Search Results (7,338)

Active sonar tracking of multiple underwater targets is frequently challenged by intense reverberation, which leads to sonar returns that are both non-stationary and non-Gaussian. In such scenarios, the generalized labeled multi-Bernoulli (GLMB) filter, which relies on a Gaussian assumption, often experiences a rise in an Optimal Subpattern Assignment (OSPA) distance, along with recurrent label switching. To mitigate this problem, a robust delta-generalized labeled multi-Bernoulli technique (ST-δ-GLMB) is introduced; it characterizes noise using a Student’s t-distribution and employs variational Bayes to estimate the corresponding parameters. More precisely, the Student’s t-distribution is utilized to represent measurement non-stationarity, and an online variational Bayesian estimation of the noise parameters is conducted within a multi-target framework based on the Student’s t-model. Moreover, without altering the GLMB data-association and label-management machinery, we derive closed-form updates and propagation for the Student’s t-parameters, thereby keeping the recursive computational burden and practical implementability under control. Finally, Monte Carlo simulations and lake-trial data demonstrate that, under non-stationary and heavy-clutter conditions, ST-δ-GLMB maintains stable track continuity and accurate target-number (cardinality) estimates in the presence of non-stationary measurements. Full article

The mathematical characterization of non-stationary signals remains a significant challenge, particularly when impulsive components are obscured by high-dimensional noise and structural coupling. This paper proposes an application-driven mathematical methodology for a learnable discrete wavelet transform (LDWT) that combines classical multi-resolution analysis with task-optimized data-driven adaptivity. Rather than introducing entirely new foundational theory, our approach strategically relaxes constraints from orthogonal wavelet theory within the non-perfect reconstruction filter bank framework, enabling controlled spectral decomposition optimized for supervised fault diagnosis. We introduce a specialized regularization term based on the half-band property to ensure spectral complementarity and minimize cross-band correlation, while a Jacobian-based stabilization approach is formulated to ensure the convergence of filter coefficients during optimization. The proposed algorithmic architecture, LDBRFnet, features a dual-branch encoder system designed to capture the mathematical synergy between sub-band-level global statistics and time-domain local morphology. This dual-view representation effectively mitigates feature leakage and overconfidence in classification. Theoretical analysis and numerical experiments demonstrate that the learned filters satisfy the frequency-shift property and maintain robust spectral partitioning even under low signal-to-noise ratios. Validation on complex vibration datasets confirms that the framework achieves superior diagnostic accuracy (over 95.5%) and computational efficiency, reducing model parameters by 96.7% compared to state-of-the-art baselines. This work provides a generalizable mathematical approach for adaptive signal decomposition and robust pattern recognition in interdisciplinary applications. Full article

Quantifying glacier contributions to river discharge is challenging because many land surface models (LSMs) lack glacier processes, whereas standalone glacier models are often disconnected from catchment hydrology. Here we develop the Conjunctive Surface–Subsurface Process model version 2-glacier coupled model (CSSPv2-GLC), and evaluate it over the Three-River Headwaters Region (TRHR) at 3 km during 1979–2017. The glacier coupling raises Nash–Sutcliffe Efficiency for monthly streamflow simulation at Tuotuohe station from 0.63 to 0.79 during calibration and from 0.61 to 0.76 during validation. CSSPv2-GLC reduces glacier surface temperature error to 1.85 K, compared with 3.09 K for the CSSPv2. Glacier meltwater contributions to total discharge reached 11.5% in July and 10.8% in August in the Yangtze headwaters. In contrast, the Lancang and Yellow headwaters contributed up to 4.5% and 1.8% in August. Dry-year contributions are 2–3 times higher than wet-year values, indicating a transient drought-buffering effect. These results demonstrate the value of integrating physically explicit glacier processes into land surface modeling frameworks for water resource assessment in glacierized headwater regions, and highlight the necessity of accounting for non-stationary glacier contributions to streamflow. Full article

High-frequency volatility and extreme tail risks in offshore wind power pose severe challenges to grid stability and economic operation. Conventional storage planning often relies on deterministic profiles or static allocation rules, failing to capture the non-stationary temporal dynamics of marine wind resources. To bridge this gap, this paper proposes a closed-loop framework that integrates ultra-short-term probabilistic forecasting with dynamic hybrid energy storage optimization. A novel Dual-Channel Residual Network is developed to provide well-calibrated predictive uncertainty quantification, which explicitly drives a Prediction-Guided Dynamic Hybrid Storage Optimization Framework. By dynamically coordinating lithium-ion batteries and liquid air energy storage based on evidential predictive variance, the proposed approach achieves superior synergy between short-term power response and long-duration energy shifting. Case studies on an offshore wind farm validate that the framework significantly reduces the Levelized Cost of Energy and loss-of-load risks while enhancing frequency regulation capabilities compared to state-of-the-art benchmarks. Full article

Monolithic zirconia restorations are frequently affected by the unnoticed growth of subcritical cracks, a failure process that is not captured by traditional imaging methods like radiographs and ultrasounds in sophisticated dental architectures. To address this evaluative inadequacy, this research introduces a hierarchical deep learning framework for microcrack detection and spatial localization. We promote a hierarchical deep learning system that integrates Acoustic Emission (AE) detection alongside signal processing. Raw AE signals utilized during dynamic loading are enhanced via Kalman filtering and Continuous Wavelet Transform (CWT) to construct high-fidelity time–frequency scalograms. The diagnostic pipeline operates in two stages: first, a hybrid CNN–BiGRU network with temporal attention fulfills zirconia component-level classification; second, a ResNet-18 backbone integrated with Bidirectional LSTM and Multi-Head Attention precisely localizes defects across five anatomical crown regions. This hierarchical design effectively captures the non-stationary, transient nature of fracture-induced stress waves. The framework achieved an F1-score of 99.00% and an AUC of 0.994, significantly outperforming conventional convolutional networks. By enabling predictive maintenance through early, non-invasive damage localization, this study demonstrates a promising laboratory framework for AE-based crack detection in zirconia dental structures and prosthetics and toward enhanced clinical reliability in digital dentistry. Full article

Detection of pre-seismic ionospheric electric field perturbation remains an open challenge in the scientific community, hindered by methodological biases and a lack of reproducible frameworks. In this study, we investigate the existence of ionospheric perturbations associated with earthquakes by developing a deep learning framework for detecting anomalous patterns in global ionospheric electric field measurements provided by the DEMETER satellite and evaluating their statistical relationship with global seismicity. We developed an unsupervised LSTM autoencoder framework trained under a rolling-window scheme with two alternative optimisation strategies. The iterative rolling-window approach enabled the preservation of long-term temporal continuity while adapting to the non-stationary ionospheric background. Anomalies detected by the model were subjected to a seismic association and evaluated statistically. Findings were consistent across multiple network configurations, independent training optimisation strategies and different segments of the dataset, demonstrating strong methodological robustness. Our study suggests that modern sequential deep-learning models, when combined with an adaptive temporal training approach and statistical evaluation, provide an effective tool for the systematic detection and statistical quantification of associations between ionospheric electric field perturbations and seismic events. Full article

Today, microfluidics has become a revolutionary field of engineering due to its wide range of applications, including lab-on-a-chip devices, microscale biochemical reactors, drug delivery systems, and disease diagnostics. Efficient fluid mixing has been a significant challenge in these systems due to the dominance of laminar flow and low-Reynolds number conditions, where mixing relies primarily on slow molecular diffusion. It is very difficult to achieve rapid mixing and homogeneous mixing within a limited length. In this study, a bioinspired passive micromixer is developed based on the cristae architecture of mitochondria, which is known for maximizing surface area and transport efficiency in biological systems. The micromixer incorporates cristae-like microstructures within a straight microchannel to produce continuous flow deflection, stretching, and folding, thereby promoting chaotic advection without relying on external energy sources. It also includes mitochondrial granules, such as micropillars, within the channel to disrupt streamline flow. Thus, a numerical investigation was conducted to design four different micromixer geometries: conventional T-channel, and T-channels with a single, double and triple matrix of cristae. The analysis was performed in COMSOL Multiphysics, in which “Laminar flow” and “Transport of diluted species” physics were used, and a stationary study was executed. Simulations were conducted at different Reynolds numbers (Re = 0.1–100) to observe the feasibility of the proposed designs. For analysis, the mixing index and concentration profiles at the outlet and along the length were also examined. The results showed that the high cristae density channel performed well, achieving a mixing index of 95.85% at Re = 0.1 and 85.84% at Re = 100, proving that the proposed mitochondria-inspired cristae Mito-mixer delivers efficient mixing over a broad Reynolds-number range while maintaining a compact, length-efficient design. Full article

The rapid expansion of renewable energy has raised the demand for accurate, long-term wind power forecasting. However, wind power series are strongly affected by meteorological factors and exhibit pronounced volatility, making long-term prediction challenging. To model these characteristics more comprehensively, we propose STFNet, a dual-branch neural architecture that integrates time-domain and frequency-domain modeling. STFNet contains two key modules: (1) an MLFE module, which explicitly captures lag effects and non-stationary transitions through parallel multi-scale convolutions and a difference-convolution branch and further enhances multivariate dependency learning via cross-variable interaction modeling, and (2) an FGFE module, which applies DCT to capture long-cycle trends and uses a learnable low-pass filter for noise suppression. Experiments on two real-world wind farm datasets (LY and HG) show that STFNet consistently outperforms strong baselines, achieving average MSE reductions of 15.9–26.6% while maintaining a high computational efficiency. Ablation studies further confirm the effectiveness of each module, indicating the strong practical potential of STFNet for wind farm operation and management. Full article

Atmospheric equatorial Kelvin waves—convective disturbances that manipulate tropical wind and rainfall patterns—can propagate eastward at speeds ranging from nearly stationary to 30 m/s, with variability determined by moist processes and advection by the background wind. Current studies on Kelvin waves lack a comprehensive climatology that explains how their structure and propagation speeds change in different background states. Thus, this work builds a variable regression model that uses ERA5 reanalysis data to reconstruct Kelvin waves during different background wind shear conditions and phases of the Madden–Julian Oscillation (MJO) and the El Niño–Southern Oscillation (ENSO) over the Pacific. Overall, Kelvin waves tend to speed up during background conditions that generate upper-tropospheric westerlies and slow down during upper-tropospheric easterlies. East Pacific Kelvin waves are faster than West Pacific Kelvin waves because of climatological westerly shear in the former and easterly shear in the latter. However, strong westerly shear over the East Pacific allows extratropical Rossby waves to impede on the Kelvin wave, while strong easterly shear over the West Pacific distorts classical Kelvin wave structure. The results provide references for weather prediction models to accurately resolve the interaction between Kelvin waves and background circulation. Full article

To address the polarization fading noise in coherent detection phase-sensitive optical time-domain reflectometry (Φ-OTDR) for distributed low-frequency vibration sensing, a Φ-OTDR sensing scheme integrating polarization diversity reception and the variational mode decomposition (VMD) algorithm is proposed. The mechanism of polarization fading induced by fiber birefringence and external perturbations is systematically analyzed. A signal–noise mathematical model for polarization diversity reception is established, and the adaptive decomposition capability of the VMD algorithm for non-stationary phase signals is elaborated. This scheme can accurately separate the additional noise introduced by polarization diversity reception from the target low-frequency vibration signals. Experimental results demonstrate that, compared with the single-path detection scheme, the proposed method eliminates the amplitude attenuation of beat frequency signals caused by polarization mismatch at the optical path level. Meanwhile, it effectively suppresses both the additional noise introduced by polarization diversity and the low-frequency phase drift resulting from unstable laser frequency. It achieves precise phase restoration of vibration signals excited at 50 Hz under three typical sensing distances of 5 km, 10 km, and 30 km. Additionally, it successfully restores low-frequency vibration signals as low as 0.6 Hz at the sensing distance of 30 km. Full article

With the implementation of market-oriented electricity trading in an increasing number of countries, accurate electricity price forecasting can not only help participants in the electricity market to make more reasonable decisions but also enable regulators to have a more reliable regulatory basis. Therefore, it is necessary to propose an appropriate electricity price forecasting method. In view of the insufficiency of the traditional models in dealing with nonlinear and non-stationary data, to improve the detection ability of the model for hidden information in data and considering the high randomness of electricity price data, this paper proposes an electricity price forecasting method based on singular spectrum analysis (SSA) to decompose the original sequence and combines it with an extreme learning machine (ELM) optimized by the grey wolf optimizer (GWO). First, SSA is used to decompose the original sequence, and then the ELM is used to predict each subsequence and add them, in which the number of neurons in the hidden layer of each ELM is jointly optimized by the GWO. To verify the effectiveness of the SSA–GWO–ELM model, a total of 2106 days of electricity price data in Victoria, Australia, were selected for modeling. The results show that the prediction accuracy of the model proposed in this paper is significantly higher than that of the other comparison models, and the R² score is as high as 0.989, which is 0.017 higher than that of the suboptimal SSA–ELM. It can also maintain strong robustness and high prediction accuracy for heterogeneous data on power demand. SSA has the potential for real-time prediction, which can provide reliable data support for electricity market participants and supervisors. Full article

Background: Multi-center resting-state functional magnetic resonance imaging (rs-fMRI) is important for neurodevelopmental disorder diagnosis, but cross-site differences in repetition time (TR) can cause temporal feature misalignment. In addition, blood-oxygen-level-dependent (BOLD) signals are non-stationary, so disease-related information may be distributed across multiple time scales. Existing methods usually do not explicitly model physical sampling intervals or coordinate temporal and spectral information across scales, which may limit cross-site generalization in heterogeneous multi-center settings. Methods: We propose Spec-RWKV, a spectrum-guided linear recurrent framework for multi-site rs-fMRI diagnosis. It includes three components: PrismTimeMix, which models temporal dynamics using decay rates derived from physical half-lives and converts them adaptively across TRs; a TR-adaptive continuous wavelet transform, which aligns spectral representations across sites by adjusting frequency coverage; and spectrum-guided adaptive temporal aggregation, which uses spectral context to weight temporal features. Results: On ABIDE-I and ADHD-200, Spec-RWKV achieved AUCs of 75.86% and 76.31%, respectively. Under leave-one-site-out validation, it achieved the best mean AUC on ABIDE-I and the best mean accuracy and AUC on ADHD-200. Conclusions: Spec-RWKV explicitly models sampling-rate differences and multi-scale spectral structure, with results supporting strong cross-site generalizability. Full article

Multivariate time series forecasting presents a challenging problem in stochastic modeling, particularly under non-stationary conditions with low signal-to-noise ratios. While recent inverted architectures enhance cross-variable dependency modeling, the conventional point-wise inversion strategy often compromises local temporal patterns. To address this limitation, we propose PiTransformer, a gated patch-wise inverted framework for multivariate time series modeling. Specifically, a Patch-wise Inverted Embedding (PIE) mechanism is introduced to segment temporal sequences into regional patches prior to inversion, enabling the preservation of localized temporal structures. In addition, a Variable–Temporal Gating (VTG) module is incorporated to regulate feature interactions based on the information bottleneck principle, thereby suppressing spurious correlations in noisy environments. Empirical evaluations on diverse benchmarks—including financial and energy datasets—demonstrate that PiTransformer achieves consistent improvements in predictive accuracy and stability over competitive baselines. These results suggest that the proposed framework provides a robust and interpretable approach for modeling high-dimensional stochastic time series under non-stationary conditions. Full article

Continuous wrist angle estimation based on surface electromyography (sEMG) is often affected by signal variability and prediction instability. Although regression models provide instantaneous outputs, their predictions may exhibit temporal fluctuations and limited robustness due to the non-stationary nature of sEMG signals. To address this issue, we propose an uncertainty-aware probabilistic fusion post-processing framework for continuous wrist motion estimation. The proposed approach decouples regression and uncertainty modeling, enabling plug-in compatibility with feature-based regression models. A local Gaussian process regression (LGPR) model is employed to estimate predictive uncertainty from a sliding feature window. The instantaneous regression output is then fused with the LGPR prediction through a Bayesian-inspired Gaussian formulation, resulting in a closed-form adaptive gain that dynamically adjusts smoothing strength according to predictive variance. Experimental results from both open-loop wrist joint motion estimation and closed-loop myoelectric control tasks demonstrate that our method outperforms existing methods in key performance indicators, including task completion time, trajectory smoothness, and trajectory tracking error. Full article

Time-series clustering is a prominent research area with extensive practical applications. Given the complexity and diversity of modern time-series data, this study proposes a novel time-series clustering method based on inter-network heterogeneity. First, each time-series is converted into a network by using two types of time-series segmentation techniques. Second, an inter-network clustering approach based on graph spectral theory is introduced: we calculate the total variation (TV) distance between the empirical spectral distributions of each network and identify distinct clusters using a hierarchical clustering algorithm. From the perspective of symmetry, networks constructed from similar time-series tend to exhibit comparable spectral structures, which reflect the underlying structural symmetries of their dynamics. Differences in spectral distributions correspond to symmetry breaking among networks, providing an effective mechanism for distinguishing heterogeneous time-series patterns. Our method effectively preserves more distinctive features inherent in the original time-series. To evaluate the performance of the proposed method, simulation studies are conducted, including the recognition of both stationary and non-stationary sequences. The method also performs well on real-world datasets, such as stock closing prices. These results demonstrate that our approach can handle non-stationary sequences and identify the intrinsic correlations in time-series. Full article