• International Journal of Highway Engineering
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• 제14권5호
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• pp.1-9
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• 2012

PURPOSES: A viscoelastic axisymmetric finite element analysis code has been developed for stress analysis of asphalt pavement structures. METHODS: Generalized Maxwell Model (GMM) and 4-node isoparametric element were employed for finite element formulation. The code was developed using $C^{+}^{+}$ computer program language and named as KICTPAVE. For the verification of the developed code, a structural model of a pavement system was constructed. The structural model was composed of three layers: asphalt layer, crushed stone layer, and soil subgrade. Two types of analysis were considered for the verification: (1)elastic static analysis, (2)viscoelastic time-dependent analysis. For the elastic static analysis, linear elastic material model was assigned to all the layers, and a static load was applied to the structural model. For the viscoelastic time-dependent analysis, GMM and linear elastic material model were assigned to the asphalt layer and all the other layers respectively, and a cyclic loading condition was applied to the structural model. RESULTS: The stresses and deformations from KICTPAVE were compared with those from ABAQUS. The analysis results obtained from the two codes showed good agreement in time-dependent response of the element under the loading area as well as the surface deformation of asphalt layer, and horizontal and vertical stresses along the axisymmetric axis. CONCLUSIONS: The validity of KICTPAVE was confirmed by showing the agreement of the analysis results from the two codes.

• The Journal of Asian Finance, Economics and Business
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• 제9권4호
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• pp.197-212
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• 2022

The study uses the audited financial statements of 26 Vietnamese commercial banks listed on the Ho Chi Minh City Stock Exchange (HOSE) and Hanoi Stock Exchange (HOSE) during the 2008-2018 period to estimate the system GMM model, which provides empirical evidence on the effect of the variables of customer deposit to total assets (DEPO) ratio, loan to assets (LTA) ratio, liquidity of commercial banks (LIQ), credit development (CRD) ratio, external funding (EFD) ratio, and credit loss provision (LLP) ratio on liquidity risk. The study confirms that commercial banks' internal factors play the most important role, and there is no empirical evidence on macro variables that affect liquidity risk. Finally, in accordance with the theoretical framework, the study uses an estimation method with the R language and the bootstrap methodology to give empirical proof of the nonlinear correlation and U-shaped graph between commercial bank size and liquidity risk. The importance of commercial bank size in absorbing and moderating the effects of liquidity shocks is demonstrated, however, excessive growth in commercial bank size would increase liquidity risk in commercial bank operations.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• 제8권4호
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• pp.33-38
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• 2009

This paper proposes an automatic equalizer control method in automobile audio system. The proposed method discriminates the music segment from the consecutive real-time audio stream of the radio and the equalizer is controlled automatically according to the classified genre of the music segment. For enhancing the accuracy of the music genre classification in real-time, timbre feature and rhythm feature extracted from the consecutive audio stream is applied to GMM(Gaussian mixture model) classifier. The proposed method evaluates the performance of the music genre classification, which classified various audio segments segmented from the audio signal of the radio broadcast in automobile audio system into one of five music genres.

• Journal of the Korea Institute of Information and Communication Engineering
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• 제20권3호
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• pp.464-470
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• 2016

This paper presents a speech recognition database verification system using speech measures, and describes a speech measure extraction algorithm which is applied to this system. In our previous study, to produce an effective speech quality measure for the system, we propose a combination of various speech measures which are highly correlated with WER (Word Error Rate). The new combination of various types of speech quality measures in this study is more effective to predict the speech recognition performance compared to each speech measure alone. In this paper, we increase the system independency by employing GMM acoustic score instead of HMM score which is obtained by a secondary speech recognition system. The combination with GMM score shows a slightly lower correlation with WER compared to the combination with HMM score, however it presents a higher relative improvement in correlation with WER, which is calculated compared to the correlation of each speech measure alone.

• Speech Sciences
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• 제9권2호
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• pp.135-146
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• 2002

Generally a speaker verification system improves its system recognition ratio by regularizing log likelihood ratio, using a speaker model and its background speaker model that are required to be verified. The speaker-based cohort method is one of the methods that are widely used for selecting background speaker model. Recently, Gaussian-based cohort model has been suggested as a virtually synthesized cohort model, and unlike a speaker-based model, this is the method that chooses only the probability distributions close to basic speaker's probability distribution among the several neighboring speakers' probability distributions and thereby synthesizes a new virtual speaker model. It shows more excellent results than the existing speaker-based method. This study compared the existing speaker-based background speaker models and virtual speaker models and then constructed new virtual background speaker model groups which combined them in a certain ratio. For this, this study constructed a speaker verification system that uses GMM (Gaussin Mixture Model), and found that the suggested method of selecting virtual background speaker model shows more improved performance.

• Phonetics and Speech Sciences
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• 제7권3호
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• pp.131-138
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• 2015

In this paper, we propose L1-norm regularization for state vector adaptation of subspace Gaussian mixture model (SGMM). When you design a speaker adaptation system with GMM-HMM acoustic model, MAP is the most typical technique to be considered. However, in MAP adaptation procedure, large number of parameters should be updated simultaneously. We can adopt sparse adaptation such as L1-norm regularization or sparse MAP to cope with that, but the performance of sparse adaptation is not good as MAP adaptation. However, SGMM does not suffer a lot from sparse adaptation as GMM-HMM because each Gaussian mean vector in SGMM is defined as a weighted sum of basis vectors, which is much robust to the fluctuation of parameters. Since there are only a few adaptation techniques appropriate for SGMM, our proposed method could be powerful especially when the number of adaptation data is limited. Experimental results show that error reduction rate of the proposed method is better than the result of MAP adaptation of SGMM, even with small adaptation data.

• Journal of Industrial Technology
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• 제38권1호
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• pp.41-52
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• 2018

Recently, object detection is a critical function for any system that uses computer vision and is widely used in various fields such as video surveillance and self-driving cars. However, the conventional methods can not detect the objects clearly because of the dynamic background change in the beach. In this paper, we propose a new technique to detect humans correctly in the dynamic videos like shores. A new background modeling method that combines spatial GMM (Gaussian Mixture Model) and temporal GMM is proposed to make more correct background image. Also, the proposed method improve the accuracy of people detection by using SVM (Support Vector Machine) to classify people from the objects and KCF (Kernelized Correlation Filter) Tracker to track people continuously in the complicated environment. The experimental result shows that our method can work well for detection and tracking of objects in videos containing dynamic factors and situations.

• Journal of Institute of Control, Robotics and Systems
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• 제16권1호
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• pp.69-76
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• 2010

The segmentation of moving object in video sequence is a core technique of intelligent image processing system such as video surveillance, traffic monitoring and human tracking. A typical method to segment a moving region from the background is the background subtraction. The steps of background subtraction involve calculating a reference image, subtracting new frame from reference image and then thresholding the subtracted result. One of famous background modeling is Gaussian mixture model (GMM). Even though the method is known efficient and exact, GMM suffers from a problem that includes false pixels in ROI (region of interest), specifically shadow pixels. These false pixels cause fail of the post-processing tasks such as tracking and object recognition. This paper presents a method for removing false pixels included in ROT. First, we subdivide a ROI by using shape characteristics of detected objects. Then, a method is proposed to classify pixels from using histogram characteristic and comparing difference of energy that converts the color value of pixel into grayscale value, in order to estimate whether the pixels belong to moving object area or shadow area. The method is applied to real video sequence and the performance is verified.

• The Journal of Asian Finance, Economics and Business
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• 제9권2호
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• pp.81-92
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• 2022

The necessity for a theoretical explanation of the negative association between capital structure and company performance is identified in this study. By focusing on accounting metrics of business performance, this study is the first to investigate the moderating effects of firm size between these variables using logical reasoning. Due to the possibility of endogeneity, this study applies a two-step system GMM approach with data from 285 non-financial enterprises from PSX over a 21-year period. For robustness, we employed pooled OLS, fixed effect, and two-step difference GMM. Our data show that leverage has a detrimental impact on business performance, with size acting as a moderator in the same direction. Our analysis empirically supports some studies while refuting others due to inconsistent results in the literature, but no study has theoretically justified their negative link. We believe that because larger companies have more and easier access to capital markets, they focus primarily on the amount of return, even if the investment is inefficient in terms of the rate of return, but small businesses do not. As a result of this thinking, firm managers' performance suffers as a result of leverage.

• The KIPS Transactions:PartB
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• 제13B권5호
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• pp.515-524
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• 2006

Background extraction is an important technique to find the moving objects in video surveillance system. This paper proposes a new on-line background extraction method for color video using vector order statistics. In the proposed method, using the fact that background occurs more frequently than objects, the vector median of color pixels in consecutive frames Is treated as background at the position. Also, the objects of current frame are consisted of the set of pixels whose distance from background pixel is larger than threshold. In the paper, the proposed method is compared with the on-line multiple background extraction based on Gaussian mixture model(GMM) in order to evaluate the performance. As the result, its performance is similar or superior to the method based on GMM.