• East Asian mathematical journal
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• v.38 no.3
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• pp.331-338
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• 2022

In this paper, we prove that the first nonzero eigenvalues λ₁ of the Laplacian and the p-Laplacian are decreasing along the inverse mean curvature flow with forced term in Euclidean space.

• Journal of Intelligence and Information Systems
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• v.9 no.1
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• pp.195-209
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• 2003

Making traditional plan of target marketing based on association rule has brought restriction to obtain the target of marketing. This paper is to present inverse association rule as a new association rule for target marketing. Inverse association rule does not use information about relation between items that customers purchase, but use information about relation between items that customers do not purchase. By adding inverse association rule to target marketing, we generate new marketing strategy to look for new target of marketing. There are three steps to apply the marketing strategy proposed by this Paper to target marketing. Firstly, a database is converted to an inverse database. Although inverse association rules can be generated from a database, it is easier to explain inverse association rule in an inverse database than in a database. Secondly, association rules and inverse association rules are generated from inverse database. Finally, two types of rules which are created in the previous steps are applied to target marketing. From new marketing rule, this paper is to show direct marketing about target item and indirect marketing about another item associated with target item to sell target item. The reason is that sales of the item associated with target item have an influence on sales of target item.

• Journal of Korea Water Resources Association
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• v.51 no.5
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• pp.451-459
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• 2018

We calculated the optimal exponent values based on the hourly rainfall data observed in South Korea by treating the exponent value as a variable without fixing it as a square in the inverse distance method. For this purpose, rainfall observation stations providing the data are classified into four groups which are located at the Han river upstream, downstream, the Geum river upstream, and the Nakdong river midstream area. A total of 52 cases were analyzed for seven stations in each group. The optimal exponent value of distance was calculated in a case including one base station and four surrounding stations in a group. We applied the golden section search method to calculating this optimum values using rainfall data for 10 years (2004~2013) and verified the optimum values for the last three years (2014~2016). We compared and analyzed two results of the conventional inverse distance method and the inverse distance method in this study. The optimal values of distance exponent obtained in this study were 3.280, 1.839, 2.181, and 2.005 respectively, in the four groups, and totally mean value was 2.326. It is shown the proposed inverse distance method applying the optimal exponent is superior to the conventional inverse distance method.

• Bulletin of the Korean Mathematical Society
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• v.44 no.2
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• pp.225-231
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• 2007

Given an injective envelope E of a left R-module M, there is an associative Galois group Gal$({\phi})$. Let R be a left noetherian ring and E be an injective envelope of M, then there is an injective envelope $E[x^{-1}]$ of an inverse polynomial module $M[x^{-1}]$ as a left R[x]-module and we can define an associative Galois group Gal$({\phi}[x^{-1}])$. In this paper we describe the relations between Gal$({\phi})$ and Gal$({\phi}[x^{-1}])$. Then we extend the Galois group of inverse polynomial module and can get Gal$({\phi}[x^{-s}])$, where S is a submonoid of $\mathbb{N}$ (the set of all natural numbers).

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.131-136
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• 1999

This paper proposes the robot inverse calibration method using a neural networks. A highorder networks called Pi-Sigma networks has been used. The Pi-Sigma networks uses linear summing units in the hidden layer and product unit in output layer. The inverse calibration model which compensates the difference of joint variables only between measuring value and analytic value about the desired pose(position, orientation) of a robot is proposed. The compensated values are determined by using the weights obtained from the learning process of the neural networks previously. To prove the reasonableness, the SCARA type direct drive robot(4-DOF) and anthropomorphic robot(6-DOF) are simulated. It shows that the proposed calibration method can reduce the errors of the joint variables from $\pm$2$^{\circ}$to $\pm$ 0.1$^{\circ}$.

• The Transactions of the Korean Institute of Power Electronics
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• v.13 no.1
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• pp.1-8
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• 2008

A dynamic model for II-SEPIC(Isolated Inverse-SEPIC) is developed based on the state-space averaging method and its control characteristics are investigated in this paper. Equations for circuit design of II-SEPIC are derived through steady state analysis and the resulted circuit parameters are used in the consequent simulation and experiment works. A structure of control system is devised to obtain better control performance. In order to verify validity and effectiveness of the design equations and dynamic model derived, dynamic control responses of II-SEPIC system against line and load variation are illustrated in both simulation and experiment.

• East Asian mathematical journal
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• v.27 no.1
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• pp.1-9
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• 2011

In this paper, we consider an iterative scheme for finding a common element of the set of fixed points of a asymptotically quasi nonexpansive mapping and the set of solutions of the variational inequality for an inverse strongly monotone mapping in a Hilbert space. Then we show that the sequence converges strongly to a common element of two sets. Using this result, we consider the problem of finding a common fixed point of a asymptotically quasi-nonexpansive mapping and strictly pseudocontractive mapping and the problem of finding a common element of the set of fixed points of a asymptotically quasi-nonexpansive mapping and the set of zeros of an inverse-strongly monotone mapping.

• East Asian mathematical journal
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• v.24 no.2
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• pp.139-144
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• 2008

Given an injective envelope E of a left R-module M, there is an associative Galois group Gal($\phi$). Let R be a left noetherian ring and E be an injective envelope of M, then there is an injective envelope E[$x^{-1}$] of an inverse polynomial module M[$x^{-1}$] as a left R[x]-module and we can define an associative Galois group Gal(${\phi}[x^{-1}]$). In this paper we extend the Galois group of inverse polynomial module and can get Gal(${\phi}[x^{-s}]$), where S is a submonoid of $\mathds{N}$ (the set of all natural numbers).

• Korea-Australia Rheology Journal
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• v.20 no.1
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• pp.35-42
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• 2008

An inverse ferrofluid composed of micron sized polymethylmethacrylate particles dispersed in ferrofluid was used to investigate the effects of test duration times on determining the flow curves of these materials under constant magnetic field. The results showed that flow curves determined using low duration times were most likely not measuring the steady state rheological response. However, at longer duration times, which are expected to correspond more to steady state behaviour, we noticed the occurrence of plateau and decreasing flow curves in the shear rate range of $0.004\;s^{-1}$ to ${\sim}20\;s^{-1}$, which suggest the presence of nonhomogeneities and shear localization in the material. This behaviour was also reflected in the steady state results from shear start up tests performed over the same range of shear rates. The results indicate that care is required when interpreting flow curves obtained for inverse ferrofluids.

• Communications for Statistical Applications and Methods
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• v.14 no.1
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• pp.215-227
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• 2007

Variable selection algorithm for Sliced Inverse Regression using penalty function is proposed. We noted SIR models can be expressed as generalized eigenvalue decompositions and incorporated penalty functions on them. We found from small simulation that the HARD penalty function seems to be the best in preserving original directions compared with other well-known penalty functions. Also it turned out to be effective in forcing coefficient estimates zero for irrelevant predictors in regression analysis. Results from illustrative examples of simulated and real data sets will be provided.