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1
Panteleev, Ivan, Aleksander Prokhorov, and Oleg Plekhov. "Strategies for Extracting Damage Induced AE Signals from Different Type Noise-Like Backgrounds for Carbon-Fibre Reinforced Polymers." Applied Sciences 11, no. 16 (August 16, 2021): 7506. http://dx.doi.org/10.3390/app11167506.
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This paper presents an algorithm for isolating a useful acoustic signal (corresponding to damage accumulation) against the background of a signal used to model the performance of an industrial rotary equipment. Acoustic emission signals induced by deformation and fracture were studied using a uniaxial tensile test on woven laminate samples cut along the fiber and weft directions. The background signal is a random composition of acoustic pulses used to model the performance of an industrial rotary equipment. A comparison of useful and noise signals enables us to develop two algorithms based on frequency filtering of a signal and its decomposition into empirical modes. These algorithms can be used to isolate useful AE pulses against the background of all signal intensities under consideration.
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Dutt, Vinayak, and James F. Greenleaf. "Ultrasound Echo Envelope Analysis Using a Homodyned K Distribution Signal Model." Ultrasonic Imaging 16, no. 4 (October 1994): 265–87. http://dx.doi.org/10.1177/016173469401600404.
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The statistics of ultrasound echo envelope signals can be used to characterize scattering media. The Rayleigh distribution and its generalized forms, the K and Rice distributions, have been previously used to model the echo signal. A more generalized statistical model, the homodyned K distribution, combines the K and Rice distribution features to better account for the statistics of the echo signal. We show that this model can give two parameters that are useful for media characterization: k, the ratio of coherent to diffuse signals, and, β, which characterizes the clustering of scatterers in the medium.
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HUANG, XIANGAO, JIANXUE XU, WEI HUANG, and ZEJUN LU. "UNMASKING CHAOTIC MASK BY A WAVELET MULTISCALE DECOMPOSITION ALGORITHM." International Journal of Bifurcation and Chaos 11, no. 02 (February 2001): 561–69. http://dx.doi.org/10.1142/s0218127401002274.
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A method is proposed for extracting a useful signal hidden in a deterministic chaotic signal using a wavelet multiscale decomposition algorithm that can separate two signals with different wavelet scales. The mask removal is done without resorting to chaos synchronization. It is a noise reduction method without knowing the dynamical model of chaotic system, and restricting the modulation ratio of the chaos signal and useful signal. So this method is effective for unmasking chaotic mask through the quantitative analyses and the numerical simulations of three examples.
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Tang, Shixi, Jinan Gu, Keming Tang, Rong Zou, Xiaohong Sun, and Saad Uddin. "A Fault-Signal-Based Generalizing Remaining Useful Life Prognostics Method for Wheel Hub Bearings." Applied Sciences 9, no. 6 (March 14, 2019): 1080. http://dx.doi.org/10.3390/app9061080.
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The goal of this work is to improve the generalization of remaining useful life (RUL) prognostics for wheel hub bearings. The traditional life prognostics methods assume that the data used in RUL prognostics is composed of one specific fatigue damage type, the data used in RUL prognostics is accurate, and the RUL prognostics are conducted in the short term. Due to which, a generalizing RUL prognostics method is designed based on fault signal data. Firstly, the fault signal model is designed with the signal in a complex and mutative environment. Then, the generalizing RUL prognostics method is designed based on the fault signal model. Lastly, the simplified solution of the generalizing RUL prognostics method is deduced. The experimental results show that the proposed method gained good accuracies for RUL prognostics for all the amplitude, energy, and kurtosis features with fatigue damage types. The proposed method can process inaccurate fault signals with different kinds of noise in the actual working environment, and it can be conducted in the long term. Therefore, the RUL prognostics method has a good generalization.
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Islam, Sheikh Md Rabiul, and Md Shakibul Islam. "Neural Mass Model-Based Different EEG Signal Generation and Analysis in Simulink." Indian Journal of Signal Processing 1, no. 3 (August 10, 2021): 1–7. http://dx.doi.org/10.35940/ijsp.c1008.081321.
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The electroencephalogram (EEG) is an electrophysiological monitoring strategy that records the spontaneous electrical movement of the brain coming about from ionic current inside the neurons of the brain. The importance of the EEG signal is mainly the diagnosis of different mental and brain neurodegenerative diseases and different abnormalities like seizure disorder, encephalopathy, dementia, memory problem, sleep disorder, stroke, etc. The EEG signal is very useful for someone in case of a coma to determine the level of brain activity. So, it is very important to study EEG generation and analysis. To reduce the complexity of understanding the pathophysiological mechanism of EEG signal generation and their changes, different simulation-based EEG modeling has been developed which are based on anatomical equivalent data. In this paper, Instead of a detailed model a neural mass model has been used to implement different simulation-based EEG models for EEG signal generation which refers to the simplified and straightforward method. This paper aims to introduce obtained EEG signals of own implementation of the Lopes da Silva model, Jansen-Rit model, and Wendling model in Simulink and to compare characteristic features with real EEG signals and better understanding the EEG abnormalities especially the seizure-like signal pattern.
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Islam, Sheikh Md Rabiul, and Md Shakibul Islam. "Neural Mass Model-Based Different EEG Signal Generation and Analysis in Simulink." Indian Journal of Signal Processing 1, no. 3 (August 10, 2021): 1–7. http://dx.doi.org/10.54105/ijsp.c1008.081321.
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The electroencephalogram (EEG) is an electrophysiological monitoring strategy that records the spontaneous electrical movement of the brain coming about from ionic current inside the neurons of the brain. The importance of the EEG signal is mainly the diagnosis of different mental and brain neurodegenerative diseases and different abnormalities like seizure disorder, encephalopathy, dementia, memory problem, sleep disorder, stroke, etc. The EEG signal is very useful for someone in case of a coma to determine the level of brain activity. So, it is very important to study EEG generation and analysis. To reduce the complexity of understanding the pathophysiological mechanism of EEG signal generation and their changes, different simulation-based EEG modeling has been developed which are based on anatomical equivalent data. In this paper, Instead of a detailed model a neural mass model has been used to implement different simulation-based EEG models for EEG signal generation which refers to the simplified and straightforward method. This paper aims to introduce obtained EEG signals of own implementation of the Lopes da Silva model, Jansen-Rit model, and Wendling model in Simulink and to compare characteristic features with real EEG signals and better understanding the EEG abnormalities especially the seizure-like signal pattern.
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Lv, Mingzhu, Shixun Liu, Xiaoming Su, and Changzheng Chen. "General Log-Linear Weibull Model Combining Vibration and Temperature Characteristics for Remaining Useful Life Prediction of Rolling Element Bearings." Shock and Vibration 2020 (August 20, 2020): 1–17. http://dx.doi.org/10.1155/2020/8829823.
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In industrial applications, the vibration and temperature measurements of rolling element bearings are known as two popular condition monitoring methods. The previously published method for remaining useful life (RUL) prediction has been limited to using the vibration signal. However, a single signal source cannot fully reflect the degradation trend of bearings, influencing the RUL prediction precision. In this paper, a novel general log-linear Weibull (GLL-Weibull) model is constructed by considering vibration and temperature condition monitoring signals to estimate the model parameters. During the feature extraction stage, the relative root mean square (RRMS) is derived from the monitored vibration signal, and the relative temperature trend value is extracted from the monitored temperature signal to eliminate individual differences in bearings and random signal fluctuations. Then, a fuzzy operator is introduced to describe the degree of an “overheated bearing” and “excessive bearing vibrations.” During the RUL prediction stage, both the extracted vibration and temperature characteristics are used to create the GLL-Weibull model. The best parameters are attained by employing the maximum likelihood estimation approach. The algorithm performance is checked with criteria like the root mean square error (RMSE) and the mean absolute percentage error (MAPE). The effectiveness and superiority of the presented approach are validated by two real-life prognosis cases. According to the experimental results, the presented approach provides superior prediction precision and lower computational cost than other approaches for bearings under constant or variable operating conditions.
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Otto, G. K. "Interferometry feedback in the laser resonator. Parametric model." Bulletin of Taras Shevchenko National University of Kyiv. Series: Physics and Mathematics, no. 3 (2019): 62–69. http://dx.doi.org/10.17721/1812-5409.2019/3.9.
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The article is devoted to an alternative view on the explanation of the mechanism of action of devices based on laser feedback interferometry (LFI-model), which is caused by contradictions between the theory of existing models and practical results. A view other than the Lang-Kobayashi model (LK model) and the parametric LFI model (P model) are proposed. Due to the theory, which is based on the parametric properties of the LFI-model, the level of advantage of LFI technology over traditional technologies is quantitatively substantiated. The article gives an example of calculating the useful signal power. It is calculated that at a distance of 500 meters, the signal according to the P-model is 34 dB higher than the signal calculated by traditional models. Thus, from the traditional models follows the inverse square dependence of the signal-to-noise ratio (S / N ratio, hereinafter - SNR) on the distance to the target l_t, viz.: SNR ~ l_t^{-2}. In practice, the SNR is much higher. Within the P-model, another dependence of SNR on l_t, is theoretically proved and experimentally confirmed, viz.: SNR ~ (l_t * ln l_t^2)^{-1/2}. Traditional models do not consider the presence of a useful signal in the pump current, while, in fact, its power is more than 10 times greater than the radiation power in the resonator. The P-model eliminates contradictions between theoretical models and practical results.
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Hamam, Zeina, Nathalie Godin, Claudio Fusco, Aurélien Doitrand, and Thomas Monnier. "Acoustic Emission Signal Due to Fiber Break and Fiber Matrix Debonding in Model Composite: A Computational Study." Applied Sciences 11, no. 18 (September 10, 2021): 8406. http://dx.doi.org/10.3390/app11188406.
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Acoustic emission monitoring is a useful technique to deal with detection and identification of damage in composite materials. Over the last few years, identification of damage through intelligent signal processing was particularly emphasized. Data-driven models are developed to predict the remaining useful lifetime. Finite elements modeling (FEM) was used to simulate AE signals due to fiber break and fiber/matrix debonding in a model carbon fiber composite and thereby better understand the AE signals and physical phenomena. This paper presents a computational analysis of AE waveforms resulting from fiber break and fiber/matrix debonding. The objective of this research was to compare the AE signals from a validated fiber break simulation to the AE signals obtained from fiber/matrix debonding and fiber break obtained in several media and to discuss the capability to detect and identify each source.
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Bae, Hyeon, Youn-Tae Kim, Sungshin Kim, Sang-Hyuk Lee, and Bo-Hyeun Wang. "Fault Detection of Induction Motors Using Fourier and Wavelet Analysis." Journal of Advanced Computational Intelligence and Intelligent Informatics 8, no. 4 (July 20, 2004): 431–36. http://dx.doi.org/10.20965/jaciii.2004.p0431.
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The motor is the workhorse of industries. The issues of preventive and condition-based maintenance, online monitoring, system fault detection, diagnosis, and prognosis are of increasing importance. This paper introduces fault detection for induction motors. Stator currents are measured by current meters and stored by time domain. The time domain is not suitable for representing current signals, so the frequency domain is applied to display signals. The Fourier Transform is employed to convert signals. After signal conversion, signal features must be extracted by signal processing such as wavelet and spectrum analysis. Features are entered in a pattern classification model such as a neural network model, a polynomial neural network, or a fuzzy inference model. This paper describes fault detection results that use Fourier and wavelet analysis. This combined approach is very useful and powerful for detecting signal features.
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Zhang, Yan, Guo Ying Zeng, Deng Feng Zhao, and Ming Yan Li. "Condition Identification of Bolted Joints Based on Autoregressive Model." Advanced Materials Research 433-440 (January 2012): 617–21. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.617.
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The difference signals, which between upper and lower shells of flange bolted joints structure, was applied to establish the autoregressive model for system condition identification. Firstly, an AR model is built by difference signals. The established AR model is used as a filter to process the difference signal in test state under the same condition and output residual series. Then the statistical parameters, such as Itakura distance, skewness, kurtosis and variance, are used to handle residual series. The results of experiment show that Itakura distance is a useful eigenvalue to identify the bolted joints condition.
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Mushenko, Alexey, Julia Dzuba, Alexey Nekrasov, and Colin Fidge. "A Data Secured Communication System Design Procedure with a Chaotic Carrier and Synergetic Observer." Electronics 9, no. 3 (March 18, 2020): 497. http://dx.doi.org/10.3390/electronics9030497.
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We have considered the problem of secure communication by means of chaotic carrier communication channels. Data protection is provided if the signal in the communication channel looks like a stochastic or noisy one, i.e., by the steganography property, and also by using advanced and secured approaches of mixing a useful signal into a chaotic carrier. The problem is to find effective and efficient techniques for useful signal reconstruction at the receiver side. We firstly use a synergetic nonlinear mathematical observer to recover the two useful signals transmitted simultaneously over a single communication channel. Compared to a known observer, the synergetic one operates with initial nonlinear models, i.e., it may be applied directly to chaotic systems. In that system structure, we consider the useful signals as unobservable variables and estimate them by the given model of a chaotic generator. A chaotic data transmission system with two channels is demonstrated; the presented mathematical procedure explains the steps of synergetic two-channel observer design. Computer simulation results prove the viability of our approach. The proposed data transmission scheme and the observer design procedure provide effective transmission and reconstruction of informational signals. Reconstruction error is up to approximately 25% but has a very short pulses shape and could be eliminated with further analog or digital filtering algorithms. As an example application, simulation of image transmission and recovery is demonstrated. The study’s results may be used as a basis for further research into secure data transmission system design.
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Huttova, Veronika, Jakub Rafl, Knut Möller, Thomas E. Bachman, Petr Kudrna, Martin Rozanek, and Karel Roubik. "Model of SpO2 signal of the neonate." Current Directions in Biomedical Engineering 5, no. 1 (September 1, 2019): 549–52. http://dx.doi.org/10.1515/cdbme-2019-0138.
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AbstractThe advantages of automatic control of the fraction of inspired oxygen in neonates have been documented in recently published clinical trials. Many control algorithms are available, but their comparison is missing in the literature. A mathematical model of neonatal oxygen transport could be a useful tool to compare and enhance both automatic control algorithms and manual control of fraction of inspired oxygen. Besides other components, the model of neonatal oxygen transport must include a module linking arterial (SaO2) and peripheral (SpO2) oxygen saturation. The pulse oximeter module must reflect issues of SpO2 measurement typical for clinical practice, such as overestimation of SpO2 over SaO2 documented by several studies, or inaccurate pulse oximeter readings due to high noise. The aim of this study was to describe both the bias between SaO2 and SpO2 and the noise, characteristic for continuous SpO2 recording, for a computer model of oxygenation of a premature infant. The SpO2-SaO2 bias, derived from available clinical data, describes a typical deviation of the SpO2 measurement as a function of the true SaO2 value in three different SaO2 intervals. The SpO2 measurement noise was considered as a random process that affects biased SpO2values at each time point with statistical properties estimated from SpO2 continuous recordings of 5 stable newborns. The results of the study will help to adjust a computer model of neonatal oxygenation to the real situations observed in the clinical practice.
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Belim, Svetlana, and Sergei Belim. "The chaotic masking message model using orthogonal functions." Transaction of Scientific Papers of the Novosibirsk State Technical University, no. 1-2 (August 26, 2020): 67–76. http://dx.doi.org/10.17212/2307-6879-2020-1-2-67-76.
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The model for chaotic signal masking is proposed in the article. The digital signal in the bit representation is encoded using a family of orthogonal functions. Random white noise is superimposed on the resulting analog signal. The white noise amplitude is significantly greater than the amplitude of the signal. The functions orthogonal property is used to retrieve a useful signal. The advantage proposed this model is that it is not necessary to match the noise generators in the source and in the receiver of the message. The integration operation is required to retrieve the message. The using a simple rectangle scheme is discussed. The comparative computer experiment is based on two families of orthogonal functions: simple trigonometric functions and orthogonal Lagrange polynomials. It has been shown that using the trigonometric function family results in fewer errors when retrieving a message.
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Du, Wenliao, Xukun Hou, and Hongchao Wang. "Time-Varying Degradation Model for Remaining Useful Life Prediction of Rolling Bearings under Variable Rotational Speed." Applied Sciences 12, no. 8 (April 16, 2022): 4044. http://dx.doi.org/10.3390/app12084044.
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It is difficult to accurately extract the health index of non-stationary signals of rolling bearings under variable rotational speed, which also leads to greater prediction error for bearing degradation models with fixed parameters. For this reason, an angular domain unscented particle filter model with time-varying degradation parameters is proposed to deal with the remaining useful life (RUL) prediction of rolling bearings. Order analysis is first performed to transform the variable-speed signal from time domain to angular domain for extracting the health index in the angular domain representation. To track the bearing degradation state, a real-time finite element model is established to guide the parameters updating the procedure of the performance degradation model. Finally, the bearing degradation state is estimated by the unscented particle filter (UPF), and then the remaining useful life of the bearing is predicted. In this way, the time-varying degradation model is developed by considering both non-stationary feature extraction and dynamic state tracking for rolling bearings. The proposed method is verified by both benchmarks: bearing experimental data, and a bearing accelerated life experiment. Compared with state-of-the-art prognostic methods, the present model can predict the bearing remaining useful life (RUL) more accurately under variable rotational speed.
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Oghogho, I. "Single user TCP downstream throughput probability models in IEEE802.11b WLAN system." Nigerian Journal of Technology 39, no. 1 (April 3, 2020): 212–18. http://dx.doi.org/10.4314/njt.v39i1.24.
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Single User, Transmission Control Protocol Downstream Throughput (TCPDST) probability models in an IEEE802.11b WLAN have been developed, validated and evaluated for performance. Measurement of single user TCPDST were taken using Tamosoft throughput test while that of signal to noise ratio (SNR) were taken using inSSIDer 2.1. The Tamosoft throughput tests were conducted using different quality of service (QoS) traffic. These QoS traffic (which were sent through an infrastructure based network) correspond to different wireless multimedia tags. Measurements were taken in free space, small offices and open corridor environments. By assuming a normal distribution, single user TCPDST Cumulative distribution function (CDF) probability models were developed for different signal categories namely: (i) all the SNR considered, (ii) strong signals only, (iii) grey signals only and (iv) weak signals only. The models were validated and their performances evaluated using root mean square (RMS) errors. RMS errors were computed by comparing model predicted values with validation data. The RMS errors for single user CDF all signals model was 0.1466%. RMS errors for strong signals models, grey signals model and weak signals model respectively were 0.1466%, 0.6756% and 0.1233% indicating acceptable performances. All signals, strong signals, grey signals and weak signals CDF probability models predicted probabilities of obtaining TCPDST values greater than 5Mbps as 74.79%, 90.55%, 13.00% and 4.77% respectively while probabilities of obtaining TCPDST values less than 2Mbps were predicted as 4.91%, 0.00%, 18.98% and 52.41% respectively. These probability models will provide additional useful information needed to design efficient distributed data networks.
Keywords: Throughput, TCP, WLAN, probability models
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Xu, Xingjian, Tian Ban, and Yuehua Li. "SPLM: A Flexible and Accurate Reliability Assessment Model for Logic Circuits." Journal of Circuits, Systems and Computers 28, no. 02 (November 12, 2018): 1950032. http://dx.doi.org/10.1142/s0218126619500324.
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Reliability evaluation by using probabilistic computational models has become an important research field in modern digital designs. Based on the profound understanding of different reliability evaluation methods, this paper proposes a universal model for signal probability and reliability analysis of logic circuits. The proposed Signal Probability Level Matrix (SPLM) provides us with the reliability and signal probability of the entire circuit as well as individual outputs. We can deal with SPLM very flexibly depending on different applications and design constraints. The accuracy and efficiency of the proposed model have been proved and verified by representative circuits in literatures. Furthermore, the proposed model is particularly useful in reliability assessment in cascade-structure circuits such as ripple carry adders.
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G. Nsaif, Baseem, and Adheed H. Sallomi. "PATH LOSS MODELING FOR URBAN WIRLESS NETWORKS IN BAGHDAD." Journal of Engineering and Sustainable Development 25, Special (September 20, 2021): 1–7. http://dx.doi.org/10.31272/jeasd.conf.2.1.2.
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An accurate propagation modeling of radio waves propagation is very important task in cellular network design as it provides the detailed useful knowledge about the wireless channel environment characteristics. Theoretical or empirical RF propagation models provide the required useful information about the signal path loss and fading to evaluate the received signal level, the coverage area, and the outage probability in specific regions. This paper aimed to develop an empirical radio wave propagation model based on observations and sets of measurement data collected from different sites through drive test. These measurements are used to determine the received signal power at some locations to create an empirical radio wave propagation model that is suitable to be appropriate in cellular network accurate design and link budget prediction at the city of Baghdad.
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Sosnowski, Tomasz, Grzegorz Bieszczad, Sławomir Gogler, Henryk Madura, Mariusz Felczak, and Robert Strąkowski. "Radiation Model of a Housing of Cooled Infrared Detector Array." Pomiary Automatyka Robotyka 25, no. 4 (December 7, 2021): 67–76. http://dx.doi.org/10.14313/par_242/67.
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The infrared camera detects infrared radiation from the observed objects, Its main element is the array of infrared detectors, which converts the received radiation into an electrical signal. The radiation sources recorded by the detector can be divided as useful, received from the observed scene, and useless received from such objects as the detector housing and lens elements. These unusable radiation sources have a significant impact on the design of the detector itself. The article presents a model of the detector housing and a quantitative analysis of the influence of various radiation sources on the effectiveness of radiation detection from the observed scene.
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Crabtree, Charles, and Holger L. Kern. "Using Electromagnetic Signal Propagation Models for Radio and Television Broadcasts: An Introduction." Political Analysis 26, no. 3 (June 1, 2018): 348–55. http://dx.doi.org/10.1017/pan.2018.8.
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This note offers an introduction to electromagnetic signal propagation models, which can be used to model terrestrial radio and television signal strength across space. Such data are useful to social scientists interested in identifying the effects of mass media broadcasts when (i) individual-level data on media exposure do not exist or when (ii) media exposure, while observed, is not exogenous. We illustrate the use of electromagnetic signal propagation models by creating a signal strength measure of military-controlled radio stations during the 2012 coup in Mali.
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AL-ASSAF, YOUSEF, and WAJDI M. AHMAD. "PARAMETER IDENTIFICATION OF CHAOTIC SYSTEMS USING WAVELETS AND NEURAL NETWORKS." International Journal of Bifurcation and Chaos 14, no. 04 (April 2004): 1467–76. http://dx.doi.org/10.1142/s0218127404009910.
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This paper addresses the problem of reconstructing a slowly-varying information-bearing signal from a parametrically modulated, nonstationary dynamical signal. A chaotic electronic oscillator model characterized by one control parameter and a double-scroll-like attractor is used throughout the study. Wavelet transforms are used to extract features of the chaotic signal resulting from parametric modulation of the control parameter by the useful signal. The vector of feature coefficients is fed into a feed-forward neural network that recovers the embedded information-bearing signal. The performance of the developed method is cross-validated through reconstruction of randomly-generated control parameter patterns. This method is applied to the reconstruction of speech signals, thus demonstrating its potential utility for secure communication applications. Our results are validated via numerical simulations.
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Wang, Qinghua, Lijuan Wang, Hongtao Yu, Dong Wang, and Asoke K. Nandi. "Utilizing SVD and VMD for Denoising Non-Stationary Signals of Roller Bearings." Sensors 22, no. 1 (December 28, 2021): 195. http://dx.doi.org/10.3390/s22010195.
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In view of the fact that vibration signals of rolling bearings are much contaminated by noise in the early failure period, this paper presents a new denoising SVD-VMD method by combining singular value decomposition (SVD) and variational mode decomposition (VMD). SVD is used to determine the structure of the underlying model, which is referred to as signal and noise subspaces, and VMD is used to decompose the original signal into several band-limited modes. Then the effective components are selected from these modes to reconstruct the denoised signal according to the difference spectrum (DS) of singular values and kurtosis values. Simulated signals and experimental signals of roller bearing faults have been analyzed using this proposed method and compared with SVD-DS. The results demonstrate that the proposed method can effectively retain the useful signals and denoise the bearing signals in extremely noisy backgrounds.
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Smith, Ann, Feng Shou Gu, and Andrew Ball. "Selection of Input Parameters for Multivariate Classifiersin Proactive Machine Health Monitoring by Clustering Envelope Spectrum Harmonics." Applied Mechanics and Materials 798 (October 2015): 308–13. http://dx.doi.org/10.4028/www.scientific.net/amm.798.308.
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In condition monitoring (CM) signal analysis the inherent problem of key characteristics being masked by noise can be addressed by analysis of the signal envelope. Envelope analysis of vibration signals is effective in extracting useful information for diagnosing different faults. However, the number of envelope features is generally too large to be effectively incorporated in system models. In this paper a novel method of extracting the pertinent information from such signals based on multivariate statistical techniques is developed whichsubstantialy reduces the number of input parameters required for data classification models. This was achieved by clustering possible model variables into a number of homogeneous groups to assertain levels of interdependency. Representatives from each of the groups were selected for their power to discriminate between the categorical classes. The techniques established were applied to a reciprocating compressorrig wherein the target was identifying machine states with respect to operational health through comparison of signal outputs for healthy and faulty systems. The technique allowed near perfect fault classification.In addition methods for identifying seperable classes are investigated through profiling techniques, illustrated using Andrew’s Fourier curves.
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Волосюк, Валерій Костянтинович, Володимир Володимирович Павліков, Семен Сергійович Жила, Микола Вікторович Руженцев, Володимир Віталійович Кошарський, and Олексій Володимирович Одокієнко. "СИНТЕЗ ОПТИМАЛЬНОГО АЛГОРИТМУ ПОЛЯРИЗАЦІЙНОЇ СЕЛЕКЦІЇ КОРИСНИХ СИГНАЛІВ НА ФОНІ ПАСИВНИХ ЗАВАД В РАДАРІ З СИНТЕЗУВАННЯМ АПЕРТУРИ." Aerospace technic and technology, no. 4 (August 28, 2020): 109–15. http://dx.doi.org/10.32620/aktt.2020.4.14.
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Using the method of maximum likelihood, the optimal algorithm of polarization selection of objects on the background of the underlying surface, hydrometeors, urban buildings from aerospace carriers of radio electronic equipment has been synthesized. To solve the problem polarimetric properties of the scattered electromagnetic radiation of different natural environments and anthropogenic objects were analyzed. Has been determined the functional-determined mathematical model of the useful signal, the structure and correlation characteristics of scattered background radiation and white noise inside the multichannel receiver. It is assumed that the wanted signals are measured with phase accuracy in a coherent receiver. When solving the optimization problem, the method of the maximum likelihood functional and the likelihood functional for correlated processes were used, contains the inverse matrix of inverse correlation functions of the observation equation. The obtained signal processing algorithm, in addition to the classical operations of accumulating the trajectory signal with the preservation of the phase structure and its matched filtering, provides for polarization and frequency selection in the line of decorrelation filters. In the case when the internal noise is very small in comparison with external interference and the correlation between the channels of vertical and horizontal polarization approaches unity, almost complete compensation of passive interference occurs. Further spectral rejection of the reflected signals is practically unnecessary and it is possible to restrict ourselves only to matched filtering of the useful signal. In the absence of background radiation correlation, the channels for receiving oscillations of different polarizations become independent and the main operation for the selection of useful signals is spectral rejection of passive interference and matched filtering of the useful signal. The developed algorithm can be implement in cognitive radars due to its adaptability to the parameters of background radiation in accordance with the change in the scattering covariance matrix of the underlying surface. Based on the results obtained, a block diagram of a multichannel polarization radar with synthesized antenna aperture has been developed.
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Shultz, Abraham M., Sangmook Lee, Mary Guaraldi, Thomas B. Shea, and Holly A. Yanco. "Robot-Embodied Neuronal Networks as an Interactive Model of Learning." Open Neurology Journal 11, no. 1 (September 30, 2017): 39–47. http://dx.doi.org/10.2174/1874205x01711010039.
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Background and Objective:The reductionist approach of neuronal cell culture has been useful for analyses of synaptic signaling. Murine cortical neurons in culture spontaneously form anex vivonetwork capable of transmitting complex signals, and have been useful for analyses of several fundamental aspects of neuronal development hitherto difficult to clarifyin situ. However, these networks lack the ability to receive and respond to sensory input from the environment as do neuronsin vivo. Establishment of these networks in culture chambers containing multi-electrode arrays allows recording of synaptic activity as well as stimulation.Method:This article describes the embodiment ofex vivoneuronal networks neurons in a closed-loop cybernetic system, consisting of digitized video signals as sensory input and a robot arm as motor output.Results:In this system, the neuronal network essentially functions as a simple central nervous system. This embodied network displays the ability to track a target in a naturalistic environment. These findings underscore thatex vivoneuronal networks can respond to sensory input and direct motor output.Conclusion:These analyses may contribute to optimization of neuronal-computer interfaces for perceptive and locomotive prosthetic applications.Ex vivonetworks display critical alterations in signal patterns following treatment with subcytotoxic concentrations of amyloid-beta. Future studies including comparison of tracking accuracy of embodied networks prepared from mice harboring key mutations with those from normal mice, accompanied with exposure to Abeta and/or other neurotoxins, may provide a useful model system for monitoring subtle impairment of neuronal function as well as normal and abnormal development.
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Mohd Zali, Hanisah, Mohd Khairil Adzhar Mahmood, Idnin Pasya, Miyuki Hirose, and Nurulazlina Ramli. "Narrowband and wideband EMW path loss in underwater wireless sensor network." Sensor Review 42, no. 1 (November 17, 2021): 125–32. http://dx.doi.org/10.1108/sr-04-2021-0128.
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Purpose Utilization of electromagnetic wave (EMW) sensors in an underwater environment has the potential to increase the data rate compared to acoustic-based sensors because of the ability to use larger signal bandwidth. Nevertheless, EMW signals has the drawback of large signal attenuation in underwater, attributed to the high relative permittivity and conductivity of water compared to the atmosphere, hence employment of wide signal bandwidth is necessary to balance the data rate-attenuation trade-off. The purpose of this paper is to analyze the characteristics of both narrowband and wideband EMW signal propagation underwater and devise a path loss model for both cases. Design/methodology/approach Path loss measurement was conducted using a point-to-point configuration in a laboratory water tank while transmitting narrowband and wideband signals between a pair of wideband underwater antennas. The wideband underwater antennas use buffer-layer structures as the impedance matching layer to optimize the antenna performance when operating underwater. The path loss for narrowband signal was modeled using a multi-layer propagation equation in lossy medium considering losses at the medium boundaries. For the case of the wideband signal, a modified version of the model introducing power integration over bandwidth is adopted. These models were formulated through numerical simulations and verified by measurements. Findings The measured narrowband path loss marked an 80 dB attenuation using 800 MHz at 2 m distance. The proposed narrowband model agrees well with the measurements, with approximately 3 dB modeling error. Utilization of the proposed wideband path loss model resulted in a reduction of the gradient of the path loss curve compared to the case of the narrowband signal. The measured wideband path loss at 2 m distance underwater was approximately −65 dB, which has been shown to enable a working signal-to-noise ratio of 15 dB. This proves the potential of realizing high data rate transmission using the wideband signal. Originality/value The paper proposed a wideband propagation model for an underwater EMW sensor network, using power integration over bandwidth. The effectiveness of using wideband EMW signals in reducing path loss is highlighted, which is seldom discussed in the literature. This result will be of useful reference for using wideband signals in designing a high data rate transmission system in underwater wireless sensor networks, for example, in link budget, performance estimation and parameter design of suitable transmission scheme.
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Bejaoui, Islem, Dario Bruneo, and Maria Gabriella Xibilia. "Remaining Useful Life Prediction of Broken Rotor Bar Based on Data-Driven and Degradation Model." Applied Sciences 11, no. 16 (August 4, 2021): 7175. http://dx.doi.org/10.3390/app11167175.
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Rotating machines such as induction motors are crucial parts of most industrial systems. The prognostic health management of induction motor rotors plays an essential role in increasing electrical machine reliability and safety, especially in critical industrial sectors. This paper presents a new approach for rotating machine fault prognosis under broken rotor bar failure, which involves the modeling of the failure mechanism, the health indicator construction, and the remaining useful life prediction. This approach combines signal processing techniques, inherent metrics, and principal component analysis to monitor the induction motor. Time- and frequency-domains features allowing for tracking the degradation trend of motor critical components that are extracted from torque, stator current, and speed signals. The most meaningful features are selected using inherent metrics, while two health indicators representing the degradation process of the broken rotor bar are constructed by applying the principal component analysis. The estimation of the remaining useful life is then obtained using the degradation model. The performance of the prediction results is evaluated using several criteria of prediction accuracy. A set of synthetic data collected from a degraded Simulink model of the rotor through simulations is used to validate the proposed approach. Experimental results show that using the developed prognostic methodology is a powerful strategy to improve the prognostic of induction motor degradation.
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Fabry, Frédéric. "For How Long Should What Data Be Assimilated for the Mesoscale Forecasting of Convection and Why? Part II: On the Observation Signal from Different Sensors." Monthly Weather Review 138, no. 1 (January 1, 2010): 256–64. http://dx.doi.org/10.1175/2009mwr2884.1.
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Abstract The ability of data assimilation to correct for initial conditions depends on the presence of a usable signal in the variables observed as well as on the capability of instruments to detect that signal. In Part I, the nature, properties, and limits in the usability of signals in model variables were investigated. Here, the focus is on studying the skill of measurements to pull out a useful signal for data assimilation systems to use. Using model runs of the evolution of convective storms in the Great Plains over an active 6-day period, simulated measurements from a variety of instruments are evaluated in terms of their ability to detect various initial condition errors and to provide a signal above and beyond measurement errors. The usability of the signal for data assimilation is also investigated. Imaging remote sensing systems targeting cloud and precipitation properties such as radars and thermal IR imagers provided both the strongest signals and the hardest ones to assimilate to recover fields other than clouds and precipitation because of the nonlinear behavior of the sensors combined with the limited predictability of the signal observed. The performance of other sensors was also evaluated, leading to several unexpected results. If used with caution, these findings can help determine assimilation priorities for improving mesoscale forecasting.
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Obuchowski, Jakub, Agnieszka Wylomanska, and Radoslaw Zimroz. "Stochastic Modeling of Time Series with Application to Local Damage Detection in Rotating Machinery." Key Engineering Materials 569-570 (July 2013): 441–48. http://dx.doi.org/10.4028/www.scientific.net/kem.569-570.441.
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Raw vibration signals measured on the machine housing in industrial conditions are complex and can be modeled as an additive mixture of several processes (with different statistical properties) related to normal operation of machine, damage related to one (or more) of its part, some noise, etc. In the case of local damage in rotating machines, contribution of informative process related to damage is hidden in the raw signal so its detection is difficult. In this paper we propose to use the statistical modeling of vibration time series to identify these components. Building the model of raw signal may be ineffective. It is proposed to decompose signal into set of narrowband sub-signals using time-frequency representation. Next, it is proposed to model each sub-signal in the given frequency range and classify all signals using their statistical properties. We have used several parameters (called selectors because they will be used for selection of sub-signals from time-frequency map for further processing) for analysis of sub-signals. They have base in statistics theory and can be useful for example in testing of normality of data set (vibration time series from machine in good condition is close to Gaussian, damaged not). Results of such modeling will be used in the sub-signals classification procedure but also in defects detection. We illustrate effectiveness of novel technique using real data from heavy machinery system.
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Ramírez-Martínez, Daniel, Mariel Alfaro-Ponce, Oleksiy Pogrebnyak, Mario Aldape-Pérez, and Amadeo-José Argüelles-Cruz. "Hand Movement Classification Using Burg Reflection Coefficients." Sensors 19, no. 3 (January 24, 2019): 475. http://dx.doi.org/10.3390/s19030475.
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Classification of electromyographic signals has a wide range of applications, from clinical diagnosis of different muscular diseases to biomedical engineering, where their use as input for the control of prosthetic devices has become a hot topic of research. The challenge of classifying these signals relies on the accuracy of the proposed algorithm and the possibility of its implementation in hardware. This paper considers the problem of electromyography signal classification, solved with the proposed signal processing and feature extraction stages, with the focus lying on the signal model and time domain characteristics for better classification accuracy. The proposal considers a simple preprocessing technique that produces signals suitable for feature extraction and the Burg reflection coefficients to form learning and classification patterns. These coefficients yield a competitive classification rate compared to the time domain features used. Sometimes, the feature extraction from electromyographic signals has shown that the procedure can omit less useful traits for machine learning models. Using feature selection algorithms provides a higher classification performance with as few traits as possible. The algorithms achieved a high classification rate up to 100% with low pattern dimensionality, with other kinds of uncorrelated attributes for hand movement identification.
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Li, Liming, Xunyi Zhou, Xingqi Zhang, and Zhenghu Zhong. "Precision degradation prediction of inertial test turntable based on Hidden Markov Model and optimized particle filtering." Advances in Mechanical Engineering 12, no. 12 (November 30, 2020): 168781402097249. http://dx.doi.org/10.1177/1687814020972498.
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In order to solve the problem that there is no effective evaluation method for the precision degradation state of inertial test turntable, a prediction model for the position precision degradation trend of test turntable was proposed based on the Hidden Markov Model (HMM) algorithm and Particle Filter (PF) algorithm. The initial parameter of the PF algorithm was optimized by the Particle Swarm Optimization (PSO) algorithm. The vibration signal was selected as the research data, which could be obtained from an velocity test of turntable precision degradation. Firstly, the original vibration signal was denoised by Ensemble Empirical Mode Decomposition and Principal Component Analysis (EEMD-PCA) algorithm, and the signal with fault characteristic was extracted for signal reconstruction; Secondly, a HMM model could be trained by using the statistical characteristic values as observation matrix, and the diagnosis of early position precision degradation and the health state indexes could be obtained. Finally, a prediction model of the test turntable precision degradation could be established by using PF algorithm, and the Remaining Useful Life (RUL) of the test turntable precision could be calculated. When the 50th group data were taken as the prediction starting point, the predicted remaining useful life was 21 years, and the actual measured result was 17 years, which are close to each other. Comparing the model calculation results and the test measurement results, it is shown that the model could effectively and accurately predict the change trend and remaining useful life of the test turntable precision.
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Zhang, Hao, Qiang Zhang, Siyu Shao, Tianlin Niu, Xinyu Yang, and Haibin Ding. "Sequential Network with Residual Neural Network for Rotatory Machine Remaining Useful Life Prediction Using Deep Transfer Learning." Shock and Vibration 2020 (September 14, 2020): 1–16. http://dx.doi.org/10.1155/2020/8888627.
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Deep learning has a strong feature learning ability, which has proved its effectiveness in fault prediction and remaining useful life prediction of rotatory machine. However, training a deep network from scratch requires a large amount of training data and is time-consuming. In the practical model training process, it is difficult for the deep model to converge when the parameter initialization is inappropriate, which results in poor prediction performance. In this paper, a novel deep learning framework is proposed to predict the remaining useful life of rotatory machine with high accuracy. Firstly, model parameters and feature learning ability of the pretrained model are transferred to the new network by means of transfer learning to achieve reasonable initialization. Then, the specific sensor signals are converted to RGB image as the specific task data to fine-tune the parameters of the high-level network structure. The features extracted from the pretrained network are the input into the Bidirectional Long Short-Term Memory to obtain the RUL prediction results. The ability of LSTM to model sequence signals and the dynamic learning ability of bidirectional propagation to time information contribute to accurate RUL prediction. Finally, the deep model proposed in this paper is tested on the sensor signal dataset of bearing and gearbox. The high accuracy prediction results show the superiority of the transfer learning-based sequential network in RUL prediction.
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Ben Nasr, Mohamed Chiheb, Sofia Ben Jebara, Samuel Otis, Bessam Abdulrazak, and Neila Mezghani. "A Spectral-Based Approach for BCG Signal Content Classification." Sensors 21, no. 3 (February 2, 2021): 1020. http://dx.doi.org/10.3390/s21031020.
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This paper has two objectives: the first is to generate two binary flags to indicate useful frames permitting the measurement of cardiac and respiratory rates from Ballistocardiogram (BCG) signals—in fact, human body activities during measurements can disturb the BCG signal content, leading to difficulties in vital sign measurement; the second objective is to achieve refined BCG signal segmentation according to these activities. The proposed framework makes use of two approaches: an unsupervised classification based on the Gaussian Mixture Model (GMM) and a supervised classification based on K-Nearest Neighbors (KNN). Both of these approaches consider two spectral features, namely the Spectral Flatness Measure (SFM) and Spectral Centroid (SC), determined during the feature extraction step. Unsupervised classification is used to explore the content of the BCG signals, justifying the existence of different classes and permitting the definition of useful hyper-parameters for effective segmentation. In contrast, the considered supervised classification approach aims to determine if the BCG signal content allows the measurement of the heart rate (HR) and the respiratory rate (RR) or not. Furthermore, two levels of supervised classification are used to classify human-body activities into many realistic classes from the BCG signal (e.g., coughing, holding breath, air expiration, movement, et al.). The first one considers frame-by-frame classification, while the second one, aiming to boost the segmentation performance, transforms the frame-by-frame SFM and SC features into temporal series which track the temporal variation of the measures of the BCG signal. The proposed approach constitutes a novelty in this field and represents a powerful method to segment BCG signals according to human body activities, resulting in an accuracy of 94.6%.
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Kundu, Pradeep, Ashish K. Darpe, and Makarand S. Kulkarni. "An ensemble decision tree methodology for remaining useful life prediction of spur gears under natural pitting progression." Structural Health Monitoring 19, no. 3 (August 6, 2019): 854–72. http://dx.doi.org/10.1177/1475921719865718.
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This article presents an ensemble decision tree–based random forest regression methodology for remaining useful life prediction of spur gears subjected to pitting failure mode. The random forest regression methodology does not require an elaborate statistics background knowledge and has an inbuilt health indicator selection capability compared to other existing data-driven remaining useful life prediction approaches. A correlation coefficient parameter based on the residual vibration signal is used for monitoring and detecting the pitting progression in spur gears. The effectiveness of the correlation coefficient of the residual vibration signal is assessed over the other existing health indicators for pitting fault progression. To show the inbuilt best health indicator selection capability of the random forest regression model, initially, eight indicators (existing seven and the correlation coefficient of the residual vibration signal) were used for model training. In addition, the effect of fusing the vibration sensor data from multiple positions on the gearbox on prediction accuracy of the random forest regression model is also evaluated. The accuracy in the remaining useful life prediction is found to increase after fusing the correlation coefficient of the residual vibration signal based health indicator derived from the accelerometers located at multiple positions on the gearbox in comparison to data from a single accelerometer. Furthermore, the accuracy of the proposed methodology is tested and proven using five accelerated run-to-failure experimental data collected from the specially built test rig.
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Vasarevičius, Dominykas, and Modestas Pikutis. "DEVELOPMENT OF MATHEMATICAL MODELS FOR INVESTIGATING MAXIMAL POWER POINT TRACKING ALGORITHMS / MATEMATINIS SAULĖS ENERGIJOS SRAUTO MODELIS DIDŽIAUSIOS GALIOS TAŠKO SAULĖS ELEMENTUOSE SEKIMO ALGORITMAMS TIRTI." Mokslas - Lietuvos ateitis 4, no. 1 (April 23, 2012): 51–55. http://dx.doi.org/10.3846/mla.2012.12.
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Solar cells generate maximum power only when the load is optimized according insolation and module temperature. This function is performed by MPPT systems. While developing MPPT, it is useful to create a mathematical model that allows the simulation of different weather conditions affecting solar modules. Solar insolation, cloud cover imitation and solar cell models have been created in Matlab/Simulink environment. Comparing the simulation of solar insolation on a cloudy day with the measurements made using a pyrometer show that the model generates signal changes according to the laws similar to those of a real life signal. The model can generate solar insolation values in real time, which is useful for predicting the amount of electrical energy produced from solar power. The model can operate with the help of using the stored signal, thus a comparison of different MPPT algorithms can be provided. Santrauka Saulės elementai didžiausią galią sukuria tik tada, kai apkrova yra parinkta pagal elementą veikiantį saulės energijos srautą ir modulio temperatūrą. Apkrovai parinkti naudojamos didžiausios galios sekimo (DGTS) sistemos. Kintant oro sąlygoms šios sistemos nespėja prisitaikyti prie saulės elemento parametrų. Kuriant ir tiriant DGTS algoritmus tikslinga sudaryti matematinį modelį, imituojantį skirtingomis oro sąlygomis veikiančius saulės elementus. Pateikiami sudaryti saulės energijos srauto (SES), debesų dangos imitavimo ir saulės modulio matematiniai modeliai Matlab/Simulink terpėje. Lyginant modeliuoto debesuotą dieną SES kitimo dėsnius su realiais piranometru pamatuotais rezultatais nustatyta, kad modeliu imituojamas SES atitinka realųjį. Modelyje generuojamos SES vertės realiajame laike yra naudingos prognozuojant saulės jėgainės pagamintos elektros energijos kiekį. Modelyje numatyta galimybė išsaugoti SES signalą, todėl juo galima atlikti tikslų skirtingų DGTS algoritmų veikimo palyginimą. Reikšminiai žodžiai: saulės energija, saulės elementai, didžiausios galios taško sekimas.
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Akpudo, Ugochukwu Ejike, and Jang-Wook Hur. "A CEEMDAN-Assisted Deep Learning Model for the RUL Estimation of Solenoid Pumps." Electronics 10, no. 17 (August 25, 2021): 2054. http://dx.doi.org/10.3390/electronics10172054.
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This paper develops a data-driven remaining useful life prediction model for solenoid pumps. The model extracts high-level features using stacked autoencoders from decomposed pressure signals (using complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm). These high-level features are then received by a recurrent neural network-gated recurrent units (GRUs) for the RUL estimation. The case study presented demonstrates the robustness of the proposed RUL estimation model with extensive empirical validations. Results support the validity of using the CEEMDAN for non-stationary signal decomposition and the accuracy, ease-of-use, and superiority of the proposed DL-based model for solenoid pump failure prognostics.
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Pu, Feng Shan, Ping Chuan Zhang, and Hang Sen Zhang. "Stochastic Material Model and Application System Analysis." Advanced Materials Research 321 (August 2011): 113–16. http://dx.doi.org/10.4028/www.scientific.net/amr.321.113.
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this paper did a profound research on the stochastic material of Electro-optic (EO) polymers and mathematic model, and analyzed the stationary stochastic processes, derived the stochastic process mean and autocorrelation function. Setup a bidirectional diode circuit model as a stochastic system to perform the analysis method. The work done here is very useful for the related subjects such as material analysis, signal processing and system analysis, or linear system design.
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Maliuk, Andrei S., Alexander E. Prosvirin, Zahoor Ahmad, Cheol Hong Kim, and Jong-Myon Kim. "Novel Bearing Fault Diagnosis Using Gaussian Mixture Model-Based Fault Band Selection." Sensors 21, no. 19 (October 1, 2021): 6579. http://dx.doi.org/10.3390/s21196579.
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This paper proposes a Gaussian mixture model-based (GMM) bearing fault band selection (GMM-WBBS) method for signal processing. The proposed method benefits reliable feature extraction using fault frequency oriented Gaussian mixture model (GMM) window series. Selecting exclusively bearing fault frequency harmonics, it eliminates the interference of bearing normal vibrations in the lower frequencies, bearing natural frequencies, and the higher frequency contents that prove to be useful only for anomaly detection but do not provide any insight into the bearing fault location. The features are extracted from time- and frequency- domain signals that exclusively contain the bearing fault frequency harmonics. Classification is done using the Weighted KNN algorithm. The experiments performed with the data containing the vibrations recorded from artificially damaged bearings show the positive effect of utilizing the proposed GMM-WBBS signal processing to filter out the discriminative data of uncertain origin. All comparison methods retrofitted with the proposed method demonstrated classification performance improvements when provided with vibration data with suppressed bearing natural frequencies and higher frequency contents.
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Shlykov, Vladyslav, Vitalii Kotovskyi, Nikolaj Višniakov, and Andžela Šešok. "Model for Elimination of Mixed Noise from MRI Heart Images." Applied Sciences 10, no. 14 (July 9, 2020): 4747. http://dx.doi.org/10.3390/app10144747.
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A method for the preliminary processing of MRI images of the heart that allows for the elimination of fluctuation and impulse noise from useful signals is proposed. These types of noise are due to the regular geometric structure of the photoelectric elements of the MRI scanner matrix and the structure of the signal transmission channel. The aim of this work is to develop a comprehensive mathematical model for eliminating noise in the signal of an MRI scanner. In this work, mathematical models of linear and median filtering of impulse noise, fluctuation, and geometric noise are implemented. The mathematical models consist of the combined use of linear and median filters for recording MRI images of the heart. In the experiments, real MRI images of the heart from six patients with different diseases were used after noise was added to them. We were able to eliminate the impulse noise, geometric noise, and fluctuation noise in the MRI images by applying our filtering techniques. The filtering technique not only removed the noise, but also increased the contrast of the cancerous volumetric heterogeneous formations in the heart region.
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Solovyeva, Elena, Steffen Schulze, and Hanna Harchuk. "Behavioral Modeling of Memristor-Based Rectifier Bridge." Applied Sciences 11, no. 7 (March 24, 2021): 2908. http://dx.doi.org/10.3390/app11072908.
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In electrical engineering, radio engineering, robotics, computing, control systems, etc., a lot of nonlinear devices are synthesized on the basis of a nanoelement named memristor that possesses a number of useful properties, such as passivity, nonlinearity, high variability of parameters, nonvolatility, compactness. The efficiency of this electric element has led to the emergence of many memristor technologies based on different physical principles and, as a result, to the occurrence of different mathematical models describing these principles. A general approach to the modeling of memristive devices is represented. The essence is to construct a behavioral model that approximates nonlinear mapping of the input signal set into the output signal set. The polynomials of split signals, which are adaptive to the class of input signals, are used. This adaptation leads to the model’s simplification important in practice. Multi-dimensional polynomials of split signals are built for the rectifier bridge at harmonic input signals. The modeling error is estimated in the mean-square norm. It is shown that the accuracy of the modeling is increased in the case of using the piecewise polynomial with split signals.
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Martincorena-Arraiza, Maite, Carlos A. De La Cruz Blas, Antonio Lopez-Martin, Cristián Molina Vicuña, and Ignacio R. Matías. "Fault Detection of Planetary Gears Based on Signal Space Constellations." Sensors 22, no. 1 (January 4, 2022): 366. http://dx.doi.org/10.3390/s22010366.
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A new method to process the vibration signal acquired by an accelerometer placed in a planetary gearbox housing is proposed, which is useful to detect potential faults. The method is based on the phenomenological model and consists of the projection of the healthy vibration signals onto an orthonormal basis. Low pass components representation and Gram–Schmidt’s method are conveniently used to obtain such a basis. Thus, the measured signals can be represented by a set of scalars that provide information on the gear state. If these scalars are within a predefined range, then the gear can be diagnosed as correct; in the opposite case, it will require further evaluation. The method is validated using measured vibration signals obtained from a laboratory test bench.
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Parshutkin, Andrey, Dmitry Levin, and Aleksey Galandzovskiy. "Simulation model of radar data processing in a station network under signal-like interference." Information and Control Systems, no. 6 (January 16, 2020): 22–31. http://dx.doi.org/10.31799/1684-8853-2019-6-22-31.
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Introduction: Radar stations, when tracking targets in a complex interference environment, form not only target marks but also false marks. A well-developed theory and technique of noise stability is not useful under signal-like interference caused by re-reflections, multi-path propagation or retransmission of the probing signals. The reliability of radar information processing under signal-like interference can be improved by joint processing of data from several spaced posts in a radar station network. Purpose: development of а simulation model which would allow you to estimate the effectiveness of radar target selection by spatial rating of its measured positions, with joint processing of the radar information obtained from two spaced radar stations. Results: We have implemented the framework of joint radar data processing for target selection in a radar station network under signal-like interference. The selection is based on using the information about the coincidence of radar target coordinates measured by spaced radar stations. A simulation model is developed to estimate the target selection probability under signal-like interference during the joint processing of data from two spaced radar stations, by analyzing the coincidence of the measured coordinates of the targets. It has been found out how the target selection probability depends on the noise interference power and the average density of false marks in the range channels of two spaced radar stations. Practical relevance: The simulation results demonstrate the possibility of increasing the range of radar target detection by network radar stations under signal-like interference, and the efficiency of using the information about coincidence of radar target coordinates measured by spaced radar stations, which is better than using only the signal features of radar target selection on the background of false marks.
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Tra, Viet, Bach-Phi Duong, Jae-Young Kim, Muhammad Sohaib, and Jong-Myon Kim. "Improving the Performance of Storage Tank Fault Diagnosis by Removing Unwanted Components and Utilizing Wavelet-Based Features." Entropy 21, no. 2 (February 4, 2019): 145. http://dx.doi.org/10.3390/e21020145.
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This paper proposes a reliable fault diagnosis model for a spherical storage tank. The proposed method first used a blind source separation (BSS) technique to de-noise the input signals so that the signals acquired from a spherical tank under two types of conditions (i.e., normal and crack conditions) were easily distinguishable. BSS split the signals into different sources that provided information about the noise and useful components of the signals. Therefore, an unimpaired signal could be restored from the useful components. From the de-noised signals, wavelet-based fault features, i.e., the relative energy (REWPN) and entropy (EWPN) of a wavelet packet node, were extracted. Finally, these features were used to train one-against-all multiclass support vector machines (OAA MCSVMs), which classified the instances of normal and faulty states of the tank. The efficiency of the proposed fault diagnosis model was examined by visualizing the de-noised signals obtained from the BSS method and its classification performance. The proposed fault diagnostic model was also compared to existing techniques. Experimental results showed that the proposed method outperformed conventional techniques, yielding average classification accuracies of 97.25% and 98.48% for the two datasets used in this study.
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Wu, Yao, Yanyong Guo, and Wei Yin. "Real Time Safety Model for Pedestrian Red-Light Running at Signalized Intersections in China." Sustainability 13, no. 4 (February 4, 2021): 1695. http://dx.doi.org/10.3390/su13041695.
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The traditional way to evaluate pedestrian safety is a reactive approach using the data at an aggregate level. The objective of this study is to develop real-time safety models for pedestrian red-light running using the signal cycle level traffic data. Traffic data for 464 signal cycles during 16 h were collected at eight crosswalks on two intersections in the city of Nanjing, China. Various real-time safety models of pedestrian red-light running were developed based on the different combination of explanatory variables using the Bayesian Poisson-lognormal (PLN) model. The Bayesian estimation approach based on Markov chain Monte Carlo simulation is utilized for the real-time safety models estimates. The models’ comparison results show that the model incorporated exposure, pedestrians’ characteristics and crossing maneuver, and traffic control and crosswalk design outperforms the model incorporated exposure and the model incorporated exposure, pedestrians’ characteristics, and crossing maneuver. The result indicates that including more variables in the real-time safety model could improve the model fit. The model estimation results show that pedestrian volume, ratio of males, ratio of pedestrians on phone talking, pedestrian waiting time, green ratio, signal type, and length of crosswalk are statistically significantly associated with the pedestrians’ red-light running. The findings from this study could be useful in real-time pedestrian safety evaluation as well as in crosswalk design and pedestrian signal optimization.
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Wu, Hao, Qing Wang, Liang Zhou, and Jin Meng. "VHF radio signal modulation classification based on convolution neural networks." MATEC Web of Conferences 246 (2018): 03032. http://dx.doi.org/10.1051/matecconf/201824603032.
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Deep learning architecture has been attracting increasing attention due to the successful applications in various fields. However, its application in radio system has not been well explored. In this paper, we consider the very high frequency (VHF) radio signal modulation classification based on convolution neural networks (CNN). The main principle of CNN is analysed and a five-layer CNN model is built. The proposed CNN-based modulation classification method is proved useful for simulated radio signals generated by MATLAB, that the overall classification accuracy is high even in low SNR. In addition, the proposed CNN-based method is used for real VHF radio signals, and the key factors effecting the classification accuracy are analysed.
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Zheng, Yuhuang. "Predicting Remaining Useful Life Based on Hilbert–Huang Entropy with Degradation Model." Journal of Electrical and Computer Engineering 2019 (February 3, 2019): 1–11. http://dx.doi.org/10.1155/2019/3203959.
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Prognostics health management (PHM) of rotating machinery has become an important process for increasing reliability and reducing machine malfunctions in industry. Bearings are one of the most important equipment parts and are also one of the most common failure points. To assess the degradation of a machine, this paper presents a bearing remaining useful life (RUL) prediction method. The method relies on a novel health indicator and a linear degradation model to predict bearing RUL. The health indicator is extracted by using Hilbert–Huang entropy to process horizontal vibration signals obtained from bearings. We present a linear degradation model to estimate RUL using this health indicator. In the training phase, the degradation detection threshold and the failure threshold of this model are estimated by the distribution of 600 bootstrapped samples. These bootstrapped samples are taken from the six training sets. In the test phase, the health indicator and the model are used to estimate the bearing’s current health state and predict its RUL. This method is suitable for the degradation of bearings. The experimental results show that this method can effectively monitor bearing degradation and predict its RUL.
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Bejarano, Gissella, David DeFazio, and Arti Ramesh. "Deep Latent Generative Models for Energy Disaggregation." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 850–57. http://dx.doi.org/10.1609/aaai.v33i01.3301850.
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Thoroughly understanding how energy consumption is disaggregated into individual appliances can help reduce household expenses, integrate renewable sources of energy, and lead to efficient use of energy. In this work, we propose a deep latent generative model based on variational recurrent neural networks (VRNNs) for energy disaggregation. Our model jointly disaggregates the aggregated energy signal into individual appliance signals, achieving superior performance when compared to the state-of-the-art models for energy disaggregation, yielding a 29% and 41% performance improvement on two energy datasets, respectively, without explicitly encoding temporal/contextual information or heuristics. Our model also achieves better prediction performance on lowpower appliances, paving the way for a more nuanced disaggregation model. The structured output prediction in our model helps in accurately discerning which appliance(s) contribute to the aggregated power consumption, thus providing a more useful and meaningful disaggregation model.
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Dayal, Aveen, Sreenivasa Reddy Yeduri, Balu Harshavardan Koduru, Rahul Kumar Jaiswal, J. Soumya, M. B. Srinivas, Om Jee Pandey, and Linga Reddy Cenkeramaddi. "Lightweight deep convolutional neural network for background sound classification in speech signals." Journal of the Acoustical Society of America 151, no. 4 (April 2022): 2773–86. http://dx.doi.org/10.1121/10.0010257.
Full textAbstract:
Recognizing background information in human speech signals is a task that is extremely useful in a wide range of practical applications, and many articles on background sound classification have been published. It has not, however, been addressed with background embedded in real-world human speech signals. Thus, this work proposes a lightweight deep convolutional neural network (CNN) in conjunction with spectrograms for an efficient background sound classification with practical human speech signals. The proposed model classifies 11 different background sounds such as airplane, airport, babble, car, drone, exhibition, helicopter, restaurant, station, street, and train sounds embedded in human speech signals. The proposed deep CNN model consists of four convolution layers, four max-pooling layers, and one fully connected layer. The model is tested on human speech signals with varying signal-to-noise ratios (SNRs). Based on the results, the proposed deep CNN model utilizing spectrograms achieves an overall background sound classification accuracy of 95.2% using the human speech signals with a wide range of SNRs. It is also observed that the proposed model outperforms the benchmark models in terms of both accuracy and inference time when evaluated on edge computing devices.
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Korkin, Oleksandr. "STOCHASTIC MODEL OF PROCESS OF ADAPTATION OF RADIOTECHNICAL SYSTEMS IS IN THE CONDITIONS OF STUDIES ON A TRAINING SAMPLE." Collection of scientific works of Odesa Military Academy, no. 16 (February 11, 2022): 144–50. http://dx.doi.org/10.37129/2313-7509.2021.16.144-150.
Full textAbstract:
On the stage of planning and development of radiotechnical systems (RТS) the special place is taken to the evaluation of their efficiency in the real terms. Taking into account complication of decision of the indicated task it is expedient to spare basic attention to development of such model with the use of method of stochastic imitation modeling, that in the first close will confirm adequacy of theoretical researches. The article is a comparison of these experimental processes of adaptation of RТS with the adaptive antenna array (AAA) for information systems and with the auto jamming canceller (АJC) for the equal terms of signal and jamming situation, when an training sample is unclassified, testifies in favor on RТS with adaptive antenna array. In RТS with ААA criterion of a of maximum of relation signal to interference plus noise ratio (SINR) will be realized, even, if there is an useful signal in the cross-correlation matrix of supervision. For situations, when the adaptive system arrives at the potential value (the process of adaptation is completed) informative losses are minimum. Not monotony of process of adaptation at a supervision for an unclassified selection can be conditioned by the hit of useful signal in the feed-back of such system. In RТS with AJC criterion of a maximum of relation SINR will not be realized in connection with its reaction as on hindrances so continuous useful signal that can is in an unclassified selection. Informative losses for such systems in nondeterministic terms are maximal. Conducted a stochastic imitation modeling allows in the first close to confirm adequacy earlier the got theoretical researches of gradient algorithms of self-reactance adaptation for ААA and АJC in the conditions of studies on an unclassified selection. Keywords: adaptation, algorithms of adaptation, adaptive antenna array, auto jamming canceller, training sample, modeling.
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Park, Pangun, Mingyu Jung, and Piergiuseppe Di Marco. "Remaining Useful Life Estimation of Bearings Using Data-Driven Ridge Regression." Applied Sciences 10, no. 24 (December 16, 2020): 8977. http://dx.doi.org/10.3390/app10248977.
Full textAbstract:
Predicting the remaining useful life (RUL) of mechanical bearings is a challenging industrial task since RUL can differ even for the same equipment due to many uncertainties such as operating condition, model inaccuracy, and sensory noise in various industrial applications. This paper proposes the RUL prediction method combining analytical model-based and data-driven approaches to forecast when a failure will occur based on the time series data of bearings. Feature importance ranking and principal component analysis construct a reliable and predictable health indicator from various statistical time, frequency, and time–frequency domain features of the observed signal. The adaptive sliding window method then optimizes the parameters of the degradation model based on the ridge regression of the time series sequence with the sliding window. The proposed adaptive scheme provides significant performance improvement in terms of the RUL estimation accuracy and robustness against the possible errors of the degradation model compared to the traditional Bayesian approaches.