• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.3
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• pp.91-102
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• 1994

For the analysis of textured image, it requires large storage space and computation time to calculate the matrix features such as SGLDM(Spatial Gray Level Dependence Matrix). NGLDM(Neighboring Gray Level Dependence Matrix). NSGLDM(Neighboring Spatial Gray Level Dependence Matrix) and GLRLM(Gray Level Run Length Matrix). In spite of a large amount of information that each matrix contains, a set of several correlated scalar features calculated from the matrix is not sufficient to approximate it. In this paper, we propose a new classifier for textured images based on these matrices in which the projected vectors of each matrix on the meaningful directions are used as features. In the proposed method, an unknown image is classified to the class of a known image that gives the maximum similarity between the projected model vector from the known image and the vector from the unknown image. In the experiment to classify images of agricultural products, the proposed method shows good performance as much as 85-95% of correct classification ratio.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2006.06a
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• pp.354-360
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• 2006

The purpose of this paper is to propose a securely personalized context-aware cooperative query that supports a multi-level data abstraction hierarchy and conceptual distance metric among data values, while considering privacy concerns around user context awareness. The conceptual distance expresses a semantic similarity among data values with a quantitative measure, and thus the conceptual distance enables query results to be ranked. To show the feasibility of the methodology proposed in this paper we have implemented a prototype system in the area of site search in a large-scale shopping mall.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.4
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• pp.105-117
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• 1998

In this paper, the general formula of disparity estimation based on Bayesian Maximum A Posteriori (MAP) algorithm is derived and implemented with simplified probabilistic models. The probabilistic models are independence and similarity among the neighboring disparities in the configuration.The formula is the generalized probabilistic diffusion equation based on Bayesian model, and can be implemented into the some different forms corresponding to the probabilistic models in the disparity neighborhood system or configuration. And, we proposed new probabilistic models in order to simplify the joint probability distribution of disparities in the configuration. According to the experimental results, the proposed algorithm outperformed the other ones, such as sum of swuared difference(SSD) based algorithm and Scharstein's method. We canconclude that the derived formular generalizes the probabilistic diffusion based on Bayesian MAP algorithm for disparity estimation, and the propsoed probabilistic models are reasonable and approximate the pure joint probability distribution very well with decreasing the computations to 0.01% of the generalized formula.

• Korean Journal of Microbiology
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• v.25 no.3
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• pp.229-237
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• 1987

To determine the characteristics of heterotrophic bacterial community in estuarine ecosystem, water and sediment samples were taden from Naktong estuary. All isolates were compared with 73 characters and described by cluster analysis. With same characters, 30 reference strains were able to divide into approximate species level at 80% similarity (S value). Diversity indices ($H^{1}$) of sediment column isolates were higher than water column isolates. The bacterial community commonly appeared in water and sediment column was reduced with going to downstream. Tolerance indices for temperature (Pt) and salinity (Ps) were also higher in sediment isolates than in water isolates. The bacterial community in sediment column is believed to be composed with diverse populations compared to water column and maintains its stability against various environmental changes with high physiological tolerances.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1177-1180
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• 1993

In this paper, a fuzzy-neural interpolating network is proposed to efficiently approximate a nonlinear function. Specifically, basis functions are first constructed by Fuzzy Membership Function based Neural Networks (FMFNN). And the fuzzy similarity, which is defined as the degree of matching between actual output value and the output of each basis function, is employed to determine initial weighting of the proposed network. Then the weightings are updated in such a way that square of the error is minimized. To show the capability of function approximation of the proposed fuzzy-neural interpolating network, a numerical example is illustrated.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.10a
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• pp.105-110
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• 2005

The penalty method has been widely applied to analyses of incompressible fluid flow. However, we have not yet found any prior studies that employed penalty method to analyze compressible fluid flow. In this study, with an eye on the apparent similarity between the slight compressible formulation and the penalty formulation, we have proposed a modified approximate approach that can analyze compressible packing process using the penalty parameter, which is an improvement on an earlier formulation (KSME, 2004B). Based on the assumption of the isothermal flow, a set of reference solutions was obtained to verify the validity of the proposed scheme. Furthermore, we have applied the proposed scheme to the analysis of the packing process of different cases.

• Computers and Concrete
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• v.30 no.1
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• pp.1-8
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• 2022

The basic purpose of the current study is to compute the numerical analysis of heat source/sink for Darcy-Forchheimer three dimensional nanofluid flow with gyrotactic microorganism by rotatable disk via porous media under the slip conditions. Due to nanoparticles, random and thermophoretic motion phenomenon occurs. The governing mathematical model is handled numerically by shooting method. Additionally, the characteristics of velocities, mass, heat, motile microorganisms and associated parameters are thoroughly analyzed via plots and tables. Different physical parameters like Forchheimer number, slip parameters like velocity, porosity parameter, Prandtl number, Brownian number, thermophoresis parameter, heat sink/source parameter, bioconvected Rayleigh number, buoyancy parameteron dimensionless velocities, temperature. Approximate values of Sherwood microorganism are analyzed.

• The KIPS Transactions:PartD
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• v.13D no.4 s.107
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• pp.455-462
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• 2006

Recently, the filtering approach using vector approximation such as VA-file[1] or LPC-file[2] have been proposed to support similarity search in high dimensional data space. This approach filters out many irrelevant vectors by calculating the approximate distance from a query vector using the compact approximations of vectors in database. Accordingly, the total elapsed time for similarity search is reduced because the disk I/O time is eliminated by reading the compact approximations instead of original vectors. However, the search time of the VA-file or LPC-file is not much lessened compared to the brute-force search because it requires a lot of computations for calculating the approximate distance. This paper proposes a new bitmap index structure in order to minimize the calculating time. To improve the calculating speed, a specific value of an object is saved in a bit pattern that shows a spatial position of the feature vector on a data space, and the calculation for a distance between objects is performed by the XOR bit calculation that is much faster than the real vector calculation. According to the experiment, the method that this paper suggests has shortened the total searching time to the extent of about one fourth of the sequential searching time, and to the utmost two times of the existing methods by shortening the great deal of calculating time, although this method has a longer data reading time compared to the existing vector approximation based approach. Consequently, it can be confirmed that we can improve even more the searching performance by shortening the calculating time for filtering of the existing vector approximation methods when the database speed is fast enough.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.5
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• pp.1161-1171
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• 1993

Model materials are developed to achieve similarity of flow patterns for mild steels in forming processes at high temperatures. The model materials consist of pure plasticine and one or two additives such as resin and lanolin. To verify the similarity of flow patterns between physical modeling and compression of mild steels at high temperatures, ring and compression tests have been carried out with the developed-model materials at various strain rates, temperatures and lubricants. The test results are in good agreement with the flow patterns obtained from upsetting of a mild steel at high temperatures.

• Korean Journal of Cognitive Science
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• v.10 no.4
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• pp.71-83
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• 1999

Conventional symbolic inference systems lack flexibility because they do not well reflect flexible semantic structure of knowledge and use symbolic logic for their basic inference mechanism. For solving this problem. we have recently proposed the 'Connectionist Semantic Network(CSN)' as a model for flexible knowledge representation and inference based on neural networks. The CSN is capable of carrying out both approximate reasoning and commonsense reasoning based on similarity and association. However. we have difficulties in representing general and structured high-level knowledge and variable binding using the connectionist framework of the CSN. In this paper. we propose a hybrid system called SymCSN(Symbolic CSN) that combines a symbolic module for representing general and structured high-level knowledge and a connectionist module for representing and learning low-level semantic structure Simulation results show that the SymCSN is a plausible model for human-like flexible knowledge representation and inference.