• Transactions of the Korean Society of Mechanical Engineers B
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• v.30 no.4 s.247
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• pp.287-297
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• 2006

The Holographic Particle Velocimetry system can be a promising optical tool for the measurements of three dimensional particle velocities. One of inherent limitations of particle holography is the very long depth of field of particle images, which causes considerable difficulty in the determination of particle positions in the optical axis. In this study, we introduced three auto-focusing parameters corresponding to the size of particles, namely, Correlation Coefficient, Sharpness Index, and Depth Intensity to determine the focal plane of a particle along the optical axis. To investigate the suitability of the above parameters, the plane image of dot array screens containing different size of dots was recorded by diffused illumination holography and the positions of each dot in the optical axis were evaluated. In addition, the effect of particle position from the holographic film was examined by changing the distance of the screen from the holographic film. All measurement results verified that the evaluated positions using suggested auto-focusing parameters remain within acceptable range of errors. These research results may provide fundamental information for the development of the holographic velocimetry system based on the automatic image processing.

• Journal of Positioning, Navigation, and Timing
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• v.9 no.2
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• pp.89-98
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• 2020

Prediction of clock behaviors is necessary to generate very high stable system time which is essential for a satellite navigation system. For the purpose, we applied the Auto-Regressive Integrated Moving Average (ARIMA) model to the prediction of two hydrogen masers' behaviors with respect to the rapid Coordinated Universal Time (UTCr). Using the packaged programming language R, we made an analysis and prediction of time series data of [UTCr - clocks]. The maximum variation width of the residuals which were obtained by the difference between the predicted and measured values, was 6.2 ns for 106 days. This variation width was just one-sixth of [UTCr-UTC (KRIS)] published by the BIPM for the same period. Since the two hydrogen masers were found to be strongly correlated, we applied the Vector Auto-Regressive Moving Average (VARMA) model for more accurate prediction. The result showed that the prediction accuarcy was improved by two times for one hydrogen maser.

• Asia pacific journal of information systems
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• v.26 no.4
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• pp.526-541
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• 2016

This research aims to examine the predictive power of multiple online stock message boards, namely, NAVER Finance and PAXNET, which are the most popular stock message boards in South Korea, in stock market activities. If predictive power exists, we then compare the predictive power of multiple online stock message boards. To accomplish the research purpose, we constructed a panel data set with close price, volatility, Spell out acronyms at first mention.PER, and number of posts in 40 companies in three months, and conducted a panel vector auto-regression analysis. The analysis results showed that the number of posts could predict stock market activities. In NAVER Finance, previous number of posts positively influenced volatility on the day. In PAXNET, previous number of posts positively influenced close price, volatility, and PER on the day. Second, we confirmed a difference in the prediction power for stock market activities between multiple online stock message boards. This research is limited by the fact that it only considered 40 companies and three stock market activities. Nevertheless, we found correlation between online stock message board and stock market activities and provided practical implications. We suggest that investors need to focus on specific online message boards to find interesting stock market activities.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.15 no.3
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• pp.73-81
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• 2006

This paper describes our primary study for a new method of recognizing materials, which is need for precision work system. This is a study of dynamic characteristics of smart sensors, new method$(R_{SAI})$ has the sensing ability of distinguishing materials. Experiment and analysis are executed for finding the proper dynamic sensing condition. First, we developed advanced smart sensor. We made smart sensors for experiment. The type of smart sensor is HH type. The smart sensor was developed for recognition of material. Second, we develop new estimation methods that have a sensing ability of distinguish materials. Dynamic characteristics of sensor are evaluated through new recognition index$(R_{SAI})$ that ratio of sensing ability index. Distinguish of object is executed with $R_{SAI}$ method relatively. We can use the $R_{SAI}$ method for finding materials. Applications of this method are finding abnormal condition of object (auto-manufacturing), feeling of object(medical product), robotics, safety diagnosis of structure, etc.

• Journal of Power System Engineering
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• v.20 no.2
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• pp.66-72
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• 2016

As a method to improve the equipment reliability, early warning researches that can be detected fault symptom of an equipment at an early stage are being performed out among developed countries. In this paper, when abnormal signal is input to actual normal signal of a pump, early detection studies on pump's fault symptom were carried out with auto-associative kernel regression as an advanced pattern recognition algorithm. From analysis, correlations among power of motor driving pump, discharge flow of pump, power output of pump, and discharge pressure of pump are exited. When the abnormal signal is input to one of those normal signals, the other expected values are changed due to the influence of the abnormal signal. Therefore, the fault symptom of pump through the early-warning index is able to detect at an early stage.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.1
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• pp.48-55
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• 2002

This paper deals with the sensing ability of the smart sensor that has the sensing ability to distinguish materials according to the input condition changing. This is a study of dynamic characteristics of sensor. We have developed a new signal processing method that can distinguish among different materials. The smart sensor was developed for recognition of materials. Experiments and analysis were executed to estimate ability to recognize objects according to the input condition. First, we developed the advanced smart sensor. Second, we developed the new method, which has the capability sensing of different materials. Dynamic characteristics of the smart sensor were evaluated relatively through a new $R_{SAI}$ method. According to frequency changing, influence of the smart sensor are evaluated through a new recognition index ($R_{SAI}$) that ratio of sensing ability index. Applications of this method are for finding abnormal conditions of objects (auto-manufacturing), feeling of objects (medical product), robotics, safely diagnosis of structure, etc.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.31 no.2 s.257
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• pp.182-189
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• 2007

This paper is concerned with the empirical flow stress constitutive equation of steel sheets for an auto-body with the variation of temperature and strain rate. In order to represent the strain rate and temperature dependent behavior of the flow stress at the intermediate strain rates accurately, an empirical hardening equation is suggested by modifying the well-known Khan-Huang-Liang model. The temperature and strain rate dependent sensitivity of the flow stress at the intermediate strain rate is considered in the hardening equation by coupling the strain, the strain rate and the temperature. The hardening equation suggested gives good correlation with experimental results at various intermediate strain rates and temperatures. In order to verify the effectiveness and accuracy of the suggested model quantitatively, the standard deviation of the fitted result from the experimental one is compared with those of the other two well-known empirical constitutive models such as the Johnson-Cook and the Khan-Huang-Liang models. The comparison demonstrates that the suggested model gives relatively well description of experimental results at various strain rates and temperatures.

• Communications for Statistical Applications and Methods
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• v.30 no.4
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• pp.369-388
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• 2023

In this paper, we develop the two-step procedure that detects and estimates the position of structural changes for multivariate nonstationary time series, either on mean parameters or second-order structures. We first investigate the presence of mean structural change by monitoring data through the aggregated cumulative sum (CUSUM) type statistic, a sequential procedure identifying the likely position of the change point on its trend. If no mean change point is detected, the proposed method proceeds to scan the second-order structural change by modeling the multivariate nonstationary time series with a multivariate locally stationary Wavelet process, allowing the time-localized auto-correlation and cross-dependence. Under this framework, the estimated dynamic spectral matrices derived from the local wavelet periodogram capture the time-evolving scale-specific auto- and cross-dependence features of data. We then monitor the change point from the lower-dimensional approximated space of the spectral matrices over time by applying the dynamic principal component analysis. Different from existing methods requiring prior information on the type of changes between mean and covariance structures as an input for the implementation, the proposed algorithm provides the output indicating the type of change and the estimated location of its occurrence. The performance of the proposed method is demonstrated in simulations and the analysis of two real finance datasets.

• Journal of Korean Institute of Industrial Engineers
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• v.42 no.6
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• pp.370-376
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• 2016

Manufacturing data analysis and its applications are getting a huge popularity in various industries. In spite of the fast advancement in the big data analysis technology, however, the manufacturing quality data monitored from the automated inspection system sometimes is not reliable enough due to the complex patterns of product quality. In this study, thus, we aim to define the level of trusty of an automated quality inspection system and improve the reliability of the quality inspection data. By correlation analysis and feature selection, this paper presents a method of improving the inspection accuracy and efficiency in an SVM-based automatic product quality inspection system using thermal image data in an auto part manufacturing case. The proposed method is implemented in the sealer dispensing process of the automobile manufacturing and verified by the analysis of the optimal feature selection from the quality analysis results.

• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.4
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• pp.105-114
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• 2012

Soil moisture plays a pivotal role in hydrological processes, especially in the forest which covers more than 64% of the national land. Soil moisture was monitored to analyze soil moisture change characteristics in terms of time and soil layers in this study. 2 Years soil moisture change data was obtained from the experimental nut pine forest and statistical analysis including auto-correlation and cross-corelation among soil moisture data from different soil layers was conducted. Using the monitored soil moisture data, a relationship between soil moisture change and precipitation was analyzed and seasonal soil moisture change characteristics were analyzed. From the result of inter-relationships among soil layers in terms of season and time lag, soil moisture change characteristics in the nut pine forest were upper soil layers were much sensitive than lowers, and seasonal variation if soil moisture for upper soil layers were bigger than lowers showing low correlation with precipitation in winter and spring due to freezing and snowfalls.