• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.4
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• pp.414-424
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• 2012

Finding or automatically generating problem instance is useful for algorithm analysis/test. The topic has been of interest in the field of hardware/software engineering and theory of computation. We apply objective value-distance correlation analysis to problem spaces, as previous researchers applied it to solution spaces. According to problems, we define the objective function by (1) execution time of tested algorithm or (2) its optimality; this definition is interpreted as difficulty of the problem instance being solved. Our correlation analysis is based on the following aspects: (1) change of correlation when we use different algorithms or different distance functions for the same problem, (2) change of that when we improve the tested algorithm, (3) relation between a problem instance space and the solution space for the same problem. Our research demonstrates the way of problem instance space analysis and will accelerate the problem instance space analysis as an initiative research.

• Management Science and Financial Engineering
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• v.2 no.1
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• pp.147-176
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• 1996

The main objective of this study is to uncover the differences in the programming behavior between methodology creators and methodology users. We conducted an experiment with methodology creators who have invented one of the major object-oriented methodologies and with professional programmers who have used the same methodology for their software-development projects. In order to explain the difference between the two groups, we propose a theoretical framework that views programming as search in four problem spaces: representation, rule, instance and paradigm spaces. The main problem spaces in programming are the representation and rule spaces, while the paradigm and instance spaces are the supporting spaces. The results of the experiment showed that the methodology creators mostly adopted the paradigm space as their supporting space, while the methodology users chose the instance space as their supporting space. This difference in terms of the supporting space leads to different search behaviors in the main problem spaces, which in turn resulted in different final programs and performance.

• Bulletin of the Korean Mathematical Society
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• v.46 no.2
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• pp.245-253
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• 2009

A new hard problem called the vector decomposition problem (VDP) was recently proposed by Yoshida et al., and it was asserted that the VDP is at least as hard as the computational Diffie-Hellman problem (CDHP) under certain conditions. Kwon and Lee showed that the VDP can be solved in polynomial time in the length of the input for a certain basis even if it satisfies Yoshida's conditions. Extending our previous result, we provide the general condition of the weak instance for the VDP in this paper. However, when the VDP is practically used in cryptographic protocols, a basis of the vector space ${\nu}$ is randomly chosen and publicly known assuming that the VDP with respect to the given basis is hard for a random vector. Thus we suggest the type of strong bases on which the VDP can serve as an intractable problem in cryptographic protocols, and prove that the VDP with respect to such bases is difficult for any random vector in ${\nu}$.

• Management Science and Financial Engineering
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• v.20 no.1
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• pp.17-19
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• 2014

In this paper, we consider an interesting variant of the inverse minimum traveling salesman problem. Given an instance (G, w) of the minimum traveling salesman problem defined on a metric space, we fix a specified Hamiltonian cycle $HC_0$. The task is then to adjust the edge cost vector w to w' so that the new cost vector w' satisfies the triangle inequality condition and $HC_0$ can be returned by the minimum spanning tree algorithm in the TSP-instance defined with w'. The objective is to minimize the total deviation between the original and the new cost vectors with respect to the $L_1$-norm. We call this problem the inverse metric traveling salesman problem against the minimum spanning tree algorithm and show that it is closely related to the inverse metric spanning tree problem.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.575-580
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• 1992

Mixed H$_{2}$/H$_{\infty}$ robust control synthesis is considered for finite dimensional linear time-invariant systems under the presence of diagonal structured uncertainties. Such uncertainties arise for instance when there is real perturbation in the nominal model of the state space system or when modeling multiple (unstructured) uncertainty at different locations in the feedback loop. This synthesis problem is reduced to convex optimization problem over a bounded subset of matrices as well as diagonal matrix having certain structure. For computational purpose, this convex optimization problem is further reduced into Generalized Eigenvalue Minimization Problem where a powerful algorithm based on interior point method has been recently developed..

• Journal of applied mathematics & informatics
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• v.31 no.5_6
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• pp.813-823
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• 2013

In this paper, a new iterative algorithm involving quasi-nonexpansive mapping in Hilbert space is proposed and proved to be strongly convergent to a point which is simultaneously a fixed point of a quasi-nonexpansive mapping, a solution of an equilibrium problem and the set of solutions of a variational inequality problem. The results of the paper extend previous results, see, for instance, Takahashi and Takahashi (J Math Anal Appl 331:506-515, 2007), P.E.Maing $\acute{e}$ (Computers and Mathematics with Applications, 59: 74-79,2010) and other results in this field.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.39 no.12
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• pp.1150-1159
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• 2011

Reliability is defined as a probability that a system will operate properly for a specified period of time under the design operating conditions without failure. Reliability-Redundancy Optimization Problem(RROP) involves selection of components with multiple choices, redundancy levels and redundancy strategy(Active or Standby) for maximizing system reliability with constraints such as cost, weight, etc. Based on the design configuration of Multi-Spectral Camera(MSC) system of KOMPSAT-2, the mathematical programming model for RROP is suggested in this study. Due to the nature of RROP, i.e. NP-hard problem, Parallel Particle Swarm Optimization(PPSO) algorithm is proposed to solve it. The result of the numerical experiment for RROP is presented as instance of recommended design configuration at some mission time.

• Journal of Navigation and Port Research
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• v.30 no.6 s.112
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• pp.457-464
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• 2006

Remarshalling operation is one of the operations considered important in an automated container terminal to perform quickly loading operations and delivery operations. It arranges the containers scattered at a verticla yard block in order to reduce the transfer time and the rehandling time of ATC(Automated Transfer Crane)s. This paper deals with the remarshalling planning problem minimizing the weighted operation time. This problem can be decomposed into 2 subproblems, storage space assignment problem and operation sequencing problem Storage space assignment problem decides to where containers are transported in terms of transportation time cost.. With results of a previous subproblem, operation sequence problem determines the ATC operation sequence, which minimizes the dead-heading of ATC This study formulates each subproblem with mixed integer program and dynamic program. To illustrate the proposed model, we propose an instance to explain the process of remarshalling planning.

• The Bulletin of The Korean Astronomical Society
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• v.35 no.2
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• pp.46.1-46.1
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• 2010

This work is a part of our project to establish a Website which provides a list of magnetic clouds (MCs) identified by WIND and ACE spacecraft. MCs are characterized by their magnetic fields that are well described by magnetic flux rope structures, whereas interplanetary coronal mass ejections (ICMEs) are interplanetary manifestations of coronal mass ejections (CMEs), usually identified by differences of plasma and magnetic field characteristics from those in the background solar wind. It is widely accepted that, while MCs are generally identified within ICMEs, the number of MCs are significantly lower than the number of ICMEs. In our effort to identify MCs, however, we have found that there was a big problem in identification method of MCs in previous works. Generally speaking, most of the previous surveys failed in identifying MCs which encounter the spacecraft at large distances from the MC axis, or near the surface of MC structures. In our survey, MCs are identified as the region of which magnetic fields are well described by appropriate flux rope models. Thus, we could selected over 45 MCs, in 1999 solar wind data for instance, while 33 ICMEs are listed in the Website of the ACE Science Center reported by Richardson and Cane.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.406-409
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• 2008

We consider the problem of exploiting a taxonomy of propositionalized attributes in order to learn compact and robust classifiers. We introduce Propositionalized Attribute Taxonomy guided Naive Bayes Learner (PAT-NBL), an inductive learning algorithm that exploits a taxonomy of propositionalized attributes as prior knowledge to generate compact and accurate classifiers. PAT-NBL uses top-down and bottom-up search to find a locally optimal cut that corresponds to the instance space from propositionalized attribute taxonomy and data. Our experimental results on University of California-Irvine (UCI) repository data sets show that the proposed algorithm can generate a classifier that is sometimes comparably compact and accurate to those produced by standard Naive Bayes learners.