• 산경연구논집
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• 제8권1호
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• pp.5-13
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• 2017

Purpose - In this article, a dynamic model like a VAR is an appropriate choice for estimating the possible interrelationship between ownership structure and firm performance as a dynamic process. Research design, data, and methodology - Data of this work are collected from Chinese stock exchange including 350 Chinese-listed firms during the period of 1999-2012. We hypothesize that this interrelationship dynamically exists between ownership structure and firm performance. To examine the correlation, a panel Vector Auto-regression (PVAR) approach generated by GMM method is utilized to test the possible dynamic relation embedded in corporate governance. Another two dynamic analysis solutions such as orthogonalized impulse-response function and variance decomposition are also used simultaneously. Results - Findings of this study indicate the evidence that dynamically endogenous relationship exists between ownership structure and firm performance. Further, there is a dynamical correlation between investment and performance. Impulse response and variance decomposition illustrate that impact of a shock to variables themselves is the main source for their variability. Conclusions - The conclusion in this study is that there is a bidirectional and inter-temporal effect between proportion of ownership and corporate performance for a long run in accordance with impulse response function. Overall, our results suggest that corporate governance in China is more market oriented.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2005년도 추계 학술발표회 논문집
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• pp.167-173
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• 2005

This paper presents an autocorrelation test that is applicable to dynamic panel data models with serially correlated errors. The residual-based GMM t-test is a significance test that is applied after estimating a dynamic model by using the instrumental variable(IV) method and is directly applicable to any other consistently estimated residuals. Monte Carlo simulations show that the t-test has considerably more power than the $m_2$ test or the Sargan test under both forms of serial correlation (i.e., AR(1) and MA(1)).

• 한국가시화정보학회:학술대회논문집
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• 한국가시화정보학회 2004년도 Proceedings of 2004 Korea-Japan Joint Seminar on Particle Image Velocimetry
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• pp.25-32
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• 2004

The new algorithm for higher performance of dynamic PIV has been proposed. Present study considered mathematical basis of PIV analysis for multiple-time-step images and it enables us to analyze the high time-resolution PIV, which is obtained by dynamic PIV system. Conventional single pair image PIV analysis gives us the velocity field data in each time step but it sometimes contains unnecessary information of target flow. Present technique utilize multi-time step correlation information, and it is analyzed.

• Journal of the Korean Statistical Society
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• 제34권4호
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• pp.367-375
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• 2005

This paper presents an autocorrelation test that is applicable to dynamic panel data models with serially correlated errors. The residual-based GMM t-test is a significance test that is applied after estimating a dynamic model by using the instrumental variable (IV) method and is directly applicable to any other consistently estimated residuals. Monte Carlo simulations show that the t-test has considerably more power than the $m_2$ test or the Sargan test under both forms of serial correlation (i.e., AR(1) and MA(1)).

• The Journal of Korean Physical Therapy
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• 제24권2호
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• pp.73-81
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• 2012

Purpose: The aim of the present study was to measure the standing balance symmetry of stroke patients using a force-plate with computer system, and to investigate the correlation between the standing balance symmetry and that of the walking function in stroke patients. Methods: 48 patients with stroke (34 men, 14 women, $56.8{\pm}11.72$ years old) participated in this study. Static standing balance was evaluated by the weight distribution on the affected and the nonaffected lower limbs, sway path, sway velocity, and sway frequency, which reflected the characteristic of body sway in quiet standing. Dynamic standing balance was evaluated by anteroposterior and mediolateral sway angle, which revealed the limit of stability during voluntary weight displacement. Symmetry index of static standing balance, (SI-SSB) calculated by the ratio of the affected weight distribution for the nonaffected weight distribution, and symmetric index of dynamic standing balance (SI-SDB) by the ratio of the affected sway angle for the nonaffected sway angle. Functional balance assessed by a Berg balance scale (BBS), and the functional walking by 10m walking velocity, as well as the modified motor assessment scale (mMAS). Results: Static balance scales and SI-SSB was the only correlation with BBS (p<0.05). Dynamic balance scales and SI-DSB, not only was correlated with BBS, but also with 10m walking velocity and mMAS (p<0.01). Additionally, there was a significant difference between SI-SSB and that of SI-DSB (p<0.01). Conclusion: The balance and the walking function relate to real life in the stroke showed strong relationships with the dynamic standing balance symmetry in the frontal plane and the ability of anterior voluntary weight displacement in sagittal plane.

• 복식
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• 제58권9호
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• pp.29-37
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• 2008

The purpose of this study was to investigate insulation of thermal clothing under still and dynamic air conditions(with 2.1m/sec air velocity) and decrease of insulation in both conditions, to analyze correlations among them, and to estimate insulation and decrease of insulation using factors, such as fabric insulation, fabric weight, clothing weight, air permeability, and water vapor resistance. A total of 25 kinds of clothing were tested(9 types for suits, 6 types of jacket, 5 types for shirts, and 5 types for trousers). The results of this study were as follows; Thermal resistance of clothing under the dynamic air condition decreased comparing to that of clothing under still air condition in all types of clothing. Decrease in shirts was the biggest(47.5%), followed by suits(39.51%), trousers(37.48%), and jackets(34.49%) in sequence. Thermal resistance of clothing under dynamic air condition showed very high correlation(0.98, p<0.01) with that of clothing under still air condition, followed by thermal resistance of fabric(0.86, p<0.01). Decrease in thermal resistance of clothing showed the highest correlation with air permeability. It didn't show correlation with other factors. Regression analysis showed that fabric thickness would be useful factor for estimating thermal resistance of clothing and air permeability also would be useful factor for estimating decrease in thermal resistance of clothing.

• Journal of Information Processing Systems
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• 제13권2호
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• pp.305-320
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• 2017

The selection of subset size is of great importance to the accuracy of digital image correlation (DIC). In the traditional DIC, a constant subset size is used for computing the entire image, which overlooks the differences among local speckle patterns of the image. Besides, it is very laborious to find the optimal global subset size of a speckle image. In this paper, a self-adaptive and bidirectional dynamic subset selection (SBDSS) algorithm is proposed to make the subset sizes vary according to their local speckle patterns, which ensures that every subset size is suitable and optimal. The sum of subset intensity variation (${\eta}$) is defined as the assessment criterion to quantify the subset information. Both the threshold and initial guess of subset size in the SBDSS algorithm are self-adaptive to different images. To analyze the performance of the proposed algorithm, both numerical and laboratory experiments were performed. In the numerical experiments, images with different speckle distribution, different deformation and noise were calculated by both the traditional DIC and the proposed algorithm. The results demonstrate that the proposed algorithm achieves higher accuracy than the traditional DIC. Laboratory experiments performed on a substrate also demonstrate that the proposed algorithm is effective in selecting appropriate subset size for each point.

• Smart Structures and Systems
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• 제26권1호
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• pp.11-21
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• 2020

The hinge joint is the key to the overall cooperative working performance of the assembled beam bridge, and it is also the weakest part during the service period. This paper proposes a method for monitoring and evaluating the lateral cooperative working performance of fabricated beam bridges based on dynamic strain correlation coefficient indicator. This method is suitable for monitoring and evaluation of hinge joints status between prefabricated girders and overall cooperative working performance of bridge, without interruption of traffic and easy implementation. The remote cloud monitoring and diagnosis system was designed and implemented on a real assembled beam bridge. The algorithms of data preprocessing, online indicator extraction and status diagnosis were given, and the corresponding software platform and scientific computing environment for cloud operation were developed. Through the analysis of real bridge monitoring data, the effectiveness and accuracy of the method are proved and it can be used in the health monitoring system of such bridges.

• Smart Structures and Systems
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• 제17권6호
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• pp.1031-1053
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• 2016

The health conditions of in-service civil infrastructures can be evaluated by employing structural health monitoring technology. A reliable health evaluation result depends heavily on the quality of the data collected from the structural monitoring sensor network. Hence, the problem of sensor fault diagnosis has gained considerable attention in recent years. In this paper, an innovative sensor fault diagnosis method that focuses on fault detection and isolation stages has been proposed. The dynamic or auto-regressive characteristic is firstly utilized to build a multivariable statistical model that measures the correlations of the currently collected structural responses and the future possible ones in combination with the canonical correlation analysis. Two different fault detection statistics are then defined based on the above multivariable statistical model for deciding whether a fault or failure occurred in the sensor network. After that, two corresponding fault isolation indices are deduced through the contribution analysis methodology to identify the faulty sensor. Case studies, using a benchmark structure developed for bridge health monitoring, are considered in the research and demonstrate the superiority of the new proposed sensor fault diagnosis method over the traditional principal component analysis-based and the dynamic principal component analysis-based methods.

• 무역학회지
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• 제47권3호
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• pp.141-156
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• 2022

This study intends to examine the regional blocs of the international crude oil futures market by analyzing the dynamic conditional correlation between the international crude oil futures markets using the DCC-GARCH model. For statistical data, from April 2, 2018 to March 31, 2022, international crude oil futures prices such as Europe, the United States, China, and Dubai were used. To summarize the results of the study, first, the phenomenon of regional blocs in the international crude oil futures market is occurring, and it is found that it is gradually strengthening as time goes by. Second, it was found that the dynamic correlation of the international crude oil futures market is temporarily strengthened when a supply-demand imbalance problem occurs due to a global shock. Third, it was found that the volatility of the Chinese crude oil futures market affects the international crude oil futures market. This study confirmed that the regional blocs phenomenon in the international crude oil futures market is strengthened as time goes by. In particular, it suggested that China's influence in the international oil market would increase.