Artificial Signals Research Articles

Adaptive Recursive Variational Mode Decomposition for Multiple Engine Faults Detection

Engine fault detection is critical to enhancing the reliability of modern equipment. However, it is challenging to obtain a large number of high-quality labeled data for engines, which is not conducive to improving the training accuracy of deep learning methods. Therefore, this article proposes a fault detection method combining adaptive recursive variational mode decomposition (ARVMD) and component energy distribution spectrum (CEDS). The paper first introduces recursive mode into VMD. Then, the mode number is dynamically selected according to the energy distribution of the power spectral density to extract the intrinsic mode functions (IMFs) continuously. The quadratic penalty term is optimized correspondingly using SNR. The decomposition results of artificial and real signals demonstrated that ARVMD has higher SNR and efficiency than VMD. Next, the center frequency and unit bandwidth energy of IMF are used to construct CEDS. Fault diagnosis is realized by the CEDS correlation ranking of various faults. Finally, two case studies are performed to illustrate the effectiveness of the proposed method. The results show that the proposed ARVMD-CEDS method provides an efficient and effective solution for single-channel engine fault diagnosis.

25-річний досвід створення та впровадження інтелектуальних інформаційних технологій оброблення біометричних сигналів складної форми

Introduction. An important area of modern information technology application is medical diagnostics, which is based on computer processing of biomedical signals The purpose of the article is to provide information on the results of basic and applied research that has ensured the practical implementation of the ECG method (fasegraphy method) in various fields of application and to outline further prospects for these studies. Methods. The technology is based on a stochastic model of generating an artificial signal of complex shape in terms of internal and external distortions. Results. It is shown that the efficiency in extracting diagnostic information from biomedical signals in conditions of the real distortions, which are not always additive in nature, can be increased by switching from a scalar signal in the time domain to a cognitive image in the phase plane. Original algorithms of adaptive filtering and smoothing have been developed, which made it possible to obtain a numerical estimate of the first derivative of the distorted signal. Recovery of the useful signal (reference cycle) for distorted implementations is carried out by averaging the phase trajectories with the subsequent return to the time domain. To increase the reliability of additional diagnostic features of the ECG in the phase space is proposed and clinical data have proven their usefulness in terms of reducing the risk of misdiagnosis. The practical results of the implementation of the diagnostic complex FASEGRAF® have confirmed the effectiveness of fasegraphy in various fields of application. Plans for further prospective research are presented. Conclusions. Continuation of research allow to create new competitive information technologies and digital medicine devices.

Dynamic phenomena prevention management in mines of SUEK-Kuzbass

The article presents information on coal seams which are hazardous and susceptible to dynamic phenomena in mines of SUEK-Kuzbass. The current geomechanical and geophysical methods used to predict dynamic phenomena, the associated problems and the potential ways of their solutions are described. The geomechanical approach to rockburst prediction uses the analysis of yield of chippings. Coal and gas outbursts are predicted based on the initial gas emission rate in drilling. The geophysical method used to predict dynamic phenomena is based on the parameters of artificial acoustic signals from SAKSM stationary facilities. When no rockburst prediction is implemented based on the artificial acoustic signal parameters for some reasons, the forecast is carried out using portable equipment ANGEL–M. This study focuses on the improvement of the method of the artificial acoustic signal parameters in order to eliminate complications of the dynamic phenomenon hazard assessment. The first circumstance is the dependence of the hazard indicator on the spacing of the geophone and the acoustic signal source. The engineering and managerial decisions discussed in this article enable maintenance of the constant spacing at an allowable error. The second circumstance is that the hazard criterion is determined as a sum of averaged hazard indicator after not less than 30 cycles of heading and two–three values of standard deviations from the average. Such criterion fails to image actual hazard. For this reason, it is planned to check the hazard criterion procedure based on the discussed approach and on the geomechanical method which has a specified hazard criterion value from regulating documents. The third circumstance is the neglect of the gas criterion as the formation gas pressure has almost zero influence on the parameters of artificial acoustic signals. This circumstance can be avoided using the proposed algorithm of prediction with adjustment of the hazard criterion based on the control data on methane concentration in mine air and on the coal strength.The authors are grateful to SUEK-Kuzbass for the information provided under the cooperation agreement with Federal Research Center for Coal and Coal Chemistry, Siberian Branch, Russian Academy of Sciences.The study was supported by the Russian Science Foundation, Project No. 17-17-01143.

Analyzing the Influence of Harmonic Parameters on Accuracy Indices When Applying Wavelet Transform

The paper deals with the evaluation of root mean square deviations and maximum absolute relative errors associated to the decomposition followed by recomposition based on Wavelet Packet Transform (WPT) of signals pol-luted with harmonics. Subtrees associated to sets of harmonics presenting practical interest for industrial applications are addressed. The study uses artificial signals generated through the superposition of perfect sinusoids with pairs of harmonics which proved to be related in an almost exclusive manner to pairs of nodes from the bottom level of a WPT tree. 4 parameters had to be considered when determining the maximum and minimum values of errors for each set: the clustered harmonics’ magnitudes and their phase-shifts relative to the component of fundamental frequency. The decomposition/recomposition are time-efficient due to an original system of flags labeling each node from the WPT tree. For each analyzed set of harmonics, 3d graphical representation of minimum and maximum errors along with the associated 3d graphical representation of the phase-shifts are provided. At the same time, per set limits of errors ranges were established and discussed while specific patterns were deduced for the context in which extreme errors appear (phase-shifts and harmonic magnitudes). The results were commented, and conclusions were drawn.

Exoplanet detection using machine learning

Abstract We introduce a new machine learning based technique to detect exoplanets using the transit method. Machine learning and deep learning techniques have proven to be broadly applicable in various scientific research areas. We aim to exploit some of these methods to improve the conventional algorithm based approaches presently used in astrophysics to detect exoplanets. Using the time-series analysis library TSFresh to analyse light curves, we extracted 789 features from each curve, which capture the information about the characteristics of a light curve. We then used these features to train a gradient boosting classifier using the machine learning tool lightgbm. This approach was tested on K2 campaign 7 data with injected artificial transit signals, which showed that it is competitive compared to the conventional box least squares fitting (BLS) method. We further found that our method produced comparable results to existing state-of-the-art deep learning models, while being much more computationally efficient and without needing folded and secondary views of the light curves. For Kepler data, the method is able to predict a planet with an AUC of 0.948, so that 94.8 per cent of the true planet signals are ranked higher than non-planet signals. The resulting recall is 0.96, so that 96 per cent of real planets are classified as planets. For the Transiting Exoplanet Survey Satellite (TESS) data, we found our method can classify light curves with an accuracy of 0.98, and is able to identify planets with a recall of 0.82 at a precision of 0.63.

Engineering a Controllable Targeted Protein Degradation System and a Derived OR-GATE-Type Inducible Gene Expression System in Synechococcus elongatus PCC 7942.

Cyanobacteria are important model organisms for exploring the mechanisms of photosynthesis and are considered as promising microbial platforms for photosynthetic biomanufacturing. The development of efficient cyanobacteria cell factories requires efficient and convenient tools to dynamically regulate and manipulate target proteins, modules, and pathways. Targeted protein degradation is important to achieve rapid responses of cellular metabolic networks to artificial or environmental signals, and there are currently limited approaches to induce protein degradation in cyanobacteria. In this work, we developed an Escherichia coli sourced ssrA-tagging system in an important cyanobacteria strain, Synechococcus elongatus PCC 7942, to achieve inducible degradation of target proteins. A modified version of the E. coli ssrA tag (ssrADAS) proved to be immune to the native ClpXP system in Synechococcus elongatus PCC 7942, while induced expression of the E. coli sourced adaptor SspB and ClpXP resulted in effective degradation of the tagged proteins. Compared to the previously developed down-regulation approaches, the inducible ssrADAS-SspB-ClpXPEc system facilitated the smart and rapid degradation of target proteins in PCC7942 cells at different growth stages. Furthermore, when used to regulate the degradation of LacI, the repressor element of LacO-LacI transcription regulation system, an efficient and stringent inducible gene expression system was obtained based on an OR-GATE type genetic circuit design. The tools developed in this work expanded the cyanobacteria synthetic biology toolbox and will facilitate the success of future dynamic metabolic engineering.

Cross-Entropy Learning for Aortic Pathology Classification of Artificial Multi-Sensor Impedance Cardiography Signals.

An aortic dissection, a particular aortic pathology, occurs when blood pushes through a tear between the layers of the aorta and forms a so-called false lumen. Aortic dissection has a low incidence compared to other diseases, but a relatively high mortality that increases with disease progression. An early identification and treatment increases patients’ chances of survival. State-of-the-art medical imaging techniques have several disadvantages; therefore, we propose the detection of aortic dissections through their signatures in impedance cardiography signals. These signatures arise due to pathological blood flow characteristics and a blood conductivity that strongly depends on the flow field, i.e., the proposed method is, in principle, applicable to any aortic pathology that changes the blood flow characteristics. For the signal classification, we trained a convolutional neural network (CNN) with artificial impedance cardiography data based on a simulation model for a healthy virtual patient and a virtual patient with an aortic dissection. The network architecture was tailored to a multi-sensor, multi-channel time-series classification with a categorical cross-entropy loss function as the training objective. The trained network typically yielded a specificity of and a sensitivity of . A study of the accuracy as a function of the size of an aortic dissection yielded better results for a small false lumen with larger noise, which emphasizes the question of the feasibility of detecting aortic dissections in an early state.

MEC-Based Jamming-Aided Anti-Eavesdropping with Deep Reinforcement Learning for WBANs

Wireless body area network (WBAN) suffers secure challenges, especially the eavesdropping attack, due to constraint resources. In this article, deep reinforcement learning (DRL) and mobile edge computing (MEC) technology are adopted to formulate a DRL-MEC-based jamming-aided anti-eavesdropping (DMEC-JAE) scheme to resist the eavesdropping attack without considering the channel state information. In this scheme, a MEC sensor is chosen to send artificial jamming signals to improve the secrecy rate of the system. Power control technique is utilized to optimize the transmission power of both the source sensor and the MEC sensor to save energy. The remaining energy of the MEC sensor is concerned to ensure routine data transmission and jamming signal transmission. Additionally, the DMEC-JAE scheme integrates with transfer learning for a higher learning rate. The performance bounds of the scheme concerning the secrecy rate, energy consumption, and the utility are evaluated. Simulation results show that the DMEC-JAE scheme can approach the performance bounds with high learning speed, which outperforms the benchmark schemes.

Wavelet transform-based cross-correlation in the time-delay estimation applications

The article discusses the application of wavelet analysis for the time-frequency time-delay estimation. The proposed algorithm is wavelet transform-based cross-correlation time delay estimation that applies discrete time wavelet transform to filter the input signal prior to computation of cross-correlation function. The distinguishing feature of the algorithm that it uses the variation of continuous wavelet transform to process the discrete signals instead of dyadic wavelet transform that is normally applied to the case. Another feature that the implication of convolution theorem is used to compute coefficients of the wavelet transform. This makes possible to omit redundant discrete Fourier transforms and significantly reduce the computational complexity. The principal applicability of the proposed method is shown in the course of a computational experiments with artificial and real-world signal. So the method demonstrated expected selectivity for the signals localized in the different frequency bands. The application of the method to practical case of pipeline leak detection was also successful. However, the study concluded that this method provides no specific advantages in comparison with the conventional one. In the future, alternative applications in biological signal processing will be considered.

Scaling K2. IV. A Uniform Planet Sample for Campaigns 1–8 and 10–18

Abstract We provide the first full K2 transiting exoplanet sample, using photometry from Campaigns 1–8 and 10–18, derived through an entirely automated procedure. This homogeneous planet candidate catalog is crucial to perform a robust demographic analysis of transiting exoplanets with K2. We identify 747 unique planet candidates and 57 multiplanet systems. Of these candidates, 366 have not been previously identified, including one resonant multiplanet system and one system with two short-period gas giants. By automating the construction of this list, measurements of sample biases (completeness and reliability) can be quantified. We carried out a light-curve-level injection/recovery test of artificial transit signals and found a maximum completeness of 61%, a consequence of the significant detrending required for K2 data analysis. Through this operation we attained measurements of the detection efficiency as a function of signal strength, enabling future population analysis using this sample. We assessed the reliability of our planet sample by testing our vetting software EDI-Vetter against inverted transit-free light curves. We estimate that 91% of our planet candidates are real astrophysical signals, increasing up to 94% when limited to the FGKM dwarf stellar population. We also constrain the contamination rate from background eclipsing binaries to less than 5%. The presented catalog, along with the completeness and reliability measurements, enable robust exoplanet demographic studies to be carried out across the fields observed by the K2 mission for the first time.

How should we model and evaluate breathing interplay effects in IMPT?

Breathing interplay effects in Intensity Modulated Proton Therapy (IMPT) arise from the interaction between target motion and the scanning beam. Assessing the detrimental effect of interplay and the clinical robustness of several mitigation techniques requires statistical evaluation procedures that take into account the variability of breathing during dose delivery. In this study, we present such a statistical method to model intra-fraction respiratory motion based on breathing signals and assess clinical relevant aspects related to the practical evaluation of interplay in IMPT such as how to model irregular breathing, how small breathing changes affect the final dose distribution, and what is the statistical power (number of different scenarios) required for trustworthy quantification of interplay effects. First, two data-driven methodologies to generate artificial patient-specific breathing signals are compared: a simple sinusoidal model, and a precise probabilistic deep learning model generating very realistic samples of patient breathing. Second, we investigate the highly fluctuating relationship between interplay doses and breathing parameters, showing that small changes in breathing period result in large local variations in the dose. Our results indicate that using a limited number of samples to calculate interplay statistics introduces a bigger error than using simple sinusoidal models based on patient parameters or disregarding breathing hysteresis during the evaluation. We illustrate the power of the presented statistical method by analyzing interplay robustness of 4DCT and Internal Target Volume (ITV) treatment plans for a 8 lung cancer patients, showing that, unlike 4DCT plans, even 33 fraction ITV plans systematically fail to fulfill robustness requirements.

High Performance Double Conductive Network Hydrogel Based on Soaking Strategy for Supercapacitors

AbstractHigh performance and high conductivity have always been important indicators for the application of hydrogels in the field of soft electronics. However, it is difficult for hydrogels to have both high conductivity and excellent mechanical properties. In this paper, a simple and feasible method is proposed, which allows polyvinyl alcohol to be embedded in the porous polyacrylamide chemical crosslinked network to form a structure of rich pores, and then soaked in salt solution to form a dense structure of hydrogels. In order to obtain good mechanical properties and electrical conductivity synchronously the hydrogel is soaked in KCl and NaCl solutions, respectively. However, different properties hydrogels are obtained after soaked in different salt solution, such as the strain of hydrogel after soaked in NaCl solution reached 750%, the conductivity of hydrogel after soaked in KCl solution is high (15.7 S m−1) and performed excellent conductivity even at low temperature (0 °C). In addition to the hydrogel after soaked in KCl solution showed 13.3 mF cm−2 high capacitance, the CV curve with regular shape and low temperature (0 °C) usage capacity when used as super capacitor. The conductive hydrogel can be used as artificial neural signal sensor, allowing produce artificial skin in medical field.

Evaluating the impact of NIR pre-processing methods via multiblock partial least-squares

Near-infrared (NIR) spectral data are used in many applications to predict physical and chemical properties. However, it can result in poor predictive models when untreated spectra are directly used to estimate these properties. Many pre-preprocessing techniques are available to reduce noise and variance unrelated to the studied property but choosing which one to apply can be tricky. Existing methods to select a pre-processing are time-consuming or do not allow for a meaningful comparison of the different techniques. Even though new methods focus on extracting complementary information from each pre-processing, an optimal combination is still required to obtain efficient predictive models and avoid extensive computational costs. Here, we propose an approach using multiblock partial least squares (MBPLS) to simultaneously compare the impact of the pre-processing techniques on spectral data and as a result on the regression models. Superloadings provide qualitative and quantitative information on pre-processed data. This tool helps compare and determine which pre-processing technique, or combinations thereof, that may be appropriate for a dataset, not just a single “best” one. Using this, the analyst is then better equipped to make a final choice when selecting which ones to include. This method is tested on artificial signals and NIR spectra from corn samples.

Artificial Intelligence Robots And Revolutionizing Society In Terms Of Technology, Innovation, Work And Power

In what ways do recent advances in robotics and artificial intelligence signal a historic economic and social transformation? This study presents a critical assessment of the subject, critiques many of the ongoing debates of technical determinism, and reframes the issues involved within a political-economic and social framework. It focuses on technological innovation's economic, political, and historical processes and their implications for employment and financial restructuring, mediated by sovereign and discursive authorities. Several epistemological and empirical issues with a transformational viewpoint are noted, and alternative views are suggested to highlight the complexity and ambiguity surrounding current and future trends. Robotics entered when the industrial revolution had many significant changes for humans. Efficiency is increased to produce various productive things. This paper uses a literature study as a method. The results of his discussion on Revolutionizing Society In Terms Of Technology, Innovation, Work And Power of Robotics.

Artificial Intelligence, Machine Learning, and Signal Processing: Researchers are using artificial intelligence, machine learning, and signal processing to build powerful three-level platforms to help meet project goals [Special Reports

Researchers in an almost endless number of fields are embracing artificial intelligence (AI) and machine learning (ML) to develop tools and systems that can predict and adapt to a wide range of changing situations, optimize system performance, and intelligently filter signals. In areas as diverse as firefighter protection, solar power optimization, and exoplanet discovery, researchers are turning to AI, ML, and signal processing to help them achieve breakthroughs that were unimaginable only a few years ago.

Adaptive Threshold Scheme for Pulse Detection under Condition of Background Nonstationary Noise

The paper describes a new adaptive threshold scheme for detecting pulses in high-frequency signals against a background of non-stationary noise. The result of the scheme operation is to determine the pulse boundaries by comparing the signal amplitude-time parameters with the threshold. The threshold value is calculated in non-overlapping windows of fixed length and depends only on the background noise level. The detected pulses undergo additional shape checking, taking into account their characteristics. The parameters of the algorithms for detecting pulses and checking their shape can be adjusted for any type of high-frequency pulse signals. This threshold scheme is tuned to detect pulses in high frequency geoacoustic emission signals. The results of the scheme operation on an artificial signal and on fragments of a geoacoustic signal are given, a comparison is made between the proposed scheme and the previously used (outdated) one. The new threshold scheme proposed by the authors is less sensitive to the choice of the initial threshold value and it is more stable in operation. When processing 15-minute fragments of a geoacoustic signal, the new scheme correctly detects, on average, 5 times more pulses.

Robust 3D-Trajectory and Time Switching Optimization for Dual-UAV-Enabled Secure Communications

This paper investigates a dual-unmanned aerial vehicle (UAV)-enabled secure communication system, in which, a UAV moves around to send confidential messages to a mobile user while another cooperative UAV transmits artificial noise signals to confuse malicious eavesdroppers. Both UAVs have energy constraints and the location information of eavesdroppers is imperfect. We consider a worst-case secrecy rate maximization problem of the mobile user over all time slots. This optimization problem is solved by jointly designing the three-dimensional (3D) trajectory of UAVs and the time allocation (recharging and service or jamming) under practical constraints including maximum UAV speed, UAV collision avoidance, UAV positioning error, and UAV energy harvesting. Specifically, we adopt a more practical UAV-ground channel model with both large-scale and small-scale fading components. Due to the non-convex feasible region constructed by the complicated constraints, directly finding the optimal solution of the original problem is intractable. To address this issue, we decouple the original optimization problem into three subproblems and develop an iterative algorithm to find its suboptimal solution by using the block coordinate descent technique. To solve each subproblem, certain advanced optimization tools, such as integer relaxation, S-procedure, and successive convex approximation techniques, are utilized. Numerical simulation results are provided to corroborate the theoretical derivations and to evaluate the performance of the proposed algorithm. Additionally, the numerical results assist to draw new insights on the 3D UAV trajectory by comparing the performance with conventional two-dimensional (2D) schemes.

Responsiveness to inhibitory signals changes as a function of colony size in honeybees (Apis mellifera).

Biological collectives, like honeybee colonies, can make intelligent decisions and robustly adapt to changing conditions via intricate systems of excitatory and inhibitory signals. In this study, we explore the role of behavioural plasticity and its relationship to network size by manipulating honeybee colony exposure to an artificial inhibitory signal. As predicted, inhibition was strongest in large colonies and weakest in small colonies. This is ecologically relevant for honeybees, for which reduced inhibitory effects may increase robustness in small colonies that must maintain a minimum level of foraging and food stores. We discuss evidence for size-dependent plasticity in other types of biological networks.

Aliasing affects ActiLife software raw accelerometry to count conversion from different sampling frequencies.

Accelerometry counts are widely used to quantify physical activity in an objective manner. ActiGraph™ accelerometers offer to record acceleration signal with different sampling frequency (fs). Nevertheless additional counts were shown to be computed by ActiLife software from acceleration signal with a sampling frequency fs>30 Hz compared to signal with default fs=30 Hz or multiple. This paper relies on the study of synthetic signals to point out the origin of this error and to recommend an adjusted method. A piecewise-frequency sinus time series (0-15 Hz) was generated at different sampling frequencies (fs=30, 50 and 100 Hz). The artificial acceleration raw signal was resampled to 30 Hz using different antialiasing lowpass filters before ActiLife count computation. The use of an antialiasing filter which did not properly attenuate aliasing replicas was found to induce aliasing frequencies within ActiLife bandpass filter which is the cause of extract activity counts. We were able to reproduce fictitious counts for acceleration around 10 Hz. A simple adjustment of antialiasing filter parameters allowed to avoid this problem. This study reproduces ActiLife counts processing from 50 and 100 Hz sampled signal. Count overestimations from fs=50 and 100 Hz signal were induced because of aliasing in the frequency bandwidth of the ActiLife count filter. This can be corrected by a relevant antialiasing filtering before ActiLife software processing or this can be done in high-level mathematical programing.

Enhanced Intrusion Detection System for an EH IoT Architecture Using a Cooperative UAV Relay and Friendly UAV Jammer

In this paper, the detection capabilities and system performance of an energy harvesting (EH) Internet of Things (IoT) architecture in the presence of an unmanned aerial vehicle (UAV) eavesdropper (UE) are investigated. The communication protocol is divided into two phases. In the first phase, a UAV relay (UR) cooperates with a friendly UAV jammer (UJ) to detect the UE, and the UR and UJ harvest energy from a power beacon (PB). In the second phase, a ground base station (GBS) sends a confidential signal to the UR using non-orthogonal multiple access (NOMA); the UR then uses its harvested energy to forward this confidential signal to IoT destinations (IDs) using the decode-and-forward (DF) technique. Simultaneously, the UJ uses its harvested energy to emit an artificial signal to combat the detected UE. A closed-form expression for the probability of detecting the UE (the detection probability, DP) is derived to analyze the detection performance. Furthermore, the intercept probability (IP) and throughput of the considered IoT architecture are determined. Accordingly, we identify the optimal altitudes for the UR and UJ to enhance the system and secrecy performance. Monte Carlo simulations are employed to verify our approach.