Search Results (391)

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

Wireless sensing networks increasingly extend into obstacle-prone deployments, where physical blockage degrades reliability and open propagation exposes transmission activity. Intelligent reflecting surfaces (IRSs) establish programmable paths around obstacles while passive elements remain constrained by severe cascaded attenuation. To address the tradeoff between reliability and covertness, we propose an unmanned aerial vehicle (UAV) -assisted active-IRS architecture under probabilistic line-of-sight and non-line-of-sight propagation conditions that accounts for direct leakage from the transmitter to the warden together with residual jammer cancellation and always-on IRS circuit noise under a finite output power budget. Furthermore, bidirectional Kullback–Leibler analysis identifies the reverse divergence as the tighter restriction and converts the covertness requirement into conservative gain bounds under warden location uncertainty and relative phase uncertainty conditions between the direct and aggregate reflected fields. Subsequently, closed-form phase control for calibrated equal-gain elements and gain monotonicity reduce the joint design to an exhaustive search over the prescribed placement grid. The numerical results demonstrate a SINR advantage over passive reflection and single-element relaying across the evaluated settings. The finite-array and hardware analyses show that gain back-off enforces a prescribed covert-outage limit while direct leakage and residual self-interference remain explicitly controlled. Overall, the framework provides a transparent basis for reliable covert sensing through UAV-assisted active reflection. Full article

We investigate reactive collision dynamics and effective interaction cross-sections using classical trajectory simulations on a reduced-dimensional reactive potential-energy surface containing reactant and product wells separated by an intermediate barrier region. The simulations are performed over a range of collision velocities for which direct scattering, transient trapping, and reactive trajectories coexist within the same interaction landscape. Trajectories are propagated using a velocity-Verlet integration scheme, while reaction probabilities are analyzed as functions of the impact parameter and initial projectile velocity. The calculated probability distributions exhibit strongly localized reactive windows in phase space separated by extended nonreactive regions, indicating pronounced sensitivity of the dynamics to both collision geometry and initial conditions. Probability maps in the (v_x,b) plane reveal a fragmented phase-space structure and highly nonuniform accessibility of the interaction region across the investigated parameter range. The simulations further show substantial variations in the relative importance of reactive, trapped, and back-scattering trajectories with increasing collision velocity, together with non-monotonic behavior of the effective reactive cross-sections. Despite the intentionally reduced dimensionality of the model, the trajectory ensembles reproduce several characteristic features of complex reactive scattering dynamics, including transient trapping, competing dynamical pathways, and broad residence-time distributions. The present results demonstrate that reduced-dimensional classical trajectory approaches can already capture important phase-space mechanisms governing dynamical accessibility and channel competition in reactive molecular collisions. Full article

Planning long-term national development under uncertainty is difficult for small states, where indicators are fragmented, frequently revised, and span tightly coupled social, economic, and environmental domains. Scenario tools can support such decisions without committing to a single forecast, but they often lack historical grounding, cross-domain consistency, reproducible update pipelines, and explicit links between policy choices and outcomes. We present the Andorra Scenario Engine, a transparent, data-grounded framework that harmonizes multi-source national indicators into a consistent state vector and propagates four contrasting pathways—Continuity, Overgrowth, Degrowth, and Density—to 2049 through bounded, coupled update rules. The engine belongs to the exploratory-modelling tradition: an upstream, auditable layer toward a national digital twin rather than a forecasting system, whose outputs are internally consistent conditional futures. A central structural modelling choice is to derive population endogenously from GDP growth via a lagged labour-immigration elasticity (0.40 at a one-year lag, 0.10 at two years), grounded in IMF (2025) and World Bank data. A back-cast benchmark quantifies the accuracy cost of this choice (9.5% population RMSE against 1.7% for a linear trend), accepted in exchange for a policy-relevant causal lever. Three macroeconomic parameters and a housing-affordability anchor are calibrated to 2010–2024 official statistics (Nelder–Mead; RMSE = 1.77% on real GDP per capita). Scenario endpoints for 2049 range from 70,049 (Degrowth) to 174,002 (Overgrowth); a sensitivity audit identifies the housing-affordability threshold and the lag-1 elasticity as the load-bearing assumptions on which the headline signals depend. The framework provides a reproducible, human-in-the-loop baseline for comparing policy-relevant trade-offs and binding constraints in data-scarce microstates. Full article

Fracture in concrete is inherently a multi-stage process, yet traditional three-stage frameworks do not explicitly distinguish between micro-crack development and macro-crack propagation, particularly under varying ligament length conditions. The influence of ligament length (notch-to-depth ratios of 0.0, 0.2, 0.3, 0.4, and 0.5) on the crack propagation characteristics in notched concrete beams under three-point bending is investigated. Three-dimensional digital image correlation (3D DIC) was employed to monitor full-field displacement and strain, enabling the evaluation of key fracture parameters including horizontal displacement, crack mouth opening displacement (CMOD), horizontal strain, fracture process zone (FPZ) length, macro-crack length, and total fracture zone length. A high-magnification industrial camera (100×) was simultaneously used for real-time observation of the notch tip. Based on the evolution of these parameters, the fracture process was divided into four distinct stages: linear elastic stage, micro-crack initiation and propagation stage, macro-crack initiation and propagation stage, and complete failure stage. The industrial camera observations confirmed macro-crack initiation at approximately 60% of the post-peak load, validating the proposed four-stage division. Quantitative results show that increasing the notch depth ratio from 0.0 to 0.5 reduces the peak load by approximately 30–40% and decreases the nominal stress proportionally. The FPZ was found to be fully developed at the 60% post-peak load threshold, after which it diminished as macro-crack propagation dominated. Aggregate bridging, crack deflection, and crack branching were consistently identified as the primary toughening mechanisms governing the ligament effect. The crack propagation mechanisms in the four stages are controlled by the combined effects of front free boundary effect, stress concentration effect, ligament effect, and back free boundary effect. These findings provide a refined understanding of concrete fracture that can inform the safety assessment and design of concrete bending members in infrastructure construction. Full article

The height of a water-conducting fractured zone (WCFZ) is directly related to the design of water-preserved coal mining and water-hazard risk assessment in ecologically fragile mining areas in western China. Existing empirical formulas have limited regional adaptability, and individual machine learning models may show insufficient stability under small-sample and nonlinear data conditions. To address this issue, a heterogeneous Stacking ensemble prediction framework was constructed based on measured data from the Yushen mining area. Mining thickness, working face length, mining method, burial depth, coal seam dip angle, and hard strata proportion coefficient were selected as input variables. The base layer consisted of support vector regression (SVR), classification and regression tree (CART), random forest (RF), extreme gradient boosting (XGBoost), and back-propagation neural network (BPNN), while Ridge regression was used as the meta-learner. Under the current data split, the test set R², RMSE, MAE, and MAPE of the Stacking model were 0.953, 10.99 m, 8.79 m, and 9.847%, respectively, indicating overall superiority over individual models and other ensemble configurations. The field validation results showed that the relative errors of the model for boreholes LD-1 and LD-2 in the fully mined area were 1.99% and 1.28%, respectively; however, an overestimation of 52.70% occurred for LD-3 in the coal-pillar-adjacent area. This indicates that the model is more suitable for the regional-scale screening of the maximum fractured-zone height and should not be directly used for fine-scale prediction in local boundary-affected zones. SHAP analysis showed that mining thickness, working face length, and hard strata proportion coefficient were the main influencing variables, and their response trends were generally consistent with key-strata control and the transition toward full-mining conditions. This study provides a reference for the rapid prediction of WCFZ height and preliminary evaluation of water-preserved coal mining in weakly cemented mining areas in western China. Full article

Pin arrangement significantly affects rubber-melt mixing and extrusion in pin-barrel cold-feed extruders. However, internal flow details are difficult to observe experimentally, and efficient prediction of flow-field parameters remains unavailable. This study used a finite-element model preliminarily validated against measured temperatures, together with particle tracing, to compare configurations with 0, 2, 4, and 6 pins per group. A dataset of 140 pin arrangements was generated by Latin hypercube sampling and numerical simulation. A mind evolutionary algorithm-optimized back-propagation neural network (MEA-BP) was then developed to predict melt volume-averaged temperature and average shear rate. Pins increased melt velocity and shear heating and improved cross-sectional temperature uniformity. Among the four uniform configurations, the 4-pin-per-group configuration showed the fastest reduction in segregation scale with a moderate residence time, achieving a favorable balance between mixing adequacy and processing efficiency. Particle tracing indicated repeated fluid splitting and recombination, whereas further increases in the number of pins yielded limited benefits. Under identical data partitions, network settings, and evaluation conditions, MEA-BP achieved R² values of 0.957 and 0.872 for temperature and shear-rate prediction, respectively, outperforming GA-BP, PSO-BP, and conventional BP. Full article

Shock wave propagation in a layered ceramic–polymer–metal (CPM) composite armor was investigated numerically using the Abaqus^© (2024) software in a plate-impact configuration, in which a copper impactor impacts a CPM plate that serves as an intermediate layer between the impactor and a protected target representing human bone. The resulting motion of the CPM back surface closes a pre-calibrated gap, initiating a secondary impact on the protected target. Because the transmitted loadings depend on the complex interaction of compressive and release waves within the layered system, the effect of gap distance on impact response is difficult to predict. Therefore, the primary objective of this study is to develop an improved understanding of the shock-mitigation mechanisms within the CPM system that enable the target to survive the impact event. The particle-velocity history at the midplane of the protected target was used to compare responses at different gap distances. The gap effect is influenced by geometry under uniaxial strain conditions, as well as by the materials’ wave speed and shock impedance. The observed trends arise from the combined effects of geometry under uniaxial strain conditions, material wave speed, and shock impedance mismatch, which govern the evolution and interaction of the compressive and release waves at different gap distances. The CPM configuration was examined over a 1–10 mm gap, and a detailed analysis was conducted for the representative gap distances of 1–3 mm. The results indicate that the midplane velocity of the protected target depends strongly on the gap distance, with a 1 mm gap producing the highest midplane velocity, followed by gaps of 3 mm and 2 mm. The CPM response depends on differences in the timing and strength of compressive and release waves reaching its free surface before gap closure, as shown by velocity histories and x–t diagrams. Full article

Plate–fin heat exchangers (PFHEs) are widely used in petrochemical, energy and electric power, aerospace, and other industries with large heat transfer requirements. The development of performance prediction and optimization methods for PFHEs has become increasingly important in the design and operation of such heat exchangers (HEs). This paper establishes a database of flow and heat transfer characteristics for four types of PFHEs with different structural parameters. Based on this database, the back-propagation artificial neural network (BP-ANN) model was optimized using the particle swarm optimization (PSO) algorithm to form the PSO-BP-ANN model for the performance prediction of these four types of PFHEs. This combination has been found to improve the prediction accuracy and generalization ability of the BP-ANN model. Additionally, the non-dominated sorting genetic algorithm II (NSGA-II) method was used to characterize the relationship between four structural parameters to be optimized (the length, height, spacing, and thickness of the HE fin) and the two objective functions (j and f) of the serrated PFHE in laminar flow. This enables the Pareto optimal solution to be obtained. The results show that, under laminar flow conditions (Re = 800), the serrated fin HE achieves the best heat transfer performance when the fin height, spacing, thickness, and length are 9.29, 1.22, 0.16, and 3.06, respectively. Full article

Narrowband Internet of Things (NB-IoT) is a 3GPP-standardised low-power wide-area network (LPWAN) technology designed for massive machine-type communications in challenging propagation environments. Despite its growing deployment, empirical channel data for Thailand’s diverse terrain—urban dense, urban outdoor, suburban, rural, and forest/mountain—remains limited in the open literature. This paper presents a composite channel characterisation study encompassing sixteen measurement sites across five environment classes in central and western Thailand. A composite channel model combining log-distance path loss, log-normal shadowing, and Nakagami-m fast fading is applied across all sites, yielding 8000 reference signal received power (RSRP) samples. Path loss exponents range from n = 2.2 (rural) to n = 4.0 (forest/mountain), back-calculated Nakagami-m parameters from m = 0.44 to m = 3.51, and shadowing standard deviations from σ_sh = 4.16 to 8.38 dB; ECL distributions are derived for all five environment classes. The back-calculated Nakagami-m parameters reveal a coherence gradient from sub-Rayleigh forest terrain (m < 1) through urban Rayleigh (m = 1.00) to near-Rician rural conditions (m > 2)—a fading hierarchy not previously reported for NB-IoT in Thailand. Results confirm that the composite channel model accurately characterises RSRP distributions and provides actionable network planning parameters for NB-IoT deployment in varied Thai terrain. Full article

Non-uniform sound velocity profiles (SVPs) cause sound-ray refraction and propagation-path bending. The straight-line mapping among propagation time, propagation distance, and target position is, therefore, disrupted, leading to systematic errors in constant-sound-speed localization. To improve the consistency between propagation correction and geometric localization, an iterative ray-path correction method based on propagation-time consistency is proposed. The method contains three coupled steps. First, a path-dependent local layered SVP model is constructed for each target-to-base-station path, rather than using a global or fixed sound-speed model. Second, the ray parameter is inverted under the constraint of measured time-of-arrival (TOA), so that the corrected ray path remains consistent with the observed propagation time. Third, the corrected slant range obtained by layered ray tracing is fed back into a known-depth weighted least squares (WLS) localization model, forming a closed-loop position update. The method is evaluated through long-baseline (LBL) simulations with multiple SVPs and propagation geometries and is validated using measured TOA data and an observation-derived SVP. The simulation results show that sub-meter accuracy can be achieved under the tested TOA-noise conditions. In measured-data validation, the planar localization error is reduced from 4.6866 m to 0.1923 m. No divergence is observed in the tested small SVP-perturbation cases. Full article

The visual quality of beverages is a major factor affecting consumers’ perception, quality evaluation, and market acceptance. Traditional colorimetric analysis is accurate but requires specialized equipment, time-consuming sample preparation, and substantial financial and time investment. The objective of this study was to develop a rapid, inexpensive, and accurate alternative method to predict the main color attributes of a date syrup–buttermilk beverage during processing and storage using an artificial neural network (ANN) approach. A multilayer perceptron ANN was developed using a back propagation algorithm. The ANN included three input variables (concentration of date syrup, storage cooling temperature, and storage time), one hidden layer with twenty neurons, and nine output color attributes (lightness, redness/greenness, yellowness/blueness, hue angle, Chroma, total color difference, browning index, whiteness index, and yellow index). To compare the effectiveness of the ANN model for the prediction of color attributes, the multiple linear regression (MLR) models were developed using the same inputs and the same training dataset. Experimental results indicated that all processing variables and their interactions had a significant effect on the color attributes of the beverage (p < 0.001). The trained ANN model exhibited excellent prediction capacity during the validation phase with high coefficients of determination (R² range was between 0.9974 and 0.9997) with lower root mean squared error than MLR. Moreover, sensitivity analysis indicated date syrup concentration as the most influential factor on the final color profile. The developed ANN model provides an effective approach for the offline prediction of color quality during processing and storage under laboratory conditions. Although the integration of the ANN model with inline sensors may offer opportunities for future intelligent quality-control applications, real-time implementation and industrial deployment were not evaluated in the present study. Full article

Accurate gear fault diagnosis under variable operating conditions remains challenging because vibration signals are affected by noise, speed-load variations, and condition-dependent feature shifts. To address these issues, this study proposes a gear fault diagnosis framework that integrates multidomain vibration feature fusion with a back-propagation neural network optimized by the sparrow search algorithm (SSA-BP). Vibration signals collected from a planetary gearbox fault-implantation platform were used to identify seven health states, including normal condition, sun gear pitting, sun gear fracture, sun gear wear, planetary gear pitting, planetary gear fracture, and planetary gear wear. For each signal segment, a 20-dimensional feature vector was constructed by combining nine time-domain features, three frequency-domain features, and eight wavelet packet energy features. SSA was employed to optimize the initial weights and biases of a double-hidden-layer BP neural network before supervised training. Experimental results show that the proposed feature fusion scheme achieved a classification accuracy of 98.30%, outperforming single-domain and pairwise feature combinations. In overall fault classification, SSA-BP obtained 98.26% accuracy, 98.26% macro-recall, 98.27% macro-precision, and 98.26% macro-F1. Moreover, SSA-BP reduced the convergence iterations from 826 to 312 compared with traditional BP and maintained 95.18% accuracy under high-speed and high-load conditions with scarce training samples. These results demonstrate that the proposed SSA-BP model provides improved convergence efficiency, diagnostic accuracy, and cross-condition robustness for intelligent gearbox condition monitoring. Full article

The performance of the aircraft anti-skid braking system is critical to the ground operational safety of an aircraft. Conventional Pressure Bias Modulation (PBM) can suffer from deep skidding under low runway friction coefficients or low aircraft speeds. To address these issues, a composite control strategy based on Gaussian Quantum Particle Swarm Optimization (GQPSO) is proposed. This strategy employs the GQPSO algorithm for offline Proportional–Integral–Derivative (PID) parameter optimization, followed by real-time adaptive scheduling through a lookup table to accommodate varying speed domains and runway conditions. Simultaneously, by integrating the main-wheel dynamics model and friction characteristics, a runway identification function based on a Back Propagation Neural Network (BPNN) is designed to provide runway status information. The stability of the controller is verified via phase-plane analysis and Monte Carlo simulation. Subsequently, comparative Hardware-in-the-Loop (HIL) tests are conducted among PBM, PSO-PID, and the proposed GQPSO-PID controller under various runway conditions. The experimental results demonstrate that this composite controller can adapt to different speed domains and runway conditions, stably track the target slip ratio, effectively suppress skidding, and significantly improve braking efficiency, as well as exhibiting excellent robustness and control performance. Full article

Rail-transit station areas concentrate commercial services, employment, and intensive land development, but their vitality is shaped by multiple built-environment conditions rather than rail accessibility alone. Focusing on 20 stations along the Osaka Metro Midosuji Line in Japan, this study uses Japanese chome units, which are small neighborhood-level address and statistical units, within an 800 m pedestrian catchment as analytical units and measures commercial-service agglomeration intensity, employment intensity, and commercial–employment composite vitality. The composite indicator measures the static co-concentration of commercial-service provision and employment carrying capacity, with pedestrian flow, consumption activity, and dwell time treated as separate dimensions of station-area vitality. Ten station-area environmental variables are examined using ordinary least squares (OLS), Lasso, Random Forest, Back-Propagation (BP) Neural Network, and extreme gradient boosting (XGBoost) models, with Shapley additive explanations (SHAP) applied to interpret variable contributions and nonlinear responses. Results show that nonlinear models generally outperform linear models. Development intensity, officially assessed land price, and network distance to the nearest metro station are the most influential variables, showing threshold, marginal, and non-monotonic effects. Split models indicate that commercial-service agglomeration is more sensitive to rail proximity and street-network conditions, whereas employment intensity is more associated with development intensity and land price. These findings support fine-grained station-area renewal and mixed-function planning. Full article