• Journal of Drive and Control
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• v.17 no.1
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• pp.44-50
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• 2020

For the requirement of accurate tracking control and the safety of physical human-robot interaction, torque control is basically desirable for humanoid robots. Because of the complexity of humanoid robot dynamics, the TDC (time-delay control) is practical because it does not require a dynamic model. However, there occurs a considerable error due to discontinuous non-linearities. To solve this problem, the TDC-FLC (fuzzy logic compensator) is applied to humanoid robots. The applied controller contains three factors: a TDE (time-delay estimation) factor, a desired error dynamic factor, and FLC to suppress the TDE error. The TDC-FLC is easy to execute because it does not require complicated humanoid dynamic calculations and the heuristic fuzzy control rules are intuitive. TDC-FLC is implemented on the whole body of a humanoid, not on biped legs even though it is performed by a virtual humanoid robot. The simulation results show the validity of the TDC-FLC for humanoid robots.

• Journal of the Korean Society for Precision Engineering
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• v.24 no.12
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• pp.57-64
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• 2007

There have been many researches for optimal controllers in multivariable systems, and they generally use accurate linear models of the plant dynamics. Real systems, however, contain nonlinearities and high-order dynamics that may be difficult to model using conventional techniques. Therefore, it is necessary a PID gain tuning method without explicit modeling for the multivariable plant dynamics. The PID tuning method utilizes the sign of Jacobian and gradient descent techniques to iteratively reduce the error-related objective function. This paper, especially, focuses on the role of I-controller when there is a steady state error. However, it is not easy to tune I-gain unlike P- and D-gain because I-controller is mainly operated in the steady state. Simulations for an overhead crane system with dynamic friction show that the proposed PID-LC algorithm improves controller performance, even in the steady state error.

• Journal of Advanced Marine Engineering and Technology
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• v.20 no.3
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• pp.286-286
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• 1996

A neural predictive tracking system for the control of structure-unknown dynamic system is presented. The control system comprises a neural network modelling mechanism for the the forward and inverse dynamics of a plant to be controlled, a feedforward controller, feedback controller, and an error prediction mechanism. The feedforward controller, a neural network model of the inverse dynamics, generates feedforward control signal to the plant. The feedback control signal is produced by the error prediction mechanism. The error predictor adopts the neural network models of the forward and inverse dynamics. Simulation results are presented to demonstrate the applicability of the proposed scheme to predictive tracking control problems.

• Journal of Advanced Marine Engineering and Technology
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• v.20 no.3
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• pp.154-161
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• 1996

A neural predictive tracking system for the control of structure-unknown dynamic system is presented. The control system comprises a neural network modelling mechanism for the the forward and inverse dynamics of a plant to be controlled, a feedforward controller, feedback controller, and an error prediction mechanism. The feedforward controller, a neural network model of the inverse dynamics, generates feedforward control signal to the plant. The feedback control signal is produced by the error prediction mechanism. The error predictor adopts the neural network models of the forward and inverse dynamics. Simulation results are presented to demonstrate the applicability of the proposed scheme to predictive tracking control problems.

• MALSORI
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• no.56
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• pp.207-223
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• 2005

The goal of language model adaptation is to improve the background language model with a relatively small adaptation corpus. This study presents a language model adaptation technique where additional text data for the adaptation do not exist. We propose the information retrieval (IR) technique with N-gram language modeling to collect the adaptation corpus from baseline text data. We also propose to use a dynamic language model interpolation coefficient to combine the background language model and the adapted language model. The interpolation coefficient is estimated from the word hypotheses obtained by segmenting the input speech data reserved for held-out validation data. This allows the final adapted model to improve the performance of the background model consistently The proposed approach reduces the word error rate by $13.6\%$ relative to baseline 4-gram for two-hour broadcast news speech recognition.

• Journal of Positioning, Navigation, and Timing
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• v.1 no.1
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• pp.51-57
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• 2012

In this paper, design of an error model is presented in which the bias characteristic of the MEMS IMU is taken into consideration for performance enhancement of the MEMS IMU-based GPS/INS integrated navigation system. The drift bias of the MEMS IMU is modeled as a 1st-order Gauss-Markov (GM) process, and the autocorrelation function is obtained from the collected IMU data, and the correlation time is estimated from this. Prior to obtaining the autocorrelation function, the noise of IMU data is eliminated based on wavelet. As a result of simulation, it is represented that the parameters of error model can be estimated correctly only when a proper denoising is performed according to dynamic behavior of drift bias, and that the integrated navigation system based on error model, in which the drift bias is considered, provides more correct navigation performance compared to the integrated navigation system based on error model in which the drift bias is not considered.

• The Journal of Asian Finance, Economics and Business
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• v.5 no.4
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• pp.85-94
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• 2018

The cultural industry is treated as the sunrise industry in modern society. It has taken an increasing role in promoting the economic growth. Due to this, this paper attempts to explore the dynamic relationship between cultural industry and the economic growth. On the grounds of Cobb-Douglas production function, the cultural industry is regarded as a determinant such as the labor input and the capital input to impact the economic growth. Meanwhile, the quarterly datum form 2000-Q1 to 2017-Q4 are employed to perform an empirical analysis via the vector error correction model. The GDP is treated as an independent variable. The input of capital, the input of labor and the total input of cultural industry are treated as dependent variables. Furthermore, a menu of statistical approaches such as the co-integration test and the impulse response function will be used to testify the dynamic relationship between cultural industry and economic growth. Via the Johansen co-integration test, the results report that the cultural industry has a obviously positive effect on economic growth. Through the vector error correction estimation, the results also report that the cultural industry also has a significantly positive effect on economic growth, but less than that of the Johansen co-integration test. This paper provides a view that the cultural industry is a kind of a determinant to promote the economic growth. Therefore, the China's government should pay much attention to the cultural industry construction.

• International Journal of Highway Engineering
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• v.16 no.3
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• pp.35-41
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• 2014

PURPOSES : The dynamic modulus for a specimen can be determined by using either the non-destructed or destructed testing method. The Impact Resonance Testing (IRT) is the one of the non-destructed testing methods. The MTS has proved the source credibility and has the disadvantages which indicate the expensive equipment to operate and need a lot of manpower to manufacture the specimens because of the low repeatability with an experiment. To overcome these shortcomings from MTS, the objective of this paper is to compare the dynamic modulus obtained from IRT with MTS result and prove the source credibility. METHODS : The dynamic modulus obtained from IRT could be determined by using the Resonance Frequency (RF) from the Frequency Response Function (FRF) that derived from the Fourier Transform based on the Frequency Analysis of the Digital Signal Processing (DSP)(S. O. Oyadigi; 1985). The RF values are verified from the Coherence Function (CF). To estimate the error, the Root Mean Squared Error (RMSE) method could be used. RESULTS : The dynamic modulus data obtained from IRT have the maximum error of 8%, and RMSE of 2,000MPa compared to the dynamic modulus measured by the Dynamic Modulus Testing (DMT) of MTS testing machine. CONCLUSIONS : The IRT testing method needs the prediction model of the dynamic modulus for a Linear Visco-Elastic (LVE) specimen to improve the suitability.

• Environmental and Resource Economics Review
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• v.11 no.3
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• pp.397-414
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• 2002

This paper presents an empirical analysis of exchange rate volatility, petroleum's import price and industrial production on petroleum imports. The GARCH framework is used to measure the exchange rate volatility. One of the most appealing features of the GARCH model is that it captures the volatility clustering phenomenon. We found one long-run relationship between petroleum imports, import price, industrial production, and exchange rate volatility using Johansen's multivariate cointegration methodology. Since there exists a cointegrating vector, therefore, we employ an error correction model to examine the short-run dynamic linkage, finding that the exchange rate volatility performs a key role in the short-run. This paper also apply impulse-response functions to provide the dynamic responses of energy consumption to the exchange rate volatility. The results show that the response of energy consumption to exchange rate volatility declines at the first month and dies out very quickly.

• Journal of Advanced Marine Engineering and Technology
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• v.30 no.8
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• pp.885-893
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• 2006

A dynamic model of turbo air compressor having multi-helical gear pairs is developed by transfer matrix method. The model accounts for the shaft and bearing flexibilities, gyroscopic effects and the force couplings among the transverse, torsion, and axial motions due to gearing. The program which can be used to analyze and predict the vibrational characteristics by the mass unbalance of the rotors and gear transmission error of turbo compressor is developed with this system model We expect this developed program to contribute the reduction of the vibration/noise on turbo compressor in the field of both design and manufacturing and can be used as a basic sub-program for CAD/CAM of low-noised gear teeth also.