• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.9B
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• pp.834-844
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• 2003

Mobile IP has some performance degrade factors such as triangle routing and packet loss by the hand-off because the original IP was designed for the fixed network. The design goal for the next generation Internet service is to guarantee QoS. So, Mobile IP also should be able to provide the guaranteed QoS with performance enhancement because it is an IP-based mobile Internet service. In this paper, we propose route optimization, smooth hand-off scheme and MIP-LDP(Mobile IP-Label Distribution Protocol) on Mobile IP-over-MPLS framework to enhance the performance of the previously researched Mobile IP-over-MPLS schemes. The proposed framework enhanced long routing path problem and packet loss problem by the hand-off.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.1
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• pp.173-183
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• 2018

It is a very difficult task to establish a collaborative procedure in a new business that requires multilateral collaboration or to revise the regulation by analyzing and proving objectively the problems in the collaborative process conducted already by multilateral collaboration. In this paper, we proposed an optimization model for collaborative process to establish the operation procedure between collaborative parties using PERT/CPM network diagram which allows us to calculate the processing time. In order to verify the effectiveness and usefulness of our model for the collaboration process optimization developed in this study, we applied the developed collaborative procedure to student selection of the work-and-study-in-parallel course associated with a degree executed by Ministry of Employment and Labor. This study can be useful not only for newly establishing or reconfiguring collaborative procedures but also for standardizing the business procedures for building information systems between collaborative organizations.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.5
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• pp.98-106
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• 1998

This paper proposes an efficient learning algorithm for improving the training performance of the neural network. The proposed method improves the training performance by applying the backpropagation algorithm of a global optimization method which is a hybrid of a stochastic approximation and a conjugate gradient method. The approximate initial point for f a ~gtl obal optimization is estimated first by applying the stochastic approximation, and then the conjugate gradient method, which is the fast gradient descent method, is applied for a high speed optimization. The proposed method has been applied to the parity checking and the pattern classification, and the simulation results show that the performance of the proposed method is superior to those of the conventional backpropagation and the backpropagation algorithm which is a hyhrid of the stochastic approximation and steepest descent method.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.6
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• pp.108-114
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• 1989

Introducing and optimizing bit-significance to the Hopfield model, ten highly correlated binary images, i.e., numbers "0" to "9", are successfully stored and retrieved in a $6{}8$ node system. Unlike many other neural network models, this model has stronger error correction capability for correlated images such as "6","8","3", and "9". The bit significance optimization is regarded as an adaptive learning process based on least-mean-square error algorithm, and may be implemented with Widrow-Hoff neural nets optimizer. A design for electro-optic implementation including the adaptive optimization networks is also introduced.

• Smart Structures and Systems
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• v.25 no.5
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• pp.605-617
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• 2020

The existence of damages in structures causes changes in the physical properties by reducing the modal parameters. In this paper, we develop a two-stages approach based on normalized Modal Strain Energy Damage Indicator (nMSEDI) for quick applications to predict the location of damage. A two-dimensional IsoGeometric Analysis (2D-IGA), Machine Learning Algorithm (MLA) and optimization techniques are combined to create a new tool. In the first stage, we introduce a modified damage identification technique based on frequencies using nMSEDI to locate the potential of damaged elements. In the second stage, after eliminating the healthy elements, the damage index values from nMSEDI are considered as input in the damage quantification algorithm. The hybrid of Teaching-Learning-Based Optimization (TLBO) with Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) are used along with nMSEDI. The objective of TLBO is to estimate the parameters of PSO-ANN to find a good training based on actual damage and estimated damage. The IGA model is updated using experimental results based on stiffness and mass matrix using the difference between calculated and measured frequencies as objective function. The feasibility and efficiency of nMSEDI-PSO-ANN after finding the best parameters by TLBO are demonstrated through the comparison with nMSEDI-IGA for different scenarios. The result of the analyses indicates that the proposed approach can be used to determine correctly the severity of damage in beam structures.

• Journal of the Korea Society of Computer and Information
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• v.25 no.11
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• pp.41-50
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• 2020

Sentimental analysis begins with the search for words that determine the sentimentality inherent in data. Managers can understand market sentimentality by analyzing a number of relevant sentiment words which consumers usually tend to use. In this study, we propose exploring performance of feature selection methods embedded with Particle Swarm Optimization Multi Objectives Evolutionary Algorithms. The performance of the feature selection methods was benchmarked with machine learning classifiers such as Decision Tree, Naive Bayesian Network, Support Vector Machine, Random Forest, Bagging, Random Subspace, and Rotation Forest. Our empirical results of opinion mining revealed that the number of features was significantly reduced and the performance was not hurt. In specific, the Support Vector Machine showed the highest accuracy. Random subspace produced the best AUC results.

• Journal of Computing Science and Engineering
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• v.12 no.1
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• pp.24-35
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• 2018

Deep convolutional neural network (DCNN) is an advanced technology in image recognition. Because of extreme computing resource requirements, DCNN implementation with software alone cannot achieve real-time requirement. Therefore, the need to implement DCNN accelerator hardware is increasing. In this paper, we present a field programmable gate array (FPGA)-based hardware accelerator design of DCNN targeting handwritten Hangul character recognition application. Also, we present design optimization techniques in SDAccel environments for searching the optimal FPGA design space. The techniques we used include memory access optimization and computing unit parallelism, and data conversion. We achieved about 11.19 ms recognition time per character with Xilinx FPGA accelerator. Our design optimization was performed with Xilinx HLS and SDAccel environment targeting Kintex XCKU115 FPGA from Xilinx. Our design outperforms CPU in terms of energy efficiency (the number of samples per unit energy) by 5.88 times, and GPGPU in terms of energy efficiency by 5 times. We expect the research results will be an alternative to GPGPU solution for real-time applications, especially in data centers or server farms where energy consumption is a critical problem.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.5
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• pp.489-495
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• 2014

A micro-genetic algorithm (MGA) is one of the improved forms of a genetic algorithm. It is used to reduce the number of iterations and the computing resources required by using small populations. The efficiency of MGAs has been proved through many problems, especially problems with 3-5 design variables. This study proposes an optimization algorithm based on the sequential design of experiments (SDOE) and an MGA. In a previous study, the authors used the SDOE technique to reduce trial-and-error in the conventional approximate optimization method by using the statistical design of experiments (DOE) and response surface method (RSM) systematically. The proposed algorithm has been applied to various mathematical examples and a structural problem.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.10
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• pp.1323-1328
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• 2011

In the current design process for the lower arm used in automobile parts, an optimal solution of its various design variables should be found through exploration of the design space approximated using the response surface model formulated with a first- or second-order polynomial equation. In this study, a multi-level computational DOE (design of experiment) was carried out to explore the design space showing nonlinear behavior, in terms of factors such as the total weight and applied stress of the lower arm, where a fractional-factorial orthogonal array based on the artificial neural network model was introduced. In addition, the tolerance robustness of the optimal solution was estimated using a tolerance optimization with six sigma constraints, taking into account the tolerances occurring in the design variables.

• Journal of the Korean Operations Research and Management Science Society
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• v.31 no.1
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• pp.91-103
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• 2006

Given a bill of materials (BOM) tree T labeled by the breadth first search (BFS) order from node 0 to node n and a general network ${\Im}=(V,A)$, where V={1,2,...,m} is the set of production facilities and A is the set of arcs representing transportation links between any of two facilities, we assume that each node of T stands for not only a component. but also a production stage which is a possible stocking point and operates under a periodic review base-stock policy, We also assume that the random demand which can be achieved by a suitable service level only occurs at the root node 0 of T and has a normal distribution $N({\mu},{\sigma}^2)$. Then our integrated model of facility location problems and safety stock optimization problem (FLP&SSOP) is to identify both the facility locations at which partitioned subtrees of T are produced and the optimal assignment of safety stocks so that the sum of production cost, inventory holding cost, and transportation cost is minimized while meeting the pre-specified service level for the final product. In this paper, we first formulate (FLP&SSOP) as a nonlinear integer programming model and show that it can be reformulated as a 0-1 linear integer programming model with an exponential number of decision variables. We then show that the linear programming relaxation of the reformulated model has an integrality property which guarantees that it can be optimally solved by a column generation method.