• 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.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.176-181
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• 2011

A role of cost modeler is that of facilitating design process by the systematic application of cost factors so as to maintain sensible and economic relationships between cost, quantity, utility and appearance. These relationships help to achieve the client's requirements within an agreed budget. The purpose of this study is to develop a parametric cost estimating model for the early design stage by using the multi-dimensional system of OLAP (On-line Analytical Processing) based on the case of quantity data related to architectural design features. The parametric cost estimating models have been adopted to support decision making in the early design stage. These models typically use a similar instance or a pattern of historical case. In order to effectively use this type of data model, it is required to set data classification and prediction methods. One of the methods is to find the similar class in line with attribute selection measure in the multi-dimensional data model. Therefore, this research is to analyze the relevance attribute influenced by architectural design features with the subject of case-based quantity data used for the parametric cost estimating model. The relevance attributes can be analyzed by Analytical Characterization. It helps determine what attributes to be included in the OLAP multi-dimension.

• Journal of Korea Multimedia Society
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• v.7 no.11
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• pp.1505-1514
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• 2004

Sequence matching in time-series databases is an operation that finds the data sequences whose changing patterns are similar to that of a query sequence. Typically, sequence matching hires a multi-dimensional index for its efficient processing. In order to alleviate the dimensionality curse problem of the multi-dimensional index in high-dimensional cases, the previous methods for sequence matching apply the Discrete Fourier Transform(DFT) to data sequences, and take only the first two or three DFT coefficients as organizing attributes of the multi-dimensional index. This paper first points out the problems in such simple methods taking the firs two or three coefficients, and proposes a novel solution to construct the optimal multi -dimensional index. The proposed method analyzes the characteristics of a target database, and identifies the organizing attributes having the best discrimination power based on the analysis. It also determines the optimal number of organizing attributes for efficient sequence matching by using a cost model. To show the effectiveness of the proposed method, we perform a series of experiments. The results show that the Proposed method outperforms the previous ones significantly.

• Nuclear Engineering and Technology
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• v.54 no.1
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• pp.97-109
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• 2022

Multi-dimensional coupling analysis is a research hot spot in nuclear reactor thermal hydraulic study and both the full-scale system transient response and local key three-dimensional thermal hydraulic phenomenon could be obtained simultaneously, which can achieve the balance between efficiency and accuracy in the numerical simulation of nuclear reactor. A one-dimensional to three-dimensional (1D-3D) coupling platform for the nuclear reactor multi-dimensional analysis is developed by XJTU-NuTheL (Nuclear Thermal-hydraulic Laboratory at Xi'an Jiaotong University) based on the CFD code Fluent and system code RELAP5 through the Dynamic Link Library (DLL) technology and Fluent user-defined functions (UDF). In this paper, the International Standard Problem (ISP) No. 43 is selected as the benchmark and the rapid boron dilution transient in the nuclear reactor is studied with the coupling code. The code validation is conducted first and the numerical simulation results show good agreement with the experimental data. The three-dimensional flow and temperature fields in the downcomer are analyzed in detail during the transient scenarios. The strong reverse flow is observed beneath the inlet cold leg, causing the de-borated water slug to mainly diffuse in the circumferential direction. The deviations between the experimental data and the transients predicted by the coupling code are also discussed.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.9
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• pp.12-17
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• 2016

In the holographic data storage system (HDSS), the data regarding the volume of a storage medium are recorded and read by the page, and the transmission rate and storage capacity can be increased because of two-dimensional, page-oriented data processing; furthermore, the multi-level HDSS can store more than one bit per pixel. For this same reason, however, and unlike conventional data-storage systems, the HDSS is hampered by two-dimensional (2D) intersymbol interference (ISI) and interpage interference (IPI). Progress regarding the published papers on 2D ISI, which is more severe in the multi-level HDSS, continues; however, mitigation of both 2D ISI and IPI in terms of the multi-level HDSS has not yet been studied. In this paper, we therefore propose a 4-level balanced-modulation code that simultaneously mitigates 2D ISI and IPI.

• Journal of Biomedical Engineering Research
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• v.10 no.2
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• pp.157-164
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• 1989

Three-dimensional imaging and manipulation of CT data are becoming increasingly important for deterRing the complex structure and pathologies. Octree which is a hierarchical data model is used to reconstruct three- dimensional objects from CT scans. Orthogonal cross sections are displayed by traverse the octree partially. Arbitrary oblique planes are derived by intersecting the square region of plane and cubic volume of octal node. Thia method enables the display of multi-structured complex organ ann the realization by personal computer.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2783-2785
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• 2003

In multi-dimensional magnetic resonance imaging, data is obtained in the spatial frequency domain. Since the signal variation in the spatial frequency domain is much larger than that in the spatial domain, analog-to-digital converts with wide conversion bits are required. In this paper, the quantization noise in magnetic resonance imaging is analyzed. The signal-to-quantization noise ratio(SQNR) in the reconstructed image is derived from the level of quantization in the data acquisition. Since the quantization noise is proportional to the signal amplitude, it becomes more dominant in high field imaging. Using the derived formula the SQNR for several MRI systems are evaluated, and it is shown that the quantization noise can be a limiting factor in high field imaging, especially in three dimensional imaging in magnetic resonance imaging.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.11a
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• pp.999-1004
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• 2004

In this study, we identify contribution of structure-borne-noise of vehicle HVAC system using Multi-Dimensional spectral analysis (MDSA) method. Firstly, to identify the applicability of MDSA method, the case of HVAC system was modeled with four input / single output system. The four inputs which is given vibration data is composed of blower, evaporator, heater and duct. The single output is noise data from driver's seat. When the blower motor is operating, we analyze the contributions of four input / single output. As a result of experiment, we identify efficiency of systems modeled with four input / single output through ordinary coherence function (OCF) and multiple coherence function (MCF).

• Communications for Statistical Applications and Methods
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• v.22 no.6
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• pp.575-587
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• 2015

The development in data collection techniques results in high dimensional data sets, where discrimination is an important and commonly encountered problem that are crucial to resolve when high dimensional data is heterogeneous (non-common variance covariance structure for classes). An example of this is to classify microbial habitat preferences based on codon/bi-codon usage. Habitat preference is important to study for evolutionary genetic relationships and may help industry produce specific enzymes. Most classification procedures assume homogeneity (common variance covariance structure for all classes), which is not guaranteed in most high dimensional data sets. We have introduced regularized elimination in partial least square coupled with QDA (rePLS-QDA) for the parsimonious variable selection and classification of high dimensional heterogeneous data sets based on recently introduced regularized elimination for variable selection in partial least square (rePLS) and heterogeneous classification procedure quadratic discriminant analysis (QDA). A comparison of proposed and existing methods is conducted over the simulated data set; in addition, the proposed procedure is implemented to classify microbial habitat preferences by their codon/bi-codon usage. Five bacterial habitats (Aquatic, Host Associated, Multiple, Specialized and Terrestrial) are modeled. The classification accuracy of each habitat is satisfactory and ranges from 89.1% to 100% on test data. Interesting codon/bi-codons usage, their mutual interactions influential for respective habitat preference are identified. The proposed method also produced results that concurred with known biological characteristics that will help researchers better understand divergence of species.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.363-373
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• 2022

Multi-omics data is difficult to interpret due to the heterogeneity of information by the volume of data, the complexity of characteristics of each data, and the diversity of omics platforms. There is not yet a system for interpreting to visualize research data on environmental diseases concerning environmental harmful substances. We provide MEE, a web-based visualization tool, to comprehensively explore the complexity of data due to the interconnected characteristics of high-dimensional data sets according to exposure to various environmental harmful substances. MEE visualizes omics data of correlation between omics data, subjects and samples by keyword searches of meta data, multi-omics data, and harmful substances. MEE has been demonstrated the versatility by two examples. We confirmed the correlation between smoking and asthma with RNA-seq and Methylation-Chip data, it was visualized that genes (P HACTR3, PXDN, QZMB, SOCS3 etc.) significantly related to autoimmune or inflammatory diseases. To visualize the correlation between atopic dermatitis and heavy metals, we selected 32 genes related immune response by integrated analysis of multi-omics data. However, it did not show a significant correlation between mercury in blood and atopic dermatitis. In the future, should continuously collect an appropriate level of multi-omics data in MEE system, will obtain data to analyze environmental substances and diseases.