• Journal of Korea Trade
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• v.25 no.4
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• pp.146-163
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• 2021

Purpose - This paper examines China's impact on Korea's ICT exports considering the direct competition channel, the production shift channel, and the indirect demand channel at once. This paper also takes China's economic rebalancing into account and discusses whether it makes any differences in the effect of the three channels. Design/methodology - To quantify the effect of the three channels, I constructed a linear panel regression model and estimated it with various estimation methods including the system GMM. China's exports toward the same destination as Korea's exports, Korea's exports toward China, and the third countries' exports toward China respectively reflect the three channels. China's GVC indicators are included as well to evaluate the effect of further China's economic rebalancing. Since the present paper has a greater interest in the effect of China rather than the determinant of bilateral trade, a (fixed effect) panel model becomes more appropriate than the gravity model because timeinvariant variables in the gravity model, such as the distance and the language, are eliminated during the estimation process. Findings - The estimation results indicate that Chinese ICT exports are complementary to Korea's ICT exports in general. However, when markets are considered in subgroups, China's ICT exports could have a negative effect in the long run, especially for SITC75 and SITC76 markets, implying a possible competitive threat of China. The production shift effect turns significant during China's economic rebalancing in the markets for the advanced economies and the SITC76 product. China's indirect demand channel is also in effect significantly for the advanced economy and SITC75 commodities during China's economic rebalancing periods. In addition, this paper shows that China's transition toward upstream in the global value chain could have a positive impact on Korea's ICT exports, especially at the Asian market. Originality/value - The contribution of this paper is threefold. First, it focuses on the ICT industry for which Korea increasingly depends on China and China becomes a global hub of the GVC. Second, this paper quantitatively studies three channels in a model in contrast to the literature which mostly examines those channels separately and pays less attention to the GVC aspect. Third, by utilizing relatively recent data from the period of 2001-2017, this paper discusses whether China's economic rebalancing affects the three channels.

• The Korean Journal of Applied Statistics
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• v.29 no.5
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• pp.807-822
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• 2016

The composite lognormal-GPD models (LN-GPD) enjoys both merits from log-normality for the body of distribution and GPD for the thick tailedness of the observation. However, in the estimation perspective, LN-GPD model performs poorly due to numerical instability. Therefore, a two-stage procedure, that estimates threshold first then estimates other parameters later, is a natural method to consider. This paper considers five nonparametric threshold estimation methods widely used in extreme value theory and compares their performance in LN-GPD parameter estimation. A simulation study reveals that simultaneous maximum likelihood estimation performs good in threshold estimation, but very poor in tail index estimation. However, the nonparametric method performs good in tail index estimation, but introduced bias in threshold estimation. Our method is illustrated to the service time of an Israel bank call center and shows that the LN-GPD model fits better than LN or GPD model alone.

• Journal of Biomedical Engineering Research
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• v.28 no.1
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• pp.29-35
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• 2007

This study was designed to investigate the feasibility to utilize the electromyogram (EMG) for estimating the muscle torque. The muscle torque estimation plays an important role in functional electrical stimulation because electrical stimulation causes muscles to fatigue much faster than voluntary contraction, and the stimulation intensity should then be modified to keep the muscle torque within the desired range. We employed the neural network method which was trained using the major EMG parameters and the corresponding knee extensor torque measured and extracted during isometric contractions. The experimental results suggested that (1) our neural network algorithm and protocol was feasible to be adopted in a real-time feedback control of the stimulation intensity, (2) the training data needed to cover the entire range of the measured value, (3) different amplitudes and frequencies made little difference to the estimation quality, and (4) a single input to the neural network led to a better estimation rather than a combination of two or three. Since this study was done under a limited contraction condition, the results need more experiments under many different contraction conditions, such as during walking, for justification.

• Journal of Korea Multimedia Society
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• v.17 no.7
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• pp.797-805
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• 2014

In this paper we propose a new frame rate up conversion scheme which is used to overcome the motion blur problem of liquid crystal display caused by its slow response. The conventional bilateral motion estimation method which is mainly used in the frame rate up conversion scheme has a drawback that it cannot find true motion vector if there are blocks with simple texture in the search range. To solve this problem, a texture adaptive bilateral motion estimation method that increases cost value of block with simple texture is proposed. Also a motion estimation scheme that utilizes neighboring motion vector effectively is proposed to reduce computation time required to estimate motion. Since the proposed scheme does not apply all available motion vectors within the search range, the execution time of frame rate up conversion can be reduced dramatically. Experimental results show that the interpolated frame image quality of the proposed method is improved in subjective as well as objective view point compared with that of the conventional method.

• The Journal of the Acoustical Society of Korea
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• v.25 no.4E
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• pp.154-158
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• 2006

In this paper, we suggest a noise estimation method for speech enhancement in nonstationary noisy environments. The proposed method consists of the following two main processes. First, in order to receive fewer affect of variable signals, a best fitting regression line is used, which is obtained by applying a least squares method to coefficient magnitudes in a node with a uniform wavelet packet transform. Next, in order to update the noise estimation efficiently, a differential forgetting factor and a correlation coefficient per subband are used, where subband is employed for applying the weighted value according to the change of signals. In particular, this method has the ability to update the noise estimation by using the estimated noise at the previous frame only, without utilizing the statistical information of long past frames and explicit nonspeech frames by voice activity detector. In objective assessments, it was observed that the performance of the proposed method was better than that of the compared (minima controlled recursive averaging, weighted average) methods. Furthermore, the method showed a reliable result even at low SNR.

• Journal of IKEEE
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• v.6 no.1 s.10
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• pp.94-101
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• 2002

In this paper, we propose a motion estimation algorithm for the discontinuity blocks. The algorithm uses sub-SAD value (i.e. the sum of absolute difference for a quarter of a block) to identify the discontinuity region, and produces additional motion vectors for these sub-blocks if necessary. We show with experimental results that, in comparison with some conventional motion estimation algorithms, the proposed algorithm achieves quality enhancement for the sequences with discontinuity blocks, and also shows the same computational quantity as to normal algorithms for sequences with less discontinuity.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.15 no.2
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• pp.123-135
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• 2011

In this paper, we present arc-length estimations for quadratic rational B$\acute{e}$zier curves using the length of polygon and distance between both end points. Our arc-length estimations coincide with the arc-length of the quadratic rational B$\acute{e}$zier curve exactly when the weight ${\omega}$ is 0, 1 and ${\infty}$. We show that for all ${\omega}$ > 0 our estimations are strictly increasing with respect to ${\omega}$. Moreover, we find the parameter ${\mu}^*$ which makes our estimation coincide with the arc-length of the quadratic rational B$\acute{e}$zier curve when it is a circular arc too. We also show that ${\mu}^*$ has a special limit, which is used for optimal estimation. We present some numerical examples, and the numerical results illustrates that the estimation with the limit value of ${\mu}^*$ is an optimal estimation.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.6
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• pp.259-265
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• 2000

The purpose of this paper is to detect and identify topological errors in order to maintain a reliable database for the state estimator. In this paper, a two stage estimation procedure is used to identify the topology errors. At the first stage, the WSAV state estimator which has characteristics to remove bad data during the estimation procedure is run for finding out the suspected branches at which topology errors take place. The resulting residuals are normalized and the measurements with significant normalized residuals are selected. A set of suspected branches is formed based on these selected measurements; if the selected measurement is a line flow, the corresponding branch is suspected; if it is an injection, then all the branches connecting the injection bus to its immediate neighbors are suspected. A new WLAV state estimator adding the branch flow errors in the state vector is developed to identify the branch topology errors. Sample cases of single topology error and topology error with a measurement error are applied to IEEE 14 bus test system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.9C
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• pp.1323-1329
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• 2004

This paper presents an enhanced MSEA(multi-level successive elimination algorithm) which is a fast algorithm of the full-search motion estimation. We predict the SAD at the final level using the values of norms at the preceding levels in MSEA and then decide on whether the processing at the following levels should be proceeded or not. We skip the computation at the following levels where the processing is not meaningful anymore. Consequently, we take computational gain. For the purpose of predicting the values of SAD at each level, we first show the theoretical analysis of the value of norm at each level, which is verified by experiments. Based on the analysis a new motion estimation method is proposed and its performance is evaluated.

• Management & Information Systems Review
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• v.7
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• pp.427-450
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• 2001

This research suggests the estimation methodology of Logistics. This paper elucidates the main problems associated with estimation in the regression model. We review the methods for estimating the parameters in the model and introduce a modified procedure in which all models are fitted and combined to construct a combination of estimates. The resulting estimators are found to be as efficient as the maximum likelihood (ML) estimators in various cases. Our method requires more computations but has an advantage for large data sets. Also, it enables to detect particular features in the data structure. Examples of real data are used to illustrate the properties of the estimators. The backgrounds of estimation of logistic regression model is the increasing logistic environment importance today. In the first phase, we conduct an exploratory study to discuss 9 independent variables. In the second phase, we try to find the fittest logistic regression model. In the third phase, we calculate the logistic estimation using logistic regression model. The parameters of logistic regression model were estimated using ordinary least squares regression. The standard assumptions of OLS estimation were tested. The calculated value of the F-statistics for the logistic regression model is significant at the 5% level. The logistic regression model also explains a significant amount of variance in the dependent variable. The parameter estimates of the logistic regression model with t-statistics in parentheses are presented in Table. The object of this paper is to find the best logistic regression model to estimate the comparative accurate logistics.