• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.4
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• pp.1654-1662
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• 2011

This paper investigates how does cash holdings have effect on investment-cash flow sensitivity in korea firms over the period 1981-2009. According to $\"{O}$.Arslan et al.(2006), I expect that financially constrained firms have more cash holdings. and financially constrained cash-rich firms are likely to have less investment-cash flow sensitivity especially in the financial crisis period. Using financial constraint classification variables(firm size, dividend, cash holdings), we divide whole sample firms into financially constrained firms and financially unconstrained firms, and then I compare investment-cash flow sensitivity in pre-financial crisis(1981-1996), financial crisis(1997-1998) and after-financial crisis(1999-2009) period. This paper's findings are as follows: First, under no financial constraint classification conditions, cash-poor firms exhibit greater investment-cash flow sensitivity than cash-rich firms do during 1981-2009 period except financial crisis period. These findings support the hypothesis that firms have more cash holdings less investment-cash flow sensitivity except in financial crisis period. In financial crisis period, cash holdings have no effect on investment-cash flow sensitivity. Second, this paper findings are somewhat different as $\"{O}$.Arslan et al.(2006)'s. Under the financial constraint classification conditions, financially unconstrained firms have more investment-cash flow sensitivity rather than constrained firms have. The reason is that both dividend and firm size are not a complete classification criteria variables. And there exists other possible determinants of investment-cash flow sensitivity. Finally, this paper find that there are common determinants of corporate cash holdings in all periods. This paper suggests that cash flow and market to book ratio are positive determinants of corporate cash holdings but short-term debt, investment and firm size are negative determinants of corporate cash holdings.

• Journal of KIISE
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• v.44 no.4
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• pp.433-438
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• 2017

Presently, the ubiquitous emergence of high-speed-network environments has led to a rapid increase of various applications, leading to constantly complicated network traffic. To manage networks efficiently, the traffic classification of specific units is essential. While various traffic-classification methods have been studied, a methods for the complete classification of network traffic has not yet been developed. In this paper, a correlation model of the network flow is defined, and a traffic-classification method for which this model is used is proposed. The proposed network-correlation model for traffic classification consists of a similarity model and a connectivity model. Suggestion for the effectiveness of the proposed method is demonstrated in terms of accuracy and completeness through experiments.

• Journal of Korean Society for Atmospheric Environment
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• v.21 no.5
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• pp.525-535
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• 2005

Using a three-dimensional computational fluid dynamics (CFD) model with the $k-{\varepsilon}$ turbulence closure scheme based on the renormalization group theory, flow regimes in urban street canyons are classified according to the building and street aspect ratios. The transition between skimming flow (SF) and wake interference flow (WIF) is determined with the size of double-eddy circulation generated behind the upwind building. The transition between WIF and isolated roughness flow (IRF) is determined with the flow reattachment distance from the upwind building. The critical aspect ratios at which the flow transition occurs are found and compared with those in previous studies. The results show that the flow-regime classification method used in this study is quite reasonable and that the values of the critical aspect ratios are generally consistent with those in fluid experiments or large-eddy simulation. The regression equation describing a relation between the building and street aspect ratios at the flow-regime transition is presented.

• Journal of Energy Engineering
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• v.23 no.4
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• pp.263-271
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• 2014

Corrective thermal performance analysis is required for thermal power plants to determine performance status of turbine cycle. We developed classification method for main feed water flow to make precise correction for performance analysis based on ASME (American Society of Mechanical Engineers) PTC (Performance Test Code). The classification is based on feature identification of status of main water flow. Also we developed predictive algorithms for corrected main feed-water through Support Vector Machine (SVM) Model for each classified feature area. The results was compared to estimations using Neural Network(NN) and Kernel Regression(KR). The feature classification and predictive model of main feed-water flow provides more practical methods for corrective thermal performance analysis of turbine cycle.

• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.628-631
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• 1988

This paper describes a fingerprint classification on the basis of feature points(whorl, core) and feature vector and uses a syntactic approach to identify the shape of flow line around the core. Fingerprint image is divided into 8 by 8 subregions and fingerprint region is separated from background. For each subregion of fingerprint region, the dominant ridge direction is obtained to use the slit window quantized in 8 direction and relaxation is performed to correct ridge direction code. Feature points(whorl, core, delta) are found from the ridge direction code. First classification procedure divides the types of fingerprint into 4 class based on whorl and cores. The shape of flow line around the core is obtained by tracing for the fingerprint which has one core or two core and is represented as string. If the string is acceptable by LR(1) parser, feature vector is obtained from feature points(whorl, core, delta) and the shape of flow line around the core. Feature vector is used hierarchically and linearly to classify fingerprint again. The experiment resulted in 97.3 percentages of sucessful classification for 71 fingerprint impressions.

• Journal of KIISE:Information Networking
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• v.36 no.3
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• pp.153-162
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• 2009

It is a challenging work for service operators to accurately classify different services, which runs on various wireless networks based upon numerous platforms. This works focuses on design and implementation of a classifier, which accurately classifies applications, which are captured horn WiBro Network. Notion of session is introduced for the classifier, instead of commonly used Flow to develop a classifier. Based on session information of given traffic, two classification algorithms are presented, Classification and Regression Tree and Support Vector Machine. Both algorithms are capable of classifying accurately and effectively with misclassification rate of 0.85%, and 0.94%, respectively. This work shows that classifier using CART provides ease of interpreting the result and implementation.

• Economic and Environmental Geology
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• v.40 no.3 s.184
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• pp.319-330
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• 2007

The Muposan Tuff is a stratigraphic unit which is distinguished as a cooling unit in the volcanic rocks of the northeastern Kyeongsang Basin. The Muposan Tuff commonly belongs to tuff field according to the granulometric classification and to vitric tuffs according to the constituent classification. The tuffs are mostly densely to partially welded to include very flattened and sometimes stretched pumices and shards, and involve several flow indicator and lateral gradings in maximum diameter and content of their constituents. Movement pattern from flow lineation, lithic and pumice imbrications, asymmetric flow folds, and lateral gradings in maximum diameter and content of their constituents indicate that the Muposan Tuff had a source from the southeastern part.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.24 no.6
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• pp.538-544
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• 2012

Analysis of thermal performance is required for the economic operation of turbine cycle of power plant. We developed corrective model of main feed water flow which is the most important parameter for the precise analysis of turbine cycle performance. Classification model for the identification of feed water flow measurement status was applied to increase the suitability of the corrective model. We used neural network and support vector machine to develop estimation model of main feed water flow with more generalization capability. The estimation model can be used practically to evaluate corrective performance of turbine cycle plant.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1239-1241
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• 2003

This paper presents the results of Landsat-TM imagery applications for detecting spatial variations of the water environments in the Saemankeum (STLR) project areas. The simulated tidal flow patterns from a two -dimensional hydro - dynamic model and water quality data from STRL project were used for relationships with the satellite data. Unsupervised classification of the tidal water body reflects the overall flow patterns at a flooding tide. Regressive equations for water quality parameters were derived and used for supervised classifications. The results were found to be useful to synoptically evaluate the water environments during the construction stages of the STLR project.

• Korean Journal of Cognitive Science
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• v.19 no.4
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• pp.477-497
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• 2008

Mood classification of blog text is an interesting problem, with a potential for a variety of services involving the Web. This paper introduces an approach to mood classification enhancements through the normalized negation n-grams which contain mood clues and corpus-specific term weighting(CSTW). We've done experiments on blog texts with two different classification methods: Enhanced Mood Flow Analysis(EMFA) and Support Vector Machine based Mood Classification(SVMMC). It proves that the normalized negation n-gram method is quite effective in dealing with negations and gave gradual improvements in mood classification with EMF A. From the selection of CSTW, we noticed that the appropriate weighting scheme is important for supporting adequate levels of mood classification performance because it outperforms the result of TF*IDF and TF.