• Korean Journal of Remote Sensing
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• v.32 no.2
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• pp.171-183
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• 2016

Land Surface Temperature (LST) retrieved from Landsat 8 measured from 2013 to 2014 and it is corrected by surface temperature observed from ground. LST maps are retrieved from Landsat 8 calculate using the linear regression function between raw Landsat 8 LST and ground surface temperature. Seasonal and annual LST maps developed an average LST from season to annual, respectively. While the higher LSTs distribute on the industrial and commercial area in urban, lower LSTs locate in surrounding rural, sea, river and high altitude mountain area over Seoul and surrounding area. In order to correct the LST, linear regression function calculate between Landsat 8 LST and ground surface temperature observed 3 Korea Meteorological Administration (KMA) synoptic stations (Seoul(ID: 108), Incheon(ID: 112) and Suwon(ID: 119)) on the Seoul and surrounding area. The slopes of regression function are 0.78 with all data and 0.88 with clear sky except 5 cloudy pixel data. And the original Landsat 8 LST have a correlation coefficient with 0.88 and Root Mean Square Error (RMSE) with $5.33^{\circ}C$. After LST correction, the LST have correlation coefficient with 0.98 and RMSE with $2.34^{\circ}C$ and the slope of regression equation improve the 0.95. Seasonal and annual LST maps represent from urban to rural area and from commercial to industrial region clearly. As a result, the Landsat 8 LST is more similar to the real state when corrected by surface temperature observed ground.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2005.04a
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• pp.85-89
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• 2005

Lineament maps are the important tools that may reveal points of groundwater recharge, flow and development. In particular, groundwater flows and yields in mountainous area, composed of crystalline rocks with many fractures, are governed mainly by the lineaments corresponding to fractures, joints and faults. Lineaments may give important information on the best distribution of wells and their management. For two districts; Pohang and Cheonan, the relationship between lineament and groundwater factors was analyzed. To compare groundwater productivity, storativity, and transmissivity of a well site along the distance to lineament, the distances to lineament was regrouped into five groups with an equal range, 100m, for the Pohang district and they are also divided into five groups with an equal range, 150m, for the Cheonan district. From the results of the Spearman Rank Correlation Analysis and Kendall Analysis for each group, the means of SPC and T of wells which are located near lineaments generally have large values. The means of SPC and T show a reverse linear relationship with a lineament distance, but the means of S shows a disperse distribution and no distinct linear relation. Result of the linear regression model between SPC and lineament length density shows that it will be effective to use the lineament length density map when finding the optimal well site on a regional scale.

• Bulletin of the Korean Mathematical Society
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• v.39 no.2
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• pp.211-224
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• 2002

In this paper we show that if D is a continuous linear Jordan derivation on a Banach algebra A satisfying [[D($x^{n}$), $x^{n}$, $x^{n}$] $\in$ rad(A) for a positive integer n and for all x${\in}$A, then D maps A into rad(A).

• Journal of applied mathematics & informatics
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• v.13 no.1_2
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• pp.425-432
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• 2003

Our main goal is to show that if there exists a continuous linear Jordan derivation D on a noncommutative Banach algebra A such that n$^{x}$ D(x)n+xD(x)x$^{n}$ $\in$ rad(A) for all x $\in$ A, then D maps A into rad(A).

• Bulletin of the Korean Mathematical Society
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• v.40 no.1
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• pp.159-165
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• 2003

We consider the stochastic differential inclusion of the form $dX_t\;\in\;\sigma(t,\;X_t)db_t+b(t,\;X_t)dt$, where $\sigma$, b are set-valued maps, B is a standard Brownian motion. We prove the boundedness of solutions under the assumption that $\sigma$ and b satisfy the local Lipschitz property and linear growth.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10b
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• pp.289-293
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• 2006

국내에서도 표준화 작업이 진행 중인 토픽맵은 분산된 정보와 지식의 관리를 지원하기 위한 국제 표준이다. 이 논문에서는 이클립스(Eclipse) 플랫폼의 플러그인 개발환경인 GMF를 이용하여 비교적 작은 도메인에 대한 토픽맵의 구조를 도식화된 방법으로 표현하고, 간편하고 직관적인 편집기능을 사용자에게 제공하며, 이러한 구조의 간단한 프로토타입 정보라 할 수 있는 Linear Topic Map Notation(LTM)을 만들어 내는 플러그 인을 소개할 것이다.

• Bulletin of the Korean Mathematical Society
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• v.60 no.2
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• pp.485-494
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• 2023

Let Y be the quintic del Pezzo 4-fold defined by the linear section of Gr(2, 5) by ℙ⁷. In this paper, we describe the locus of double lines in the Hilbert scheme of coincs in Y. As a corollary, we obtain the desigularized model of the moduli space of stable maps of degree 2 in Y. We also compute the intersection Poincaré polynomial of the stable map space.

• Communications of the Korean Mathematical Society
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• v.37 no.1
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• pp.105-112
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• 2022

Let g : X ⟶ Y and f : Y ⟶ Z be two maps between real normed linear spaces. Then f is called generalized isometry or g-isometry if for each x, y ∈ X, ║f(g(x)) - f(g(y))║ = ║g(x) - g(y)║. In this paper, under special hypotheses, we prove that each generalized isometry is affine. Some examples of generalized isometry are given as well.

• ETRI Journal
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• v.35 no.5
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• pp.911-914
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• 2013

This letter presents Progression-Preserving Projection, a dimension reduction technique that finds a linear projection that maps a high-dimensional sensor dataset into a two- or three-dimensional subspace with a particularly useful property for visual exploration. As a demonstration of its effectiveness as a visual exploration and diagnostic means, we empirically evaluate the proposed technique over a dataset acquired from our own virtual-reality-enhanced ball-intercepting training system designed to promote the upper extremity movement skills of individuals recovering from stroke-related hemiparesis.

• Bulletin of the Korean Mathematical Society
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• v.34 no.2
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• pp.259-271
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• 1997

Let K be a nonempty subset of a normed linear space X and let x $\in$ X. An element k$_0$ in K satisfying $\$\mid$$x - k$_0$$\$\mid$$ = d(x, K) := (equation omitted) $\$\mid$$x - k$\$\mid$$ is called a best approximation to x from K. For any x $\in$ X, the set of all best approximations to x from K is denoted by P$_K$(x) = {k $\in$ K : $\$\mid$$ x - k $\$\mid$$ = d(x, K)}. (omitted)