PARAMETER IDENTIFICATION IN OFFSHORE PLATFORM USING ARMA MODEL AND TECHNOLOGY OF EXTRACTING FREE VIBRATION SIGNAL

A procedure for identifying the dynamic parameter of offshore platform is presented. The present procedure consists of two key features. First uses random decrement (RD) technology to extract free vibration signal in strong noise environment in which it may not white noise. Second technology which called autoregressive moving average (ARMA) was used to model the data treated by the random decrement method. In order to get rid of the color noise in the output signal response from the offshore platform an imaginary system is added in RD system and make the course of extracting performed under the state of color input by choosing the breakover condition and lead time. For eliminating multi_values of parameters identified, an updating moving average method is used. The dynamic parameters of structure under arbitrary input are identified. Example of the method as applied to a scale_model offshore platform was used to evaluate the technology of efficiency and the value of on_line.

Noise reduction and periodic signal extraction for GNSS height data in the study of vertical deformation

Global navigation satellite system(GNSS)technique has irreplaceable advantages in the continuous monitoring of surface deformation.Reducing noise to improve the signal-to-noise ratio(SNR)and extract the concerned signals is of great significance.As an improved algorithm of empirical mode decomposition(EMD),complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)algorithm has better signal processing ability.Using the CEEMDAN algorithm,the height time series of 29GNSS stations in Chinese mainland were analyzed,and good denoising effects and extraction from periodic signals were achieved.The numerical results showed that the annual signal obtained with the CEEMDAN algorithm was significantly based on Lomb_Scargle spectrum analysis,and large differences in the long-term signals were found between the stations at different locations in Chinese mainland.With respect to data denoising,compared with the EMD and wavelet denoising algorithms,the CEEMDAN algorithm respectively improved the SNR by 29.35% and 36.54%,increased the correlation coefficient by 8.67% and 11.96%,and reduced root mean square error(RMSE)by 44.68% and 43.48%,indicating that the CEEMDAN algorithm had better denoising behavior than the other two algorithms.In addition,the results demonstrated that different denoising methods had little influence on estimating the annual vertical deformation velocity.The extraction of periodic signals showed that more components were retained by using the CEEMDAN algorithm than the EMD algorithm,which indicated that the CEEMDAN algorithm had advantages over frequency aliasing.In conclusion,the CEEMDAN algorithm was recommended for processing the GNSS height time series to analyze the vertical deformation due to its excellent features of denoising and the extraction of periodic signals.

Extraction of periodic signals in chaotic secure communication using Duffing oscillators

This paper presents a novel approach to extract the periodic signals masked by a chaotic carrier. It verifies that the driven Duffing oscillator is immune to the chaotic carrier and sensitive to certain periodic signals. A preliminary detection scenario illustrates that the frequency and amplitude of the hidden sine wave signal can be extracted from the chaotic carrier by numerical simulation. The obtained results indicate that the hidden messages in chaotic secure communication can be eavesdropped utilizing Duffing oscillators.

Implication of two-coupled tri-stable stochastic resonance in weak signal detection

Stochastic resonance （SR） has been proved to be an effective approach to extract weak signals overwhelmed in noise. However, the detection effect of current SR models is still unsatisfactory. Here, a coupled tri-stable stochastic resonance （CTSSR） model is proposed to further increase the output signal-to-noise ratio （SNR） and improve the detection effect of SR. The effects of parameters a, b, c, and r in the proposed resonance system on the SNR are studied, by which we determine a set of parameters that is relatively optimal to implement a comparison with other classical SR models. Numerical experiment results indicate that this proposed model performs better in weak signal detection applications than the classical ones with merits of higher output SNR and better anti-noise capability.

Piezoresistive Response Extraction for Smart Cement-based Composites/Sensors

A kind of piezoresistive response extraction method for smart cement-based composites/sensors was proposed.Two kinds of typical piezoresistive cement-based composites/sensors were fabricated by respectively adding carbon nanotubes and nickel powders as conductive fillers into cement paste or cement mortar.The variation in measured electrical resistance of such cement-based composites/sensors was explored without loading and under repeated compressive loading and impulsive loading.The experimental results indicate that the measured electrical resistance of piezoresistive cement-based composites/sensors exhibits a two-stage variation trend of fast increase and steady increase with measurement time without loading,and an irreversible increase after loading.This results from polarization caused by ionic conduction in these composites/sensors.After reaching a plateau,the measured electrical resistance can be divided into an electrical resistance part and an electrical capacity part.The piezoresistive responses of electrical resistance part in measured electrical resistance to loading can be extracted by eliminating the linear electrical capacity part in measured electrical resistance.

Time-shared channel identification for adaptive noise cancellation in breath sound extraction

Noise artifacts are one of the key obstacles in applying continuous monitoring and computer-assisted analysis of lung sounds. Traditional adaptive noise cancellation (ANC) methodologies work reasonably well when signal and noise are stationary and independent. Clinical lung sound auscultation encounters an acoustic environment in which breath sounds are not stationary and often correlate with noise. Consequendy, capability of ANC becomes significantly compromised. This paper introduces a new methodology for extracting authentic lung sounds from noise-corrupted measurements. Unlike traditional noise cancellation methods that rely on either frequency band separation or signal/noise independence to achieve noise reduction, this methodology combines the traditional noise canceling methods with the unique feature of time-split stages in breathing sounds. By employing a multi-sensor system, the method first employs a high-pass filter to eliminate the off-band noise, and then performs time-shared blind identification and noise cancellation with recursion from breathing cycle to cycle. Since no frequency separation or signal/noise independence is required, this method potentially has a robust and reliable capability of noise reduction, complementing the traditional methods.

The Algorithm of Balanced Orthogonal Multiwavelets and Its Application in Denoising

In order to extract fault features of a weak signal from the strong noise and maintain signal smoothness, a new method of denoising based on the algorithm of balanced orthogonal multiwavelets is proposed. Multiwavelets have several scaling functions and wavelet functions, and possess excellent properties that a scalar wavelet cannot satisfy simultaneously, and match the different characteristics of signals. Moreover, the balanced orthogonal multiwavelets can avoid the Gibbs phenomena and their processes have the advantages in denoising. Therefore, the denoising based on the algorithm of balanced orthogonal multiwavelets is introduced into the signal process. The algorithm of bal- anced orthogonal multiwavelet and the implementation steps of this denoising are described. The experimental compar- ison of the denoising effect between this algorithm and the traditional multiwavelet algorithm was done. The experi- ments indieate that this method is effective and feasible to extract the fault feature submerged in heavy noise.

Bicoid Signal Extraction with a Selection of Parametric and Nonparametric Signal Processing Techniques

The maternal segmentation coordinate gene bicoid plays a significant role during Drosophila embryogenesis. The gradient of Bicoid, the protein encoded by this gene, determines most aspects of head and thorax development. This paper seeks to explore the applicability of a variety of signal processing techniques at extracting bicoid expression signal, and whether these methods can outperform the current model. We evaluate the use of six different powerful and widely-used models representing both parametric and nonparametric signal processing techniques to determine the most efficient method for signal extraetion in bicoid. The results are evaluated using both real and simulated data. Our findings show that the Singular Spectrum Analysis technique proposed in this paper outperforms the synthesis diffusion degradation model for filtering the noisy protein profile of bicoid whilst the exponential smoothing technique was found to be the next best alternative followed by the autoregressive integrated moving average.

Acoustic fault signal extraction via the line-defect phononic crystals

Rotating machine fault signal extraction becomes increasingly important in practical engineering applications.However,fault signals with low signal-to-noise ratios(SNRs)are difficult to extract,especially at the early stage of fault diagnosis.In this paper,2D line-defect phononic crystals(PCs)consisting of periodic acrylic tubes with slit are proposed for weak signal detection.The defect band,namely,the formed resonance band of line-defect PCs enables the incident acoustic wave at the resonance frequency to be trapped and enhanced at the resonance cavity.The noise can be filtered by the band gap.As a result,fault signals with high SNRs can be obtained for fault feature extraction.The effectiveness of weak harmonic and periodic impulse signal detection via line-defect PCs are investigated in numerical and experimental studies.All the numerical and experimental results indicate that line-defect PCs can be well used for extracting weak harmonic and periodic impulse signals.This work will provide potential for extracting weak signals in many practical engineering applications.

Design and implement of the signal generator in the magnetic focused conductivity tomography system

To improve stability and performance of the signal source and sweeping detection,as well as to extract abundant and reliable signal,the direct digital synthesis technology was employed to design the generator of the source which formed sweeping frequencies of sine wave output from 1 to 20 MHz.The planar spiral coil was con-nected as an amplitude modulation circuit.The same coil adopted differential architecture for signal detection and extraction.The MC1595 was utilized to compose a phase detector in which difference of phases varies with the change of frequencies.A low pass filter was designed to filter the carry waves of the sweeping source.Thereby the system gained abundant data and its stability was improved.Further,the spatial resolution of the system was enhanced.All of the above favors the use of software in the magnetic focused conductivity tomography system(MFCT)to reconstruct the image of conductivity within the human body.

Extraction of mode attenuation coefficient from low frequency reverberation signal

A method of extracting normal mode attenuation coefficient from low frequency reverberation signal has been proposed.Pseudo-inverse normal mode filtering method is implemented to get single mode reverberation field firstly.Based on the assumption of separability of modal back-scattering matrix,effective back-scattering matrix element can be calculated using single mode average reverberation intensity.Finally,mode attenuation coefficient is extracted by comparing effective back-scattering matrix elements at different ranges.The extracted mode attenuation coefficients are used to predict sound transmission loss at the same experiment area. Results show that the predicted transmission loss agrees well with the measured data.This method avoids the difficult of treating the coupling between bottom scattering attenuation and normal mode propagation attenuation.Research on extraction of mode attenuation coefficient from low frequency reverberation signal is useful for both geoacoustic inversion and rapid underwater environment assessment.

Banking with blockchain-ed big data

Blockchain is disrupting the banking industry and contributing to the increased big data in banking.However,there exists a gap in research and development into blockchain-ed big data in banking from an academic perspective,and this gap is expected to have a significant negative impact on the adoption and development of blockchain technology for banking.In hope of motivating more active engagement by academics,researchers and bankers alike,we present the most comprehensive review of the impact of blockchain in banking to date by summarizing the opportunities and challenges from a bankers perspective.In addition,we also discuss the impact that big data from blockchain will have on banking data analytics in future and show the increasing importance of filtering and signal extraction for the banking industry.Whilst there is evidence of selected banks adopting blockchain technology in isolation or small groups,we find the need for extensive research and development into several aspects of banking with blockchain to overcome the challenges which are currently hindering its adoption in banking across the globe.

A Harmful-Intrusion Detection Method Based on Background Reconstruction and Two-Dimensional K-S Test in an Optical Fiber Pre-Warning System

The key technology and main difficulty for optical fiber intrusion pre-warning systems （OFIPS） is the extraction of harmful-intrusion signals. After being processed by a phase-sensitive optical time-domain reflectometer （O-0TDR）, vibration signals can be preliminarily extracted. Generally, these include noises and intrusions. Here, intrusions can be divided into harmful and harmless intrusions. With respect to the close study of signal characteristics, an effective extraction method of harmful intrusion is proposed in the paper. Firstly, in the part of the background reconstruction, all intrusion signals are first detected by a constant false alarm rate （CFAR）. We then reconstruct the backgrounds by extracting two-part information of alarm points, time and amplitude. This ensures that the detection background consists of intrusion signals. Secondly, in the part of the two-dimensional Kolmogorov-Smirnov （K-S） test, in order to extract harmful ones from all extracted intrusions, we design a separation method. It is based on the signal characteristics of harmful intrusion, which are shorter time interval and higher amplitude. In the actual OFIPS, the detection method is used in some typical scenes, which includes a lot of harmless intrusions, for example construction sites and busy roads. Results show that we can effectively extract harmful intrusions.

A computationally efficient method for identification of steady state in time series data from ship monitoring

Most applied time series are non-stationary,or exhibit some kind of non-stationarity for at least parts of the time series.For time series analyses or mathematical modeling purposes,the non-stationarities can be difficult to handle.Therefore,identification of stationary and non-stationary behavior is of great practical interest in time series analysis.In this study a robust and computationally efficient method to identify steady state parts of time series data is presented.The method is based on the class of deterministic trend models using a sliding window,and is focused towards being easy to implement,efficient and practical in use and to preserve data completeness.To demonstrate the performance of the steady state identifier,the method is applied on different sets of time series data from two ships equipped with systems for in-service monitoring.The method is shown to be reliable and practical for identifying steady state parts of time series data,and can serve as a practical preprocessing tool for time series data analysis.

An approach for automatic sleep stage scoring and apnea-hypopnea detection

In this article we present an application of data mining to the medical domain sleep research, an approach for automatic sleep stage scoring and apnea-hypopnea detec- tion. By several combined techniques （Fourier and wavelet transform, derivative dynamic time warping, and waveform recognition）, our approach extracts meaningful features （fre- quencies and special patterns like k-complexes and sleep spindles） from physiological recordings containing EEG, ECG, EOG and EMG data. Based on these pieces of in- formation, an ensemble of decision trees is constructed us- ing the principle of bagging, which classifies sleep epochs in their sleep stages according to the rules by Rechtschaf- fen and Kales and annotates occurrences of apnea-hypopnea （total or partial cessation of respiration）. After that, case- based reasoning is applied in order to improve quality. We tested and evaluated our approach on several large public databases from PhysioBank, which showed an overall accu- racy of 95.2% for sleep stage scoring and 94.5% for classify- ing minutes as apneic or non-apneic.