• Journal of Digital Contents Society
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• v.11 no.4
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• pp.597-606
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• 2010

This paper describes a real-time camera tracking framework specifically designed to track a monocular camera in an AR workspace. Typically, the Kalman filter is often employed for the camera tracking. In general, however, tracking performances of conventional methods are seriously affected by unpredictable situations such as ambiguity in feature detection, occlusion of features and rapid camera shake. In this paper, a recursive Bayesian sampling framework which is also known as the particle filter is adopted for the camera pose estimation. In our system, the camera state is estimated on the basis of the Gaussian distribution without employing additional uncertainty model and sample weight computation. In addition, the camera state is directly computed based on new sample particles which are distributed according to the true posterior of system state. In order to verify the proposed system, we conduct several experiments for unstable situations in the desktop AR environments.

• The Journal of the Acoustical Society of Korea
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• v.34 no.4
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• pp.316-320
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• 2015

In this paper, the pole filtering concept is applied to the Mel-frequency cepstral coefficient (MFCC) feature vectors in the conventional cepstral mean normalization (CMN) and cepstral mean and variance normalization (CMVN) frameworks. Additionally, performance of the cepstral mean and scale normalization (CMSN), which uses scale normalization instead of variance normalization, is evaluated in speech recognition experiments in noisy environments. Because CMN and CMVN are usually performed on a per-utterance basis, in case of short utterance, they have a problem that reliable estimation of the mean and variance is not guaranteed. However, by applying the pole filtering and scale normalization techniques to the feature normalization process, this problem can be relieved. Experimental results using Aurora 2 database (DB) show that feature normalization method combining the pole-filtering and scale normalization yields the best improvements.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.333-335
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• 2006

Position of surface objects can be fixed in many ways. The most popular radionavigational systems, including satellite systems, make possible obtaining nearly continuous and very precise ship's position. However, under the water application of radionavigational systems is impossible. Underwater navigation requires other tools and solutions then these encountered in surface and air navigation. In underwater environment vehicles and submarines, operate that have to possess alternative navigational systems. Underwater vehicles, in order to perform their tasks require accurate information about their own, current position. At present, they are equipped with inertial navigational systems (INS). Accuracy of INS is very high but in relatively short periods. Position error is directly proportional to time of working of the system. The basic feature of INS is its autonomy and passivity. This characteristic mainly decides that INS is broadly used on submarines and other underwater vehicles. However, due to previously mentioned shortcoming i.e. gradually increasing position error, periodical calibration of the system is necessary. The simplest calibration method is surface or nearly surface application of GPS system. Another solution, which does not require interruption of performed task and emergence on the surface, is application of comparative navigation technique. Information about surrounding environment of the ship, obtained e.g. by means sonic depth finder or board sonar, and comparing it with accessible pattern can be used in order to fix ship's position. The article presents a structure and a description of working of underwater vehicle navigation system simulator. The simulator works on the basis of comparative navigation methods which exploit in turn digital images of echograms and sonograms. The additional option of the simulator is ability to robust estimation of measurements. One can do it in order to increase accuracy of position fixed with comparative navigation methods application. The simulator can be a basis to build future underwater navigation system.

• The KIPS Transactions:PartB
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• v.15B no.3
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• pp.205-210
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• 2008

A new approach is proposed for restoration of corrupted regions and segmentation in natural text images. The challenge is to fill in the corrupted regions on the basis of color feature analysis by second order symmetric stick tensor. It is show how feature analysis can benefit from analyzing features using tensor voting with chromatic and achromatic components. The proposed method is applied to text images corrupted by manifold types of various noises. Firstly, we decompose an image into chromatic and achromatic components to analyze images. Secondly, selected feature vectors are analyzed by second-order symmetric stick tensor. And tensors are redefined by voting information with neighbor voters, while restore the corrupted regions. Lastly, mode estimation and segmentation are performed by adaptive mean shift and separated clustering method respectively. This approach is automatically done, thereby allowing to easily fill-in corrupted regions containing completely different structures and surrounding backgrounds. Applications of proposed method include the restoration of damaged text images; removal of superimposed noises or streaks. We so can see that proposed approach is efficient and robust in terms of restoring and segmenting text images corrupted.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.9
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• pp.1060-1069
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• 2019

Pedestrian-based camera self-calibration methods are suitable for video surveillance systems since they do not require complex calibration devices or procedures. However, using arbitrary pedestrians as calibration targets may result in poor calibration accuracy due to the unknown height of each pedestrian. To solve this problem in the real surveillance environments, this paper proposes a novel Bayesian approach. By assuming known statistics on the height of pedestrians, we construct a probabilistic model that takes into account uncertainties in both the foot/head locations and the pedestrian heights, using foot-head homology. Since solving the model directly is infeasible, we use variational Bayesian inference, an approximate inference algorithm. Accordingly, this makes it possible to estimate the height of pedestrians and to obtain accurate camera parameters simultaneously. Experimental results show that the proposed algorithm is robust to noise and provides accurate confidence in the calibration.

• Corrosion Science and Technology
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• v.18 no.2
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• pp.61-71
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• 2019

Flow-Accelerated Corrosion (FAC) is a phenomenon in which a protective coating on a metal surface is dissolved by a flow of fluid in a metal pipe, leading to continuous wall-thinning. Recently, many countries have developed computer codes to manage FAC in power plants, and the FAC prediction model in these computer codes plays an important role in predictive performance. Herein, the FAC prediction model was developed by applying a machine learning method and the conventional nonlinear regression method. The random forest, a widely used machine learning technique in predictive modeling led to easy calculation of FAC tendency for five input variables: flow rate, temperature, pH, Cr content, and dissolved oxygen concentration. However, the model showed significant errors in some input conditions, and it was difficult to obtain proper regression results without using additional data points. In contrast, nonlinear regression analysis predicted robust estimation even with relatively insufficient data by assuming an empirical equation and the model showed better predictive power when the interaction between DO and pH was considered. The comparative analysis of this study is believed to provide important insights for developing a more sophisticated FAC prediction model.

• The Journal of the Acoustical Society of Korea
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• v.40 no.3
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• pp.213-218
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• 2021

It is known that total least-squares method shows better estimation performance than least-squares method when noise is present at the input and output at the same time. When total least squares method is applied to data with time series characteristics, Recursive Total Least Squares (RTS) algorithm has been proposed to improve the real-time performance. However, RTLS has numerical instability in calculating the inverse matrix. In this paper, we propose an algorithm for reducing numerical instability as well as having similar convergence to RTLS. For this algorithm, we propose a new RTLS using Iterative Wiener Filter (IWF). Through the simulation, it is shown that the convergence of the proposed algorithm is similar to that of the RTLS, and the numerical robustness is superior to the RTLS.

• The Korean Journal of Franchise Management
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• v.13 no.4
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• pp.39-48
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• 2022

Purpose: COVID-19 has negatively influenced the financial performance of restaurant firms. Previous literature suggests that the franchising strategy effectively helps restaurant firms recover from difficult business conditions through various methods for expanding business size and enhancing business efficiency. According to risk-sharing theory, restaurant franchisors may minimize operational risks by sharing the risks with their franchisees. For instance, restaurant franchisors could generate more stable cash flow using franchise fees from their franchisees. However, research on the effect of franchise's risk reduction factor on business performance during pandemic is scarce. Thus, this study aims to examine the positive moderating effect of franchising between COVID-19 and restaurants' financial performance. Research design, data, and methodology: Panel data including financial information and franchising status of restaurant firms were collected for analysis. In order to control for unobserved firm-specific factors, generalized least squared estimation in fixed effects model was conducted. Huber-White robust standard errors were used to deal with heteroscedasticity issues. Results: It was found that COVID-19 pandemic has a negative effect on the restaurants' financial performance such as ROA (return on assets), ROE (return on equity), and PM (profit margins), which confirms the findings from existing literature. More importantly, results show that the degree of franchising has a positive moderating effect on the relationship between COVID-19 and financial performance of restaurant firms. This suggests that more active engagement in franchising may decrease negative impacts of COVID-19 on the restaurants' financial performance. Conclusions: The study supports existing literature related to risk-sharing theory, by confirming that pandemics, such as COVID-19, negatively affect financial performance of the restaurants. Furthermore, it was found that franchising strategy can help lessen negative impacts of pandemics on the firm performance. These findings can contribute to the franchise and restaurant management literature by suggesting the role of franchising in reducing business risks, thereby positively affecting financial performance. Moreover, this study offers business managers of franchisors and franchisees insights for utilizing franchising in restaurant risk management. Policymakers may also gain information on aiding restaurant firms during global crisis, such as COVID-19.

• Journal of Korea Trade
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• v.24 no.7
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• pp.54-72
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• 2020

Purpose - This study explores the contextual factors that affect the relationship between CEO overseas experience and firm internationalization. This study incorporates a wide range of contextual factors, including mega, macro, and micro variables. In particular, this study goes a step further from prior studies by incorporating a higher-order variable i.e., the global financial crisis that can constrain the managerial discretion of a CEO. Design/methodology - To structure the balanced data set before and after the 2008 global financial crisis, we used the data for the years from 2002 to 2014 from a sample of Korean manufacturing firms. Ultimately, 1101 firm-year unbalanced panel observations from 101 firms were used for the analysis. Findings - Our main findings can be summarized as follows. CEO overseas experience is positively related to firm internationalization. However, this relationship varies depending on the CEOs level of managerial discretion. As for the constraining moderation, the global financial crisis weakened the positive relationship between CEO overseas experience and firm internationalization. As for the enabling moderation, the CEOs tenure strengthened the relationship. Originality/value - This study adopted the knowledge, skills, and abilities (KSA) framework to explain the relationship between CEO overseas experience and firm internationalization. Moreover, we argue that the CEO-internationalization relationship depends on the specific context of the managerial discretion, focusing on the 2008 global financial crisis. Empirically, this study adopted the 2SLS procedure to correct endogeneity. Instead of taking the actual value of prior internationalization as a control, we estimated prior internationalization using the instrument variables at an industry level. This procedure made our estimation more robust.

• Journal of Ocean Engineering and Technology
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• v.36 no.5
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• pp.340-352
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• 2022

This article presents a modeling method for the uncorrelated measurement error of the ultra-short baseline (USBL) acoustic positioning system for aiding navigation of underwater vehicles. The Mahalanobis distance (MD) and principal component analysis are applied to decorrelate the errors of USBL measurements, which are correlated in the x- and y-directions and vary according to the relative direction and distance between a reference station and the underwater vehicles. The proposed method can decouple the radial-direction error and angular direction error from each USBL measurement, where the former and latter are independent and dependent, respectively, of the distance between the reference station and the vehicle. With the decorrelation of the USBL errors along the trajectory of the vehicles in every time step, the proposed method can reduce the threshold of the outlier decision level. To demonstrate the effectiveness of the proposed method, simulation studies were performed with motion data obtained from a field experiment involving an autonomous underwater vehicle and USBL signals generated numerically by matching the specifications of a specific USBL with the data of a global positioning system. The simulations indicated that the navigation system is more robust in rejecting outliers of the USBL measurements than conventional ones. In addition, it was shown that the erroneous estimation of the navigation system after a long USBL blackout can converge to the true states using the MD of the USBL measurements. The navigation systems using the uncorrelated error model of the USBL, therefore, can effectively eliminate USBL outliers without loss of uncontaminated signals.