• Korean Business Review
• /
• v.22 no.1
• /
• pp.27-44
• /
• 2009

This paper compares the accounting standards for stock option of Japan and Korea. Especially, tire setting process of accounting standards for stock option, accounting methods and disclosures for stock option in two countries are analyzed. The results provide that two countries shaw different characteristics in accounting standards for stock option. First, in Japan, acquired services are reported as compensation costs and capital adjustments. On the other hand, in Korea, in case of cash-settled share- based payment transactions, acquired services are reported as compensation costs and capital adjustments, but in case of equity-settled share- based payment transactions, acquired services are reported as compensation costs and debt. Second, when tire stock option rights are abandoned, they are reported as extraordinary items in Japan and are reported as other surplus in Korea. Third, though both countries do not choose specific stock option pricing model, Japan prefers Black-Sholes Model and Korea regards binomial model as proper model.

• Journal of Electrical Engineering and Technology
• /
• v.13 no.2
• /
• pp.1030-1041
• /
• 2018

The aim of this paper is to design a centrifugal deformation error compensation method with guaranteed performance that allows angular velocity measurement of the magnetically suspended sensitive gyroscopes (MSSGs). The angular velocity measurement principle and the structure of the MSSG are described, and the analytical model of errors caused by MSSG rotor centrifugal deformation is established. Then, an on-line rotor centrifugal deformation error compensation method based on measurement of rotor spinning speed in real-time has been designed. The common issues caused by centrifugal deformation of spinning rotors can be effectively resolved by the proposed method. Comparative experimental results before and after compensation demonstrate the validity and superiority of the error compensation method.

• MALSORI
• /
• no.62
• /
• pp.97-112
• /
• 2007

The speech recognizer in general operates in noisy acoustical environments. Many research works have been done to cope with the acoustical variations. Among them, the multiple-HMM model approach seems to be quite effective compared with the conventional methods. In this paper, we consider a multiple-model approach combined with the model compensation method and investigate the necessary number of the HMM model sets through noisy speech recognition experiments. By using the data-driven Jacobian adaptation for the model compensation, the multiple-model approach with only a few model sets for each noise type could achieve comparable results with the re-training method.

• The Journal of the Acoustical Society of Korea
• /
• v.24 no.1
• /
• pp.58-68
• /
• 2005

In this paper, we propose a scheme to improve discriminative property in the feature compensation method for robust speech recognition under noisy environments. The estimation of noisy speech model used in existing feature compensation methods do not guarantee the computation of posterior probabilities which discriminate reliably among the Gaussian components. Estimation of Posterior probabilities is a crucial step in determining the discriminative factor of the Gaussian models, which in turn determines the intelligibility of the restored speech signals. The proposed scheme employs minimum classification error (MCE) training for estimating the parameters of the noisy speech model. For applying the MCE training, we propose to identify and determine the 'competing components' that are expected to affect the discriminative ability. The proposed method is applied to feature compensation based on parallel combined mixture model (PCMM). The performance is examined over Aurora 2.0 database and over the speech recorded inside a car during real driving conditions. The experimental results show improved recognition performance in both simulated environments and real-life conditions. The result verifies the effectiveness of the proposed scheme for increasing the performance of robust speech recognition systems.

• Journal of electromagnetic engineering and science
• /
• v.10 no.4
• /
• pp.231-236
• /
• 2010

This study proposes a temperature compensation method of the complex permittivities of biological tissues and organs. The method is based on the temperature dependence of the Debye model of water, which has been thoroughly investigated. This method was applied to measured data at room temperature for whole blood, kidney cortex, bile, liver, and heart muscle. It is shown that our method can compensate for the Cole-Cole model using measured data at 20 $^{\circ}C$, given the Cole-Cole model based on measured data at 35 $^{\circ}C$, with a root-mean-squared deviation of 3~11 % and 2~6 % for the real and imaginary parts of the complex permittivities, respectively, among the measured tissues.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1996.11a
• /
• pp.219-224
• /
• 1996

Piezoelectric actuators exhibit limited accuracy in tracking control due to their hysteresis nonlinearity. In this study a digital tracking control approach for a piezoelectric actuator based on incorporating a feedback linearization loop with a PID feedback controller is presented. The hysteresis nonlinearity of the piezoelectric actuator is modeled in the feedback compensation loop using the Maxwell slip model. Experiments were performed on a piezoelectric 2-axis linear positioner for tracking linearly decaying sinusoidal waveforms and circles. The experimental results show that the tracking control performance is noticeably improved by augmenting the feedback loop with a model of hysteresis in the feedback compensation loop.

• Speech Sciences
• /
• v.15 no.2
• /
• pp.55-65
• /
• 2008

Recently, the multi-model based speech recognizer has been used quite successfully for noisy speech recognition. For the selection of the reference HMM (hidden Markov model) which best matches the noise type and SNR (signal to noise ratio) of the input testing speech, the estimation of the SNR value using the VAD (voice activity detection) algorithm and the classification of the noise type based on the GMM (Gaussian mixture model) have been done separately in the multi-model framework. As the SNR estimation process is vulnerable to errors, we propose an efficient method which can classify simultaneously the SNR values and noise types. The KL (Kullback-Leibler) distance between the single Gaussian distributions for the noise signal during the training and testing is utilized for the classification. The recognition experiments have been done on the Aurora 2 database showing the usefulness of the model compensation method in the multi-model based speech recognizer. We could also see that further performance improvement was achievable by combining the probability density function of the MCT (multi-condition training) with that of the reference HMM compensated by the D-JA (data-driven Jacobian adaptation) in the multi-model based speech recognizer.

• Journal of Institute of Control, Robotics and Systems
• /
• v.20 no.2
• /
• pp.180-185
• /
• 2014

DC motor is a representative electric motor commonly utilized in various motion control fields. However, DC motor-based motion control systems suffer from degradation of position precision due to nonlinear static friction. In order to enhance control precision, friction model-based compensators have been introduced in previous researches, where friction models are identified and counter inputs are added to control inputs for cancelling out the identified friction forces. In this paper, a static friction compensator is proposed without use of a friction model. The proposed compensation algorithm utilizes internal state manipulation to generate compensation pulses, and related parameters are easily tuned experimentally. The proposed friction compensator is applied to a DC motor-based motion control system, and results are presented in comparison with those without a friction compensator.

• Journal of Institute of Control, Robotics and Systems
• /
• v.9 no.11
• /
• pp.963-968
• /
• 2003

A feedback control implementation for a high speed train pressurization system is proposed based on CAN (Controller Area Network). Firstly, system model including network latencies by CAN arbitration mechanisms is proposed, and an analytical compensation method of control parameters based on the system model is proposed for the network latencies. For the practical implementation of the control, global synchronization is adopted for controller to measure network latencies and to utilize them for the compensation of the control parameters. Simulation results are shown with practical tunnel data response. The proposed method is evaluated to be the most effective for the system through the control performances comparing among a controller not considering network latencies, other two off-line compensation methods, and the proposed method.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.9 no.4
• /
• pp.327-332
• /
• 2009

This paper proposes a method to improve the accuracy of a short-term electrical load forecasting (STLF) system based on neuro-fuzzy models. The proposed method compensates load forecasts based on the error obtained during the previous prediction. The basic idea behind this approach is that the error of the current prediction is highly correlated with that of the previous prediction. This simple compensation scheme using error information drastically improves the performance of the STLF based on neuro-fuzzy models. The viability of the proposed method is demonstrated through the simulation studies performed on the load data collected by Korea Electric Power Corporation (KEPCO) in 1996 and 1997.