• The KIPS Transactions:PartC
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• v.11C no.7 s.96
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• pp.905-916
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• 2004

Self-organizing hierarchical ad hoc network (SOHAN) is a new ad-hoc network architecture designed to improve the scalability properties of conventional 'flat' ad hoc networks. This network architecture consists of three tiers of ad-hoc nodes, i.e.. access points, forwarding nodes and mobile nodes. This paper presents a topology discovery and routing protocol for the self-organization of SOHAN. We propose a cross-layer path metric based on link quality and MAC delay which plays a key role in producing an optimal cluster-based hierarchical topology with high throughput capacity. The topology discovery protocol provides the basis for routing which takes place in layer 2.5 using MAC addresses. The routing protocol is based on AODV with appropriate modifications to take advantage of the hierarchical topology and interact with the discovery protocol. Simulation results are presented which show the improved performance as well as scalability properties of SOHAN in terms of through-put capacity, end-to-end delay, packet delivery ratio and control overhead.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.1
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• pp.88-98
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• 1994

A new neural network architecture called the Parallel. Self-Organizing Hierarchical Neural Network (PSHNN) is presented. The new architecture involves a number of stages in which each stage can be a particular neural network (SNN). The experiments performed in comparison to multi-layered network with backpropagation training and indicated the superiority of the new architecture in the sense of classification accuracy, training time,parallelism.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.1
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• pp.34-40
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• 1994

The design and implementation of a fuzzy self-organizing controller for an environmental temperature chamber is discussed. The chamber is a non-linear, time-variant system with delay-time and dead-time. And the parameter tuning is required in PI control when the performance degraded. However the proposed fuzzy-SOC monitors the performance of the process. modifies the data base, and performs the delay-time compensation based on the idealized process model. A series of experiments was performed for the conventional PI and the fuzzy-SOC. These experimental results show the usefulness of the fuzzy-SOC.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.1086-1088
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• 1993

A robust fuzzy self-organizing controller(SOC) is proposed for an environmental temperature chamber. Although fuzzy SOC can improve the performance of nonlinear system, the controller is ineffective to solve the performance degradation owing to the time varying factors. In this paper, we construct the fuzzy SOC with a predictive scheme based on the 386PC. The usefulness of the proposed scheme is shown through the comparison of the PI controller and the fuzzy controller.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.102.1-102
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• 2002

In this paper, we propose Self-organizing neurofuzzy networks(SONFN) and discuss their comprehensive design methodology. The proposed SONFN is generated from the mutually combined structure of both neurofuzzy networks (NFN) and polynomial neural networks(PNN) for model identification of complex and nonlinear systems. NFN contributes to the formation of the premise part of the SONFN. The consequence part of the SONFN is designed using PNN. The parameters of the membership functions, learning rates and momentum coefficients are adjusted with the use of genetic optimization. We discuss two kinds of SONFN architectures and propose a comprehensive learning algorithm. It is shown that this network...

• The Transactions of the Korea Information Processing Society
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• v.4 no.1
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• pp.184-194
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• 1997

In the recent years number of commercial off-line character recognition systems have been appeared in the Korean market. This paper describes a "self -organizing" data structure for representing a large dictionary which can be searched in real time and uses a practical amount of memory, and presents a study on the character correction for off-line printed sentence marks, Japanese, English, and Chinese character recognition. Self-organizing algorithm can be recommenced as particularly appropriate when we have reasons to suspect that the accessing probabilities for individual words will change with time and theme. The wrongly recognized characters generated by OCR systems are collected and analyzed Error types of English characters are reclassified and 0.5% errors are corrected using an English character confusion table with a self-organizing dictionary containing 25,145 English words. And also error types of Chinese characters are classified and 6.1% errors are corrected using a Chinese character confusion table with a self-organizing dictionary carrying 34,593 Chinese words.ese words.

• The KIPS Transactions:PartB
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• v.15B no.5
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• pp.407-412
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• 2008

Color segmentation is one of the most challenging problems in image processing especially in case of handling the images with cluttered background. Great amount of color segmentation methods have been developed and applied to real problems. In this paper, we suggest a new methodology. Our approach is focused on background extraction, as a complimentary operation to standard foreground object segmentation, using self-organizing feature selective property of unsupervised self-learning paradigm based on the competitive algorithm. The results of our studies show that background segmentation can be achievable in efficient manner.

• Journal of the Institute of Convergence Signal Processing
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• v.4 no.3
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• pp.49-54
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• 2003

In this paper, we propose a controller with the self-organizing neural network compensator for compensating PID controller's response. PID controller has simple design method but needs a lot of trials and errors to determine coefficients. A neural network control method does not have optimal structure as the parameters are pre-specified by designers. In this paper, to solve this problem, we use a self-organizing neural network which has Back Propagation Network algorithm using a Gaussian Potential Function as an activation function of hidden layer nodes for compensating PID controller's output. Self-Organizing Neural Network's learning is proceeded by Gaussian Function's Mean, Variance and number which are automatically adjusted. As the results of simulation through the second order plant, we confirmed that the proposed controller get a good response compare with a PID controller. And we implemented the of controller performance hydraulic servo motor system using the DSP processor. Then we observed an experimental results.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.115.4-115
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• 2001

This paper deals with recognizing ten korean voiced vowels using Self Organizing Map. SOM is a good classifier. The output layer is composed of two dimensions. The input vector is the frequency values having the characteristic of voiced vowels. The short time frequency transform is used getting input vector. The final neural networks is attached SOM output layer.

• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.591-593
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• 1997

When input circumstance is changed abrubtly, many nodes of Competitive Learning Neural Networks far from new input vector may never win, and therefore never learn. Various techniques to prevent these phenomena have been reported. We proposed a new technique based on Self Creating and Organizing Neural Networks, and which is compared to Self Organizing Feature Map and Frequency Sensitive Neural Networks.