• Proceedings of the Korean Society of Precision Engineering Conference
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• 1992.04a
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• pp.157-161
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• 1992

Up to now, there has been a lot of researches on the sliding mode control which has the insensitive characteristics to the variations of plant parameters, nonlinearities and external disturbances. One dificulty in applying the sliding mode control is the need for the knowledge of the full-state vector. The use of state observer is a natural step towards the relaxation of this condition. However, the exact plant-modeling is assumed to be known. Recently, there has been a remarkable advance in the microprocessor and one can construct the controller which could not realize due to hardware restriction in the past. Therefore in this paper, the PID sliding mode controller which has only one output feedback signal is suggested by means of microprocessor and the performance of electro-hydraulic servosystem compersated with this controller is proved.

• Journal of Electrical Engineering and information Science
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• v.2 no.4
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• pp.53-58
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• 1997

In this paper, the power system stabilizer(PSS) using the sliding mode observer-model following(SMO-MF) with closed-loop feedback (CLF) for single machine system is extended to multimachine system. This a multimachine SMO-MF PSS for unmeasureable plant state variable is obtained by combining the sliding mode-model following(SM-MF) including closed-loop feedback(CLF) with the full-order observer(FOO). And the estimated control input for unmeasurable plant sate variables is derived by Lyapunov's second method to determine a control input that keeps the system stable. Time domain simulation results for the torque angle and for the angular velocity show that the proposed multimachine SMO-MF PSS including CLF for unmeasurable plant sate variables is able to damp out the low frequency oscillation and to achieve asymptotic tracking error between the reference model state at different initial conditions and at step input.

• Journal of Power Electronics
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• v.18 no.3
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• pp.766-777
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• 2018

In this paper, a sliding mode and proportional plus integral (SM-PI) control combined with self-sustained phase shift modulation (SSPSM) for LLC resonant converters is presented. The proposed control scheme improves the transient response while preserving good steady-state performance. An averaged large signal model of an LLC converter with the ZVS modulation technique is developed for the SM control design. The sliding surface is obtained based on the input-output linearization concept. A system identification method is adopted to obtain the transform function of the LLC resonant converter, which is used to design the PI control. In order to reduce the inherent chattering problem in the steady state, the combined SM-PI control strategy is derived with fuzzy control, where the SM control is responsive during the transient state while the PI control prevails in the steady state. The combination of SSPSM and the SM-PI control provides ZVS operation, robustness and a fast transient response against step load variations. Simulation and experimental results validate the theoretical analysis and the attractive features of the proposed scheme.

• Transactions of Materials Processing
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• v.11 no.5
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• pp.447-456
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• 2002

A study has been made to investigate the boundary sliding and its accommodation mode with respect to the variation of $\alpha$$_2$/$eta$ volume fraction during superplastic deformation of two-phase Ti$_3$Al-xNb intermetallics. Step strain rate and load relaxation tests have been performed at 950, 970 and 99$0^{\circ}C$ to obtain the flow stress curves and to analyze the deformation characteristics by the theory of inelastic deformation. The results show that the grain matrix deformation and boundary sliding of the three intermetallics containing 21, 50 and 77% in $eta$ volume fractions are well described by the plastic deformation and viscous flow equations. Due to the equal accommodation of both $a^2$ and $\beta$ phases, the accommodation modes for fine-grained materials are in good agreement with the iso-strain rate models. The sliding resistance analyzed for the different boundaries is the lowest in the $\alpha$$_2$/$\alpha$$_2$ boundary, and increases in the order of $\alpha$$_2$/$\alpha$$_2$<< $\alpha$$_2$/$\beta$ = $\beta$/$\beta$, which plays an important role in controlling the superplasticity of the alloys with the various $\alpha$$_2$/$\beta$ phase ratio.

• Korean Journal of Remote Sensing
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• v.1 no.1
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• pp.5-27
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• 1985

A hill-sliding technique was devised to extract Gaussian clusters from the multivariate probability density estimates of sample data for the first step of iterative unsupervised classification. The underlying assumption in this approach was that each cluster possessed a unimodal normal distribution. The key idea was that a clustering function proposed could distinguish elements of a cluster under formation from the rest in the feature space. Initial clusters were extracted one by one according to the hill-sliding tactics. A dimensionless cluster compactness parameter was proposed as a universal measure of cluster goodness and used satisfactorily in test runs with Landsat multispectral scanner (MSS) data. The normalized divergence, defined by the cluster divergence divided by the entropy of the entire sample data, was utilized as a general separability measure between clusters. An overall clustering objective function was set forth in terms of cluster covariance matrices, from which the cluster compactness measure could be deduced. Minimal improvement of initial data partitioning was evaluated by this objective function in eliminating scattered sparse data points. The hill-sliding clustering technique developed herein has the potential applicability to decomposition of any multivariate mixture distribution into a number of unimodal distributions when an appropriate diatribution function to the data set is employed.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.8A
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• pp.1218-1226
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• 1999

In decoding the turbo codes, the sliding window BCJR algorthm, derived from the BCJR algorithm, permits a continuous decoding of the coded sequence without requiring trellis fermination of the constituent codes and uses reduced memory span. However, the number of computation required is greater than that of BCJR algorithm and no study on the effect of the window length has been reported. In this paper, we propose an eddicient sliding window type scheme which maintains the advantages of the conventional sliding window algorithm, reduces its computational burdens, and improves is BER performance. A guideline is first presented to determine the proper window length and then a computationally efficient sliding window BCJR algorithm is obtained by allowing the window to be forwarded in multi-step. Simulation results show that the proposed scheme outperforms the conventional sliding window BCJR algorithm with reduced complexity. It gains 0.1dB SNR improvements over the conventional method for the constraint length 3 and BER $10^{-4}$

• The Journal of the Acoustical Society of Korea
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• v.41 no.2
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• pp.159-165
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• 2022

In this paper, a frame synchronization structure for the Binary Phase Shift Keying (BPSK) modulation method in underwater acoustic communication was proposed. The proposed frame synchronization structure is largely divided into two. First, the approximate position and frequency offset of the frame are obtained by non-coherent correlation and sliding Fast Fourier Transform (FFT) method. Second, after compensating for the frequency error to the received signal, the exact position of the frame is obtained by coherent correlation method. Maritime experiments were conducted to confirm the performance of the 2-STEP frame synchronization structure. It was showed that the limitations of the non-coherent correlation and sliding FFT method can be verified when the power of the received signal was greatly reduced due to the channel characteristics. As a result, stable frame synchronization could be obtained by compensating for the frequency error and then using the coherent correlation method.

• 대한방사선방어학회:학술대회논문집
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• 2011.11a
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• pp.102-103
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• 2011

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.3
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• pp.314-318
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• 2006

This study is to analyze the intensity of welding part by simulating the dynamic procedure during the fracture of plates with spot welding. The upper and tower plates attached with spot welding can be seen to fall apart at the elapsed time of 0.64 ms after the upper plate is stretched from the lower plate. The maximum von Mises stress is shown at the welding part in the mid of upper and lower plates. The internal energy decreases largely and the kinetic energy increases suddenly near the elapsed time of 0.64 ms when welding part breaks down. The sliding energy decreases with step-by-step style as the time elapses. The value of this energy becomes 0 at the elapsed time of 0.2 ms and on the contrary, two plates stick each other as this value becomes a minus after this time.

• Smart Media Journal
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• v.9 no.4
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• pp.26-35
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• 2020

Attempts to predict future stock prices have been studied steadily since the past. However, unlike general time-series data, financial time-series data has various obstacles to making predictions such as non-stationarity, long-term dependence, and non-linearity. In addition, variables of a wide range of data have limitations in the selection by humans, and the model should be able to automatically extract variables well. In this paper, we propose a 'sliding time step normalization' method that can normalize non-stationary data and LSTM autoencoder to compress variables from all variables. and 'moving transfer learning', which divides periods and performs transfer learning. In addition, the experiment shows that the performance is superior when using as many variables as possible through the neural network rather than using only 100 major financial variables and by using 'sliding time step normalization' to normalize the non-stationarity of data in all sections, it is shown to be effective in improving performance. 'moving transfer learning' shows that it is effective in improving the performance in long test intervals by evaluating the performance of the model and performing transfer learning in the test interval for each step.