• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.11
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• pp.2801-2809
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• 1996

Scheduling problems in real-time systems are known to be NP-hard. the heuristic approaches aregenerally aplied to solve a certain class of systems. One of such cases is to allocate periodic tasks to multiprocessors while the moethod assures the requirement of the deadine constraints of real-time systems. The study on the allocation of periodic taks includes RMNF, RMFF, FFDUF and Next-Fit-M algorithms, which make a set of task grups first and then allocate to processors. This papre proposes the various algorithms which are based on the Next-Fit-M. To analyze the four proposed methods, simulation was carried on, in which the sample tasks are randomly generated with the various time intervals. The proposed algorithms reduce the number of processors compared with the conventional methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.9B
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• pp.569-576
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• 2007

This paper is on the enhancement of our heuristics for GOSST problem that could apply to the design of communication networks offering graduated services. This problem hewn as one of NP-Hard problems finds a network topology meeting the G-Condition with minimum construction cost. In our prior research, we proposed two heuristics. We suggest methods of selecting G-Node and disconnections for Global or Local locating heuristic in this research. The ameliorated Local locating heuristic retrenches 17% more network construction cost saving ratio and the reformed Global locating heuristic does 14% more than our primitives.

• Management Science and Financial Engineering
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• v.22 no.1
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• pp.5-12
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• 2016

We consider a time-cost tradeoff problem with multiple milestones under a chain precedence graph. In the problem, some penalty occurs unless a milestone is completed before its appointed date. This can be avoided through compressing the processing time of the jobs with additional costs. We describe the compression cost as the convex or the concave function. The objective is to minimize the sum of the total penalty cost and the total compression cost. It has been known that the problems with the concave and the convex cost functions for the compression are NP-hard and polynomially solvable, respectively. Thus, we consider the special cases such that the cost functions or maximal compression amounts of each job are identical. When the cost functions are convex, we show that the problem with the identical costs functions can be solved in strongly polynomial time. When the cost functions are concave, we show that the problem remains NP-hard even if the cost functions are identical, and develop the strongly polynomial approach for the case with the identical maximal compression amounts.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.3
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• pp.622-627
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• 2006

TSP(Traveling Salesman Problem) is a problem finding out the shortest distance out of many courses where given cities of the number of N, one starts a certain city and turns back to a starting city, visiting every city only once. As the number of cities having visited increases, the calculation rate increases geometrically. This problem makes TSP classified in NP-Hard Problem and genetic algorithm is used representatively. To obtain a better result in TSP, various operators have been developed and studied. This paper suggests new method of population initialization and of sequential transformation, and then proves the improvement of capability by comparing them with existing methods.

• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.3
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• pp.65-73
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• 2017

The fifth generation(5G) of wireless networks will connect not only smart phone but also unimaginable things. Therefore, 5G cellular network is facing the soaring traffic demand of numerous user devices. To solve this problem, a huge amount of 5G base stations will need to be installed. The base station positioning problem is an NP-hard problem that does not know how long it will take to solve the problem. Because, it can not find an answer other than to check the number of all cases. In this paper, to solve the NP hard problem, we compare the tabu search and the genetic algorithm using real maps for optimal cell planning. We also perform Monte Carlo simulations to study the performance of the Tabu search and Genetic algorithm for 5G cell planning. As a results, Tabu search required 2.95 times less computation time than Genetic algorithm and showed accuracy difference of 2dBm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.6
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• pp.595-603
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• 1992

It has been establisued that the reliability of communication networks is NP hard problem owing to computationally and complexity as the number of componeuts is Increased in large networks. This paper proposed an algorithm for determining upper and lower bounds In the reliability of source-to-terminal in communication networks to solve this problem. The evaluation method follows the next procedures. First, minimal pathset and minimal cut set are serched. Second, it is sorted that the number of components is the same events and the reliability bounds Is evaluated by the section function to extract common variable. The performance of proposed algorithm is also estimate(1 as compared to the reliability of Esary-Proschan, Shogan and Copal.

• Journal of the Korea Society of Computer and Information
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• v.20 no.7
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• pp.33-40
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• 2015

The time slot assignment problem (TSAP) or Satellite Communications scheduling problem (SCSP) for a satellite performs $n{\times}n$ ground station data traffic switching has been known NP-hard problem. This paper suggests $O(n^2)$ time complexity algorithm for TSAP of a satellite that performs $n^2{\times}n^2$ ground station data traffic switching. This problem is more difficult than $n{\times}n$ TSAP as NP-hard problem. Firstly, we compute the average traffic for n-transponder's basic coverage zone and applies ground station exchange method that swap the ground stations until all of the transponders have a average value as possible. Nextly, we transform the D matrix to $D_{LB}$ traffic matrix that sum of rows and columns all of transponders have LB. Finally, we select the maximum traffic of row and column in $D_{LB}$, then decide the duration of kth switch mode to minimum traffic from selected values. The proposed algorithm can be get the optimal solution for experimental data.

• Journal of the Korea Society of Computer and Information
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• v.4 no.4
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• pp.40-46
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• 1999

The task scheduling has an effect on system execution time in a precedence constrained task graph onto the multiprocessor system. This problem is known to be NP-hard. many people made an effort to obtain near optimal schedule. We compared modified critical path schedule with many other methods(CP, MH, DL Swapping) For testing this subject, we created randomly a directed acyclic task graph with many root nodes and terminal nodes simulation result convinced for us that the modified critical path algorithm is superior to the other scheduling algorithm.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.4
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• pp.177-182
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• 2018

The home-and-way round-robin sports leagues scheduling problem with minimum brake is very hard to solve in polynomial time. This problem is NP-hard, the complexity status is not yet determined. This paper suggests round-robin sports leagues scheduling algorithm not computer-aided program but by hand with O(n) time complexity for arbitrary number of teams n with always same pattern. The algorithm makes a list of mathes using $n{\times}n$ canonical latin square for n=even teams. Then trying to get home(H) and away(A) with n-2 minimum number of brakes. Also, we get the n=odd scheduling with none brakes delete a team own maximum number of brakes from n=even scheduling.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.649-651
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• 2017

A clustering problem is one of the organizational problems to improve the network lifetime and scalability in wireless ad-hoc networks. This problem is a difficult combinatorial optimization problem associated with the design and operation of these networks. In this paper, we propose an efficient clustering algorithm to maximize the network lifetime and consider scalability in wireless ad-hoc networks. The clustering problem is known to be NP-hard. We thus solve the problem by using optimization approaches that are able to efficiently obtain high quality solutions within a reasonable time for a large size network. The proposed algorithm selects clusterheads and configures clusters by considering both nodes' power and the clustering cost. We evaluate this performance through some experiments in terms of nodes' transmission energy. Simulation results indicate that the proposed algorithm performs much better than the existing algorithms.