• Journal of Korea Society of Industrial Information Systems
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• v.13 no.1
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• pp.30-36
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• 2008

Recently, ere are many researches to improve the quality of e medical service such as Point of care (POC). To improve the quality of the medical service, not only good medical device but also more man power is required. Especially, the number of nurses are very few in Korea that is almost the lowest rank compared to OECD countries. If the simple repetition works of the nurse could be removed, it is possible to use the skillful nurse for other works and provide better quality services. There are many simple repetition works which the nurses have to do, such as replacing the ringer's solution. To replace the ringer's solution automatically, it is necessary to detect residual quantity of the ringer's solution. In this paper, image processing is used to detect the residual quantity of ringer's solution, and modified self quotient image (SQI) algorithm is used to strong background lights. After modified SQI algorithm, the simple histogram accumulation is done to find the residual quantity of the ringer's solution. The implemented algorithm could be use to replace the ringer's solution automatically or alarm to the nurses to replace the solution.

• Journal of the Institute of Convergence Signal Processing
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• v.3 no.3
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• pp.63-69
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• 2002

Detailed routing is a problem assigning each net to a track after global routing. The most popular algorithms for detailed routing include left-edge algorithm, dogleg algorithm, and greedy channel routing algorithm. In this paper we propose a genetic algorithm searching solution space for the detailed routing problem. We compare the performance of proposed genetic algorithm(GA) for detailed routing with that of greedy channel routing algorithm by analyzing the results of each implementation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.1
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• pp.201-209
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• 2014

This paper proposes a genetic algorithm to maximize the connectivity among the mobile nodes for the cooperative communication in ad-hoc networks. In general, as the movement of the mobile nodes in the networks increases, the amount of calculation for finding the solution would be too much increased. To obtain the optimal solution within a reasonable computation time for a high-density network, we propose a genetic algorithm to obtain the optimal solution for maximizing the connectivity. In order to make a search more efficient, we propose some efficient neighborhood generating operations of the genetic algorithm. We evaluate those performances through some experiments in terms of the maximum number of connections and the execution time of the proposed algorithm. The comparison results show that the proposed algorithm outperforms other existing algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.11
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• pp.4347-4366
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• 2015

Distinguished with traditional strategies which offload an application's computation to a single server, parallel computation offloading can promote the performance by simultaneously delivering the computation to multiple computing resources around the mobile terminal. However, due to the variability of communication and computation environments, static application component multi-partitioning algorithms are difficult to maintain the optimality of their solutions in time-varying scenarios, whereas, over-frequent algorithm executions triggered by changes of environments may bring excessive algorithm costs. To this end, an adaptive application component mapping algorithm for parallel computation offloading in variable environments is proposed in this paper, which aims at minimizing computation costs and inter-resource communication costs. It can provide the terminal a suitable solution for the current environment with a low incremental algorithm cost. We represent the application component multi-partitioning problem as a graph mapping model, then convert it into a pathfinding problem. A genetic algorithm enhanced by an elite-based immigrants mechanism is designed to obtain the solution adaptively, which can dynamically adjust the precision of the solution and boost the searching speed as transmission and processing speeds change. Simulation results demonstrate that our algorithm can promote the performance efficiently, and it is superior to the traditional approaches under variable environments to a large extent.

• International Journal of Computer Science & Network Security
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• v.23 no.2
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• pp.55-64
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• 2023

The travelling salesman problem (TSP) is an important combinatorial optimization problem that is used in several engineering science branches and has drawn interest to several researchers and scientists. In this problem, a salesman from an arbitrary node, called the warehouse, starts moving and returns to the warehouse after visiting n clients, given that each client is visited only once. The objective in this problem is to find the route with the least cost to the salesman. In this study, a meta-based ant colony system algorithm (ACSA) is suggested to find solution to the TSP that does not use local pheromone update. This algorithm uses the global pheromone update and new heuristic information. Further, pheromone evaporation coefficients are used in search space of the problem as diversification. This modification allows the algorithm to escape local optimization points as much as possible. In addition, 3-opt local search is used as an intensification mechanism for more quality. The effectiveness of the suggested algorithm is assessed on a several standard problem instances. The results show the power of the suggested algorithm which could find quality solutions with a small gap, between obtained solution and optimal solution, of 1%. Additionally, the results in contrast with other algorithms show the appropriate quality of competitiveness of our proposed ACSA.

• International Journal of Control, Automation, and Systems
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• v.1 no.3
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• pp.358-367
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• 2003

We consider discrete-time factorial Markov Decision Processes (MDPs) in multiple decision-makers environment for infinite horizon average reward criterion with a general joint reward structure but a factorial joint state transition structure. We introduce the "localization" concept that a global MDP is localized for each agent such that each agent needs to consider a local MDP defined only with its own state and action spaces. Based on that, we present a gradient-ascent like iterative distributed algorithm that converges to a local optimal solution of the global MDP. The solution is an autonomous joint policy in that each agent's decision is based on only its local state.cal state.

• Journal of Korean Institute of Industrial Engineers
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• v.16 no.2
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• pp.91-98
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• 1990

This paper studies a heuristic method for the Maximum Origin-Destination Flow Path (MODFP) in an acyclic transportation network. We construct a mathematical formulation for finding the MODFP. Then by applying Benders' partitioning method, we generate two subproblems which should be solved in turn so that they may give an optimal solution. We solve one subproblem by an optimal seeking algorithm and the other by a hueristic method. so that, we finally obtain a good solution. The computational complexity of calculating the optimal solution of the first subproblem is 0(mn) and that of calculating the heuristic solution of the other subproblem is $0(n^2).$ From the computational experiments, we estimated the performance of the heuristic method as being 99.3% and the computing time relative to optimal algorithm as being 28.76%.

• Journal of Korean Institute of Industrial Engineers
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• v.28 no.1
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• pp.87-98
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• 2002

A competitive coevolutionary algorithm is a probabilistic search method that imitates coevolution process through evolutionary arms race. The algorithm has been used to solve adversarial problems. In the algorithms, the selection of competitors is needed to evaluate the fitness of an individual. The goal of this study is to compare and analyze several competition strategies in terms of solution quality, convergence speed, balance between competitive coevolving species, population diversity, etc. With two types of test-bed problems, game problems and solution-test problems, extensive experiments are carried out. In the game problems, sampling strategies based on fitness have a risk of providing bad solutions due to evolutionary unbalance between species. On the other hand, in the solution-test problems, evolutionary unbalance does not appear in any strategies and the strategies using information about competition results are efficient in solution quality. The experimental results indicate that the tournament competition can progress an evolutionary arms race and then is successful from the viewpoint of evolutionary computation.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.6 no.3
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• pp.11-20
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• 1986

The purpose of this study is to devise an efficient and economic solution algorithm for the nonlinear finite element equations. First, procedures and characteristics of the solution methods of ordinary nonlinear equations are critically reviewed and discussed. Based on the discussion, some promising nonlinear finite element analysis procedures are presented as an algorithmic form. Finally, a conceptually combined algorithm for a solution of nonlinear finite element equations is proposed and analyzed, in which the computational effort is minimized and numerical difficulties can be avoided.

• Journal of Satellite, Information and Communications
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• v.3 no.2
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• pp.32-37
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• 2008

Global Navigation Satellite System (GNSS) is been developing in many countries. The satellite navigation system has the importance in economic and military fields. For utilizing satellite navigation system properly, the technology of GNSS Ground Station is needed. GNSS Ground Station monitors the signal of navigation satellite and analyzes navigation solution. This study deals with the navigation software for GNSS Ground Station. This paper will introduce the navigation solution algorithm for GNSS Ground Station. The navigation solution can be calculated by the code-carrier smoothing method, the Kalman-filter method, the least-square method, and the weight least square method. The performance of each navigation algorithm in this paper is presented.