• Journal of Korea Port Economic Association
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• v.40 no.1
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• pp.87-118
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• 2024

Coastal ports play an essential role in developing a country and a city. Port efficiency is an important factor affecting port trade, and the importance of port efficiency for port performance has been recognized in previous literature. DEA (Data Envelopment Analysis) and SFA (Stochastic Frontier Analysis) are widely used in this field of research. However, these two methods are limited in selecting input and output variables. In addition, the literature studies on Chinese coastal ports mainly focus on the study of port clusters in local areas, which lacks a holistic approach and generally lacks up-to-date data. Therefore, to fill the gap in this area of research, this paper introduces a model combining principal component analysis and data envelopment analysis to analyze the operational efficiency of the top 17 coastal ports in China in terms of throughput based on the most recent data available in 2021. This paper identifies container throughput as the output variable, and 13 second indicators are selected as input variables from four primary indicators: land, capital, labor, and infrastructure. Four principal components were selected from 13 second indicators using PCA.After that, DEA (BBC) and DEA (CCR) were used to analyze the 17 ports, among which five were Shanghai, Ningbo-Zhoushan, Guangzhou, Xiamen, and Dongguan, respectively, DEA efficient, and the remaining 12 ports were non-DEA efficient. Finally, improvement directions for each port are derived, and brief suggestions are made. This paper provides some reference value for developing and constructing coastal ports in China.

• Journal of the Korean Association of Geographic Information Studies
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• v.9 no.4
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• pp.129-141
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• 2006

The image data amount is increasing rapidly that used geography, sea information etc. with great development of a remote sensing technology using artificial satellite. Therefore, people need automatic method that use image processing description than macrography for analysis remote sensing image. In this paper, we propose that acquire texture information to use GLCM(Gray Level Co-occurrence Matrix) in red tide area of artificial satellite remote sensing image, and detects red tide area by PCA(principal component analysis) automatically from this data. Method by sea color that one feature of remote sensing image of existent red tide area detection was most. but in this paper, we changed into 2 principal component accumulation images using GLCM's texture feature information 8. Experiment result, 2 principal component accumulation image's variance percentage is 90.4%. We compared with red tide area that use only sea color and It is better result.

• Journal of Practical Agriculture & Fisheries Research
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• v.22 no.2
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• pp.59-67
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• 2020

Growth tests on the Wando and Baengnyeongdo cultivars of Saccharina japonica were performed at the Myeongcheon and Gyedo aquafarms, Goheung in Jeollanamdo, from February to July in 2003. Five environmental conditions and 2 traits were measured monthly. The data were used to analyze the growth patterns, relationships between traits and principal component. Box plots were used to display the growth patterns. Scatter plots and regression and correlation coefficients were used to determine the strength of relationships between the traits. A principal component analysis revealed that the first principal component explained more than 91.4% and 90.5% of the total sample variance in the Myeongcheon and Gyedo aquafarms. From the viewpoint of the economic traits (blade length, blade weight), the growth of populations from the Gyedo aquafarm was stronger than that of those from the Myeongcheon aquafarm, and the growth of the Baengnyeongdo cultivar was superior to that of the Wando one.

• Journal of Korea Water Resources Association
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• v.38 no.12 s.161
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• pp.985-994
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• 2005

We examined problems of the principal component analysis(PCA), which is able to analyze at the low dimensionality as a methodologv to assess hydrologic time series, and introduced the theory and characteristics of independent component analysis(ICA) that can supplement problems of principal component analysis. We also applied the global sea surface temperature(SST) of the Nino region and assessed the correlation between El $\tilde{n}ino$-Southern Oscillation(ENSO) and SST. The results of examining separation-ability of principal components using mixed signals indicate that the independent component analysis is statistically superior compared to that of the principal component analysis. Finally, we assessed correlation between ENSO and global anomaly SST. The independent component analysis was applied to the $5^{\circ}{\times}5^{\circ}$(latitude and longitude) global anomaly SST in the Nino+3.4 region that is the El $\tilde{n}ino$ observation section. We assessed the correlation with the ENSO years. These results of the analysis show that only one independent component($86\%$) was able to represent the entire behavior and was consistent with the main ENSO years. Finally, we carried out independent component analysis for summer seasonal rainfalls at nine stations and could extract ICs to reflect geographical characteristics. The increasing trend has been shown at IC-1 and IC-2 since 1970s.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.12
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• pp.2792-2799
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• 2015

In this paper, we utilize source mel-frequency cepstral coefficients (SMFCCs), skewness, and kurtosis extracted in glottal flow signals to improve speaker recognition performance. Generally, because the high band magnitude response of glottal flow signals is somewhat flat, the SMFCCs are extracted using the response below the predefined cutoff frequency. The extracted SMFCC, skewness, and kurtosis are concatenated with conventional feature parameters. Then, dimensional reduction by the principal component analysis (PCA) and the linear discriminat analysis (LDA) is followed to compare performances with conventional systems under equivalent conditions. The proposed recognition system outperformed the conventional system for large scale speaker recognition experiments. Especially, the performance improvement was more noticeable for small Gaussan mixtures.

• Near Infrared Analysis
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• v.1 no.2
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• pp.21-27
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• 2000

Temperature-dependent changes in near-infrared (NIR) spectra have been measured for oleic acid, and nonanoic acid in the pure liquid state. Particular attention has been paid to the 5400-4800 cm$\^$-1/ region where a number of combination bands appear. The NIR spectra of oleic acid show that a band at 5303 cm$\^$-1/ increases with temperature while that at 5270 cm/sup-1/ decreases. It ha been found from their second derivative spectra that these spectral changes take place stepwisely with two break points at 30 and 53$\^{C}$, which correspond to the phase transition temperatures oleic acid reported previously. Principle component analysis (PCA) has been carried out for the NIR spectra of oleic acid in the 5400-4800 cm$\^$-1/ region measured over a temperature range of 15-80$\^{C}$. core plots of the first and second principal components (PCs) show that the NIR spectra are classified into three groups; the spectra measured in the temperature range of 15-30$\^{C}$, those in the range of 31-53$\^{C}$, and those in the range of 54-80$\^{C}$. These temperature ranges correspond to those for quasi-smectic liquid crystal, disordered liquid crystal, and isotropic liquid of oleic acid in the pure liquid state. In other words, PCA provides unambiguous evidence for the phase transitions. similar studies have been carried out for petroselinic acid and nonanoic acid in the pure liquid states, but they do not show any evidence for phase transitions.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.33 no.2
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• pp.145-155
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• 2023

Objectives: A fit test panel is needed to identify the fit performance of a respirator and its face seal. This is a criterion for selecting subjects that can represent the facial characteristics of users. Although anthropometry data has been developed for people in United States and China it is not yet present in Korea. This study aimed to develop a Korean fit test panel and test headform. Methods: For the 7th and 8th waves of the Size Korea anthropometry data, facial measurements of 11,429 people aged 15 to 69 years were used for analysis. PCA and bivariate panel were classified using the ISO16976-2:2022(E) anthropometrics analysis method. Based on this result, a static headform was developemed and a fit test chamber was constructed. Results: Of the 11,429 Korean people used for principal component analysis, 11,300 were included in the ellipse, marking an acceptance rate of 98.87% on PCA panel. The face types were classified into five types. Among them, a large, medium, and small static headform were printed using a 3D printer. In addition, 10,985 people (96.12%) were included in the bivariate panel based on face length and face width. The y-axis (face length) boundary was 97.87 to 134.59 mm, and the x-axis (face width) boundary was 120.75 to 158.23 mm. Conclusions: Compared to the ISO analysis, the Korean principal component was narrower in the width item (PC1) and longer in the length item (PC2). For the future, it is necessary to conduct a fit test using the developed headform and chamber device to confirm the usefulness of this Korean test panel. Therefore, this study is considered valuable as basic research for Korean test panels.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.475-475
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• 2022

The aim of this study is to identify the significant pollutant sources from the tributaries that are affecting the water quality of the study site, the Geum River and provide a solution to enhance the water quality. Multivariate statistical analysis modles such as cluster analysis, Principal component analysis (PCA) and positive matrix factorization (PMF) were applied to identify and prioritize the major pollutant sources of the two major tributaries, Gab-cheon and Miho-cheon, of the Geum River. PCA identifies three major pollutant sources for Gab-cheon and Miho-cheon, respectively. For Gab-cheon, wastewater treatment plant (WWTP), urban, and agricultural pollutions are identified as major pollutant sources. For Miho-cheon, agricultural, urban, and forest land are identified as major pollutant sources. On the contrary, PMF identifies three pollutant sources in Gab-cheon, same as PCA result and two pollutant sources in Miho-cheon. Water quality control scenarios are formulated and improvement of water quality in the river locations are simulated and analyzed with the Environmental Fluid Dynamic Code (EFDC) model. Scenario results were evaluated using a water quality index developed by Canadian Council of Ministers of the Environment. PCA and PMF appears to be effective to identify water pollution sources for the Geum river and also its tributaries in detail and thus can be used for the development of water quality improvement alternative of the above water bodies.

• Journal of the Korea Society of Computer and Information
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• v.22 no.2
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• pp.21-27
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• 2017

In this paper, we propose a pupil detection method using PCA(principal component analysis) and Hough transform. To reduce error to detect eyebrows as pupil, eyebrows are detected using projection function in eye region and eye region is set to not include the eyebrows. In the eye region, pupil candidates are detected using rank order filter. False candidates are removed by using symmetry. The pupil candidates are grouped into pairs based on geometric constraints. A similarity measure is obtained for two eye of each pair using PCA and hough transform, we select a pair with the smallest similarity measure as final two pupils. The experiments have been performed for 1000 images of the BioID face database. The results show that it achieves the higher detection rate than existing method.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.504-509
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• 2008

Support Vector Machine (SVM) is one of powerful learning machine and has been applied to varying task with generally acceptable performance. The success of SVM for classification tasks in one domain is affected by features which represent the instance of specific class. Given the representative and discriminative features, SVM learning will give good generalization and consequently we can obtain good classifier. In this paper, we will assess the problem of feature choices for human detection tasks and measure the performance of each feature. Here we will consider HOG-family feature. As a natural extension of SVM, we combine SVM with Principal Component Analysis (PCA) to reduce dimension of features while retaining most of discriminative feature vectors.