• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.4
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• pp.626-648
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• 2011

Energy consumption of wireless access networks is in permanent increase, which necessitates development of more energy-efficient network management approaches. Such management schemes must result with adaptation of network energy consumption in accordance with daily variations in user activity. In this paper, we consider possible energy savings of wireless local area networks (WLANs) through development of a few integer linear programming (ILP) models. Effectiveness of ILP models providing energy-efficient management of network resources have been tested on several WLAN instances of different sizes. To cope with the problem of high computational time characteristic for some ILP models, we further develop several heuristic algorithms that are based on greedy methods and local search. Although heuristics obtains somewhat higher results of energy consumption in comparison with the ones of corresponding ILP models, heuristic algorithms ensures minimization of network energy consumption in an amount of time that is acceptable for practical implementations. This confirms that network management algorithms will play a significant role in practical realization of future energy-efficient network management systems.

• Geomechanics and Engineering
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• v.8 no.1
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• pp.97-111
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• 2015

The kinematical approach of limit analysis is used to estimate the three dimensional stability analysis of rock slopes with nonlinear Hoek-Brown criterion under earthquake forces. The generalized tangential technique is introduced, which makes limit analysis apply to rock slope problem possible. This technique formulates the three dimensional stability problem as a classical nonlinear programming problem. A nonlinear programming algorithm is coded to search for the least upper bound solution. To prove the validity of the present approach, static stability factors are compared with the previous solutions, using a linear failure criterion. Three dimensional seismic and static stability factors are calculated for rock slopes. Numerical results of indicate that the factors increase with the ratio of slope width and height, and are presented for practical use in rock engineering.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.10
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• pp.137-146
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• 2004

This is the second of two companion papers concerned with the analysis and design of a pneumatic vibration isolation system. The properties of the system are clarified by observation of the transmissibility surface calculated by the models and algorithm developed in the first paper of this research. It Is shown that the nonlinear model proposed in this research is more closer to experimental results than the linear model that have been used in previous studies. The design optimization of the major design variables that affect the performance of the system is achieved by using the condition for attenuation, disturbance rejection and maximum damping in resonance peak. The design space search method is adopted for the optimization of the orifice area. The models, transmissibility calculation algorithms and design optimization techniques developed in this research are shown to be greatly helpful to the optimal design of the pneumatic vibration isolation system by experiment.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.24 no.6
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• pp.845-851
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• 2000

In this paper, we show the numerical and the experimental results of two-dimensional fluid motions inside a rectangular container subjected to a background rotation added by a rotational oscillation. In the PlY experiment we apply a new algorithm, new three step search(NTSS), to the velocity calculation. In the numerical computation, the linear Ekman-pumping model was used to take the bottom friction effect into account. It was found that it well produces the experimental results at low e number.

• Advances in Computational Design
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• v.1 no.4
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• pp.315-327
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• 2016

Damage detection and localisation in structures is essential since it can be a means for preventive maintenance of those structures under service conditions. The use of structural modal data for detecting the damage is one of the most efficient methods. This paper presents comparative performance of various state-of-the-art meta-heuristics for use in structural damage detection based on changes in modal data. The metaheuristics include differential evolution (DE), artificial bee colony algorithm (ABC), real-code ant colony optimisation (ACOR), charged system search (ChSS), league championship algorithm (LCA), simulated annealing (SA), particle swarm optimisation (PSO), evolution strategies (ES), teaching-learning-based optimisation (TLBO), adaptive differential evolution (JADE), evolution strategy with covariance matrix adaptation (CMAES), success-history based adaptive differential evolution (SHADE) and SHADE with linear population size reduction (L-SHADE). Three truss structures are used to pose several test problems for structural damage detection. The meta-heuristics are then used to solve the test problems treated as optimisation problems. Comparative performance is carried out where the statistically best algorithms are identified.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.7
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• pp.424-433
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• 2003

Evolutionary design related to the optimal design of Polynomial Neural Networks (PNNs) structure for model identification of complex and nonlinear system is studied in this paper. The PNN structure is consisted of layers and nodes like conventional neural networks but is not fixed and can be changable according to the system environments. three types of polynomials such as linear, quadratic, and modified quadratic is used in each node that is connected with various kinds of multi-variable inputs. Inputs and order of polynomials in each node are very important element for the performance of model. In most cases these factors are decided by the background information and trial and error of designer. For the high reliability and good performance of the PNN, the factors must be decided according to a logical and systematic way. In the paper evolutionary algorithm is applied to choose the optimal input variables and order. Evolutionary (genetic) algorithm is a random search optimization technique. The evolved PNN with optimally chosen input variables and order is not fixed in advance but becomes fully optimized automatically during the identification process. Gas furnace and pH neutralization processes are used in conventional PNN version are modeled. It shows that the designed PNN architecture with evolutionary structure optimization can produce the model with higher accuracy than previous PNN and other works.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.52 no.7
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• pp.424-424
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• 2003

Evolutionary design related to the optimal design of Polynomial Neural Networks (PNNs) structure for model identification of complex and nonlinear system is studied in this paper. The PNN structure is consisted of layers and nodes like conventional neural networks but is not fixed and can be changable according to the system environments. three types of polynomials such as linear, quadratic, and modified quadratic is used in each node that is connected with various kinds of multi-variable inputs. Inputs and order of polynomials in each node are very important element for the performance of model. In most cases these factors are decided by the background information and trial and error of designer. For the high reliability and good performance of the PNN, the factors must be decided according to a logical and systematic way. In the paper evolutionary algorithm is applied to choose the optimal input variables and order. Evolutionary (genetic) algorithm is a random search optimization technique. The evolved PNN with optimally chosen input variables and order is not fixed in advance but becomes fully optimized automatically during the identification process. Gas furnace and pH neutralization processes are used in conventional PNN version are modeled. It shows that the designed PNN architecture with evolutionary structure optimization can produce the model with higher accuracy than previous PNN and other works.

• Journal of Space Technology and Applications
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• v.2 no.2
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• pp.67-80
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• 2022

The missile early-warning satellite systems have been developed and upgraded by some space-developed nations, under the inevitable trend that the space is more strongly considered as another battle field than before. As the key function of such a satellite-based early warning system, the prediction algorithm of the missile flight trajectory is studied in the paper. In particular, the evolution computation, receiving broad attention in the artificial intelligence area, is applied to the proposed prediction method so that the global optimum-like solution is found avoiding disadvantage of the previous non-linear optimization search tools. Moreover, using the prediction simulator of the launch vehicle flight trajectory which is newly developed in C# and Python, the paper verifies the performance and the feature of the proposed algorithm.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.3
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• pp.103-108
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• 2024

Kakuro puzzles are NP-complete problems where no way to solve puzzles in polynomial time is known. Until now, a brute-force search method or a linear programming method has been applied to substitute all possible cases. This paper finds a magic rule, a rule for box sizes and unfilled numbers according to sum clues. Based on the magic rule, numbers that cannot enter empty cells were deleted from the box for row and column sum clues. Next, numbers that cannot enter the box were deleted based on the sum clue value. Finally, cells with only a single number were confirmed as clues. As a result of applying the proposed algorithm to seven benchmarking experimental data, it was shown that solutions could be obtained for all problems.

• Journal of the Korean Data and Information Science Society
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• v.20 no.4
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• pp.639-647
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• 2009

From development of computer science, genetic algorithm has been applied to many fields for search like non-linear problem based on various variables and optimization process. Among others, in the data mining field, there are methods to select the best input variables for model accuracy and various predict models which were merged by using the genetic algorithm. In the meantime, to improve and preserve quality of the Hanwoo (Korean cattle) which is represented the agricultural industry in our country, we need to find out outstanding economical traits of Hanwoo in having specific genotype of single nucleotide polymorphism (SNP) which is inherited to next generation. According to, This research proposed the selecting method to find genotype of SNPs marker which affects economical traits of the Hanwoo by using the genetic algorithm. And we selected the best genotypes of the principal SNPs marker by applying to real data on Hanwoo genetic.