Search Results (649)

To address traditional methods’ limitations in neglecting the interaction between random shock loads and progressive degradation, as well as failure correlations, this study proposes a dynamic reliability framework integrating Gamma processes, homogeneous Poisson processes (HPP), and mixed Copula functions. The framework develops a wind turbine gearbox reliability model under shock–degradation coupling while quantifying failure correlations. Gamma processes characterize continuous degradation, with parameters estimated from P-S-N curves. Based on stress–strength interference theory, random shocks within damage thresholds are integrated to form a coupled reliability model. A Gumbel–Clayton–Frank mixed Copula with a multi-layer nested algorithm quantifies failure correlations, with correlation parameters estimated via the RSS principle and genetic algorithms. Validation using a 2 MW gearbox’s planetary gear-stage system covers four scenarios: natural degradation, shock–degradation coupling, and both scenarios with failure correlations. The results show that compared to independent assumptions, the model accelerates reliability decline, increasing failure rates by >37%. Relative to natural degradation-only models, failure rates rise by >60%, validating the model’s effectiveness and alignment with real-world operational conditions. Full article

Molybdenum disulfide is more attractive and valuable at the molecular level due to its unique structure and exceptional properties. Here, new-type MoS₂-ring chains are constructed and theoretically investigated for relevant electronic properties influenced by the length of the chain and the bias. Different from traditional wires, our findings demonstrate that the conductance of such a new-type chain presents unusually non-exponential decay with the length of the chain, with a particularly anomalous length of seven rings, which shows stronger equilibrium conductance than a shorter four-ring chain. Multi-peaks of electron transmission and delocalized electronic states contribute such uniqueness. Mo atoms play a vital role in electron transport. Essentially, a narrower “HOMO-LUMO” (the two closest energy levels to the Fermi level of MoS₂-ring chain) gap compensates for the lower device density of states of new-type molybdenum disulfide-ring chains. The usual electronic structure of a seven-ring chain is derived from its slightly arched structure and mainly originates from interference, which is the resonance occurring between the electrodes. Noticeably, the bias could greatly enhance conductance, which could reach 1000 times more than the equilibrium conductance. At a certain bias, the conductance of a seven-ring chain even exceeds the shortest one- or two-ring chain. Furthermore, the threshold voltage (at which the maximum conductance appears) gradually decreases with the length of the chain and eventually remains at 0.7 V. The valuable negative differential resistance (NDR) effect could be found in such a molecular chain, which becomes more obvious as the length rises until the seven-ring chain reaches the peak. Our findings shed light on the relations between electronic properties and the length of a new-type molybdenum disulfide-ring chain, and provide support for such new-type chains in applications of innovative low-power and controllable electronics. Full article

The robust detection of small targets is crucial in infrared (IR) search and tracking applications. Considering that many state-of-the-art (SOTA) methods are still unable to suppress various edges satisfactorily, especially under complex backgrounds, an effective infrared small target detection algorithm inspired by modified fast saliency and the weighted guided image filter (WGIF) is presented in this paper. Initially, the fast saliency map modulated by the steering kernel (SK) is calculated. Then, a set of edge-preserving smoothed images are produced by WGIF using different filter radii and regularization parameters. After that, utilizing the fuzzy sets technique, the background image is predicted reasonably according to the results of the saliency map and smoothed or non-smoothed images. Finally, the differential image is calculated by subtracting the predicted image from the original one, and IR small targets are detected through a simple thresholding. Experimental results on four sequences demonstrate that the proposed method can not only suppress background clutter effectively under strong edge interference but also detect targets accurately with a low false alarm rate. Full article

Background: Combined cognitive and exercise training improves exercise endurance, including submaximal muscular endurance. Its effects on maximal muscular strength have yet to be determined. Accordingly, we tested the effects of combined training on muscular strength (one repetition maximum, 1RM) and endurance (as many repetitions as possible, AMRAP). Methods: Resistance-trained adults (five males, three females) completed ten sessions (four testing, six training) over 4 weeks. In each testing session, they were assessed for bench press 1RM before they completed AMRAP at 50% of initial 1RM. In each training session, they performed five bench press sets (five repetitions at 80% current 1RM), with each set followed by a hard 5 min cognitive task (Time-Load Dual-Back or Color Multi-Source Interference). Ratings of perceived exertion (RPE) were averaged to provide a session RPE. At the end of each session, participants completed a Psychomotor Fatigue Threshold Test and rated mental fatigue. Results: ANOVAs (four testing sessions) showed that combined training increased 1RM (p < 0.001; averaging 8.0 kg or 11% from sessions 1–4) and AMRAP (p < 0.01; 5.1 repetitions or 22%). Moreover, training increased RPE (p < 0.05; 0.3 or 5%) and decreased mental fatigue ratings (p < 0.001; −1.2 or −49%) but did not affect Psychomotor Fatigue Threshold Test reaction times (p > 0.05; 2 ms or 0%). Conclusions: A 4-week training program that combined high-intensity cognitive and resistance exercise tasks improved maximal and submaximal resistance exercise performance. This pilot study provides preliminary evidence that high-intensity combined training can enhance muscular strength and endurance. Full article

We analyze the high-order above-threshold ionization (HATI) process of a small polyatomic molecule with C₃ symmetry, which is induced by a bicircular strong laser field. This field consists of two coplanar, counter-rotating, circularly polarized components with frequencies $r\omega$ and $s\omega$ where r and s are integers. In our study, we use an improved molecular strong-field approximation to obtain electron energy-angle-resolved and momentum spectra of the BF₃ molecule. We analyze the contributions of individual atoms as well as the impact of molecular symmetries on these spectra. We find that these spectra are significantly affected by the characteristics of the molecule and the laser-field parameters. Furthermore, we observe pronounced interference minima in the HATI spectra. We demonstrate that these minima result from the destructive interference of rescattered wave packets from different atomic centers, and we determine the conditions under which they occur, including two-, three-, and four-center interference. Full article

Optical coherence tomography (OCT) employs light to acquire high-resolution 3D images and is widely applied in fields such as ophthalmology and forensic science. A popular technique for visualizing the top view (en face) is to slice it with flat horizontal plane or apply statistical functions along the depth axis. However, when the target appears as a thin layer, strong reflections from other layers can interfere with the target, rendering the flat-plane approach ineffective. We apply Otsu-based thresholding to extract the object’s foreground, then use least squares (with Tikhonov regularization) to fit a polynomial curve that describes the sample’s structural morphology. The surface is then used to obtain the latent fingerprint image and its residues at different depths from a translucent tape, which cannot be analyzed using conventional en face OCT due to strong reflection from the diffusive surface, achieving FSIM of 0.7020 compared to traditional en face of 0.6445. The method is also compatible with other signal processing techniques, as demonstrated by a thermal-printed label ink thickness measurement confirmed by a microscopic image. Our approach empowers OCT to observe targets embedded in samples with arbitrary postures and morphology, and can be easily adapted to various optical imaging technologies. Full article

With the rapid development of sixth-generation (6G) wireless networks and large-scale multiple-input multiple-output (MIMO) technology, the number of antennas deployed at base stations (BSs) has increased significantly, resulting in a high probability that users are in the near-field region. Note that it is difficult for the traditional far-field plane-wave model to meet the demand for high-precision beamforming in the near-field region. In this paper, we jointly optimize the power and the number of antennas to achieve the maximum energy efficiency for the users located in the near-field region. Particularly, this paper considers the resolution constraint in the formulated optimization problem, which is designed to guarantee that interference between users can be neglected. A low-complexity optimization algorithm is proposed to realize the joint optimization of power and antenna number. Specifically, the near-field resolution constraint is first simplified to a polynomial inequality using the Fresnel approximation. Then the fractional objective of maximizing energy efficiency is transformed into a convex optimization subproblem via the Dinkelbach algorithm, and the power allocation is solved for a fixed number of antennas. Finally, the number of antennas is integrally optimized with monotonicity analysis. The simulation results show that the proposed method can significantly improve the system energy efficiency and reduce the antenna overhead under different resolution thresholds, user angles, and distance configurations, which provides a practical reference for the design of green and low-carbon near-field communication systems. Full article

Wearable laser Doppler devices are susceptible to complex noise interferences, such as Gaussian white noise, baseline drift, and motion artifacts, with motion artifacts significantly impacting clinical diagnostic accuracy. Addressing the limitations of existing denoising methods—including traditional adaptive filtering that relies on prior noise information, modal decomposition techniques that depend on empirical parameter optimization and are prone to modal aliasing, wavelet threshold functions that struggle to balance signal preservation with smoothness, and the high computational complexity of deep learning approaches—this paper proposes an ISSA-VMD-AWPTD denoising algorithm. This innovative approach integrates an improved sparrow search algorithm (ISSA), variational mode decomposition (VMD), and adaptive wavelet packet threshold denoising (AWPTD). The ISSA is enhanced through cubic chaotic mapping, butterfly optimization, and sine–cosine search strategies, targeting the minimization of the envelope entropy of modal components for adaptive optimization of VMD’s decomposition levels and penalty factors. A correlation coefficient-based selection mechanism is employed to separate target and mixed modes effectively, allowing for the efficient removal of noise components. Additionally, an exponential adaptive threshold function is introduced, combining wavelet packet node energy proportion analysis to achieve efficient signal reconstruction. By leveraging the rapid convergence property of ISSA (completing parameter optimization within five iterations), the computational load of traditional VMD is reduced while maintaining the denoising accuracy. Experimental results demonstrate that for a 200 Hz test signal, the proposed algorithm achieves a signal-to-noise ratio (SNR) of 24.47 dB, an improvement of 18.8% over the VMD method (20.63 dB), and a root-mean-square-error (RMSE) of 0.0023, a reduction of 69.3% compared to the VMD method (0.0075). The processing results for measured human blood flow signals achieve an SNR of 24.11 dB, a RMSE of 0.0023, and a correlation coefficient (R) of 0.92, all outperforming other algorithms, such as VMD and WPTD. This study effectively addresses issues related to parameter sensitivity and incomplete noise separation in traditional methods, providing a high-precision and low-complexity real-time signal processing solution for wearable devices. However, the parameter optimization still needs improvement when dealing with large datasets. Full article

Recent studies primarily focus on how the fiber content and curing age influence the pore structure and strength of concrete. However, The interfacial bonding mechanism in basalt-fiber-reinforced concrete hydration remains unclear. The lack of a long-term performance-prediction model and insufficient research on multi-field coupling effects form key knowledge gaps, hindering the systematic optimal design and wider engineering applications of such materials. By integrating X-ray computed tomography (CT) with the watershed algorithm, this study proposes an innovative gray scale threshold method for pore quantification, enabling a quantitative analysis of pore structure evolution and its correlation with mechanical properties in basalt-fiber-reinforced concrete (BFRC) and normal concrete (NC). The results show the following: (1) Mechanical Enhancement: the incorporation of 0.2% basalt fiber by volume demonstrates significant enhancement in the mechanical performance index. At 28 days, BFRC exhibits compressive and splitting tensile strengths of 50.78 MPa and 4.07 MPa, surpassing NC by 19.88% and 43.3%, respectively. The early strength reduction in BFRC (13.13 MPa vs. 22.81 MPa for NC at 3 days) is attributed to fiber-induced interference through physical obstruction of cement particle hydration pathways, which diminishes as hydration progresses. (2) Porosity Reduction: BFRC demonstrates a 64.83% lower porosity (5.13%) than NC (11.66%) at 28 days, with microscopic analysis revealing a 77.5% proportion of harmless pores (<1.104 × 10⁷ μm³) in BFRC versus 67.6% in NC, driven by densified interfacial transition zones (ITZs). (3) Predictive Modeling: a two dimensional strength-porosity model and a three-dimensional age-dependent model are developed. The proposed multi-factor model demonstrates exceptional predictive capability (R² = 0.9994), establishing a quantitative relationship between pore micro structure and mechanical performance. The innovative pore extraction method and mathematical modeling approach offer valuable insights into the micro-structural evolution mechanism of fiber concrete. Full article

To address the coupled fault diagnosis challenge between temperature sensors and equipment in traction converter cooling systems, this paper proposes a dual-level intelligent diagnostic architecture. This method achieves online sensor fault isolation and early equipment anomaly warning by leveraging spatiotemporal correlation modeling of multimodal sensor data and ensemble learning-based prediction. At the first level, it integrates multi-source parameters such as outlet temperature and pressure to establish dynamic prediction models, which are combined with adaptive threshold mechanisms for detecting various sensor faults including offset, open-circuit, and noise interference. At the second level, it monitors the status of temperature sensors through time-series analysis of inlet temperature data. Verified on an edge computing platform, the proposed method effectively resolves the coupling misdiagnosis between sensor distortion and equipment faults while maintaining physical interpretability, thereby significantly enhancing diagnostic robustness under complex operating conditions. Full article

With the rapid development of wireless communication technologies and the increasing demand for efficient spectrum utilization, broadband spectrum sensing has become critical in both civilian and military fields. Detecting narrowband signals under broadband environments, especially under low-signal-to-noise-ratio (SNR) conditions, poses significant challenges due to the complexity of time–frequency features and noise interference. To this end, this study presents a signal detection model named deformable feature-enhanced network–You Only Look Once (DFN-YOLO), specifically designed for blind signal detection in broadband scenarios. The DFN-YOLO model incorporates a deformable channel feature fusion network (DCFFN), replacing the concatenate-to-fusion (C2f) module to enhance the extraction and integration of channel features. The deformable attention mechanism embedded in DCFFN adaptively focuses on critical signal regions, while the loss function is optimized to the focal scaled intersection over union (Focal_SIoU), improving detection accuracy under low-SNR conditions. To support this task, a signal detection dataset is constructed and utilized to evaluate the performance of DFN-YOLO. The experimental results for broadband time–frequency spectrograms demonstrate that DFN-YOLO achieves a mean average precision (mAP50–95) of 0.850, averaged over IoU thresholds ranging from 0.50 to 0.95 with a step of 0.05, significantly outperforming mainstream object detection models such as YOLOv8, which serves as the benchmark baseline in this study. Additionally, the model maintains an average time estimation error within $5.55\times {10}^{-5}$ s and provides preliminary center frequency estimation in the broadband spectrum. These findings underscore the strong potential of DFN-YOLO for blind signal detection in broadband environments, with significant implications for both civilian and military applications. Full article

Frequency-modulated continuous-wave (FMCW) LiDAR systems frequently experience noise interference during multi-target measurements in real-world applications, resulting in target overlapping and diminished detection accuracy. Conventional denoising approaches—such as Empirical Mode Decomposition (EMD) and wavelet thresholding—are often constrained by challenges like mode mixing and the attenuation of weak target signals, which limits their detection precision. To address these limitations, this study presents a novel denoising framework that integrates an optimized Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm and singular value decomposition (SVD). The CEEMDAN algorithm’s two critical parameters—the noise standard deviation and the number of noise additions—are optimally determined using particle swarm optimization (PSO), with the envelope entropy of the intrinsic mode functions (IMFs) serving as the fitness criterion. IMFs are subsequently selected based on spectral and amplitude comparisons with the original signal to facilitate initial signal reconstruction. Following CEEMDAN-based decomposition, SVD is employed with a normalized soft thresholding technique to further suppress residual noise. Validation using both synthetic and experimental datasets demonstrates the superior performance of the proposed approach over existing methods in multi-target scenarios. Specifically, it reduces the root mean square error (RMSE) by 45% to 59% and the mean square error (MSE) by 34% to 69%, and improves the signal-to-noise ratio (SNR) by 1.85–4.38 dB and the peak signal-to-noise ratio (PSNR) by 1.18–6.94 dB. These results affirm the method’s effectiveness in enhancing signal quality and target distinction in noisy FMCW LiDAR measurements. Full article

The current study presents the results of a methodical investigation into the ionization of rare gas atoms, specifically focusing on argon. In this study, two configurations are examined: ionization via a near-infrared (NIR) laser field alone, and ionization caused by extreme ultraviolet (XUV) radiation in the presence of a strong, synchronized NIR pulse. The theoretical investigation is conducted using an ab initio method to solve the time-dependent Schrödinger equation within the single active electron (SAE) approximation. The simulation results show a sequence of above-threshold ionization (ATI) peaks that shift to lower energies with increasing laser intensity. This behavior reflects the onset of the Stark effect, which modifies atomic energy levels and increases the number of photons required for ionization. An examination of the two-color photoionization spectrum, which includes sideband structures and harmonic peaks, shows how the ionization probability is redistributed between the direct path (single XUV photon absorption) and sideband pathways (XUV ± n × IR) as the intensity of the infrared field increases. Quantum interference between continuum states is further revealed by the photoelectron angular distribution, clearly indicating the control of ionization dynamics by the IR field. Full article

Partial discharge (PD) in insulators will not only lead to the gradual degradation of insulation performance but even cause power system failure in serious cases. Because there is strong noise interference in the field, it is difficult to accurately locate the position of the PD source. Therefore, this paper proposes a three-dimensional spatial localization method of the PD source with a four-element ultra-high-frequency (UHF) array based on improved wavelet packet dynamic threshold denoising and the $G_{xx}-\beta$ generalized cross-correlation algorithm. Firstly, considering the field noise interference, the PD signal is decomposed into sub-signals with different frequency bands by the wavelet packet, and the corresponding wavelet packet coefficients are extracted. By using the improved threshold function to process the wavelet packet coefficients, the PD signal with low distortion rate and high signal-to-noise ratio (SNR) is reconstructed. Secondly, in order to solve the problem that the amplitude of the first wave of the PD signal is small and the SNR is low, an improved weighting function, $G_{xx}-\beta$, is proposed, which is based on the self-power spectral density of the signal and is adjusted by introducing an exponential factor to improve the accuracy of the first wave arrival time and time difference calculation. Finally, the influence of different sensor array shapes and PD source positions on the localization results is analyzed, and a reasonable arrangement scheme is found. In order to verify the performance of the proposed method, simulation and experimental analysis are carried out. The results show that the improved wavelet packet denoising algorithm can effectively realize the separation of PD signal and noise and improve the SNR of the localization signal with low distortion rate. The improved $G_{xx}-\beta$ weighting function significantly improves the estimation accuracy of the time difference between UHF sensors. With the sensor array designed in this paper, the relative localization error is 3.46%, and the absolute error is within 6 cm, which meets the requirements of engineering applications. Full article

Gravitational wave (GW) signals are often affected by noise interference in the detection system; in order to attenuate the impact of detector noise and enhance the waveform characteristics of the signal, this paper proposes a space-based GW signal denoising method that combines the Parrot algorithm (PO) with the improved wavelet threshold (IWT) to optimize the variational mode decomposition (VMD). To address the challenge of selecting the number of modes K and the penalty factor $\alpha$ in VMD, PO is introduced to select the optimal parameters, achieving a good balance between global search and local optimization. The components after modal decomposition are divided into preserved modal components and noise modal components, and the IWT is introduced to further denoise the noise modal components; finally, the signal is reconstructed to achieve the purpose of denoising the GW signal. The algorithm is verified by the GW simulation signal and the measured signal. The experimental results show that the algorithm is superior to other algorithms in the noise separation of GW signals, significantly improves the SNR, improves the detection accuracy of GW, and provides a new technical means for the extraction and analysis of GW signals. Full article