Search Results (73)

Long-span bridges in mountainous areas are usually exposed to nonstationary winds (e.g., thunderstorms), which pose a significant threat to the driving safety of vehicles. However, current analysis on the wind–vehicle–bridge interaction was mainly implemented based on the assumption of stationary wind input, which would lead to the distortion in the assessment of driving safety under nonstationary extreme wind events. In this study, a nonstationary wind–vehicle–bridge coupling analysis framework was found to investigate the dynamic response and driving safety under nonstationary events. Firstly, the Wavelet–Hilbert scheme was introduced to simulate the nonstationary wind velocity, and the two-dimension indicial function was employed to model the transient aerodynamic loads. Then, the nonstationary wind–vehicle–bridge coupling system was developed, and the separate iteration method was employed to obtain the response of the coupling system. Finally, the driving safety is evaluated based on statistical accident risk coefficients, derived from wheel contact forces. The results show that the vertical contact forces transference ratio, lateral contact forces, and vehicle accident risk coefficients under nonstationary winds are higher than those resulting from equivalent stationary winds. In addition, the accident risk coefficients increase with the transient wind velocity, duration, and vehicle velocity. In particular, the risk coefficient increases by approximately 201%, 36%, and 79%, respectively, with the increase in transient wind velocity, duration, and vehicle velocity. Full article

In large-scale fluid transport systems, distributed pump and valve stations must coordinate their operations to prevent overpressure while minimizing energy use and control effort. This paper presents a communication-aware, game-theoretic coordination framework in which stations act as rational agents that iteratively adjust operating setpoints based on locally computed utilities. Existing station-level pressure controllers regulate local pressures and flows, while a slower supervisory negotiation layer governs inter-station coordination using steady-state hydraulic surrogates derived from pump affinity laws and pipeline loss relationships. The proposed framework does not rely on centralized optimization or exhaustive enumeration of strategies. Instead, stations update setpoints sequentially, evaluating incremental changes in utility to determine beneficial adjustments and detect equilibrium conditions. Cooperative behavior emerges naturally when communication is available, enabling stations to internalize the hydraulic impact of their actions on neighboring stations. When communication is lost, the system transitions seamlessly to a non-cooperative mode in which each station optimizes its local objective while maintaining safe operation. Simulation studies conducted on a multi-station pipeline with mixed actuator types demonstrate measurable performance improvements over fixed-setpoint operation. Cooperative coordination reduces total system energy usage from 39.6 MW to 38.8 MW while increasing average control valve openness from 60.4% to 63.7%. Non-cooperative operation converges more rapidly but results in higher energy consumption (39.2 MW) and greater valve throttling. Under partial communication loss, the system preserves near-cooperative energy performance (38.8 MW) with a modest increase in convergence time, demonstrating robustness to degraded communication. Across all simulated scenarios, the iterative game converged to stationary operating points consistent with Nash-equilibrium behavior in non-cooperative settings and Pareto-stationary solutions in cooperative communication settings. Full article

Outlier detection in time series is traditionally formulated as the identification of rare or extreme observations with respect to global statistical properties. While effective for stationary processes, this perspective becomes insufficient in complex and non-stationary systems, where atypical behavior may manifest as disruptions of stable relationships rather than numerical extremeness. This paper proposes a rule-based framework for detecting structural outliers in non-stationary time series. Regular system behavior is represented by an interpretable set of deterministic IF–THEN rules describing stable relational patterns between features. Each rule defines a logical context and an admissible range of a diagnostic quantity, estimated nonparametrically from historical observations satisfying the rule condition. For a given observation, the set of active rules is identified and a structural inconsistency score is computed as the fraction of violated rule consequences. Additionally, observations lacking support from high-frequency contexts are treated as candidates for structural atypicality. The method is deterministic and avoids the need for explicit probabilistic modeling or iterative parameter learning, which simplifies interpretation and implementation. The framework is illustrated on daily EUR/USD data (2010–2022) using technical indicators (EMA, RSI) and absolute log-returns as the diagnostic measure. Results provide evidence that structurally atypical events can be identified even when global statistical thresholds remain unviolated, suggesting the practical relevance of relational analysis for non-stationary time series monitoring contexts. 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

The paper discusses a greedy algorithm that can be used to solve non-smooth optimization problems in which its objective function can be represented as a minimum of a compactly parameterized family of uniformly smooth functions. The algorithm guarantees a sparse solution by adding one atom from the dictionary to the solution at each iteration. The algorithm employs a gradient greedy step that maximizes a linear functional using gradient information from the previous iteration. However, the algorithm is considered “weak” because it only solves the linear subproblems approximately. By employing the duality gap evaluated at each gradient-greedy step, the paper proves convergence of the algorithm to Clarke stationary points. Explicit upper bounds on the duality gap are derived, yielding a quantitative measure of proximity to stationarity and establishing the corresponding rates of convergence. Full article

This study addresses the problem of optimal resource allocation in nonlinear multi-module dynamic systems arising in complex computational and techno-economic processes, where numerical stability and strict enforcement of structural constraints are critical. The objective is to develop a computationally efficient optimal control algorithm capable of handling bilateral control constraints and external balance conditions without resorting to large-scale nonlinear programming or boundary-value shooting. The proposed method is based on a modified Lagrangian formulation, in which bilateral Karush–Kuhn–Tucker (KKT) conditions are analytically embedded into the optimality system. The resulting computational scheme consists of a coupled system of matrix and vector differential equations solved through a non-iterative backward–forward integration procedure. Numerical experiments conducted on a nonlinear model with Cobb–Douglas-type operators demonstrate the stable convergence of the trajectories toward a stationary regime, strict satisfaction of bilateral constraints, and consistent enforcement of balance relations throughout the planning horizon. Empirical scalability analysis indicates approximately cubic computational complexity with respect to the state dimension, while sensitivity tests confirm the numerical robustness across different integration tolerances and ODE solvers. These results demonstrate that the proposed structure-preserving framework provides a computationally stable and practically implementable approach to constrained optimal control in nonlinear multi-module systems. Full article

An HPO (Hunter–Prey Optimizer)-optimized Bidirectional LSTM (HPO-BiLSTM) model is introduced to address the challenges in predicting gas concentration within coal mining working faces. This study aims to adaptively adjust the key hyperparameters (such as learning rate and number of hidden layer units) of the BiLSTM network through intelligent optimization algorithms. While the BiLSTM architecture inherently mitigates gradient vanishing and exploding problems through its gating mechanisms, the proposed HPO method focuses on addressing the inefficiency of manual parameter tuning and the risk of trapping in local optima that traditional methods encounter when dealing with nonlinear and non-stationary gas concentration time series. The experiment utilized the actual methane monitoring data from the 15117 working face of Jishazhuang Coal Mine in Jinzhong City, Shanxi Province (with a sampling interval of 2 min). The proposed HPO-BiLSTM model was compared with baseline models such as LSTM, BiLSTM, GA-BiLSTM, and PSO-BiLSTM in terms of performance. This study systematically compares the performance of LSTM, BiLSTM, and BiLSTM models optimized with GA, PSO, and HPO. Results demonstrate that all optimized models outperform the baselines, with HPO-BiLSTM achieving the best overall performance. It attained the lowest RMSE and highest R² across the training, validation, and test sets, showcasing superior fitting and generalization capabilities. Furthermore, HPO-BiLSTM converged to the lowest loss value (0.00062) in only 15 iterations, demonstrating significantly greater efficiency and stability than both GA-BiLSTM (loss 0.00072, 25 iterations) and PSO-BiLSTM (loss 0.00071, 30 iterations). The experiments confirm that the HPO algorithm effectively configures BiLSTM hyperparameters, mitigates overfitting, and provides a more accurate and robust solution for gas concentration prediction in coal mines. Full article

Modern Hopfield networks are energy-based associative memory models whose performance critically depends on the structure and optimization of their energy functions. While recent formulations substantially improve storage capacity, the resulting non-convex energy landscapes are often optimized using heuristic update rules that can be sensitive to initialization and may not provide monotonic energy descent or rigorous convergence guarantees. In this work, we propose a new energy formulation for modern Hopfield networks together with a principled iterative optimization scheme. The proposed energy admits a natural decomposition that allows optimization via the concave–convex procedure (CCCP), yielding well-defined network dynamics with guaranteed energy descent beyond classical Hopfield updates. We establish fundamental theoretical properties of the proposed framework, including non-negativity, boundedness, and monotonic decrease in the energy along iterations. In particular, we prove that the induced dynamics converge to a stationary point of the energy function, providing explicit convergence guarantees for the resulting Hopfield-type model. We further evaluate the proposed approach on synthetic classification tasks and compare its optimization behavior with that of the original Hopfield network and several standard machine learning baselines. Experimental results demonstrate improved stability, convergence behavior, and competitive classification performance. We also validate the approach on real-world benchmark datasets to demonstrate utility beyond controlled experiments. Overall, this work provides a theoretically grounded energy-based optimization framework for modern Hopfield networks, clarifying the role of principled optimization in achieving stable and convergent associative memory dynamics. Full article

This study investigates the selection and scaling of recorded strong ground motions in the time-domain spectral matching framework to realistically represent the seismic demands on the superstructure and secondary systems in the seismic design of complex facilities such as marine ports. The time-domain spectral matching method iteratively adjusts the original record in the time domain by adding wavelets with limited durations and specific period ranges to achieve compatibility with the specified target acceleration response spectrum. A site-specific probabilistic seismic hazard analysis (PSHA) was performed for a port facility in İskenderun Bay, an area affected by the 6 February 2023 earthquakes. Horizontal Ground-Motion Response Spectra (GMRS) were derived for different return periods. Based on the hazard deaggregation, recorded ground motions compatible with the seismotectonic context of the region and the site conditions were selected. These records were then processed using time-domain spectral matching (TDSM) to match their elastic response spectra with the target GMRS over specific period ranges. The method utilizes spectral matching in the time domain to improve the match with the target spectrum while preserving the phase information and non-stationary nature of the records. The results show that the mean spectral acceleration curves of the scaled records are highly consistent with the target GMRS over a wide range of periods and that near-fault pulse-like characteristics, when present, are reasonably preserved. These results confirm that time-domain spectral matching provides a reliable framework for the performance-based assessments of complex port infrastructures by achieving high compatibility with the target spectra while preserving the physical characteristics of the waveforms Full article

Aiming to improve the currently low accuracy of fault diagnosis due to the difficulty of extracting the non-stationary and nonlinear features of rolling bearing fault signals, a multi-condition fault diagnosis method for rolling bearings was proposed based on enhanced singular spectrum decomposition (ESSD), optimized multi-scale mean permutation entropy (MMPE), and support vector machine (SVM). Firstly, aiming to address the problem of singular spectrum decomposition (SSD) producing false components and signals with low energy proportions that cannot be accurately decomposed when the residual energy ratio is used as the final iteration termination condition, an enhanced singular spectral decomposition method is proposed. Secondly, the effect of the MMPE extraction of fault features depends on the selection of parameters, and after comprehensively considering the interaction between MMPE parameters, a method to optimize MMPE based on the particle swarm optimization (PSO) algorithm is proposed to maximize the performance of the extracted features. Finally, considering that the classification performance of SVM is affected by the penalty factor c and kernel function g, the fault characteristics proposed by ESSD + PSO - MMPE are identified by an SVM classifier model that is optimized by the particle swarm algorithm, so as to realize the effective diagnosis of multi-condition faults in rolling bearings. Using rolling bearing simulation signals, the Case Western Reserve University bearing dataset, and the online monitoring signal from the front bearings of a wind farm’s 1.5 MW wind turbine, the proposed method is compared with EMD + MMPE + SVM, SSD + MMPE + PSO - SVM, ESSD + MMPE + PSO - SVM, and other methods, and the results show that the proposed method can effectively identify multi-working faults in rolling bearings. Full article

Software-defined networking (SDN) requires adaptive control strategies to handle dynamic traffic conditions and heterogeneous network environments. Reinforcement learning (RL) has emerged as a promising solution, yet deep RL methods often face instability, non-stationarity, and reproducibility challenges that limit practical deployment. To address these issues, a kernel-based RL framework is introduced, embedding transition dynamics into reproducing kernel Hilbert spaces (RKHS) and combining kernel ridge regression with policy iteration. This approach enables stable value estimation, enhanced sample efficiency, and interpretability, making it suitable for large-scale and evolving SDN scenarios. Experimental evaluation demonstrates consistent convergence and robustness under traffic variability, with cumulative rewards exceeding those of baseline deep RL methods by more than 22%. The findings highlight the potential of kernel-embedded RL as a practical and theoretically grounded solution for adaptive SDN management and contribute to the broader development of intelligent systems in complex environments. Full article

The article presents Modified Scaled Adaptive Daqrouq Wavelet (MSADW) as an autonomous wavelet framework to overcome the analysis obstacles of traditional wavelets (Morlet and Daubechies) for signals with non-stationary characteristics. MSADW adjusts its waveform shape and frequency in real time based on the specific characteristics of the signal, allowing it to outperform conventional wavelet methods. The system reaches adaptability through three core methods featuring gradient-dependent scale adjustments for fast transient detection and smooth regions, and instantaneous frequency monitoring achieved by a combination of STFT and Hilbert transforms and an iterative error reduction process using gradient descent and genetic algorithms. Continuous Wavelet Transform (CWT) combined with Discrete Wavelet Transform (DWT) extracts features from ECG and speech signals. Throughout this process, MSADW maintains great time precision to detect transients as well as maintain sensitivity for the audio’s base stability. Testing MSADW in practical use reveals its superior performance because it detects R-peaks accurately within 0.01 s through zero-crossing methods, which combine P/T-wave detection with effective ECG signal segmentation and noise-free reconstructed speech ($\mathit{MSE:} 1.17\times {10}^{-31}$). The localized parameterization framework of MSADW, enabled by feedback refinement, fulfills missing aspects in biomedical signal evaluation and creates space for low-cost real-time evaluation methods for medical devices and arrhythmia and ischemic detection platforms. The theoretical backbone for MSADW establishes itself because this work shows how wavelet analysis can transition toward managing non-stationary and noise-prone domains. Full article

This paper presents a novel non-uniform, non-stationary Loop subdivision that directly interpolates arbitrary initial triangular meshes. This subdivision is derived by assigning distinct parameters for “vertex-point” and “edge-point” generation within the stencils of a uniform, non-stationary Loop subdivision. This underlying uniform, non-stationary scheme is obtained based on a suitably chosen iterative process. Crucially, we derive the limit positions of the initial points under this non-uniform scheme and decrease the assigned parameters to a single shape parameter when interpolating the initial mesh. Compared with the existing methods interpolating the initial mesh using approximating subdivision, this new one achieves interpolation in finite steps and without any additional adjustment to the initial mesh or subdivision rules. Several numerical examples are given to show the scheme’s interpolation accuracy and shape control capabilities. Full article

Object detection in single synthetic aperture radar (SAR) imagery has always been essential for SAR interpretation. Over the years, the saliency-based detection method is considered as a strategy that can overcome some inherent deficiencies in traditional SAR detection and arouses widespread attention. Considering that the conventional saliency method usually suffers performance loss in saliency map generation from lacking specific task priors or highlighted non-object regions, this paper is devoted to achieving excellent salient object detection in single SAR imagery via a two-channel framework integrating Bayesian inference and adaptive iteration. Our algorithm firstly utilizes the two processing channels to calculate the object/background prior without specific task information and extract four typical features that can enhance the object presence, respectively. Then, these two channels are fused to generate an initial saliency map by Bayesian inference, in which object areas are assigned with high saliency values. After that, we develop an adaptive iteration mechanism to further modify the saliency map, during which object saliency is progressively enhanced while the background is continuously suppressed. Thus, in the final saliency map, there will be a distinct difference between object components and the background, allowing object detection to be realized easily by global threshold segmentation. Extensive experiments on real SAR images from the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset and SAR Ship Detection Dataset (SSDD) qualitatively and quantitatively demonstrate that our saliency map is superior to those of four classical benchmark methods, and final detection results of the proposed algorithm present better performance than several comparative methods across both ground and maritime scenarios. Full article

Effective noise elimination is essential for ensuring data reliability in high-accuracy measurement systems. However, selecting the optimal denoising strategy for diverse and non-stationary signal types remains a major challenge. This study presents a wavelet-based denoising optimization framework that systematically identifies and applies the most suitable noise reduction model for each signal segment. By evaluating multiple wavelet types and thresholding strategies, the proposed method enables adaptive and automated selection tailored to the specific characteristics of each signal. The framework was validated using synthetic, open-access, and experimentally acquired signals in both reference-based and reference-free scenarios. Extensive testing covered signals from power quality disturbance (PQD) events, electrocardiogram (ECG) data, and electroencephalogram (EEG) recordings, all of which represent critical applications where signal integrity under noise is essential. The method achieved optimal model selection in 22.15 s (across 4558 iterations) on a standard PC, with an average denoising time of 4.86 ms per signal window. These results highlight its potential for real-time and embedded applications, including smart grid monitoring systems, wearable health devices, and automated biomedical diagnostic platforms, where adaptive, fast, and reliable denoising is vital. The framework’s versatility makes it highly relevant for deployment in smart grid monitoring systems and intelligent energy infrastructures requiring robust signal conditioning. Full article