• Asia-Pacific Journal of Business
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• v.2 no.2
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• pp.1-35
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• 2011

Facing information overload in today's complex environments, managers look to a concise set of marketing metrics to provide direction for marketing decision making. While there have been several papers dealing with the theoretical aspects of dashboard creation, no research creates and tests a dashboard using scientific techniques. This study develops and demonstrates an empirical approach to dashboard metric selection. In a fast moving consumer goods category, this research selects leading indicators for national-brand and store-brand sales and revenue premium performance from 99 brand-specific and relative-to-competition variables including price, brand equity, usage occasions, and multiple measures of awareness, trial/usage, purchase intent, and liking/satisfaction. Plotting impact size and wear-in time reveals that different kinds of variables predict sales at distinct lead times, which implies that managerial action may be taken to turn the metrics around before performance itself declines.

• Journal of the Korean Mathematical Society
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• v.41 no.1
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• pp.209-229
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• 2004

Let $\Sigma_{$\mid$\alpha$\mid$=m}\;s_{\alpha}z^{\alpha},\;z\;{\in}\;{\mathbb{C}}^n$ be a unimodular m-homogeneous polynomial in n variables (i.e. $$\mid$s_{\alpha}$\mid$\;=\;1$ for all multi indices $\alpha$), and let $R\;{\subset}\;{\mathbb{C}}^n$ be a (bounded complete) Reinhardt domain. We give lower bounds for the maximum modules $sup_{z\;{\in}\;R\;$\mid$\Sigma_{$\mid$\alpha$\mid$=m}\;s_{\alpha}z^{\alpha}$\mid$$, and upper estimates for the average of these maximum moduli taken over all possible m-homogeneous Bernoulli polynomials (i.e. $s_{\alpha}\;=\;{\pm}1$ for all multi indices $\alpha$). Examples show that for a fixed degree m our estimates, for rather large classes of domains R, are asymptotically optimal in the dimension n.

• Structural Engineering and Mechanics
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• v.92 no.1
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• pp.111-120
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• 2024

Sampling methods are powerful approaches to solving the problems of structural reliability analysis and estimating the failure probability of structures. In this paper, a new sampling method is proposed offering lower variance and lower computational cost for complex and high-dimensional problems. The method is called Optimal Latinized partially stratified sampling (OLPSS) as it is based upon the Latinized Partially Stratified Sampling (LPSS) which itself is based on merging Stratified Sampling (SS) and Latin Hypercube Sampling (LHS) algorithms. While LPSS has a low variance, it may suffer from a lack of good space-filling of its generated samples in some cases. In the OLPSS, this issue has been resolved by employing a new columnwise-pairwise exchange optimization procedure for sample generation. The efficiency of the OLPSS has been tested and reported under several benchmark mathematical functions and structural examples including structures with a large number of variables (e.g., a structure with 67 variables). The proposed method provides highly accurate estimates of the failure probability of structures with a significantly lower variance relative to the Monte Carlo simulations, Latin Hypercube, and standard LPSS.

• Asian Journal of Innovation and Policy
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• v.8 no.3
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• pp.343-361
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• 2019

The Practical Guide of Technology Valuation (the guide) by the Ministry of Industry, Trade and Energy is dominant in technology valuation in the public sector in Korea. The guide was released in 2011 and revised every three years. However, there are several guidelines or manuals for technology valuation issued by other agencies under different ministries. This paper compares the several guidelines for technology valuation and figures out the similarity and differences, from the view of the US and international standards of valuation. The first aspect found is that the guide is evolving toward the basic principles of valuation. Second, all the guidelines should comply with the guide but have sector-specific characteristics in methods, variables and data. Third, although the guide recommends only two valuation methods, some guidelines introduce various other methods. Fourth, the methods are still too complex and having unnecessary ingredients. Finally, this paper suggests further development of the guide and other guidelines.

• Sleep Medicine and Psychophysiology
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• v.6 no.1
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• pp.26-31
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• 1999

"Attention" is not defined sufficiently. This term incorporates several dimensions or complex information processes such as alertness, spatial distribution, focused attention, sustained attention, divided attention and supervisory attentional control. In practice, however, various aspects of attention cannot be assessed separately with a single test. Moreover, a particular test is never assessing attention only, because the several intervening variables may influence the attentional component. Therefore, one can only assess a certain aspect of human behavior with special interest for its attentional component. This paper attempted to clarify various concepts of attention, reviewed signal detection theories with receiver operating characteristic(ROC) curves, and listed practical methods for assessment of attention.

• Coupled systems mechanics
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• v.11 no.2
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• pp.167-198
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• 2022

In this paper we deal with classical instability problems of heterogeneous Euler beam under conservative loading. It is chosen as the model problem to systematically present several possible solution methods from simplest deterministic to more complex stochastic approach, both of which that can handle more complex engineering problems. We first present classical analytic solution along with rigorous definition of the classical Euler buckling problem starting from homogeneous beam with either simplified linearized theory or the most general geometrically exact beam theory. We then present the numerical solution to this problem by using reduced model constructed by discrete approximation based upon the weak form of the instability problem featuring von Karman (virtual) strain combined with the finite element method. We explain how such numerical approach can easily be adapted to solving instability problems much more complex than classical Euler's beam and in particular for heterogeneous beam, where analytic solution is not readily available. We finally present the stochastic approach making use of the Duffing oscillator, as the corresponding reduced model for heterogeneous Euler's beam within the dynamics framework. We show that such an approach allows computing probability density function quantifying all possible solutions to this instability problem. We conclude that increased computational cost of the stochastic framework is more than compensated by its ability to take into account beam material heterogeneities described in terms of fast oscillating stochastic process, which is typical of time evolution of internal variables describing plasticity and damage.

• The Korean Journal of Applied Statistics
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• v.35 no.6
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• pp.703-724
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• 2022

Although longitudinal studies mainly produce multivariate longitudinal data, most of existing statistical models analyze univariate longitudinal data and there is a limitation to explain complex correlations properly. Therefore, this paper describes various methods of modeling the covariance matrix to explain the complex correlations. Among them, modified Cholesky decomposition, modified Cholesky block decomposition, and hypersphere decomposition are reviewed. In this paper, we review these methods and analyze Korean children and youth panel (KCYP) data are analyzed using the Bayesian method. The KCYP data are multivariate longitudinal data that have response variables: School adaptation, academic achievement, and dependence on mobile phones. Assuming that the correlation structure and the innovation standard deviation structure are different, several models are compared. For the most suitable model, all explanatory variables are significant for school adaptation, and academic achievement and only household income appears as insignificant variables when cell phone dependence is a response variable.

• Industrial Engineering and Management Systems
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• v.10 no.4
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• pp.272-278
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• 2011

Decision tree as a classification tool is being used successfully in many areas such as medical diagnosis, customer churn prediction, signal detection and so on. The main advantage of decision tree classifiers is their capability to break down a complex structure into a collection of simpler structures, thus providing a solution that is easy to interpret. Since decision tree is a top-down algorithm using a divide and conquer induction process, there is a risk of reaching a local optimal solution. This paper proposes a procedure of optimally determining thresholds of the chosen variables for a decision tree using an adaptive particle swarm optimization (APSO). The proposed algorithm consists of two phases. First, we construct a decision tree and choose the relevant variables. Second, we find the optimum thresholds simultaneously using an APSO for those selected variables. To validate the proposed algorithm, several artificial and real datasets are used. We compare our results with the original CART results and show that the proposed algorithm is promising for improving prediction accuracy.

• Kyungpook Mathematical Journal
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• v.64 no.1
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• pp.133-159
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• 2024

In a previous work we studied generalized Stirling numbers of the second kind S^{(a₂,b₂,p₂)}_a1,b1 (p₁, k), where a₁, a₂, b₁, b₂ are given complex numbers, a₁, a₂ ≠ 0, and p₁, p₂ are non-negative integers given. In this work we use these generalized Stirling numbers to define Bernoulli polynomials in two variables B_p1,p2 (x₁, x₂), and Euler polynomials in two variables E_p1p2 (x₁, x₂). By using results for S^{(1,x₂,p₂)}_1,x1 (p₁, k), we obtain generalizations, to the bivariate case, of some well-known properties from the standard case, as addition formulas, difference equations and sums of powers. We obtain some identities for bivariate Bernoulli and Euler polynomials, and some generalizations, to the bivariate case, of several known identities for Bernoulli and Euler numbers and polynomials of the standard case.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.33 no.6
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• pp.401-409
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• 2020

Seismic fragility assessments include a procedure to combine the random variables of response and capacity to produce the relationship between failure probability and seismic intensity. The evaluation of the failure probability of simultaneous multiple failures of two or more components assumes that the failure probability of each component is independent of those of the others. However, a correlation is expected to exist because several random factors have the same cause. The multiple-failure probability can differ depending on this correlation and may be unconservative without considering the seismic correlation. Therefore, a practical methodology for fragility assessment should be evaluated using the seismic correlation and correlation coefficient for each random variable. In this study, several random variables were selected for numerical evaluation of the correlation coefficient. The correlation coefficient was then compared with each variable and the combined variables. The correlation coefficient using simplified and complex models were also compared to determine and analyze the differences between each of the approaches.