Kombinacija vremensko-frekvencijske analize signala i strojnoga učenja uz primjer u detekciji gravitacijskih valova

This paper presents a method for classifying noisy, non-stationary signals in the time-frequency domain using artificial intelligence. The preprocessed time-series signals are transformed into time-frequency representations (TFrs) from Cohen’s class resulting in the TFr images, which are used as input to the machine learning algorithms. We have used three state-of-the-art deep-learning 2d convolutional neural network (Cnn) architectures (ResNet-101, Xception, and EfficientNet). The method was demonstrated on the challenging task of detecting gravitational-wave (gw) signals in intensive real-life, non-stationary, non-gaussian, and non-white noise. The results show excellent classification performance of the proposed approach in terms of classification accuracy, area under the receiver operating characteristic curve (roC auC), recall, precision, F1 score, and area under the precision-recall curve (PR AUC). The novel method outperforms the baseline machine learning model trained on the time-series data in terms of all considered metrics. The study indicates that the proposed technique can also be extended to various other applications dealing with non-stationary data in intensive noise.Ovaj rad predstavlja metodu klasifikacije šumom narušenih nestacionarnih signala u vremensko-frekvencijskoj domeni korištenjem umjetne inteligencije. Naime, signali u obliku vremenskih nizova transformirani su nakon predobrade u vremensko-frekvencijske prikaze (TFR) iz Cohenove klase, rezultirajući TFR slikama korištenim kao ulaz u algoritme strojnoga učenja. Korištene su tri suvremene metode dubokoga učenja u obliku 2D arhitektura konvolucijskih neuronskih mreža (CNN) (ResNet-101, Xception i EfficientNet). Metoda je demonstrirana na zahtjevnom problemu detekcije signala gravitacijskih valova (GW) u intenzivnom stvarnom i nestacionarnom šumu koji nema karakteristike ni Gaussovog ni bijelog šuma. Rezultati pokazuju izvrsne performanse klasifikacije predloženoga pristupa s obzirom na točnost klasifikacije, površinu ispod krivulje značajke djelovanja prijamnika (ROC AUC), odziv, preciznost, F1-mjeru i površinu ispod krivulje preciznost-odziv (PR AUC). Nova metoda nadmašuje osnovni model strojnoga učenja treniran na podatcima u obliku vremenskih nizova s obzirom na razmatrane metrike. Istraživanje pokazuje da se predložena tehnika može proširiti i na različite druge primjene koje uključuju nestacionarne podatke u intenzivnom šumu

On the anti‐intercept features of noise radars

Robustness against Electronic Warfare/Electronic Defence attacks represents an important advantage of Noise Radar Technology (NRT). An evaluation of the related Low Probability of Detection (LPD) and of Intercept (LPI) is presented for Continuous Emission Noise Radar (CE‐NR) waveforms with different operational parameters, that is, “tailored”, and with various “degrees of randomness”. In this frame, three different noise radar waveforms, a phase Noise (APCN) and two “tailored” noise waveforms (FMeth and COSPAR), are compared by time–frequency analysis. Using a correlator (i.e. a two antennas) receiver, assuming a complete knowledge of the band (B) and duration (T) of the coherent emission of these waveforms, it will be shown that the LPD features of a CE‐NR do not significantly differ from those of any CE radar transmitting deterministic waveforms. However, in real operations, B and T are unknown; hence, assuming an instantaneous bandwidth estimation will show that the duration T can be estimated only for some specific “tailored” waveforms (of course, not to be operationally used). The effect of “tailoring” is analysed with prospects for future work. Finally, some limitations in the classification of these radar signals are analysed

Seismic characterisation based on time-frequency spectral analysis

We present high-resolution time-frequency spectral analysis schemes to better resolve seismic images for the purpose of seismic and petroleum reservoir characterisation. Seismic characterisation is based on the physical properties of the Earth's subsurface media, and these properties are represented implicitly by seismic attributes. Because seismic traces originally presented in the time domain are non-stationary signals, for which the properties vary with time, we characterise those signals by obtaining seismic attributes which are also varying with time. Among the widely used attributes are spectral attributes calculated through time-frequency decomposition. Time-frequency spectral decomposition methods are employed to capture variations of a signal within the time-frequency domain. These decomposition methods generate a frequency vector at each time sample, referred to as the spectral component. The computed spectral component enables us to explore the additional frequency dimension which exists jointly with the original time dimension enabling localisation and characterisation of patterns within the seismic section. Conventional time-frequency decomposition methods include the continuous wavelet transform and the Wigner-Ville distribution. These methods suffer from challenges that hinder accurate interpretation when used for seismic interpretation. Continuous wavelet transform aims to decompose signals on a basis of elementary signals which have to be localised in time and frequency, but this method suffers from resolution and localisation limitations in the time-frequency spectrum. In addition to smearing, it often emerges from ill-localisation. The Wigner-Ville distribution distributes the energy of the signal over the two variables time and frequency and results in highly localised signal components. Yet, the method suffers from spurious cross-term interference due to its quadratic nature. This interference is misleading when the spectrum is used for interpretation purposes. For the specific application on seismic data the interference obscures geological features and distorts geophysical details. This thesis focuses on developing high fidelity and high-resolution time-frequency spectral decomposition methods as an extension to the existing conventional methods. These methods are then adopted as means to resolve seismic images for petroleum reservoirs. These methods are validated in terms of physics, robustness, and accurate energy localisation, using an extensive set of synthetic and real data sets including both carbonate and clastic reservoir settings. The novel contributions achieved in this thesis include developing time-frequency analysis algorithms for seismic data, allowing improved interpretation and accurate characterisation of petroleum reservoirs. The first algorithm established in this thesis is the Wigner-Ville distribution (WVD) with an additional masking filter. The standard WVD spectrum has high resolution but suffers the cross-term interference caused by multiple components in the signal. To suppress the cross-term interference, I designed a masking filter based on the spectrum of the smoothed-pseudo WVD (SP-WVD). The original SP-WVD incorporates smoothing filters in both time and frequency directions to suppress the cross-term interference, which reduces the resolution of the time-frequency spectrum. In order to overcome this side-effect, I used the SP-WVD spectrum as a reference to design a masking filter, and apply it to the standard WVD spectrum. Therefore, the mask-filtered WVD (MF-WVD) can preserve the high-resolution feature of the standard WVD while suppressing the cross-term interference as effectively as the SP-WVD. The second developed algorithm in this thesis is the synchrosqueezing wavelet transform (SWT) equipped with a directional filter. A transformation algorithm such as the continuous wavelet transform (CWT) might cause smearing in the time-frequency spectrum, i.e. the lack of localisation. The SWT attempts to improve the localisation of the time-frequency spectrum generated by the CWT. The real part of the complex SWT spectrum, after directional filtering, is capable to resolve the stratigraphic boundaries of thin layers within target reservoirs. In terms of seismic characterisation, I tested the high-resolution spectral results on a complex clastic reservoir interbedded with coal seams from the Ordos basin, northern China. I used the spectral results generated using the MF-WVD method to facilitate the interpretation of the sand distribution within the dataset. In another implementation I used the SWT spectral data results and the original seismic data together as the input to a deep convolutional neural network (dCNN), to track the horizons within a 3D volume. Using these application-based procedures, I have effectively extracted the spatial variation and the thickness of thinly layered sandstone in a coal-bearing reservoir. I also test the algorithm on a carbonate reservoir from the Tarim basin, western China. I used the spectrum generated by the synchrosqueezing wavelet transform equipped with directional filtering to characterise faults, karsts, and direct hydrocarbon indicators within the reservoir. Finally, I investigated pore-pressure prediction in carbonate layers. Pore-pressure variation generates subtle changes in the P-wave velocity of carbonate rocks. This suggests that existing empirical relations capable of predicting pore-pressure in clastic rocks are unsuitable for the prediction in carbonate rocks. I implemented the prediction based on the P-wave velocity and the wavelet transform multi-resolution analysis (WT-MRA). The WT-MRA method can unfold information within the frequency domain via decomposing the P-wave velocity. This enables us to extract and amplify hidden information embedded in the signal. Using Biot's theory, WT-MRA decomposition results can be divided into contributions from the pore-fluid and the rock framework. Therefore, I proposed a pore-pressure prediction model which is based on the pore-fluid contribution, calculated through WT-MRA, to the P-wave velocity.Open Acces

Human Multi-activities Classification using mmWave Radar:Feature Fusion in Time-Domain and PCANet

This study introduces an innovative approach by incorporating statistical offset features, range profiles, time–frequency analyses, and azimuth–range–time characteristics to effectively identify various human daily activities. Our technique utilizes nine feature vectors consisting of six statistical offset features and three principal component analysis network (PCANet) fusion attributes. These statistical offset features are derived from combined elevation and azimuth data, considering their spatial angle relationships. The fusion attributes are generated through concurrent 1D networks using CNN-BiLSTM. The process begins with the temporal fusion of 3D range–azimuth–time data, followed by PCANet integration. Subsequently, a conventional classification model is employed to categorize a range of actions. Our methodology was tested with 21,000 samples across fourteen categories of human daily activities, demonstrating the effectiveness of our proposed solution. The experimental outcomes highlight the superior robustness of our method, particularly when using the Margenau–Hill Spectrogram for time–frequency analysis. When employing a random forest classifier, our approach outperformed other classifiers in terms of classification efficacy, achieving an average sensitivity, precision, F1, specificity, and accuracy of 98.25%, 98.25%, 98.25%, 99.87%, and 99.75%, respectively

Radar intra-pulse modulation classification using convolutional neural networks

This dissertation presents a detailed investigation into the classification of radar intra-pulse modulation schemes. Recent years have seen increased waveform diversity in radar systems which, while making many aspects of pulse analysis more challenging, have presented new opportunities and features for the _eld of classification. This dissertation aims to address the increasing difficulty of pulse classification through the use of modern machine learning techniques - more specifically, by utilising convolutional neural networks. A wide range of modulation schemes was considered and simulated with realistic imperfections to create a dataset that was as representative of real-world scenarios as possible. Data representations of varying levels of abstraction were analysed in order to investigate the effects of data formatting on the performance of various classifiers. A classifier which made use of manual feature extraction was evaluated against a series of convolutional neural network classifiers in order to establish whether improvements in classification accuracy and throughput could be realised. This study also presents research into the viability of classifying data that has been degraded by real transmitter and channel effects using classifiers trained entirely on simulated data. The operation of the tested classifiers is analysed, and parallels are drawn between the feature extraction steps in convolutional neural networks and conventional signal features. The primary research questions in this study are whether machine learning approaches are able to improve on non-machine learning based classification techniques, and which data representations are best suited to convolutional neural network based classification. Classifiers were tested across 28 classes of modulation, with signal-to-noise ratios uniformly distributed between -5 dB and 20 dB. It was found that substantial performance and stability improvements could be achieved when convolutional neural networks were used over the tested non-machine learning based classification technique. The most promising classifier made use of time-frequency representations as an input, and was able to achieve a classification accuracy of 98%, while exhibiting extreme robustness against noise and pulse imperfections

Fault diagnosis of rolling element bearings using artificial neural network

Bearings are essential components in the most electrical equipment. Procedures for monitoring the condition of bearings must be developed to prevent unexpected failure of these components during operation to avoid costly consequences. In this paper, the design of a monitoring system for the detection of rolling element-bearings failure is proposed. The method for detecting and locating this type of fault is carried out using advanced intelligent techniques based on a Perceptron Multilayer Artificial Neural Network (MLP-ANN); its database uses statistical indicators characterizing vibration signals. The effectiveness of the proposed method is illustrated using experimentally obtained bearing vibration data, and the results have shown good accuracy in detecting and locating defects

Multi-view Temporal Ensemble for Classification of Non-Stationary Signals

In the classification of non-stationary time series data such as sounds, it is often tedious and expensive to get a training set that is representative of the target concept. To alleviate this problem, the proposed method treats the outputs of a number of deep learning sub-models as the views of the same target concept that can be linearly combined according to their complementarity. It is proposed that the view’s complementarity be the contribution of the view to the global view, chosen in this work to be the Laplacian eigenmap of the combined data. Complementarity is computed by alternate optimization, a process that involves the cost function of the Laplacian eigenmap and the weights of the linear combination. By blending the views in this way, a more complete view of the underlying phenomenon can be made available to the final classifier. Better generalization is obtained, as the consensus between the views reduces the variance while the increase in the discriminatory information reduces the bias. Data experiment with artificial views of environment sounds formed by deep learning structures of different configurations shows that the proposed method can improve the classification performance

An intelligent fault diagnosis method of rotating machinery based on deep neural networks and time-frequency analysis

As the crucial part of the health management and condition monitoring of mechanical equipment, the fault diagnosis and pattern recognition using vibration signal are essential researching contents. The time-frequency representation method cannot identify the fault patterns from time-frequency representation effectively because of the complex work conditions of rotating machinery parts and the interference of strong background noise. Considering these disadvantages, a new reliable and effective method based on the time-frequency representation and deep convolutional neural networks is presented. In this method, the time-frequency features are calculated by the short time Fourier transform (STFT), and the pseudo-color map as the new identification objects. A novel feature learning method based on the sparse autoencode with linear decode is used to extract these time-frequency features, which is an unsupervised feature learning method with the goal of minimizing the loss function. The convoluting and pooling are applied to establish the hierarchical deep convolutional neural networks and filter the useful features layer by layer from the output of sparse autoencode. And a softmax classifier is used to obtain the faults classification. The experimental datasets from roller bearing and gearbox have been taken to verify the reliability and effectiveness of the proposed method for fault diagnosis and pattern recognition. The results show that the proposed method have excellent performance of the recognized objects