• Journal of Advanced Navigation Technology
• /
• v.27 no.4
• /
• pp.396-409
• /
• 2023

In the field of autonomous vehicles, where high accuracy and reliability are critical, various satellite navigation augmentation systems have been developed to improve system performance. These systems generate correction and integrity information based on measurements and navigation data collected from ground reference stations, enhancing user positioning accuracy. Thus, the performance of the system heavily relies on the deployment and spacing of reference stations. To construct an effective satellite navigation augmentation system, careful consideration must be given to the installation points of reference stations. This paper presents a user positioning performance modeling formula and proposes a method for selecting the installation points of new reference stations. The proposed method involves selecting a candidate group area that can optimize the user's positioning performance. By utilizing this method, the system's performance can be improved, ensuring high accuracy and reliability for autonomous vehicle applications.

• Nuclear Engineering and Technology
• /
• v.55 no.10
• /
• pp.3716-3724
• /
• 2023

Loose parts monitoring and detecting alarm type in real Nuclear Power Plant have challenges such as background noise, insufficient alarm data, and difficulty of distinction between alarm data that occur during start and stop. Although many signal processing methods and alarm determination algorithms have been developed, it is not easy to determine valid alarm and extract the meaning data from alarm signal including background noise. To address these issues, this paper proposes a denoising autoencoder-based majority vote classification. Training and test data are prepared by acquiring alarm data from real NPP and simulation facility for data augmentation, and noisy data is reproduced by adding Gaussian noise. Using DAEs with 3, 5, 7, and 9 layers, features are extracted for each model and classified into neural networks. Finally, the results obtained from each DAE are classified by majority voting. Also, through comparison with other methods, the accuracy and the false alarm rate are compared, and the excellence of the proposed method is confirmed.

• Journal of Korea Multimedia Society
• /
• v.14 no.2
• /
• pp.329-338
• /
• 2011

Recent augmentation reality demands more realistic multimedia data with the mixture of various media. High-technology for multimedia data which combines existing media data with various media such as audio and video dominates entire media industries. In particular, there is a growing need to serve augmentation reality, 3-dimensional contents and realtime interaction system development which are communication method and visualization tool in Internet. The existing services do not correspond to generate depth value for 3-dimensional space structure recovery which is to form solidity in existing contents. Therefore, it requires research for effective depth-map generation using 2-dimensional video. Complementing shortcomings of existing depth-map generation method using 2-dimensional video, this paper proposes an enhanced depth-map generation method that defines the depth direction in regard to loss location in a video in which none of existing algorithms has defined.

• The Korean Journal of Applied Statistics
• /
• v.29 no.1
• /
• pp.99-112
• /
• 2016

Asymmetric jump-diffusion models are effectively used to model the dynamic behavior of asset prices with abrupt asymmetric upward and downward changes. However, the estimation of their extension to the multivariate asymmetric jump-diffusion model has been hampered by the analytically intractable likelihood function. This article confronts the problem using a data augmentation method and proposes a new Bayesian method for a multivariate asymmetric Laplace jump-diffusion model. Unlike the previous models, the proposed model is rich enough to incorporate all possible correlated jumps as well as mention individual and common jumps. The proposed model and methodology are illustrated with a simulation study and applied to daily returns for the KOSPI, S&P500, and Nikkei225 indices data from January 2005 to September 2015.

• Journal of the Korea Society of Computer and Information
• /
• v.26 no.4
• /
• pp.21-28
• /
• 2021

This study proposes a deep learning-based flat foot classification methodology using transfer learning. We used a transfer learning with VGG16 pre-trained model and a data augmentation technique to generate a model with high predictive accuracy from a total of 176 image data consisting of 88 flat feet and 88 normal feet. To evaluate the performance of the proposed model, we performed an experiment comparing the prediction accuracy of the basic CNN-based model and the prediction model derived through the proposed methodology. In the case of the basic CNN model, the training accuracy was 77.27%, the validation accuracy was 61.36%, and the test accuracy was 59.09%. Meanwhile, in the case of our proposed model, the training accuracy was 94.32%, the validation accuracy was 86.36%, and the test accuracy was 84.09%, indicating that the accuracy of our model was significantly higher than that of the basic CNN model.

• Journal of Positioning, Navigation, and Timing
• /
• v.13 no.3
• /
• pp.221-235
• /
• 2024

Errors included in Global Navigation Satellite System (GNSS) measurements degrade the performance of user position estimation but can be mitigated by spatial correlation properties. Augmentation systems providing correction data can be broadly categorized into State Space Representation (SSR) and Observation Space Representation (OSR) methods. The satellite-based cm-level augmentation service based on the SSR broadcasts correction data via satellite signals, unlike the traditional Real-Time Kinematic (RTK) and Network RTK methods, which use OSR. To provide a large amount of correction data via the limited bandwidth of the satellite communication, efficient message structure design considering service area, correction generation, and broadcast intervals is necessary. For systematic message design, it is necessary to analyze the influence of error components included in GNSS measurements. In this study, errors in satellite orbits, satellite clocks for GPS, Galileo, BeiDou, and QZSS satellite constellations ionospheric and tropospheric delays over one year were analyzed, and their spatial decorrelations and linear modeling characteristics were examined.

• Journal of Energy Engineering
• /
• v.12 no.2
• /
• pp.139-144
• /
• 2003

Augmentation of CHF by ultrasonic vibration in water pool is experimentally investigated under pool boiling condition. The experiments are carried out using copper coated plates and distilled water. Measurements of CHF on flat plate heated surface were made with and without ultrasonic wave and with variations in inclined angle of the surface and water subcooling. Experimental apparatus consists of a bath, power supply, test section, ultrasonic generator, and data acquisition system. The measurements show that ultrasonic wave enhances CHF and its extent is dependent upon inclination angle as well as water subcooling. The rate of increase in CHF increases with an increase in water subcooling while it decreases with an increase in inclination angle. Visual observation shows that the cause of CHF augmentation is closely related with the dynamic behavior of bubble generation and departure in acoustic field.

• The Journal of the Korea Contents Association
• /
• v.5 no.2
• /
• pp.251-256
• /
• 2005

When English monosyllabic words are adapted to the Korean language, the loanwords tend to carry extra syllables. The purpose of this paper is to find the syllable augmentation conditions in loanword adaptation and further to provide an algorithm to predict the syllable numbers of English monosylabic loanwords. Three syllable augmentation conditions are found as follows: 1) the existence of diphthong, 2) the existence of consonant clusters, and 3) the quality of the final consonant (and the preceding vowel). Based on these three conditions, an algorithm to predict the syllable number of English monosyllabic loanwords are proposed as three rules applied iteratively with ordering. In addition, the applications of the algorithm to data are given.

• Journal of the Korean Data and Information Science Society
• /
• v.13 no.2
• /
• pp.105-113
• /
• 2002

The statistical analysis of incomplete data sometimes requires handling of incomplete observations. Towards this end, each case with some missing values generally should be deleted, namely, resulting in only use of non-missing cases. EM algorithm(Dempster et al., 1977) which involves prediction and estimation steps is a general method among others. In this article, we use the free software NORM developed for multiple imputation, which uses DA(Data Augmentation) algorithm in its imputation, and evaluate its efficiency through a numerical example.

• Journal of the Korean Statistical Society
• /
• v.28 no.3
• /
• pp.311-324
• /
• 1999

The problem of 'outliers', observations which look suspicious in some way, has long been one of the most concern in the statistical structure to experimenters and data analysts. We propose a model for an outlier problem and also analyze it in linear regression model using a Bayesian approach. Then we use the mean-shift model and SSVS(George and McCulloch, 1993)'s idea which is based on the data augmentation method. The advantage of proposed method is to find a subset of data which is most suspicious in the given model by the posterior probability. The MCMC method(Gibbs sampler) can be used to overcome the complicated Bayesian computation. Finally, a proposed method is applied to a simulated data and a real data.