Cyclic Features Research Articles

Generalizable and Discriminative Representations for Adversarially Robust Few-Shot Learning.

Few-shot image classification (FSIC) is beneficial for a variety of real-world scenarios, aiming to construct a recognition system with limited training data. In this article, we extend the original FSIC task by incorporating defense against malicious adversarial examples. This can be an arduous challenge because numerous deep learning-based approaches remain susceptible to adversarial examples, even when trained with ample amounts of data. Previous studies on this problem have predominantly concentrated on the meta-learning framework, which involves sampling numerous few-shot tasks during the training stage. In contrast, we propose a straightforward but effective baseline via learning robust and discriminative representations without tedious meta-task sampling, which can further be generalized to unforeseen adversarial FSIC tasks. Specifically, we introduce an adversarial-aware (AA) mechanism that exploits feature-level distinctions between the legitimate and the adversarial domains to provide supplementary supervision. Moreover, we design a novel adversarial reweighting training strategy to ameliorate the imbalance among adversarial examples. To further enhance the adversarial robustness without compromising discriminative features, we propose the cyclic feature purifier during the postprocessing projection, which can reduce the interference of unforeseen adversarial examples. Furthermore, our method can obtain robust feature embeddings that maintain superior transferability, even when facing cross-domain adversarial examples. Extensive experiments and systematic analyses demonstrate that our method achieves state-of-the-art robustness as well as natural performance among adversarially robust FSIC algorithms on three standard benchmarks by a substantial margin.

Electrochemistry of Redox Active Ferrocene Covalently Attached to Glassy Carbon Electrodes

Formation of well-ordered redox active monolayers is desirable for systematic study of electron transfer for sensor applications and generation of tailored hybrid materials. We have covalently attached 6-bromohexylferrocene (Fc(CH2)6Br) to glassy carbon electrodes through a Grignard reaction and used cyclic voltammetry to characterize interfacial electron transfer and probe disorder in these modified electrodes. X-ray photoelectron spectroscopy (XPS) and cyclic voltammetry confirmed attachment of the expected Fe(II) species and reversible electron transfer is observed. However, the full width at half max of the cyclic voltammogram features deviates from the ideal value of 90.6 mV and varies with surface coverage, indicating a disordered surface. We determine this disorder arises from phase segregation where some domains have a high loading of the hexylferrocene chains while others have a lower loading of hexylferrocene resulting in different local environments that are distinguishable by cyclic voltammetry.

Multiscale cyclic frequency demodulation-based feature fusion framework for multi-sensor driven gearbox intelligent fault detection

Accurate fault detection is extremely important to ensure stable gearbox operation. Data-driven schemes using cyclic spectral have received significant attention due to their robust demodulation performance. However, these schemes are mainly applied to process single sensor signals, and they are unable to accurately obtain precise fault features. This paper proposed a novel multiscale cyclic frequency demodulation (MCFD)-based feature fusion framework for multi-sensor driven gearbox intelligent fault diagnosis. Firstly, the MCFD is proposed to analyze the vibration signals from multi-sensor driven gearbox, which acquires the multi-sensor mode information without setting parameters in advance. Thereafter, the grey relational degree between the multi-sensor mode information and original signal is calculated, and its results are normalized to obtain the relationship coefficients. Finally, the acquired coefficients are performed for multi-sensor information fusion to form the covariance matrix for gearbox fault diagnosis. The effectiveness of the proposed feature fusion framework is validated using the gearbox case. The comparative experiments indicate that this framework outperforms comparative algorithms for multi-sensor driven gearbox fault diagnosis.

Rolling bearing fault diagnosis method based on multi-information fusion characteristics under complex working conditions

To address the problem of low accuracy of traditional neural network models for rolling bearing fault diagnosis under complex working conditions, a novel method based on the multi-information fusion axis attention mechanism (MFA) and improved multi-scale convolutional neural network (MFA-IMSCNN) is proposed. Firstly, a multi-information fusion axis attention mechanism is presented to accurately extract the feature channel information and the number axis location information at various scales. Secondly, a multi-scale dense cyclic feature extraction module (MSDC) is built to improve the feature extraction ability of the MFA-IMSCNN under small sample conditions. The module better captures the potential feature information of time series and ensures the integrity of the information. Finally, two different datasets are imported into the MFA-IMSCNN model to evaluate its effectiveness, and the experimental results verify that the MFA-IMSCNN obtains better classification performance and stronger robustness under various experimental conditions.

Feature extraction algorithm of anti-jamming cyclic frequency of electronic communication signal

Abstract Anti-jamming cyclic frequency feature extraction is an important link in identifying communication interference signals, which is of great significance for eliminating electronic communication interference factors and improving the security of electronic communication environment. However, when the traditional feature extraction technology faces a large number of data samples, the processing capacity is low, and it cannot solve the multi-classification problems. For this type of problem, a method of electronic communication signal anti-jamming cyclic frequency feature extraction based on particle swarm optimization-support vector machines (PSO-SVM) algorithm is proposed. First, the SVM signal feature extraction model is proposed, and then the particle swarm optimization (PSO) algorithm is used. Optimize the kernel function parameter settings of SVM to raise the classifying quality of the SVM model. Finally, the function of the PSO-SVM signal feature extraction model is tested. The results verify that the PSO-SVM model begins to converge after 60 iterations, and the loss value remains at about 0.2, which is 0.2 lower than that of the SVM technique. The exactitude of signal feature extraction is 90.4%, and the recognition effect of binary phase shift keying signal is the best. The complete rate of signal feature extraction is 85%. This shows that the PSO-SVM model enhances the sensitivity of the anti-jamming cyclic frequency feature, improves the accuracy of the anti-jamming cyclic frequency feature recognition, reduces the running process, reduces the time cost, and greatly increases the performance of the SVM method. The good model performance also improves the application value of the method in the field of electronic communication.

Pairing monitoring datasets with probabilistic forecasts to provide early warning of drought in Australia

Droughts are a cyclical feature of Australia's climate that have compounding impacts on agricultural productivity and the wellbeing of drought-affected communities. Understanding anticipated drought conditions in context of antecedent observations is critical to providing early warning of drought. In this study we paired probabilistic seasonal rainfall forecasts with precipitation, soil moisture and evapotranspiration data that were objectively combined using Principal Component Analysis (PCA). The final Drought Early Warning System (DEWS) maps overlay forecasting information with the multi-variate PCA-weighted maps at 1-, 3- and 6-month timescales over the common period of overlap between all datasets (1982–2018). In this study period, the 1982–1983 Ash Wednesday tinder drought and the 1997–2001 Millennium drought were investigated. We validated PCA-weighted maps with satellite vegetation data and found performance was strongest over the Murray Darling Basin region (R = 0.63, p = 0.009) and poorest over Central interior Australia (insignificant correlations). We also validated PCA-weighted maps using agricultural commodity data from ABARES. Significant negative correlations at 95 %, 99 % and 99.9 % confidence intervals were found between %-Area in drought category and crop cultivation area; export volume/value; crop yield; and rural debt. Our findings indicate that early warning of drought can be categorised by concern – wherein dry antecedent conditions and dry forecasted conditions are of the highest concern. The developed proof-of-concept DEWS contributes to the growing body of proactive drought management research. In a drought vulnerable future, operationalising and communicating drought early warnings will be critical to reducing the harmful impacts of drought on communities, economies, and environments.

Blind estimation of modulation parameters for PCMA signals using frame cyclic features

Blind receiver technologies for paired carrier multiple access (PCMA) signals have always been a challenging task with many technical difficulties, among which the estimation of modulation parameters is a fundamental but important element. Despite some achievements in previous studies, more systematic and sophisticated estimation methods have not been adequately investigated. In this paper, schemes for the blind estimation of the symbol timing phase, amplitude attenuation, frequency offset, and carrier phase for PCMA signals in satellite communications are proposed. The data flow transmitted in satellite communication often has a certain frame structure, the most important of which is the synchronization data, namely the so-called cycle features. The proposed schemes assume that the modulated signals have fixed frame length and frame sync code and that the symbol rate has been estimated when the signals are encoded asynchronously. Distinct from the previous methods, our schemes exploit the sync waveform and the overlapping waveform, which are estimated via singular value decomposition (SVD) (using the frame cyclic features) and interference cancelation, together with their demodulation results as aid data, for the estimation of the desired parameters. The simulation results demonstrate that the schemes are effective in the parameters estimation of PCMA signals and outperform the comparison algorithms.

Multi-horizon short-term load forecasting using hybrid of LSTM and modified split convolution.

Precise short-term load forecasting (STLF) plays a crucial role in the smooth operation of power systems, future capacity planning, unit commitment, and demand response. However, due to its non-stationary and its dependency on multiple cyclic and non-cyclic calendric features and non-linear highly correlated metrological features, an accurate load forecasting with already existing techniques is challenging. To overcome this challenge, a novel hybrid technique based on long short-term memory (LSTM) and a modified split-convolution (SC) neural network (LSTM-SC) is proposed for single-step and multi-step STLF. The concatenating order of LSTM and SC in the proposed hybrid network provides an excellent capability of extraction of sequence-dependent features and other hierarchical spatial features. The model is evaluated by the Pakistan National Grid load dataset recorded by the National Transmission and Dispatch Company (NTDC). The load data is pre-processed and multiple other correlated features are incorporated into the data for performance enhancement. For generalization capability, the performance of LSTM-SC is evaluated on publicly available datasets of American Electric Power (AEP) and Independent System Operator New England (ISO-NE). The effect of temperature, a highly correlated input feature, on load forecasting is investigated either by removing the temperature or adding a Gaussian random noise into it. The performance evaluation in terms of RMSE, MAE, and MAPE of the proposed model on the NTDC dataset are 500.98, 372.62, and 3.72% for multi-step while 322.90, 244.22, and 2.38% for single-step load forecasting. The result shows that the proposed method has less forecasting error, strong generalization capability, and satisfactory performance on multi-horizon.

ECG Matching: An Approach to Synchronize ECG Datasets for Data Quality Comparisons.

Clinical assessment of newly developed sensors is important for ensuring their validity. Comparing recordings of emerging electrocardiography (ECG) systems to a reference ECG system requires accurate synchronization of data from both devices. Current methods can be inefficient and prone to errors. To address this issue, three algorithms are presented to synchronize two ECG time series from different recording systems: Binned R-peak Correlation, R-R Interval Correlation, and Average R-peak Distance. These algorithms reduce ECG data to their cyclic features, mitigating inefficiencies and minimizing discrepancies between different recording systems. We evaluate the performance of these algorithms using high-quality data and then assess their robustness after manipulating the R-peaks. Our results show that R-R Interval Correlation was the most efficient, whereas the Average R-peak Distance and Binned R-peak Correlation were more robust against noisy data.

Improving and analysing the spectral access performance of QAM-64 optical NOMA using a hybrid ED-CSD algorithm

Abstract Cognitive radio (Cr) is one of among the most promising developments, which combines optical non-orthogonal multiple access (O-NOMA) methods to increase the fifth generation (5G) spectrum’s usage efficiency. In this work, we provide a hybrid approach based on cyclic stationary feature detection (CFD) and energy detection (ED). The probability of false alarm (PFA), on which it is based, is used to choose which spectrum sensing (SS) approach to use. When PFA is less than 0.5, the ED is chosen; otherwise, the MF is. The suggested detection method in O-NOMA is investigated using Matlab-2016 simulation with reference to several metrics, such as bit error rate (BER), signal-to-noise ratio (SNR), probability of detection (PD), and PFA.

Automatic borehole fracture detection and characterization with tailored Faster R-CNN and simplified Hough transform

Understanding the geological information of fractures from borehole images is essential for conducting a thorough geological analysis in engineering. Recently, the convolutional neural network (CNN) has been employed to extract high-level features of fractures from images. We propose an improved Faster R-CNN (region-based CNN) model for fracture detection, which is suitably equipped with a tailored region proposal network f-RPN for borehole fractures. The anchors generated by f-RPN are specifically designed with a fixed width and varying heights, and only the anchors with the maximum intersection-over-union values are labeled as positive. By taking advantage of the cyclic feature of borehole images, the images are expanded in four orientations to increase the amount and diversity of the model input. The detection results of the model can further be utilized to simplify the Hough transform for characterizing dip angles and dip directions of fractures. The proposed method is validated with borehole images from multiple projects in hydraulic engineering. Experiments reveal that the average detection accuracy is approximately 91.5%, which exhibits an improvement of 11.06% compared to the original Faster R-CNN. Further analysis indicates that both f-RPN and the multi-orientation expansion contribute to improving the prediction accuracy. It is also shown that this method demonstrates robustness against blurred image background and outperforms traditional image processing methods in the presence of noise. The fracture characterization by the simplified Hough transform turns out to be computationally efficient and accurate, with the absolute errors less than 5° compared to the manual identification results.

Macro-prudential policies to contain the effect of structural risks on financial downturns

We investigate the extent to which various structural risks exacerbate the materialization of credit risk and affect financial cycle downturns. We use a large database covering all sorts of cyclical and structural features of the financial sector and the real economy for a panel of 30 countries over the period 2006Q1–2019Q4. We show that elevated levels of structural risks may have an important role in explaining the severity of credit risk materialization during financial cycle contractions. Among these risks, private and public sector indebtedness, banking sector resilience and concentration of real estate exposures stand out. Moreover, we show that the elevated levels of some of the structural risks identified may be related to long-standing accommodative economic policy. Our evidence implies a stronger role for macroprudential policy, especially in countries with higher levels of structural risks.

Cyclic Hardening and Fatigue Damage Features of 51CrV4 Steel for the Crossing Nose Design

A crossing nose is a component of railway infrastructure subject to very severe loading conditions. Depending on the severity of these loads, the occurrence of structural fatigue, severe plastic deformation, or rolling fatigue may occur. Under fatigue conditions with high plastic deformation, cyclic plasticity approaches, together with local plasticity models, become more viable for mechanical design. In this work, the fatigue behavior in strain-controlled conditions of 51CrV4 steel, applicable to the crossing nose component, was evaluated. In this investigation, both strain-life and energy-life approaches were considered for fatigue prediction analysis. The results were considered through obtaining a Ramberg-Osgood cyclic elasto-plastic curve. Since this component is subject to cyclic loading, even if spaced in time, the isotropic and kinematic cyclic hardening behavior of the Chaboche model was subsequently analyzed, considering a comparative approach between experimental data and the FEM. As a result, the material properties and finite element model parameters presented in this work can contribute to the enrichment of the literature on strain-life fatigue and cyclic plasticity, and they could be applied in mechanical designs with 51CrV4 steel components or used in other future analyses.

Experimental and constitutive modelling studies of type 316L stainless steel based on internal stress under low cycle fatigue and creep-fatigue interaction

Low cycle fatigue (LCF) and creep-fatigue interaction (CFI) behaviour of 316L stainless steel at 550 °C were investigated using experimental and modelling approaches. Results demonstrate that friction and back stress contribute to the initial hardening stage, whereas the former is primarily responsible for the subsequent softening stage. Besides, friction stress also accounts for the non-masing feature and varied softening rate. Increasing holding time induces a rise followed by a decline in friction stress, while the back stress decreases steadily to a saturated value. Moreover, a short tensile holding duration leads to planar structures and higher dislocation density. However, a longer tensile holding time promotes dislocation annihilation and subgrains growth, and elongates dislocation cells. Finally, an improved viscoplastic constitutive model was proposed based on the internal stress analysis. The kinematic hardening rule was improved by considering the cycle-dependent plastic modulus and stress relaxation, and the non-masing and variable softening rate were captured by modifying the isotropic hardening rule. In addition, holding time-related factors were also coupled into the model. The proposed model is robust for describing the cyclic features under both LCF and CFI.

TS-WHT: A Two-Step Walsh–Hadamard Transform Approach for Blind Error Correcting Code Classification

Blind classification of error-correcting codes is essential for intelligent wireless communication, cognitive radio, and non-cooperative communication. The object of this paper is to investigate the differences in the Walsh spectrum between linear block code, convolutional code, and Turbo code. Based on these differences, a two-step Walsh Hadamard transform (TS-WHT) strategy is proposed for error-correcting code blind classification. Further, according to the detection theory, the peak detection threshold is detailed and optimized with the cyclic feature of CRC codes. The results indicate that when employing the optimized threshold, probabilities of correct classification are superior for linear block codes and convolutional codes. Moreover, with the help of the fault-tolerant feature of the WHT technique, the classification accuracy of the proposed method is substantially better than existing approaches under low signal-to-noise ratio(SNR) conditions.

Determination of Optimal Predictors and Sampling Frequency to Develop Nutrient Soft Sensors Using Random Forest.

Despite advancements in sensor technology, monitoring nutrients in situ and in real-time is still challenging and expensive. Soft sensors, based on data-driven models, offer an alternative to direct nutrient measurements. However, the high demand for data required for their development poses logistical issues with data handling. To address this, the study aimed to determine the optimal subset of predictors and the sampling frequency for developing nutrient soft sensors using random forest. The study used water quality data at 15-min intervals from 2 automatic stations on the Main River, Germany, and included dissolved oxygen, temperature, conductivity, pH, streamflow, and cyclical time features as predictors. The optimal subset of predictors was identified using forward subset selection, and the models fitted with the optimal predictors produced R2 values above 0.95 for nitrate, orthophosphate, and ammonium for both stations. The study then trained the models on 40 sampling frequencies, ranging from monthly to 15-min intervals. The results showed that as the sampling frequency increased, the model's performance, measured by RMSE, improved. The optimal balance between sampling frequency and model performance was identified using a knee-point determination algorithm. The optimal sampling frequency for nitrate was 3.6 and 2.8 h for the 2 stations, respectively. For orthophosphate, it was 2.4 and 1.8 h. For ammonium, it was 2.2 h for 1 station. The study highlights the utility of surrogate models for monitoring nutrient levels and demonstrates that nutrient soft sensors can function with fewer predictors at lower frequencies without significantly decreasing performance.

Cross-Category Highlight Detection via Feature Decomposition and Modality Alignment

Learning an autonomous highlight video detector with good transferability across video categories, called Cross-Category Video Highlight Detection(CC-VHD), is crucial for the practical application on video-based media platforms. To tackle this problem, we first propose a framework that treats the CC-VHD as learning category-independent highlight feature representation. Under this framework, we propose a novel module, named Multi-task Feature Decomposition Branch which jointly conducts label prediction, cyclic feature reconstruction, and adversarial feature reconstruction to decompose the video features into two independent components: highlight-related component and category-related component. Besides, we propose to align the visual and audio modalities to one aligned feature space before conducting modality fusion, which has not been considered in previous works. Finally, the extensive experimental results on three challenging public benchmarks validate the efficacy of our paradigm and the superiority over the existing state-of-the-art approaches to video highlight detection.

Edge UAV Detection Based on Cyclic Spectral Feature: An Intelligent Scheme

With the commercialization of the fifth-generation mobile communication network (5G), the scale of the unmanned aerial vehicle (UAV) industry has continued to expand. However, the unregistered UAV has caused frequent harassment incidents at international airports, and the problem of UAV crimes is increasing. Radio technology supports long-distance detection of unregistered UAV and can be used as an efficient early warning method for unregistered UAV, which has attracted extensive attention from academia and industry. The classic UAV detection based on remote control signal method faces technical bottlenecks such as being easily affected by environmental noise, high complexity, and low detection accuracy. In the paper, an UAV remote control signal detection method is proposed based on cyclic spectrum features. More specifically, a dataset of UAV remote control signal UAV-CYCset is firstly constructed in the frequency domain. Based on UAV-CYCset dataset, a network architecture is proposed based on improved AlexNet, and the average detection accuracy of the improved model reaches 85% (from -10 dB to 10 dB) according to the simulation experiments.

Influence of cyclic hardening characteristic on seismic performance of welded austenitic stainless steel H-section beam-column

Previous studies have revealed that the cyclic hardening feature of austenitic stainless steel depended on the applied strain amplitude and exhibited unsaturation prior to failure, which greatly affected the seismic response of austenitic stainless steel structures. In view of the limited research on the seismic performance and design of structural stainless steel members, this study investigates the influence of cyclic hardening characteristic on the performance of welded austenitic stainless steel H-section beam-column under combined uniaxial cyclic bending and constant compression. The finite element (FE) models of unsaturated cyclic hardening austenitic stainless steel grade S30408 beam-columns with H-section were initially created and validated with the available test data. The effects of loading history in conjunction with the axial compressive load level and the cross-section slenderness on the seismic performance of specimens were then studied through extensive parameter analysis. Results show that the capacities of load-carrying and deformation for the austenitic stainless steel member are discrete under different loading histories. Apparently, the unsaturated cyclic hardening property of stainless steel S30408 influences the performance of the member under seismic loading. Moreover, current codified design methods and the Continuous Strength Method (CSM) underestimated the ultimate strengths of stainless steel beam-columns under seismic loading. The over-strength factor and seismic deformation capacity design formulae of welded austenitic stainless steel H-section beam-columns were finally proposed.

Gaussian-Distributed Spread-Spectrum for Covert Communications.

Covert communication techniques play a crucial role in military and commercial applications to maintain the privacy and security of wireless transmissions from prying eyes. These techniques ensure that adversaries cannot detect or exploit the existence of such transmissions. Covert communications, also known as low probability of detection (LPD) communication, are instrumental in preventing attacks such as eavesdropping, jamming, or interference that could compromise the confidentiality, integrity, and availability of wireless communication. Direct-sequence spread-spectrum (DSSS) is a widely used covert communication scheme that expands the bandwidth to mitigate interference and hostile detection effects, reducing the signal power spectral density (PSD) to a low level. However, DSSS signals possess cyclostationary random properties that an adversary can exploit using cyclic spectral analysis to extract useful features from the transmitted signal. These features can then be used to detect and analyse the signal, making it more susceptible to electronic attacks such as jamming. To overcome this problem, a method to randomise the transmitted signal and reduce its cyclic features is proposed in this paper. This method produces a signal with a probability density function (PDF) similar to thermal noise, which masks the signal constellation to appear as thermal white noise to unintended receivers. This proposed scheme, called Gaussian distributed spread-spectrum (GDSS), is designed such that the receiver does not need to know any information about the thermal white noise used to mask the transmit signal to recover the message. The paper presents the details of the proposed scheme and investigates its performance in comparison to the standard DSSS system. This study used three detectors, namely, a high-order moments based detector, a modulation stripping detector, and a spectral correlation detector, to evaluate the detectability of the proposed scheme. The detectors were applied to noisy signals, and the results revealed that the moment-based detector failed to detect the GDSS signal with a spreading factor, N = 256 at all signal-to-noise ratios (SNRs), whereas it could detect the DSSS signals up to an SNR of -12 dB. The results obtained using the modulation stripping detector showed no significant phase distribution convergence for the GDSS signals, similar to the noise-only case, whereas the DSSS signals generated a phase distribution with a distinct shape, indicating the presence of a valid signal. Additionally, the spectral correlation detector applied to the GDSS signal at an SNR of -12 dB showed no identifiable peaks on the spectrum, providing further evidence of the effectiveness of the GDSS scheme and making it a favourable choice for covert communication applications. A semi-analytical calculation of the bit error rate is also presented for the uncoded system. The investigation results show that the GDSS scheme can generate a noise-like signal with reduced identifiable features, making it a superior solution for covert communication. However, achieving this comes at a cost of approximately 2 dB on the signal-to-noise ratio.