• 한국연소학회:학술대회논문집
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• 2015.12a
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• pp.295-296
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• 2015

We presented the methods calculating the reduction efficiency of nitrogen oxide for the low $NO_x$ burner as the pollution prevention facilities. The standard $NO_x$ concentration was used on the emission factor of LNG, $3.7g/m^3$. The $NO_x$ reduction efficiency based on the $NO_x$ concentration was presented and the relationships between the $NO_x$ concentration and the emission factor or the specific heat emission factor were derived. These results could be accurately reflected on calculating the amount of the nitrogen oxide emissions. In addition, according to the arrangement of the low $NO_x$ burners the methods of applying their $NO_x$ reduction efficiency were proposed. The $NO_x$ reduction efficiency for the facilities consisting of the low $NO_x$ burners and the non-low $NO_x$ burners could be estimated with information about the reduction efficiency of each low $NO_x$ burners, the fuel consumption rate, and the heating value of fuel.

• Korean System Dynamics Review
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• v.14 no.1
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• pp.55-68
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• 2013

It is necessary to evaluate the greenhouse gases (GHGs) reduction policy by central and regional governments to set up the suitable GHG emissions measures. Quantitative, qualitative and synthetic methods have been adopted by previous researches to estimate GHG reduction policy. However, these methods mostly focused on the results of the reduction policy, rather than understanding and fixing the integrated structures of GHG emissions. In this research, System Dynamics(SD) was applied to 1 million green homes program, self-carfree-day system and carbon point program. The results showed that SD analyses could be appliable for the estimation of GHG reduction policy by developing the feedback loops and dynamic simulation model. SD can be consider as a supplementary tool to estimate the GHG reduction policies through the recognition of the structure in complex real system.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.1
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• pp.1-5
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• 2012

Data reduction has been used widely in data mining for convenient analysis. Principal component analysis (PCA) and factor analysis (FA) methods are popular techniques. The PCA and FA reduce the number of variables to avoid the curse of dimensionality. The curse of dimensionality is to increase the computing time exponentially in proportion to the number of variables. So, many methods have been published for dimension reduction. Also, data augmentation is another approach to analyze data efficiently. Support vector machine (SVM) algorithm is a representative technique for dimension augmentation. The SVM maps original data to a feature space with high dimension to get the optimal decision plane. Both data reduction and augmentation have been used to solve diverse problems in data analysis. In this paper, we compare the strengths and weaknesses of dimension reduction and augmentation for classification and propose a classification method using data reduction for classification. We will carry out experiments for comparative studies to verify the performance of this research.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.19 no.2
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• pp.171-188
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• 2015

In this paper, the efficient column-wise/row-wise lattice reduction (LR) updating and downdating methods are developed and their complexities are analyzed. The well-known LLL algorithm, developed by Lenstra, Lenstra, and Lov${\acute{a}}$sz, is considered as a LR method. When the column or the row is appended/deleted in the given lattice basis matrix H, the proposed updating and downdating methods modify the preconditioning matrix that is primarily computed for the LR with H and provide the initial parameters to reduce the updated lattice basis matrix efficiently. Since the modified preconditioning matrix keeps the information of the original reduced lattice bases, the redundant computational complexities can be eliminated when reducing the lattice by using the proposed methods. In addition, the rounding error analysis of the proposed methods is studied. The numerical results demonstrate that the proposed methods drastically reduce the computational load without any performance loss in terms of the condition number of the reduced lattice basis matrix.

• Maxillofacial Plastic and Reconstructive Surgery
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• v.27 no.4
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• pp.372-380
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• 2005

Condylar process of mandible, has the specialized anatomic structure compared with any other body structure, acts directly in connection with mastication and speech and so on. In general, mandibular condyle fractures have been managed by two methods as open and closed reduction. But, there are no reasonable consensus about the proper management of this injury. This study was designed for analysis of the prognosis of two methods of treatment, open and closed reduction, with positional change of fractured condyle and complications within 6 months post-intermaxillary fixation period. We conducted a retrospective analysis of 154 patients whose unilateral mandibular condyle fractures were treated by open or closed reduction in our department. The horizontal, sagittal, and coronal change of the condyle was examined using modified Towne's and panoramic radiographs before intermaxillary fixation(IMF), immediately after IMF, and at 6 months after IMF. Patients, whose mandibular condyle fractures were treated by closed reduction, had significantly shorter ramus height on the side of injury(P<0.05). But, fractured condylar fragments were displaced insignificantly with aspect to sagittal and coronal plane(P>0.05). The level of the fracture influenced the ramus length and the degree of coronal change in the closed reduction group(P<0.05). There was no significant correlation among the level of the fracture, treatment methods and complications(P>0.05). From the results obtained in this study, fractured mandibular condyles, were treated by closed reduction, had a tendency that continuous condylar displacement was occurred with aspect to horozontal and coronal plane in treatment period including intermaxillary fixation. And then there was a correlation between the level of the fracture and the position change in close reduction group statistically. These result suggested that care must be taken in basing treatment decisions on the degree of displacement of the condyle and in treating the mandibular condyle fractures for a long time.

• Journal of the Korea Society of Computer and Information
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• v.23 no.12
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• pp.21-26
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• 2018

As the large amount of data can be efficiently stored, the methods extracting meaningful features from big data has become important. Especially, the techniques of converting high- to low-dimensional data are crucial for the 'Data visualization'. In this study, principal component analysis (PCA; linear dimensionality reduction technique) and Isomap (non-linear dimensionality reduction technique) are introduced and applied to neural big data obtained by the functional magnetic resonance imaging (fMRI). First, we investigate how much the physical properties of stimuli are maintained after the dimensionality reduction processes. We moreover compared the amount of residual variance to quantitatively compare the amount of information that was not explained. As result, the dimensionality reduction using Isomap contains more information than the principal component analysis. Our results demonstrate that it is necessary to consider not only linear but also nonlinear characteristics in the big data analysis.

• Communications for Statistical Applications and Methods
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• v.26 no.5
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• pp.519-526
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• 2019

Response dimension reduction in a sufficient dimension reduction (SDR) context has been widely ignored until Yoo and Cook (Computational Statistics and Data Analysis, 53, 334-343, 2008) founded theories for it and developed an estimation approach. Recent research in SDR shows that a semi-parametric approach can outperform conventional non-parametric SDR methods. Yoo (Statistics: A Journal of Theoretical and Applied Statistics, 52, 409-425, 2018) developed a semi-parametric approach for response reduction in Yoo and Cook (2008) context, and Yoo (Journal of the Korean Statistical Society, 2019) completes the semi-parametric approach by proposing an unstructured method. This paper theoretically discusses and provides insightful remarks on three versions of semi-parametric approaches that can be useful for statistical practitioners. It is also possible to avoid numerical instability by presenting the results for an orthogonal transformation of the response variables.

• Communications for Statistical Applications and Methods
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• v.28 no.2
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• pp.205-215
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• 2021

Sufficient dimension reduction is useful dimension reduction tool in regression, and sliced inverse regression (Li, 1991) is one of the most popular sufficient dimension reduction methodologies. In spite of its popularity, it is known to be sensitive to the number of slices. To overcome this shortcoming, the so-called fused sliced inverse regression is proposed by Cook and Zhang (2014). Unfortunately, the two existing methods do not have the direction application to large p-small n regression, in which the dimension reduction is desperately needed. In this paper, we newly propose seeded sliced inverse regression and seeded fused sliced inverse regression to overcome this deficit by adopting iterative projection approach (Cook et al., 2007). Numerical studies are presented to study their asymptotic estimation behaviors, and real data analysis confirms their practical usefulness in high-dimensional data analysis.

• Communications for Statistical Applications and Methods
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• v.31 no.2
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• pp.191-202
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• 2024

The goal of this paper is to show how multivariate regression analysis with high-dimensional responses is facilitated by the response dimension reduction. Multivariate regression, characterized by multi-dimensional response variables, is increasingly prevalent across diverse fields such as repeated measures, longitudinal studies, and functional data analysis. One of the key challenges in analyzing such data is managing the response dimensions, which can complicate the analysis due to an exponential increase in the number of parameters. Although response dimension reduction methods are developed, there is no practically useful illustration for various types of data such as so-called large p-small n data. This paper aims to fill this gap by showcasing how response dimension reduction can enhance the analysis of high-dimensional response data, thereby providing significant assistance to statistical practitioners and contributing to advancements in multiple scientific domains.

• Applied Biological Chemistry
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• v.47 no.1
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• pp.1-16
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• 2004

The purpose of this paper is to review the occurrence, analytical methods and reduction methods of aflatoxins in foods. Aflatoxins are produced by the secondary metabolism of various fungal species and have the highest toxicity among mycotoxins. Due to their toxicity including carcinogenic activity, aflatoxins affect not only the health of humans ana animals but also the economics of agriculture and food. As a food-importing country, because aflatoxins could contaminate raw commodities and foodstuffs, there should be inspection on the exposure and the regulation of risk assessment as a food safety measure. In addition, studies on rapid analytical methods and reduction of toxicity by various processes for aflatoxins should be carried out in conjunction with those of the risk assessment of aflatoxins.