Test Signal Research Articles

Statistical analysis of the matrix method for measuring the radar characteristics of objects

The matrix method for measuring the radar characteristics of objects is examined. The method involves irradiating the object with a system of independent test fields, receiving the set of scattered fields, and performing posterior processing of the obtained results based on the superposition principle to determine the required radar characteristics of the object. An analysis of the information capabilities of the matrix method for measuring linear and nonlinear radar characteristics of objects is conducted, considering random errors in the registration of the amplitude and phase of the test signals. The variances of the desired linear and nonlinear radar characteristics of objects are determined, assuming a high signal-to-noise ratio in each test signal reception channel and maximum likelihood estimation of the amplitude and phase of the test signals. It is established that the matrix method for measuring radar characteristics of objects is functional under real conditions involving noise and interference.

Untreated Vestibular Schwannoma: Analysis of the Determinants of Growth.

The growth rate of sporadic VS varies considerably, posing challenges for consistent clinical management. This systematic review examines data on factors associated with VS growth, following a protocol registered in the PROSPERO database. The analysis reveals that key predictors of tumor growth include tumor location, initial size, and specific clinical symptoms such as hearing loss and imbalance. Additionally, several studies suggest that growth observed within the first year may serve as an indicator of subsequent progression, enabling the earlier identification of high-risk cases. Emerging factors such as the posture swing test and MRI signal intensity have also been identified as novel predictors that could further refine growth assessments. Our meta-analysis confirms that tumor location, initial size, cystic components, and vestibular symptoms are closely linked to the likelihood of VS growth. This review provides valuable guidance for clinicians in identifying patients who may require closer monitoring or early intervention. By integrating these predictive factors into clinical practice, this review supports more personalized treatment and contributes to the development of more accurate prognostic models for managing untreated sporadic VS.

A New Type of Dynamic Vibration Fiber Sensor.

A new-type vibration sensor based on a fiber Bragg grating combined with a special structure-packaged design is proposed for monitoring the mechanical vibration signals. Three different sensing structures, including the film squeeze type, new film squeeze type, and elastic tape squeeze type are proposed for measuring the vibration signals with the frequency range from tens to thousands of Hz. In the comparison to experimental results, the new film squeeze structure has a nice sensing performance in the range from 100 to 1000 Hz with a sensitivity of 0.302 mV/g. For the elastic tape squeeze structure, the elastic tape is designed to encapsulate the optical fiber with a good frequency response from 1100 to 3100 Hz. In addition, by using the new film squeeze structure to measure the steady-state and non-steady-state vibration signals, the spectral components of sensing signals are analyzed by using the wavelet transformation for confirming the testing signals. These vibration fiber sensors can be applied in the measurement of high-end manufacture-facility vibration or earthquake vibrations etc.

Exploring the effects of waste plastic aggregate on styrene-butadiene-styrene-modified asphalt binders for sustainable rural pavements

In addressing the imperative need for sustainable and cost-effective solutions in rural pavement development, this study navigates the intricate balance of environmental and financial constraints to ensure the resilience of infrastructure in communities with limited resources. The focal point is the integration of waste plastic aggregate (WPA) into hot mix asphalt, augmented by the inclusion of styrene-butadiene-styrene (SBS) for an elevated level of performance. The findings underscore a gradual decrease in the tensile strength ratio, emphasizing a manageable impact, transitioning from 82.4% in the control to 73.7% at 6% WPA. Noteworthy is the observation of marginal reductions in indirect tensile strength and stiffness, particularly notable at higher WPA levels. Dynamic modulus testing highlights susceptibility to rutting at lower frequencies, while high-frequency results demonstrate stability up to 6% WPA. The Hamburg wheel tracking test signals heightened rutting at 3% and 6% WPA, indicating potential challenges in deformation resistance. Despite a slight dip in strength, the discernible magnitude of this reduction is not substantial. This affirms that the incorporation of WPA achieves a harmonious enhancement of sustainability without compromising critical mechanical properties.

Terahertz Non-Destructive Testing of Porosity in Multi-Layer Thermal Barrier Coatings Based on Small-Sample Data

Accurately characterizing the internal porosity rate of thermal barrier coatings (TBCs) was essential for prolonging their service life. This work concentrated on atmospheric plasma spray (APS)-prepared TBCs and proposed the utilization of terahertz non-destructive detection technology to evaluate their internal porosity rate. The internal porosity rates were ascertained through a metallographic analysis and scanning electron microscopy (SEM), followed by the reconstruction of the TBC model using a four-parameter method. Terahertz time-domain simulation data corresponding to various porosity rates were generated employing the time-domain finite difference method. In simulating actual test signals, white noise with a signal-to-noise ratio of 10 dB was introduced, and various wavelet transforms were utilized for denoising purposes. The effectiveness of different signal processing techniques in mitigating noise was compared to extract key features associated with porosity. To address dimensionality challenges and further enhance model performance, kernel principal component analysis (kPCA) was employed for data processing. To tackle issues related to limited sample sizes, this work proposed to use the Siamese neural network (SNN) and generative adversarial network (GAN) algorithms to solve this challenge in order to improve the generalization ability and detection accuracy of the model. The efficacy of the constructed model was assessed using multiple evaluation metrics; the results indicate that the novel hybrid WT-kPCA-GAN model achieves a prediction accuracy exceeding 0.9 while demonstrating lower error rates and superior predictive performance overall. Ultimately, this work presented an innovative, convenient, non-destructive online approach that was safe and highly precise for measuring the porosity rate of TBCs, particularly in scenarios involving small sample sizes facilitating assessments regarding their service life.

Pulsar Signal Adaptive Surrogate Modeling

As the number of spacecraft heading beyond Earth’s orbit increased in recent years, autonomous navigation solutions have become increasingly important. One such solution is pulsar-based navigation. The availability of pulsar signals for simulations and HIL testing is essential for the development of pulsar-based navigation. This study proposes a method to develop a surrogate model of pulsar signals based on radio pulsar observations. The selection of suitable pulsars for the radio telescope is discussed, and a series of observations are conducted. The collected data are processed using the PRESTO software, and the pulsar parameters for the model are derived. Unlike current pulsar signal models, the proposed model anticipates pulsar signal parameters to change over time. It can provide dynamic input parameters for known synthetic pulsar signal generators, resulting in a more realistic signal.

A novel method measuring orifice conductance and testing time-of-flight mass spectrometer for water vapor–binary gas

Abstract The miniature time-of-flight mass spectrometer (TOF-MS) is a crucial instrument for detecting water ice in Chinese Lunar Exploration Program (CLEP), so it is necessary to compare its detection results for pure water vapor and water vapor-binary gas (such as H2O-N2, H2O-CH4, and H2O-Ar) to evaluate its water detection performance. The throughput must be calculated using the measured conductance to test the miniature TOF-MS. According to the VΔp method, the pΔt method, whose uncertainty is less than 14.2%, is proposed to measure orifice conductance for water vapor-binary gas, and an apparatus was developed based on those two methods. The orifice conductance of four kinds of pure gases (N2, H2O, CH4, and Ar) was measured using those two methods separately, and the measurement results allowed to calculate the conductance of the water vapor-binary gas through the Equivalent Single Gas method. The conductance of the water vapor-binary gas was measured using the pΔt method, and the difference between the calculated and measured results is less than 7%. Hence, the measured conductance allows the miniature TOF-MS to be tested for the water vapor-binary gas. As throughput is from 10-9 to 10-6 Pa m3 s-1, the difference between the test signals of water vapor-binary gas and pure water vapor is less than 40%.

Improving multi-wavelength overlay measurement time by the development of color-mixing light source

This paper introduces an innovative approach to streamline the time-consuming process of multiple-wavelength measurement in semiconductor overlay metrology. To achieve this, a color-mixing light source was developed, which employed light sources of different wavelengths, each modulated with different sine wave frequencies. By aligning these sources and directing them simultaneously onto the test sample, we mixed all wavelength information into a single test signal. Leveraging the known modulation frequencies, we can accurately extract the amplitudes of each wavelength using a three-parameter sine wave fitting method. These amplitudes effectively represent the sought-after signal for each wavelength. A 4-wavelength mixed light source was developed to demonstrate this method's feasibility. Compared with the intensities received independently for each wavelength, the reconstructed amplitude error was below 5 %. Finally, in the test of multiple-wavelength overlay measurement, the overlay difference between this and traditional methods was only 0.078 nm. The experimental results confirm that this method enables a reduction in the measurement time for multiple wavelengths from 0.8 sec to 0.2 sec, i.e., a 75 % reduction).

Оцінка точності комп’ютерного моделювання окуло-моторної системи на основі моделей Вольтерри

Integral nonlinear models are used to create mathematical models of the human oculo-motor system (OMS). These models take into account both inertial and nonlinear properties of the objects under study. To obtain empirical data for model construction, experimental studies are conducted with OMS using «input-output» data. Visual stimuli are used as test signals, displayed on a computer monitor at various distances from the starting position, which formally corresponds to the action of step signals with varying amplitudes on the object of study. In this process, the responses of the OMS are recorded using innovative eye-tracking technology. Mathematical models in the form of Volterra series and polynomials are employed for computer modeling of the OMS. The aim of this research is to analyze the accuracy of OMS identification as multidimensional transient functions based on eye-tracking data, examining the dependency of computation errors for models of different orders on the amplitudes and quantities of the test signals used. The subject of the study includes various methods for identifying OMS, algorithms, and Python-based software tools for computing the dynamic characteristics of OMS using eye-tracking technology. The research explores identification methods: compensation, approximation, and least squares methods. The accuracy of the linear, quadratic, and cubic OMS models is evaluated. The most accurate models, constructed from real experimental data, are found to be quadratic or cubic OMS models obtained using the least squares method with three test signals.

Аналіз даних теплового режиму комутаційного обладнання комп’ютерних мереж на основі відновлення сигналів температурних датчиків

The article considers the task of analyzing distributed data of temperature modes of chips of switching equipment of computer networks. For this, a temperature measurement system using a temperature sensor is used. In the case when the temperature sensor is inside the chip, the speed of the measurement system's response to temperature changes is satisfactory. However, when the sensor is outside the chip, due to the inertness of the thermal contact, the response speed of the measuring system is low. In this situation, the effectiveness of the control system becomes unsatisfactory. To overcome the inertness of temperature sensors, it is proposed to analyze the distributed data that is received in digital form on the data processing server by restoring the distorted signals of nonlinear measuring subsystems «chip – temperature sensor» based on the use of their mathematical models in the form of a partial sum of the Volterra integro-power series. The identification of the mathematical model of the measurement subsystem is carried out on a finite interval of time by conducting a series of experiments using test signals. The method of reducing the number of test signals based on taking into account the specificity of the impact of nonlinearity on the results of experiments is considered. The obtained model is the basis for solving the inverse problem of restoring the signal of temperature influence at the sensor input. Since this problem is incorrect, it is suggested to supplement the model with a regularization parameter and reduce the problem to a correct one. To use the model over an infinite period of time, a computer modeling technique is proposed using restarts of computing processes, which are carried out in several streams with a shift in time. The result of calculations is formed by combining fragments from different streams. To check the reliability of the results obtained by applying the developed method, the solutions of the model problems are given.

Estimates of the accuracy of identification of a nonlinear dynamic system using step test signals

Machine learning (ML) has become widespread in everyday life. On its basis, programs with artificial intelligence work, on the basis of which many virtual assistants have evolved. ML plays an important role in various spheres of activity of many enterprises. ML helps to automate many processes, simplifying the functioning of the company. Named Entity Recognition (NER) models allow to automatically select and search for information according to certain criteria in mobile data extracted, for example, by the logistic method. Support for NER by Python models makes it possible to flexibly program specific requests that are generated in the forensic examination process. Open source creates a unique opportunity to continuously improve the model by training it on datasets. A powerful NER package is the spaCy framework, which helps to simplify data and extract detailed information from input data, train a model, perform model tuning, and more. spaCy is compatible with 64-bit CPython 3.7+ and runs on Unix/Linux, macOS/OS X and Windows. The latest spaCy releases are available over pip and conda.

STAKgram: a method for optimal demodulation band selection in bearing fault diagnosis under complex interference

Abstract Within rotating machinery, bearings constitute critical and vulnerable components. When they operate in a compromised state, they generate periodic shock pulses. While spectral kurtosis (SK) has been demonstrated as an effective tool for pulse detection, in complex operating environments, the vibration signals of damaged bearings acquired through sensors often contain a variety of interfering pulses, such as ambient background noise, episodic single pulses, which cause SK to produce poor computational results. In this paper, an innovative bearing fault diagnostic method named subband trimmed average kurtogram (STAKgram) is proposed, aiming to minimize the effect of interfering signals on signal processing results, thus ensuring reliable diagnostic results even in complex interference environments. Firstly, the signal is segmented into N sub-signals by employing a sliding window method. Following, the subband kurtosis is calculated for every sub-signal. Ultimately, the trimmed average kurtosis of the respective sub-band is computed to construct the STAKgram, indicating the optimal demodulation frequency band. It is used for squared envelope demodulation analysis. By simulation analysis and the application of test signals obtained under conditions closely resembling authentic bearing operating conditions, the STAKgram method is proven to provide more accurate results for diagnosing bearing faults in complex interference.

Equivalent damping coefficient of a bistable piezoelectric cantilever beam energy harvester via experimental-analytical balanced energy method

This study shed light on the simultaneous effects of bistability and electrical resistor on the equivalent damping of a piezoelectric cantilever beam energy harvester. Damping coefficient has a direct effect on the frequency bandwidth of an oscillating system. This paper, extends the experimental-analytical balanced energy (BE) method to obtain the damping change due to bistability of a piezoelectric energy harvester. By using extended Hamilton principle and Euler-Lagrange formulation with nonlinear magnetic field relations, the governing equations of the system are derived. Then the equivalent BE viscous damping model was adopted and the equivalent damping term is achieved. In BE method, the test signals are registered into the proposed BE algorithm, then the equivalent viscous damping coefficient of the system will be obtained. In the end, the effects of bistability and electrical resistor of the power circuit of piezoelectric patch is discussed. According to the results, by increasing the bistability of the system, the equivalent damping of the system will also increase. Besides by escalation the value of electrical load in piezoelectric power circuit, the value of the equivalent damping coefficient reaches a maximum value. This behavior is originated from the extracted power of the system.

Near- and remote-field pulsed eddy current integrated testing for enhancement in imaging of buried flaws in layered conductive structures

ABSTRACT In a bid to simultaneously exploit the advantages of Pulsed Eddy Current testing (PEC) and Pulsed Remote-field Eddy Current Testing (PRFEC) for evaluation of buried defects in layered conductors, in this paper the Near- and Remote-field Pulsed Eddy Current integrated testing (NRPEC) alongside an axi-symmetric probe has been proposed. Simulations are conducted to reveal the field characteristics underlying the proposed method and analyse the signal response to hidden flaws. In parallel, an NRPEC system has been built up. Based on the images of near-field and remote-field signal features, a fusion algorithm is proposed to fuse the signal features of near-field and remote-field signals for defect testing with the enhanced signal-to-noise ratio. Results from simulations and experiments indicate that based on the fused signal feature, high-accuracy imaging of hidden defects in a double-layer conductor can be achieved via NRPEC with the improved signal-to-noise ratio.

Wind farm power optimization using system identification

The wake effect reduces the total power production of wind farms. This paper presents a method for wind farm power optimization through wake effect reduction. The proposed method optimizes the yaw angle offsets and de-rating settings of all turbines to maximize total power generation. The optimization approach is gradient-based, with gradients at each iteration obtained through system identification using field test data, eliminating the need for physical models. In system identification, test signal design, model estimation and model validation problems are solved in a systematic manner; in the gradient-based optimization, in order to achieve fast convergence, methods for initial value and initial step-size determination, variable step-size iteration and iteration termination are developed. The method is verified using the FLORIS wind farm model developed by National Renewable Energy Laboratory (NREL), USA. The studied wind farm consists of 80 wind turbines configured similarly to the Horns Rev I offshore wind farm in Denmark. The result of the developed optimization method is highly consistent with those obtained using FLORIS's built-in optimization tool.

Nanozyme coating-gated multifunctional COF composite based dual-ratio enhanced dual-mode sensor for highly sensitive and reliable detection of organophosphorus pesticides in real samples

The reliable detection of organophosphorus pesticides (OPs) in complex matrices remains an enormous challenge due to inevitable interference of sample matrices and testing factors. To address this issue, we designed a nanozyme-coated mesoporous COF with guest molecule loading, and successfully used it to construct a dual-ratio dual-mode sensor through target-regulated signal generation. The multifunctional COF-based composite (MB/COF@MnO2, MCM) featured high loading of methylene blue (MB), oxidase-like MnO2 coatings as gatekeepers, and specific recognition of thiocholine (TCh). TCh, a regulator produced from acetylcholinesterase (AChE)-catalyzed hydrolysis of acetylthiocholine, could decompose MnO2 coatings, triggering the release of abundant MB and oxidation of few o-phenylenediamine (OPD). OPs, strong inhibitors of AChE, could restrain TCh production and MnO2 decomposition, thereby controlling the release of less MB and oxidation of more OPD. This regulation boosted the dual-ratio dual-mode assay of OPs by using the released MB and oxidized OPD in the solution as testing signals, measured by both fluorescent and electrochemical methods. Experimental results demonstrated the sensitive detection of dichlorvos with LODs of 0.083 and 0.026 ng/mL via the fluorescent/electrochemical mode, respectively. This study represented a creative endeavor to develop dual-ratio dual-mode sensors for OPs detection in complex samples, offering high sensitivity, excellent selectivity, and good reliability.

Power system event detection using Teager Kaiser Energy Operator and autoencoder based algorithm

As the global demand for electricity increases, modern power systems are operating closer to their stability limits. This increases power system events that vary in intensity. It is essential to detect these power system events as early as possible and to take the necessary control actions to prevent escalation. This paper proposes a power system event detection method based on the Teager Kaiser Energy Operator (TKEO) and Autoencoder. In the proposed method, the rules-based algorithm performs primary detection, and the detected events are confirmed using the TKEO and autoencoder-based algorithms. The effectiveness of the proposed method was tested using test signals, simulated power system signals, and real-time power system data obtained from an actual power system. The results reveal the robustness and accuracy of the proposed method in detecting single and multiple power system events in the PMU data.

The Development of a Novel Transient Signal Analysis: A Wavelet Transform Approach

This paper presents a new method for the analysis of transient signals in the frequency domain based on the Continuous Wavelet Transform (CWT). The proposed case study involves test signals measured from an electronic switch considering open and close operations. The source is connected to inductive, resistive, and capacitive loads. Resonance behaviors are introduced and compared with the Discrete Fourier Transform (DFT). Multiple factors, such as reliability, repeatability, high noise attenuation, and the smoothing of the analyzed spectrum, are considered in this study. This proposed study highlights the effectiveness of CWT in signal processing, especially in obtaining a detailed spectrum that reveals the behavior of electrical circuits. Resonance behaviors were analyzed, demonstrating that the signal processing performed by CWT is better for spectrum analysis than DFT. This study shows the potential of CWT to analyze transient electrical signals, specifically for identifying and characterizing the behavior of load connections and disconnections.

Study on ultrasonic lamb wave testing technology of honeycomb sandwich structures

Abstract Honeycomb sandwich composite panels have complex structures, large sizes, many types of defects, and significant sound attenuation, which lead to low efficiency and poor sensitivity of ultrasonic non-destructive testing. In the study, a non-linear ultrasonic lamb wave testing technique for honeycomb sandwich composite panels is proposed. Firstly, the anisotropic propagation characteristics and modal structure of lamb waves in honeycomb sandwich composite panels are analyzed, and the lamb wave modes with non-linear ultrasonic accumulation are excited. Secondly, the data extraction method and imaging algorithm of non-linear ultrasonic lamb wave imaging testing are proposed. Finally, an enhanced imaging algorithm is proposed to improve image quality based on the sliding window algorithm. Based on the comparative analysis of air-coupled ultrasonic testing images and metallographic tests, it can be seen that non-linear ultrasonic lamb wave testing signals can be used to characterize the damage distribution characteristics of honeycomb sandwich composite panels, display debonding and weak debonding defects, and have the advantages of being able to perform on the same side testing and high efficiency, which can be used for non-destructive testing of honeycomb sandwich composite panels.

Overcoming the coherence time barrier in quantum machine learning on temporal data

The practical implementation of many quantum algorithms known today is limited by the coherence time of the executing quantum hardware and quantum sampling noise. Here we present a machine learning algorithm, NISQRC, for qubit-based quantum systems that enables inference on temporal data over durations unconstrained by decoherence. NISQRC leverages mid-circuit measurements and deterministic reset operations to reduce circuit executions, while still maintaining an appropriate length persistent temporal memory in the quantum system, confirmed through the proposed Volterra Series analysis. This enables NISQRC to overcome not only limitations imposed by finite coherence, but also information scrambling in monitored circuits and sampling noise, problems that persist even in hypothetical fault-tolerant quantum computers that have yet to be realized. To validate our approach, we consider the channel equalization task to recover test signal symbols that are subject to a distorting channel. Through simulations and experiments on a 7-qubit quantum processor we demonstrate that NISQRC can recover arbitrarily long test signals, not limited by coherence time.