Search Results (283)

To address the challenges of unstable vibration signals, indistinct fault features, and difficulties in feature extraction during rolling bearing operation, this paper presents a novel fault diagnosis method based on the fusion of PSR-CRP and DenseNet. The Phase Space Reconstruction (PSR) method transforms one-dimensional bearing vibration data into a three-dimensional space. Euclidean distances between phase points are calculated and mapped into a Color Recurrence Plot (CRP) to represent the bearings’ operational state. This approach effectively reduces feature extraction ambiguity compared to RP, GAF, and MTF methods. Fault features are extracted and classified using DenseNet’s densely connected topology. Compared with CNN and ViT models, DenseNet improves diagnostic accuracy by reusing limited features across multiple dimensions. The training set accuracy was 99.82% and 99.90%, while the test set accuracy is 97.03% and 95.08% for the CWRU and JNU datasets under five-fold cross-validation; F1 scores were 0.9739 and 0.9537, respectively. This method achieves highly accurate diagnosis under conditions of non-smooth signals and inconspicuous fault characteristics and is applicable to fault diagnosis scenarios for precision components in aerospace, military systems, robotics, and related fields. Full article

Medical image registration is an indispensable preprocessing step to align medical images to a common coordinate system before in-depth analysis. The registration precision is critical to the following analysis. In addition to representative image features, the initial pose settings and multiple poses in images will significantly affect the registration precision, which is largely neglected in state-of-the-art works. To address this, the paper proposes a dual-pose medical image registration algorithm based on improved differential evolution. More specifically, the proposed algorithm defines a composite similarity measurement based on contour points and utilizes this measurement to calculate the similarity between frontal–lateral positional DRR (Digitally Reconstructed Radiograph) images and X-ray images. In order to ensure the accuracy of the registration algorithm in particular dimensions, the algorithm implements a dual-pose registration strategy. A PDE (Phased Differential Evolution) algorithm is proposed for iterative optimization, enhancing the optimization algorithm’s ability to globally search in low-dimensional space, aiding in the discovery of global optimal solutions. Extensive experimental results demonstrate that the proposed algorithm provides more accurate similarity metrics compared to conventional registration algorithms; the dual-pose registration strategy largely reduces errors in specific dimensions, resulting in reductions of 67.04% and 71.84%, respectively, in rotation and translation errors. Additionally, the algorithm is more suitable for clinical applications due to its lower complexity. Full article

Digital holography can capture and reconstruct 3D object information, making it valuable for biomedical imaging and materials science. However, traditional holographic reconstruction methods require the use of phase shift operation in the time or space domain combined with complex computational processes, which, to some extent, limits the range of application areas. The integration of deep learning (DL) advancements with physics-informed methodologies has opened new avenues for tackling this challenge. However, most of the existing DL-based holographic reconstruction methods have high model complexity. In this study, we first design a lightweight model with fewer parameters through the synergy of deep separable convolution and Swish activation function and then employ it as a teacher to distill a smaller student model. By reducing the number of network layers and utilizing knowledge distillation to improve the performance of a simple model, high-quality holographic reconstruction is achieved with only one hologram, greatly reducing the number of parameters in the network model. This distilled lightweight method cuts computational expenses dramatically, with its parameter count representing just 5.4% of the conventional Unet-based method, thereby facilitating efficient holographic reconstruction in settings with limited resources. Full article

The thermal data of the hypersonic wind tunnel field accurately reflect the aerodynamic performance and key parameters of the aircraft model. However, the prediction of the temperature in hypersonic wind tunnels has problems such as a large delay, nonlinearity and multivariable coupling. In order to reduce the influence brought by temperature changes and improve the accuracy of temperature prediction in the field control of hypersonic wind tunnels, this paper first combines kernel principal component analysis (KPCA) with phase space reconstruction to preprocess the temperature data set of wind tunnel tests, and the processed data set is used as the input of the temperature-prediction model. Secondly, support vector regression is applied to the construction of the temperature prediction model for the hypersonic wind-tunnel temperature field. Meanwhile, aiming at the problem of difficult parameter-combination selection in support vector regression machines, an Improved African Vulture Optimization Algorithm (IAVOA) based on adaptive chaotic mapping and local search enhancement is proposed to conduct combination optimization of parameters in support vector regression. The improved African Vulture Optimization Algorithm (AVOA) proposed in this paper was compared and analyzed with the traditional AVOA, PSO (Particle Swarm Optimization Algorithm) and GWO (Grey Wolf Optimizer) algorithms through 10 basic test functions, and the superiority of the improved AVOA algorithm proposed in this paper in optimizing the parameters of the support vector regression machine was verified in the actual temperature data in wind-tunnel field control. Full article

The primary measurement involves detecting tiny (picometer-level) pathlength fluctuations between satellites using heterodyne laser interferometry for space-based gravitational wave detection. The interference of two laser beams with a MHz-level frequency difference produces a MHz beat-note, in which the gravitational wave signal is encoded in the phase of the beat-note. The phasemeter then performs micro-radian accuracy phase measurement and communication information demodulation for this beat-note. To mitigate the impact of phase modulation, existing solutions mostly alleviate it by reducing the modulation depth and optimizing the structure of the pseudo-random noise (PRN) codes. Since the phase modulation is not effectively separated from the phase of the beat-note phase measurement, it has a potential impact on the phase extraction of the micro-radian accuracy of the beat-note. To solve this problem, this paper analyzes the influence mechanism of phase modulation on beat-note phase measurement and proposes a method to separate the modulated phase based on complex rotation. The beat-note is processed by complex conjugate rotation, which can effectively eliminate the PRN modulated phase. Simulation and analysis results demonstrate that this method can significantly enhance the purity of the measured phase in the beat-note while maintaining the ranging and communication functions. Targeting the application of the micro-radian phasemeter in space-based gravitational wave detection, this study presents the reconstruction and separation method of the ranging and communication phase in beat-note, which also provides a new direction for the final selection of modulation depth in the future. Full article

This study examines the evolution, structure, and dynamic and thermodynamic mechanisms of a Mediterranean tropical-like cyclone (TLC), or medicane (from Mediterranean–Hurricane), that occurred in the central Mediterranean region from 15 to 19 September 2020. This event is considered an extreme meteorological phenomenon, particularly impacting the Greek area and affecting the country’s economic and social structures. It is one of the most significant recorded Mediterranean cyclone phenomena in the broader Mediterranean region. The synoptic and dynamic environment, as well as the thermodynamic structure of this atmospheric disturbance, were analyzed using thermodynamic parameters. The system’s development can be described through three distinct phases, characterized by its symmetrical structure and warm core, as illustrated in the phase space diagrams and further supported by dynamical analysis. During the first phase, on 15 September, the structure of the upper tropospheric layers began to strengthen the parent barometric low, which had been in the Sirte Bay region since 13 September. The influence of upper-level dynamical processes was responsible for the reconstruction of the weakened barometric low. In the second phase, during the formation of the Mediterranean cyclone, low-level diabatic processes determined the evolution of the surface cyclone without significant support from upper-tropospheric baroclinic processes. Therefore, in this phase, the system is characterized as barotropic. In the third phase, the system remained barotropic but showed a continuous weakening tendency as the sea surface pressure steadily increased. This comprehensive analysis highlights the intricate processes involved in the development and evolution of Mediterranean cyclones with tropical characteristics. Full article

To address the conflicting demands of the energy crisis, environmental pollution, and economic growth, the electric vehicle (EV) industry has expanded rapidly, facilitating the widespread adoption of power batteries. This paper investigates the use of chaos theory and machine learning for predicting the remaining useful life (RUL) of lithium-ion batteries. Firstly, the mutual information method determines the time delay of the monitoring sequence, while the improved false nearest neighbor method (Cao algorithm) establishes the embedding dimension, yielding the phase space reconstruction parameters. Secondly, the maximum Lyapunov exponent identifies the chaotic properties of the capacity decay time series, and a prediction dataset is constructed based on phase space reconstruction theory. Finally, leveraging the chaotic time-series features, a support vector machine (SVM) model is developed for lithium-ion battery RUL prediction. The algorithm is subsequently validated through simulation using the NASA battery dataset. The results demonstrate that the proposed method achieves high predictive accuracy and stability, providing reliable RUL estimates for the battery management system (BMS). Full article

We propose Phase-Adaptive Federated Learning (PAFL), a novel framework for privacy-preserving personalized travel itinerary generation that dynamically balances privacy and utility through a phase-dependent aggregation mechanism inspired by phase-change materials. (1) PAFL’s primary objective is to dynamically optimize the privacy–utility trade-off in federated travel recommendation systems through phase-adaptive anonymization. The phase parameter φ ∈ [0, 1] operates as a tunable control variable that continuously adjusts the latent space geometry between differentially private (φ→1) and utility-optimized (φ→0) representations via a thermodynamic-inspired transformation. Conventional federated learning approaches often rely on static privacy-preserving techniques, which either degrade recommendation quality or inadequately protect sensitive user data; PAFL addresses this limitation through three key innovations: a latent-space phase transformer, a differential privacy-gradient inverter with mathematically provable reconstruction bounds (εt ≤ 1.0), and a lightweight sequential transformer. (2) PAFL’s core innovation lies in its phase-adaptive mechanism that dynamically balances privacy preservation through differential privacy and utility maintenance via gradient inversion, governed by the tunable phase parameter φ. Experimental results demonstrate statistically significant improvements, with 18.7% higher HR@10 (p < 0.01) and 62% lower membership inference risk compared to state-of-the-art methods, while maintaining εtotal < 2.3 over 100 training rounds. The framework advances federated learning for sensitive recommendation tasks by establishing a new paradigm for adaptive privacy–utility optimization. Full article

Concrete-like composites are widely used in the building of civil engineering applications such as houses, dams, and roads. Mesoscale modeling is a powerful tool for the physical and mechanical analysis of concrete-like composites. A novel discrete element method (DEM)-enhanced external force-free method for multi-phase concrete-like composite modeling with an interface transition zone (ITZ) is presented in this paper. Firstly, randomly distributed particles with arbitrary shapes are generated based on a grading curve. Then, a Minkowski sum operation for particles is implemented to control the minimum gap between adjacent particles. Secondly, a transition from particles to clumps is realized using the overlapping discrete element cluster (ODEC) method and is randomly placed into a specific space. Thirdly, the DEM simulation with a simple linear contact model is employed to separate the overlapped clumps. Meanwhile, the initial position, displacement, and rotation of clumps are recorded. Finally, the mesoscale model is reconstructed based on the displacement and rotation information. The results show that this method can efficiently generate multi-phase composites with arbitrary particle shapes, high volume fractions, an overlapped ITZ, and a periodic structure. This study proposes a novel, efficient tool for analyzing and designing composite materials in resilient civil infrastructure. Full article

This study quantitatively analyzes the effects of various ventilation parameters on airflow stability in confined spaces ventilated by opposed jets, a common configuration in high-density settings. Using large eddy simulations (LES), we evaluate how changes in supply velocity, airflow configuration, enclosure geometry, and thermal gradients influence airflow dynamics. Findings show that higher supply velocities, up to 1.92 m/s, lead to a measurable increase in oscillation period (from 7.7 s to 11.3 s) and reduce small-scale flow disturbances. The free jet configuration exhibits higher oscillation amplitude and a more disordered structure compared to the attached jet, resulting in uneven airflow distribution. Aspect ratio has a pronounced effect, with increased ratios extending oscillation periods from 10.6 s to 18.1 s and intensifying turbulence. Thermal gradients, with floor temperatures rising from 15 °C to 35 °C, and the oscillation period are increased, further dispersing airflow and reducing stability. Phase space reconstruction and power spectral analysis provide quantitative benchmarks for oscillation frequencies and patterns, correlating velocity time series with airflow structural changes. The findings from this study can serve as a foundation for future research on thermal comfort and air quality management in enclosed environments. Full article

To address the temporal jitter with low segmentation accuracy and the lack of high-precision transformations for specific object classes in video generation, we propose the fish body pattern sync-style network for ornamental fish videos. This network innovatively integrates dynamic texture transfer with instance segmentation, adopting a two-stage processing architecture. First, high-precision video frame segmentation is performed using Mask2Former to eliminate background elements that do not participate in the style transfer process. Then, we introduce the wavelet-gated styling network, which reconstructs a multi-scale feature space via discrete wavelet transform, enhancing the granularity of multi-scale style features during the image generation phase. Additionally, we embed a convolutional block attention module within the residual modules, not only improving the realism of the generated images but also effectively reducing boundary artifacts in foreground objects. Furthermore, to mitigate the frame-to-frame jitter commonly observed in generated videos, we incorporate a contrastive coherence preserving loss into the training process of the style transfer network. This enhances the perceptual loss function, thereby preventing video flickering and ensuring improved temporal consistency. In real-world aquarium scenes, compared to state-of-the-art methods, FSSNet effectively preserves localized texture details in generated videos and achieves competitive SSIM and PSNR scores. Moreover, temporal consistency is significantly improved. The flow warping error index decreases to 1.412. We chose FNST (fast neural style transfer) as our baseline model and demonstrate improvements in both model parameter count and runtime efficiency. According to user preferences, 43.75% of participants preferred the dynamic effects generated by this method. Full article

The synchronization of chaotic systems is a fundamental phenomenon in nonlinear dynamics. Most known synchronization techniques suggest that the trajectories of coupled systems converge at an exponential rate. However, this requires transferring a substantial data array to achieve complete synchronization between the master and slave oscillators. A recently developed approach, called time-reversible synchronization, has been shown to accelerate the convergence of trajectories. This approach is based on the special properties of time-symmetric integration. This technique allows for achieving the complete synchronization of discrete chaotic systems at a superexponential rate. However, the validity of time-reversible synchronization between discrete and continuous systems has remained unproven. In the current study, we expand the applicability of fast time-reversible synchronization to a case of digital and analog chaotic systems. A circuit implementation of the Sprott Case B was taken as an analog chaotic oscillator. Given that real physical systems possess more complicated dynamics than simplified models, analog system reidentification was performed to achieve a reasonable relevance between a discrete model and the circuit. The result of this study provides strong experimental evidence of fast time-reversible synchronization between analog and digital chaotic systems. This finding opens broad possibilities in reconstructing the phase dynamics of partially observed chaotic systems. Utilizing minimal datasets in such possible applications as chaotic communication, sensing, and system identification is a notable development of this research. Full article

Magnetic flux leakage (MFL) inspection systems are widely used for detecting pipeline defects in industrial sites. However, the acquired MFL signals are affected by field noise, such as electromagnetic interference and mechanical vibrations, which degrade the performance of the developed models. In addition, the noise type or intensity is unknown or changes dynamically during the test phase in contrast to the training phase. To address the above challenges, this paper introduces a novel noise-robust deep-learning framework to remove the noise component in the original signal and learn its noise-invariant feature representation. This can handle the unseen noise pattern and mitigate the impact of dynamic noises on MFL inspection systems. Specifically, we propose a transformer-based architecture for denoising, which encodes noisy input signals into a latent space and reconstructs them into clean signals. We also devise an up–down sampling denoising block to better filter the noise component and generate a noise-invariant representation for weld-defect detection. Finally, extensive experiments demonstrate that the proposed approach effectively improves detection accuracy under both static and dynamic noise conditions, highlighting its value in real-world industrial applications. Full article

By extending synthetic aperture technology from a microwave band to laser wavelength, the synthetic aperture ladar (SAL) achieves extremely high spatial resolution independent of the target distance in long-range imaging. Nonlinear phase correction is a critical challenge in SAL imaging. To address the issue of phase noise during the imaging process, we first analyze the theoretical impact of nonlinear phase noise in imaging performance. Subsequently, a reconstruction and compensation method based on orthonormal complete basis functions is proposed to mitigate nonlinear phase noise in SAL imaging. The simulation results validate the accuracy and robustness of the proposed method, while experimental data demonstrate its effectiveness in improving system range resolution and reducing the peak side lobe ratio by 3 dB across various target scenarios. This advancement establishes a solid foundation for the application of SAL technology in ground-based remote sensing and space target observation. Full article

In this work, we explore an optimization idea for the complexity guidance of a phase retrieval solution for a single acquired hologram. This method associates free-space backpropagation with the fast iterative shrinkage-thresholding algorithm (FISTA), which incorporates an improvement in the total variation (TV) to guide the complexity of the phase retrieval solution from the complex diffracted field measurement. The developed procedure can provide excellent phase reconstruction using only a single acquired hologram. Full article