• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.3 no.3
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• pp.309-330
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• 2009

In this paper, we introduce the architecture of Genetic Algorithm(GA) based Feed-forward Polynomial Neural Networks(PNNs) and discuss a comprehensive design methodology. A conventional PNN consists of Polynomial Neurons, or nodes, located in several layers through a network growth process. In order to generate structurally optimized PNNs, a GA-based design procedure for each layer of the PNN leads to the selection of preferred nodes(PNs) with optimal parameters available within the PNN. To evaluate the performance of the GA-based PNN, experiments are done on a model by applying Medical Imaging System(MIS) data to a multi-variable software process. A comparative analysis shows that the proposed GA-based PNN is modeled with higher accuracy and more superb predictive capability than previously presented intelligent models.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.195-196
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• 2007

The purpose of this paper is to analyze the resource allocation problem in a self organizing network from the viewpoint of game theory. The main focus is to suggest the model and analyze a power control algorithm in wireless ad-hoc networks using non cooperative games. Our approach is based on a model for the level of satisfaction and utility a wireless user in a self organizing network derives from using the system. Using this model, we show a distributed power control scheme that maximizes utility of each user in the network. Formulating this as a non-cooperative game we will show the feasibility of such power control as well as existence of the Nash Equilibrium achieved by the non-cooperative game.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.09b
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• pp.1110-1111
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• 2005

• Journal of the Korean Geophysical Society
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• v.9 no.2
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• pp.99-103
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• 2006

3D object recognition independent of translation and rotation using an ultrasonic sensor array, invariant moment vectors and SOFM(Self Organizing Feature Map) neural networks is presented. Using invariant moment vectors of the acquired 16×8 pixel data of square, rectangular, cylindric and regular triangular blocks, 3D objects could be classified by SOFM neural networks. Invariant moment vectors are constant independent of translation and rotation. The recognition rates for the training and testing data were 95.91% and 92.13%, respectively.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.1
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• pp.40-49
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• 2004

In this paper, we propose a new architecture of Genetic Algorithms(GAs)-based Self-Organizing Polynomial Neural Networks(SOPNN), discuss a comprehensive design methodology and carry out a series of numeric experiments. The conventional SOPNN is based on the extended Group Method of Data Handling(GMDH) method and utilized the polynomial order (viz. linear, quadratic, and modified quadratic) as well as the number of node inputs fixed (selected in advance by designer) at Polynomial Neurons (or nodes) located in each layer through a growth process of the network. Moreover it does not guarantee that the SOPNN generated through learning has the optimal network architecture. But the proposed GA-based SOPNN enable the architecture to be a structurally more optimized network, and to be much more flexible and preferable neural network than the conventional SOPNN. In order to generate the structurally optimized SOPNN, GA-based design procedure at each stage (layer) of SOPNN leads to the selection of preferred nodes (or PNs) with optimal parameters- such as the number of input variables, input variables, and the order of the polynomial-available within SOPNN. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization (predictive) abilities of the model. A detailed design procedure is discussed in detail. To evaluate the performance of the GA-based SOPNN, the model is experimented with using two time series data (gas furnace and NOx emission process data of gas turbine power plant). A comparative analysis shows that the proposed GA-based SOPNN is model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• Journal of Korea Multimedia Society
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• v.11 no.9
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• pp.1213-1226
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• 2008

A recommender system supports people in making recommendations finding a set of people who are likely to provide good recommendations for a given person, or deriving recommendations from implicit behavior such as browsing activity, buying patterns, and time on task. We proposed new recommender system which combined SOM(Self-Organizing Map) neural networks with the Collaborative filtering which most recommender systems hat applied First, we segmented user groups according to demographic characteristics and then we trained the SOM with people's preferences as ito inputs. Finally we applied the classic collaborative filtering to the clustering with similarity in which an recommendation seeker belonged to, and therefore we didn't have to apply the collaborative filtering to the whose data set. Experiments were run for EachMovies data set. The results indicated that the predictive accuracy was increased in terms of MAE(Mean-Absolute-Error).

• International Journal of Control, Automation, and Systems
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• v.2 no.4
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• pp.423-434
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• 2004

In this paper, we introduce a new topology of Self-Organizing Polynomial Neural Networks (SOPNN) based on genetically optimized Multi-Layer Perceptron (MLP) and discuss its comprehensive design methodology involving mechanisms of genetic optimization. Let us recall that the design of the 'conventional' SOPNN uses the extended Group Method of Data Handling (GMDH) technique to exploit polynomials as well as to consider a fixed number of input nodes at polynomial neurons (or nodes) located in each layer. However, this design process does not guarantee that the conventional SOPNN generated through learning results in optimal network architecture. The design procedure applied in the construction of each layer of the SOPNN deals with its structural optimization involving the selection of preferred nodes (or PNs) with specific local characteristics (such as the number of input variables, the order of the polynomials, and input variables) and addresses specific aspects of parametric optimization. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between the approximation and generalization (predictive) abilities of the model. To evaluate the performance of the GA-based SOPNN, the model is experimented using pH neutralization process data as well as sewage treatment process data. A comparative analysis indicates that the proposed SOPNN is the model having higher accuracy as well as more superb predictive capability than other intelligent models presented previously.reviously.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.3A
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• pp.259-266
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• 2011

Femtocells are expected as the surest way to increase the system capacity with higher-quality links and more spatial reuse in future networks. In spite of their great potential, the system capacity is highly susceptible to network density because a large portion of users are exposed to inter-cell interference (ICI). In this work, we proposed a dynamic interference avoidance scheme in densely deployed cell environments. Our proposed DDIA (Distributed Dynamic ICI Avoidance) scheme not only works in a fully distributed manner, but also controls interference link connectivity of users with high agility so that it is suited for self-organizing networks (SONs). We introduced the concept of ICI-link and two-tier scheduling in designing the DDIA scheme. To avoid ICI without any central entity, our scheme tries to harmonize all base stations (BSs) with users adaptively. Through extensive simulations, it was shown that our proposed scheme improves the throughput of users by more than twice on average compared to the frequency reuse factor 1 scheme, who are exposed to ICI while maintaining or even improving overall network performance. Our scheme operates well regardless of network density and topology.

• Journal of Korean Society on Water Environment
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• v.24 no.5
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• pp.611-617
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• 2008

It is necessary to analysis the data characteristics of discharge and water quality for efficient water resources management, aggressive alternatives to inundation by flood and various water pollution accidents, the basic information to manage water quality in lakes and to make environmental policy. Therefore, the present study applied Self-Organizing Map (SOM) showing excellent performance in classifying patterns with weights estimated by self-organization. The result revealed five patterns and TOC versus rainfall-storage data according to the respective patterns were depicted in two-dimensional plots. The visualization presented better understanding of data distribution pattern. The result in the present study might be expected to contribute to the modeling procedure for data prediction in the future.