• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.27 no.9
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• pp.808-816
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• 2016

In this paper, a new technique and cost function which can be to classify jamming signal and target signal from the spectral distribution of received signal in order to minimize the main beam distortion of target signal and to form nulls in the direction of jamming signal in array antennas of single port system is proposed. The proposed cost function is applied to the adaptive algorithm which has the fast convergence and stable nulling performance through the combination of the PSO(Particle Swam Optimization) algorithm and the gradient-based perturbation algorithm, which shows stable nulling performance adaptively even under the moving jamming signal where the incident direction of the jamming signal is changing with time.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.2
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• pp.342-348
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• 2016

We propose an obstacle detection algorithm for unmanned ground vehicle on uneven terrain. The key ideas of the proposed algorithm are the use of two-layer laser range data to calculate the gradient of a target, which is characterized as either ground or obstacles. The proposed obstacle detection algorithm includes 4-steps: 1) Obtain the distance data for each angle from multiple lidars or a multi-layer scan lidar. 2) Calcualate the gradient for each angle of the uneven terrain. 3) Determine ground or obstacle for each angle on the basis of reference gradient. 4) Generate a new distance data for each angle for a virtual laser scanner. The proposed algorithm is verified by various experiments.

• Korean Journal of Artificial Intelligence
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• v.12 no.2
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• pp.17-23
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• 2024

Various methods have been developed to predict the flight path of an air-launched weapon to intercept a fast-moving target in the air. However, it is also getting more challenging to predict the optimal firing zone and provide it to a pilot in real-time during engagements for advanced weapons having new complicated guidance and thrust control. In this study, a method is proposed to develop an optimized weapon engagement zone by the SCG (Scaled Conjugate Gradient) algorithm to achieve both accurate and fast estimates and provide an optimized launch display to a pilot during combat engagement. SCG algorithm is fully automated, includes no critical user-dependent parameters, and avoids an exhaustive search used repeatedly to determine the appropriate stage and size of machine learning. Compared with real data, this study showed that the development of a machine learning-based weapon aiming algorithm can provide proper output for optimum weapon launch zones that can be used for operational fighters. This study also established a process to develop one of the critical aircraft-weapon integration software, which can be commonly used for aircraft integration of air-launched weapons.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.11
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• pp.73-84
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• 1992

In this paper, an algorithm for recovering the 3-D shape from a single shaded image is proposed. In the proposed algorithm, by using the relation between the height and surface gradient (p, q), a set of linear equations is derived from the linearized reflectance function. Then the 3-D surface is recovered by employing the conjugate gradient technique. In order to improve the convergence speed of the solution, we also employ the hierarchical basis function and multiresolution images in the algorithm. A method for determining the regularization parameter, which is determined by trial and error in the conventional approach, is also introduced. In addition, the proposed algorithm attempts to recover the 3-D surface without requiring the boundary conditions, making it suitable for a real-time implementation. Simulation results for real image as well as synthetic image are provided to demonstrate the performance of the proposed algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.3C
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• pp.232-238
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• 2003

In this paper, we propose a spatially adaptive image restoration algorithm using local statistics. The local mean, variance, and maximum values are utilized to constrain the solution space, and these parameters are computed at each iteration step using partially restored image. A parameter defined by the user determines the degree of local smoothness imposed on the solution. The resulting iterative algorithm exhibits increased convergence speed when compared to the non-adaptive algorithm. In addition, a smooth solution with a controlled degree of smoothness is obtained. Experimental results demonstrate the capability of the proposed algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.7A
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• pp.978-985
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• 2000

Blind equalizers recover the transmitted data using statistical characteristics of the signal alone. Among many alternatives, steepest gradient descent type algorithms such as the CMA and Sato algorithm are most widely utilized in practice. In this paper we propose a dual-mode NCMA algorithm, which combines the advantages of the dual mode CMA and Normalized CMA (NCMA) with the dual mode phase recovery algorithm. In addition, we perform computer simulations to demonstrate the performance improvement of the proposed algorithm with a QAM system. Simulation results show that the presented algorithm has a faster convergence speed and smaller steady-state residual error than the CMA and dual-mode CMA.

• Journal of Electrical Engineering and information Science
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• v.2 no.6
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• pp.191-196
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• 1997

Fuzzy clustering has been playing an important role in solving many problems. Fuzzy c-Means(FCM) algorithm is most frequently used for fuzzy clustering. But some fixed point of FCM algorithm, know as Tucker's counter example, is not a reasonable solution. Moreover, FCM algorithm is impossible to perform the on-line learning since it is basically a batch learning scheme. This paper presents unsupervised learning networks as an attempt to improve shortcomings of the conventional clustering algorithm. This model integrates optimization function of FCM algorithm into unsupervised learning networks. The learning rule of the proposed scheme is a result of formal derivation based on the gradient descent procedure of a fuzzy objective function. Using the result of formal derivation, two algorithms of fuzzy cluster analysis, the batch learning version and on-line learning version, are devised. They are tested on several data sets and compared with FCM. The experimental results show that the proposed algorithms find out the reasonable solution on Tucker's counter example.

• Journal of applied mathematics & informatics
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• v.34 no.5_6
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• pp.467-478
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• 2016

In this paper, we propose an Ishikawa-type extra-gradient iterative algorithm for finding a solution of split feasibility, fixed point problems and equilibrium problems of quasi-nonexpansive mappings. It is proven that under suitable conditions, the sequences generated by the proposed iterative algorithms converge weakly to a solution of the split feasibility, fixed point problems and equilibrium problems. An example is given to illustrate the main result of this paper.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1998.10a
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• pp.481-488
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• 1998

The drift and inter-story drift control method for steel structures subjected to seismic forces is formulated into a structural optimization problem in this paper. The formulated optimization problem with constraints on drift, inter-story drifts, and member strengthes are transformed into an unconstrained optimization problem. For the solution of the tranformed optimization problem an searching algorithm based on the gradient projection method utilizing gradient information on eigenvalues and eigenvectors are developed and presented in detail. The performance of the proposed algorithm is demonstrated by application to drift control of a verifying example.

• Communications of the Korean Mathematical Society
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• v.16 no.3
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• pp.509-518
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• 2001

The purpose of this paper is to propose a numerical algorithm for the problem of coefficient identification of the scalar wave equation based on a local observation on the boundary: Determine the unknown coefficient function with the knowledge of simultaneous Dirichlet and Neumann boundary values on a part of boundary. To find the unknown coefficient function, the unknown Neumann boundary value is also identified. We recast our inverse problem to variational problem. The gradient method is applied to find the minimizing functions. We confirm the effectiveness of our algorithm by numerical experiments.