• Journal of the Korea Safety Management & Science
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• v.13 no.4
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• pp.225-235
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• 2011

Autoregressed Controller, which have trend algorithm, seeks to minimize variability by transferring the output variable to the related process input variable, while multivariate process control techniques seek to reduce variability by detecting and eliminating assignable causes of variation. In the case of process control, a very reasonable objective is to try to minimize the variance of the output deviations from the target or set point. We also investigate algorithm with relevant Shewhart chart, Theoretical control charts, precontrol and process capability. To help the people who want to make the theoretical system, we compare the main techniques in "a study on the relation between multivariate process control techniques and trend algorithms".

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.470-472
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• 2006

Sensor networks are the results of convergence of very important technologies such as wireless communication and micro electromechanical systems. In recent years, sensor networks found a wide applicability in various fields such as environment and health, industry scene system monitoring, etc. A very important step for these many applications is pattern classification and recognition of data collected by sensors installed or deployed in different ways. But, pattern classification and recognition are sometimes difficult to perform. Systematic approach to pattern classification based on modem learning techniques like Multivariate Gaussian mixture models, can greatly simplify the process of developing and implementing real-time classification models. This paper proposes a new recognition system which is hierarchically composed of many sensor nodes having the capability of simple processing and wireless communication. The proposed system is able to perform context classification of sensed data using the Multivariate Gaussian function. In order to verify the usefulness of the proposed system, it was applied to intelligent dust collecting system.

• Communications for Statistical Applications and Methods
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• v.8 no.3
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• pp.697-709
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• 2001

In this paper we study two vector-valued process capability indices $C_{p}$=($C_{px}$, $C_{py}$ ) and C/aub pm/=( $C_{pmx}$, $C_{pmy}$) considering process capability indices $C_{p}$ and $C_{pm}$ . First, two asymptotic distributions of plug-in estimators $C_{p}$=($C_{px}$, $C_{py}$ ) and $C_{pm}$ =) $C_{pmx}$, $C_{pmy}$) are derived.. With the asymptotic distributions, we propose asymptotic confidence regions for our indices. Next, obtaining the asymptotic distributions of two bootstrap estimators $C_{p}$=($C_{px}$, $C_{py}$ )and $C_{pm}$ =( $C_{pmx}$, $C_{pmy}$) with our bootstrap algorithm, we will provide the consistency of our bootstrap for statistical inference. Also, with the consistency of our bootstrap, we propose bootstrap asymptotic confidence regions for our indices. (no abstract, see full-text)see full-text)e full-text)

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.12
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• pp.991-995
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• 2005

Sensor networks are the results of convergence of very important technologies such as wireless communication and micro electromechanical systems. In recent years, sensor networks found a wide applicability in various fields such as health, environment and habitat monitoring, military, etc. A very important step for these many applications is pattern classification and recognition of data collected by sensors installed or deployed in different ways. But, pattern classification and recognition are sometimes difficult to perform. Systematic approach to pattern classification based on modern teaming techniques like Multivariate Gaussian mixture models, can greatly simplify the process of developing and implementing real-time classification models. This paper proposes a new recognition system which is hierarchically composed of many sensor nodes haying the capability of simple processing and wireless communication. The proposed system is able to perform classification of sensed data using the Multivariate Gaussian function. In order to verify the usefulness of the proposed system, it was applied to hand gesture recognition system.

• Journal of Korean Institute of Industrial Engineers
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• v.24 no.2
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• pp.211-221
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• 1998

It is very important to have a satisfactory measurement system, since it is useless to try to improve the manufacturing process without an adequate measurement system. Therefore, evaluation of the measurement system is the first step for the quality improvement of the manufacturing process. To estimate the measurement error we must conduct a controlled gage repeatability and reproducibility(gage R&R) study. Many manufacturers use a gage or instrument to measure multiple dimensions for the overall quality of the manufactured parts. In this case, it is necessary to estimate the gage R&R for multiple dimensions. When a gage measures a large number of dimensions of a part, it is very time-consuming and costly to measure all the dimensions. In this paper we propose the use of the principal component analysis method to identify a few principal components out of the original multivariate measurement capability to explain most of the measurement system variation pattern.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1123-1123
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• 2001

In recent years, a miniature spectrometer has been extensively developed due to the marriage of fiber optics and semiconductor detector array. This type of miniature spectrometer has advantages of low price and robustness due to the capability of mass production and no moving parts are required such as lenses, mirrors and scanning monochromator. These systems are ideal for use in teaching labs, process monitoring and field analyses. A portable near infrared (NIR) system has been developed for qualitative and quantitative analysis. This system includes a tungsten halogen lamp for light source, a fiber optics connected a light source, and a sample module to the microspectrometer, The size of spectrometer can be as small as 2.5 cm x 1.5 cm x 0.1 cm. Wavelength ranges can be chosen as 360-800 nm, 800-1100 nm and 1100-1900 nm depending on the type of detector. The software consists of various tools for multivariate analysis and pattern recognition techniques. To evaluate the system, long and short-term stability, wavelength accuracy, and stray light have been investigated and compared with conventional scanning type NIR spectrometer. This developed system can be sufficiently used for quantitative and qualitative analysis for various samples such as agricultural product, herbal medicine, food, petroleum, and pharmaceuticals, etc.

• Knowledge Management Research
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• v.21 no.1
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• pp.27-40
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• 2020

Response surface methodology (RSM) empirically studies the relationship between a response variable and input variables in the product or process development phase. The ultimate goal of RSM is to find an optimal condition of the input variables that optimizes (maximizes or minimizes) the response variable. RSM can be seen as a knowledge management tool in terms of creating and utilizing data, information, and knowledge about a product production and service operations. In the field of product or process development, most real-world problems often involve a simultaneous consideration of multiple response variables. This is called a multiple response surface (MRS) problem. Various approaches have been proposed for MRS optimization, which can be classified into loss function approach, priority-based approach, desirability function approach, process capability approach, and probability-based approach. In particular, the loss function approach is divided into univariate and multivariate approaches at large. This paper focuses on the univariate approach. The univariate approach first obtains the mean square error (MSE) for individual response variables. Then, it aggregates the MSE's into a single objective function. It is common to employ the weighted sum or the Tchebycheff metric for aggregation. Finally, it finds an optimal condition of the input variables that minimizes the objective function. When aggregating, the relative weights on the MSE's should be taken into account. However, there are few studies on how to determine the weights systematically. In this study, we propose an interactive procedure to determine the weights through considering a decision maker's preference. The proposed method is illustrated by the 'colloidal gas aphrons' problem, which is a typical MRS problem. We also discuss the extension of the proposed method to the weighted MSE (WMSE).

• 한국지구물리탐사학회:학술대회논문집
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• 2003.11a
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• pp.142-149
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• 2003

Blind hydraulic backfilling is a commonly used technique for subsidence control of the strata over unapproachable waterlogged underground excavations. In this investigation model studies on all the three variants of this technique, namely, hydro-pneumatic or air-assisted gravity backfilling, pumped-slurry backfilling and simple gravity backfilling, have been carried out in fully transparent models of the underground excavations. On examination of the filling process, it was revealed that in all the three cases, the basic process of filling occurs by sand transport along one or more meandering channels. The relative influence of sand, water and air flow rates on the area of filling from a single inlet point and the hydraulic pressure loss per unit length were studied in details. In hydro-pneumatic backfilling process, the air bubbles while moving upward through the meandering channels provide an additional buoyant force over and above the available hydraulic head. In this way the area of filling from a single borehole may be quite large even at small flow rates of water. During actual field implementation the injected air, if not released completely from the rise side holes, may cause troubles by way of creating potholes on the surface. The pumped-slurry technique has shown its capability of filling a relatively larger area at faster rate, especially when high-volume, low-pressure method was selected. But simple gravity filling was also found to be equally effective method as slurry pumping, especially when flow rates were high. In the second and third method discussed above, examination of variations of injection pressure was also done and its relation with physical phenomenon was also attempted. Some empirical relationships were also developed using multivariate regression with a view to help the practicing engineers.

• Journal of The Korean Society of Grassland and Forage Science
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• v.35 no.3
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• pp.257-263
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• 2015

This study was conducted to assess the potential of using NIRS to accurately determine the chemical composition and fermentation parameters in fresh coarse sorghum and sudangrass silage. Near Infrared Spectroscopy (NIRS) has been increasingly used as a rapid and accurate method to analyze the quality of cereals and dried animal forage. However, silage analysis by NIRS has a limitation in analyzing dried and ground samples in farm-scale applications because the fermentative products are lost during the drying process. Fresh coarse silage samples were scanned at 1 nm intervals over the wavelength range of 680~2500 nm, and the optical data were obtained as log 1/Reflectance (log 1/R). The spectral data were regressed, using partial least squares (PLS) multivariate analysis in conjunction with first and second order derivatization, with a scatter correction procedure (standard normal variate and detrend (SNV&D)) to reduce the effect of extraneous noise. The optimum calibrations were selected on the basis of minimizing the standard error of cross validation (SECV). The results of this study showed that NIRS predicted the chemical constituents with a high degree of accuracy (i.e. the correlation coefficient of cross validation ($R^2{_{cv}}$) ranged from 0.86~0.96), except for crude ash which had an $R^2{_{cv}}$ of 0.68. Comparison of the mathematical treatments for raw spectra showed that the second-order derivatization procedure produced the best result for all the treatments, except for neutral detergent fiber (NDF). The best mathematical treatment for moisture, acid detergent fiber (ADF), crude protein (CP) and pH was 2,16,16 respectively while the best mathematical treatment for crude ash, lactic acid and total acid was 2,8,8 respectively. The calibrations of fermentation products produced poorer calibrations (RPD < 2.5) with acetic and butyric acid. The pH, lactic acid and total acids were predicted with considerable accuracy at $R^2{_{cv}}$ 0.72~0.77. This study indicated that NIRS calibrations based on fresh coarse sorghum and sudangrass silage spectra have the capability of assessing the forage quality control