• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.11a
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• pp.335.1-335
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• 2002

Among many structural dynamics modification methods for plate and shell vibration problems, embedding an emboss to the surface is very efficient. But deciding an optimal position and shape using optimization algorithm needs defining geometry and remeshing the model for every iteration step to implement the method, which takes much numerical cost. (omitted)

• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.4
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• pp.708-712
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• 1987

This paper deals with minimizing layout area of VLSI design. A long wire in a VLSI layout causes delay which can be reduced by using a driver. There can be significant area increase when many drivers are introduced in a layout. This paper describes a method to obtain tight bound on the worst-case increase in area when drivers are introduced along many long wires in a layout. The area occupied by minimum-area embedding for a circuit can depend on the aspect ratio of the bounding rectangle of the layout. This paper presents a separator-based area optimal embeddings for VLSI graphs in rectangles of several aspect ratios.

• Journal of Information Processing Systems
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• v.7 no.1
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• pp.75-84
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• 2011

The enhanced pyramid graph was recently proposed as an interconnection network model in parallel processing for maximizing regularity in pyramid networks. We prove that there are two edge-disjoint Hamiltonian cycles in the enhanced pyramid networks. This investigation demonstrates its superior property in edge fault tolerance. This result is optimal in the sense that the minimum degree of the graph is only four.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.10a
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• pp.365-373
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• 1999

Recently, numerous studies have demonstrated that artificial intelligence such as neural networks can be an alternative methodology for classification problems to which traditional statistical methods have long been applied. In building neural network model, the selection of independent and dependent variables should be approached with great care and should be treated as a model construction process. Irrespective of the efficiency of a learning procedure in terms of convergence, generalization and stability, the ultimate performance of the estimator will depend on the relevance of the selected input variables and the quality of the data used. Approaches developed in statistical methods such as correlation analysis and stepwise selection method are often very useful. These methods, however, may not be the optimal ones for the development of neural network models. In this paper, we propose a genetic algorithms approach to find an optimal or near optimal input variables for neural network modeling. The proposed approach is demonstrated by applications to bankruptcy prediction modeling. Our experimental results show that this approach increases overall classification accuracy rate significantly.

• Composites Research
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• v.25 no.6
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• pp.224-229
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• 2012

The structural performance is degraded in case of embedding the array antenna for reconnaissance and surveillance into the wing skin structures. In this paper, the optimal design for the thickness of composite hat-shaped stiffener which is reinforced embedded array antenna on the simplified composite wing box was conducted. To select the basic shape of hat-shaped stiffener, structural analysis was carry out using the commercial finite element analysis program while changing the web slope and flange length of hat-shaped stiffener. The optimal thickness of the composite hat-shaped stiffeners was determined by using commercial optimization program such as VisualDOC and commercial FEA program with considering stresses and buckling constraints.

• Journal of KIISE:Computer Systems and Theory
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• v.36 no.3
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• pp.149-157
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• 2009

The crossed cube, a variation of the hypercube, possesses a better topological property than the hypercube in its diameter that is about half of that of the hypercube. It has been known that an N-node complete binary tree is a subgraph of an (N+1)-node crossed cube [P. Kulasinghe and S. Bettayeb, 1995]. However, efficient embedding methods have not been known for the case that the number of nodes of the complete binary tree is greater than that of the crossed cube. In this paper, we show that an N-node complete binary tree can be embedded into an M-node crossed cube with dilation 1 and load factor [N/M], N>M$\geq$2. The dilation and load factor is optimal. Our embedding has a property that the tree nodes on the same level are evenly distributed over the crossed cube nodes. The property is especially useful when tree-structured algorithms are processed on a crossed cube in a level-by-level way.

• Journal of KIISE:Computer Systems and Theory
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• v.36 no.5
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• pp.335-343
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• 2009

The crossed cube has received great attention because it has equal or superior properties compared to the hypercube that is widely known as a versatile parallel processing system. It has been known that disjoint two copies of a mesh of size $4{\times}2^m$ or disjoint four copies of a mesh of size $8{\times}2^m$ can be embedded into a crossed cube with dilation 1 and expansion 1 [Dong, Yang, Zhao, and Tang, 2008]. However, it is not known that disjoint multiple copies of a mesh with more than eight rows and columns can be embedded into a crossed cube with dilation 1 and expansion 1. In this paper, we show that disjoint $2^{n-1}$ copies of a mesh of size $2^n{\times}2^m$ can be embedded into a crossed cube with dilation 1 and expansion 1 where $n{\geq}1$ and $m{\geq}3$. Our result is optimal in terms of dilation and expansion that are important measures of graph embedding. In addition, our result is practically usable in allocating multiple jobs of mesh structure on a parallel computer of crossed cube structure.

• Journal of KIISE
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• v.44 no.7
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• pp.719-725
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• 2017

To reduce management costs and improve performance, the European Telecommunication Standards Institute (ETSI) introduced the concept of network function virtualization (NFV), which can implement network functions (NFs) on cloud/datacenters. Within the NFV architecture, NFs can share physical resources by hosting NFs on physical nodes (commodity servers). For network service providers who support NFV architectures, an efficient resource allocation method finds utility in being able to reduce operating expenses (OPEX) and capital expenses (CAPEX). Thus, in this paper, we analyzed the network service chain embedding problem via an optimization formulation and found a close-optimal solution based on the Markov approximation framework. Our simulation results show that our approach could increases on average CPU utilization by up to 73% and link utilization up to 53%.

• Journal of Broadcast Engineering
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• v.24 no.1
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• pp.58-66
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• 2019

Recognition of named entity such as proper nouns in conversation sentences is the most fundamental and important field of study for efficient conversational information prediction. The most important part of a task-oriented dialogue system is to recognize what attributes an object in a conversation has. The named entity recognition model carries out recognition of the named entity through the preprocessing, word embedding, and prediction steps for the dialogue sentence. This study aims at using user - defined dictionary in preprocessing stage and finding optimal parameters at word embedding stage for efficient dialogue information prediction. In order to test the designed object name recognition model, we selected the field of daily chemical products and constructed the named entity recognition model that can be applied in the task-oriented dialogue system in the related domain.

• Journal of the Korean Society for Library and Information Science
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• v.53 no.2
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• pp.179-199
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• 2019

In this paper, we propose optimal methodologies for classifying patent literature by examining various feature extraction methods, machine learning and deep learning models, and provide optimal performance through experiments. We compared the traditional BoW method and a distributed representation method (word embedding vector) as a feature extraction, and compared the morphological analysis and multi gram as the method of constructing the document collection. In addition, classification performance was verified using traditional machine learning model and deep learning model. Experimental results show that the best performance is achieved when we apply the deep learning model with distributed representation and morphological analysis based feature extraction. In Section, Class and Subclass classification experiments, We improved the performance by 5.71%, 18.84% and 21.53%, respectively, compared with traditional classification methods.