• Journal of the Korean Society of Fisheries and Ocean Technology
• /
• v.25 no.3
• /
• pp.137-142
• /
• 1989

A pole assignment problem in a specified region is solved using a bilinear transformation method. It is well known that the problem designing discrete-time system or vice versa is called redesign problem, But there is not so much study that is cyclic type of design, i.e. from continuous system to discrete system and from the latter to the former system. In this paper, the cyclic type of design for the continuous-time system is proposed using the bilinear transformation. In the view of a pole assignment method with poles in a specified region, it will be possible to design a regulator or a servo system considering damping ratio, stability degree and band with which are resulted to the characteristics of the closed-loop system.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.8 no.11
• /
• pp.4087-4102
• /
• 2014

Two-way relaying is an effective way of improving system spectral efficiency by making use of physical layer network coding. However, energy efficiency in OFDM-based bidirectional relaying with asymmetric traffic requirement has not been investigated. In this study, we focused on subcarrier transmission mode selection, bit loading, and power allocation in a multicarrier single amplified-and-forward relay system. In this scheme, each subcarrier can operate in two transmission modes: one-way relaying and two-way relaying. The problem is formulated as a mixed integer programming problem. We adopt a structural approximation optimization method that first decouples the original problem into two suboptimal problems with fixed subcarrier subsets and then finds the optimal subcarrier assignment subsets. Although the suboptimal problems are nonconvex, the results obtained for a single-tone system are used to transform them to convex problems. To find the optimal subcarrier assignment subsets, an iterative algorithm based on subcarrier ranking and matching is developed. Simulation results show that the proposed method can improve system performance compared with conventional methods. Some interesting insights are also obtained via simulation.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.5 no.2
• /
• pp.269-286
• /
• 2011

In this paper, we study a problem that is concerning how to construct a delay-constrained multicast tree on a wireless mesh network (WMN) such that the number of serviced clients is maximized. In order to support high-quality and concurrent interference-free transmission streams, multiple radios are implemented in each mesh node in the WMNs. Instead of only orthogonal channels used for the multicast in the previous works, both orthogonal and partially overlapping channels are considered in this study. As a result, the number of links successfully allocated channels can be expected to be much larger than that of the approaches in which only orthogonal channels are considered. The number of serviced subscribers is then increased dramatically. Hence, the goal of this study is to find interference-free and delay-constrained multicast trees that can lead to the maximal number of serviced subscribers. This problem is referred as the MRDCM problem. Two heuristics, load-based greedy algorithm and load-based MCM algorithm, are developed for constructing multicast trees. Furthermore, two load-based channel assignment procedures are provided to allocate interference-free channels to the multicast trees. A set of experiments is designed to do performance, delay and efficiency comparisons for the multicast trees generated by all the approximation algorithms proposed in this study.

• Journal of Intelligence and Information Systems
• /
• v.16 no.2
• /
• pp.129-141
• /
• 2010

An Internet shopping mall for clothing operates a warehouse for packing and shipping products to fulfill its orders. All the products in the warehouse are put into the boxes of same brands and the boxes are stored in a row on shelves equiped in the warehouse. To make picking and managing easy, boxes of the same brands are located side by side on the shelves. When new products arrive to the warehouse for storage, the products of a brand are put into boxes and those boxes are located adjacent to the boxes of the same brand. If there is not enough space for the new coming boxes, however, some boxes of other brands should be moved away and then the new coming boxes are located adjacent in the resultant vacant spaces. We want to minimize the movement of the existing boxes of other brands to another places on the shelves during the warehousing of new coming boxes, while all the boxes of the same brand are kept side by side on the shelves. Firstly, we define the adjacency of boxes by looking the shelves as an one dimensional series of spaces to store boxes, i.e. cells, tagging the series of cells by a series of numbers starting from one, and considering any two boxes stored in the cells to be adjacent to each other if their cell numbers are continuous from one number to the other number. After that, we tried to formulate the problem into an integer programming model to obtain an optimal solution. An integer programming formulation and Branch-and-Bound technique for this problem may not be tractable because it would take too long time to solve the problem considering the number of the cells or boxes in the warehouse and the computing power of the Internet shopping mall. As an alternative approach, we designed a fast heuristic method for this reallocation problem by focusing on just the unused spaces-empty cells-on the shelves, which results in an assignment problem model. In this approach, the new coming boxes are assigned to each empty cells and then those boxes are reorganized so that the boxes of a brand are adjacent to each other. The objective of this new approach is to minimize the movement of the boxes during the reorganization process while keeping the boxes of a brand adjacent to each other. The approach, however, does not ensure the optimality of the solution in terms of the original problem, that is, the problem to minimize the movement of existing boxes while keeping boxes of the same brands adjacent to each other. Even though this heuristic method may produce a suboptimal solution, we could obtain a satisfactory solution within a satisfactory time, which are acceptable by real world experts. In order to justify the quality of the solution by the heuristic approach, we generate 100 problems randomly, in which the number of cells spans from 2,000 to 4,000, solve the problems by both of our heuristic approach and the original integer programming approach using a commercial optimization software package, and then compare the heuristic solutions with their corresponding optimal solutions in terms of solution time and the number of movement of boxes. We also implement our heuristic approach into a storage location assignment system for the Internet shopping mall.

• Journal of the Institute of Electronics Engineers of Korea SD
• /
• v.41 no.1
• /
• pp.81-90
• /
• 2004

The state assignment for a finite state machine greatly affects the delay, area, power dissipation, and testabilities of the sequential circuits. In order to improve the testabilities and power consumption, a new state assignment technique . based on m-block partition is introduced in this paper. By the m-block partition algorithm, the dependencies among groups of state variables are minimized and switching activity is further reduced by assigning the codes of the states in the same group considering the state transition probability among the states. In the sequel the length and number of feedback cycles are reduced with minimal switching activity on state variables. It is inherently contradictory problem to optimize the testability and power consumption simultaneously, however our new state assignment technique is able to achieve high fault coverage with less number of scan nfp flops by reducing the number of feedback cycles while the power consumption is kept low upon the low switching activities among state variables. Experiment shows drastic improvement in testabilities and power dissipation for benchmark circuits.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.40 no.11
• /
• pp.988-996
• /
• 2012

This paper presents a distributed task assignment algorithm for the suppression of enemy air defense (SEAD) mission of heterogeneous UAVs, based on the consensus-based bundle algorithm (CBBA). SEAD mission can be modeled as a task assignment problem of multiple UAVs performing multiple air defense targets, and UAVs performing SEAD mission consist of the weasel for destruction of enemy's air defense system and the striker for the battle damage assessment (BDA) or other tasks. In this paper, a distributed task assignment algorithm considering path-planning in presence of terrain obstacle is developed for heterogeneous UAVs, and then it is applied to SEAD mission. Through numerical simulations the performance and the applicability of the proposed method are tested.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.32 no.3
• /
• pp.118-126
• /
• 2009

This research investigates the problem of minimizing setup costs in resequencing jobs having first-in, first-out(FIFO) constraints at conveyorized production or assembly systems. Sequence changing at conveyor junctions in these systems is limited due to FIFO restriction. We first define the general problem of resequencing jobs to workstations satisfying precedence relationships between jobs(Generalized Sequential Ordering Problem, GSOP). Then we limit our scope to FIFO precedence relationships which is the conveyor selection problem at a diverging junction(Diverging Sequential Ordering Problem, DSOP), modeling it as a 0-1 integer program. With the capacity constraint removed, we show that the problem can be modeled as an assignment problem. In addition, we proposed and evaluated the heuristic algorithm for the case where the capacity constraint cannot be removed. Finally, we discuss the case study which motivated this research and numerical results.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.23 no.5
• /
• pp.151-158
• /
• 2023

The researchers entirely focused on meta-heuristic method for generalized assignment problem(GAP) that is known as NP-hard problem because of the optimal solution within polynomial time algorithm is unknown yet. On the other hand, this paper proposes a heuristic greedy algorithm with rules for finding solutions. Firstly, this paper reduces the weight matrix of original data to w_ij ≤ b_i/l in order to n jobs(items) pack m machines(bins) with l = n/m. The maximum profit of each job was assigned to the machine for the reduced data. Secondly, the allocation was adjusted so that the sum of the weights assigned to each machine did not exceed the machine capacity. Finally, the k-opt swap optimization was performed to maximize the profit. The proposed algorithm is applied to 50 benchmarking data, and the best known solution for about 1/3 data is to solve the problem. The remaining 2/3 data showed comparable results to metaheuristic techniques. Therefore, the proposed algorithm shows the possibility that rules for finding solutions in polynomial time exist for GAP. Experiments demonstrate that it can be a P-problem from an NP-hard.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.18 no.3
• /
• pp.35-49
• /
• 1993

The node coloring problem is a problem to color the nodes of a graph using the minimum number of colors possible so that any two adjacent nodes are colored differently. This problem, along with the edge coloring problem, has a variety of practical applications particularly in item loading, resource allocation, exam timetabling, and channel assignment. The node coloring problem is an NP-hard problem, and thus many researchers develop a number of heuristic algorithms. In this paper, we survey and classify those heuristics with the emphasis on how an algorithm orders the nodes and colors the nodes using a determined ordering.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2000.11a
• /
• pp.163-167
• /
• 2000

The eligibility is used to solve the credit-assignment problem which is one of important problems in reinforcement learning. Conventional eligibilities which are accumulating eligibility and replacing eligibility make ineffective use of rewards acquired in learning process. Because only an executed action in a visited state is learned by these eligibilities. Thus, we propose a new eligibility, called the weighted eligibility with which not only an executed action but also neighboring actions in a visited state are to be learned. The fuzzy Q-learning algorithm using proposed eligibility is applied to a cart-pole balancing problem, which shows improvement of learning speed.