• Proceedings of the KSR Conference
• /
• 2009.05b
• /
• pp.311-320
• /
• 2009

Most advanced countries are researching to apply light weight materials far rolling stock because weight reduction for railway body derives cost-saving, energy-saving, and high-speed. Likewise, current Korea rolling stock field makes arduous effects of weight-reduction, miniaturization, and high-efficiency to achieve a high-speed railway. Aluminum becomes suitable material for these projects because it is much lighter than steel or stainless. Manufacturing the railway car body by using the Aluminum is increasing because Aluminum is not bringing the corrosion by unique oxidation-passivate. Aluminum extrusion profile far railway body requires a high mechanical property, accurate shape dimension, and stable quality because the railway body is composed with many different kinds of extruded profiles. Therefore, it is necessary to research about Aluminum precision-extrusion technology to maintain exit temperature and die load. The goal of this project is applying the Aluminum extrusion profile to next-generation railway car body by developing the Aluminum extrusion profile according to precision-extrusion technology which may maintain isothermal exit temperature.

• Journal of the Korean Data Analysis Society
• /
• v.20 no.6
• /
• pp.2733-2746
• /
• 2018

The two inverse regression estimation methods, SIR and SAVE to estimate the central space are computationally easy and are widely used. However, SIR and SAVE may have poor performance in finite samples and need strong assumptions (linearity and/or constant covariance conditions) on predictors. The two non-parametric estimation methods, MAVE and dMAVE have much better performance for finite samples than SIR and SAVE. MAVE and dMAVE need no strong requirements on predictors or on the response variable. MAVE is focused on estimating the central mean subspace, but dMAVE is to estimate the central space. This paper explores and compares four methods to explain the dimension reduction. Each algorithm of these four methods is reviewed. Empirical study for simulated data shows that MAVE and dMAVE has relatively better performance than SIR and SAVE, regardless of not only different models but also different distributional assumptions of predictors. However, real data example with the binary response demonstrates that SAVE is better than other methods.

• Communications for Statistical Applications and Methods
• /
• v.28 no.3
• /
• pp.233-250
• /
• 2021

This paper studies if the accuracies of mortality models (LC model vs. 4-parametric model) are aggravated if a mortality structure changes due to the impact of COVID-19. LC model (LCM) uses dimension reduction for fitting to the log mortality matrix so that the performance of the dimension reduction method may not be good when the matrix structure changes. On the other hand, 4-parametric factor model (4-PFM) is designed to use factors for fitting to log mortality data by age groups so that it would be less affected by the change of the mortality structure. In fact, the forecast accuracies of LCM are better than those of 4-PFM when life-tables are used whereas those of 4-PFM are better when the mortality structure changes. Thus this result shows that 4-PFM is more reliable in performance to the structural changes of the mortality. To support the accuracy changes of LCM the functional aspect is explained by computing eigenvalues produced by singular vector decomposition

• Communications for Statistical Applications and Methods
• /
• v.29 no.2
• /
• pp.193-202
• /
• 2022

Semi-supervised learning has gained significant attention in recent applications. In this article, we provide a selective overview of popular semi-supervised methods and then propose a simple but effective algorithm for semi-supervised classification using support vector machines (SVM), one of the most popular binary classifiers in a machine learning community. The idea is simple as follows. First, we apply the dimension reduction to the unlabeled observations and cluster them to assign labels on the reduced space. SVM is then employed to the combined set of labeled and unlabeled observations to construct a classification rule. The use of SVM enables us to extend it to the nonlinear counterpart via kernel trick. Our numerical experiments under various scenarios demonstrate that the proposed method is promising in semi-supervised classification.

• Communications for Statistical Applications and Methods
• /
• v.31 no.2
• /
• pp.235-246
• /
• 2024

In high-dimensional data analysis, sufficient dimension reduction (SDR) has been considered as an attractive tool for reducing the dimensionality of predictors while preserving regression information. The principal support vector machine (PSVM) (Li et al., 2011) offers a unified approach for both linear and nonlinear SDR. This article comprehensively explores a variety of SDR methods based on the PSVM, which we call principal machines (PM) for SDR. The PM achieves SDR by solving a sequence of convex optimizations akin to popular supervised learning methods, such as the support vector machine, logistic regression, and quantile regression, to name a few. This makes the PM straightforward to handle and extend in both theoretical and computational aspects, as we will see throughout this article.

• Proceedings of the Korean Geotechical Society Conference
• /
• 2005.03a
• /
• pp.854-861
• /
• 2005

Studied accuracy and practical use possibility of joint measurement that using 3D laser scanner to rock slope. Measured joint of Rock slope and comparison applied 3 dimension laser scanner and clinometer. 3D laser scanning system preserves on computer calculating to 3 dimension coordinate scaning laser to object. and according to laser measurement method of interior, produce correct vector value from charge-coupled device(CCD) or laser reciver and telegram register and time measuring equipment. Create of object x, y, z point coordinates to 3 dimension space of computer. Such 3 dimension point datum (Point Clouds) forms relocate position informations that exist to practical space to computer space. Practical numerical values related between each other. Compared joint distribution and direction that measured by laser scanner and clinometer. By the result, Distribution of joint projected almost equally. Could get more joint datas by measurement of 3 dimension scanner than measured by clinometer. Therefore, There is effect that objectification of rock slope investigation data, shortening of investigation periods, investigation reduction of cost. could know that it is very effective method in joint measuring.

• The Korean Journal of Applied Statistics
• /
• v.37 no.2
• /
• pp.119-138
• /
• 2024

Text indicators are increasingly valuable in economic forecasting, but are often hindered by noise and high dimensionality. This study aims to explore post-processing techniques, specifically noise filtering and dimensionality reduction, to normalize text indicators and enhance their utility through empirical analysis. Predictive target variables for the empirical analysis include monthly leading index cyclical variations, BSI (business survey index) All industry sales performance, BSI All industry sales outlook, as well as quarterly real GDP SA (seasonally adjusted) growth rate and real GDP YoY (year-on-year) growth rate. This study explores the Hodrick and Prescott filter, which is widely used in econometrics for noise filtering, and employs sufficient dimension reduction, a nonparametric dimensionality reduction methodology, in conjunction with unstructured text data. The analysis results reveal that noise filtering of text indicators significantly improves predictive accuracy for both monthly and quarterly variables, particularly when the dataset is large. Moreover, this study demonstrated that applying dimensionality reduction further enhances predictive performance. These findings imply that post-processing techniques, such as noise filtering and dimensionality reduction, are crucial for enhancing the utility of text indicators and can contribute to improving the accuracy of economic forecasts.

• Journal of Korea Multimedia Society
• /
• v.16 no.7
• /
• pp.820-828
• /
• 2013

Extracting unique features from an image is a fundamental issue when making panorama images, acquiring stereo images, recognizing objects and analyzing images. Generally, the task to compare features to other images requires much computing time because some features are formed as a vector which has many elements. In this paper, we present a method that compares features after reducing the feature dimension extracted from an image using PCA(principal component analysis) and sorting the features in a linked list. SURF(speeded up robust features) is used to describe image features. When the dimension reduction method is applied, we can reduce the computing time without decreasing the matching accuracy. The proposed method is proved to be fast and robust in experiments.

• The KIPS Transactions:PartB
• /
• v.13B no.4 s.107
• /
• pp.393-400
• /
• 2006

There are many feature extraction methods for face recognition. We need a new method to overcome the small sample problem that the number of feature variables is larger than the sample size for face image data. The paper considers partial least squares(PLS) as a new dimension reduction technique for feature vector. Principal Component Analysis(PCA), a conventional dimension reduction method, selects the components with maximum variability, irrespective of the class information. So, PCA does not necessarily extract features that are important for the discrimination of classes. PLS, on the other hand, constructs the components so that the correlation between the class variable and themselves is maximized. Therefore PLS components are more predictive than PCA components in classification. The experimental results on Manchester and ORL databases shows that PLS is to be preferred over PCA when classification is the goal and dimension reduction is needed.

• KIPS Transactions on Software and Data Engineering
• /
• v.10 no.7
• /
• pp.263-270
• /
• 2021

Reinforcement learning(RL) is the method to find an optimal policy through training. and it is one of popular methods for solving lifesaving and disaster response problems effectively. However, the conventional reinforcement learning method for disaster response utilizes either simple environment such as. grid and graph or a self-developed environment that are hard to verify the practical effectiveness. In this paper, we propose the design of a disaster response RL environment which utilizes the detailed property information of the disaster simulation in order to utilize the reinforcement learning method in the real world. For the RL environment, we design and build the reinforcement learning communication as well as the interface between the RL agent and the disaster simulation. Also, we apply the dimension reduction method for converting non-image feature vectors into image format which is effectively utilized with convolution layer to utilize the high-dimensional and detailed property of the disaster simulation. To verify the effectiveness of our proposed method, we conducted empirical evaluations and it shows that our proposed method outperformed conventional methods in the building fire damage.