• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.10
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• pp.217-228
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• 2013

In this paper, the brain function in relation with human's visual attention was investigated by means of coherence and bicoherence methods. Throughout experimentation with attentional visual stimulus ERP (Event Related Potential) data and synthesized simulated data with different combinations of parameters, it is demonstrated that bicoherence and coherence can be useful to reveal the phase synchronies between different frequency bands at fixed scalp location, and between different scalp locations at fixed frequency band, respectively. Both methods are also affected by time interval from the onset, and the level of white noise added. The phase coupled relationships among ${\Theta}$, ${\delta}$, and ${\alpha}$ bands, and between frontal and central lobes were observed for attentional tasks, while those were little observable for inattentional tasks, which can show brain's functional spatio-spectral differences associated with human's attention.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.45 no.3
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• pp.53-60
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• 2008

Recently, various recording technologies are studied for optical data storage. After standardization of BD (Blue-ray Disc) and HD-DVD (High-Definition Digital Versatile Disc), the industry is looking for a suitable technology for next generation optical data storage. Super-RENS (Super-resolution near field structure) technique, which is capable of compatibility with other systems, is one of next optical data storage. In this paper, we analyze the nonlinearity of Super-RENS read-out signal through the bicoherence test, which uses HOS (Higher-Order Statistics) and apply neural networks for nonlinear modeling. The model structure considered in this paper is the NARX (Nonlinear AutoRegressive eXogenous) model. The experiment results indicate that the read-out signals have nonlinear characteristics. In addition, it verified the possibility that neural networks can be utilized for nonlinear modeling of Super-RENS systems.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.12 no.1
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• pp.39-52
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• 2000

The triad interactions have been known to be important only for shoaling waves or finite depth wind waves. In deep water, they are insignificant compared with the quadruplet interactions in respect to the evolution of wind waves due to energy transfer among the wave components. However, the triad interactions may be important even for deep water waves because they may closely be related to the wave steepness, which definitely affects wave breaking, drag of air flow over t.'Ie sea, or navigation of ships, especially during the early stage of the development of wind waves. This study reports a series of laboratory experiments, whose data are subjected to bispectral analyses to investigate the triad interactions of deep-water wind waves. It is found that the bicoherence at the spectral peak frequency and the wave steepness are almost directly proportional, indicating that the steep waves with peaked crests and flat troughs are resulted from the triad interactions. Both bicoherence and wave steepness increase with the wave age during the early stage of wave generation and then drop off as the waves grow old. It seems that the energy of the secondary spectral peak developed by the triad interactions during the early stage of wave generation is redistributed to the neighboring frequencies by the quadruplet interactions during the later stage.

• Industrial Engineering and Management Systems
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• v.8 no.4
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• pp.247-256
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• 2009

In mass production industries such as steel making that have large equipment, sudden stops of production process due to machine failure can cause severe problems. To prevent such situations, machine diagnosis techniques play important roles. Many methods have been developed focusing on this subject. In this paper, we propose a method for the early detection of the failure on rotating machine, which is the most common theme in the machine failure detection field. A simplified method of calculating autocorrelation function is introduced and is utilized for ARMA model identification. Furthermore, an absolute deterioration factor such as Bicoherence is introduced. Machine diagnosis can be executed by this simplified calculation method of system parameter distance with weight. Proposed method proved to be a practical index for machine diagnosis by numerical examples.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.05a
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• pp.235-239
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• 1999

This thesis is concerned with the development of useful engineering techniques to detect and analyze nonlinearities in mechanical systems. The methods developed are based on the concepts of higher order spectra, in particular the bispectrum. The study of higher order statistics has been dominated by work on the bispectrum. The bispectrum can be viewed as a decomposition of the third moment(skewness) of a signal over frequency and as such is blind to symmetric nonlinearities. Initially auto higher order spectra are studied in detail with particular attention being paid to normalization method. Traditional method based on the bicoherence are studied. Under certain conditions, notably narrow band signals, the above normalization method is shown to fail and so a new technique based on prewhitening the signal in the time domain is developed.

• Industrial Engineering and Management Systems
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• v.4 no.1
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• pp.68-74
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• 2005

Among many dimensional and dimensionless amplitude parameters, kurtosis (4-th normalized moment of probability density function) is generally regarded as a sensitive good parameter for machine diagnosis. However, higher order moment may be supposed to be much more sensitive. Bicoherence is an absolute deterioration factor whose range is 1 to 0. The theoretical value of n-th moment divided by n-th moment calculated by measured data would behave in the same way. We propose a simplified calculation method for an absolute index of n-th moment and name this as simplified absolute index of n-th moment. Some favorable results are obtained.

• Smart Structures and Systems
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• v.20 no.6
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• pp.669-682
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• 2017

The normalised version of bispectrum, the so-called bicoherence, has often proved a reliable method of damage detection on engineering applications. Indeed, higher-order spectral analysis (HOSA) has the advantage of being able to detect non-linearity in the structural dynamic response while being insensitive to ambient vibrations. Skewness in the response may be easily spotted and related to damage conditions, as the majority of common faults and cracks shows bilinear effects. The present study tries to extend the application of HOSA to damage localisation, resorting to a neural network based classification algorithm. In order to validate the approach, a non-linear finite element model of a 4-meters-long cantilever beam has been built. This model could be seen as a first generic concept of more complex structural systems, such as aircraft wings, wind turbine blades, etc. The main aim of the study is to train a Neural Network (NN) able to classify different damage locations, when fed with bispectra. These are computed using the dynamic response of the FE nonlinear model to random noise excitation.