• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.4
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• pp.409-415
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• 2003

Recently, the modular learning are very popular and receive much attention for pattern classification. The modular learning method based on the "divide and conquer" strategy can not only solve the complex problems, but also reach a better result than a single classifier′s on the learning quality and speed. In the neural network area, some researches that take the modular learning approach also have been made to improve classification performance. In this paper, we propose a simple modular neural network for characters recognition of vehicle number plate and evaluate its performance on the clustering methods of feature vectors used in constructing subnetworks. We implement two clustering method, one is grouping similar feature vectors by K-means clustering algorithm, the other grouping unsimilar feature vectors by our proposed algorithm. The experiment result shows that our algorithm achieves much better performance.

• IE interfaces
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• v.12 no.4
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• pp.532-539
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• 1999

The first step to design a cellular manufacturing system is to make part-families and machine-cells. This process is called the machine-part grouping. This paper considers a machine-cell size when grouping machine-cells. By considering a machine-cell size, an unrealistically big size of machine-cell which may be caused by the chaining effect can be avoid. A heuristic algorithm which minimizes the number of inter-cell movement of parts considering a machine-cell size is presented. The effectiveness and performance of the proposed heuristic algorithm are compared with those of several heuristic algorithms previously reported. The comparison shows that the proposed heuristic algorithm is efficient and reliable in minimizing the number of inter-cell movement of parts and also prevents the chaining effect.

• Journal of Korean Institute of Industrial Engineers
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• v.31 no.1
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• pp.1-9
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• 2005

This paper proposes the heuristic algorithm for the generalized GT problems to consider the restrictions which are given the number of machine cell and maximum number of machines in machine cell as well as minimum number of machines in machine cell. This approach is split into two phase. In the first phase, we use the similarity coefficient which proposes and calculates the similarity values about each pair of all machines and sort these values descending order. If we have a machine pair which has the largest similarity coefficient and adheres strictly to the constraint about birds of a different feather (BODF) in a machine cell, then we assign the machine to the machine cell. In the second phase, we assign parts into machine cell with the smallest number of exceptional elements. The results give a machine-part grouping. The proposed algorithm is compared to the Modified p-median model for machine-part grouping.

• Journal of Electrical Engineering and Technology
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• v.3 no.1
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• pp.130-134
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• 2008

The increasing size of very large scale integration (VLSI) circuits, high transistor density, and popularity of low-power circuit and system design are making the minimization of power dissipation an important issue in VLSI design. Test Power dissipation is exceedingly high in scan based environments wherein scan chain transitions during the shift of test data further reflect into significant levels of circuit switching unnecessarily. Scan chain or cell modification lead to reduced dissipations of power. The ETC algorithm of previous work has weak points. Taking all of this into account, we therefore propose a new algorithm. Its name is RE_ETC. The proposed modifications in the scan chain consist of Exclusive-OR gate insertion and scan cell reordering, leading to significant power reductions with absolutely no area or performance penalty whatsoever. Experimental results confirm the considerable reductions in scan chain transitions. We show that modified scan cell has the improvement of test efficiency and power dissipations.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.3A
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• pp.293-302
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• 2004

Wireless LAN is a rather mature communication technology that connects mobile terminals. A typical wireless LAN system is composed of one AP and more than one terminals, which is called a BSS. Terminals near to an AP can receive high rate data but terminals far from an AP may not receive data with guaranteed high speed rate because received signal strength is weakened. This paper proposes a method to allow high speed data transmission by grouping terminals and using part of wireless terminals as repeaters. We compare and analyze proposed grouping algorithms based on Depth First Search and Breadth First Search via simulations. A grouping algorithm based on Breadth First Search is shown to be more effective in term of efficiency and coverage.

• Journal of Digital Contents Society
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• v.19 no.1
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• pp.199-204
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• 2018

In this paper, we propose a fast collision detection method for interacting with objects in virtual space. First, in the mesh grouping step, the entire space is divided into small spaces of a predetermined size, and the positions and sizes of the smallest basic stereoscopic shapes (cube or sphere) including all of the meshes belonging to each small space are determined. In the collision detection step, it is checked whether a haptic interface point (HIP) is included in a three-dimensional figure representing a group. When a collision with a specific three-dimensional figure is confirmed, searching is performed only for the meshes in the group to find a mesh on which a possible real collision with HIP occurred. The effectiveness of the proposed algorithm is verified by measuring and comparing the computation time of the proposed method with and without the proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1468-1473
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• 2005

In this paper, I proposed the INTelligent Agent System by Kohonen's Self Organizing Neural Network (INTAS). INTAS creates each user's profile from the information. Based on it, learning community grouping suitable to each individual is automatically executed by using unsupervised learning algorithm. In INTAS, grouping and learning are automatically performed on real time by multiagents, regardless of the number of learners. A new framework has been proposed to generate multiagents, and it is a feature that efficient multiagents can be executed by proposing a new negotiation mode between multiagents..

• Journal of information and communication convergence engineering
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• v.5 no.1
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• pp.78-82
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• 2007

This paper propose a building reconstruction method of urban area for a 3D GIS with stereo images. The 3D reconstruction is performed by the grouping 3D line segments extracted from the stereo matching of salient edges which are derived from multiple images. The grouping is achieved by conditions of degrees and distances between lines. Building objects are determined by the junction combinations of the grouped line segments. The proposed algorithm demonstrates effective results of 3D reconstruction of buildings with 2D aerial images.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.31A no.6
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• pp.169-176
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• 1994

This paper describes an efficient parallel evaluation algorithm for accelerating fault simulation, which can be applied to combinational circuits. The method is based on a combination of all the advantages in parallel, deductive and concurrent schemes in terms of evaluation and propagation of fautly gates for achieving high performance and handling multi-valued signal. We also propose a new fault grouping procedure to increase parallel operation of fault bits by packing active faults which occur in the same signal line densely into the same fault group. The algorithm has been implemented in C language on a Sun 3/260, and experimental results for ISCAS'85 benchmark circuits have been shown that this algorithm is 2.6 to 8.2 times faster than the conventional cocurrent fault simulation algorithm.

• Nuclear Engineering and Technology
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• v.46 no.1
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• pp.109-116
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• 2014

An electrical resistance tomography (ERT) technique combining the particle swarm optimization (PSO) algorithm with the Gauss-Newton method is applied to the visualization of two-phase flows. In the ERT, the electrical conductivity distribution, namely the conductivity values of pixels (numerical meshes) comprising the domain in the context of a numerical image reconstruction algorithm, is estimated with the known injected currents through the electrodes attached on the domain boundary and the measured potentials on those electrodes. In spite of many favorable characteristics of ERT such as no radiation, low cost, and high temporal resolution compared to other tomography techniques, one of the major drawbacks of ERT is low spatial resolution due to the inherent ill-posedness of conventional image reconstruction algorithms. In fact, the number of known data is much less than that of the unknowns (meshes). Recalling that binary mixtures like two-phase flows consist of only two substances with distinct electrical conductivities, this work adopts the PSO algorithm for mesh grouping to reduce the number of unknowns. In order to verify the enhanced performance of the proposed method, several numerical tests are performed. The comparison between the proposed algorithm and conventional Gauss-Newton method shows significant improvements in the quality of reconstructed images.