• Proceedings of the Korean Information Science Society Conference
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• 2008.06d
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• pp.485-490
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• 2008

무선 인터넷의 구조적 특성상 한 셀에서 대역폭을 공유하고 그 안에서 각기 다른 QoS를 요구하는 서비스들이 한정된 자원을 사용한다. 트래픽의 변화와 패턴을 예측하기 위한 분석은 실제 서비스를 제공하기 전인 기획단계에서 매우 중요한 도구로 사용이 된다. 무선망의 트래픽을 예측하기 위해서는 유선망의 분석과는 다른 방법이 필요하기 때문에 정확한 분류를 위해서 본 연구에서는 세션의 단위로 분석할 것을 제안한다. 또한 Classification and Regression Tree(CART) 와 Support Vector Machine(SVM) 의 두 개의 판별 분류 기법을 서로 비교하고 그 성능을 평가한다. 두 개의 판별 기법의 오차는 CART의 경우 0.0094 그리고 SVM의 경우 0.0089로 둘 다 우수한 성능을 보였지만 쉬운 결과 해석이 가능한 CART가 사용하기 용이함을 보인다.

• Journal of the Korean Data and Information Science Society
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• v.21 no.3
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• pp.471-480
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• 2010

Cluster analysis is one of the useful methods to find out number of groups and member’s belongings. With the rapid development of computer application in statistics, variety of new methods in clustering analysis were studied such as EM algorism and Self organization maps. The goals of cluster analysis is finding the number of groupings that are meaningful to me. If data are analyzed perfectly with cluster analysis, we can get the same results from discernment analysis.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10c
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• pp.595-597
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• 2002

현재 인터넷의 발달에 인한 다양한 공격의 가능성의 이유로 침입 탐지 시스템(IDsystem, IDS)의 중요성은 날로 커지고 있으며 네트워크의 보안을 보장하기 위한 방안으로서 널리 이용되고 있다. 그러나 작은 네트워크 환경에서도 IDsystem에 적용되는 audit data의 양이 많아짐으로서 시간당 처리속도와 IDsystem의 설정을 위한 시간이 더욱더 요구되며 전체적인 효율성이 감소하게 된다. 본 연구에서는 IDsystem으로 빠른 훈련과정과 일반화 능력, 구조적인 단순함으로 다양한 분야에서 연구가 진행 중인 신경망 모델 중 하나인 Radial Basis Function(RBF)를 사용하였으며, 효율성 제고를 위하여 RBF에 적용 할 입력 간들의 중요성을 선 처리 단계에서 판별하여 불필요한 입력 값들을 축약하기 위해 결정계수(R-square)같을 측정, 알려지지 않은 공격과 알려진 공격들을 판별 할 수 있는 IDsystem을 제안하였다.

• The KIPS Transactions:PartB
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• v.17B no.4
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• pp.263-274
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• 2010

To provide context-aware music recommendation service, first of all, we need to catch music mood that a user prefers depending on his situation or context. Among various music characteristics, music mood has a close relation with people‘s emotion. Based on this relationship, some researchers have studied on music mood detection, where they manually select a representative segment of music and classify its mood. Although such approaches show good performance on music mood classification, it's difficult to apply them to new music due to the manual intervention. Moreover, it is more difficult to detect music mood because the mood usually varies with time. To cope with these problems, this paper presents an automatic method to classify the music mood. First, a whole music is segmented into several groups that have similar characteristics by structural information. Then, the mood of each segments is detected, where each individual's preference on mood is modelled by regression based on Thayer's two-dimensional mood model. Experimental results show that the proposed method achieves 80% or higher accuracy.

• Journal of the Korea institute for structural maintenance and inspection
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• v.20 no.5
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• pp.26-34
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• 2016

The failure modes of steel column-base plate connection arranged on the basis of AISC Design Guide-#1 and -#10 are base plate tension and compression side flexural yielding, yielding, pull-out and shear failure of anchor rod, concrete crushing in concrete footing and steel column yielding. The bending moment capacity and failure mode in this connection are predicted using limit-state function and we compare these results and test result. In the case that thickness of base plate is relatively thick, bending moment capacity and failure mode in steel column-base plate connection accurately predicted. But in the case that thickness of base plate is relatively thin and axial force do not exist, prediction of failure mode in this connection is somewhat inaccurate.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.12
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• pp.993-997
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• 2009

Clustering is well known method and that can be used in statistical machine translation. In this paper we propose a corpus clustering method using syntactic structure and POS information of dependency grammar. And using this cluster language model as additional feature to phrased-based statistical machine translation system to improve translation Quality.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.47 no.10
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• pp.695-704
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• 2019

In hypersonic regime, the complicated interaction between the air and surface of aircraft results in intensive aerodynamic heating on body. Provided this phenomenon occurs on a hypersonic vehicle, the temperature of the body extremely increases. And consequently, thermal deformation is produced and material properties are degraded. Furthermore, those affect both the aerothermoelastic stability and thermal safety of structures significantly. With the background, thermal safety and dynamic stability are studied according to the altitude, flight time and Mach number. Based on the investigation, design guideline is suggested to guarantees the structural integrity of hypersonic vehicles in terms of both of thermal safety and dynamic stability.

• Journal of the Korea Concrete Institute
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• v.15 no.6
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• pp.794-801
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• 2003

As it is important to measure the degree of the deterioration and predict service life caused by chloride in concrete structure the methods of measuring chloride in the concrete is raised important problems. This study is to set a new standard for using of the colorimetric method through grasping the character of the colorimetric distinction method, and measuring the chloride content at the place discolored. Also, to predict chloride content around embeded bar and time reaching limit chloride concentration through measuring the chloride content of concrete surface by colorimetric distinction method and this study presents the new concept of concrete degradation and diagnosis of the durability by salt damage. According the results, it is possible to use colorimetric distinction method as simplified measurement to measure the fixed quantity of the chloride concentration. What is more, it would make calculation of concrete surface chloride had a wide fluctuation at the general environment extended. Also, it would be make estimating durability of reinforced concrete structures applied to the basic data.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.735-740
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• 2012

In this paper, we introduce design methodologies of polynomial radial basis function neural network classifier with the aid of Principal Component Analysis(PCA) and Linear Discriminant Analysis(LDA). By minimizing the information loss of given data, Feature data is obtained through preprocessing of PCA and LDA and then this data is used as input data of RBFNNs. The hidden layer of RBFNNs is built up by Fuzzy C-Mean(FCM) clustering algorithm instead of receptive fields and linear polynomial function is used as connection weights between hidden and output layer. In order to design optimized classifier, the structural and parametric values such as the number of eigenvectors of PCA and LDA, and fuzzification coefficient of FCM algorithm are optimized by Artificial Bee Colony(ABC) optimization algorithm. The proposed classifier is applied to some machine learning datasets and its result is compared with some other classifiers.

• The Korean Journal of Applied Statistics
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• v.34 no.3
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• pp.401-410
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• 2021

Popular in discriminant classification analysis, k-nearest neighbor classification methods have limitations that do not reflect the local characteristic of the data, considering only the number of fixed neighbors. Considering the local structure of the data, the adaptive nearest neighbor method has been developed to select the number of neighbors. In the analysis of high-dimensional data, it is common to perform dimension reduction such as random projection techniques before using k-nearest neighbor classification. Recently, an ensemble technique has been developed that carefully combines the results of such random classifiers and makes final assignments by voting. In this paper, we propose a novel discriminant classification technique that combines adaptive nearest neighbor methods with random projection ensemble techniques for analysis on high-dimensional data. Through simulation and real-world data analyses, we confirm that the proposed method outperforms in terms of classification accuracy compared to the previously developed methods.