• Journal of applied mathematics & informatics
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• v.32 no.5_6
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• pp.635-647
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• 2014

In this paper, a generalized perturbed midpoint rule is established. Various error bounds for this generalization are also obtained.

• Journal of applied mathematics & informatics
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• v.13 no.1_2
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• pp.67-84
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• 2003

A quadrature rule for the finite Hilbert Trasnform via trapezoid type inequalities is obtained . Some numerical experiments for different divisions of the interval [a, b] are also presented.

• Bulletin of the Korean Mathematical Society
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• v.42 no.2
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• pp.213-230
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• 2005

Companions of Ostrowski's integral inequality for absolutely continuous functions and applications for composite quadrature rules and for p.d.J.'s are provided.

• Journal of Digital Convergence
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• v.12 no.4
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• pp.277-283
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• 2014

Fuzzy modeling is generally using the given data and the fuzzy rules are established by the input variables and the space division by selecting the input variable and dividing the input space for each input variables. The premise part of the fuzzy rule is presented by selection of the input variables, the number of space division and membership functions and in this paper the consequent part of the fuzzy rule is identified by polynomial functions in the form of linear inference and modified quadratic. Parameter identification in the premise part devides input space Min-Max method using the minimum and maximum values of input data set and C-Means clustering algorithm forming input data into the hard clusters. The identification of the consequence parameters, namely polynomial coefficients, of each rule are carried out by the standard least square method. In this paper, membership function of the premise part is dividing input space by using trapezoid-type membership function and by using gas furnace process which is widely used in nonlinear process we evaluate the performance.

• Bulletin of the Korean Mathematical Society
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• v.47 no.2
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• pp.221-229
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• 2010

Some new $\check{C}$eby$\check{s}$ev type inequalities have been developed by working on functions whose first derivatives are absolutely continuous and the second derivatives belong to the usual Lebesgue space $L_{\infty}[a,\;b]$. A unified treatment of the special cases is also given.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.807-810
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• 1997

Digital control of robot manipulator employs discrete-time robot models. It is important to explore effective discrete-time robot models and to analyze their properties in control system designs. This paper presents a new type discrete-time robot model. The model is derived by using trapezoid rule to approximate the convolution integral term, then eliminating nonlinear force terms from robot dynamical equations. The new model obtained has very simple structure, and owns the properties of independence to the nonlinear force terms. According to evaluation criteria, three aspects of the model properties: model accuracy, model validity range and model simplicity are examined and compared with commonly used discrete-time robot models. The validity of the proposed model and its advantages to control system designs are verified by simulation results.

• The Journal of the Korea Contents Association
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• v.11 no.4
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• pp.74-82
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• 2011

To do fuzzy modelling of a nonlinear process needs to analyze the characteristics of input-output of fuzzy inference systems according to the division of entire input spaces and the fuzzy reasoning methods. For this, fuzzy model is expressed by identifying the structure and parameters of the system by means of input variables, fuzzy partition of input spaces, and consequence polynomial functions. In the premise part of the fuzzy rules Min-Max method using the minimum and maximum values of input data set and C-Means clustering algorithm forming input data into the clusters are used for identification of fuzzy model and membership functions are used as a series of triangular, gaussian-like, trapezoid-type membership functions. In the consequence part of the fuzzy rules fuzzy reasoning is conducted by two types of inferences such as simplified and linear inference. The identification of the consequence parameters, namely polynomial coefficients, of each rule are carried out by the standard least square method. And lastly, using gas furnace process which is widely used in nonlinear process we evaluate the performance and the system characteristics.

• Journal of the Korean Society of Costume
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• v.59 no.3
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• pp.131-144
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• 2009

This study is about single-layered $Chog{\breve{a}}ri$ excavated from SongHyosang(宋效商, 1430-1490, SHS hereafter), SongHeeJong(宋喜從, the late 1500s, SHJ hereafter) tombs. There are 7 single-layered $Chog{\breve{a}}ris$ for men. We focus on comparison of their design and sowing method. 1. Design: Investigating collar, $Chog{\breve{a}}ris$ from SHS have MokpanGit and $Chog{\breve{a}}ris$ from SHJ have KalGit. KalGit has been seen from SHJ to 17th, 18th and 19th centuries. After the late 1500s, there is no MokpanGit single-laTered $Chog{\breve{a}}ri$ for men. Side panel under arm has various shapes(triangle, trapezoid, triangle+trapezoid) in 15th century. After the late 1500s, It changes into no side panel. Two $Chog{\breve{a}}ris$ with no side panel from SHJ reveals that the late 1500s is a period of transition. 2. Sewing method: First, researching lengthwise grainline of the fly, the left fly has lengthwise grainline outside In four, inside in three. The right fly has lengthwise grainline outside in just one, the others have lengthwise grainline inside. Compared with today's way, there is a great difference, but in those times there isn't an established rule. This is true of side panel under arm. The sewing method are backstitch, running stitch, and hemming. Researching the construction method of seam, in putting two selvages together, open seam and plain seam are used. In putting selvage and bias, bias and bias together, flat felled seam and french seam are used. This study shows that single-layered $Chog{\breve{a}}ri$ far men from 15C. to 16C. has changes of design such as collar(Git) and side panel undo. arm. But there is little change in sewing method.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.11
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• pp.5164-5171
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• 2011

In fuzzy modeling for nonlinear process, typically using the given data, the fuzzy rules are formed by the input variables and the space division by selecting the input variable and dividing the input space for each input variables. The premise part of the fuzzy rule is identified by selection of the input variables, the number of space division and membership functions and the consequent part of the fuzzy rule is identified by polynomial functions in the form of simplified and linear inference. In general, formation of fuzzy rules for nonlinear processes using the given data have the problem that the number of fuzzy rules exponentially increases. To solve this problem complex nonlinear process can be modeled by separately forming the fuzzy rules by means of fuzzy division of each input space. Therefore, this paper utilizes individual input space to generate fuzzy rules. The premise parameters of the fuzzy rules are identified by Min-Max method using the minimum and maximum values of input data set and membership functions are used as a series of triangular, gaussian-like, trapezoid-type membership functions. And lastly, using the data which is widely used in nonlinear process we evaluate the performance and the system characteristics.