• Journal of the Optical Society of Korea
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• v.19 no.3
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• pp.255-259
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• 2015

We have proposed and experimentally verified a pulse-width modulation (PWM) generator which directly generated a PWM signal in the optical domain. Output waveforms were clear at the repetition rate of 16 MHz; the duty cycle (DC) was from 14.7% to 72.1%; and the DC-control resolution was about 4.399%/dB. The PWM generator' operation principle is based on the injection-locking property of a single-mode Fabry-$P{\acute{e}}rot$ laser diode (SMFP-LD). The SMFP-LD, which has a self-locked mode wavelength at ${\lambda}_{PWM}$, was used to detect the power of the injection-locking signal (optical analog input). If the analog input power is high, the SMFP-LD is locked to the wavelength of the input signal ${\lambda}_a$ and there is no output after an optical bandpass filter (OBF). If the analog input power is low, the SMFP-LD is unlocked and there is output signal at ${\lambda}_{PWM}$ after the OBF. Thus, the SMFP-LD plus the OBF provide digital output for an analog input. The DC of the output PWM signal can be controlled by tuning the power of the analog input.

• Journal of the Optical Society of Korea
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• v.1 no.1
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• pp.1-4
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• 1997

The theoretical limit of the noise correlation between the signal and idler outputs of a nondegenerate optical parametric amplifier (NOPA) with a coherent state signal and vacuum state idler input can be enhanced if a non-vacuum coherent state idler input is employed. By choosing a balanced signal and idler input, the noise correlation is $1/{({\root}g + {\root}{g-1})}^2$, where g is the intensity gain of the NOPA, and that is superior to the prediced outputs with single signal input by approximately 3dB. The result is applicable to all the schemes that use the NOPA to produce a sub-shot noise light generation such as feed-back or feed-forward control.

• Journal of the Korea Institute of Military Science and Technology
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• v.21 no.2
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• pp.204-210
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• 2018

As the 4th industrial revolution and artificial intelligence technology develop, it is expected that there will be a revolutionary changes in the security robot. However, artificial intelligence system requires enormous hardwares for tremendous computing loads, and there are many challenges that need to be addressed more technologically. This paper introduces precise tracking control technique of autonomous system that need to move repetitive paths for security purpose. The input feedforward signal is generated by using the inverse based iterative learning control theory for the 2 input 2 output nonminimum-phase system which was difficult to overcome by the conventional feedback control system. The simulation results of the input signal generation and precision tracking of given path corresponding to the repetition rate of extreme, such as bandwidth of the system, shows the efficacy of suggested techniques and possibility to be used in military security purposes.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.6
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• pp.119-127
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• 1996

The proposed method uses a signal of the smae frequency as the input modulating carrier frequency and of a different phase. This signal is generated in the digital automatic frequency control part to decide the input color demodulated signal. And the phase error from the burst signal is calculated. The calculated phase error is utilized to rmove the phase error contained inthe demodulated color signal. In this paper, digital signal processing of automatic color control is proposed for VCR system campatible with both NTSC and PAL TV systems.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.08a
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• pp.17-20
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• 2000

In a digital communication system, the transmission channel may introduce error into the digital signal being transmitted. It would be useful if a process could be devised so that the error could be removed in order to recover the transmitted digital signal. We design a corrective filter that is inverse filter, which will generate an output signal identical to the input signal. in order for two systems connected in cascade to produce an output which is identical to the input signal, the over-all unit sample response of the cascade connection must be a unit sample function.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.8
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• pp.495-497
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• 2005

Recently Neural Network techniques have widely used in adaptive and learning control schemes for production systems. However, generally it costs a lot of time for learning in the case applied in control system. Furthermore, the physical meaning of neural networks constructed as a result is not obvious. And this method has been used as a learning algorithm to estimate the parameter of a neural network used for identification of the process dynamics of s signal input and signal output system and it was shown that this method offered superior capability over the conventional back propagation algorithm. This controller is designed by using three-layered neural networks. The effectiveness of the proposed Neural Network-based control scheme is investigated through an application for a production control system. This control method can enable a plant to operate smoothy and obviously as the plant condition varies with any unexpected accident. This paper goal estimate input signal about controller using neural networks.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.18-20
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• 2005

Recently Neural Network techniques have widely used in adaptive and learning control schemes for production systems. However, generally it costs a lot of time for learning in the case applied in control system. Furthermore, the physical meaning of neural networks constructed as a result is not obvious. And this method has been used as a learning algorithm to estimate the parameter of a neural network used for identification of the process dynamics of s signal input and signal output system and it was shown that this method offered superior capability over the conventional back propagation algorithm. This controller is designed by using three-layered neural networks. The effectiveness of the proposed Neural Network-based control scheme is investigated through an application for a production control system. This control method can enable a plant to operate smoothy and obviously as the plant condition varies with any unexpected accident. This paper goal estimate input signal about controller using neural networks.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.05a
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• pp.855-861
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• 2002

This paper demonstrates the reconstruction of input signals from only the measured signal for the simulation and endurance test of automobiles. The aim of this research is concerned with input signal reconstruction using various iterative teaming algorithm under the condition of system characteristics. From a linear to nonlinear systems which provides the output signals are estimated in this algorithm which is based on the frequency domain. Our concerns are that the algorithm can assure an acceptable stability and convergence compared to the ordinary iterative learning algorithm. As a practical application, a f car model with nonlinear damper system is used to verify the restoration of input signal especially with a modified iterative loaming algorithm.

• Nuclear Engineering and Technology
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• v.33 no.5
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• pp.457-467
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• 2001

It is well known that the performance of a fuzzy neural network strongly depends on the input features selected for its training. In its applications to sensor signal estimation, there are a large number of input variables related with an output As the number of input variables increases, the training time of fuzzy neural networks required increases exponentially. Thus, it is essential to reduce the number of inputs to a fuzzy neural network and to select the optimum number of mutually independent inputs that are able to clearly define the input-output mapping. In this work, principal component analysis (PCA), genetic algorithms (CA) and probability theory are combined to select new important input features. A proposed feature selection method is applied to the signal estimation of the steam generator water level, the hot-leg flowrate, the pressurizer water level and the pressurizer pressure sensors in pressurized water reactors and compared with other input feature selection methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.1
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• pp.43-51
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• 1995

In this paper, we propose a neural network with fuzzy preprocessor not only for improving the classifi¬cation accuracy but also for being able to classify objects whose attribute values do not have clear bound¬aries. The fuzzy input signal representation scheme is included as a preprocessing module. It transforms imprecise input in linguistic form and precisely stated numerical input into multidimensional numerical values. 'The transformed input is processed in the postprocessing module. The experimental results indi-cate the superiority of fuzzy input signal representation scheme in comparison to binary input signal rep¬resentation scheme and decimal input signal representation scheme.