Search Results (3,463)

In bridge health monitoring, structural responses are strongly coupled with temperature effects and vehicle load effects, making it difficult for conventional fixed thresholds and single data-driven approaches to simultaneously achieve environmental adaptability and quantitative reliability assessment. To address this issue, this study proposes a deep-learning-based dynamic early-warning method for bridge structures, using health-monitoring data from an in-service long-span cable-stayed bridge as the research background. First, a two-month mid-span deflection time series is processed using variational mode decomposition optimized by the Porcupine Optimization Algorithm to separate temperature-induced effects. Subsequently, a hybrid prediction model integrating Informer and SEnet is constructed. Temperature and temperature-induced deflection components are used as input features, and a sliding-window strategy is adopted to achieve high-accuracy prediction of the temperature-induced deflection trend, which serves as the time-varying baseline of the dynamic threshold. On this basis, vehicle load effects are modeled by combining Pareto extreme value theory with finite element analysis and superimposed to establish a two-level dynamic early-warning threshold system that satisfies code requirements. Furthermore, a stochastic finite element Monte Carlo method is introduced to probabilistically model uncertainties associated with material parameters, load effects, and model prediction errors. The threshold failure probability at each time instant is taken as the evaluation metric, enabling quantitative characterization of threshold reliability. The results indicate that under combined multiple working conditions, the proposed method reduces the maximum failure probability of the first-level warning by 32.68% and that of the second-level warning by 93.48%, with more stable and consistent probabilistic responses. In engineering applications, simulation experiments based on stochastic traffic loading show that the warning accuracy is improved by up to 19.27%, while the error rate is reduced by up to 16.16%. The study demonstrates that the proposed method possesses a clear physical and statistical foundation as well as good engineering feasibility and provides a viable pathway for transforming bridge early-warning systems from experience-based schemes toward data-driven and risk-oriented frameworks. Full article

Adulteration of milk powder (MP) is performed, especially in underdeveloped countries, by adding corn starch (CS) or wheat flour (WF) without mentioning it. Multiple techniques have been established to reduce these deceptive methods. Most of these techniques require samples to be sent to the laboratory for results through a time-consuming, expert-requiring, and destructive procedure. Raman spectroscopy (RS) has seen application due to the availability of portable modalities and its non-destructive, water-insensitive nature. Using principal component analysis (PCA), the differences and similarities between MP and the adulterants (CS and WF) have been evaluated. To quantify the percentages of CS and WF binary mixtures independently with MP, partial least squares regression (PLSR) has been employed. A total of 70 MP samples independently adulterated with CS and WF were prepared. Thirteen chemometric modes were developed by combining the first and second derivatives with Standard Normal Variate (SNV) and Multiplicative Scatter Correction (MSC) to quantify adulteration. The results obtained for CS and WF mixtures show errors of 0.76 and 0.77 %w/w, respectively, with the optimized math pretreatment. These results demonstrate that the portable RS modality can be used as an effective technique for detecting adulterants in milk powder. Full article

To reduce the impact of periodic pulsating torque and non-periodic disturbances on the speed control performance of permanent magnet synchronous motors (PMSMs), a sliding mode variable structure control method incorporating iterative learning compensation and load torque observation compensation is proposed. First, iterative learning control (ILC) is designed to address periodic disturbances and suppress periodic torque ripple. A load torque observation compensator is developed to counteract non-periodic disturbances, thereby enhancing the system’s robustness against uncertain disturbances. Second, numerical simulations compare the proposed method with sliding mode control (SMC), sliding mode control with load torque observation compensation (SMC + LO), and linear active disturbance rejection control (LADRC). The simulation results demonstrate that the proposed control strategy achieves reduced torque ripple, improved system tracking, and strong robustness. Finally, physical experiments are conducted, and the results closely align with the simulations. Both simulation and experimental outcomes confirm the effectiveness of the proposed control strategy in enhancing the speed performance of permanent magnet synchronous motors. Full article

Cyclonic extreme events have recently undergone an important boost over the Mediterranean Sea, a trend closely linked to ongoing strong climate variations. Several studies are explaining the combination of many different effects that increase the frequency of mesoscale vortices’ intensification, namely Mediterranean tropical-like cyclones (TLCs), until the stage of Medicanes. Among these effects, processes like sea–atmosphere energy exchanges, baroclinic instability, and the release of latent heat lead to the intensification of these systems into fully tropical-like structures. This study investigates the formation and development of Ianos, the most intense Mediterranean tropical-like cyclone recorded in recent years, which affected the Ionian Sea and surrounding regions in September 2020. Using satellite observations and remote sensing data, the study applies a dual approach to characterise the system evolution across the spatial and temporal scales. Firstly, proper orthogonal decomposition (POD) is exploited to assess temperature and pressure fluctuations derived from the geostationary database of Meteosat Second Generation (MSG-11)/SEVIRI. POD allows for the identification of dominant modes of variability and the quantification of energy distribution across different spatial structures during the cyclone’s lifecycle. The decomposition reveals that a small number of orthogonal modes capture a significant proportion of the total variance, highlighting the emergence and persistence of coherent structures associated with the cyclone’s core and peripheral convection. To support scale-dependent energy organisation and dissipation within Ianos, total-period and three-period analyses were carried out, in addition to early-stage intensification patterns and implications for meteorological scale assessments. From the study on the temperatures’ spatio-temporal evolution, a comparison in the POD spectra and of the structures during the peak of intensity was carried out between the Ianos TLC and the Faraji and Freddy tropical cyclones. Additional multi-sensor data from Suomi NPP and Sentinel-3 satellites were integrated to analyse the evolution of the same parameters, also taking into account an evaluation of the vertical temperature gradient, over a 4-day period encompassing the full life cycle of Ianos. The study of the daily evolution helps investigate the spatial trends around the warm core regions, identifying the pressure minima for a comparison with the BOLAM and ERA5 databases of the mean sea level pressure. Overall, this study demonstrates the value of combining dynamic decomposition methods with high-resolution satellite datasets to gain insight into the multiscale structure and convective energetics of Mediterranean tropical-like cyclones. Some significant patterns come out from the spatial organisation of deep convection that seem to be linked to the permanent structures of atmospheric fluctuations near the warm core centre. Full article

To address the problems existing in nuclear reactor accident emergency training, a design scheme and system prototype of radiation detection training equipment for penetrating radiation fields in enclosed spaces, based on inertial sensors and wireless Bluetooth communication is proposed. First, the penetrating radiation field is modeled. On this basis, a calculation model of the neutron/γ dose equivalent rate is established. This model is based on the motion path of simulated radiation detection equipment. Second, the MPU6050 inertial sensor is designed and developed. It monitors the three-axis acceleration and three-axis angular acceleration values in real time. This enables the indoor positioning function of the simulated detection training equipment. The Digital Motion Processor (DMP) filtering algorithm is used to process the measured data. This improves the detection accuracy. Finally, a Bluetooth communication module is designed and developed. It transmits the detected position data to the main control computer in real time. The main control computer performs calculation and analysis to obtain the radiation intensity value. This value is sent to the Arduino controller. The Arduino controller controls the display of the value on the liquid crystal screen. Experimental verification is carried out. Experimental verification indicates that the maximum error of the system’s three-dimensional spatial positioning is 0.08 m, the mean relative error of the radiation dose rate simulation is 4.81%, and the maximum relative error is 7.8%. The system relatively accurately achieves radiation dose simulation and radiation source localization according to different working modes, providing a high cost-effectiveness training method for radiation detection training with high safety and good economy. Full article

Non-crimp fabric (NCF) composites are increasingly adopted for structural components due to their high mechanical performance and processability. Like other fibre-reinforced plastics, NCFs remain vulnerable to in-service damage from tool drops or unintended collisions, which can substantially reduce load-bearing capacity. This study aimed to develop a validated numerical model capable of simulating damage initiation and post-impact behaviour through an integrated experimental–numerical approach. The mechanical properties of a representative unidirectional NCF composite were first experimentally established. Then, tubular NCF subcomponents were fabricated and tested under a two-phase loading protocol. In the first phase, damage was introduced using quasi-static indentation or controlled low-velocity impact. In the second phase, the residual load-bearing capacity of the damaged subcomponents was assessed under four-point bending. To support the research objective, a finite element model was developed in LS-DYNA to simulate both phases, using the MAT_ENHANCED_COMPOSITE_DAMAGE (MAT54) material formulation. Non-measurable input parameters, including stress limit factors and erosion strain thresholds, were calibrated via parameter estimation, sensitivity analysis, and iterative refinement. The final model showed close agreement with experiments in predicted damage location, deformation mode, and residual strength. X-ray computed tomography was used to validate delamination predictions. The findings support the development of reliable and cost-effective numerical tools for damage assessment in advanced composite structures. Full article

Accurate assessment of source-load complementarity and system regulation capacity is critical for secure dispatch and planning in high-penetration renewable power systems. Addressing limitations of existing methods—which rely heavily on static metrics, struggle to capture temporal and tail dependence characteristics, and provide insufficient support for dispatch decisions—this paper proposes a multi-level integrated evaluation framework. First, from a source—load matching perspective, we develop a novel complementarity metric, integrating real-time rate of change, temporal consistency, and tail dependency. An improved adaptive noise-complete set empirical mode decomposition combined with a hybrid Copula model is employed to isolate noise and to precisely quantify dynamic dependency structures. Second, we introduce the Minkowski measure and construct a net load fluctuation domain accounting for extreme fluctuations and coupling relationships. Subsequently, combining the Analytic Hierarchy Process (AHP) with probabilistic convolution enables multi-level comparative quantification of resource capacity and fluctuation domain requirements under varying confidence levels. Simulation results demonstrate that the proposed framework not only provides a more robust assessment of source-load complementarity but also quantitatively outputs the adequacy and risk level of system regulation capacity. This delivers hierarchical, actionable decision support for dispatch planning, significantly enhancing the engineering applicability of evaluation outcomes. Full article

In response to the need for cost-effective and resilient drivetrain architectures in renewable energy emulation platforms, this paper proposes a wind turbine emulator (WTE) designed to enhance the operational efficiency of variable-speed wind turbines (WTs) connected to electric generators in power grid applications. The proposed emulator relies on a robust sensorless vector-controlled induction motor (IM) drive fed by a reduced-switch soft–voltage source inverter (Soft-VSI) topology. The proposed control chain combines a second-order super-twisting sliding-mode flux observer, based on stator measurements, with a modified MRAS speed estimator whose Popov hyperstability offers explicit PI tuning and ensures stable sensorless speed convergence. The complete WTE design, from the aerodynamic model to the Soft-VSI induction motor drive, is implemented and evaluated in MATLAB/Simulink environment. A Mexican hat wind speed profile is used to excite the emulator and assess its dynamic behavior under diverse transient conditions. The simulation results demonstrate fast convergence of the estimated flux and speed, stable closed-loop operation when using the estimated speed, and strong robustness against no-loaded and loaded operations and rotor-resistance variations. Moreover, a comparative analysis between the proposed control scheme and a conventional first-order sliding-mode flux observer is carried out to highlight the enhanced flux and speed estimation accuracy, reduced chattering, and improved dynamic robustness of the WTE. The proposed framework provides a flexible tool to support the energy transition through the development of advanced wind energy system control strategies. Full article

Frequent rockbursts in staggered roadways beneath residual coal pillars pose a critical challenge for the slice mining of ultra-thick coal seams. Taking the LW250101-2 of Huating Coal Mine as a case study, this paper systematically reveals the stress evolution laws and rockburst mechanism induced by irregular residual pillars by integrating microseismic (MS) monitoring, moment tensor inversion, and numerical simulation. First, source mechanism inversion analysis elucidated that compressive-shear failure of coal pillars was the dominant rupture mode in five of the eight recorded rockburst events. Second, numerical simulations demonstrate that the width of the left wing and the thickness of the right wing of the “L-shaped” coal pillar structure are the key geometric factors controlling rockburst risk; larger dimensions correlate with more intense stress concentration and higher-energy MS events. Moreover, the stress deflection effect of “L-shaped” coal pillars causes the haulage gateway of the LW250101-2 to remain in a state of stress accumulation, increasing its susceptibility to rockburst. Finally, a synergistic prevention system consisting of deep-hole roof blasting, large-charge coal blasting, and ultra-deep large-diameter boreholes was implemented. Field monitoring confirms that these measures dissipated high-stress concentrations, reduced rockburst frequency to zero and ensured safe mining. Full article

With the integration of high-proportion renewable energy, the operation modes of the power system are becoming increasingly complex and diverse. The typical operation modes selected with manual experience cannot comprehensively represent system operating characteristics. To more accurately analyze system operating characteristics, an analysis method for power system operation modes based on autoencoder clustering is proposed. Compared to other clustering methods, the autoencoder clustering method can adapt to data of different types and structures, extract features and perform clustering in a reduced-dimensional space, and suppress noise in the data to a certain extent. First, multi-dimensional analysis metrics for power system operation modes are proposed. The metrics are used to evaluate system characteristics such as cleanliness, security, flexibility, and adequacy. The evaluation metrics for clustering are designed based on the metrics. Second, an operation mode analysis framework is constructed. The framework uses an autoencoder to extract implicit coupling relationships between system operation variables. The encoded feature vectors are used for clustering, which helps to find the internal similarities of the operation modes. Regulation resources such as pumped hydro storage are also considered in the framework. Finally, the proposed method is tested on the IEEE 39-node system. In the test, the comparison of clustering evaluation metrics and operation mode analysis errors shows that the proposed method has the best clustering performance and operation mode analysis effect compared to other clustering methods. The results prove that the proposed method can effectively extract the inner correlations and coupling relations of high-dimensional operating vectors, form consistent operation mode clusters, select typical operation modes, and accurately assess the characteristics and risks of the power system with high-proportion renewable energy integration. This paper helps to build a stronger power system that can integrate a higher proportion of renewable energy, replace fossil fuel generation, and contribute to a higher level of sustainable development. Full article

The accuracy of online parameter identification for permanent magnet synchronous motors (PMSMs) is constrained by discrete model errors, rank deficiency in the steady-state identification matrix, and voltage deviations resulting from inverter nonlinearities. This paper proposes a multi-parameter identification method acting as a high-precision virtual sensor, based on Zero-Order Hold (ZOH) discretization and an inverter nonlinear voltage compensation scheme utilizing a dual-sampling strategy. First, a discrete model of the PMSM, accounting for rotor position variations within the control period, is established using the ZOH discretization method. Compared with the forward Euler discretization method, this approach effectively minimizes discretization model errors, especially under high-speed operating conditions where rotor position variations are significant. Second, the rank deficiency problem of the steady-state identification matrix is overcome by combining d-axis small-signal injection with a dual-sampling strategy. Furthermore, the Forgetting Factor Recursive Least Squares (FFRLS) algorithm is introduced to achieve online multi-parameter identification. Finally, the influence mechanisms of the dead-time effect, power switch voltage drop, and turn-on delay on the output voltage are analyzed. Consequently, an inverter nonlinear voltage compensation strategy tailored for the dual-sampling mode is proposed. Experimental results demonstrate that the proposed method significantly enhances parameter identification accuracy across the entire speed range. Specifically, under high-speed conditions, the identification errors for resistance, inductance, and flux linkage are maintained within 5.47%, 4.05%, and 2.46%, respectively. Full article

This paper addresses the attitude tracking control problem for laterally symmetric vehicles during the boost phase under aerodynamic parameter variations and high-altitude wind disturbances. A neural disturbance observer-based nonsingular predefined-time sliding mode control scheme is proposed. First, a Lyapunov-based predefined-time stability criterion is established, which facilitates the design of an adaptive predefined-time observer using radial basis function neural networks. Without requiring prior knowledge of disturbance bounds, this observer ensures that disturbance estimation errors converge to a neighborhood of the origin within a predefined time parameter. Second, a novel nonsingular predefined-time sliding surface is constructed using hyperbolic tangent functions, leading to an integrated predefined-time sliding mode controller. The proposed scheme guarantees that the upper bound of the convergence time for initial attitude tracking errors is independent of the initial boost-phase states and can be arbitrarily predefined. Unlike conventional predefined-time control methods, the proposed approach eliminates controller singularity issues while avoiding the introduction of piecewise continuous functions or double-integral terms in either the sliding surface or the control law, thereby reducing structural complexity. Theoretical analysis confirms the boundedness of all closed-loop signals during attitude tracking. Numerical simulations demonstrate the effectiveness of the proposed control strategy under complex flight conditions. Full article

The LLC resonant converter constitutes a high-order switching system characterized by multiple operational modes and region-dependent switching sequences. This complexity poses significant challenges to system modeling and dynamic analysis. Furthermore, its inherent high-order nonlinearity tends to induce detrimental nonlinear phenomena, including bifurcation and chaos, which are particularly undesirable in power electronic systems that demand the utmost priority for stability and reliability. To address these concerns, this work focuses on investigating the dynamic behaviors and stability of LLC resonant converter control systems. This study aims to elucidate the origins and evolution of these nonlinear characteristics, thereby facilitating the design of higher-performance power electronic systems. First, a continuous-time model of the closed-loop controlled LLC resonant converter system was established using the sigmoid function modeling method. This model allows direct application of continuous system theory to analyze dynamic behavior, significantly reducing analytical complexity. Second, the system’s bifurcation characteristics and stability were comprehensively investigated through Floquet theory, bifurcation diagrams, and Lyapunov exponent spectra. Results reveal that PFM-controlled LLC resonant converters exhibit rich nonlinear dynamics under variations in key parameters. Experiments successfully captured the observed nonlinear phenomena, validating the evolution of system dynamics and stability. This work provides a novel perspective for stability analysis and parameter design in multi-resonant converter systems. Full article

To investigate the hypersonic boundary layer transition over complex three-dimensional configurations, this study conducted an experiment using infrared thermography, Rayleigh scattering visualization, and high-frequency pressure sensors in a Mach 6 Ludwieg wind tunnel. The infrared results indicate that increasing the Reynolds number promotes boundary layer transition on the model surface. Spectral analysis reveals a high-frequency peak centered at 250 kHz on the finless side of the windward surface. Comprehensive analysis indicates this represents high-frequency secondary instability triggered by the traveling crossflow mode in its nonlinear phase. On the finless side of the leeward surface, a typical Mack second-mode high-frequency instability amplification process is observed within the 140–280 kHz frequency band. Additionally, the spectrum results for the fin–cone junction became more complex. On the windward side, the primary energy concentration in the junction zone is observed between 80 and 200 kHz, with calculated wave packet velocities higher than those on the finless side. Wavelet analysis reveals that low-frequency modes are amplified first and gradually excite high-frequency components, with significant modal coupling appearing in the high-frequency region of the bicoherence. The leeward fin–cone junction exhibits dual-band characteristics at 60–120 kHz and 180–260 kHz, demonstrating stronger intermodal interactions. Both the windward and leeward surfaces of the fin show low-frequency transverse flow-like modes around 70–180 kHz. The spectral results for the windward and leeward sides are largely consistent, with only slight differences in amplitude levels and saturation positions. Full article

Placental abruption (PA) without vaginal bleeding is known to be associated with severe outcomes when compared to symptomatic cases; the presence of hypertensive disorders of pregnancy (HDP) is an additional negative prognostic factor. According to guidelines, severe HDP are indications for prompt delivery after maternal–fetal stabilization. Considering gestational age, parity and clinical obstetric examination, the induction of labor should be prioritized to avoid additional risks associated with cesarean section. However, since only a minority of cases of PA may be detected by ultrasonography (US), findings consistent with this suspicion should contribute to the establishment of an appropriate mode of delivery. We present two cases affected by severe HDP, eclampsia and HELLP syndrome, admitted to St. Luke Catholic Hospital, Wolisso, Ethiopia. In both cases, obstetric point-of-care (POC) US revealed a live premature fetus and a solid heterogeneous placental mass, raising the suspicion of concealed placental abruption. To expedite delivery, cesarean section was promptly offered. PA was confirmed in both cases; the first had stillbirth and postpartum hemorrhage, while the second ended up with healthy mother and newborn. In conclusion, POC-US imaging could play a role in optimizing delivery mode and timing for patients with HDP in low-resourced settings. Additional research is warranted to determine the impact of this technique in the management of obstetric emergencies. Full article