• Proceedings of the Acoustical Society of Korea Conference
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• 1995.06a
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• pp.291-294
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• 1995

본논문은 POW algorithm과 알고리즘을 통해 수행된 결과인 large scale word recognizer를 위한 POW set에 대하여 설명하겠다. Large scale word recognizer를 위한 speech database를 구축하기 위해서는 모든 가능한 phonological phenomenon이 POW set에 포함 되어얗 ks다. 또한 POW set의 음운 현상들의 분포는 추출하고자 하는 모집단의 음운현상들의 분포와 유사해야 한다. 위와 같은 목적으로 다음과 같이 3가지 성질을 갖는 POW set을 추출하기 위한 새로운 algorithm을 제안한다. 1. 모집단에서 발생하는 모든 음운현상을 포함해야 한다. 2, 최소한의 단어 집합으로 구성되어야 한다. 3. POW set과 모집단의 음운현상의 분포가 유사해야 한다. 우리는 약 300만 어절의 한국어 text corpus로부터 5천 단어의 고빈도 어절을 추출하고 이로부터 한국어 POW set을 추출하였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.1
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• pp.15-21
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• 2015

In support vector machine, the values of parameters included in kernels affect strongly generalization ability. It is often difficult to determine appropriate values of those parameters in advance. It has been observed through our studies that the burden for deciding the values of those parameters in support vector regression can be reduced by utilizing ensemble learning. However, the straightforward application of the method to large scale problems is too time consuming. In this paper, we propose a method in which the original data set is decomposed into a certain number of sub data set in order to reduce the burden for parameter tuning in support vector regression with large scale data sets and imbalanced data set, particularly.

• Journal of the Korea Society for Simulation
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• v.10 no.2
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• pp.61-74
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• 2001

The reliability evaluation of the large scale network becomes very complicate according to the growing size of network. Moreover if the reliability is not constant but follows probability distribution function, it is almost impossible to compute them in theory. This paper studies the network evaluation methods in order to overcome such difficulties. For this an efficient path set algorithm which seeks the path set connecting the start and terminal nodes efficiently is developed. Also, various variance reduction techniques are applied to compute the system reliability to enhance the simulation performance. As a numerical example, a large scale network is given. The comparisons of the path set algorithm and the variance reduction techniques are discussed.

• Proceedings of the KSME Conference
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• 2008.11b
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• pp.2558-2561
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• 2008

In the present study, a parallel Taylor-Galerkin/level set based two-phase flow code was developed using finite element discretization and domain decomposition method based on MPI (Message Passing Interface). The proposed method can be utilized for the analysis of a large scale free surface problem in a complex geometry due to the feature of FEM and domain decomposition method. Four-step fractional step method was used for the solution of the incompressible Navier-Stokes equations and Taylor-Galerkin method was adopted for the discretization of hyperbolic type redistancing and advection equations. A Parallel ILU(0) type preconditioner was chosen to accelerate the convergence of a conjugate gradient type iterative solvers. From the present parallel numerical experiments, it has been shown that the proposed method is applicable to the simulation of large scale free surface flows.

• Journal of the Korean Institute of Navigation
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• v.1 no.1
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• pp.17-26
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• 1977

A large number of the set-nets are set in Namhaedo coast of Korea. The floats of these set-nets are not only small even in case of large floats but also they scarcely have distinguishable marks such as light buoys or flags, so that they are very hard to be recognized by naked eyes and thus became probable obstacles to navigation for the passing ships and the fishing vessels. In order to research the capability of detecting such nets with Radar, the author investigated a maximum detectable range of the ordinarly large floatsand of a floating corner reflectors of various size and shape by Radar. The results obtained are as follows; 1. A maximum detectable range of large floats at a close range can be calculated by the Radar equation in sufficient accuracy. 2. Large floats of the large set-nets are also detectable by Radar even though it's detectable range boundary was within 0.2-0.65 miles. And the Radar picture of large floats was easier to be found with somewhat higher setting of the gain control on shorter range scale of the 1 mile. 3. Floating corner reflector rather suitable for set-net floats of "S" type reflector proposed in this paper, of which the dimension must be above 17cm in diameter to be detectable by Radar at 2 miles.t 2 miles.

• The Korean Journal of Applied Statistics
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• v.23 no.1
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• pp.197-206
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• 2010

In this paper, we propose boosting algorithms when data are very large or coming in batches sequentially over time. In this situation, ordinary boosting algorithm may be inappropriate because it requires the availability of all of the training set at once. To apply to large scale data or data batch stream, we modify the AdaBoost and Arc-x4. These algorithms have good results for both large scale data and data batch stream with or without concept drift on simulated data and real data sets.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1100-1122
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• 2023

Scheduling user-submitted cloud tasks to the appropriate virtual machine (VM) in cloud computing is critical for cloud providers. However, as the demand for cloud resources from user tasks continues to grow, current evolutionary algorithms (EAs) cannot satisfy the optimal solution of large-scale cloud task scheduling problems. In this paper, we first construct a large- scale multi-objective cloud task problem considering the time and cost functions. Second, a multi-objective optimization algorithm based on multi-factor optimization (MFO) is proposed to solve the established problem. This algorithm solves by decomposing the large-scale optimization problem into multiple optimization subproblems. This reduces the computational burden of the algorithm. Later, the introduction of the MFO strategy provides the algorithm with a parallel evolutionary paradigm for multiple subpopulations of implicit knowledge transfer. Finally, simulation experiments and comparisons are performed on a large-scale task scheduling test set on the CloudSim platform. Experimental results show that our algorithm can obtain the best scheduling solution while maintaining good results of the objective function compared with other optimization algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.4
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• pp.452-474
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• 2010

Formidable growth of Internet technologies has revealed challenging issues about its scale and performance evaluation. Modeling and simulation play a central role in the evaluation of the behavior and performance of the large-scale network systems. Large numbers of nodes affect simulation performance, simulation execution time and scalability in a weighty manner. Most of the existing simulators have numerous problems such as size, lack of system theoretic approach and complexity of modeled network. In this work, a scalable discrete-event modeling approach is described for studying networks' scalability and performance traits. Key fundamental attributes of Internet and its protocols are incorporated into a set of simulation models developed using the Discrete Event System Specification (DEVS) approach. Large-scale network models are simulated and evaluated to show the benefits of the developed network models and approaches.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.4
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• pp.1110-1127
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• 2022

To improve the training efficiency and generalization performance of a support vector machine (SVM) in a large-scale set, an optimal SVM learning method based on adaptive sparse sampling and the granularity shift factor is presented. The proposed method combines sampling optimization with learner optimization. First, an adaptive sparse sampling method based on the potential function density clustering is designed to adaptively obtain sparse sampling samples, which can achieve a reduction in the training sample set and effectively approximate the spatial structure distribution of the original sample set. A granularity shift factor method is then constructed to optimize the SVM decision hyperplane, which fully considers the neighborhood information of each granularity region in the sparse sampling set. Experiments on an artificial dataset and three benchmark datasets show that the proposed method can achieve a relatively higher training efficiency, as well as ensure a good generalization performance of the learner. Finally, the effectiveness of the proposed method is verified.

• Korean Institute of Interior Design Journal
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• no.22
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• pp.16-22
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• 2000

The purpose of this study is to analyze characteristics of set-booth in beauty salon as well as to suggest the basic design data for franchise beauty salon. The method of this research was based on field observation of the franchise beauty salon in Seoul. The results of this research are as follows. 1) In set-booth type analysis, set-mirror wall type and set-mirror partition type are mainly used, but set-mirror table type is not showed in this research. 2) In terms of scale, wall type and partition type are classified as large scale, wall type and partition type are used as meduim scale. In shop front analysis, the result is shown in two things. If it is type, they used partition type and if it is close type, they used wall type. 3) Set-mirror is consisted of mirror and drawer and it is classified by 4 types with combination method. In a result, most of them used separated mirror type because they want to emphasize the separation between set booth and its layout. 4) Lighting method has 4 types; corniced type, bracket type, pendant type and downlight type. Among them, downlight is showed as the most-used.