• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.4
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• pp.118-129
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• 2008

This paper describes an IEEE 802.15.4-based hierarchical sensor network model for a VAWS(Vehicle Approach Warning System) which provides the drivers of vehicles approaching a sharp turn with the information about vehicles approaching the same turn from the opposite end. In the proposed network model, a tree-structured topology, that can prolong the lifetime of network is formed in a self-organizing manner by a topology control protocol. A simple but efficient routing protocol, that creates and maintains routing tables based on the network topology organized by the topology control protocol, transports data packets generated from the sensor nodes to the base station which then forwards it to a display processor. These protocols are designed as a network layer extension to the IEEE 802.15.4 MAC. In the simulation, which models a scenario with a sharp turn, it is shown that the proposed network model achieves a high-level performance in terms of both energy efficiency and throughput simultaneously.

• Journal of Korea Game Society
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• v.10 no.3
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• pp.47-59
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• 2010

Differing from most of current studies which recognizing game item cash-trade as simple static system, this study approaches game item cash-trade as Complex Adaptive System through historical analysis. The item-trade is a complex phenomenon converging between cyber-economy and real-economy, and production and consumption process of game-item are evolving dynamically over time. The results are following: first, the early item-trade emerges in endogenously rather than results from purposed system designed by singular actor. Second, after the early item-trade, the trade system as a CAS which various voluntary actors(single user, factory, game company, user community, agency, etc.) participates in is self-organizing for trading safety and efficiency. Third, the complex adaptive item-trade system satisfies actor's needs interdependently and accelerate positive feedback powerfully. This study implies that purposeful control disregarding emergent adaptive item-trade system distorts system efficiency and can lead to unintended policy failure.

• Korean Institute of Interior Design Journal
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• no.14
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• pp.63-73
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• 1998

The aim of this study is to understand and redefine the nature of architectural environment within the paradigm of natural science. The chaos theory non-equilibrium thermodynamics theory self-organization of modern physics offer new insights to explain not only natural phenomena but also to define creative and dynamic architectural environment. First natural laws in modern physics like the arrow of time but is related not only with certainty but also possibility so nature is understood as a constantly changing process of evolution. Second the new architectural environment is defined as a kind of fluid and irreducible organic biosytem that cannot be fully understood by modernist idea of architecture. It is conceived of as a fluid constantly changing self-oraganizing system that consists of different situations events movements and programs in uncertain and irreducible time frame. Third insights and implications of natural science offer new language and strategy for design and the two disciplines can be understood as interdependent and co-evolving

• Journal of Intelligence and Information Systems
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• v.14 no.4
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• pp.89-101
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• 2008

The most used tool for quality control is control chart in manufacturing industry. But it has limitations at current situation where most of manufacturing facilities are automated and several manufacturing processes have interdependent relationship such as CCM assembly line. To Solve problems, we propose quality management system based on data mining that are consisted of monitoring system where it monitors flows of processes at single window and feature extraction system where it predicts the yield of final product and identifies which processes have impact on the quality of final product. The quality management system uses decision tree, neural network, self-organizing map for data mining. We hope that the proposed system can help manufacturing process to produce stable quality of products and provides engineers useful information such as the predicted yield for current status, identification of causal processes for lots of abnormality.

• Korean Journal of Remote Sensing
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• v.37 no.1
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• pp.85-95
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• 2021

Recently, as data mining and artificial neural network techniques are developed, analyzing large amounts of data is proposed to reduce the dimension of the data. In general, empirical orthogonal function (EOF) used to reduce the dimension in the ocean data and recently, Self-organizing maps (SOM) algorithm have been investigated to apply to the ocean field. In this study, both algorithms used the monthly Sea level anomaly (SLA) data from 1993 to 2018 around the East Asia Coasts. There was dominated by the influence of the Kuroshio Extension and eddy kinetic energy. It was able to find the maximum amount of variance of EOF modes. SOM algorithm summarized the characteristic of spatial distributions and periods in EOF mode 1 and 2. It was useful to find the change of SLA variable through the movement of nodes. Node 1 and 5 appeared in the early 2000s and the early 2010s when the sea level was high. On the other hand, node 2 and 6 appeared in the late 1990s and the late 2000s, when the sea level was relatively low. Therefore, it is considered that the application of the SOM algorithm around the East Asia Coasts is well distinguished. In addition, SOM results processed by SLA data, it is able to apply the other climate data to explain more clearly SLA variation mechanisms.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.120.2-120
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• 2002

In this paper auto-tuning scheme of weight function in the neural networks has been suggested by immune algorithm for nonlinear process. A number of structures of the neural networks are considered as learning methods for control system. A general view is provided that they are the special cases of either the membership functions or the modification of network structure in the neural networks. On the other hand, since the immune network system possesses a self organizing and distributed memory, it is thus adaptive to its external environment and allows a PDP (parallel distributed processing) network to complete patterns against the environmental situation. Also. It can provi..

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.103.1-103
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• 2002

In variable structure control algorithms, The control law used to realized the desired sliding mode dynamics is discontinuous on the switching manifold. However, due to imperfections in switching, such as time delays, the system trajectory chatters instead of sliding along the switching manifold. This chattering is undesirable because it may excite unmodeled high frequency dynamics in the physical system. In this paper, to overcome this drawback a self-organizing fuzzy sliding mode control algorithm using gradient descent method is proposed. The proposed method has the characteristics which are viewed in conventional VSC, e.g. insensitivity to a class of disturbance, parameter variations and uncertainties ill the sliding mode. To demonstrate its performance, the proposed control algorithm is applied to an inverted pendulum system. The results show that both alleviation of chattering and performance are achieved.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.4
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• pp.334-340
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• 2002

This paper proposes a self-organizing fuzzy modeling which can create a new hyperplane-shaped cluster by applying multiple regression to input/output data with relatively large fuzzy entropy, add the new cluster to fuzzy rule base and adjust parameters of the fuzzy model in repetition. Tn the coarse tuning, weighted recursive least squared algorithm and fuzzy C-regression model clustering are used and in the fine tuning, gradient descent algorithm is used to adjust parameters of the fuzzy model precisely And learning rates are optimized by utilizing meiosis-genetic algorithm. To check the effectiveness and feasibility of the suggested algorithm, four representative examples for system identification are examined and the performance of the identified fuzzy model is demonstrated in comparison with that of the conventional fuzzy models.

• The KIPS Transactions:PartA
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• v.13A no.7 s.104
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• pp.623-632
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• 2006

We propose a peer-to-peer search scheme in which super peers with high rapacity constitute a ring by themselves and all peers utilize the ring for their query and publishing keys. In a dynamic peer-to-peer environment, the size of the super peer ring changes adaptively according to the changes of the system, but more powerful peers are continuously promoted to super peers. By simulations, we show that our proposed search scheme improves the query success rate of Gnutella+1hop replication search method, and maintains shorter query delay than JXTA as a static ring.

• Transactions on Control, Automation and Systems Engineering
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• v.3 no.1
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• pp.43-50
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• 2001

In this paper, we propose the design procedure of advance Polynomial Neural Networks(PNN) architecture for optimal model identification of complex and nonlinear system. The proposed PNN architecture is presented as the generic and advanced type. The essence of the design procedure dwells on the Group Method of Data Handling(GMDH). PNN is a flexible neural architecture whose structure is developed through learning. In particular, the number of layers of the PNN is not fixed in advance but is generated in a dynamic way. In this sense, PNN is a self-organizing network. With the aid of three representative numerical examples, compari-sons show that the proposed advanced PNN algorithm can produce the model with higher accuracy than previous other works. And performance index related to approximation and generalization capabilities of model is evaluated and also discussed.