Search Results (941)

Indoor temperature time series in district-heating buildings are often contaminated by anomalies embedded in nonstationary, multiscale thermal dynamics. This study proposes a hybrid Empirical Wavelet Transform and Long Short-Term Memory (EWT-LSTM) framework for adaptive anomaly detection and localized reconstruction. Evaluated on 15 min interval data from 45 residential units over a 112-day heating season, the framework operates via a highest-frequency branch for anomaly detection and a full-modal branch for signal repair. Quantitative results show that the EWT Highest-Frequency LSTM (EWT(HF)-LSTM) achieved the best anomaly discrimination among decomposition variants with an average F1-score of 0.531. For anomaly repair, the full EWT-LSTM produced the highest fidelity with a localized Root Mean Square Error ($RMS{E}_a$) of 0.818 °C. Furthermore, thermal comfort validation demonstrated that EWT-LSTM successfully prevented the severe comfort degradation of up to −82% in Exceeded Degree-Hours caused by unstable Empirical Mode Decomposition (EMD)-based reconstructions. These concrete results confirm that the proposed framework effectively provides clean, physically coherent temperature data for downstream district heating operations. Full article

This study utilized three controlled Sitika spruce beam specimens and established a parameterized transfer-function model based on force–acceleration frequency response functions (FRFs) to characterize and reconstruct the frequency-domain modal response of beam specimens. The specimens were tested using non-contact magnetic swept-sine excitation, laser Doppler vibration measurement, and synchronous FFT analysis methods under free–free boundary conditions. In the experiment, one specimen was used for modeling and the other two specimens were used for consistency verification. Based on the measured complex FRF, a 1st–5th order modal transfer-function model was established in the frequency range of 0–1000 Hz. The experiment identified five resonance frequencies of the specimen, which were 65.0, 198.5, 370.5, 620.0, and 930.0 Hz, respectively. The model can reconstruct the measured magnitude and phase responses, with magnitude residuals within ±5 dB, resonance-peak magnitude errors of 0.03–0.73 dB, and wrapped-phase deviation around the poles of 0.20–5.08°. The Nyquist trajectory was continuous and smooth, with all poles located in the left half-plane, indicating that the model has stable pole behavior. The research results support the specimen vibration response as an approximate linear time-invariant system under small-magnitude and controlled testing conditions. The model can provide a physically interpretable and reconstructable modal-parameter expression for evaluating frequency-domain vibration responses of controlled wooden beam specimens. Full article

Driven by sea-level rise and frequent compound coastal flooding, accurate inundation simulation is essential for disaster mitigation and urban planning. To address the topologically disconnected overestimation errors inherent in the traditional bathtub model, this study proposes a dynamic coastal inundation simulation framework based on an 8-neighbor seed-spread algorithm. Within this framework, a digital elevation model (DEM) is resampled to a 10 m spatial resolution, and a high frequency tidal sequence with a 5-min temporal resolution is reconstructed from typical spring tides. The vertical datums of both the topography and tidal water levels are strictly unified to the Mean Sea Level (MSL) to maintain physical consistency. Comparative experiments across multiple water level scenarios reveal a distinct threshold effect and non-linear expansion characteristics in inundation responses under complex geomorphological conditions. Because the traditional bathtub model fails to account for the blocking effects of inland physical barriers, its overestimation increases significantly once the water level exceeds critical flood protection thresholds. By generating high resolution Time of Arrival (ToA) maps, the proposed framework provides a robust spatial–temporal basis for precise coastal risk assessment, evacuation planning, and defense resource allocation. Full article

This paper proposes a voltage-sensitivity estimation and droop-allocation framework for virtual power plant (VPP)-based frequency regulation in partially observable distribution feeders. In practical distribution systems, active-power adjustments by distributed energy resources (DERs) for frequency regulation may cause voltage excursions, while full real-time feeder information is often unavailable. To address this issue, an anchor-least-squares (Anchor-LS)-aided sensitivity-estimation method is developed using only point-of-common-coupling (PCC) voltage measurements and feeder-network information. Unlike state-estimation-based, data-driven, or optimization-heavy approaches that typically require wider measurement coverage, large training datasets, or repeated centralized computation, the proposed framework is designed for fast VPP-based frequency regulation under partial observability using only limited PCC measurements and feeder information. The proposed method reconstructs an approximate operating point and derives an operating-point-sensitive PCC voltage-magnitude-sensitivity matrix based on a coupled Z-bus formulation. Based on the estimated sensitivity, a voltage-constrained asymmetric droop-allocation framework is developed for under-frequency and over-frequency events, together with a practical iterative droop-adjustment method that mitigates PCC voltage violations without relying on a full optimization-based dispatch model. The proposed framework is validated through two case studies. In Monte Carlo simulations on the IEEE 33-bus feeder, the proposed sensitivity model reduced the mean RMSE by about 117 times compared with the common-path resistance method and by about 30 times compared with the conventional Z-bus method. In simulations on a practical 115-bus Korean distribution feeder, the proposed method achieved acceptable droop capacities comparable to those of a centralized LP baseline while reducing the mean computation time by about 3.2 times for both under-frequency and over-frequency events. These results confirm the practical usefulness of the proposed framework for fast VPP-based frequency regulation in real distribution networks under partial observability. Full article

Radio Frequency Identification (RFID) is an emerging technology in Industry 4.0 for low-cost logistics, yet direction and orientation estimation typically requires multiple antennas, and robustness under industrial multipath fading, operator variability, and signal fragmentation has not been evaluated. To address this gap, this study proposes a single-antenna RFID system that evaluated thirteen architectures spanning unsupervised methods (clustering algorithms) and supervised methods (classical machine learning, deep learning, and hybrid architectures) on Received Signal Strength Indicator (RSSI) and phase time-series reconstructed through a pipeline of Savitzky–Golay smoothing, phase unwrapping, and cubic spline resampling to $N$ = 50–300 samples, preserving signal morphology across variable-length RFID passes. The system further incorporates a physics-informed augmentation strategy that encodes multipath fading, distance variation, and fragmentation into synthetic training samples for cross-domain generalization without hardware modification. In controlled laboratory experiments, both direction and orientation tasks achieved $>$99.5% accuracy, while direction tracking was additionally validated on an industrial shop floor under varying distances, Non-Line-of-Sight (NLoS) occlusions, and signal fragmentation. Zero-shot transfer caused accuracy to degrade to near-chance levels for several configurations, confirming a pronounced domain gap. Domain adaptation with XGBoost recovered direction accuracy to $>$97% under severe fragmentation under NLoS conditions, with an inference latency of ≈150 $\mu$s. Under domain-adapted shop floor conditions, direction accuracy exceeded the 75–92% reported in prior single-antenna laboratory studies, suggesting that physics-informed domain adaptation is a promising approach for single-antenna RFID tracking in Industrial Internet of Things (IIoT) logistics environments. Full article

Sweeping jet (SWJ) actuators are widely used in active flow control, but explicitly resolving actuator-scale unsteadiness in full-configuration computational fluid dynamics (CFD) remains prohibitively expensive because of the small geometric scales and high-frequency oscillations involved. Existing reduced-order boundary-condition models constructed from exit-plane data alone can reproduce the observed switching waveform, but they treat the actuator as an input–output black box and provide limited insight into the internal dynamics that generate the response. This work develops a causal state-space reduced-order modeling framework that links internal mixing-chamber dynamics to time-resolved exit-plane boundary conditions. Proper orthogonal decomposition (POD) is used to obtain a low-dimensional representation of the internal flow, and a data-driven linear evolution operator is identified in the reduced space by least-squares regression of successive snapshot pairs. A POD truncation rank of $r=60$ is selected from cumulative-energy and validation-error sensitivity analyses, capturing well above 99% of the fluctuation energy while lying within the converged performance regime. A corresponding reduced operator is identified for the exit plane, and spectral comparison reveals near-neutrally stable oscillatory modes in both regions. Using a $\pm 1\%$ relative frequency-matching tolerance, the dominant reduced-operator modes exhibit a 28.3% frequency overlap, providing operator-level evidence that exit-plane oscillations are dynamically linked to internal coherent structures. This correspondence is further supported by cross-spectral coherence analysis between representative internal and exit-plane probe signals, which shows strong coherence at dynamically relevant frequencies. A delayed causal output mapping is then formulated in which the internal reduced state drives the exit-plane response after an identified lag of 149 time steps, corresponding to $2.98\times {10}^{-3}$ s. This delay provides a physically interpretable convective transport timescale from the mixing chamber to the actuator exit. Over the validation interval, the model maintains a mean relative $L_2$ error below 0.02, with maximum normalized errors below 0.04 for most of the prediction horizon, and localized increases are confined to rapid jet-switching events. Field-level reconstructions of streamwise velocity and total pressure show that the model captures both phases of the jet-switching cycle, with errors concentrated primarily in high-gradient shear-layer regions. Compared with exit-only reduced-order models, the proposed internal-driven formulation improves amplitude and phase fidelity over extended prediction horizons. The resulting framework provides a compact, interpretable, operator-based representation of SWJ actuator dynamics suitable for use as a CFD-embeddable dynamic boundary condition. Full article

The dependence of electronic products on printed circuit boards (PCBs) is increasing. In recent years, PCB defect detection technology has achieved considerable results, but the defect targets are small, the background is complex, and there are many integrated components, which poses great challenges to product quality control. Therefore, this paper proposes a printed circuit board defect detection network that integrates coordinate attention and multi-level gradient flow optimization, called EP-YOLO, to improve the detection accuracy of printed circuit board defects. Firstly, this paper improves the model’s ability to capture small target details by reconstructing the detection head of the small target. Based on this, a Shallow Context Feature Extraction (SCFE) network is designed to fuse shallow features with multi-scale features, effectively preventing the loss of shallow detail information and texture features. At the same time, in order to suppress background noise, this paper designs a multi-level feature gradient flow optimization module (C2f_CA, abbreviated as CCA) that integrates coordinate attention and a Cross-Stage Partial Frequency-domain Omni-Kernel module (CSPFOK) to enhance feature extraction capability. Finally, the SCYLLA-IoU (SIoU) optimization model training process was introduced. The experimental results showed that EP-YOLO achieved 97.5% mAP50 on the PKU-Market-PCB dataset and 98.5% mAP50 on the DeepPCB dataset, with a parameter reduction of approximately 12.55%, outperforming popular object detection networks. The results highlight the potential capabilities of EP-YOLO, providing a powerful and effective solution for industrial PCB defect detection. Full article

Today, the rapid progress in non-invasive light–matter interaction analysis is transforming the landscape of biomedical and life sciences driven by low-intensity light detection technologies. As the complexity of photonic applications continues to grow, the importance of single-photon detection techniques becomes pivotal. Among them, Time-Correlated Single-Photon Counting (TCSPC) has become the gold standard for precise, time-resolved reconstruction of rapid and faint optical signals. However, TCSPC has long been constrained by pile-up distortion, which worsens with increasing acquisition speed, typically limiting it to 5% of the excitation frequency. To overcome the operational constraints of conventional implementations, a novel TCSPC acquisition methodology has been introduced, independent of photodetector dead time, excitation intensity, and prior optical signal knowledge, still enabling distortion-free reconstruction of the measured light profiles. In this context, the development of single-photon detectors with short dead time and low timing jitter becomes crucial. This work presents a single-photon detection module based on a Silicon Photomultiplier, which delivers 750 ps FWHM output pulses with a 33.5 ps RMS IRF. Its performance is showcased through fluorescence measurements employing the constraint-free TCSPC methodology, achieving a photon count rate up to 166% of the excitation frequency with a minimal lifetime estimation error of just −1.46%. Full article

The frequency identification technology of differential frequency-hopping (DFH) signals is the key to decoding at the receiver. Aiming to improve frequency identification accuracy under low signal-to-noise ratio (SNR) conditions, a method based on super-resolution image reconstruction technology is proposed for the instantaneous frequency identification of DFH signals. Firstly, the time–frequency image of the DFH signal is obtained using short-time Fourier transform (STFT). Then, a U-Net neural network with an attention mechanism is designed to suppress noise and interference components in the time–frequency image and reconstruct a super-resolution time–frequency image. Furthermore, based on the correlation between adjacent hop signals in accordance with the frequency transfer function, a ResNet neural network is designed to identify frequencies from the super-resolution time–frequency image of DFH signals. Simulation results demonstrate that the designed U-Net neural network can effectively suppress noise and interference components and reconstruct high-quality super-resolution time–frequency images. Comparative experimental results show that the proposed ResNet neural network can significantly improve the identification accuracy of DFH signals under low-SNR conditions. Specifically, the identification accuracy can reach more than 90% when the low SNR is not less than −10 dB, which is a significant improvement compared with other methods. Ablation experiment results indicate that the attention mechanism can improve model performance by 3.74%. Full article

We propose NPMV-SLAM, an RGB-Depth neural implicit dense SLAM system based on multi-resolution voxels and feature point clouds. The method demonstrates exceptional performance in tracking accuracy and reconstruction quality, aiming to address the shortcomings of traditional visual SLAM in texture detail modeling and geometric consistency, as well as the limitations of existing neural implicit methods in real-time performance and scene scalability. (1) We innovatively propose a position-enhanced encoding mechanism that fuses multi-resolution hash voxel grids with feature point clouds. This design fully leverages the high sensitivity of point clouds to high-frequency geometric details and the global structural continuity provided by voxels, achieving complementary advantages during network training and inference, thereby comprehensively enhancing the system’s reconstruction generalization capability. (2) Furthermore, we design an adaptive sampling strategy guided by point cloud density priors. This strategy fundamentally alleviates the core issue of insufficient scene scalability through data-driven online point cloud reconstruction. By filtering out invalid, non-surface sampling points, it concentrates computational resources on object surface regions, significantly reducing computational redundancy in empty areas, and achieves efficient point cloud spatial indexing with the aid of a vector database similarity search algorithm. While maintaining operational efficiency, our method significantly improves both detailed reconstruction capability and global reconstruction completeness. Experiments conducted on multiple indoor scenes from the Replica and TUM datasets show that our approach achieves notable improvements in tracking accuracy, rendering quality, and mapping accuracy, successfully balancing precision and efficiency. Full article

Spaceborne interrupted frequency-modulated continuous-wave synthetic aperture radar (iFMCW SAR) employs a single antenna on a single spacecraft operating in a time-division transmit/receive mode, effectively avoiding mutual interference between transmitted and received signals and thereby overturning the design paradigm of spaceborne FMCW SAR systems. However, the periodic switching of the antenna between transmit and receive states results in periodic data gaps along the azimuth direction in the echo signal, leading to spurious artifacts in the reconstructed images and severely degrading image quality. Sparse signal recovery techniques based on compressive sensing models have been shown to effectively suppress such spurious targets. Nevertheless, the generalized orthogonal matching pursuit (GOMP) algorithm requires prior knowledge of the signal sparsity, a condition that is often impractical in real-world scenarios. To address this limitation, this paper investigates the variation pattern of the residual norm with respect to sparsity in the GOMP algorithm and proposes a modified GOMP algorithm based on binary search. This approach enables rapid and accurate determination of the true sparsity level without prior knowledge, thereby achieving sparsity-adaptive reconstruction with GOMP and significantly enhancing the imaging quality of iFMCW SAR. Simulation experiments involving both point and scene targets are provided to demonstrate the effectiveness and potential of the proposed algorithms for practical applications. Full article

Compressed sensing (CS) applied in the ambiguity domain offers an effective approach for recovering time–frequency distributions (TFDs) of non-stationary signals from sparse representations. Existing methods predominantly rely on the Wigner–Ville distribution (WVD) as the initial representation due to its simplicity and high auto-term concentration. However, the WVD performs poorly for signals with higher-order frequency-modulated (FM) components and is highly sensitive to interference and noise, which then propagate into the reconstruction. This paper introduces the systematic use of the L-Wigner distribution (LWD) as the initial representation for CS-based reconstruction, providing front-end adaptability to signal characteristics. By generating a controllable family of TFDs ranging from the spectrogram to cross-term-free polynomial WVDs, the LWD enables effective suppression of interference and noise while simultaneously enhancing auto-term localization for nonlinear FM components. The proposed LWD-based reconstruction framework is evaluated against the conventional WVD-based method using several state-of-the-art reconstruction algorithms, whose parameters are jointly optimized through a multi-objective meta-heuristic framework to ensure a fair comparison. Experiments on noisy synthetic signals and real-world gravitational-wave data demonstrate consistent performance gains. On synthetic signals, the proposed approach reduces the average reconstruction error index by up to 36.6%, improves the ${\ell }_1$-reconstruction error by up to 75.8%, and achieves complete elimination of cross-term energy. In addition, robustness analysis under additive white Gaussian noise shows up to a 75% improvement in ${\ell }_1$ performance. For real gravitational-wave data, the method reduces the mean reconstruction index by up to 5.8% while maintaining auto-term preservation and eliminating cross-term artifacts. These results establish the LWD-based initialization as an effective and general strategy for TFD reconstruction in complex signal environments. Full article

Unsupervised anomaly detection in industrial fabric inspection remains a formidable challenge due to the complexity of background textures and the subtle, irregular nature of real-world defects. Although the teacher-student distillation paradigm has demonstrated promising performance without reliance on anomalous data, existing methods still struggle in the presence of complex textures, largely due to limited semantic guidance, insufficient frequency modeling, and inadequate multi-scale representation. To address these limitations, we propose a novel reverse distillation framework tailored for fabric defect detection. The core of our method is the frequency decoupling Feature fusion module (FDFM), which achieves frequency domain alignment between teacher and student features through spatially adaptive and learnable filter banks, namely the adaptive high-pass filter (AHPF) and the adaptive low-pass filter (ALPF). Specifically: (1) the high-frequency pathway employs deconvolutional residual enhancement to emphasize boundary details; (2) the low-frequency pathway leverages the CARAFE operator to Handle these normal fluctuations to prevent the model from mistakenly identifying background changes as abnormal areas. This design not only maintains a lightweight architecture but also significantly improves sensitivity to fine-grained anomalies. Furthermore, we introduce a cross-layer residual alignment mechanism that guides the student network in reconstructing deep semantic representations from the teacher-student feature pairs. To balance detection accuracy and deployment efficiency, we develop two model variants: a high-capacity version optimized for precision, and a lightweight version tailored for real-time industrial applications. Compared with other methods from recent years, the experimental results of FAD-RNet validate its superiority in relevant metrics. It should be noted that this study is conducted based on the data organization and processing protocol of the ZJU-Leaper dataset, which may introduce certain dataset-specific characteristics. Full article

This paper investigates an adaptive pilot-assisted channel estimation framework for orthogonal frequency-division multiplexing (OFDM)-based high-speed railway (HSR) communications over non-stationary wideband channels. Within this framework, a channel-aware adaptive pilot insertion (CA-API) mechanism is combined with an linear minimum mean square error (LMMSE) shrinkage technique to adjust pilot density based on temporal channel variations. Using the refined pilot-domain observations, three time-domain channel estimators namely piecewise cubic Hermite interpolation (PCHIP), autoregressive (AR), and Gaussian process regression (GPR), are comparatively evaluated under measurement-based HSR channel models. Simulation results across Remote Area (RA), Closer Area (CEA), and Close Area (CA) conditions demonstrate that the benefit of adaptive pilot scheduling is strongly scenario-dependent. In RA and CEA, the CA-API scheme reduces overhead while maintaining channel reconstruction accuracy close to that of the fixed-pilot baseline, with average overhead reductions of 38% and 30%, respectively. Under the more dispersive CA condition, the adaptive mechanism tends to increase pilot density to preserve reliable channel tracking. Among the evaluated algorithms, GPR delivers the highest estimation accuracy, AR provides a balanced trade-off between accuracy and implementation complexity, and PCHIP is less accurate but remains attractive because of its low complexity. This study provides practical insights into the joint design of adaptive pilot scheduling and channel estimation for HSR wireless communication systems. Full article

While 3D Gaussian Splatting enables real-time rendering of large-scale scenes, its explicit representation leads to near-linear growth in storage requirements as scene scale expands. Furthermore, existing block-based strategies often suffer from geometric discontinuities and storage redundancy. To address these limitations, we present StitchGS, a high-fidelity and lightweight reconstruction scheme tailored for city-scale environments. To mitigate boundary artifacts caused by physical segmentation, we design a Stochastic Interwoven Stitching mechanism. This technique utilizes Oriented Bounding Boxes to define soft transition zones and employs a confidence-driven competition strategy to achieve smooth sub-pixel fusion of primitives within overlapping regions. To alleviate high storage costs, we further introduce a Spectral-Aware Adaptive Compression strategy. By analyzing the energy spectrum distribution of Spherical Harmonics, this method adaptively prunes redundant high-frequency parameters in diffuse regions. Moreover, it incorporates Quantization-Aware Fine-Tuning to balance storage efficiency with visual fidelity. Experiments demonstrate that StitchGS achieves 1.7×–4.0× storage reduction across our benchmarks while maintaining rendering quality competitive with state-of-the-art methods, enabling efficient deployment of large-scale scenes. Full article