• Water Engineering Research
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• v.1 no.4
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• pp.309-320
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• 2000

In this study, laboratory study of the behavior of wastewater discharged from the Boston ocean outfall was compared with the predictions of mathematical models. The dta sets cover broad ranges of discharge conditions and oceanic conditions, and are associated with a typical type of outfall discharges with multiport diffusers. The laboratory data sets were obtained in density stratified towing tanks. These data sets were used to evaluate four commonly used models: UM, UDKHDEN, RSB and CORMI$\times$2 for minimum dilution, the height to the top of the wastefield, and wastefield thickness. For minimum diluation and height to the top of the wastefield, UM and RSB predictions agree well with laboratory data. UDKHDEN overestimated the minimum dilution and height to the top of the wastefield while CORMI$\times$2 underestimated these values. All of the model predictions for the wastefield thickness were widely scattered. about the measured values. The hydraulic model study reproduced the major features observed in the laboratory. It also afforded considerable insight into the mechanics of mixing of multiport risers which could not have been obtained either from the laboratory test or the mathematical models.

• Journal of Integrative Natural Science
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• v.6 no.2
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• pp.111-117
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• 2013

We propose a mean value function for software failures in NHPP software reliability model. And we deal with the maximum likelihood estimation and the least squares estimation in the proposed mean value function. The explicit mean value function solution for the proposed model is presented by MLE and LSE in two data sets. The values of SSE and MSE is presented in two data sets by MLE and LSE. We compare the predicted number of faults with the actual two data sets using the proposed mean value function.

• Korean Journal of Remote Sensing
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• v.24 no.3
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• pp.251-256
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• 2008

Morphing method, one of classic image processing algorithms, has been used in various application fields. The motivation of this work is to investigate its applicability in consideration to geo-spatial data including airborne or space-borne images. For this purpose, the Beier and Neely morphing algorithm is tentatively implemented in the form of a prototype with user interface. As the results, this feature-based morphing with paired image sets can be used for general users: image simulation using two or more images and construction of color-blending image between source image and destination image in different types. Some simple application cases were demonstrated. This scheme is the simple and useful approach for those who want to utilize both geo-spatial data sets and airborne/space-borne image sets.

• ETRI Journal
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• v.44 no.2
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• pp.327-338
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• 2022

Classification models for human action recognition require robust features and large training sets for good generalization. However, data augmentation methods are employed for imbalanced training sets to achieve higher accuracy. These samples generated using data augmentation only reflect existing samples within the training set, their feature representations are less diverse and hence, contribute to less precise classification. This paper presents new data augmentation and action representation approaches to grow training sets. The proposed approach is based on two fundamental concepts: virtual video generation for augmentation and representation of the action videos through robust features. Virtual videos are generated from the motion history templates of action videos, which are convolved using a convolutional neural network, to generate deep features. Furthermore, by observing an objective function of the genetic algorithm, the spatiotemporal features of different samples are combined, to generate the representations of the virtual videos and then classified through an extreme learning machine classifier on MuHAVi-Uncut, iXMAS, and IAVID-1 datasets.

• Journal of the Korea Society of Computer and Information
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• v.14 no.6
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• pp.135-142
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• 2009

Data mining is an emerging area of computational intelligence that offers new theories, techniques, and tools for analysis of large data sets. The major techniques used in data mining are mining association rules, classification and clustering. Since these techniques are used individually, it is necessary to develop the methodology for rule extraction using a process of integrating these techniques. Rule extraction techniques assist humans in analyzing of large data sets and to turn the meaningful information contained in the data sets into successful decision making. This paper proposes an autonomous method of rule extraction using clustering and rough set theory. The experiments are carried out on data sets of UCI KDD archive and present decision rules from the proposed method. These rules can be successfully used for making decisions.

• Proceedings of the KSRS Conference
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• v.2
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• pp.586-589
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• 2006

High resolution satellite imagery is regarded as one of the important data sets to engineering application, as well as conventional scientific application. However, despite this general view, there are a few target applications using this information. In this study, the possibility for the future wide uses in associated with smart graphics of this information is investigated. The concept of smart graphics can be termed intelligent graphics with XML-based structure and knowledge related to semantic web, which is a useful component for the data dissemination framework model in a multi-layered web-based application. In the first step in this study, high resolution imagery is transformed to GML (Geographic Markup Language)-based structure with attribute schema and geo-references. In the second, this information is linked with GIS data sets, and this fused data set is represented in the X3D (eXtensible 3D), ISO-based web 3D graphic standard, with styling attributes, in the next stop. The main advantages of this approach using GML and X3D are the flourished representations of a source data according to user/clients’ needs and structured 3D visualization linked with other XML-based application. As for the demonstration of this scheme, 3D urban modelling case with actual data sets is presented.

• Journal of Computing Science and Engineering
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• v.10 no.1
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• pp.21-31
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• 2016

With the application and development of biomedical techniques such as next-generation sequencing, mass spectrometry, and medical imaging, the amount of biomedical data have been growing explosively. In terms of processing such data, we face the problems surrounding big data, highly intensive computation, and high dimensionality data. Fortunately, cloud computing represents significant advantages of resource allocation, data storage, computation, and sharing and offers a solution to solve big data problems of biomedical research. In order to improve the efficiency of resource management in cloud computing, this paper proposes a clustering method and adopts Radial Basis Function in order to compress comprehensive data sets found in biology and medicine in high quality, and stores these data with resource management in cloud computing. Experiments have validated that with such a data-compression-based resource management in cloud computing, one can store large data sets from biology and medicine in fewer capacities. Furthermore, with reverse operation of the Radial Basis Function, these compressed data can be reconstructed with high accuracy.

• MALSORI
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• no.65
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• pp.105-124
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• 2008

This paper presents the effects of different phone-like-unit (PLU) sets in order to propose an optimal PLU set for the performance improvement of Korean automatic speech recognition (ASR) systems. The examination of 9 currently used PLU sets indicates that most of them include a selection of allophones without any sufficient phonetic base. In this paper, a total of 34 PLU sets are designed based on Korean phonetic characteristics arid the effects of each PLU set are evaluated through experiments. The results show that the accuracy rate of each phone is influenced by different phonetic constraint(s) which determine(s) the PLU sets, and that an optimal PLU set can be anticipated through the phonetic analysis of the given speech data.

• The Korean Journal of Applied Statistics
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• v.25 no.5
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• pp.847-857
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• 2012

This article is concerned with choice-based conjoint analysis versus rating-based and ranking-based conjoint analysis. Choice-based conjoint analysis has a definite advantage in that the respondent's task of choosing the most preferred profile from several competing profiles adequately mimics consumer marketplace behavior. It is crucial to design the choice sets appropriate for the choice-based conjoint. Thus, this article suggests a new method to design the choice sets that are well-balanced. It augments the balanced incomplete block design and then obtains the dual design of the result to accommodate various numbers of profiles. In consequence, the choice sets designed by the new method have the desirable characteristics that each profile is presented to the same number of respondents, and pairs of any two distinct profiles occur together in the same number of choice sets. The balancing of the design increases the efficiency of the conjoint analysis. In addition, the pair-comparison scheme can improve the quality of data through the identification of contradictory responses.

• Journal of the Korean Statistical Society
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• v.25 no.4
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• pp.589-601
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• 1996

Geometric approach to extend the classical two-set theory of canonical correlation analysis to three or more sets is considered. It provides statistical graphs to represent the data in a low dimensional space. Procedures are developed for computing the canonical variables and the corresponding properties are investigated. The solution is equivalent to that of the usual problem in the case of two sets. Goodness-of-fit of the proposed plots is studied and a numerical example is included.