Newsfeeds

Measurement Science and Technology - latest papers

Latest articles for Measurement Science and Technology

IOPscience

  • Count-nonlinearity correction of single-photon avalanche diodes using laser superposition and reference-photon-rate validation
    Single-photon avalanche diode (SPAD) count outputs exhibit rate-dependent compression at high count rates, limiting their linear operating range in weak-light radiometry. We present a measurement-based correction framework combining continuous-wave laser-superposition linearity evaluation, a wavelength-shared rate-dependent effective-dead-time model, and independent reference-photon-rate validation using a silicon trap detector. The model was identified from 33 superposition groups at 460 nm, 532 nm, and 633 nm, covering approximately – , and was frozen before independent validation. After correction, the RMS superposition residuals were , , and , respectively, with a pooled RMS of ; all 33 groups satisfied the linearity criterion. Odd–even and leave-one-wavelength-out tests yielded test RMS values below . Independent validation using 78 separately acquired points extending to reduced zero-intercept fitting RMS residuals from – to – . Overall, 77 of 78 points satisfied the criterion, while all 30 points at or above satisfied the criterion. These results demonstrate substantially improved SPAD count linearity within the validated continuous-wave operating range.

  • A 1dcnn-based multi-modal fusion network for quantitative in-situ detection of subsurface cracks in CFRP via eddy current testing
    Rapid in-situ quantitative evaluation of subsurface cracks in carbon fiber-reinforced polymer (CFRP) structures remains challenging. This study proposes an end-to-end method that exploits the anisotropy-dependent eddy-current response of CFRP by combining eddy-current testing (ECT), entropy-difference-based multimodal fusion, and a one-dimensional convolutional neural network. Amplitude, phase, real, and imaginary components were acquired at two excitation frequencies and fused using self-optimizing weights derived from their entropy differences relative to an intact reference. The fused one-dimensional sequence was then used to regress crack center position, width, and subsurface depth simultaneously. On the held-out test set, the model achieved mean absolute errors (MAEs) of 0.44 mm for center position, 0.17 mm for width, and 0.02 mm for depth, with R2 values of at least 0.95. Tests on lightning-shaped crack geometries not represented in the training set further demonstrated geometric transferability within the tested machined-crack domain. These results show that the proposed method effectively fuses complementary information from the four impedance components without direct concatenation, enabling accurate quantitative evaluation of subsurface cracks in CFRP from raw ECT signals.

  • Distributed photovoltaic power forecasting based on dynamic fusion of physical priors and numerical weather prediction
    Distributed photovoltaic (PV) power forecasting remains difficult because existing data-driven methods mainly learn statistical correlations from historical samples and often produce physically inconsistent outputs under rapidly changing weather. From a measurement perspective, this task requires reliable inference of an unobserved future power quantity from heterogeneous and uncertain inputs, including historical power measurements and numerical weather predictions. To address this issue, this paper proposes a forecasting framework that dynamically fuses physical priors and forecast meteorological information. Its novelty lies in treating the physical power envelope and baseline as interpretable measurement references rather than merely concatenating them with data-driven features. A physical power upper bound and a physical baseline power are first constructed from irradiance, temperature, wind speed, and humidity. A dual-branch TimeMixer encoder separately models the forecast meteorological and physical-prior sequences, while multi-scale cross attention and dynamic gating adaptively fuse weather evolution with physical power evolution. Residual correction around the physical baseline is further combined with physics-constrained losses to suppress over-bound and nighttime-positive predictions. Experiments on three distributed PV sites show that the proposed method achieves values of 0.874, 0.898, and 0.766 and root mean square error (RMSE) values of 5.603, 9.854, and 6.423 kW, respectively, with a false alarm rate (FAR) of 0 across all sites. Compared with the best-performing baseline at each site, it reduces RMSE by up to 35.8% under normal conditions and by up to 37.7% under complex weather; even in complex-weather scenarios, it maintains values of 0.679–0.801 and a FAR of 0. These results demonstrate improved accuracy, traceability to physically meaningful reference quantities, and robustness under challenging weather, with broader relevance to measurement-informed monitoring, grid operation, and energy management.

  • Aeromagnetic interference compensation method based on V-Coherence feature screening and dual-channel time–frequency hybrid driving
    Owing to the integration of numerous electronic devices into flight platforms, the resulting complex magnetic interference environment significantly impacts geomagnetic field measurement accuracy. To improve the compensation accuracy of traditional models and enhance their ability to characterize non-maneuvering interference, this study proposes a dual-channel hybrid-driven aeromagnetic compensation method incorporating time–frequency features. First, a model-data hybrid-driven approach is introduced to model different types of magnetic interference based on the classification of aeromagnetic interference. Additionally, the V-Coherence method is employed to select current signals from onboard equipment that are highly correlated with magnetic interference. Second, to further enrich feature representation, wavelet packet decomposition is introduced to expand the time–frequency characteristics of network inputs, and a dual-channel network is constructed to achieve effective compensation for complex magnetic interference from the aircraft platform. Finally, based on the proposed model, a cross-training strategy for calibrated flight data is explored. The proposed method is validated using field magnetic survey data in a public dataset. The results show that the proposed method achieves superior performance across all test regions compared to traditional approaches, effectively improves the accuracy of geomagnetic field measurements.

  • ICG-VIMOD: infrared-characteristic-guided visible-infrared multi-modal object detection network
    Object detection in complex lighting and harsh environments significantly benefits from the synergistic deployment of visible and infrared spectra. However, most existing dual-modality methods follow a two-stage pipeline termed ‘fusion-then-detection’, inevitably entailing substantial model complexity and intensive computational overhead. Moreover, existing enhancement strategies tend to bias toward visible imagery, lacking adequate adaptation to distinct infrared properties. To address these challenges, this paper proposes an infrared-characteristic-guided visible-infrared multi-modal object detection network (ICG-VIMOD). By integrating dual-modality feature fusion and detection within a unified architecture, an Infrared Position-aware feature Direct Connection mechanism is introduced to enable the end-to-end joint optimization, thereby maximizing the utility of infrared feature. Specifically, to mitigate the feature sparsity and imbalanced thermal distribution inherent in infrared images, two core components are designed: 1) An Edge-Guided Feature Extraction Module, which integrates the explicit boundary-capturing capability from edge operators with the adaptability of learnable convolutions to alleviate detail attenuation; And 2) A Region-adaptive Hybrid Attention mechanism, which dynamically allocates attention resources based on regional detection difficulty to improve infrared feature representation. Comprehensive benchmarks on the LLVIP, M3FD, and a self-built dataset validate the efficacy of our framework. On LLVIP and M3FD, ICG-VIMOD achieves a mAP@0.5 of 97.0% and 85.2%, and a mAP@0.5:0.95 of 67.0% and 57.9%, respectively. Featuring a mere 4.16 M parameters and 10.2G FLOPs, our model obtains improved detection accuracy while maintaining an ultra-lightweight footprint.