• Bulletin of the Korean Mathematical Society
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• v.42 no.4
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• pp.837-849
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• 2005

In this paper, we obtain a successive quadratic programming algorithm for solving the semidefinite programming (SDP) relaxation of the binary quadratic programming. Combining with a randomized method of Goemans and Williamson, it provides an efficient approximation for the binary quadratic programming. Furthermore, its convergence result is given. At last, We report some numerical examples to compare our method with the interior-point method on Maxcut problem.

• Journal of Electrical Engineering and Technology
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• v.9 no.2
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• pp.415-422
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• 2014

This paper presents a new approach to solve the multi area unit commitment problem (MAUCP) using an evolutionary programming based particle swarm optimization (EPPSO) method. The objective of this paper is to determine the optimal or near optimal commitment schedule for generating units located in multiple areas that are interconnected via tie lines. The evolutionary programming based particle swarm optimization method is used to solve multi area unit commitment problem, allocated generation for each area and find the operating cost of generation for each hour. Joint operation of generation resources can result in significant operational cost savings. Power transfer between the areas through the tie lines depends upon the operating cost of generation at each hour and tie line transfer limits. Case study of four areas with different load pattern each containing 7 units (NTPS) and 26 units connected via tie lines have been taken for analysis. Numerical results showed comparing the operating cost using evolutionary programming-based particle swarm optimization method with conventional dynamic programming (DP), evolutionary programming (EP), and particle swarm optimization (PSO) method. Experimental results show that the application of this evolutionary programming based particle swarm optimization method has the potential to solve multi area unit commitment problem with lesser computation time.

• Journal of Korea Multimedia Society
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• v.22 no.12
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• pp.1481-1490
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• 2019

Arduino makes it easy to connect objects and computers. As a result, programming learning using physical computing has been proposed as an effective alternative to SW training for beginners. In this paper, we propose an Arduino-based physical computing education method that can be applied to basic programming subjects. To this end, we propose a basic programming training method based on Arduino analog signals. Currently, physical computing courses focus on digital control when connecting input sensors and output devices in Arduino. However, the contents of programming education using analog signals of Arduino boards are insufficient. In this paper, we proposed and tested the teaching method used for programming education using low-cost materials used for Arduino analog signal-based computing.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.96-99
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• 1992

Currently, there are various robot programming methods for articulated robots. Although each method has merits and drawbacks, they have commonly weak points for practical application, and especially the weak point can be even more vulnerable when the robot programming requires the subtle feelings of human being. This is because the movement of a human being is synthetic while the robot programming is analytic. Therefore, the present method of programming has limits in performing these kinds of subtle robot movement. In this paper, we propose a direct robot programming method, which generates robot programs based on the force/torque vector applied to a force/torque sensor by the human operator. The method reduces the effort required in the robot programming.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.879-882
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• 1999

The effects of the substrate bias on the performance of programming erasing in p-channel flash memory cell have been investigated. It is found that applying positive substrate bias can improve the programming and erasing speed. This improvements can be explained by Substrate Current Induced Hot Electron Injection. From the results, we can confirm that BTB programming method is better in programming and erasing speed than CHE programming method.

• Journal of the Korean Mathematical Society
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• v.48 no.5
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• pp.985-999
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• 2011

A new filled function method is presented in this paper to solve box-constrained nonlinear integer programming problems. It is shown that for a given non-global local minimizer, a better local minimizer can be obtained by local search starting from an improved initial point which is obtained by locally solving a box-constrained integer programming problem. Several illustrative numerical examples are reported to show the efficiency of the present method.

• Journal of the Korean Society of Safety
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• v.18 no.1
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• pp.94-100
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• 2003

Generally, cable-stayed bridges are both statically indeterminate structure with a high degree of redundancy and flexible structure. So it is very important to ensure precision control during both fabrication and construction. In precision control of cable-stayed bridges, precision control under multi-objective programming method is needed, because precision control problem of cable-stayed bridges is a multi-objective programming problem in which many objective functions are regard as variables. In previous studies, it was regarded as a single-objective problem, so it had many problems in respect of usefulness and rationalness. In this study, precision control under multi-objective programming method is proposed considering economy, efficiency, and safety at best in precision control of cable-stayed bridges. Precision control problem of cable-stayed bridges is formulated with satisfying trade-off method which is a kind of multi-objective programming method, then it is optimized with min-max method. A computer program is presented including above process.

• Journal of applied mathematics & informatics
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• v.16 no.1_2
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• pp.91-104
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• 2004

A modification of the gradient method of convex programming is introduced. Also, we describe symbolic implementation of the gradient method and its modification by means of the programming language MATHEMATICA. A few numerical examples are reported.

• Journal of the Korea Society of Computer and Information
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• v.22 no.9
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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.

• Journal of the Korea Society of Computer and Information
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• v.23 no.12
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• pp.1-9
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• 2018

The multiple-choice multidimensional knapsack problem (MMKP) is a variant of the well known 0-1 knapsack problem, which is known as an NP-hard problem. This paper proposes a method for solving the MMKP using the integer programming-based local search (IPbLS). IPbLS is a kind of a local search and uses integer programming to generate a neighbor solution. The most important thing in IPbLS is the way to select items participating in the next integer programming step. In this paper, three ways to select items are introduced and compared on 37 well-known benchmark data instances. Experimental results shows that the method using linear programming is the best for the MMKP. It also shows that the proposed method can find the equal or better solutions than the best known solutions in 23 data instances, and the new better solutions in 13 instances.