• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.8A
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• pp.794-804
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• 2008

In this paper, we analyze the performance of probabilistic slotted anti-collision algorithm used in EPCglobal Class-1 Generation-2 (Gen2). To increase throughput and system efficiency, and to decrease tag identification time and collision ratio, we propose new tag anti-collision algorithms, which are FAFQ (fired adjustable flamed Q) algorithm and AAFQ (adaptive adjustable framed Q) algorithm, by using QueryAdjust command. We also propose grouping method based on Gen2 to improve the efficiency of tag identification. The simulation results show that all the proposed algorithms outperform Q algorithm, and AAFQ algorithm performs the best. That is, AAFQ has an increment of 5% of system efficiency and a decrement of 4.5% of collision ratio. For FAFQ and AAFQ algorithm, the performance of grouping method is similar to that of ungrouping method. However, for Q algorithm in Gen2, grouping method can increase throughput and system efficiency, and decrease tag identification time and collision ratio compared with ungrouping method.

• International Journal of Contents
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• v.1 no.1
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• pp.1-5
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• 2005

This paper presents the development and application of electrical resistance imaging techniques for the visualization of two-phase flow fields. Two algorithms, the so-called the mesh grouping and the boundary estimation, are described for potential applications of electrical resistance tomography (ERT) and results from extensive numerical simulations are also presented. In the electrical resistance imaging for two-phase flows, numerical meshes fairly belonging to each phase can be grouped to improve the reconstruction performance. In many cases, the detection of phase boundary is a key subject and a mathematical model to estimate phase boundary can be formulated in a different manner. Our results indicated that the mesh grouping algorithm is effective to enhance computational performance and image quality, and boundary estimation algorithm to determine the phase boundary directly.

• Journal of Korean Institute of Industrial Engineers
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• v.27 no.3
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• pp.281-288
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• 2001

We consider in this article a multi-objective part-machine grouping problem in which parts have alternative process plans and expected annual demand of each part is known. This problem is characterized as optimally determining part sets and corresponding machine cells such that total sum of distance (or dissimilarity) between parts and total sum of load differences between machines are simultaneously minimized. Two heuristic algorithms are proposed, and examples are given to compare the performance of the algorithms.

• Journal of information and communication convergence engineering
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• v.10 no.1
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• pp.40-44
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• 2012

Data mining, which is attracting public attention, is a process of drawing out knowledge from a large mass of data. The key technique in data mining is the ability to maximize the similarity in a group and minimize the similarity between groups. Since grouping in data mining deals with a large mass of data, it lessens the amount of time spent with the source data, and grouping techniques that shrink the quantity of the data form to which the algorithm is subjected are actively used. The current grouping algorithm is highly sensitive to static and reacts to local minima. The number of groups has to be stated depending on the initialization value. In this paper we propose a gene algorithm that automatically decides on the number of grouping algorithms. We will try to find the optimal group of the fittest function, and finally apply it to a data mining problem that deals with a large mass of data.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.61
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• pp.137-146
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• 2000

One of the problems faced in implementing cellular manufacturing systems is machine-part group formation. This paper proposes machine-part grouping algorithms based on Self-Organizing Map(SOM) neural networks and K-Means algorithm in cellular manufacturing systems. Although the SOM spreads out input vectors to output vectors in the order of similarity, it does not always find the optimal solution. We rearrange the input vectors using SOM and determine the number of groups. In order to find the number of groups and grouping efficacy, we iterate K-Means algorithm changing k until we cannot obtain better solution. The results of using the proposed approach are compared to the best solutions reported in literature. The computational results show that the proposed approach provides a powerful means of solving the machine-part grouping problem. The proposed algorithm Is applied by simple calculation, so it can be for designer to change production constraints.

• Journal of The Korean Association of Information Education
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• v.14 no.3
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• pp.291-300
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• 2010

Group-based learning is known to be an effective means to improve scholastic achievement in online learning. Therefore, there are some previous researches for the group-based learning. A lot of previous researches define factors for grouping from the characteristics of classes, teacher's decision and students' preferences and then generate a group based on the defined factors. However, many algorithms proposed by previous researches depend on a specific class and is not a general approach since there exist several differences in terms of the need of courses, learners, and teachers. Moreover it is hard to find a automatic system for group generation. This paper proposes a grouping system which automatically generate a learner group according to characteristics of various classes. the proposed system automatically generates a learner group by using basic information for a class or additional factors inputted from a user. The proposed system defines a set of rules for learner grouping which enables automatic selection of a learner grouping algorithm tailored to the characteristics of a given class. This rule based approach allows the proposed system to accommodate various learner grouping algorithms for a later use. Also we show the usability of our system by serviceability evaluation.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.4
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• pp.321-329
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• 2015

This paper addresses a new algorithm for a stereo visual odometry to measure the ego-motion of a vehicle. The new algorithm introduces an inlier grouping method based on Delaunay triangulation and vanishing point computation. Most visual odometry algorithms rely on RANSAC in choosing inliers. Those algorithms fluctuate largely in processing time between images and have different accuracy depending on the iteration number and the level of outliers. On the other hand, the new approach reduces the fluctuation in the processing time while providing accuracy corresponding to the RANSAC-based approaches.

• 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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.2A
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• pp.112-120
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• 2010

In this paper, we propose semi-ML adaptive parallel QRDM (APQRDM) and iterative QRDM (AIQRDM) algorithms based on set grouping. Using the set grouping, the tree-search stage of QRDM algorithm is divided into partial detection phases (PDP). Therefore, when the treesearch stage of QRDM is divided into 4 PDPs, the APQRDM latency is one fourth of that of the QRDM, and the hardware requirements of AIQRDM is approximately one fourth of that of QRDM. Moreover, simulation results show that in $4{\times}4$ system and at Eb/N0 of 12 dB, APQRDM decreases the average computational complexity to approximately 43% of that of the conventional QRDM. Also, at Eb/N0 of 0dB, AIQRDM reduces the computational complexity to about 54% and the average number of metric comparisons to approximately 10% of those required by the conventional QRDM and AQRDM.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.19 no.4 s.74
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• pp.357-367
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• 2006

Drift design methods using resizing algorithms have been presented as a practical drift design method since the resizing algorithms proposed easily find drift contribution of each member, called member displacement participation factor, to lateral drift to be designed without calculation of sensitivity coefficient or re-analysis. Weight of material to be redistributed for minimization of the lateral drift is determined according to the member displacement participation factors. However, resizing algorithms based on energy theorem must consider loading conditions because they have different displacement contribution according to different loading conditions. Furthermore, to improve practicality of resizing algorithms, structural member grouping is required in application of resizing algorithms to drift control of high-rise buildings. In this study, three resizing algorithms on considering load condition and structural member grouping are developed and applied to drift design of a 20-story steel-frame shear-wall structure and a 50-story frame shear-wall system with outriggers.