• Journal of Advanced Navigation Technology
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• v.18 no.2
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• pp.114-120
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• 2014

In this paper, we reviews the need and contents of aerodrome control simulator for air traffic controllers' training. In the view of managing the aviation safety, the departure and landing phases of aircraft are very important, because more than 60% of aircraft accidents and incidents have occurred in the take-off and landing phases. According to the benchmark each as practice type, simulation device and fidelity of reality of the air traffic control simulator, we have evaluated the implementation level of the domestic air traffic control simulator and checked up the current simulator's problems through the air traffic controllers' survey. Therefore, we suggest to the direction of developing a HI-FI simulator for aerodrome controllers.

• Journal of KIISE:Computer Systems and Theory
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• v.29 no.6
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• pp.361-368
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• 2002

This paper presents a new data path synthesis algorithm which takes into account simultaneously three important design criteria: testability, design area, and total execution time. We define a goodness measure on the testability of a circuit based on three rules of thumb introduced in prior work on synthesis for testability. We then develop a stepwise refinement synthesis algorithm which carries out the scheduling and allocation tacks in an integrated fashion. Experimental results for benchmark and other circuit examples show that we are able to enhance the testability of circuits with very little overheads on design area and execution time.

• Journal of Computing Science and Engineering
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• v.9 no.2
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• pp.39-50
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• 2015

Genetic algorithm (GA) and particle swarm optimization (PSO) are two excellent approaches to multimodal optimization problems. However, slow convergence or premature convergence readily occurs because of inappropriate and inflexible evolution. In this paper, a novel optimization algorithm with a flexible grid optimization (FGO) is suggested to provide adaptive trade-off between exploration and exploitation according to the specific objective function. Meanwhile, a uniform agents array with adaptive scale is distributed on the gird to speed up the calculation. In addition, a dominance centroid and a fitness center are proposed to efficiently determine the potential guides when the population size varies dynamically. Two types of subregion division strategies are designed to enhance evolutionary diversity and convergence, respectively. By examining the performance on four benchmark functions, FGO is found to be competitive with or even superior to several other popular algorithms in terms of both effectiveness and efficiency, tending to reach the global optimum earlier. Moreover, FGO is evaluated by applying it to a parameter decision in a least squares support vector machine (LS-SVM) to verify its practical competence.

• Journal of Computing Science and Engineering
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• v.5 no.4
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• pp.305-313
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• 2011

Due to the expansion of high-speed Internet access, the need for secure and reliable networks has become more critical. The sophistication of network attacks, as well as their severity, has also increased recently. As such, more and more organizations are becoming vulnerable to attack. The aim of this research is to classify network attacks using neural networks (NN), which leads to a higher detection rate and a lower false alarm rate in a shorter time. This paper focuses on two classification types: a single class (normal, or attack), and a multi class (normal, DoS, PRB, R2L, U2R), where the category of attack is also detected by the NN. Extensive analysis is conducted in order to assess the translation of symbolic data, partitioning of the training data and the complexity of the architecture. This paper investigates two engines; the first engine is the back-propagation neural network intrusion detection system (BPNNIDS) and the second engine is the radial basis function neural network intrusion detection system (BPNNIDS). The two engines proposed in this paper are tested against traditional and other machine learning algorithms using a common dataset: the DARPA 98 KDD99 benchmark dataset from International Knowledge Discovery and Data Mining Tools. BPNNIDS shows a superior response compared to the other techniques reported in literature especially in terms of response time, detection rate and false positive rate.

• International Journal of Aeronautical and Space Sciences
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• v.10 no.2
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• pp.77-85
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• 2009

A two-dimensional cross-section analysis program based on the finite element method has been developed for composite blades with arbitrary cross-section profiles and material distributions. The modulus weighted approach is used to take into account the non-homogeneous material characteristics of advanced blades. The CLPT (Classical Lamination Plate Theory) is applied to obtain the effective moduli of the composite laminate. The location of shear center for any given cross-sections are determined according to the Trefftz' definition while the torsion constants are obtained using the St. Venant torsion theory. A series of benchmark examples for beams with various cross-sections are illustrated to show the accuracy of the developed cross-section analysis program. The cross section cases include thin-walled C-channel, I-beam, single-cell box, NACA0012 airfoil, and KARI small-scale blades. Overall, a reasonable correlation is obtained in comparison with experiments or finite element analysis results.

• Journal of the Korea Society of Computer and Information
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• v.10 no.5 s.37
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• pp.11-16
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• 2005

Recently, there are a lot of endeavor to implement a fuzzy theory to artificial neural network. Goh proposed the fuzzy single layer perceptron algorithm and advanced fuzzy perceptron based on the generalized delta rule to solve the XOR Problem and the classical Problem. However, it causes an increased amount of computation and some difficulties in application of the complicated image recognition. In this paper, we propose an enhanced fuzzy single layer Perceptron using the dynamic adjustment of threshold. This method is applied to the XOR problem, which used as the benchmark in the field of pattern recognition. The method is also applied to the recognition of digital image for image application. In a result of experiment, it does not always guarantee the convergence. However, the network show improved the learning time and has the high convergence rate.

• Journal of Advanced Marine Engineering and Technology
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• v.38 no.8
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• pp.1004-1009
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• 2014

The redundancy allocation problem has usually considered only the component redundancy at the lowest-level for the enhancement of system reliability. A system can be functionally decomposed into system, module, and component levels. Modular redundancy can be more effective than component redundancy at the lowest-level because in modular systems, duplicating a module composed of several components can be easier, and requires less time and skill. We consider a multi-level redundancy allocation problem in which all cases of redundancy for system, module, and component levels are considered. A tabu search of memory-based mechanisms that balances intensification with diversification via the short-term and long-term memory is proposed for its solution. To the best of our knowledge, this is the first attempt to use a tabu search for this problem. Our tabu search algorithm is compared with the previous genetic algorithm for the problem on the new composed test problems as well as the benchmark problems from the literature. Computational results show that the proposed method outstandingly outperforms the genetic algorithm for almost all test problems.

• International Journal of Control, Automation, and Systems
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• v.6 no.4
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• pp.571-582
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• 2008

This paper analyzes global convergence of the univariate dynamic encoding algorithm for searches (uDEAS) and provides an application result to function optimization. uDEAS is a more advanced optimization method than its predecessor in terms of the number of neighborhood points. This improvement should be validated through mathematical analysis for further research and application. Since uDEAS can be categorized into the generating set search method also established recently, the global convergence property of uDEAS is proved in the context of the direct search method. To show the strong performance of uDEAS, the global minima of four 30 dimensional benchmark functions are attempted to be located by uDEAS and the other direct search methods. The proof of global convergence and the successful optimization result guarantee that uDEAS is a reliable and effective global optimization method.

• Computational Structural Engineering : An International Journal
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• v.3 no.1
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• pp.61-68
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• 2003

A recently developed semi-active control system employing magneto-rheological (MR) fluid dampers is applied to vibration control of a wind excited tall building. The semi-active control system with MR fluid dampers appears to have the reliability of passive control devices and the adaptability of fully active control systems. The system requires only small power source, which is critical during severe events, when the main power source may fail. Numerical simulation studies are performed to demonstrate the efficiency of the MR dampers on the third ASCE benchmark problem. Multiple MR dampers are assumed to be installed in the 76-story building. Genetic algorithm is applied to determine the optimal locations and capacities of the MR dampers. Clipped optimal controller is designed to control the MR dampers based on the acceleration feedback. To verify the robustness with respect to the variation of the external wind force, several cases with different wind forces are considered in the numerical simulation. Simulation results show that the semi-actively controlled MR dampers can effectively reduce both the peak and RMS responses the tall building under various wind force conditions. The control performance of the MR dampers for wind is found to be fairly similar to the performance of an active tuned mass damper.

• Nuclear Engineering and Technology
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• v.17 no.2
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• pp.129-135
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• 1985

Effects of differences between fine- and broad-group structures and spectrum as a weighting function at the resonance energy region of iron on a neutron shielding calculation were analyzed with the ANISN code and ENDF/B-IV data. The problems analyzed are the broad-group effect, the effect for variation of iron thickness, and the effect of problem-dependent weighting spectrum. In order to verify the group data and method used, a calculational benchmark was performed with the continuous-energy Monte Carlo code VIM. The result was compared with the ANISN calculations using the fine- and broad-group data.