• Proceedings of the Korean Institute of Surface Engineering Conference
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• 2012.05a
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• pp.177-178
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• 2012

In this experiment, post-nitriding treatment has been performed at $400^{\circ}C$ on AISI 316 stainless steel which is plasma carburized previously at $430^{\circ}C$ for 15 hours. Plasma nitriding was implemented on AISI 316 stainless steel at various gas compositions (25% N2, 50% N2 and 75% N2) for 4 hours. Additionally, during post nitriding Ar gas was used with H2 and N2 to observe the improvement of treatment. After treatment, the behavior of the hybrid layer was investigated by optical microscopy, X-ray diffraction, and micro-hardness testing. Potentiodynamic polarization test was also used to evaluate the corrosion resistance of the samples. Meanwhile, it was found that the surface hardness increased with increasing the nitrogen gas content. Also small percentage of Ar gas was introduced in the post nitriding process which improved the hardness of the hardened layer but reduces the corrosion resistance compared with the carburized sample. The experiment revealed that AISI 316L stainless steel showed better hardness and excellent corrosion resistance compared with the carburized sample, when 75% N2 gas was used during the post nitriding treatment. Also addition of Ar gas during post nitriding treatment were degraded the corrosion resistance of the sample compared with the carburized sample.

• Journal of Biomedical Engineering Research
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• v.42 no.4
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• pp.186-192
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• 2021

In this paper, we evaluated prediction accuracy of Euler angle spectrograph classification method using a convolutional neural networks (CNN) for hand gesture recognition in augmented reality (AR) cognitive rehabilitation system based on Leap Motion Controller (LMC). Hand gesture recognition methods using a conventional support vector machine (SVM) show 91.3% accuracy in multiple motions. In this paper, five hand gestures ("Promise", "Bunny", "Close", "Victory", and "Thumb") are selected and measured 100 times for testing the utility of spectral classification techniques. Validation results for the five hand gestures were able to be correctly predicted 100% of the time, indicating superior recognition accuracy than those of conventional SVM methods. The hand motion recognition using CNN meant to be applied more useful to AR cognitive rehabilitation training systems based on LMC than sign language recognition using SVM.

• The Korean Journal of Applied Statistics
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• v.7 no.1
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• pp.103-112
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• 1994

An asymptotic property of the estimated eigenvalues for multivariate AR(p) process which consists of vector of nonstationary process and vector of stationary process is developed. All components of the nonstationary process are assumed to reveal random walk behavior. The asymptotic property is helpful in understanding multiple unit roots. In this paper we show the stationay part in multivariate AR(p) process does not affect the limiting distribution of estimated eigenvalues associated with the nonstationary process. A test statistic based on the ordinary least squares estimator for testing a certain number of multiple unit roots is suggested.

• Laser Solutions
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• v.10 no.4
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• pp.1-6
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• 2007

This study includes the effects of shielding gas types and flow rate on Nd:YAG Laser weldability of sintered material. The types of shielding gas were evaluated for He, Ar and N2. Bending strength, porosity rate, hardness and aspect ratio testing of laser weld are carried out to evaluated the weldability. As a results, Ar gas was showed the best welding strength even it has the most porosity content on weld metal, and depend on increases the gas flow rate, it was not only got deeper penetration depth but also showed higher bending strength. Therefore we could know that bending strength is not only affect the porosity content but also melting area.

• Journal of Korean Society for Quality Management
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• v.16 no.2
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• pp.68-81
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• 1988

Testing procedures for a detection of change point in the regression model with correlated errors are discussed. A Bayesian approach is adopted and applied to a regression model with errors following an AR(1) model.

• Journal of the Korean Statistical Society
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• v.36 no.1
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• pp.149-156
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• 2007

This study suggests a new method for testing seasonal unit roots in a momentum threshold autoregressive (MTAR) process. This sign test is robust against heteroscedastic or heavy tailed errors and is invariant to monotone data transformation. The proposed test is a seasonal extension of the sign test of Park and Shin (2006). In the case of partial seasonal unit root in an MTAR model, a Monte-Carlo study shows that the proposed test has better power than the seasonal sign test developed for AR model.

• Journal of the Korean Statistical Society
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• v.29 no.3
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• pp.351-360
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• 2000

This paper considers the problem of testing a variance change in infinite order autoregressive models. A cusum of squares test based on the residuals from an AR(q) model is constructed analogous to Inclan and Tiao (1994)'s test statistic, where q is a sequence of positive integers diverging to $\infty$. It is shown that under regularity conditions the limiting distribution of the test statistic is the sup of a standard Brownian bridge. Simulation results are given to illustrate the performance of the test.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.11 no.5
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• pp.129-136
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• 2012

On this study, $CO_2$ gas, net of Ar gas, and mixed gas in solid wire(AWS ER 70S-6) and flux cored wire(AWS E71T-1) were used to weld on Mild steel(SS400). After the progress, the status changes of the welds in Mild steel(SS400) were investigated with compositional changes. For stable experiments, welding was conducted using the automatic feeder. Radiation testing, hardness testing, chemical composition analysis and penetrated cross-section were measured. Through these experiments, shapes of penetrated cross-section, chemical composition changes, and weld defects according to the variation of welding gas were known. Weld defects and weld cross-sectional shapes by the variation of the welding voltage were also detected.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.1
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• pp.24-30
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• 2021

In this study, an augmented reality (AR) app that detects the screw-assembly state of a car valve-body and assists the assembly work is developed and the effectiveness of the app is shown through testing. The app creates the contents indicating the screw-assembly position and order, and the screw-assembly state. Then, the contents are registrated onto the valve-body image on a smart-phone screen to be shown to the worker during assembly. To this end, the features are extracted from the 2D image of the valve-body and the location of the valve-body is tracked. By extracting the areas where the screws are to be assembled, and periodically determining the luminance of these areas, it is checked whether the screws are assembled in order at the predetermined position of the valve-body. When an error is detected during assembly, a warning sound is notified to the worker, and the worker can check the assembly state on the smart-phone screen and handle the error, immediately. Study results found that it takes about 65 ms to detect the assembly state of the five screws, and the assembly state is detected without error for 1 hour.

• Journal of the Korea Society of Computer and Information
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• v.24 no.11
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• pp.61-69
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• 2019

In this paper, we propose a multisensory(i.e., visual-auditory-tactile) based AR system for the education of cultural heritage. The proposed system provides a multisensory interaction by designing a user to experience with a 3D printed artifact which is mapped by a virtual 3D content of digital heritage. Compared with the existing systems of cultural heritage education based on augmented reality(AR) technology, this system focused on not only providing learning experience via a sense of visual and auditory, but also a sense of tactile. Furthermore, since this systems mainly provided the direct interactions using a 3D printed model, it gives a higher degree of realism than existing system that use touch or click motions on a 2D display of mobile phones and tablets. According to a result of user testing, we concluded that the proposed system delivered the excellent presence and learning flow to users. Particularly, from the usability evaluation, a 3D printed target artifact which is similar in shape to original heritage artifact, achieved the highest scores among the various tested targets.