Search Results (24,857)

Accurate and low-cost fault diagnosis is essential for improving the reliability of cascaded neutral-point-clamped (NPC) inverters. This paper proposes a single-sensor fault diagnosis method for a single-phase three-module cascaded NPC inverter. Only one DC-side current sensor is required for the diagnostic algorithm, while the voltage sensor used in the outer voltage-control loop is not involved in fault-feature extraction. The measured DC-side current is decomposed via Fourier analysis, and a low-dimensional feature vector is constructed using the amplitudes of the zeroth, 2nd, 3rd, and 4th harmonics together with the phases of the 1st and 3rd harmonics. The six Fourier features are normalized using feature-wise Min–max parameters determined exclusively from the training data. A back-propagation (BP) neural network is then adopted to identify and locate 24 single-switch open-circuit faults in the three-module system. The investigated inverter produces 13 output-voltage levels under healthy operation, and the BP network converges after 5835 training iterations to an error threshold of 1 × 10⁻⁶. An adaptive confirmation criterion based on consecutive diagnosis-code consistency and inter-window feature convergence is introduced. For the nominal 25-class simulation test set, the accuracy, macro-precision, macro-recall, and macro-F1-score are all 100%. In addition, 134 of the 136 dynamic-condition simulation runs are correctly diagnosed, corresponding to an overall robustness-test accuracy of 98.53%. One confirmed, but incorrect final code occurs under the load disturbance applied at 90° of the output-voltage fundamental, and another occurs at an SNR of 20 dB, while no unconfirmed run is observed. Under the severe RL-load condition with τ/T₀ = 1, the mean and maximum diagnostic delays are 41.7 ms and 52 ms, respectively. Full article

Traditional zoom semi-active laser seekers cannot adaptively adjust the spot size while maintaining a compact structure, which degrades angle measurement accuracy. This paper proposes a method based on aberration theory to analyze angle measurement designs an electrowetting dual-liquid zoom optical system that meets compactness constraints. The dynamic curvature control architecture replaces traditional mechanical zoom to achieve optimal detection spot for four-quadrant detectors throughout the entire trajectory. The Gaussian bracket method is employed to calculate and distribute the total optical power of the system, and a dynamic zoom optical system meeting compactness requirements is designed and simulated. Quantitative equations relating the spatial position of the liquid lens to aberrations are derived. A global optimization of the liquid lens zoom optical system for the seeker is performed on the ZEMAX 2024 platform, enabling the system to meet the detection requirements of the entire trajectory through voltage control. Design results show a total system length of 61.6 mm, with distortion controlled within 0.1% during continuous focallength adjustment from 30 to 57 mm. A quantitative evaluation model for aberration-induced angle measurement error is established, and calculations indicate a 62.8% reduction in the RMS angle measurement error over the full zoom range. Full article

Background/Objectives: Suicide-risk assessment during child and adolescent psychiatric hospitalization draws on patient report, clinical history, and professional observation. Whether nocturnal autonomic physiology is concurrently associated with a structured admission-window assessment after accounting for depressive symptoms and self-reported suicidal ideation remains uncertain. Methods: We analyzed 212 hospitalized children and adolescents receiving inpatient care for a major depressive episode. All had a Nurses’ Global Assessment of Suicide Risk (NGASR) rating, self-report measures, covariates, and first admission-night non-contact autonomic data. Principal component analysis was used to derive a nocturnal autonomic dysregulation factor from heart-rate and heart-rate-variability (HRV) summaries. Ordinary least squares regression with HC3 robust standard errors estimated its concurrent association with NGASR after adjustment for age, sex, BMI, Beck Depression Inventory score, and Chinese Beck Scale for Suicide Ideation score. Score-appropriate sensitivity analyses used Poisson, negative-binomial, and predefined NGASR-category models. Results: Higher autonomic dysregulation was associated with higher NGASR after adjustment for depressive symptoms and self-reported suicidal ideation (standardized beta = 0.182, 95% CI 0.067 to 0.297; p = 0.002; q = 0.005), accounting for an additional 3.2 percentage points of explained variance. The association was similar in a robust Poisson model (incidence-rate ratio = 1.059, 95% CI 1.022 to 1.097; p = 0.001) and remained stable after adjustment for subjective sleep quality, AHI, sleep efficiency, and monitoring timing. Separate component models showed larger associations for SDNN and RMSSD than for heart rate or LF/HF, but did not decompose the composite effect. Conclusions: Admission-window nocturnal autonomic dysregulation showed a modest concurrent association with clinical suicide-risk assessment after adjustment for depressive symptoms and self-reported suicidal ideation. The finding does not establish disclosure-independent assessment, temporal improvement of admission assessment, or prediction of future suicidal behavior. Full article

To address the engineering challenges of long detection cycles and the inability to provide real-time early warning for the traditional measurement of 5-day biochemical oxygen demand (BOD₅) in drainage networks, coupled with the limited availability of monitored indicators in practice, this study proposes a BOD₅ concentration prediction method based on a BP neural network. Based on 10,511 sets of field measurements from a city’s drainage network, only three routinely measurable indicators, chemical oxygen demand (COD), ammonia nitrogen (NH₄⁺-N), and total phosphorus (TP), were selected as input features. A COD-only baseline model was first constructed to validate the reliability of the core predictive factor (R² = 0.9401). NH₄⁺-N and TP were then incorporated to build a three-feature synergistic prediction model, which improved the R² to 0.9541. By comparing three optimization algorithms (L-BFGS, Adam, and SGD) and performing hyperparameter optimization, the final test-set R² reached 0.957 with an MAE of 8.834 mg/L (MAPE = 12.89%), representing a practical error of approximately 10.3% of the mean BOD₅ concentration. The prediction accuracy was significantly superior to support vector regression (SVR, R² = 0.930) and multiple linear regression (MLR, R² = 0.955). Generalization ability was validated using three external independent datasets from Jinan (China), Hohhot (China), and Melbourne (Australia). The results show that the model maintains reasonable predictive capability within the same region (R²= 0.844), but accuracy declines substantially when transferring across regions (MAPE increasing from 12.89% to 35.07–58.63%) and countries. This method provides a high-accuracy, low-cost, real-time estimation scheme for early warning of water quality in drainage networks under limited monitoring conditions. Full article

This study evaluates operational optical to near-infrared (NIR) band (400–1050 nm) atmospheric transmittance simulation at the Ali-CPT site (5250 m above sea level). Monthly mean profiles from a 20-year Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) reanalysis were combined with precipitable water vapor (PWV) constraints from the POM-02 sun photometer and validated against DTF-8 sun photometer measurements under fixed aerosol parameters (visibility of 75 km, Clean Continental mode). Mode 1 used real-time lidar extinction profiles; Mode 2 used built-in aerosol modes scaled by aerosol optical depth (AOD). Both modes achieved correlation coefficients greater than 0.93. Mode 1 showed root mean square errors (RMSEs) of 0.034–0.040 in the 400–870 nm range, while Mode 2 exhibited a systematic negative bias (RMSE 0.040–0.052) due to the mismatch between the fixed visibility assumption and the much cleaner winter conditions. The errors in the 940 nm water vapor absorption band were dominated by the vertical structural deviation of the MERRA-2 water vapor profile during high-PWV summer conditions. The results confirm the feasibility of reanalysis-based operational transmittance simulation at data-sparse high-altitude sites. Full article

Accurate localization and mapping are essential for autonomous mobile robots operating in unknown environments. This study investigates the impact of Extended Kalman Filter (EKF)-based sensor fusion on the performance of three widely used two-dimensional (2D) LiDAR Simultaneous Localization and Mapping (SLAM) algorithms: GMapping, Karto SLAM, and SLAM Toolbox. Wheel encoder longitudinal velocity and inertial measurement unit (IMU) yaw angular velocity were fused using an EKF and compared with raw wheel odometry using the MIT Stata Center dataset. Localization performance was evaluated both before and after SLAM using translational and rotational Absolute Pose Error (APE) across multiple trajectory segments. Five repeated executions were performed for each SLAM configuration to characterize run-to-run variability. Prior to SLAM, EKF-filtered odometry reduced translational APE root mean square error (RMSE) by approximately 61–75% and rotational APE RMSE by approximately 65–77% relative to raw odometry. After SLAM, translational differences between the two odometry sources were substantially smaller and varied according to the evaluated algorithm and trajectory, while rotational performance exhibited larger and less consistent changes. These results demonstrate that substantial improvements in upstream odometry accuracy do not necessarily produce proportional improvements in final SLAM localization and that the influence of sensor fusion varied across the evaluated SLAM algorithm and trajectory segments, providing practical guidance for selecting localization strategies in autonomous mobile robots. Full article

Background/Objectives: Apical periodontitis is among the most prevalent dental pathologies, and its radiographic severity is graded on panoramic radiographs using the ordinal periapical index (PAI 3 < 4 < 5). Existing computer-aided systems typically treat these grades as unrelated classes and down-sample the radiograph to a fixed input, which erases the small periradicular radiolucencies that define early disease. This study evaluated a lesion-preserving, ordinal-aware YOLO framework for the simultaneous detection and severity grading of periapical lesions. Methods: A total of 3924 expert-annotated panoramic radiographs (PAI 3/4/5) were processed with an adaptive lesion-preserving tiling algorithm that mapped each radiograph onto native-resolution 640 × 640 tiles so that every lesion appeared intact within exactly one tile, yielding 4641 tiles. Six consecutive YOLO generations (YOLOv8, YOLOv9, YOLOv10, YOLO11, YOLO12, and YOLO26) were benchmarked under one identical pipeline on a locked hold-out test set of 985 lesions, of which 628 were PAI 3, 274 PAI 4, and 83 PAI 5. YOLO12m, which attained the highest point estimates and the smallest run-to-run variation, was then progressively augmented with an ordinal-aware detection loss, a high-resolution P2 head, severity class-weighting and test-time augmentation. Localization was measured with COCO mAP and severity grading with severity-weighted average precision (Sev-wAP), quadratic weighted kappa (QWK), a catastrophic off-by-two error rate, and per-grade recall. Results: Lesion-preserving tiling improved detection for every generation, raising the mAP@0.50 of YOLO12m from 0.494 to 0.615, the highest point estimate among the six generations, although the margin over the next two was within the resolution of this test set. Cumulatively adding the ordinal loss, the P2 head, severity weighting, and test-time augmentation raised mAP@0.50 to 0.664, mAP@0.50:0.95 to 0.333, Sev-wAP to 0.704, and QWK to 0.780. PAI 5 average precision rose from 0.268 to 0.331 and PAI 5 recall from 0.602 to 0.663, 55 of 83 severe lesions, while catastrophic confusion between mild and severe lesions stood at 0.41%, 4 of the 985 reference lesions. Conclusions: Coupling lesion-preserving tiling with ordinal severity-aware detection improves localization and severity-weighted precision by resolvable margins, shifts every severe-grade measure in a clinically preferable direction, and provides a reproducible reference protocol for comparing detector architectures on this task. Full article

This work presents a microwave attitude sensing method and device based on localized perturbation of a split-ring resonator (SRR). The sensor comprises a planar SRR, parallel microstrip feed lines and a metallic disk that can move along a circular trajectory. When the sensor’s orientation is modified in a plane perpendicular to the ground, the metallic disk moves within the constrained structure under the influence of gravity and changes its position relative to the SRR, modulating the local near field and the microstrip coupling state. Consequently, variations in angle are observed across multiple S-parameter channels. The mechanism is validated through simulation and experimental measurements. The measured S-parameters are used to construct a circular residual mixture-of-experts Gaussian process regression (MoE-GPR) model, which is developed for 360° angle reconstruction. In leave-one-angle-out (LOAO) validation on data sampled at 2.5° intervals, the proposed reconstruction method achieves a mean absolute error (MAE) of 0.700°. When trained on data sampled at 10° intervals and tested on a dataset sampled at 2.5° intervals, the proposed method achieves an MAE of 1.125°, demonstrating its generalization across different angular sampling conditions. As no active electronics are required at the moving sensing element, the proposed configuration has potential for integration with RF sensing and communication platforms, as well as for inclination sensing referenced to gravity, orientation detection and structural health monitoring. Full article

Oil–water relative permeability is a critical parameter for reservoir fluid property evaluation and productivity prediction, playing a vital role in reservoir identification and development. However, the correlation between relative permeability and resistivity remains unclear for complex conductive mineral-bearing argillaceous sandstone reservoirs. In this study, twenty-eight artificial core samples were prepared and compacted under simulated reservoir temperature and pressure conditions. Simultaneous oil–water relative permeability and resistivity measurements were performed on these samples at two brine salinities (7000 ppm and 3000 ppm). The analysis reveals that under the same resistivity index, both conductive-mineral content and shale content exhibit a negative correlation with water-phase relative permeability but a positive correlation with oil-phase relative permeability. In contrast, porosity–permeability conditions and injected-water salinity show a positive correlation with water-phase relative permeability and a negative correlation with oil-phase relative permeability. Furthermore, the effect of salinity on the relative permeability–resistivity relationship intensifies as porosity–permeability conditions improve. Based on the consistency between movable water seepage and conductive paths, a novel relative permeability–resistivity model was developed for the target reservoirs by integrating seepage and conductive theories, with a generalized Archie’s equation adopted to calibrate the effects of conductive minerals and shale on rock conductivity. The model parameters are optimized using the Newton–Raphson iteration algorithm, and empirical correlations between the undetermined coefficients and reservoir parameters are developed. Validated against two independent cores with the most complex mineral compositions (SH2-3 and SH3-3), the model yields average relative errors of 9.8% and 13.4% for water-phase relative permeability, and 4.4% and 5.4% for oil-phase relative permeability, respectively, demonstrating its high calculation accuracy. It enables efficient prediction of reservoir relative permeability using resistivity data, providing a new theoretical and technical support for low-resistivity oil reservoir identification and productivity assessment. Full article

In multi-hop UAV ad hoc networks employing the Statistical Priority-based Multiple Access (SPMA) protocol, the HELLO broadcast interval cannot be arbitrarily shortened due to the inherent upper bound on the per-slot transmission probability of each node in saturated networks, which fundamentally limits the estimation accuracy of Channel Occupancy Statistics (COS). To address this problem, this paper proposes a spatial correlation-aided multi-source asynchronous Kalman filtering method, abbreviated as SMA-KF. On the basis of conventional COS broadcasting, SMA-KF introduces two complementary observation sources: COS measurements piggybacked on data packets, and spatially correlated observations from common neighbors compensated by historical biases. These three types of observations are integrated into a unified Kalman filtering framework, and a state-space model suitable for asynchronous intermittent observations is constructed. Theoretical analysis verifies the convergence of the algorithm. Simulation results demonstrate that the proposed algorithm significantly outperforms the EWMA and TW algorithms across all test scenarios, and achieves overall lower error than BiLSTM. Under the extremely sparse observation condition with a HELLO broadcast interval of 600 slots, the Normalized Root Mean Square Error (NRMSE) of SMA-KF is 28.28%, which is 32.8% and 26.2% lower than those of EWMA (42.11%) and TW (38.33%), respectively. In the heavy-traffic scenario with an average data packet arrival interval of 20 slots, the NRMSE of SMA-KF is as low as 4.80%, whereas those of EWMA and TW are 17.49% and 15.40%, respectively, corresponding to reductions of 72.6% and 68.8%. In comparison with BiLSTM, SMA-KF achieves lower NRMSE in five out of seven traffic configurations, while BiLSTM exhibits only marginal and statistically insignificant advantages in the remaining two configurations. Link interruption experiments show that SMA-KF maintains NRMSE between 5.68% and 10.40% across the entire meaningful interruption coverage range of 0% to 53%, consistently outperforming all benchmark algorithms. Moreover, SMA-KF consistently achieves the lowest estimation error under varying node mobility speeds. Parameter sensitivity analysis confirms that SMA-KF maintains stable performance across a wide range of parameter values. These results validate the effectiveness of multi-source observation fusion and spatial cooperative estimation in improving both the accuracy and robustness of COS estimation. Full article

Background/Objectives The evaluation of treatment effectiveness largely depends on the outcome measures used in the study. Patient-reported outcome measures (PROMs), such as the visual analog scale (VAS), and more objective measures, such as pressure pain thresholds (PPT), are commonly employed to capture pain-sensory profiles and hypoalgesic effects. The NOD is a novel digital algometer designed to assess the PPT. This study aimed to evaluate the validity, reliability, and responsiveness of the NOD algometer using a conventional algometer as a reference instrument for PPT assessment. Methods: A prospective longitudinal study with repeated measures was conducted following the COSMIN recommendations for studies evaluating measurement properties. Healthy participants with latent trigger points were recruited from the University of Valencia, where an experimental pain induction procedure was used to generate transient changes in PPT values and evaluate the responsiveness of the NOD device. The PPT was assessed at four time points: baseline, immediately after the intervention, and at 30 and 60 min post-intervention. Results: A total of 40 participants were included in the study (mean age: 22.0 ± 3.35 years; F = 35%). For concurrent validity, a strong positive association was observed between the reference algometer and NOD (Spearman’s ρ = 0.90, p < 0.001), and the limits of agreement ranged from −1.13 to 1.34 (−25.45% to 30.18%). For reliability, intra-session reliability was good to excellent in both sessions (ICC = 0.825 and 0.859), with a standard error of measurement (SEM = 0.63 to 0.82) and minimal detectable change (MDC = 1.75 to 2.26). For responsiveness, significant reductions in NOD values were observed at all post-intervention time points compared to baseline (p ≤ 0.005). The magnitude of change increased over time, with a standardized response mean (SRM = −0.57 to −0.74) and moderate-to-large effect sizes (Cohen’s d = 0.57–0.73). Conclusions: Our findings indicate that the NOD may be a valid and reliable tool for measuring PPT at latent trigger points in healthy participants. The NOD showed a strong association with a reference algometer and good relative reliability under standardized experimental conditions. The device was also able to detect experimentally induced changes in PPT. Nevertheless, further studies are required to establish its interchangeability with conventional algometers, responsiveness to clinically meaningful change, and applicability in clinical populations. Full article

Continuous gas leakage in thin-walled sealed spacecraft structures produces sustained broadband acoustic-emission (AE) signals from which reliable first-arrival picking is difficult. This study presents an interpretable two-stage workflow that decouples leak screening from regional localization. Experiments used a 500 × 500 × 2.5 mm 3A21 aluminum-alloy plate with eight piezoelectric AE sensors under 0.1 MPa pressure difference. Intact, circular-hole, and slit-leak conditions were each tested three times with a 0.5–1.5 s steady-state window. Leak screening employed the 20–200 kHz mean spectral amplitude (Hann window). A provisional threshold of 0.85, set from three intact records, separated all leak records from intact controls. Regional localization used relative logarithmic RMS amplitudes in an energy-decay plus maximum-likelihood-estimation (ED+MLE) cost function on a 10 mm grid. The equivalent attenuation coefficient, empirically selected as 2.3 m⁻¹ using the 1 mm central-hole calibration case, gave a 10 mm grid error for that calibration demonstration. With this coefficient fixed, the four transfer conditions yielded mean localization errors of 19.6–45.7 mm. The results provide preliminary feasibility evidence for leak detection and regional localization under the controlled laboratory conditions investigated in this study. Further validation requires independent channel calibration, larger background datasets, leak-rate measurements, parameter-sensitivity analysis, and testing on more representative structures. Full article

Deep visual recognition can turn ordinary video into interpretable motor knowledge, yet coordination among the joints of a single body during social interaction remains largely unexplored. We present an interactive playback visualizer that couples markerless pose estimation with the cross-wavelet transform to quantify amplitude co-modulation between all joint pairs. Amplitude co-modulation proved anatomically structured: bilateral homologous pairs exceeded cross-limb and head–body pairs even among pairs sharing no keypoint, where correlated tracking error cannot produce it. Within-limb pairs also scored high but share keypoints, so their elevation is confounded with measurement error and not treated as established. Co-modulation concentrated at low postural frequencies and showed no systematic temporal trend; each profile remained temporally consistent within the session, and equivalence to a flat trend was not established. Vocal activity correlated with upper-limb movement at gesture frequencies. Each participant’s 78-dimensional profile was individually distinctive: split-half identification reached 28.6% (95% CI 19.3–40.1%) against 1.4% chance, demonstrating within-session identifiability rather than a cross-session trait. No sex differences were detected in overall or category-level co-modulation, and equivalence was not established for any measure; women exceeded men in the micro-movement band, the narrowest and most attenuated by preprocessing, so that difference is reported but not interpreted. Full article

Accurate radar tracking of highly maneuverable unmanned aerial vehicles (UAVs) is a key enabling technology for low-altitude airspace surveillance, counter-UAS defense, and UAS traffic management (UTM). Once a non-cooperative UAV has been detected, estimating its motion state must cope with nonlinear polar-coordinate observations, unknown maneuver-mode switching, and multi-scale state variations driven by agile drone flight, making it difficult for classical IMM/UKF filters and deep sequence models to preserve local maneuver response and long-term temporal consistency. We propose MSGate, a multi-scale gated temporal network organized along an “observation-representation-fusion-constraint” pipeline. A non-learnable physical front end maps polar measurements into a Cartesian observation trajectory of the same dimension as the UAV state. Multi-scale gated convolution and RoPE-Transformer encoding extract local maneuver responses and long-range dependencies. A shared gated dual-path decoder fuses the two paths adaptively at each time step and channel, and velocity-smoothness and position-velocity kinematic consistency terms regularize the predicted trajectory. On the real-UAV datasets UZH-FPV, EuRoC MAV, and NeuroBEM, under a unified range-azimuth observation protocol, MSGate attains the lowest average position and velocity errors (Pos-RMSE $0.0486\mathrm{m}$; Vel-RMSE $0.1767\mathrm{m}/\mathrm{s}$), outperforming the strongest time-series baseline TimeMixer, and generalizes to a separate nano-quadrotor dataset (NanoBench). MSGate provides an accurate, maneuver-robust solution for radar state estimation of highly maneuverable UAVs. Full article

Hypertension is a major risk factor for cardiovascular disease, yet cuffless blood pressure monitoring remains challenging because most existing methods rely on intermittent cuff-based measurements or multimodal physiological signals. This work proposes PPG-FusionNet, a dual-branch deep learning architecture for simultaneous systolic and diastolic blood pressure estimation using only photoplethysmography (PPG) signals. The model combines a local dilated convolutional encoder and a global AutoCorrelation encoder operating on a shared patch embedding, whose representations are integrated through cross-attention fusion and optimized with a constrained dual-head regression objective. The model was trained and evaluated on the PulseDB benchmark using Bayesian hyperparameter optimization and systematic ablation studies to assess each architectural component. PPG-FusionNet achieved mean absolute errors of 7.46 mmHg for systolic blood pressure and 4.72 mmHg for diastolic blood pressure, with near-zero mean errors and compliance with the ANSI/AAMI standard and BHS Grade B for diastolic estimation. Ablation experiments revealed that trend-seasonal decomposition, despite its success in long-horizon forecasting, degraded performance on short PPG windows, whereas cross-attention fusion and AutoCorrelation improved estimation accuracy. These results demonstrate that heterogeneous dual-branch representation learning provides an effective, scalable framework for cuffless blood pressure estimation from a single PPG sensor. Full article