Search Results (2,016)

Vibration signals of planetary gearboxes under variable-speed conditions exhibit strong non-stationarity and modulation, so a single feature representation cannot comprehensively describe intricate fault patterns, and distribution discrepancies across rotating speeds degrade the cross-condition generalization of diagnostic models. To address these limitations, this paper proposes an adaptive domain-aligned multi-modal feature fusion network (ADAMFFN). Three parallel branches extract complementary features from dual-channel vibration signals: spatial coupling features from orbit images, time–frequency energy features from continuous wavelet transform (CWT) representations, and frequency-domain statistical (FreqStat) features from power and envelope spectra. Heterogeneous features are mapped into a shared latent subspace through a unified projection layer, deep cross-modal interaction is realized by a progressive fusion network, and a domain alignment mechanism based on a domain-adversarial neural network (DANN) is introduced to eliminate source–target distribution gaps via adversarial training. On eight leave-one-speed-out (LOSO) cross-speed tasks constructed on the public WT-Planetary Gearbox dataset, ADAMFFN achieves an average accuracy of 99.25%, outperforming the best single-branch and dual-branch schemes by 2.63 and 0.40 percentage points, respectively; ablation experiments verify the complementarity of the three modalities and the effectiveness of domain alignment. Cross-condition external validation on the Southeast University (SEU) gearbox dataset further demonstrates its generalization capability under a different test rig and acquisition conditions. Full article

Monocular camera relocalization recovers a six-degree-of-freedom pose from one RGB image, but direct absolute pose regression typically predicts translation and rotation from one terminal representation. We propose Decoupled SwinPose, a hierarchical Swin-Tiny regressor that instead reads translation from a shallow, higher-resolution Stage 1 map and rotation from the deep, contextual Stage 3 representation, testing this asymmetric-readout hypothesis through three falsifiable predictions. Across three TUM RGB-D motion regimes and all seven Microsoft 7-Scenes environments, against constant-pose, retrieval, and matched shared-terminal controls, it achieves the lowest three-seed mean translation and rotation error on all ten evaluated sequences within the matched reimplemented cohort. Relative to the strongest alternative learned model within this cohort, translation error falls by up to 29.6% on TUM RGB-D and 52.6% on 7-Scenes, and rotation error by up to 27.2% and 54.4%, respectively. Routing ablations, including a capacity-matched control, support T1-R3 as the strongest overall trade-off among six tested configurations, and matched profiling shows the readout adds only 0.29% parameters with essentially identical FLOPs and FP32 cost relative to a shared-terminal baseline on an NVIDIA L4. These results support asymmetric multilevel readouts as an effective, low-cost architectural prior for transformer-based monocular pose regression in the evaluated indoor setting. Full article

Rotating machinery prognostics is essential for ensuring the reliability and operational safety of industrial systems. Although gated recurrent units (GRUs) have achieved competitive performance in remaining useful life (RUL) prediction, whether their internal dynamics are consistent with irreversible degradation mechanisms remains largely unexplored. This study revisits GRU-based prognostics from a physics-consistency perspective and analyzes the potential mismatch between gating mechanisms and degradation evolution. A full-life benchmarking framework is developed based on the XJTU-SY bearing run-to-failure dataset. A training-based health indicator (HI) is constructed through multi-domain vibration feature extraction and principal component analysis, where the degradation-state representation and RUL prediction objective are explicitly distinguished to avoid physically inconsistent supervision. Several representative approaches, including statistical models and deep learning architectures (LSTM, GRU, TCN, and Transformer), are evaluated using both prediction accuracy metrics (RMSE, MAE, and R²) and physical consistency criteria (monotonicity index, monotonicity violation index, and degradation trend consistency). Experimental results demonstrate that superior prediction accuracy does not necessarily guarantee physically consistent degradation modeling. Although GRU provides competitive RUL prediction performance, its hidden-state evolution and gating responses exhibit noticeable non-monotonic behaviors during degradation progression. These findings reveal a potential discrepancy between prediction-oriented recurrent learning mechanisms and irreversible degradation dynamics, highlighting the importance of incorporating physics-consistency evaluation into reliable data-driven prognostic models. Full article

Tourism destinations increasingly require data-driven tools to interpret competitiveness, capacity use, housing-related pressure, and post-shock change. This study develops a machine-learning-based destination marketing intelligence framework for a non-probability analytical sample of 29 European destinations observed annually between 2015 and 2024. Destinations were retained when sufficiently comparable information was available across the common study window for the six raw indicators required to construct the performance-pressure framework. Tourism demand, accommodation capacity, labor, investment intensity, and housing-cost pressure are transformed into normalized indicators and analyzed using principal component analysis, k-means clustering, classification trees, random forests, and robustness checks. The first three principal components explain 84.2% of total variance. Although silhouette favors three clusters, the four-cluster solution provides stronger Calinski–Harabasz separation and leave-one-destination-out stability. The retained solution identifies four relative destination-state configurations: lower performance with near-average pressure; high rotation, moderate performance, and lower pressure; high performance with lower pressure; and extreme housing pressure. Under leave-one-destination-out validation, random forests achieve 86.6% accuracy and a Cohen’s kappa of 76.9%. The configurations are pressure-sensitive marketing-intelligence categories rather than comprehensive sustainability classifications or permanent country typologies. Full article

Oriented object detection (OOD) in remote sensing images (RSIs) suffers from insufficient feature representation caused by arbitrary rotation angles and small spatial resolutions. Existing spatial–frequency fusion paradigms merely implement single-granularity feature interaction, either global image-level frequency compensation or local instance-level feature refinement, and fail to simultaneously capture global scene semantic consistency and local object fine-grained discriminability. In this paper, we propose a unified dual-level spatial–frequency collaborative detector (DSCDet) for remote sensing OOD tasks. Different from previous decoupled designs, the proposed DSCDet constructs a complete spatial–frequency collaborative fusion paradigm that shares a generic wavelet-based frequency extraction mechanism and cross-feature fusion module, which is adaptively deployed at both image-level and instance-level granularities. Specifically, our method introduces Haar wavelet transform to extract multi-scale frequency mutation features. On this basis, a generic cross-domain attention fusion (GCDAF) is constructed with granularity-dependent positional encoding constraints. The core difference between dual granularity fusion lies in geometric positional encoding, where image-level fusion adopts global scene positional embedding to maintain overall semantic stability, and instance-level fusion leverages local pairwise instance positional embedding to optimize fine-grained target feature interaction. The unified dual-level fusion architecture comprehensively integrates global semantic integrity and local target specificity, forming a robust and universal spatial–frequency feature representation system. Extensive experiments on three public remote sensing datasets, including DOTA-v1.0, DOTA-v1.5 and DIOR-R, demonstrate that the proposed DSCDet achieves competitive and superior performance against state-of-the-art OOD detectors. Full article

This paper presents a modified two-degree-of-freedom wave buoy with an embedded tunable magnetic-spring electromagnetic energy harvester. The proposed device is modeled as a branched kinematic chain composed of a rotational–rotational buoy mechanism and a rotational–prismatic harvester branch sharing the first revolute joint. Two harvester orientations are considered and compared. The mathematical model is formulated using homogeneous transformations, velocity Jacobians and Lagrange equations. Particular attention is paid to the structure of the inertia matrix and to the way in which its inverse transmits generalized forces between the rotational coordinates and the translational motion of the moving magnet. The model is implemented in MATLAB/Simulink R2024b and evaluated under free-response, regular-wave, and bidirectional frequency-sweep excitation scenarios. Under regular-wave excitation, Config. 1 produces approximately 19.3 and 2.98 times greater average load power than Config. 2 for moving-assembly masses of 5 g and 268 g, respectively. The frequency-sweep results show that the preferred harvester orientation depends on the excitation frequency and moving-assembly mass. No resolved sweep-direction dependence is observed for 5 g, whereas for 268 g the identified hysteresis intervals are approximately 0.53 rad/s for Config. 2 and 0.64 rad/s for Config. 1. These results provide design guidelines for selecting the harvester orientation and moving mass in compact wave-excited buoy systems. Full article

This paper presents a comparative study of two approaches for solving second-order ordinary differential equations arising from the membrane theory of shells of revolution. The considered equations originate from the rotational symmetry of shell structures, which enables the reduction of the governing partial differential equations to a sequence of ordinary differential equations corresponding to individual circumferential harmonics. The study compares the classical Differential Transform Method (DTM) with Physics-Informed Neural Networks (PINNs). Both initial value and boundary value problems are investigated, including benchmark examples with known analytical solutions and a systematic analysis of the influence of PINN architecture on the solution accuracy. For the PINN approach, the effects of the number of collocation points, hidden layers, and neurons per layer on the approximation error and training time are examined. The results demonstrate that DTM provides an efficient framework for constructing analytical solutions of initial value problems with minimal computational cost. However, its application to boundary value problems requires the introduction of additional auxiliary parameters and the solution of supplementary nonlinear equations, considerably increasing the analytical complexity of the procedure. In contrast, PINNs achieve high accuracy for both initial and boundary value problems while naturally incorporating boundary conditions through the loss function. The presented results demonstrate how the exploitation of geometric symmetry, combined with modern scientific machine learning techniques, provides an effective computational framework for solving differential equations arising in shell mechanics. Full article

Low Earth orbit (LEO) satellite downlinks broadcast over wide ground footprints, exposing Earth-observation and remote-sensing sensor data to passive eavesdropping. Affine frequency division multiplexing (AFDM) is a candidate waveform for the doubly dispersive LEO channel and a natural integrated sensing and communication (ISAC) waveform whose delay–Doppler structure supports target parameter estimation; yet existing secure-AFDM schemes act only in the discrete affine Fourier transform (DAFT) parameter domain, leaving the transmitted waveform structurally recognizable. To address this gap, this paper applies time-domain waveform obfuscation to AFDM as physical layer encryption. Using a secret key, the transmitter permutes the inverse-DAFT samples and applies a phase rotation before chirp-periodic-prefix generation; the mask is unitary, so the peak-to-average power ratio is preserved exactly, and the key-holding receiver retains AFDM’s full delay–Doppler sensing capability, while a no-key receiver obtains a dense composite response that destroys target localization (sensing concentration drops from 0 dB to −16.6 dB). Secret pilot phases enable channel estimation at the legitimate receiver while blocking a naive composite-channel attack. Simulations at $N=64$ and 128 show that a wrong-key eavesdropper achieves uncoded BER within $0.01$ of $0.5$ across 0–20 dB and that blind Viterbi–Viterbi phase recovery is no more effective under QPSK (BER $0.46$–$0.48$), while the legitimate SNR penalty stays below $0.5$ dB. The mask also suppresses AFDM’s internal structure to the AWGN level under AFDM-aware processing. Time-domain obfuscation offers a complementary physical-layer security layer for confidential LEO remote-sensing data downlink and ISAC waveforms. Full article

In the TJ-II stellarator, a finite net toroidal plasma current can modify the rotational transform profile and shift the radial position of low-order rational surfaces. Because these surfaces can influence edge gradients, turbulence and confinement in low-shear configurations, estimating the current waveform is relevant for the preparation and interpretation of scenarios programmed with the ohmic heating (OH) coil. This work compares two data-driven estimators using 87 selected OH-programmed discharges from the fixed magnetic configuration 100_44_64, divided into 50 training, 20 validation and 17 test cases. The first estimator uses MultiGene Genetic Programming (MGGP) to obtain explicit equations. The second is a nonlinear autoregressive recurrent neural network with exogenous inputs (NARX-RNN), implemented with long short-term memory (LSTM) branches. The symbolic equations and their settings were selected using the training and validation discharges, while the test set was used only for final evaluation. On the original 17-discharge test partition, the general OH-normalized equation achieved an overall root-mean-square error (RMSE) of 0.4898 kA, whereas the compact equation achieved 0.5700 kA with substantially lower expression complexity. After routine signal quality control, two test records were excluded, and the final comparison used 15 discharges. On this set, the mean discharge-wise RMSE was 0.350 kA for the NARX-RNN, 0.515 kA for the general symbolic equation and 0.576 kA for the compact equation. The NARX-RNN also gave a median RMSE of 0.230 kA. Whether this current error level is sufficient for positioning a particular rational surface depends on the local rotational transform response and magnetic shear, so no universal current error threshold for edge control is assigned here. The symbolic models were less accurate on average but provided explicit relations involving OH amplitude, previous current information, electron cyclotron resonance heating, fuelling and wall-conditioning variables. Full article

To address the insufficient representation of fault characteristics in rotating machinery vibration signals, the sensitivity of conventional Transformers to variations in input response amplitudes, and the limited ability of fixed sequence embedding to preserve continuous temporal information, a rotating machinery fault diagnosis method based on Amplitude-Calibrated Sequence Embedding (ACSE) and a Response-Normalized Transformer (RNformer) is proposed. First, ACSE is designed to construct local temporal feature representations through continuous convolutional mapping, while an amplitude response estimation and adaptive amplitude calibration mechanism is employed to dynamically recalibrate the response intensity at different temporal positions. Rather than simply rescaling the signal amplitude range, amplitude calibration adaptively strengthens the feature contribution of regions associated with fault-induced impacts according to the vibration response intensity, thereby highlighting fault-sensitive information while suppressing the influence of noncritical amplitude fluctuations. In this way, continuous temporal characteristics are preserved while fault-relevant information is enhanced. Second, RNformer is constructed by incorporating a response normalization mechanism into the Transformer encoder to mitigate the interference of abnormal amplitude responses with global feature modeling, thereby improving the stability and robustness of feature representations under complex operating conditions. Finally, a lightweight channel attention mechanism is introduced to further enhance critical fault features and perform fault classification. Experiments were conducted on the Paderborn University bearing dataset and the University of Connecticut gear dataset. The proposed method achieved average diagnostic accuracies of 99.36% and 99.28%, respectively, outperforming the best-performing baseline methods by 1.82 and 1.56 percentage points. These results demonstrated the effectiveness of the proposed method for fault diagnosis. Full article

This study developed a composite rejuvenator comprising recycled polyethylene wax (PREW), waste cooking oil (WCO), and epoxidized soybean oil (ESO) activated by the tertiary amine catalyst BDMA to improve the intermediate-temperature rejuvenation of aged SBS-modified asphalt binder. The binder was subjected to combined rolling thin-film oven and pressure aging vessel aging. Conventional tests, rotational viscosity, bending beam rheometer, multiple stress creep recovery, fluorescence microscopy, and Fourier transform infrared spectroscopy were used to evaluate macroscopic, rheological, and microstructural properties. Aging hardened and embrittled the binder, increased softening point and viscosity, reduced penetration and ductility, and disrupted the polymer-rich phase. PREW reduced flow resistance and retained relatively high-temperature structural stability, whereas WCO improved flexibility and flowability, although excessive softening impaired high-temperature stability. ESO/BDMA treatment was accompanied by changes in oxygen-containing functional group-related absorption regions and improved apparent connectivity of the SBS-rich phase. Among the tested temperatures, 120 °C provided the best overall balance among the evaluated properties, satisfying low-temperature stress-relaxation requirements while limiting high-temperature creep deformation. These results identify 120 °C as the preferred treatment temperature for the PREW/WCO/ESO-BDMA rejuvenation system. Full article

The micro-motion in radar echoes can produce time-frequency flashes, the characteristics of which vary markedly among different targets. However, the physical mechanism governing such flash variations has not yet been fully elucidated. To address this gap, this study investigates the mechanism responsible for variations in the time-frequency flashes of rotor targets. Pronounced changes in the time-frequency flash patterns of rotor echoes are observed as the blade number or rotational speed increases. Based on the established scattering point model, the time-frequency characteristics of the echoes are analyzed using the short-time Fourier transform. On this basis, the underlying mechanism is derived, and the conditions under which the phenomenon occurs are identified. The theoretically derived conditions are validated through numerical simulations. The results indicate that an excessive rotational speed or an increased blade count causes the blades to traverse an angular interval greater than a certain range within a single slow-time interval, thereby suppressing the resolvable temporal variation in the instantaneous frequency and ultimately causing the frequency time-varying characteristics to disappear. Moreover, the STFT window length also governs the visibility and representation of the flash characteristics. Clarifying this mechanism provides a theoretical basis for aircraft target feature extraction and recognition and offers practical guidance for rotor target discrimination in practice. Full article

Image restoration lies between camera acquisition and geometric estimation, but pixel improvements may not transfer to motion estimates. We evaluated directional-kernel Richardson–Lucy (RL) deblurring under kernel-length and direction controls. The restoration analysis covered 3071 paired GoPro, RealBlur-J, and RealBlur-R images. An exploratory feature analysis used a fixed 155-image subset with Oriented FAST and Rotated BRIEF (ORB), scale-invariant feature transform (SIFT), two geometry models, ten random directions, NAFNet, and Restormer. A separate task analysis used ten red–green–blue plus depth (RGB-D) sequences from the Technical University of Munich (TUM) benchmark, synthetic 20 ms exposures, and fixed RGB-D perspective-n-point odometry. Estimated directions contained information relative to random angles, yet the tested global RL branches remained below Blur Input on average. Changes in sequence-mean absolute trajectory error (ATE) RMSE ranged from +0.004 to +0.051 m for ORB and from −0.008 to +0.036 m for SIFT. Seeds were averaged within each sequence before inference. No tested branch achieved a robust ATE improvement across both detectors. Pixel, raw-feature, normalized-feature, geometry-state, and trajectory endpoints produced different method rankings. These findings motivate endpoint-specific evaluation. The task experiment does not validate naturally blurred long-exposure video, a deployed simultaneous localization and mapping system, or sensor hardware. Full article

In the era of intelligence, accurate remaining useful life (RUL) prediction is essential to ensure the reliable operation of smart equipment, particularly for rolling bearings—critical components that are highly susceptible to degradation in rotating machinery. However, as faults progressively develop, the vibration signals of rolling bearings exhibit strong non-stationarity and complex degradation patterns. Existing RUL prediction methods, particularly standard Transformer-based models, often struggle to capture local transient features within non-stationary signals and fail to effectively decouple long-term degradation trends from periodic variations. To overcome these limitations, a novel RUL prediction method that integrates time-series analysis techniques with the FEDformer architecture is proposed, termed TE-FEDformer. Firstly, a feature enhancement module is employed at the input stage to reconstruct and strengthen the original sequence, aiming to strengthen the representation of weak fault features that are often overlooked by global attention mechanisms. Then, deep time-series representations are extracted via the encoder. In the decoding stage, a frequency enhancement mechanism and a sequence decomposition mechanism are jointly utilized to explicitly model the coupling between degradation trends and periodic variations, thus resolving the spectral interference commonly encountered in complex degradation processes. Comparative experimental results on the PHM2012 and XJTU-SY datasets demonstrate that TE-FEDformer outperforms other benchmark models. Ablation studies further validate that each module contributes positively to the overall performance, confirming the effectiveness of the proposed approach for RUL prediction. Full article

Yellow River irrigation districts must maintain food production under variable inflows, rigid diversion quotas, sedimentation, groundwater depletion, and soil salinization. This systematic review synthesized 79 journal articles from Web of Science and CNKI to clarify how resilience can be assessed and managed at the irrigation-district scale. The evidence indicates that resilience is a time-dependent combination of resistance, recovery, adaptability, and transformability within a coupled water source–canal–field–drainage–ecology–institution system. Although composite indices and hydrological–crop models have advanced, three gaps remain: operational thresholds rarely connect indicators to failure and recovery; farmer and institutional feedbacks are weakly represented; and assessments seldom translate into executable schedules. We, therefore, propose an irrigation-district-specific framework that couples water, sediment, salt, crops, ecology, and governance across basin–district–field scales without transferring risk between scales. Management priorities differ spatially: upstream districts require coordinated water–salt control; middle-reach well–canal systems require surface-water substitution and groundwater recovery; and downstream diversion districts require multi-source allocation and adaptive intake. A digital twin-based closed loop—continuous monitoring, forecasting, optimization, operational commands, and feedback correction—can translate diagnosis into canal rotation, recharge, drainage, and emergency actions. This review provides operational indicators and a decision-oriented research agenda for resilient irrigation modernization. Full article