• Journal of Institute of Control, Robotics and Systems
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• v.7 no.10
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• pp.819-826
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• 2001

Genetic algorithms are becoming more popular because of their relative simplicity and robustness. Genetic algorithms are global search techniques for nonlinear optimization. However, traditional genetic algorithms, though robust, are generally not the most successful optimization algorithm on any particular domain because they are poor at hill-climbing, whereas simulated annealing has the ability of probabilistic hill-climbing. Therefore, hybridizing a genetic algorithm with other algorithms can produce better performance than using the genetic algorithm or other algorithms independently. In this paper, we propose an efficient hybrid optimization algorithm named the adaptive random signal-based learning. Random signal-based learning is similar to the reinforcement learning of neural networks. This paper describes the application of genetic algorithms and simulated annealing to a random signal-based learning in order to generate the parameters and reinforcement signal of the random signal-based learning, respectively. The validity of the proposed algorithm is confirmed by applying it to two different examples.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.6
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• pp.2619-2643
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• 2018

In this paper, a new clustering algorithm based on the connected region generation (CRG-clustering) is proposed. It is an effective and robust approach to clustering on the basis of the connectivity of the points and their neighbors. In the new algorithm, a connected region generating (CRG) algorithm is developed to obtain the connected regions and an isolated point set. Each connected region corresponds to a homogeneous cluster and this ensures the separability of an arbitrary data set theoretically. Then, a region expansion strategy and a consensus criterion are used to deal with the points in the isolated point set. Experimental results on the synthetic datasets and the real world datasets show that the proposed algorithm has high performance and is insensitive to noise.

• Journal of Information Processing Systems
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• v.13 no.2
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• pp.256-267
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• 2017

An active research area in computer vision, stereo matching is aimed at obtaining three-dimensional (3D) information from a stereo image pair captured by a stereo camera. To extract accurate 3D information, a number of studies have examined stereo matching algorithms that employ adaptive support weight. Among them, the adaptive census transform (ACT) algorithm has yielded a relatively strong matching capability. The drawbacks of the ACT, however, are that it produces low matching accuracy at the border of an object and is vulnerable to noise. To mitigate these drawbacks, this paper proposes and analyzes the features of an improved stereo matching algorithm that not only enhances matching accuracy but also is also robust to noise. The proposed algorithm, based on the ACT, adopts the truncated absolute difference and the multiple sparse windows method. The experimental results show that compared to the ACT, the proposed algorithm reduces the average error rate of depth maps on Middlebury dataset images by as much as 2% and that is has a strong robustness to noise.

• Journal of Astronomy and Space Sciences
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• v.31 no.3
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• pp.205-214
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• 2014

In this study, a star identification algorithm which utilizes pivot patterns instead of apparent magnitude information was developed. The new star identification algorithm consists of two steps of recognition process. In the first step, the brightest star in a sensor image is identified using the orientation of brightness between two stars as recognition information. In the second step, cell indexes are used as new recognition information to identify dimmer stars, which are derived from the brightest star already identified. If we use the cell index information, we can search over limited portion of the star catalogue database, which enables the faster identification of dimmer stars. The new pivot algorithm does not require calibrations on the apparent magnitude of a star but it shows robust characteristics on the errors of apparent magnitude compared to conventional pivot algorithms which require the apparent magnitude information.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.7
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• pp.302-307
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• 2002

Digital watermarking is the technique, which embeds an invisible signal including signal including owner identification and copy control information into multimedia data such as audio, video, and images for copyright protection. A new MPEG watermark embedding algorithm using complex block effect based on the Human Visual System(HVS) is introduced in this paper. In this algorithm, $8{\times}8$ dark blocks are selected, and the watermark is embedded in the DC component of the discrete cosine transform(DCT) by using quantization and modulus calculation. This algorithm uses a blind watermark retrieval technique, which detects the embedded watermark without using the original image. The experimental results show that the proposed watermark technique is robust against MPEG coding, bitrate changes, and various GOP(Group of Picture) changes.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.503-506
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• 1996

An integrated Robot control system for SCARA robot is developed. The system consists of an off-line programming(OLP), software and a robot controller using four digital signal processor(TMS32OC50). The OLP has functions of teaching task, dynamic simulator, three dimensional animation, and trajectory planning. To develop robust dynamic control algorithm, a new sliding mode control algorithm for the robot is proposed. The trajectory tracking performance of these algorithm is evaluated by implementing to SCARA robot(SM5 type) using DSP controller which has conventional PI-FF control algorithm. To make SCARA robot operate according to off-line teaching, an interface between OLP and robot controller in the integrated system is designed. To demonstrate performance of the integrated system, the proposed control algorithm is applied to the system.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.781-784
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• 1998

An image segmentation approach based on the fuzzy worm searching and MIN-MAx clusterng algorithm is proposed in this paper. This algorithm deals with fuzzy worm value and min-max node at a gross scene level, which investigates the edge information including fuzzy worm action. But current segmentation methods based edge extraction methods generally need the mask information for the algebraic model, and take long run times at mask operation, wheras the proposed algorithm has single operation ccording to active searching of fuzzy worms. In addition, we also genetic min-max clustering using genetic algorithm to complete clustering and fuzyz searching on grey-histogram of image for the optimum solution, which can automatically determine the size of rnages and has both strong robust and speedy calculation. The simulation results showed that the proposed algorithm adaptively divided the quantized images in histogram region and performed single searching methods, significantly alleviating the increase of the computational load and the memory requirements.

• Korean Journal of Computational Design and Engineering
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• v.17 no.4
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• pp.262-273
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• 2012

We present a novel GPU algorithm to compute outer cell boundaries of 3D arrangement subdivided by a given set of triangles. An outer cell boundary is defined as a 2-manifold surface consisting of subdivided polygons facing outward. Many geometric problems, such as Minkowski sum, sweep volume, lower/upper envelop, Bool operations, can be reduced to finding outer cell boundaries with specific properties. Computing outer cell boundaries, however, is a very time-consuming job and also is susceptible to numerical errors. To address these problems, we develop an algorithm based on GPU with a robust scheme combining interval arithmetic and multi-level precisions. The proposed algorithm is tested on Minkowski sum of several polygonal models, and shows 5-20 times speedup over an existing algorithm running on CPU.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.1
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• pp.54-64
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• 1997

In this paper, a method of dynamic modeling and a dynamic control of a mobile robot are presented to show the superiority of the dynamic control comparing to the PD control. This dynamic model is derived from the cartesian coordinates using lagrange equations. Based upon the derived dynamic model, we implemented the dynamic control of the mobile robot using the computed torque method. Time varying non-linear friction terms are not incroporated in this dynamic model. Instead, those are considered as disturbances. This uncertainty in dynamic model of mobile robot is compensated by the outer loop controller using PD algorithm. The validity of this model and the control algorithm are confirmed through the experiments, where the dynamic control algorithm demonstrated robust velocity tracking performance against the unmodeled non-linear frictions. The superiority of this algorithm is demonstrated by comparing to classical PD control algorithm.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.10
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• pp.502-508
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• 2000

This paper presents a dynamic simulation algorithm for studying the effect of United Power Flow Controller(UPFC) on the low frequency power system oscillations and transient stability studies. The proposed algorithm is a Newton-type one and uses current injection type UPFC model, which gives a fast convergence characteristics. The algorithm is applied to studying inter-area power oscillation damping enhancement of a sample two-area power system both in time domain and frequency domain. The case study results show that the proposed algorithm is very efficient and UPFC is very effective and robust against operating point change.