Search Results (320)

This paper proposes an Adam-optimized Deep Belief Networks (Adam-DBNs) denoising method for throat-attached piezoelectric signals. The method aims to process mechanical vibration signals captured through polyvinylidene fluoride (PVDF) sensors attached to the throat region, which are typically contaminated by environmental noise and physiological noise. First, the short-time Fourier transform (STFT) is utilized to convert the original signals into the time–frequency domain. Subsequently, the masked time–frequency representation is reconstructed into the time domain through a diagonal average-based inverse STFT. To address complex nonlinear noise structures, a Deep Belief Network is further adopted to extract features and reconstruct clean signals, where the Adam optimization algorithm ensures the efficient convergence and stability of the training process. Compared with traditional Convolutional Neural Networks (CNNs), Adam-DBNs significantly improve waveform similarity by 6.77% and reduce the local noise energy residue by 0.099696. These results demonstrate that the Adam-DBNs method exhibits substantial advantages in signal reconstruction fidelity and residual noise suppression, providing an efficient and robust solution for throat-attached piezoelectric sensor signal enhancement tasks. Full article

To circumvent the computational bottlenecks associated with the intermediate steps (e.g., least squares fitting) in conventional sine wave similarity principles and directly acquire the energy metrics required for stabilized sinusoidal waveform characterization, this study leverages time domain probability distribution theory. From a complementary advantage perspective, a novel transformer inrush current identification criterion is developed using the Wasserstein distance metric. The methodology employs feature discretization to extract target/template signals, transforming them into state vectors for sample labelling. By quantifying inter-signal energy distribution disparities through this framework, it achieves a precise waveform similarity assessment in sinusoidal regimes. The theoretical analysis and simulations demonstrate that the approach eliminates frequency domain computations while maintaining implementation simplicity. Compared with conventional sine wave similarity methods, the solution streamlines protection logic and significantly enhances practical applicability with accelerated response times. Furthermore, tests conducted on field-recorded circuit breaker closing waveforms using MATLAB R2022a confirm the effectiveness of the proposed method in improving transformer protection performance. Full article

Insulation materials are critical for the reliability and performance of electrical power systems, particularly in high-voltage rotating machines. While failures can arise from thermal, mechanical, or electrical stress, they predominantly manifest as electrical breakdowns. Prior research has primarily concentrated on aging in straight winding sections, despite evidence indicating that failures frequently occur in the bending regions of turn insulation. This study explores the influence of high-frequency pulsed electrical stress on the lifetime behavior of Type II insulation systems used in high-voltage rotating machines. Practical samples, designed with geometric configurations and technology akin to that in rotating machines, were tested under conditions characterized by voltage slew rates (dv/dt) exceeding 10 kV/μs, with variations in frequency and waveform shape. The findings reveal that the rate of electrical aging remains consistent across differing pulse widths, risetimes, and polarities, displaying a similar lifetime exponent. Nonetheless, insulation durability is markedly more compromised under pulsed conditions. At the identical times-to-failure, the sinusoidal waveform necessitated nearly twice the applied peak voltage as the bipolar pulse waveform. This finding clearly suggests that pulsed excitation exacerbates insulation degradation more effectively due to the sharp rise times and high (dv/dt) rates imposing substantial electrical stress on dielectric materials. Full article

To improve the efficiency and accuracy of microseismic event extraction from time-series data and enhance the detection of anomalous events, this paper proposes a Multi-scale Fusion Convolution and Dilated Convolution Autoencoder (MDCAE) combined with a Constraint Shape-Based Distance algorithm incorporating volatility (CSBD-Vol). MDCAE extracts low-dimensional features from waveform signals through multi-scale fusion and dilated convolutions while introducing the concept of waveform volatility (Vol) to capture variations in microseismic waveforms. An improved Shape-Based Distance (SBD) algorithm is then employed to measure the similarity of these features. Experimental results on a microseismic dataset from the 802 working faces of a mining site demonstrate that the CSBD-Vol algorithm significantly outperforms SBD, Shape-Based Distance with volatility (SBD-Vol), and Constraint Shape-Based Distance (CSBD) in classification accuracy, verifying the effectiveness of constrained time windows and volatility in enhancing performance. The proposed clustering algorithm reduces time complexity from $O\left(n^2\right)$ to $O(nlogn)$, achieving substantial improvements in computational efficiency. Furthermore, the MDCAE-CSBD-Vol approach achieves 87% accuracy in microseismic time-series waveform classification. These findings highlight that MDCAE-CSBD-Vol offers a novel, precise, and efficient solution for detecting anomalous events in microseismic systems, providing valuable support for accurate and high-efficiency monitoring in mining and related applications. Full article

Monitoring forearm muscle contraction force in home-based rehabilitation remains challenging. Electromyography (EMG), as a standard technique, is considered impractical and complex for independent use by patients at home, which poses a risk of device misattachment and inaccurate recorded data. Considering the muscle-related modality, several studies have demonstrated an excellent correlation between stretch sensors and EMG, which provides significant potential for addressing the monitoring issue at home. Additionally, due to its flexible nature, it can be attached to the finger, which facilitates the logging of the kinematic mechanisms of a finger. This study proposes a method for estimating forearm muscle contraction in a cylinder grasping environment during home-based rehabilitation using a stretch-sensor glove. This study employed support vector machine (SVM), multi-layer perceptron (MLP), and random forest (RF) to construct the estimation model. The root mean square (RMS) of the EMG signal, representing the muscle contraction force, was collected from 10 participants as the target learning for the stretch-sensor glove. This study constructed an experimental design based on a home-based therapy protocol known as the graded repetitive arm supplementary program (GRASP). Six cylinders with varying diameters and weights were employed as the grasping object. The results demonstrated that the RF model achieved the lowest root mean square error (RMSE) score, which differed significantly from the SVM and MLP models. The time series waveform comparison revealed that the RF model yields a similar estimation output to the ground truth, which incorporates the contraction–relaxation phases and the muscle’s contraction force. Additionally, despite the subjectivity of the participants’ grasping power, the RF model could produce similar trends in the muscle contraction forces of several participants. Utilizing a stretch-sensor glove, the proposed method demonstrated great potential as an alternative modality for monitoring forearm muscle contraction force, thereby improving the practicality for patients to self-implement home-based rehabilitation. Full article

P-wave first-motion polarity is a critical parameter for determining earthquake focal mechanisms. Extracting relative P-wave arrival times and polarity information using waveform cross-correlation techniques can enhance the accuracy of earthquake location and focal mechanism inversion. However, seismic noise severely hampers the reliable detection of P-wave onsets and their first-motion polarities. To address this issue, we propose a noise-resistant polarity detection method based on the normal time–frequency transform (NTFT), termed the time–frequency similarity coefficient (TFSC). The TFSC method computes relative delays and similarity coefficients by calculating the real part of the NTFT coefficients between two seismic signals. We validated the proposed approach using both synthetic and real earthquake data. Without any filtering or preprocessing, the TFSC method demonstrated significantly greater robustness and reliability compared to the conventional time-domain normalized cross-correlation (NCC) method. These results indicate that the TFSC method has strong potential for practical application and provides a novel perspective for accurate detection of P-wave first-motion polarity in noisy seismic environments. Full article

A linear frequency modulation (LFM) signal and its corresponding de-chirp operation are one of the basic methods for wideband radar signal processing, which can reduce the burden of the radar system sampling rate and is more suitable for large-bandwidth signal processing. More importantly, most existing methods against interrupted sampling repeater jamming (ISRJ) are based on time–frequency (TF) or frequency domain analysis of the de-chirped signal. However, the above anti-ISRJ methods cannot be directly applied to multiple-input multiple-output (MIMO) radar with multiple beams, because the angular waveform (AW) in mainlobe directions does not possess the TF properties of the LFM signal. Consequently, this work focuses on the co-optimization of transmit beampattern and AW similarity in wideband MIMO radar systems. Different from the existing works, which only concern the space–frequency pattern of the transmit waveform, we recast the transmit beampattern and AW expressions for wideband MIMO radar in a more compact form. Based on the compact expressions, a co-optimization model of the transmit beampattern and AWs is formulated where the similarity constraint is added to force the AW to share the TF properties of the LFM signal. An algorithm based on the alternating direction method of multipliers (ADMM) framework is proposed to address the aforementioned problem. Numerical simulations show that the optimized waveform can form the desired transmit beampattern and its AWs have similar TF properties and de-chirp results to the LFM signal. Full article

Based on the wide variety of electrical appliances, it is difficult to detect similar current waveforms when different appliances experience arc faults due to insufficient extraction of fault arc characteristics and low detection accuracy. To address these issues, a series arc fault detection method combining artificial hummingbird algorithm (AHA) and XGboost has been proposed. According to GB14287.4—2014, an experimental platform for fault arcs was designed and built to collect fault arc signals. By leveraging the global search capability and dynamic adaptive mechanism of AHA, key feature subsets sensitive to arcs are selected from high-dimensional time–frequency domain features. Combining the parallel computing advantages and regularization strategies of XGBoost, a low-complexity, highly interpretable fault classification model is constructed. The hyperparameters of XGBoost are simultaneously optimized by AHA. Experimental results show that the proposed method achieves a fault arc detection accuracy rate of 98.098%, effectively identifying series arc faults. Full article

The current pre-clinical testing standards for total hip replacements (THRs), ISO standards, use simplified loading waveforms that do not fully replicate real-world biomechanics. These standards provide a benchmark of data that may not accurately predict in vivo wear, necessitating the evaluation of physiologically relevant loading conditions. Previous studies have incorporated activities of daily living (ADLs) such as walking, jogging and stair negotiation into wear simulations. However, these studies primarily used simplified adaptations that increased axial forces and applied accelerated sinusoidal waveforms, rather than fully replicating the complex kinematics experienced by THR patients. To address this gap, this study applied patient-derived ADL profiles—jogging and stair negotiation—using a three-station hip simulator, obtained through 3D motion analysis of total hip arthroplasty patients, processed via a musculoskeletal multibody modelling approach to derive realistic hip contact forces (HCFs). The results indicate that jogging significantly increased wear rates compared to the ISO walking gait waveform, with wear increasing from 15.24 ± 0.55 to 28.68 ± 0.87 mm³/Mc. Additionally, wear was highly sensitive to changes in lubricant protein concentration, with an increase from 17 g/L to 30 g/L reducing wear by over 60%. Contrary to predictive models, stair descent resulted in higher volumetric wear (8.62 ± 0.43 mm³/0.5 Mc) compared to stair ascent (4.15 ± 0.31 mm³/0.5 Mc), despite both profiles having similar peak torques. These findings underscore the limitations of current ISO standards in replicating physiologically relevant wear patterns. The application of patient-specific loading profiles highlights the need to integrate ADLs into pre-clinical testing protocols, ensuring a more accurate assessment of implant performance and longevity. Full article

To enhance the signature of an extended target in a SAR image, a robust waveform design method is presented for spectrally dense environments. First, the problem is formulated by maximizing the worst-case signal-to-clutter ratio (SCR) over the uncertainty set of statistics for both target and background scattering characteristics, subject to energy, similarity, and spectrum constraints. Second, the closed-form solutions for the uncertain statistics are derived. The problem of maximizing worst-case SCR is boiled down to a nonconvex fractional quadratically constrained quadratic problem (QCQP). Resorting to the Dinkelbach’s algorithm and Lagrange duality, the QCQP is split into a series of solvable semidefinite programming problems. A convergence analysis is conducted, where a sufficient condition for global convergence is derived. Finally, numerical examples are presented to demonstrate the performance of the proposed scheme. Full article

Landslide-induced impulse waves (LIIWs) are significant natural hazards, frequently occurring in mountain reservoirs, which threaten the safety of waterways and dam project. To predict the impact of impulse waves induced by Rongsong (RS) potential landslide on the dam, during the layered construction period and maximum water level operation period of Rumei (RM) Dam (unbuilt), a large-scale three-dimensional similar physical model with a similarity scale of 200:1 (prototype length to model length) was established. The experiments set five water levels during the dam’s layered construction period and recorded and analyzed the generation and propagation laws of LIIWs. The findings indicate that, for partially granular submerged landslides, no splashing waves are generated, and the waveform of the first wave remains intact. The amplitude of the first wave exhibits stable attenuation while the third one reaches the largest. After the first three columns of impulse waves, water on the dam surface oscillates between the two banks. This study specifically discusses the impact of different water depths on LIIWs. The results show that the wave height increases as the water depth decreases. Two empirical formulas to calculate the wave attenuation at the generation area and to calculate the maximum vertical run-up height on the dam surface were derived, showing strong agreement between the empirical formulas and experimental values. Based on the model experiment results, the wave height data in front of the RM dam during the construction and operation periods of the RM reservoir were predicted, and engineering suggestions were given for the safety height of the cofferdam during the construction and security measures to prevent LIIW overflow the dam top during the operation periods of the RM dam. Full article

The codesign of the receive filter and transmit waveform under similarity constraints is one of the key technologies in frequency diverse array multiple-input multiple-output (FDA-MIMO) radar systems. This paper discusses the design of constant modulus waveforms and filters aimed at maximizing the signal-to-interference-and-noise ratio (SINR). The problem’s non-convexity renders it challenging to solve. Existing studies have typically employed relaxation-based methods, which inevitably introduce relaxation errors that degrade system performance. To address these issues, we propose an optimization framework based on the joint complex circle manifold–complex sphere manifold space (JCCM-CSMS). Firstly, the similarity constraint is converted into the penalty term in the objective function using an adaptive penalty strategy. Then, JCCM-CSMS is constructed to satisfy the waveform constant modulus constraint and filter norm constraint. The problem is projected into it and transformed into an unconstrained optimization problem. Finally, the Riemannian limited-memory Broyden–Fletcher–Goldfarb–Shanno (RL-BFGS) algorithm is employed to optimize the variables in parallel. Simulation results demonstrate that our method achieves a 0.6 dB improvement in SINR compared to existing methods while maintaining competitive computational efficiency. Additionally, waveform similarity was also analyzed. Full article

Voice conversion (VC) is an advanced technology that enables the transformation of raw speech into high-quality audio resembling the target speaker’s voice while preserving the original linguistic content and prosodic patterns. In this study, we propose a voice conversion algorithm, Multi-Dimensional Perception Flow Matching (MPFM-VC). Unlike traditional approaches that directly generate waveform outputs, MPFM-VC models the evolutionary trajectory of mel spectrograms with a flow-matching framework and incorporates a multi-dimensional feature perception network to enhance the stability and quality of speech synthesis. Additionally, we introduce a content perturbation method during training to improve the model’s generalization ability and reduce inference-time artifacts. To further increase speaker similarity, an adversarial training mechanism on speaker embeddings is employed to achieve effective disentanglement between content and speaker identity representations, thereby enhancing the timbre consistency of the converted speech. Experimental results for both speech and singing voice conversion tasks show that MPFM-VC achieves competitive performance compared to existing state-of-the-art VC models in both subjective and objective evaluation metrics. The synthesized speech shows improved naturalness, clarity, and timbre fidelity in both objective and subjective evaluations, suggesting the potential effectiveness of the proposed approach. Full article

When analyzing and designing Q-switched Nd:YAG lasers, the impact of lower-energy-level relaxation on the pulse waveform is often ignored. This approximation typically does not result in significant deviations when the laser pulse duration is much longer than the relaxation time of the lower energy level. However, when the pulse duration approaches the nanosecond range, the spontaneous emission time of lower energy level in the Nd:YAG crystal, which is approximately 30 ns, can severely affect the pulse waveform. In this study, a theoretical model is proposed to investigate the influence of lower-energy-level relaxation on the output pulse waveform of an Nd:YAG laser. Specifically, the output waveform of a narrow-pulse-width Q-switched Nd:YAG laser is simulated. The results indicate that for narrow-pulse-width laser output, lower-energy-level relaxation causes a secondary peak to appear after the main peak of the Q-switched pulse. The energy of this secondary peak is more than two times higher than that of the main peak. An experimental system for acousto-optic Q-switched Nd:YAG lasers has also been established, and the Q-switched pulse waveforms are measured under conditions similar to those in the simulations. The tail peak phenomenon observed in the experiments is consistent with the simulation results, verifying the accuracy of the theoretical model. These findings provide a crucial theoretical foundation for understanding and optimizing Nd:YAG lasers and have significant implications for the development of similar technologies. In laser technology, particularly for applications requiring high precision and performance, considering such factors is essential for optimizing the design and functionality of laser systems. Full article

Electroluminescence imaging is increasingly used in photovoltaic power plant inspections due to its effectiveness in detecting various types of failures in solar cells. This article presents a novel technique that enables the modulation of an arbitrary electroluminescence signal in PV modules using an electronic device that controls the signal by modulating an arbitrary current waveform in a photovoltaic module, utilizing the string current as its energy source. As a result, measurements do not require a power supply and can be performed during the normal operation of a PV string. Throughout the paper, this method is compared to a more conventional approach that relies solely on a power supply to generate the current signal. Capturing a sequence of images while modulating the current with different waveforms allows the application of the Fast Fourier Transform to suppress background signals caused by sunlight, resulting in high-quality EL images. Experimental results demonstrate that the proposed method delivers imaging quality comparable to that achieved with a power supply, while effectively detecting a broad range of solar cell failures. Furthermore, the calculated signal-to-noise ratio for both approaches yields similar values, indicating comparable quality in quantitative terms. Finally, square wave modulation has shown slightly better performance than other waveforms, such as sinusoidal and half-sinusoidal modulation. Full article