• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2008.06a
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• pp.525-526
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• 2008

For measuring nonlinear Vehicle Modeling based on 7-Depth Sensor, the neural networks are proposed m adaptive and in realtime. The structure of it is similar to recurrent neural networks; a delayed output as the input and a delayed error between the output of plant and neural networks as a bias input. In addition, we compute the desired value of hidden layer by an optimal method instead of transfering desired values by backpropagation and each weights are updated by RLS(Recursive Least Square). Consequently, this neural networks are not sensitive to initial weights and a learning rate, and have a faster convergence rate than conventional neural networks. This new neural networks is Error Estimated Neural Networks. We can estimate nonlinear models in realtime by the proposed networks and control nonlinear models.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.53 no.4
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• pp.195-200
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• 2004

Many kinds of speed sensorless control system of induction motor had been developed. But it is difficult to implement at the real system because of complex algorithm and equations. This paper investigates a novel speed sensorless control of induction motor using neural networks. The proposed control strategy is based on neural networks using stator current and output of neural model based on state observer. The errors between the stator current and the output of neural model are back-propagated to adjust the rotor speed, so that adaptive state variable will coincide with the desired state variable. This algorithm may overcome several shortages of conventional model, such as integrator problems, small EMF at low speed and relatively large sensitivity of stator resistance variation. Also, this paper presents a newly developed optimal equation about the momentum constant and the learning rate. The proposed algorithms are verified through simulation.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.35 no.9
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• pp.380-387
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• 1986

We have developed a dual control algorithm by means of innovations approach and established the stability of dual by introducing the pole-placement method suggested by Berger on the non-dual control. The dual controller realizing this algorithm decreases control loss sharply when compared with that of a non-dual controller, and shows the characteristics of suppressing the output deviation in transient state effectively. The total control energy and the accumulated square misdistance of this dual controller are shown to be 1-10% and 0.1-10% of those of CE control, respectively. Consequently this controller solves the non-minimum phase problem encountered when discretizing the system equation, and can be used to overcome the uncertainty of system effectively by adjusting the learning rate of the controller.

• Environmental Engineering Research
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• v.25 no.4
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• pp.545-553
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• 2020

Rainfall-Runoff modeling plays a crucial role in various aspects of water resource management. It helps significantly in resolving the issues related to flood control, protection of agricultural lands, etc. Various Machine learning and statistical-based algorithms have been used for this purpose. These techniques resulted in outcomes with an acceptable rate of success. One of the pertinent machine learning algorithms namely Adaptive Neuro Fuzzy Inference System (ANFIS) has been reported to be a very effective tool for the purpose. However, the computational complexity of ANFIS is a major hindrance in its application. In this paper, we resolved this problem of ANFIS by incorporating one of the evolutionary algorithms known as Particle Swarm Optimization (PSO) which was used in estimating the parameters pertaining to ANFIS. The results of the modified ANFIS were found to be satisfactory. The performance of this modified ANFIS is then compared with conventional ANFIS and another popular statistical modeling technique namely ARIMA model with respect to the forecasting of runoff. In the present investigation, it was found that proposed PSO-ANFIS performed better than ARIMA and conventional ANFIS with respect to the prediction accuracy of runoff.

• ETRI Journal
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• v.38 no.6
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• pp.1064-1073
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• 2016

An adaptive speech streaming method to improve the perceived speech quality of a software-based multipoint control unit (SW-based MCU) over IP networks is proposed. First, the proposed method predicts whether the speech packet to be transmitted is lost. To this end, the proposed method learns the pattern of packet losses in the IP network, and then predicts the loss of the packet to be transmitted over that IP network. The proposed method classifies the speech signal into different classes of silence, unvoiced, speech onset, or voiced frame. Based on the results of packet loss prediction and speech classification, the proposed method determines the proper amount and bitrate of redundant speech data (RSD) that are sent with primary speech data (PSD) in order to assist the speech decoder to restore the speech signals of lost packets. Specifically, when a packet is predicted to be lost, the amount and bitrate of the RSD must be increased through a reduction in the bitrate of the PSD. The effectiveness of the proposed method for learning the packet loss pattern and assigning a different speech coding rate is then demonstrated using a support vector machine and adaptive multirate-narrowband, respectively. The results show that as compared with conventional methods that restore lost speech signals, the proposed method remarkably improves the perceived speech quality of an SW-based MCU under various packet loss conditions in an IP network.

• Nuclear Engineering and Technology
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• v.56 no.9
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• pp.3740-3749
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• 2024

To reduce the losses caused by aging failure of insulation gate bipolar transistor (IGBT), which is the core components of nuclear power plant rod position indicating and rod control (RPC) system. It is necessary to conduct studies on its life prediction. The selection of IGBT failure characteristic parameters in existing research relies heavily on failure principles and expert experience. Moreover, the analysis and learning of time-domain degradation data have not been fully conducted, resulting in low prediction efficiency as the monotonicity, time correlation, and poor anti-interference ability of extracted degradation features. This paper utilizes the advantages of the stacked denoising autoencoder(SDAE) network in adaptive feature extraction and denoising capabilities to perform adaptive feature extraction on IGBT time-domain degradation data; establishes a long-short-term memory (LSTM) prediction model, and optimizes the learning rate, number of nodes in the hidden layer, and number of hidden layers using the Gray Wolf Optimization (GWO) algorithm; conducts verification experiments on the IGBT accelerated aging dataset provided by NASA PCoE Research Center, and selects performance evaluation indicators to compare and analyze the prediction results of the SDAE-LSTM model, PSOLSTM model, and BP model. The results show that the SDAE-LSTM model can achieve more accurate and stable IGBT life prediction.

• Journal of the Korean Society for Precision Engineering
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• v.5 no.4
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• pp.32-38
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• 1988

Apaptive cintrol of machine tool is aimed to change cutting state satis- factorily without aid of a machine operator, if the cuting state is abnomal such as formation of tangled ribbon type chip, built-up edge and generation of chattering and so on. Among these the recognition of chip type is one of the most important since it has imlications relate to : 1. Safety of operator 2. Stoppage of work due to entanglment in tool and workpiece of chip 3. Problem of producted chip control In this paper the chip type is discriminatied by the pattern recognition technique. It is found that the power spectrum of cutting force for each chip type has it's own special pattern. Linear discriminant function for the recognition of the chip type is obtained by learning process. The discriminant function can be the basis of adaptive control for the rate of success of recognition by pattern recognition technique is at leasthigher than 83%.

• Journal of the Korea Society of Computer and Information
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• v.10 no.6 s.38
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• pp.187-194
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• 2005

The background subtraction method is mainly used for the real-time extraction and tracking of moving objects from image sequences. In the outdoor environment, there are many changeable environment factors such as gradually changing illumination, swaying trees and suddenly moving objects , which are to be considered for an adaptive processing. Normally, GMM(Gaussian Mixture Model) is used to subtract the background by considering adaptively the various changes in the scenes, and the adaptive GMMs improving the real-time Performance were Proposed and worked. This paper, for on-line background subtraction, employed the improved adaptive GMM, which uses the small constant for learning rate a and is not able to speedily adapt the suddenly movement of objects, So, this paper Proposed and evaluated the dynamic control method of a using the adaptive selection of the number of component distributions and the global variances of pixel values.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.747-752
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• 2006

In this paper, the adaptive fuzzy sliding mode controller with two systems is designed. The existing sliding mode controller used to $approximation{\^{u}}(t)$ with discrete sgn function and sat function for keeping the state trajectories on the sliding surface[1]. The proposed controller decrease the disturbance for uncertain control gain and This paper is concerned with an Adaptive Fuzzy Sliding Mode Control(AFSMC) that the fuzzy systems ate used to approximate the unknown functions of nonlinear system. In the adaptive fuzzy system, we adopt the adaptive law to approximate the dynamics of the nonlinear plant and to adjust the parameters of AFSMC. The stability of the suggested control system is proved via Lyapunov stability theorem, and convergence and robustness properties ate demonstrated. Futhermore, fuzzy tuning improve tracking abilities by changing some sliding conditions. In the traditional sliding mode control, ${\eta}$ is a positive constant. The increase of ${\eta}$ has led to a significant decrease in the rise time. However, this has resulted in higher overshoot. Therefore the proposed ${\eta}$ tuning AFSMC improve the performances, so that the controller can track the trajectories faster and more exactly than ordinary controller. The simulation results demonstrate that the performance is improved and the system also exhibits stability.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.9A
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• pp.1329-1338
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• 1999

In this paper, an efficient signal interference control technique to improve the convergence speed of LMS algorithm is introduced. The convergence characteristics of the proposed algorithm, whose coefficients are multiply adapted in a symbol time period by recycling the received data, are analyzed to prove theoretically the improvement of convergence speed. According as the step-size parameter $\mu$ is increased, the rate of convergence of the algorithm is controlled. Also, a increase in the step-size parameter $\mu$ has the effect of reducing the variation in the experimentally computed learning curve. Increasing the eigenvalue spread has the effect of controlling down the rate of convergence of the adaptive equalizer and also increasing the steady-state value of the mean squared error and also demonstrate the superiority of signal interference control to the filter algorithm increasing convergence speed by (B+1) times due to the data-recycling LMS technique.