• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.05a
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• pp.837-842
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• 2004

The positions of rotating sound sources have been localized by experiments with the Doppler effects removed. In order to do-Dopplerize the sound signals emitted from moving sources, two kinds of signal reconstruction methods were applied. One is the forward propagation method and the other is the backward propagation method. Forward propagation method analyze the source emission time based on the instantaneous distance between sensors and the assumed source position, then the signals are reconstructed with respect to the emission time. On the other hand, the backward method uses time delay to do-Dopplerize the acquired data for the received time of reference. In both techniques, the reconstructed signal data were processed using beamforming algorithm to produce power distributions at the frequency of interest. Experiments have been carried out for varying frequencies, rotating speeds and the object distances. Forward propagation method has shown better performance in locating source position than the backward propagation method.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.9
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• pp.49-61
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• 1996

Back-propagation(BP) algorithm needs a lot of time to train the artificial neural network (ANN) to get high accuracy level in classification tasks. So there have been extensive researches to process back-propagation algorithm on parallel processors. This paper prsents a linear systolic array which calculates forward-backward propagation of BP algorithm at the same time using effective space-time transformation and PE structure. First, we analyze data flow of forwared and backward propagations and then, represent the BP algorithm into data dapendency graph (DG) which shows parallelism inherent in the BP algorithm. Next, apply space-time transformation on the DG of ANN is turn with orthogonal direction projection. By doing so, we can get a snakelike systolic array. Also we calculate the interval of input for parallel processing, calculate the indices to make the right datas be used at the right PE when forward and bvackward propagations are processed in the same PE. And then verify the correctness of output when forward and backward propagations are executed at the same time. By doing so, the proposed system maximizes parallelism of BP algorithm, minimizes th enumber of PEs. And it reduces the execution time by 2 times through making idle PEs participate in forward-backward propagation at the same time.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.14 no.12
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• pp.1338-1346
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• 2004

The positions of rotating sound sources have been localized by experiments with the Doppler effects removed. In order to de-Dopplerize the sound signals emitted from moving sources, two kinds of signal reconstruction methods were applied. One is the forward propagation method and the other is the backward propagation method. Forward propagation method analyze the source emission time based on the instantaneous distance between sensors and the assumed source position, then the signals are reconstructed with respect to the emission time. On the other hand, the backward method uses time delay to do-Dopplerize the acquired data for the received time of reference. In both techniques. the reconstructed signal data were processed using beamforming algorithm to produce power distributions at the frequencies of interest. Experiments have been carried out for varying frequencies, rotating speeds and the object distances. It is shown that the forward propagation method gives better performance in locating source position than the backward propagation method.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.11
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• pp.984-990
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• 2002

We propose a new type of nonlinear fixed interval smoother to which an existing nonlinear smoother is modified. The nonlinear smoother is derived from two-filter formulas. For the backward filter. the propagation and the update equation of error states are derived. In particular, the modified update equation of the backward filter uses the estimated error terms from the forward filter. Data fusion algorithm, which combines the forward filter result and the backward filter result, is altered into the compatible form with the new type of the backward filter. The proposed algorithm is more efficient than the existing one because propagation in backward filter is very simple from the implementation point of view. We apply the proposed nonlinear smoothing algorithm to off-line navigation system and show the proposed algorithm estimates position, and altitude fairly well through the computer simulation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.171-174
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• 2007

A realization for FF-PID(Feed-Forward PID) controlling system with backward propagation algorithm and image pattern recognition is presented in this paper. The pattern recognition used backward propagation of nervous network is teaming. FF-PID is enhanced the response characteristic of moving image by using the controlling value which is output error for the target value of nervous system. In conclusion of experiment, the system is shown that the response is worked as 2.7sec that is enhanced round 15% in comparison with general difference image algorithm. The system is able to control a moving object with effect.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.177.4-177
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• 2001

In this paper, we propose a new type of nonlinear fixed interval smoothing filter which is modified from the existing nonlinear smoothing filter. A nonlinear smoothing filter is derived from two-filter formulas. For the backward filter, the propagation and update equation of error states are derived. Particularly the modified update equation of the backward filter use the estimated error terms from the forward filter. Smoothing algorithm is altered into the compatible form with the new type of the backward fitter. An advantage of the proposed algorithm is more efficient than the existing one because propagation in backward filter is very simple from the implementation point of view. We apply the proposed nonlinear smoothing ...

• The Journal of Information Technology and Database
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• v.1 no.1
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• pp.63-81
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• 1994

The trigger mechanism in active object-oriented database systems is known to be a good tool for describing user-defined integrity constraints. It cannot adequately support, however, certain integrity constrains specified on the objects in class composition hierarchy. Those are the cases where the constraints must be maintained in the forward direction along the composition hierarchy as well as in the backward direction We call theses kinds of problems "backward propagation problem" and investigate several ways to resolve them using the currently available techniques. Based on them, a new constructor, called CONSTRAIN $T^{cch}$, is proposed. The constructor can be realized with enhanced facilities for active OODBMS which we recommend in this paper.d facilities for active OODBMS which we recommend in this paper.r.

• Journal of the Korean Society for Nondestructive Testing
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• v.33 no.3
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• pp.270-275
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• 2013

In this study, focusing of ultrasonic Lamb wave by negative refraction with mode conversion from antisymmetric to symmetric mode was investigated. When a wave propagates backward by negative refraction, the energy flux is antiparallel to the phase velocity. Backward propagation of Lamb wave is quite well known, but the behavior of backward Lamb wave at an interface has rarely been investigated. A pin-type transducer is used to detect Lamb wave propagating on a steel plate with a step change in thickness. Conversion from forward to backward propagating mode leads to negative refraction and thus wave focusing. By comparing the amplitudes of received Lamb waves at a specific frequency measured at different distance between transmitter and interface, the focusing of Lamb wave due to negative refraction was confirmed.

• Journal of Biomedical Engineering Research
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• v.12 no.3
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• pp.191-202
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• 1991

This paper describes ECG data compression algorithm using neural network. As a learning method, we use back error propagation algorithm. ECG data compression is performed using learning ability of neural network. CSE database, which is sampled 12bit digitized at 500samp1e/sec, is selected as a input signal. In order to reduce unit number of input layer, we modify sampling ratio 250samples/sec in QRS complex, 125samples/sec in P & T wave respectively. hs a input pattern of neural network, from 35 points backward to 45 points forward sample Points of R peak are used.

• Journal of the Korea Society of Computer and Information
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• v.7 no.2
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• pp.95-100
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• 2002

By feedback channel, the decoder reports the addresses of corrupted macroblocks induced by transmission errors back to the encoder With these negative acknowledgements, the encoder can make the next frame having propagated errors by using forward dependency based on GOBs and MBs of the frame happening transmission errors. The encoder can precisely calculate and track the propagated errors by examining the backward motion dependency for each of four comer pixels in the current encoding frame until before-mentioned the next frame. The error-propagation effects can be terminated completely by INTRA refreshing the affected macroblocks. Such a fast algorithm further reduce the computation and memory requirements. The advantages of the low computation complexity and the low memory requirement are Particularly suitable for real-time implementation.