• Bulletin of the Society of Naval Architects of Korea
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• v.24 no.2
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• pp.20-28
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• 1987

In general, preliminary hull form design is performed by changing a parent hull form using a computer to satisfy given requirements, e.g., principal dimensions, displacement, $L_{CB}$, and etc. Principal dimensions, $C_b,\;L_{CB}$ and midship sections are the only parameters to be modified in the traditional hull form variation methods available for preliminary design. In this paper, a method is presented in which local cross sections as well as principal dimensions and midship sections are modified according to design requirements. The method gives hydrostatic curves of modified hull form simultaneously. An optimization technique to satisfy the constraints of hydrostatic characteristics such as maximizing KM as a design requirement is also considered.

• Journal of Sensor Science and Technology
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• v.30 no.5
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• pp.314-319
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• 2021

In this study, biomarkers were analyzed and segmented using tunable infrared gas sensors after performing the principal component analysis. The free spectral range of the device under test (DUT) was around 30 nm and DUT-5580 yielded the highest output voltage property among the others. The biomarkers (isoprophyl alcohol, ethanol, methanol, and acetone solutions) were sequentially mixed with deionized water and their mists were carried into the gas chamber using high-purity nitrogen gas. A total of 17 different mixed gases were tested with three tunable infrared gas sensors, namely DUT-3144, DUT-5580, and DUT-8010. DUT-8010 resolved the infrared absorption spectra of whole mixed gases. Based on the principal component analysis with each DUT and their combinations, each mixed gas and the trends in increasing gas concentration could be well analyzed when the contributions of the eigenvalues of the first and second were higher than 70% and 10%, respectively, and their sum was greater than 90%.

• 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.

• Journal of Korea Multimedia Society
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• v.23 no.12
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• pp.1586-1593
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• 2020

This study was conducted on the face ratio of mammals. It can also be applied to character automation by checking factors about the difference between animal and human face shapes. This paper used the face and face area data generated for Deep Learning learning. In detail, the proportion factors of the area comprising the faces of 20 species of animals and humans were defined and the average ratio was calculated. Next, the proportion of each animal was analyzed using the Principal Component Analysis (PCA). Through this, we would like to propose the golden ratio of mammals.

• Asia-Pacific Journal of Business
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• v.12 no.3
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• pp.87-99
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• 2021

Purpose - In this study, I attempt to clarify three theories of firm boundaries (vertical integration): the principal-agent theory, transaction cost theory, and property rights theory. Although these theories have been widely cited and much discussed, it has been found that understanding the commonalities and distinctions of these seemingly familiar theories is difficult. Design/methodology/approach - I present the three theories about the decisions that firms make concerning their boundaries. Then, I compare elemental versions of the theories of the firm. Findings - Comparing the ingredients of the elemental property rights and principal-agent theories shows that they provide a unified account of the costs and benefits of vertical integration. However, the property rights theory in no sense formalizes the transaction cost theory. Research implications or Originality - Clarifying the three theories of the firm can help to construct empirical models and interpret its results.

• Communications for Statistical Applications and Methods
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• v.29 no.2
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• pp.203-223
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• 2022

We propose a Bayesian approach to cumulative logistic regression model for the ordinal response based on the orthogonal principal components via singular value decomposition considering the multicollinearity among predictors. The advantage of the suggested method is considering dimension reduction and parameter estimation simultaneously. To evaluate the performance of the proposed model we conduct a simulation study with considering a high-dimensional and highly correlated explanatory matrix. Also, we fit the suggested method to a real data concerning sprout- and scab-damaged kernels of wheat and compare it to EM based proportional-odds logistic regression model. Compared to EM based methods, we argue that the proposed model works better for the highly correlated high-dimensional data with providing parameter estimates and provides good predictions.

• Communications for Statistical Applications and Methods
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• v.29 no.6
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• pp.721-733
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• 2022

In this paper we compare parameter estimation by Grassmann manifold optimization and sequential candidate set algorithm in a structured principal fitted component (PFC) model. The structured PFC model extends the form of the covariance matrix of a random error to relieve the limits that occur due to too simple form of the matrix. However, unlike other PFC models, structured PFC model does not have a closed form for parameter estimation in dimension reduction which signals the need of numerical computation. The numerical computation can be done through Grassmann manifold optimization and sequential candidate set algorithm. We conducted numerical studies to compare the two methods by computing the results of sequential dimension testing and trace correlation values where we can compare the performance in determining dimension and estimating the basis. We could conclude that Grassmann manifold optimization outperforms sequential candidate set algorithm in dimension determination, while sequential candidate set algorithm is better in basis estimation when conducting dimension reduction. We also applied the methods in real data which derived the same result.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.113-118
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• 2024

Pursuance Sentiment Analysis on Twitter is difficult then performance it's used for great review. The present be for the reason to the tweet is extremely small with mostly contain slang, emoticon, and hash tag with other tweet words. A feature extraction stands every technique concerning structure and aspect point beginning particular tweets. The subdivision in a aspect vector is an integer that has a commitment on ascribing a supposition class to a tweet. The cycle of feature extraction is to eradicate the exact quality to get better the accurateness of the classifications models. In this manuscript we proposed Term Frequency-Inverse Document Frequency (TF-IDF) method is to secure Principal Component Analysis (PCA) with Naïve Bayes Classifiers. As the classifications process, the work proposed can produce different aspects from wildly valued feature commencing a Twitter dataset.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.1
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• pp.173-185
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• 2015

This study aims to investigate the characteristics and types of car accidents and establish a prediction model by analyzing 456 car accidents having occurred in the 11 tunnels in Busan, through statistical analysis techniques. The results of this study can be summarized as below. As a result of analyzing the characteristics of car accidents, it was found that 64.9% of all the car accidents took place in the tunnels between 08:00 and 18:00, which was higher than 45.8 to 46.1% of the car accidents in common roads. As a result of analyzing the types of car accidents, the car-to-car accident type was the majority, and the sole-car accident type in the tunnels was relatively high, compared to that in common roads. Besides, people at the age between 21 and 40 were most involved in car accidents, and in the vehicle type of the first party to car accidents, trucks showed a high proportion, and in the cloud cover, rainy days or cloudy days showed a high proportion unlike clear days. As a result of analyzing the principal components of car accident influence factors, it was found that the first principal components were road, tunnel structure and traffic flow-related factors, the second principal components lighting facility and road structure-related factors, the third principal factors stand-by and lighting facility-related factors, the fourth principal components human and time series-related factors, the fifth principal components human-related factors, the sixth principal components vehicle and traffic flow-related factors, and the seventh principal components meteorological factors. As a result of classifying car accident spots, there were 5 optimized groups classified, and as a result of analyzing each group based on Quantification Theory Type I, it was found that the first group showed low explanation power for the prediction model, while the fourth group showed a middle explanation power and the second, third and fifth groups showed high explanation power for the prediction model. Out of all the items(principal components) over 0.2(a weak correlation) in the partial correlation coefficient absolute value of the prediction model, this study analyzed variables including road environment variables. As a result, main examination items were summarized as proper traffic flow processing, cross-section composition(the width of a road), tunnel structure(the length of a tunnel), the lineal of a road, ventilation facilities and lighting facilities.

• Journal of Sericultural and Entomological Science
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• v.31 no.2
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• pp.102-112
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• 1989

Principal component analysis and cluster analysis were performed on the nine quantitative characters of the one hundred and forty eight silkworm genetic stocks. The six major quantitative characters such as cocoon yield, cocoon weight, cocoon shell weight, cocoon shell percentage, larval period of the 5th instar silkworm, and total larval period showed significantly positive correlation between them. The first three principal components extracted form the initial nine variables by principal component analysis accounted for about eighty percent of original information. The first and second principal components were characterized as factors related to silk productivity, and cocoon productivity, respectively. On the basis of multivariate analysis using city block distance determined from the first three principal components to measure the phenotypic diversity, the one hundred and forty eight silkworm genetic stocks could be clustered into seven varietal groups, and the phenotypic diversity between the varietal groups was partly related to their geographical origins. Among 7 varietal group, group II and IV revealed higher silk and cocoon productivity.