• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.23-26
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• 2000

This paper descrtbcs our prlmary study for a new mncthod of recogninng materials. which is need for precision work system. This IS a study of dynarnlc characteristics of sensor. new melhod ($R_{SAI}$) has thc sensing ability of distinguishing materials. Experiment and annlysis are executed for proper dynamic scnslng condition. First. we developed advanced smart sensor Second, we develop new methods that have a sensing ability of distinguish matarialsAccording to frequency changing. mtluence of smart sensor are evaluated through new recognition Index ($R_{SAI}$) that ratioof sensing ability index. Disungush of object is cxucuted wllh RsA, method relalivcly according to liequency changing. Wecan use the RsAl for finding materids. Applfciltionr of thls method are linding abnormal condition of obicct (automanufacturing).keling ofobject (medlcal product). tobolics, safety diagnosis of structure, etc

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.05a
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• pp.318-322
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• 2001

This paper deals with sensing ability of smart sensor that has a sensing ability to distinguish materials according to surface types of smart sensor. We have developed a new signal processing method that can distinguish among different materials. The smart sensor was developed for recognition of materials. We made two types of smart sensors in our experiment. Then, we estimated the ability to recognize objects according to smart sensor type. We estimated the sensing ability of smart sensor with the $R_{SAI}$ method. Experiments and analysis were executed to estimate the ability to recognize objects according to surface types of smart sensor. Sensing ability of smart sensors was evaluated relatively through a new $R_{SAI}$ method. Applications of smart sensors are for finding abnormal conditions of objects (auto-manufacturing), feeling of objects (medical product), robotics, safety diagnosis of structure, etc.etc.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.13 no.1
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• pp.22-27
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• 2004

A new method that estimates the sensing ability of HH smart sensor is proposed. The new signal processing method have been developed that can distinguish among different materials relatively. The HH smart sensor was developed far recognition of materials. The HH smart sensor was made for experiment. Then, it was estimated the ability to recognize objects according to acceleration value. The sensing ability of HH smart sensor has been estimated with the $R_{SAI}$ method. Experiments and analysis were executed to estimate the ability to recognize objects according to acceleration value changing. Dynamic characteristics of HH smart sensor were evaluated relatively through a new $R_{SAI}$ method that uses the power spectrum density. Applications of this method are for finding abnormal conditions of objects (auto-manufacturing), feeling of objects (medical product), robotics, safety diagnosis of structure, etc.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.11b
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• pp.922-926
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• 2001

This paper deals with sensing ability of smart sensor that has a sensing ability to distinguish materials according to frequency changing. We have developed a new signal processing method that can distinguish among different materials. The smart sensor was developed for recognition of materials. We estimated the sensing ability of smart sensor with the $R_{SAI}$ method according to frequency changing. Experiments and analysis were executed to estimate the ability to recognize objects according to frequency changing. Sensing ability of smart sensors was evaluated relatively through a new $R_{SAI}$ method. Applications of smart sensors are for finding abnormal conditions of objects (auto-manufacturing), feeling of objects (medical product), robotics, safety diagnosis of structure, etc.etc.

• The Korean Journal of Applied Statistics
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• v.9 no.1
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• pp.91-109
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• 1996

Exponentially weighted moving averae(EWMA) control chart has been used widely for process monitoring and process adjustment recently, but there has not been many studies about the selection of the parameters. Design of the control chart can be classified into the statistical design and the economic design. The purpose of the economic design is to minimize the cost function in which all the possible costs occurring during the process are probability given the Type I error probability. In this paper the optimal parameters of the EWMA chart are selected for the economic design as well as for the statistical design. The optimal parameters for the economic design show significantly different from those of the statistical design, and especially the weight is always larger than that used in the statistical design. In the economic design, we divide the model into the single assignable cause model and the multiple assignable causes model caacording to number of which is used as the average context of the multiple assignable causes, it shows that the selection of the parameters may be misleading when the multiple assignable causes exist in practice.

• The Korean Journal of Applied Statistics
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• v.37 no.2
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• pp.177-189
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• 2024

The run length is defined as the number of samples or subgroups taken before the control chart statistic exceeds the control limits. Because the distribution of run length is typically asymmetric and has a large variability, it may not be appropriate to use ARL (average run length) alone to design control charts and evaluate performance. In this paper, we introduce the concept of percentile (PL)-based design of control charts, and propose the procedure for PL-based design of EWMA (exponentially weighted moving average) charts. For the PL-based design of EWMA, we present a fitted function for the control chart coefficient, given specific percentile parameters. Additionally, we perform simulations to compare the proposed design with the ARL-based design. The simulation results show that the proposed design yields improvements in monitoring in-control processes while maintaining the ability to detect out-of-control performance.

• Journal of Korea Water Resources Association
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• v.42 no.9
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• pp.681-690
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• 2009

In this research, design precipitation was calculated by reflecting the climatic indices and its uncertainty assessment was evaluated. Climatic indices used the sea surface temperature and moisture index which observed globally. The correlation coefficients were calculated between the annual maximum precipitation and the climatic indices. and then climatic indices which have the larger correlation coefficient were selected. Therefore, the regression relationship was established by a locally weighted polynomial regression. Next, climatic indices were generated by montecarlo simulation using kernel function. Finally, the design rainfall was calculated by the locally weighted polynomial regression using generated climatic indices. At the result, the comparison of design rainfall between the reflection of the climatic indices and the frequency analysis did not indicate a significant difference. Also, this result can be used as basic data for calculation of probability precipitation to reflect climate change.

• Proceedings of the KIEE Conference
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• 1990.07a
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• pp.51-54
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• 1990

A parametrization for a linear system is presented to design a direct model reference adaptive pole placement controler. This parametrized model is one of the structured nonminimal models. The exponentially weighted least-squres algorithm is employed to estimate the control parameters. The direct adaptive controller has the exponential weighting properties by the proposed method of selecting the characteristic polynomials of the sensitivity function filters in connection with the reference models.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.2
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• pp.608-616
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• 2018

If a general nonlinear system is linearized by the successive multiplication of the 1st and 2nd order systems, then there are four types of poles in this linearized system: the pole of the 1st order system and the equal poles, two distinct real poles, and complex conjugate pair of poles of the 2nd order system. Linear Quadratic (LQ) control is a method of designing a control law that minimizes the quadratic performance index. It has the advantage of ensuring the stability of the system and the pole placement of the root of the system by weighted matrix adjustment. LQ control by the weighted matrix can move the position of the pole of the system arbitrarily, but it is difficult to set the weighting matrix by the trial and error method. This problem can be solved using the characteristic equations of the Hamiltonian system, and if the control weighting matrix is a symmetric matrix of constants, it is possible to move several poles of the system to the desired closed loop poles by applying the control law repeatedly. The paper presents a method of calculating the state weighting matrix and the control law for moving the equal poles with Jordan blocks to two real poles using the characteristic equation of the Hamiltonian system. We express this characteristic equation with a state weighting matrix by means of a trigonometric function, and we derive the relation function (${\rho},\;{\theta}$) between the equal poles and the state weighting matrix under the condition that the two real poles are the roots of the characteristic equation. Then, we obtain the moving-range of the two real poles under the condition that the state weighting matrix becomes a positive semi-finite matrix. We calculate the state weighting matrix and the control law by substituting the two real roots selected in the moving-range into the relational function. As an example, we apply the proposed method to a simple example 3rd order system.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.6 no.3
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• pp.43-49
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• 1992

Forecasting of electric power demand has been a basic element for electric power system operation and system development, and it's accuracy has very strong influence on reliability and economical efficience of power supply. So, in this paper, long―term maximum electric power demand has been forecasted by using the triple exponential smoothing method initiated R.G.Brown. It has been regarded this method as high accuracy and operational convenience. The smoothing function is a liner combination of all past observations and the weight given to previous observations decreases geometrically with age.