• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.201-208
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• 2021

This study aims to provide the relevant basis upon which prediction of dyslipidemia should be made based on body information. Using the National Health Insurance big data (3,312,971 people) canonical correlation analysis was performed between body information and lipid-profile. Body information included age, height, weight and waist circumference, while the lipid-profile included total cholesterol, triglycerides, HDL cholesterol and LDL cholesterol. As a result, when the waist circumference and the weight are large, triglycerides increase and HDL cholesterol level decreases. In terms of age, weight, waist circumference, and HDL cholesterol, the canonical variates (the degree of influence) were significantly different according to sex. In particular, the canonical variate was dramatically changed around the forties and fifties in women in terms of weight, waist circumference, and HDL cholesterol. The canonical correlation results of the health care big data presented in this study will help construct a predictive model that can evaluate an individual's health status based on body information that can be easily measured in a non-invasive manner.

• Journal of Ginseng Research
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• v.46 no.5
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• pp.675-682
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• 2022

Background: Korean Red Ginseng (KRG) was reported to play an anti-inflammatory role, however, previous studies largely focused on the effects of KRG on priming step, the inflammation-preparing step, and the anti-inflammatory effect of KRG on triggering, the inflammation-activating step has been poorly understood. This study demonstrated anti-inflammatory role of KRG in caspase-11 non-canonical inflammasome activation in macrophages during triggering of inflammatory responses. Methods: Caspase-11 non-canonical inflammasome-activated J774A.1 macrophages were established by priming with Pam3CSK4 and triggering with lipopolysaccharide (LPS). Cell viability and pyroptosis were examined by MTT and lactate dehydrogenase (LDH) assays. Nitric oxide (NO)-inhibitory effect of KRG was assessed using a NO production assay. Expression and proteolytic cleavage of proteins were examined by Western blotting analysis. In vivo anti-inflammatory action of KRG was evaluated with the LPS-injected sepsis model in mice. Results: KRG reduced LPS-stimulated NO production in J774A.1 cells and suppressed pyroptosis and IL-1β secretion in caspase-11 non-canonical inflammasome-activated J774A.1 cells. Mechanistic studies demonstrated that KRG suppressed the direct interaction between LPS and caspase-11 and inhibited proteolytic processing of both caspase-11 and gasdermin D in caspase-11 non-canonical inflammasome-activated J774A.1 cells. Furthermore, KRG significantly ameliorated LPS-mediated lethal septic shock in mice. Conclusion: The results demonstrate a novel mechanism of KRG-mediated anti-inflammatory action that operates through targeting the caspase-11 non-canonical inflammasome at triggering step of macrophage-mediated inflammatory response.

• Food Science of Animal Resources
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• v.38 no.4
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• pp.794-805
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• 2018

To identify correlation between fresh meat and processed meat products, we performed canonical correlation analysis (CCA) to predict the relationship between pork supply and meat product production in Korea. Results of CCA showed a canonical correlation of 0.8576 in the first canonical pair (p<0.01). The production of meat products showed the highest correlation with pork import but the lowest correlation with the production of domestic pork. Although Korean consumer preferred meat products produced by fresh domestic pork, inexpensively imported pork with high share in meat products was supplied in the market. Therefore, securing domestically produced raw meat is important for expanding consumption of domestic meat products. Results of this study suggest that meat processor and pig producer can achieve the $6^{th}$ industrialization by combining the production of raw pork materials, meat processing, and sales service.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.4D
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• pp.569-577
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• 2008

The purpose of this study is focused on the development of compound classification process by mixing multitemporal data and annexing a specific image enhancement technique with a specific image classification algorithm, to gain more accurate land information from satellite imagery. That is, this study suggests the classification process using canonical correlation classification technique after principal component analysis for the mixed multitemporal data. The result of this proposed classification process is compared with the canonical correlation classification result of one date images, multitemporal imagery and a mixed image after principal component analysis for one date images. The satellite images which are used are the Landsat 5 TM images acquired on July 26, 1994 and September 1, 1996. Ground truth data for accuracy assessment is obtained from topographic map and aerial photograph, and all of the study area is used for accuracy assessment. The proposed compound classification process showed superior efficiency to appling canonical correlation classification technique for only one date image in classification accuracy by 8.2%. Especially, it was valid in classifying mixed urban area correctly. Conclusively, to improve the classification accuracy when extracting land cover information using Landsat TM image, appling canonical correlation classification technique after principal component analysis for multitemporal imagery is very useful.

• Smart Structures and Systems
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• v.17 no.6
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• pp.1031-1053
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• 2016

The health conditions of in-service civil infrastructures can be evaluated by employing structural health monitoring technology. A reliable health evaluation result depends heavily on the quality of the data collected from the structural monitoring sensor network. Hence, the problem of sensor fault diagnosis has gained considerable attention in recent years. In this paper, an innovative sensor fault diagnosis method that focuses on fault detection and isolation stages has been proposed. The dynamic or auto-regressive characteristic is firstly utilized to build a multivariable statistical model that measures the correlations of the currently collected structural responses and the future possible ones in combination with the canonical correlation analysis. Two different fault detection statistics are then defined based on the above multivariable statistical model for deciding whether a fault or failure occurred in the sensor network. After that, two corresponding fault isolation indices are deduced through the contribution analysis methodology to identify the faulty sensor. Case studies, using a benchmark structure developed for bridge health monitoring, are considered in the research and demonstrate the superiority of the new proposed sensor fault diagnosis method over the traditional principal component analysis-based and the dynamic principal component analysis-based methods.

• The Korean Journal of Applied Statistics
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• v.28 no.1
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• pp.49-60
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• 2015

Various kinds of ordination methods such as correspondence analysis and canonical correspondence analysis are used in community ecology to visualize relationships among species, sites, and environmental variables. Ter Braak (1986), Jackson and Somers (1991), Parmer (1993), compared the ordination methods using eigenvalue and distance graph. However, these methods did not show the relationship between population and biplot because they are only based on surveyed data. In this paper, a method that measures the extent to show population information to biplot was introduced to compare ordination methods objectively.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.45 no.4
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• pp.267-271
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• 2000

A total of 1,830 soybean collections were grown in the field and characterized for 10 morphological traits to determine the diversity and relationship within and among geographical regions. Phenotypic variation was found within all regions for most characters. The Shannon-Weaver diversity index ranged from 0.49 to 0.62 across regions, and 0.09 to 1.00 across characters. Canonical discriminant analysis and clustering of the canonical means delineated 3 regional clusters: (ⅰ) Kyunggi, Chungchong, Kangwon, Chulla, and Kyungsang; (ⅱ) Heilongjiang; and (ⅲ) Jilin, Manchuria, central China, south China, Others (China), Hokkaido, Honshu, and Others (Japan).

• Korean Management Science Review
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• v.28 no.2
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• pp.93-103
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• 2011

This paper has aimed to identify marketing variables which affect physicians' prescription of drug. Based on a literature review this paper derives the three factors (indirect commercial source, direct commercial source, academic information source) of information sources that physicians rely on for medicines, the three factors (research supporting activity, marketing supporting activity, medicine information supporting activity) of promotion activities physicians prefer, and the four factors (indirect quality of medicine, direct quality of medicine, experience of using medicine, price and design of medicine) of prescription criteria physicians use. Then it investigates using canonical correlation analysis whether or not physicians' prescriptions are affected by the information sources, the promotion activities, and the type of physicians. From the canonical correlation analysis this paper derives the meaningful three canonical functions of prescription for drugs. The first function explains the prescription which is insensitive to marketing activities, the second function does the prescription which is sensitive to them, and the final function does the prescription which is not affected by them.

• Industrial Engineering and Management Systems
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• v.11 no.2
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• pp.134-141
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• 2012

It is difficult to find an appropriate ordering policy for a many types of items. One of the reasons for this difficulty is that each item has a different demand trend. We will classify items by shipment trend and then decide the ordering policy for each item category. In this study, we indicate that categorizing items from their statistical characteristics leads to an ordering policy suitable for that category. We analyze the ordering policy and shipment trend and propose a new method for selecting the ordering policy which is based on finding the strongest relation between the classification of the items and the ordering policy. In our numerical experiment, from actual shipment data of about 5,000 items over the past year, we calculated many statistics that represent the trend of each item. Next, we applied the canonical correlation analysis between the evaluations of ordering policies and the various statistics. Furthermore, we applied the cluster analysis on the statistics concerning the performance of ordering policies. Finally, we separate items into several categories and show that the appropriate ordering policies are different for each category.

• Journal of Korean Society for Quality Management
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• v.8 no.1
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• pp.8-15
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• 1980

There are many methods of pattern recognition. In this paper we assume that the responses of independent m groups are described by p-variate normal random variables with distinct mean vectors and a common covariance matrix. Under the assumption we give pattern recognition of m groups by means of canonical discrininant analysis and it's computer program. An example is presented.