross-validation. As a result of the experiment, XGBoost showed relatively high predictive performance compared to other models with an accuracy of 0.9929, specificity of 0.9372, F₁-score of 0.9963, and logarithmic loss of 0.0209. The classifier developed in this study is expected to improve productivity by enabling effective management of the manufacturing process for the STS303 small rolling wire rods.