• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.25-25
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• 2023

The ensemble optimal interpolation (EnOI) scheme is a sub-optimal alternative to the ensemble Kalman filter (EnKF) with a reduced computational demand making it potentially more suitable for operational applications. Since only one model is integrated forward instead of an ensemble of model realizations, online estimation of the background error covariance matrix is not possible in the EnOI scheme. In this study, we investigate two Gaussian noise based ensemble generation strategies to produce dynamic covariance matrices for assimilation of water level observations into a distributed hydrological model. In the first approach, spatially correlated noise, sampled from a normal distribution with a fixed fractional error parameter (which controls its standard deviation), is added to the model forecast state vector to prepare the ensembles. In the second method, we use an adaptive error estimation technique based on the innovation diagnostics to estimate this error parameter within the assimilation framework. The results from a real and a set of synthetic experiments indicate that the EnOI scheme can provide better results when an optimal EnKF is not identified, but performs worse than the ensemble filter when the true error characteristics are known. Furthermore, while the adaptive approach is able to reduce the sensitivity to the fractional error parameter affecting the first (non-adaptive) approach, results are usually worse at ungauged locations with the former.

• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.981-988
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• 2018

In order to acquire an accurate TOF, this paper proposes a method that produces TOF by using a mathematical model for the envelope of the received signal obtained from a system dynamic model of ultrasonic transducer. The proposed method estimates the arrival time of the received signal retrospectively by comparing its wave form obtained after triggering point with its mathematical envelope model. Experimental result shows that the error due to variation of triggering point can be dramatically decreased by implementing the proposed method.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.5
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• pp.838-847
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• 1994

Although there is an analytical proof of modeling capability of the neural network, the convergency error in nonlinearity modeling is inevitable, since the steepest descent based practical larning algorithms do not guarantee the convergency of modeling error. Therefore, it is difficult to apply the neural network to control system in critical environments under an on-line learning scheme. Although the convergency of modeling error of a neural network is not guatranteed in the practical learning algorithms, the convergency, or boundedness of tracking error of the control system can be achieved if a proper feedback control law is combined with the neural network model to solve the problem of modeling error. In this paper, the neural network is introduced for compensating a system uncertainty to control a nonlinear dynamic system. And for suppressing inevitable modeling error of the neural network, an iterative neural network learning control algorithm is proposed as a virtual on-line realization of the Adaptive Variable Structure Controller. The efficiency of the proposed control scheme is verified from computer simulation on dynamics control of a 2 link robot manipulator.

• Proceedings of the KSR Conference
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• 2009.05a
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• pp.1557-1565
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• 2009

According to the GM/RT2141, to assess the safety of vehicle, the validated the vehicle dynamic model should be applied. The validation of the vehicle model is against the static test, some kind of vehicle type test results have been used to determine the suspension characteristics and the vehicle dynamic characteristics. To validate the vehicle model and the test results, first the test results has been analysed as to specified the suspension characteristics and than the parameters to related with the test result has to be adjusted to show the same results of the test. In this process the parameters of vehicle model have been determined to show the coherence of the two results of the simulation and the test by trial & error. In this report, the optimization tool has been introduced in this model validation process and shows the efficient and well validated model.

• Journal of Sensor Science and Technology
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• v.13 no.1
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• pp.1-11
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• 2004

Due to various types of errors added to dynamic weight measurement data, proper methods to reduce measurement errors are required to produce reliable weights. To cope with parasitic types of errors in real systems, information provided by the various sensors is utilized and combined in such a way to reduce the measurement errors of load cells. In addition to four channels of load cells from a trailer, an accelerometer is used to obtain the information to compensate the error induced from vertical movement of the vehicle due to the variation of ground level. A model trailer system is run to verify the effectiveness of the proposed method to reduce noise of dynamic weight measurements. Experiments show that the processed error magnitudes of less than 20 g can be obtained for 10 Kg experimental loads.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.1
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• pp.37-46
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• 2020

The paper examines the dynamic relationship of domestic credit and stock market liquidity on the economic growth of the Philippines from 1995 to 2018 applying the autoregressive distributed lag (ARDL) bounds testing approach to cointegration, together with Granger causality test based on vector error correction model (VECM). The ARDL model indicated a long-run relationship of domestic credit and stock market liquidity on GDP growth. When the GDP per capita is the dependent variable there is weak cointegration. Also, the Johansen cointegration test confirmed the existence of long-run relationship of domestic credit and stock market liquidity both on GDP growth and GDP per capita. The VECM concludes a long-run causality running from domestic credit and stock market liquidity to GDP growth. At levels, domestic credit has significant short-run causal relationship with GDP growth. As for stock market liquidity at first lag, has significant short-run causal relationship with GDP growth. With regards to VECM for GDP per capita, domestic credit and stock market liquidity indicates no significant dynamic adjustment to a new equilibrium if a disturbance occurs in the whole system. At levels, the results indicated the presence of short-run causality from stock market liquidity and GDP per capita. The CUSUMSQ plot complements the findings of the CUSUM plot that the estimated models for GDP growth and GDP per capita were stable.

• Transactions of the Korean Society of Mechanical Engineers
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• v.13 no.1
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• pp.29-39
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• 1989

In this work, a numerical and experimental study was done to get an optimum cam profile considering dynamic characteristics of a cam-valve system. First of all, a four degree of freedom dynamic model was set up for an OHV type cam-valve acceleration while not modifying original cam shape greatly. Also another optimization which aims to enlarge the valve displacement area while reducing the peak valve acceleration, was tried. The optimized cam profile was tested experimentally and found that the measured valve displacement and pushrod force show only very small error from the analytically predicted model simulation results.

• The KIPS Transactions:PartC
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• v.12C no.4 s.100
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• pp.519-524
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• 2005

Due to various types of errors added to dynamic weight measurement data, proper methods to reduce measurement errors are required to produce reliable weights. It is very difficult to reduce the measurement error due to excessive oscillation of the system. To cope with parasitic types of errors in real systems, information provided by the photo sensors are utilized and combined in such a way to reduce the measurement errors of load cells. In addition to four channels of load cells from a model trailer, photo sensors are used to obtain the information to compensate the error induced from vertical movement of the vehicle due to the variation of ground level. A model trailer system is run to verify the effectiveness of the proposed method to reduce noise of dynamic weight measurements.

• Environmental and Resource Economics Review
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• v.12 no.2
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• pp.327-346
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• 2003

Unlike previous studies on the causal relationship between energy consumption and economic growth, this paper analyses the dynamic causal relationship between these variables using the dynamic vector using Johansen's multiple cointegration procedure, dynamic vector error-correction model and impulse response function. The empirical results show that while the energy consumption to a shock in income responds positively, the income responds positively to the shocks in energy consumption in the first place and then the responses become negative. We also find that the impact of energy consumption shock on the income is short-lived and causes higher inflationary pressure.

• International Journal of Control, Automation, and Systems
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• v.6 no.6
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• pp.948-953
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• 2008

In high dynamic situations, the GPS carrier tracking loop requires a wide bandwidth to track a carrier signal because the Doppler frequency changes more rapidly with time. However, a wide bandwidth allows noises within the bandwidth of the tracking loop to pass through the loop filter. As these noises are used in the numerical controlled oscillator(NCO), the carrier tracking loop of a GPS receiver shows a degraded performance in high dynamic situations. To solve this problem, an adaptive two-stage Kalman filter, which offers the NCO a less noisy phase error, can be used. This filter is based on a carrier phase dynamic model and can adapt to an incomplete dynamic model and a quickly changed Doppler frequency. The performance of the proposed tracking loop is verified by several simulations.