• Bulletin of the Korean Chemical Society
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• v.12 no.5
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• pp.574-582
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• 1991

The CO molecules adsorbed on Ni(111) surface is studied in the cluster approximation employing EH method with self-consistent charge iteration. The effect of CO coverage is simulated by allowing the variation of valence state ionization potentials of each Ni atom in model cluster according to the self-consistent charge iteration method. The CO coverage dependent C-O stretching frequency shift, adsorption site conversion, and metal work function change are attributed to the charge transfer between metal surface and adsorbate. For CO/Ni(111) system, net charge transfer from Ni surface to chemisorbed CO molecules makes surface Ni atoms be more positive with increasing coverage, and lowers Ni surface valence band. This leads to a weaker interaction between metal surface valence band and Co $2{\pi}^{\ast}$ MO, less charge transfer to a single CO molecule, and the bule shift of C-O stretching frequency. Further increase of coverage induces the conversion of 3-fold site CO to lower coordination site CO as well as the blue shift of C-O stretching frequency. This whole process is accompanied by the continuous increase of metal work function.

• International Journal of Control, Automation, and Systems
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• v.6 no.6
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• pp.928-938
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• 2008

This study presents an approach for approximation an unknown function from a numerical data set based on the synthesis of a neuro-fuzzy model. An adaptive input data space parting method, which is used for building hyperbox-shaped clusters in the input data space, is proposed. Each data cluster is implemented here as a fuzzy set using a membership function MF with a hyperbox core that is constructed from a min vertex and a max vertex. The focus of interest in proposed approach is to increase degree of fit between characteristics of the given numerical data set and the established fuzzy sets used to approximate it. A new cutting procedure, named NCP, is proposed. The NCP is an adaptive cutting procedure using a pure function $\Psi$ and a penalty function $\tau$ for direction the input data space parting process. New algorithms named CSHL, HLM1 and HLM2 are presented. The first new algorithm, CSHL, built based on the cutting procedure NCP, is used to create hyperbox-shaped data clusters. The second and the third algorithm are used to establish adaptive neuro- fuzzy inference systems. A series of numerical experiments are performed to assess the efficiency of the proposed approach.

• The Bulletin of The Korean Astronomical Society
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• v.40 no.1
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• pp.49.2-49.2
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• 2015

IntraCluster Medium (ICM) located at the galaxy cluster is in the state of very hot, tenuous, magnetized, and highly ionized X-ray emitting plasmas. High temperature and low density make ICM very viscous and conductive. In addition to the high conductivity, fluctuating random plasma motions in ICM, occurring at all evolution stages, generate and amplify the magnetic fields in such viscous ionized gas. The amplified magnetic fields in reverse drive and constrain the plasma motions beyond the viscous scale through the magnetic tension. Moreover, without the influence of resistivity viscous damping effect gets balanced only with the magnetic tension in the extended viscous scale leading to peculiar ICM energy spectra. This overall collisionless magnetohydrodynamic (MHD) turbulence in ICM was simulated using a hyper diffusivity method. The results show the plasma motions and frozen magnetic fields have power law of $E_V^k{\sim}k^{-3}$, $E_M^k{\sim}k^{-1}$. To explain these abnormal power spectra we set up two simultaneous differential equations for the kinetic and magnetic energy using an Eddy Damped Quasi Normal Markovianized (EDQNM) approximation. The solutions and dimensions of leading terms in the coupled equations derive the power spectra and tell us how the spectra are formed. We also derived the same results with a more intuitive balance relation and stationary energy transport rate.

• Journal of KIISE:Databases
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• v.29 no.2
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• pp.128-137
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• 2002

In this paper, we propose the LPC+-file for efficient indexing of high-dimensional image data. With the proliferation of multimedia data, there Is an increasing need to support the indexing and retrieval of high-dimensional image data. Recently, the LPC-file (5) that based on vector approximation has been developed for indexing high-dimensional data. The LPC-file gives good performance especially when the dataset is uniformly distributed. However, compared with for the uniformly distributed dataset, its performance degrades when the dataset is clustered. We improve the performance of the LPC-file for the strongly clustered image dataset. The basic idea is to adaptively partition the data space to find subspaces with high-density clusters and to assign more bits to them than others to increase the discriminatory power of the approximation of vectors. The total number of bits used to represent vector approximations is rather less than that of the LPC-file since the partitioned cells in the LPC+-file share the bits. An empirical evaluation shows that the LPC+-file results in significant performance improvements for real image data sets which are strongly clustered.

• Journal of KIISE:Software and Applications
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• v.30 no.11
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• pp.1054-1061
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• 2003

Q-Learning, a representative algorithm of reinforcement learning, experiences repeatedly until estimation values about all state-action pairs of state space converge and achieve optimal policies. When the state space is high dimensional or continuous, complex reinforcement learning tasks involve very large state space and suffer from storing all individual state values in a single table. We introduce Q-Map that is new function approximation method to get classified policies. As an agent learns on-line, Q-Map groups states of similar situations and adapts to new experiences repeatedly. State-action pairs necessary for fine control are treated in the form of rule. As a result of experiment in maze environment and mountain car problem, we can achieve classified knowledge and extract easily rules from Q-Map

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.2
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• pp.1-10
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• 2016

In this paper, we present a new way to derive the mean cycle time of the G/G/m failure prone queue when the loading of the system approaches to zero. The loading is the relative ratio of the arrival rate to the service rate multiplied by the number of servers. The system with low loading means the busy fraction of the system is low. The queueing system with low loading can be found in the semiconductor manufacturing process. Cluster tools in semiconductor manufacturing need a setup whenever the types of two successive lots are different. To setup a cluster tool, all wafers of preceding lot should be removed. Then, the waiting time of the next lot is zero excluding the setup time. This kind of situation can be regarded as the system with low loading. By employing absorbing Markov chain model and renewal theory, we propose a new way to derive the exact mean cycle time. In addition, using the proposed method, we present the cycle times of other types of queueing systems. For a queueing model with phase type service time distribution, we can obtain a two dimensional Markov chain model, which leads us to calculate the exact cycle time. The results also can be applied to a queueing model with batch arrivals. Our results can be employed to test the accuracy of existing or newly developed approximation methods. Furthermore, we provide intuitive interpretations to the results regarding the expected waiting time. The intuitive interpretations can be used to understand logically the characteristics of systems with low loading.

• The Journal of the Korea Contents Association
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• v.6 no.1
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• pp.23-30
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• 2006

A clustering method for temporal data takes a model-based approach. This uses automata based model for each cluster. It is necessary to construct global models for a set of data in order to elicit individual models for the cluster. The preparation for building individual models is completed by determining the number of clusters inherent in the data set. In this paper, BIC(Bayesian Information Criterion) approximation is used to determine the number clusters and confirmed its applicability. A search technique to improve efficiency is also suggested by analyzing the relationship between data size and BIC values. A number of experiments have been performed to check its validity using artificially generated data sets. BIC approximation measure has been confirmed that it suggests best number of clusters through experiments provided that the number of data is relatively large.

• Journal of Digital Convergence
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• v.13 no.11
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• pp.157-164
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• 2015

This paper describes subspace clustering of categorical data for convergence and integration. Because categorical data are not designed for dealing only with numerical data, The conventional evaluation measures are more likely to have the limitations due to the absence of ordering and high dimensional data and scarcity of frequency. Hence, conditional entropy measure is proposed to evaluate close approximation of cohesion among attributes within each cluster. We propose a new objective function that is used to reflect the optimistic clustering so that the within-cluster dispersion is minimized and the between-cluster separation is enhanced. We performed experiments on five real-world datasets, comparing the performance of our algorithms with four algorithms, using three evaluation metrics: accuracy, f-measure and adjusted Rand index. According to the experiments, the proposed algorithm outperforms the algorithms that were considered int the evaluation, regarding the considered metrics.

• Spatial Information Research
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• v.13 no.4 s.35
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• pp.355-364
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• 2005

Vector data sets (e.g. maps) are currently major sources of displaying, querying, and identifying locations of spatial features in a variety of applications. Especially in mobile environment, the needs for using spatial data is increasing, and the relative large size of vector maps need to be smaller. Recently, there have been several studies about vector map compression. There was clustering-based compression method with novel encoding/decoding scheme. However, precedent studies did not consider that spatial data have to be updated periodically. This paper explores the problem of existing clustering-based compression method. We propose an adaptive approximation method that is capable of handling data updates as well as reducing error levels. Experimental evaluation showed that when an updated event occurred the proposed adaptive approximation method showed enhanced positional accuracy compared with simple cluster based compression method.

• Journal of computational fluids engineering
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• v.12 no.3
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• pp.20-28
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• 2007

A fast and robust method of grid generation to multiple functions has been developed for flow analysis in three dimensional space. It is based on the Non-Uniform Rational B-Spline(NURBS) of an approximation method. Many of NURBS intrinsic properties are introduced and much more easily understood. The grid generation method, details of numerical implementation. examples of application, and potential extensions of the current method are illustrated in this paper. The object of this study is to develop the surface grid generation and the grid cluster techniques capable of resolving complex flows with shock waves, expansion waves, shear layers. The knot insert method of Non-Uniform Rational B-Spline seems well worked. In addition, NURBS has been widely utilized to generate grids in the computational fluid dynamics community. Computational examples associated with practical configurations have shown the utilization of the algorithm.