Search Results (8,781)

Offshore wind energy is an important source of clean energy. Single-post platforms, due to their simple structure and strong stability, can adapt to deep water environments through buoyancy and ballast systems, have small motion responses, and have low construction and maintenance costs. They are suitable for offshore wind energy development in deep-sea areas and help expand the application of offshore wind power. This paper conducts a coupled response analysis of offshore wind turbine foundations and mooring systems, as well as an optimization study on the form and number of mooring lines. Under the premise of considering the safety and economy of floating wind turbines, the mooring lines have been optimally arranged. The study calculates frequency-domain responses, time-domain responses, and mooring line forces under the constraints of the original three-line mooring system. Based on this benchmark, the study further optimizes the mooring forms and numbers for the same platform, analyzing four, six, and eight single mooring lines, as well as three groups of single-line, double-line, and triple-line mooring configurations. Finally, using AQWA software (2024 R1), the responses and mooring line forces of different mooring configurations were calculated, and the preferred mooring arrangement for this stepped single-post platform was determined to be a three-group, three-line system (a total of nine mooring lines). The mooring line tension decreased substantially from the original 3.2 × 10⁶ N to 1.8 × 10⁶ N, while the dynamic response was reduced to one-sixth of its original level. Meanwhile, this study provides strong support for the utilization of offshore wind energy and the construction of offshore wind turbine platforms and mooring systems. Full article

In the oil and gas industry, fiber-optic telemetry is hindered by transmission degradation from high-temperature macro-bend loss. In this study, to address the lack of a unified model, we develop a numerical framework incorporating both bending-dominated effects and thermo-optic modulation. We systematically analyze the coupled responses of multimode (MMF) and single-mode (SMF) fibers at 1.55 μm across varying temperatures (303.15~483.15 K) and bending radii (1~12 mm). Power spectral density (PSD) and phase spectra are utilized to characterize the loss response and explore its modulation mechanisms. Our results indicate that the MMF temperature response is relatively smooth, with a peak magnitude of 10³. In the frequency domain, increased bending raises the MMF PSD main peak by over an order of magnitude, enhancing structural features. While the MMF phase response exhibits a wide dynamic range under tight bending, it becomes unstable in weak modulation regions. Conversely, SMF exhibits more pronounced structural fluctuations (order of 10⁴) but maintains a continuous, smooth phase gradient, demonstrating superior stability. Furthermore, MMF frequency-domain characteristics are highly wavelength-dependent (1.2~2.0 μm), whereas SMF fluctuations remain below 10%, indicating a higher parameter robustness. These findings provide a theoretical foundation for optimizing downhole fiber-optic telemetry selection and signal processing strategies. 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

High-frequency transformer (HFT) noise is a pivotal indicator of equipment performance. To conduct a comprehensive evaluation, this study systematically performed testing and evaluation on the noise generated by a 70 kW HFT under no-load conditions. Acoustic data were collected using acoustic sensors and a head-and-torso simulator, followed by an analysis of noise characteristics focusing on the impacts of voltage levels and operating frequencies. A multi-dimensional evaluation of HFT noise was carried out using sound quality parameters to unravel its intrinsic attributes under electrical parameter excitation. The key findings are as follows: HFT noise exhibits steady-state time-domain behavior and distinct tonal frequency-domain features; the dominant frequency is twice the operating frequency, with prominent harmonics. The noise intensity increases with the voltage levels (~47.0 dB (A) at 200 V to ~72.0 dB (A) at 750 V at 5 kHz) but decreases with the operating frequencies (~82.0 dB (A) at 4 kHz to ~47.0 dB (A) at 10 kHz at 750 V). This study establishes correlations between the electrical parameters and sound quality metrics; the loudness, sharpness, tone-to-noise ratio and prominence ratio are sensitive to the electrical parameters of HFT. Single-frequency noise from HFT exhibits remarkable perceptual salience, exacerbating the perceived annoyance. Thus, HFT design should prioritize reducing single-frequency noise to alleviate such issues. Full article

Earthquake-induced landslide reactivation poses a sustained hazard for years following major seismic events, yet operational prediction remains constrained by heterogeneous multi-modal data, sparse supervision, and the absence of uncertainty-aware frameworks. This paper presents PIMSEL, a physically guided multi-modal semi-supervised framework for post-seismic landslide reactivation risk assessment. PIMSEL integrates satellite-derived morphological features, precipitation time series, and seismic hazard attributes through four components: entropy-regularized optimal transport for cross-modal semantic alignment without paired supervision; causally constrained hierarchical fusion enforcing domain-consistent modal weighting; scenario-based prototype mutation for semi-supervised learning from sparse expert annotations; and prototype-anchored variational graph clustering that simultaneously stratifies landslides into HIGH, MEDIUM, and LOW risk tiers and produces decomposed aleatoric and epistemic uncertainty estimates for operational triage. The HIGH risk tier operationally corresponds to predicted reactivation, validated against 598 documented reactivation events across 7482 co-seismic landslides from three Sichuan Province earthquake sequences: the 2013 Lushan (Mw 7.0), 2017 Jiuzhaigou (Mw 7.0), and 2022 Luding (Mw 6.8) events. PIMSEL achieves 82.5% reactivation recall and 66.4% precision, outperforming twelve baselines across clustering quality, classification, and uncertainty calibration metrics. Ablation studies confirm that optimal transport alignment contributes the largest individual performance gain. Current limitations include quarterly assessment frequency and dependence on optical imagery under cloud cover, which future integration of real-time meteorological triggers and SAR data should address. Full article

Aero-engine defects often exhibit micro-scale and high-frequency characteristics under complex metallic textures, which makes precise segmentation difficult. Most existing pixel-level methods rely on spatial-domain modeling and lack frequency-domain decoupling. As a result, high-frequency details are easily hidden by low-frequency background information. In addition, repeated downsampling weakens the representation of fine-grained structures, leading to inaccurate boundary localization and limited robustness. To address these issues, a spectrum-driven hierarchical learning network is proposed for aero-engine defect segmentation. First, a dual-band spectral module is constructed using the discrete cosine transform to separate high-frequency and low-frequency components, providing stable and physically meaningful frequency-domain priors for the network. Second, a detail-guided module is designed where high-frequency features adaptively guide skip connections, compensating information loss during encoding and improving boundary recovery. Furthermore, a low-frequency-driven region-aware modeling module is developed. The internal defect regions, boundary areas, and background regions are modeled hierarchically. A dynamic hyper-kernel generation mechanism performs region-sensitive convolutional modeling, improving adaptation to complex structural variations. Extensive experiments on the Turbo19 and NEU-Seg datasets demonstrate that the proposed method produces accurate defect boundaries and achieves mIoU scores of 89.82% and 91.44%, improving over the second-best method by 5.22% and 4.42%, respectively. 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

Improving the prediction accuracy of renewable energy power generation units is an important goal of the “source-storage integration” approach. However, the abundance of anomalous data and indistinct features in renewable energy station data seriously affects the health status prediction of these generator sets. To effectively enhance the performance of renewable energy generation prediction, this paper proposes an efficient data cleaning method for renewable energy stations based on anomaly detection and feature enhancement. First, anomaly detection is achieved by calculating a baseline power curve and partitioning data, utilizing the Density-Based Spatial Clustering of Applications with Noise (DBSCAN). Subsequently, considering that current models often learn low-frequency features while ignoring high-frequency features when processing time-series data, a data feature enhancement method is proposed. The proposed method integrates high-/low-frequency data decomposition, time–frequency domain conversion, and an improved attention mechanism to effectively enhance the high-frequency features of renewable energy station data, and reduces the RMSE of mainstream forecasting models significantly. Finally, using data from a renewable energy station in a region of China, the effectiveness and superiority of the anomaly detection and feature enhancement methods are analyzed. The results show that for renewable energy generation data, the proposed method reduces the RMSE of LSTM and Transformer models by 15.12%, 16.67% and 16.24%, 18.32% respectively, significantly improving prediction accuracy. 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 increasing demand for intelligent fault monitoring, acoustic-based diagnosis has emerged as a promising solution for industrial applications such as pipeline leakage and electrical equipment fault detection. However, complex working conditions and domain shifts significantly degrade model performance, especially when unseen target domain data is unavailable. To address this, we propose an amplitude-phase collaborative augmentation network named AP-CANet tailored for acoustic fault diagnosis. Specifically, the network adaptively aligns amplitude and phase features across multiple source domains and performs label-consistent sample augmentation to enrich data diversity while preserving semantic consistency. A frequency–spatial interaction module further integrates global spectral information with local temporal details to improve feature discriminability. Moreover, we introduce a manifold triplet loss that scales shortest path distances in the feature manifold, encouraging the model to better capture subtle distinctions among hard samples and improving intra-class compactness and inter-class separability. We evaluate the proposed method on two publicly available datasets: the Pipeline Leak Acoustic Dataset (GPLA-12) and the Electrical Sound Dataset (MIMII-DG). Experimental results demonstrate superior performance under domain-shift scenarios, highlighting the method’s potential for scalable and low-cost acoustic fault diagnosis in real-world industrial environments. Full article

This paper presents a time-domain closed-loop resistive temperature sensor architecture. The design employs a frequency-locked loop (FLL)-based oscillator as the sensing element, generating a monotonic frequency response to temperature variations. The output frequency is digitized on-chip and converted into a temperature code. Within the oscillator core, a switched-capacitor technique converts frequency to voltage for closed-loop control, reducing charging/discharging voltage swings and significantly lowering dynamic power consumption. The FLL topology enhances frequency stability, minimizes distortion, and suppresses power supply sensitivity. Fabricated in a 180 nm CMOS process with a core area of 0.12 mm², the sensor achieves a peak-to-peak inaccuracy of +1.2 °C/−0.9 °C from −40 °C to 125 °C. Operating at 1 V, the circuit consumes only 10 μW with a resolution of 51 mK within 12 ms. Full article

To address the difficulty of detecting demagnetization severity in permanent magnet synchronous motors (PMSMs), this paper proposes a demagnetization severity detection method based on temperature signal and Convolutional Neural Network (CNN). First, the differences between local demagnetization and eccentricity fault in stator current harmonics are analyzed from an electromagnetic perspective, and fast Fourier transform (FFT) is used for frequency-domain analysis of the stator current to identify local demagnetization faults. On this basis, an electromagnetic–thermal coupling model is established by considering motor losses and heat dissipation boundary conditions to obtain the winding temperatures under different demagnetization severities and operating conditions. Furthermore, the temperature time series, together with speed and load torque, is constructed into a three-dimensional state space, and the proposed Conditionally Modulated Multi-Scale Convolutional Neural Network (CMSCNN) is introduced for feature learning to achieve demagnetization severity detection. Experimental results show that the proposed method achieves an average detection accuracy of 98.06% on the simulation test set and outperforms the baseline CNN model. On measured data collected from the faulty prototype, the average detection accuracy reaches 93.34%, verifying the effectiveness of the proposed method for demagnetization severity detection. Full article

To improve the feature representation and cross-condition generalization of bearing fault diagnosis, this paper proposes an adaptive multi-sensor fusion network with a skip fusion module and a parameter-efficient dual-convolution diagnosis block. The vibration and current signals are first augmented by overlapping segmentation and transformed into the frequency domain using FFT. Multi-scale depthwise convolutions are then employed in parallel branches to capture fault patterns at different receptive fields, and an attention-based skip fusion mechanism selectively aggregates cross-sensor features for complementary enhancement. After fusion, self-calibrated convolution and dilated convolution are alternately applied to strengthen discriminative representation without increasing model complexity. Experiments on multiple bearing datasets under both constant and variable operating conditions demonstrate that the proposed method achieves consistently higher accuracy and robustness than representative CNN-based baselines, verifying its effectiveness for practical bearing fault diagnosis. Full article

Non-destructive characterization of electromagnetic (EM) wave propagation properties in drill cores is gaining prominence as a foundation for reliable geophysical inversion, improved rock-physics modeling, and increasingly data-driven mineral exploration workflows. Lab-based rock characterization requires benchmarks that link the density, elastic, electrical, magnetic, and EM properties of studied cores to lithology and mineralization, enabling more accurate interpretation of geophysical data. This study develops a robust high-frequency EM (HFEM) wave velocity measurement technique and incorporates it within a standardized non-destructive framework validated across multiple mineral systems in Newfoundland and Labrador, Canada. The developed method derives EM velocities from two-way travel time through drill cores positioned above a metallic reflector, supported by finite-difference time-domain simulations to optimize antenna frequency and test geometry. A repeatable signal-processing workflow was implemented to enhance reflection picking. Results reveal systematic EM velocity contrasts among host rocks and oxide or sulfide-bearing systems, with oxide-rich and massive sulfide intervals exhibiting higher density, elevated conductivity and susceptibility with strong EM attenuation. The integrated dataset shows that conductivity and magnetic susceptibility significantly influence EM velocity response and detectability limits. The proposed multi-parameter benchmark enables enhanced discrimination of lithological and mineralization controls in mineral exploration workflows and supports more accurate time–depth conversion in HFEM geophysical and ground-penetrating radar (GPR) methods. Full article

Point cloud semantic segmentation is a pivotal technology for realizing non-contact body measurement and refined management of livestock. However, processing sheep point clouds in smart livestock scenarios presents specific challenges, primarily due to non-rigid posture deformations and severe background interference. To address these issues, this paper proposes a novel symmetric encoder–decoder architecture named Local–Global Aggregation Network (LGA-Net), which achieves high-precision parsing of sheep point clouds by constructing a dual-scale feature aggregation mechanism. First, a Dual Attention Aggregation (DAA) module is designed to jointly encode geometric and color features, significantly enhancing the network’s ability to capture fine-grained local boundaries, such as sheep ears and hooves. Second, a Global Semantic Relation (GSR) module is introduced, utilizing spatial occupancy ratios to establish long-range dependencies, thereby effectively resolving semantic ambiguity caused by posture variations. Furthermore, a plug-and-play Dual-domain Feature Enhancement (DFE) module is proposed. By fusing bilinear interactions between explicit 3D space and implicit feature space, the DFE module constructs a high-pass filtering mechanism to suppress low-frequency background noise. Extensive experiments on a self-constructed point cloud dataset involving two semantic classes (Sheep and Fence) demonstrate that LGA-Net achieves a mIoU of 97.3%, an OA of 99.0%, and a mAcc of 97.8%. These results indicate that the proposed method outperforms existing mainstream algorithms in both segmentation accuracy and robustness. This study not only proposes a feasible solution for precise sheep extraction under the tested experimental conditions, but also provides solid technical support for subsequent automated body measurement and behavior analysis. Full article