• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.4
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• pp.449-455
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• 2011

In this paper, a time-delay problem of a tele-operated control system is investigated and compensated by neural network. The smith predictor requires an exact system model to deal with a time-delay in the system. To compensate for modeling errors in the configuration of the Smith predictor, a neural network approach is presented. Based on forming the Smith predictor structure, the radial basis function(RBF) neural network estimator is used. Simulation and experimental studies are conducted to show the functionality of the proposed method.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.4
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• pp.112-118
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• 2010

This paper describes a noise suppression system based on a normalization method using a time-delay neural network and line spectrum pair having a parameter of frequency domain. First, a time-delay neural network is trained using line spectrum pair values of noisy speech signals obtained by linear prediction analysis. After trained the time-delay neural network, the proposed system enhances speech signals that are degraded by a background noise. Accordingly, the proposed time-delay neural network restores from the line spectrum pair values of noisy speech signals to the line spectrum pair values of clean speech signals. It is confirmed that this system is effective for speech signals degraded by a background noise, judging from spectral distortion measurement.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.446-449
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• 1997

We propose a control law in discrete time domain of the bilateral feedback teleoperation system using neural network and the reference model type of adaptive control. Different from traditional teleoperation systems, the transmission time delay irregularly changes. The proposed control method controls master and slave systems through identification of master and slave models using neural networks.

• Proceedings of the KOSOMBE Conference
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• v.1994 no.12
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• pp.165-168
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• 1994

We proposed a new model of neural network, called Time Delay Counter-Propagation Neural network (TDCPN). This model is combined properly by the merits of Time Delay Neural Network (TDNN) structure and those of Counter - Propagation Neural network (CPN) learning rule, so that increase recognition rate but decrease total teaming time. And we use this model to simulate classification of EMG signals, and compare the recognition rate and teaming time with those of another neural network model. As a result of simulation, the proposed model is proved to be very effective.

• Journal of Korea Multimedia Society
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• v.6 no.5
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• pp.778-787
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• 2003

Intrusion detection systems based on rules are not efficient for mutated attacks, because they need additional rules for the variations. In this paper, we propose an intrusion detection system using the time delay neural network. Packets on the network can be considered as gray images of which pixels represent bytes of them. Using this continuous packet images, we construct a neural network classifier that discriminates between normal and abnormal packet flows. The system deals well with various mutated attacks, as well as well known attacks.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.3 s.303
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• pp.121-128
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• 2005

On the research field for speech signal, neural network mainly uses for the category classification in speech recognition and applies to signal processing. Accordingly, this paper proposes a noise reduction system using a time-delay neural network, which implements the mapping from the space of speech signal degraded by noise to the space of clean speech signal. It is confirmed that this method is effective for speech degraded not only by white noise but also by colored noise using the noise reduction system, which restores the amplitude component of fast Fourier transform.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.94-97
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• 1999

In this paper, a new real-time neural network connection admission controller is proposed. The proposed controller measures traffic flows, cell loss rate and cell delay periodically each classes. The Neural network learns the relation between those measured information and service quality by real-time. Also the proposed controller uses the DWRR multiplexer with buffer dedicated to every traffic source in order to measure the delay that cells experience in buffer. Experimental result shows that the proposed method can control effectively heterogeneous traffic sources with diverse QoS requirement.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.4
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• pp.419-426
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• 1999

A novel motion analysis system is presented in this paper. The proposed system is inspired by processing functions observed in the fly visual system, which detects changes in input light intensities, determines motion on both the local and the wide-field levels. The system has several differences from conventional motion analysis system. First, conventional systems usually focused on matching similar feature or optical flow, but neural network is applied in this system. Back propagation is used by learning method, and Tine Delay Neural Network (TDNN) is also used as analysis method. Second, while conventional systems usually limited on only two frames of sequence, the proposed system accept multiple frames of sequence. The experimental results showed a 94.7% correct rate with a speed of 71.47 milli seconds for real and synthetic images.

• International Journal of Control, Automation, and Systems
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• v.6 no.3
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• pp.444-452
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• 2008

We propose an intelligent predictive control approach for a nonlinear networked control system (NCS) with time-varying delay and random observation. The control is given by the sum of a nominal control and a corrective control. The nominal control is determined analytically using a linearized system model with fixed time delay. The corrective control is generated online by a neural network optimizer. A Markov chain (MC) dynamic Bayesian network (DBN) predicts the dynamics of the stochastic system online to allow predictive control design. We apply our proposed method to a satellite attitude control system and evaluate its control performance through computer simulation.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.491-494
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• 2006

Kalman filtering (KF) is hard to be applied to the SINS (Strap-down Inertial Navigation System)/RDSS (Radio Determination Satellite Service) integrated navigation system directly because the time delay of RDSS positioning in active mode is random. BP (Back-Propagation) Neuron computing as a powerful technology of Artificial Neural Network (ANN), is appropriate to solve nonlinear problems such as the random time delay of RDSS without prior knowledge about the mathematical process involved. The new algorithm betakes a BP neural network (BPNN) and velocity feedback to aid KF in order to overcome the time delay of RDSS positioning. Once the BP neural network was trained and converged, the new approach will work well for SINS/RDSS integrated navigation. Dynamic vehicle experiments were performed to evaluate the performance of the system. The experiment results demonstrate that the horizontal positioning accuracy of the new approach is 40.62 m (1 ${\sigma}$), which is better than velocity-feedback-based KF. The experimental results also show that the horizontal positioning error of the navigation system is almost linear to the positioning interval of RDSS within 5 minutes. The approach and its anti-jamming analysis will be helpful to the applications of SINS/RDSS integrated systems.