• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.4C
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• pp.355-361
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• 2007

In this paper, the hop-timing estimator, which NED and ELF are adopted to, has been proposed. The estimation performance of the proposed scheme and the conventional scheme is compared through computer simulations. The simulation results show that the new system has less hop-timing error than the conventional system in partial band noise jamming channel. The lover Eb/Nj and ratio of jamming bandwidth(rho) we, the bigger performance enhancement of the proposed system is.

• Journal of the Korea Institute of Military Science and Technology
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• v.13 no.1
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• pp.30-35
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• 2010

As of today, Frequency Hopping techniques are widely used for over-channel interference and anti-jamming communication systems. In this paper, analysis the performance of robustness on the focus of some general jamming channel. In FH/SS systems, usually SFH(Slow Frequency Hopping) and FFH(Fast Frequency Hopping) are took up on many special communication systems, the SFH, FFH are also combined with a channel diversity algorithm likes NED(Normalized Envelop Detection), EGC(Equal Gain Combines) and Clipped Combines to overcome jammer's attack. This paper propose Adaptive-NED and shows A-NED will be worked well than the others in the some general jamming environments.

• Journal of the Korea Society of Computer and Information
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• v.19 no.11
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• pp.17-24
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• 2014

This paper proposes a fault detection method for low-speed rolling element bearings of an induction motor using acoustic emission signals and histogram modeling. The proposed method performs envelop modeling of the histogram of normalized fault signals. It then extracts and selects significant features of each fault using partial autocorrelation coefficients and distance evaluation technique, respectively. Finally, using the extracted features as inputs, the support vector regression (SVR) classifies bearing's inner, outer, and roller faults. To obtain optimal classification performance, we evaluate the proposed method with varying an adjustable parameter of the Gaussian radial basis function of SVR from 0.01 to 1.0 and the number of features from 2 to 150. Experimental results show that the proposed fault identification method using 0.64-0.65 of the adjustable parameter and 75 features achieves 91% in classification performance and outperforms conventional fault diagnosis methods as well.