• 한국컴퓨터정보학회논문지
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• 제22권9호
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• pp.9-16
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• 2017

The course timetabling problem is a problem assigning a set of subjects to the given classrooms and different timeslots, while satisfying various hard constraints and soft constraints. This problem is defined as a constraint satisfaction optimization problem and is known as an NP-complete problem. Various methods has been proposed such as integer programming, constraint programming and local search methods to solve a variety of course timetabling problems. In this paper, we propose an iterative improvement search method to solve the problem based on constraint programming. First, an initial solution satisfying all the hard constraints is obtained by constraint programming, and then the solution is repeatedly improved using constraint programming again by adding new constraints to improve the quality of the soft constraints. Through experimental results, we confirmed that the proposed method can find far better solutions in a shorter time than the manual method.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 22-24 Oct. 1991
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• pp.191-196
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• 1991

LQG/LTR method have a theoretical constraint that it cannot applied to nonminimum phase plant. In this paper, we suggest two methods of approximation of minimum phase plant for a given nonminimum phase plant to solve this constraint. Error is described by additive form which can reduce its magnitude in broad frequency range. A optimal approximation method was suggesetd by using Hankel operator theory and Nehari theory. It is showen by example that the methods suggested can resolve the frequency domain constraint arised in Stein and Athans approximation.

• 한국통신학회논문지
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• 제16권10호
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• pp.972-980
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• 1991

LQG/LTR method have a theoretical constraint that it cannot applied to nonminimum phase plant. In this paper we suggest two methods of approximation of minimum phase plant for a given nonminimum phase plant to solve this constraint. Error is described by additive form which can reduce its magnitude in broad frequency range. A optimal approximation method was suggested by using Hankel operator theory and Nehan theory it is shown by example that the methods suggested can resolve the frequency domain constraint arised in Stein and Athans approximation.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2008년도 학술대회 논문집 정보 및 제어부문
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• pp.323-324
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• 2008

In this paper, we present performance comparison of two methods to localize multiple robots. One is extended Kalman filter and the other is constraint propagation technique. Extended Kalman filter is conventional probabilistic method which gives the sub-optimal estimation rather than guarantee any boundary for true position of robot. In case of constraint propagation, it can give a boundary containing true robot position value. Especially, we deal with cooperative localization problem in outdoor environment for multiple robots equipped with GPS, gyro meter, wheel encoder. In simulation results, we present strength and weakness for localization methods based on extend Kalman filter and constraint propagation technique.

• 한국CDE학회논문집
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• 제3권4호
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• pp.211-222
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• 1998

Parametric design is an important modeling paradigm in CAD/CAM applications, enabling efficient design modifications and variations. One of the major issues in parametric design is to develop a geometric constraint solver that can handle a large set of geometric configurations efficiently and robustly. In this appear, we propose a new approach to geometric constraint solving that employs a graph-based method to solve the ruler-and-compass constructible configurations and a numerical method to solve the ruler-and-compass non-constructible configurations, in a way that combines the advantages of both methods. The geometric constraint solving process consists of two phases: 1) planning phase and 2) execution phase. In the planning phase, a sequence of construction steps is generated by clustering the constrained geometric entities and reducing the constraint graph in sequence. in the execution phase, each construction step is evaluated to determine the geometric entities, using both approaches. By combining the advantages of the graph-based constructive approach with the universality of the numerical approach, the proposed approach can maximize the efficiency, robustness, and extensibility of geometric constraint solver.

• 한국전산유체공학회:학술대회논문집
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• 한국전산유체공학회 2009년 추계학술대회논문집
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• pp.1-1
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• 2009

In this presentation, I talk about various fluid simulation methods that have been developed for computer graphics special effects since 1996. They are all based on CFD but sacrifice physical reality for visual plausability and time. But as the speed of computer increases rapidly and the capability of GPU (graphics processing unit) improves, methods for more physical realism have been tried. In this talk, I will focus on four aspects of fluid simulation methods for computer graphics: (1) particle level-set methods, (2) particle-based simulation, (3) methods for exact satisfaction of incompressibility constraint, and (4) GPU-based simulation. (1) Particle level-set methods evolve the surface of fluid by means of the zero-level set and a band of massless marker particles on both sides of it. The evolution of the zero-level set captures the surface in an approximate manner and the evolution of marker particles captures the fine details of the surface, and the zero-level set is modified based on the particle positions in each step of evolution. (2) Recently the particle-based Lagrangian approach to fluid simulation gains some popularity, because it automatically respects mass conservation and the difficulty of tracking the surface geometry has been somewhat addressed. (3) Until recently fluid simulation algorithm was dominated by approximate fractional step methods. They split the Navier-Stoke equation into two, so that the first one solves the equation without considering the incompressibility constraint and the second finds the pressure which satisfies the constraint. In this approach, the first step introduces error inevitably, producing numerical diffusion in solution. But recently exact fractional step methods without error have been developed by fluid mechanics scholars), and another method was introduced which satisfies the incompressibility constraint by formulating fluid in terms of vorticity field rather than velocity field (by computer graphics scholars). (4) Finally, I want to mention GPU implementation of fluid simulation, which takes advantage of the fact that discrete fluid equations can be solved in parallel.

• 한국도로학회논문집
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• 제15권4호
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• pp.155-165
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• 2013

PURPOSES : Benefits for improvement of travel time reliability obtained from construction of new highways should be considered as a major factor in the feasibility study for highway constructions. The purpose of this study is to develop a method of estimation for the value of travel time reliability. METHODS : Highway type (urban/rural highway) and traffic flow type(interrupted/uninterrupted) was considered to estimate he value of travel time reliability. And Double-bounded Dichotomous Choice among Contingent Valuation Method(CVM) was applied to survey the willingness-to-pay of drivers when travel time reliability is improved. Finally the value of travel time reliability was estimated using the results of survey and logit model. The value of travel time reliability was estimated considering travel objectives, time constraint travel and non-time constraint travel. RESULTS: The value of travel time reliability of business trip is higher than that of non-business trip. The value of travel time reliability of time constraint travel is higher than that of non-time constraint travel. The value of travel time reliability in urban area is higher than that in rural area. CONCLUSIONS: It was concluded that the proposed method in this study is more realistic and proper to estimate the value of travel time reliability because it reflects the situations of time constraint travel and non-time constraint travel.

• Journal of Mechanical Science and Technology
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• 제16권8호
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• pp.1072-1078
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• 2002

The constrained motion requires the determination of constraint force acting on unconstrained systems for satisfying given constraints. Most of the methods to decide the force depend on numerical approaches such that the Lagrange multiplier method, and the other methods need vector analysis or complicated intermediate process. In 1992, Udwadia and Kalaba presented the generalized inverse method to describe the constrained motion as well as to calculate the constraint force. The generalized inverse method has the advantages which do not require any linearization process for the control of nonlinear systems and can explicitly describe the motion of holonomically and/or nongolonomically constrained systems. In this paper, an explicit equation to describe the constrained motion is derived by minimizing the performance index, which is a function of constraint force vector, with respect to the constraint force. At this time, it is shown that the positive-definite weighting matrix in the performance index must be the inverse of mass matrix on the basis of the Gauss's principle and the derived differential equation coincides with the generalized inverse method. The effectiveness of this method is illustrated by means of two numerical applications.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2006년도 추계학술발표대회
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• pp.55-58
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• 2006

This paper presents solving the Sudoku puzzle as a constraint satisfaction problem (CSP). After introducing the rules and characteristics of the puzzle, we formulate the puzzle as a CSP and develop various methods of solving the problem. Blind search, minimum remaining value (MRV) heuristic, and some advanced methods are investigated, and their algorithms are implemented in this undergraduate project. The performance comparisons of these methods are discussed in the paper.

• 대한산업공학회지
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• 제20권1호
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• pp.133-146
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• 1994

Conditional constraints are frequently used to represent relations. To use these conditional constraints, it is necessary to develop an appropriate logic in which these conditional constraints can be represented and manipulated. Nevertheless, there has been little research that addresses interval-based conditional constraints. The proposed approach addresses the use of conditional constraints involving intervals in constraint networks. Two algorithms are presented: (1) a propagation algorithm for an interval-based conditional constraint, which is similar to one for an exact-value conditional constraint; (2) a propagation algorithm for interval-based conditional constraints which satisfy some conditions. The former can be applied to any conditional constraint. However, with the former algorithm, conditional constraints are usually categorized into the cases that they cannot be propagated. After investigating several methods in which most conditional constraints can be propagated, we propose the latter algorithm under certain condition that usually results in smaller resulting design space comparing to the former.