• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.376-386
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• 2023

Due to climate changes, landslide hazards in the Republic of Korea (hereafter South Korea) continuously increase. To establish the effective landslide mitigation strategies, such as erosion control works, landslide hazard estimation in the long-term perspective should be proceeded considering the influence of climate changes. In this study, we examined the change in landslide-damaged areas in South Korea responding to climate change scenarios using the multivariate regression method. Data on landslide-damaged areas and rainfall from 1981-2010 were used as a training dataset. Sev en indices were deriv ed from rainfall data as the model's input data, corresponding to rainfall indices provided from two SSP scenarios for South Korea: SSP1-2.6 and SSP5-8.5. Prior to the multivariate regression analysis, we conducted the VIF test and the dimension analysis of regression model using PCA. Based on the result of PCA, we developed a regression model for landslide damaged area estimation with two principal components, which cov ered about 93% of total v ariance. With climate change scenarios, we simulated landslide-damaged areas in 2030-2100 using the regression model. As a result, the landslide-damaged area will be enlarged more than the double of current annual mean landslide damaged area of 1981-2010; It infers that landslide mitigation strategies should be reinforced considering the future climate condition.

• Journal of Korean Society of Transportation
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• v.26 no.3
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• pp.53-66
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• 2008

This research is to the establishment of a conceptual framework that supports road characteristic classification from a new point of view in order to complement of the existing road functional classification and examine of traffic pattern. The road characteristic classification(RCC) is expected to use important performance criteria that produced a policy guidelines for transportation planning and operational management. For this study, the traffic data used the permanent traffic counters(PTCs) located within the national highway between 2002 and 2006. The research has described for a systematic review and assessment of how exploratory factor analysis should be applied from 12 explanatory variables. The optimal number of components and clusters are determined by interpretation of the factor analysis results. As a result, the scenario including all 12 explanatory variables is better than other scenarios. The four components is produced the optimal number of factors. This research made contributions to the understanding of the exploratory factor analysis for the road characteristic classification, further applying the objective input data for various analysis method, such as cluster analysis, regression analysis and discriminant analysis.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.1C
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• pp.65-71
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• 2004

We improved the MLLR speaker adaptation algorithm with reduction of the order of HMM parameters using PCA(Principle Component Analysis) or ICA(Independent Component Analysis). To find a smaller set of variables with less redundancy, we adapt PCA(principal component analysis) and ICA(independent component analysis) that would give as good a representation as possible, minimize the correlations between data elements, and remove the axis with less covariance or higher-order statistical independencies. Ordinary MLLR algorithm needs more than 30 seconds adaptation data to represent higher word recognition rate of SD(Speaker Dependent) models than of SI(Speaker Independent) models, whereas proposed algorithm needs just more than 10 seconds adaptation data. 10 components for ICA and PCA represent similar performance with 36 components for ordinary MLLR framework. So, compared with ordinary MLLR algorithm, the amount of total computation requested in speaker adaptation is reduced by about 1/167 in proposed MLLR algorithm.

• Journal of the Korean Society of Tobacco Science
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• v.17 no.2
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• pp.177-183
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• 1995

This study was conducted to develop the most precise NIR(near infrared spectrometric) calibration for rapid determination of chemical composition in ground samples of toasted burley tobacco using stepwise, stepup, principal component regression(PCR), partial least square(PLS) and modified partial least square(MPLS) calibration method. The number of wavelength(W) selected by stepup multiple linear regression using: second derivative spectra was as follows: total sugar(TS)-4 W, nicotine-9 W, total nitrogen(TN)-2 W, ash-8 W, total volatile base(TVB)-5 W, chlorine4 W, L of color-6 W, a of color-6 W and b of color-7 W. Comparing the calibration equations followed by each chemical components, the most precise calibration equation was MPLS for 75, a and b of color, PLS for nicotine, ash, TVB, chlorine and L of color and stepup for TN. The standard error of calibration(SEC) and standard error of performance(SEP) between result of near infrared analysis and standard laboratory analysis were 0.18, 0.40% for 75, 0.06, 0.08% for nicotine, 0.18, 0.16% for TN, 0.33, 0.46% for ash, 0.04, 0.03% for TVB, 0.08, 0.06% for chlorine, 0.54, 0.58 for L of color, 0.22, 0.22 for a of color and 0.27, 0.27 for b of color, respectively. The SEC and SEP of ash and TVB were within allowable error of standard laboratory analysis, nicotine, TN and chlorine were 1.2-2.0 times and 75 were 2.1-4.0 times larger than allowable error of standard laboratory analysis. The ratio of SEC and SEP to mean were 1.5, 1.6% for L of color, 3.7, 3.8% for a of color and 1.8, 1.8% for b of color, respectively. Key words : burley tobacco chemistry, near infrared spectroscopy.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.10
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• pp.6019-6023
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• 2013

Lung cancer, one of the leading causes of cancer-related deaths, usually appears as solitary pulmonary nodules (SPNs) which are hard to diagnose using the naked eye. In this paper, curvelet-based textural features and clinical parameters are used with three prediction models [a multilevel model, a least absolute shrinkage and selection operator (LASSO) regression method, and a support vector machine (SVM)] to improve the diagnosis of benign and malignant SPNs. Dimensionality reduction of the original curvelet-based textural features was achieved using principal component analysis. In addition, non-conditional logistical regression was used to find clinical predictors among demographic parameters and morphological features. The results showed that, combined with 11 clinical predictors, the accuracy rates using 12 principal components were higher than those using the original curvelet-based textural features. To evaluate the models, 10-fold cross validation and back substitution were applied. The results obtained, respectively, were 0.8549 and 0.9221 for the LASSO method, 0.9443 and 0.9831 for SVM, and 0.8722 and 0.9722 for the multilevel model. All in all, it was found that using curvelet-based textural features after dimensionality reduction and using clinical predictors, the highest accuracy rate was achieved with SVM. The method may be used as an auxiliary tool to differentiate between benign and malignant SPNs in CT images.

• International Journal of Highway Engineering
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• v.16 no.6
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• pp.137-147
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• 2014

PURPOSES : Daejeon is basically divided into an old downtown and a new downtown, and the recent relocation of the Chungcheongnam-do Provincial Government of Republic of Korea from the old downtown and the opening of governmental buildings in the new downtown as well have made this new downtown only densely populated with industrial and business facilities. Such changes in the downtowns have promoted the conditions of the new downtown while, consequently, dragging down the old downtown. Out of concern for those unbalanced developments of the two downtowns, Daejeon is now carrying out several city projects to revive the old downtown. In the light of that, as a part of the project to promote the old downtown, this study aims to conduct an evaluation on landscape of the culture and arts street in Daeheungdong which was built upon those ideas of a theme street project by Daejeon. METHODS : Based on the findings from the questionnaire not only on the components that would design the streetscape of the culture and arts street but also on the public satisfaction with the streetscape, the study defined how those changes in the components affect emotional factors of the pedestrians. In order to achieve the research goal, the study made changes in D/H ratio of the street structural components as well as the roadside trees. In terms of the questionnaire method, the study used the SD scale, and proceeded with its investigation through the frequency analysis, the principal component analysis (the factor analysis) and the structural equation model. RESULTS : According to the results from the factor analysis and the regression analysis, of those three factors, such as the openness, the comfortable sensation and the safety, the openness followed by the comfortable sensation and the safety was determined to have the most positive influence on the total satisfaction. The structural model analysis reported that the D/H and the structural components of the roadside trees and planting have a positive effect on the emotional image, and this emotional image also appeared to be positively related to the total satisfaction. CONCLUSIONS : This study looked into how the changes in the street structural components of the culture and arts street in Daeheungdong would affect the satisfaction with the streetscape, and finally confirmed that the D/H and the planting are what would have a positive effect on this satisfaction. What has been learned from this study will be the basic data to figure out how to promote and improve the culture and arts street in Daeheung-dong as this data will also help designing and developing of those specialized streets in other regions.

• Korean Journal of Clinical Pharmacy
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• v.25 no.2
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• pp.94-101
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• 2015

Background: In order to achieve the goals of community pharmacy practice, its legal, labour-related, and economic barriers need to be identified. This study examined pharmacists' perceptions of constraints on providing optimal pharmacy services in order to identify underlying factors and analyse the associations between barriers and pharmaceutical services in community pharmacies. Methods: A survey targeting pharmacy owners was conducted from May to June 2012 using a structured questionnaire including nine pharmaceutical service items. According to the service provision level, we classified pharmacists as inactive (fewer than 5 items among the listed 9 service items) and active providers (5 or more items). Principal component analysis was used to group significant factors for barriers into four thematic components. Associations between the participants' demographics and pharmacy characteristics and the services provided were explored by logistic regression analyses. Results: Participants were 402 pharmacists. Over 60% provided disease management services for hypertension, diabetes, and hyperlipidaemia. Variables that affected pharmaceutical services included the lack of separate areas for patient counselling (OR: 2.12, 95% CI: 1.18-3.80), and clinical knowledge and information-related barriers (OR: 0.59, 95% CI: 0.36-0.97). Conclusion: Strategies for improving clinical knowledge and providing expeditious information are necessary in order to improve community pharmacy services.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.1
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• pp.231-235
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• 2012

In order to explore the role of nutrients and bioactive related substances in colorectal cancer, we conducted a case-control in Uruguay, which is the country with the highest production of beef in the world. Six hundred and eleven (611) cases afflicted with colorectal cancer and 1,362 controls drawn from the same hospitals in the same time period were analyzed through unconditional multiple logistic regression. This base population was submitted to a principal components factor analysis and three factors were retained. They were labeled as the meat-based, plant-based, and carbohydrates patterns. They were rotated using orthogonal varimax method. The highest risk was positively associated with the meat-based pattern (OR for the highest quartile versus the lowest one 1.63, 95 % CI 1.22-2.18, P value for trend = 0.001), whereas the plant-based pattern was strongly protective (OR 0.60, 95 % CI 0.45-0.81, P value for trend <0.0001. The carbohydrates pattern was only positively associated with colon cancer risk (OR 1.46, 95 % CI 1.02-2.09). The meat-based pattern was rich in saturated fat, animal protein, cholesterol, and phosphorus, nutrients originated in red meat. Since herocyclic amines are formed in the well-done red meat through the action of amino acids and creatine, it is suggestive that this pattern could be an important etiologic agent for colorectal cancer.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.18
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• pp.8541-8551
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• 2016

Background: The coverage of breast and cervical cancer screening has only slightly increased in the past decade in Thailand, and these cancers remain leading causes of death among women. This study identified socioeconomic and contextual factors contributing to the variation in screening uptake and coverage. Materials and Methods: Secondary data from two nationally representative household surveys, the Health and Welfare Survey (HWS) 2007 and the Reproductive Health Survey (RHS) 2009 conducted by the National Statistical Office were used. The study samples comprised 26,951 women aged 30-59 in the 2009 RHS, and 14,619 women aged 35 years and older in the 2007 HWS were analyzed. Households of women were grouped into wealth quintiles, by asset index derived from Principal components analysis. Descriptive and logistic regression analyses were performed. Results: Screening rates for cervical and breast cancers increased between 2007 and 2009. Education and health insurance coverage including wealth were factors contributing to screening uptake. Lower or non-educated and poor women had lower uptake of screenings, as were young, unmarried, and non-Buddhist women. Coverage of the Civil Servant Medical Benefit Scheme increased the propensity of having both screenings, while the universal coverage scheme increased the probability of cervical screening among the poor. Lack of awareness and knowledge contributed to non-use of both screenings. Women were put off from screening, especially Muslim women on cervical screening, because of embarrassment, fear of pain and other reasons. Conclusions: Although cervical screening is covered by the benefit package of three main public health insurance schemes, free of charge to all eligible women, the low coverage of cervical screening should be addressed by increasing awareness and strengthening the supply side. As mammography was not cost effective and not covered by any scheme, awareness and practice of breast self examination and effective clinical breast examination are recommended. Removal of cultural barriers is essential.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.3950-3969
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• 2021

Radio Frequency (RF)-based indoor localization technologies play significant roles in various Internet of Things (IoT) services (e.g., location-based service). Most such technologies require that all the devices comply with a specified technology (e.g., WiFi, ZigBee, and Bluetooth). However, this requirement limits its application scenarios in today's IoT context where multiple devices complied with different standards coexist in a shared environment. To bridge the gap, in this paper, we propose a cross-technology localization approach, which is able to localize target nodes using a different type of devices. Specifically, the proposed framework reuses the existing WiFi infrastructure without introducing additional cost to localize Non-WiFi device (i.e., ZigBee). The key idea is to leverage the interference between devices that share the same operating frequency (e.g., 2.4GHz). Such interference exhibits unique patterns that depend on the target device's location, thus it can be leveraged for cross-technology localization. The proposed framework uses Principal Components Analysis (PCA) to extract salient features of the received WiFi signals, and leverages Dynamic Time Warping (DTW), Gradient Boosting Regression Tree (GBRT) to improve the robustness of our system. We conduct experiments in real scenario and investigate the impact of different factors. Experimental results show that the average localization accuracy of our prototype can reach 1.54m, which demonstrates a promising direction of building cross-technology technologies to fulfill the needs of modern IoT context.