• Journal of Institute of Control, Robotics and Systems
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• v.4 no.6
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• pp.780-785
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• 1998

In this paper, it is shown that the conventional methods for dealing with the singularity problem of a manipulator can be generalized as a local minimization problem with differently weighted objective functions. A new damping method proposed in this article automatically determines the damping amounts for singular values, which are inversely proportional to the magnitude of the singular values. Furthermore, this can be done without explicitly computing the singular values. The proposed method can be applied to all the manipulators with revolute joints.

• Journal of applied mathematics & informatics
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• v.25 no.1_2
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• pp.305-314
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• 2007

This paper considers single machine scheduling problems where the processing time of a job increases as a function of its position in the sequence. In this model, the later a given job is scheduled in the sequence, the longer its processing time. It is shown that the optimal schedule may be very different from that of the classical version of the problem. We introduce polynomial solutions for the makespan minimization problem, the sum of completion times minimization problem and the sum of earliness penalties minimization problem. For two resource constrained problems, based on the analysis of the problems, the optimal resource allocation methods are presented, respectively.

• Journal of applied mathematics & informatics
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• v.7 no.3
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• pp.887-894
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• 2000

In this paper we consider the corrected version of the algorithm of the state-minimization for the nondeterministic finite automata: we correct here a mistake of the previous paper, where the same problem was considered.

• Korean Journal of Computational Design and Engineering
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• v.17 no.4
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• pp.282-293
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• 2012

This paper presents an error-bounded B-spline fitting technique to approximate unorganized data within a prescribed error tolerance. The proposed approach includes two main steps: leastsquares minimization and error-bounded approximation. A B-spline hypervolume is first described as a data representation model, which includes its mathematical definition and the data structure for implementation. Then we present the least-squares minimization technique for the generation of an approximate B-spline model from the given data set, which provides a unique solution to the problem: overdetermined, underdetermined, or ill-conditioned problem. We also explain an algorithm for the error-bounded approximation which recursively refines the initial base model obtained from the least-squares minimization until the Euclidean distance between the model and the given data is within the given error tolerance. The proposed approach is demonstrated with some examples to show its usefulness and a good possibility for various applications.

• International journal of advanced smart convergence
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• v.13 no.2
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• pp.88-93
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• 2024

The main problem with existing edge detection techniques is that they have many limitations in detecting edges for complex and diverse images that exist in the real world. This is because only edges of a defined shape are discovered based on an accurate definition of the edge. One of the methods to solve this problem is the cost minimization method. In the cost minimization method, cost elements and cost functions are defined and used. The cost function calculates the cost for the candidate edge model generated according to the candidate edge generation strategy, and if the cost is found to be satisfactory, the candidate edge model becomes the edge for the image. In this study, we proposed an enhanced candidate edge generation strategy to discover edges for more diverse types of images in order to improve the shortcoming of the cost minimization method, which is that it only discovers edges of a defined type. As a result, improved edge detection results were confirmed.

• Proceedings of the KIEE Conference
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• 2002.07a
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• pp.320-326
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• 2002

Distribution system loss minimization re-configuration is 0-1 planning problem, and the number of combinations requiring searches is extremely large when dealing with typical system scales. For this reason, the application of a genetic algorithm (GA) seems attactive to solve this problem. Although Genetic algorithms are a type of random number search method, they incorporate a multi-point search feature and are therefore superior to one-point search techniques. The efficiency of GAs for solving large combinational problem has received wide attention. Further, parallel searching can be performed and the optimal solution is more easily reached. In this paper, for improving GA convergence characteristics in the distribution system loss minimization re-configeration problem, a chromosome "Limited Life" concept is intro duced. Briefly, considering the population homogenization and genetic drift problems, natural selection is achieved by providing this new concept, in addition to natural selection by fitness. This is possible because individuals in a population have an age value. Simulations were carried out using a model system to check this method's validity.

• Proceedings of the KIEE Conference
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• 2001.07a
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• pp.6-9
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• 2001

This paper presents a new method which applies a genetic algorithm(GA) for determining which sectionalizing switch to operate in order to solve the distribution system loss minimization re-configuration problem. The distribution system loss minimization re-configuration problem is in essence a 0-1 planning problem which means that for typical system scales the number of combinations requiring searches becomes extremely large. In order to deal with this problem, a new approach which applies a GA was presented. Briefly, GA are a type of random number search method, however, they incorporate a multi-point search feature. Further, every point is not is not separately and respectively renewed, therefore, if parallel processing is applied, we can expect a fast solution algorithm to result.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.24 no.2
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• pp.161-197
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• 2020

Total variation minimization is standard in mathematical imaging and there have been numerous researches over the last decades. In order to process large-scale images in real-time, it is essential to design parallel algorithms that utilize distributed memory computers efficiently. The aim of this paper is to illustrate recent advances of domain decomposition methods for total variation minimization as parallel algorithms. Domain decomposition methods are suitable for parallel computation since they solve a large-scale problem by dividing it into smaller problems and treating them in parallel, and they already have been widely used in structural mechanics. Differently from problems arising in structural mechanics, energy functionals of total variation minimization problems are in general nonlinear, nonsmooth, and nonseparable. Hence, designing efficient domain decomposition methods for total variation minimization is a quite challenging issue. We describe various existing approaches on domain decomposition methods for total variation minimization in a unified view. We address how the direction of research on the subject has changed over the past few years, and suggest several interesting topics for further research.

• Management Science and Financial Engineering
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• v.13 no.1
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• pp.93-113
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• 2007

In the previous research, we considered a logistics brokerage problem with the objective of minimizing total transportation lead time of freights in a logistics e-marketplace, in which a logistics brokerage agent intermediates empty vehicles and freights registered by car owners and shippers [7]. However, in the logistics e-marketplace, transportation due date tardiness is more important than the transportation lead time, since transportation service level is critically determined by whether the due date is met or not. Therefore, in this paper, we deal with the logistics brokerage problem with the objective of minimizing total tardiness of freights. Hungarian method based matching algorithms, real time matching(RTM), periodic matching(PM), and fixed matching(FM), are used for solving the problem considered in this paper. In order to test performance of the proposed algorithms, we perform computational experiments on a various problem instances. The results show that the waiting-and-matching algorithms, PM and FM, also give better performance than real time matching strategy, RTM, for the total tardiness minimization problem as the algorithms did for the total lead time minimization problem.

• Nonlinear Functional Analysis and Applications
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• v.26 no.4
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• pp.649-662
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• 2021

Recently the author studied a one-parameter family of divergences and considered the related median minimization problem of finite points over these divergences in general symmetric cones. In this article, to utilize the results practically, we deal with a special symmetric cone called second order cone, which is important in optimization fields. To be more specific, concrete computations of divergences with its gradients and the unique minimizer of the median minimization problem of two points are provided skillfully.