• Korean Journal of Organic Agriculture
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• v.21 no.4
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• pp.487-500
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• 2013

The purpose of this study is to estimate efficiency of environmental-friendly agricultural product by using Data Envelopment Analysis. A proposed method employs a bootstrapping approach to generating efficiency estimates through Monte Carlo simulation resampling process. The technical efficiency, pure technical efficiency, and scale efficiency measure of strawberry by pesticide-free certification is 0.967, 0.995, 0.968 respectively. However those of bias-corrected estimates are 0.918, 0.983, 0.934. We know that the DEA estimator is an upward biased estimator. In technical efficiency, average lower and upper confidence bounds of 0.807 and 0.960. According to these results, the DEA bootstrapping model used here provides bias-corrected and confidence intervals for the point estimates, it is more preferable.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.12
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• pp.1196-1201
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• 2004

H₂ controller is proposed for networked control systems with time-varying delay. The time-varying network delay is assumed to be unknown, but its lower and upper bounds are assumed to be known. The time-varying delay is treated like a parameter variation and robust control technique is used to deal with the time-varying delay. The proposed controller can be computed by solving linear matrix inequalities. Through numerical simulations, the proposed controller is verified.

• Journal of applied mathematics & informatics
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• v.36 no.3_4
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• pp.307-315
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• 2018

Let G be a simple connected graph with n vertices and m edges, with a sequence of vertex degrees $d_1{\geq}d_2{\geq}{\cdots}{\geq}d_n$ > 0. A vertex-degree topological index, referred to as harmonic index, is defined as $H={\sum{_{i{\sim}j}}{\frac{2}{d_i+d_j}}$, where i ~ j denotes the adjacency of vertices i and j. Lower and upper bounds of the index H are obtained.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.36 no.2
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• pp.73-79
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• 1987

This paper describes a new algorithm to evaluate the production cost for a generation system including energy-limited hydroelectric plants. The algorithm is based upon the analytical production costing model developed under the assumption of Gaussian probabilistic distribution of random load fluctuations and plant outages. Hydro operation and pumped storage operation have been dealt with in the previous papers using the concept of peak-shaving operation. In this paper, the hydro problem is solved by using a new version of the gradient projection method that treats the upper / lower bounds of variables saparately and uses a specified initial active constraint set. Accuracy and validity of the algorithm are demonstrated by comparing the result with that of the peak-shaving model.

• Journal of Korean Society for Quality Management
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• v.26 no.3
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• pp.71-81
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• 1998

In reliability analysis, the time difference between the expected next failure time and the current failure time or the Mean Time Between Failure(MTBF) is of significant interest. Until recently, in reliability growth studies, the reciprocal of the intensity function at current failure time has been used as being equal to MTBE($t_n$)at the n-th failure time $t_n$. That is MTBF($t_n$)=l/$\lambda (t_n)$. However, such a relationship is only true for Homogeneous Poisson Process(HPP). Tsokos(1995) obtained the upper bound and lower bound for the MTBF($t_n$) and proposed an estimator for the MTBF($t_n$) as the mean of the two bounds. In this paper, we provide the estimator for the MTBF($t_n$) which does not depend on the value of the shape parameter. The result of the Monte Carlo simulation shows that the proposed estimator has better efficiency than Tsokos's estimator.

• Journal of Korean Institute of Industrial Engineers
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• v.9 no.2
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• pp.3-8
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• 1983

The multiple choice knapsack problem is modified. To solve this modified multiple choice knapsack problem, Lagrangian relaxation is used, and to take advantage of the special structure of subproblems obtained by decomposing this relaxed Lagrangian problem, a modified ranking algorithm is used. The K best rank order solutions obtained from each subproblem as a result of applying modified ranking algorithm are used to formulate restricted problems of the original problem. The optimality for the original problem of solutions obtained from the restricted problems is judged from the upper bound and lower bounds calculated iteratively from the relaxed problem and restricted problems, respectively.

• Bulletin of the Korean Mathematical Society
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• v.53 no.5
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• pp.1353-1362
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• 2016

Using the notion of complete nonabelian exterior square $G\hat{\wedge}G$ of a pro-p-group G (p prime), we develop the theory of the exterior degree $\hat{d}(G)$ in the infinite case, focusing on its relations with the probability of commuting pairs d(G). Among the main results of this paper, we describe upper and lower bounds for $\hat{d}(G)$ with respect to d(G). Here the size of the second homology group $H_2(G,\mathbb{Z}_p)$ (over the p-adic integers $\mathbb{Z}_p$) plays a fundamental role. A further result of homological nature is placed at the end, in order to emphasize the influence of $H_2(G,\mathbb{Z}_p)$ both on G and $\hat{d}(G)$.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2007.04a
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• pp.141-144
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• 2007

Given a number of training data, a traditional BPN is normally trained by minimizing the absolute difference between target outputs and approximate outputs. When BPN is used as a meta-model for inequality constraint function, approximate optimal solutions are sometimes actually infeasible in a case where they are active at the constraint boundary. The paper describes the development of the efficient BPN based meta-model that enhances the constraint feasibility of approximate optimal solution. The modified BPN based meta-model is obtained by including the decision condition between lower/upper bounds of a constraint and an approximate value. The proposed approach is verified through a simple mathematical function and a ten-bar planar truss problem.

• Journal of Korean Society of Transportation
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• v.26 no.2
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• pp.89-100
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• 2008

In an urban network controlled by traffic signals, there is an interaction between the signal timing and the routes chosen by individual road users. This study develops a bi level programming model for traffic signal optimization in networks with path based traffic assignment. In the bi level programming model, genetic algorithm approach has been proposed to solve upper level problem for a signalized road network. Path based traffic assignment using column generation technique which is proposed by M.H. Xu, is applied at the lower-level. Genetic Algorithm provieds a feasible set of signal timings within specified lower and upper bounds signal timing variables and feeds into lower level problem. The performance of this model is investigated in numerical experiment in a sample network. In result, optimal signal settings and user equilibrium flows are made.

• Communications of the Korean Mathematical Society
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• v.17 no.2
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• pp.321-330
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• 2002

Consider the random assignment (or bipartite matching) problem with iid uniform edge costs t(i, j). Let $A_{n}$ be the optimal assignment cost. Just recently does Aldous [2] give a rigorous proof that E $A_{n}$ longrightarrowζ(2). In this paper we establish the upper and lower bounds for Var $A_{n}$ , i.e., there exist two strictly positive but finite constants $C_1$ and $C_2$ such athat $C_1$ $n^{(-5}$2)/ (log n)$^{(-3}$2)/ $\leq$ Var $A_{n}$ $\leq$ $C_2$ $n^{-1}$ (log n)$^2$.EX>.