Search Results (74)

High penetration of distributed renewable generation in active distribution networks frequently leads to severe voltage fluctuations and challenges system voltage stability, as the inherent power-flow symmetry is disrupted by stochastic and bidirectional power injections. To address these issues, this paper proposes a symmetry-preserving coordinated control strategy that integrates topology-constrained clustering with state-of-charge (SoC) balancing of battery energy storage systems to actively mitigate voltage fluctuations and enhance operational stability. First, an agglomerative hierarchical clustering algorithm that explicitly enforces physical line connectivity is employed to partition the distribution network into multiple structurally symmetric autonomous control zones. Within each zone, the SoC of distributed storage units is balanced and aggregated into a virtual battery model, thus maintaining energy-level symmetry and reducing the risk of uneven charging/discharging that would otherwise exacerbate voltage deviations. A model predictive control-based rolling optimization framework is then developed to coordinate photovoltaic inverters and energy storage systems across the zones, explicitly targeting the suppression of voltage fluctuations and the maintenance of short-term voltage stability under varying operating conditions. The proposed method is validated on a modified IEEE 34-bus test feeder with high renewable penetration. Simulation results demonstrate that the strategy effectively limits voltage fluctuation magnitudes, keeps nodal voltages within the required bounds, improves the SoC balance among the storage units, and maintains the nodal voltages within the required bounds across all evaluated coordination strategies. Full article

Metal-halide perovskite light-emitting diodes (PeLEDs) have rapidly achieved external quantum efficiencies comparable to established organic and quantum-dot LEDs. Their narrow emission spectra, tunable bandgaps, high photoluminescence efficiencies, and low-temperature processing make them promising for displays, lighting, optical communication, and flexible electronics, although their commercialization remains limited by short operational lifetime, efficiency roll-off, unstable blue emission, ion migration, interfacial degradation, and poor large-area uniformity. This review provides a device-engineering-centered analysis connecting perovskite materials and processing conditions with charge injection, radiative recombination, optical extraction, and stability. It introduces the essential characteristics of 3D, 2D/quasi-2D, and nanocrystal perovskites and evaluates major engineering approaches, including composition and dimensionality control, crystallization regulation, defect passivation, transport-layer and interface modification, charge balancing, and optical outcoupling. An emphasis of this review is its comparison of the problems addressed by these approaches and their trade-offs among efficiency, spectral stability, lifetime, and manufacturing compatibility. Degradation under electrical operation is examined with attention to Joule heating, ion migration, charge accumulation, and interfacial reactions. Emerging patterned, reconfigurable, flexible, transparent, and communication devices are also discussed. Finally, the review identifies operational stability, efficient blue emission, scalable fabrication, high-resolution patterning, lead toxicity, and competition with OLEDs as the principal challenges for PeLED commercialization. Full article

Accurate modeling of the tooth friction coefficient is central to analyses of efficiency, vibration, and durability in enclosed gear drives. Physics-based models describe these contacts using a large set of coupled thermal, contact, and lubrication laws whose individual parameters have uncertainties that are difficult to propagate. This study proposes a compact alternative: the HAF model, a three-parameter phenomenological description of the friction coefficient as a function of the slide-to-roll ratio (SRR). The three parameters have distinct tribological interpretations: h (hysteresis) governs the steepness of the sigmoidal transition, a (attrition) governs the slope of the plateau, and ν (friction level) governs the overall magnitude. The model is calibrated using nonlinear regression with fourteen two-disc traction curves acquired with a one-factor-at-a-time (star) design around a reference operating point (1.6 GPa, 80 °C, and 20 m/s): the contact pressure (1.2, 1.6, and 1.9 GPa), the injection temperature (40, 80, and 100 °C), and the mean speed (10, 20, and 30 m/s) are each varied in turn, for both smooth and rough discs. Parameter uncertainty is quantified using the residual-resampling bootstrap (BIG) established in a companion paper and applied over 10⁵ iterations; it yields near-Gaussian, weakly correlated parameter distributions. The three HAF parameters are then expressed as linear functions of load, temperature, speed, and roughness; least-squares inference across the fourteen conditions shows that eleven of the fifteen regression coefficients differ significantly from zero at the 5% level, with roughness having the strongest effect on the friction level (t = 7.98). Substituting these laws into the HAF equation and reoptimizing the resulting expression globally yields a single closed-form model that reproduces the measured friction coefficient with a residual spread of σ ≈ 0.001 in friction-coefficient units (R² ≈ 0.996) and approximately Gaussian, zero-mean, and homoscedastic residuals with no evident systematic structure. Being differentiable and equipped with bootstrap confidence intervals, the model predicts friction throughout the tested operating envelope—across which the maximum friction coefficient varies by a factor of eight, from 0.0049 to 0.0388—and supports gradient-based optimization of low-friction operating conditions. The contribution of this study is a compact, interpretable, and statistically characterized predictive law for tooth friction, expressed in closed form as a function of the operating conditions and of the surface state. Full article

In confined waters such as cylindrical aquaculture cages and ponds, underwater robots for cleaning, harvesting and other tasks that use single-beam mechanical scanning sonar for positioning and navigation are susceptible to multipath interference in complex water environments. Meanwhile, under extreme sea conditions, the severe attitude swaying of the robot and the slow-scanning characteristic of the sonar superimpose on each other, causing range stretching and helical deformation of the acoustic point cloud. To address these problems, this paper analyzes the deformation mechanism of single-beam sonar and proposes a spatiotemporal joint deformation compensation and localization-mapping method. First, an attitude-derived probabilistic confidence model is introduced as a lightweight robustness safeguard to characterize the geometric reliability of sonar echoes and reduce the contribution of low-confidence measurements during subsequent registration. Second, a beam-level spatiotemporal joint de-deformation algorithm is designed: the slant range in polar coordinates is flattened to eliminate nonlinear swaying deformation, and a beam-level displacement back-estimation based on the beam time offset and feedback velocity is employed to remove helical misalignment, thereby enhancing the underlying correction capability for dynamic deformation processes. Finally, a lightweight SLAM architecture that integrates keyframe-based dynamic sub-maps is constructed, where a confidence-weighted ICP is used to estimate the planar position with the heading provided by the compass and provide velocity-based closed-loop feedback, effectively mitigating the problem of global matching divergence caused by underlying dynamic deformations. Real-data-driven semi-physical disturbance tests based on measured pool data show that, under the injected ±45° roll disturbance and translational drift, the proposed method reduces the maximum point-to-reference error MaxAE from 1.059 m to 0.098 m. The reported mean internal registration residual decreases from 0.275 m for the traditional navigation odometry SLAM to 0.158 m for the proposed method, corresponding to a numerical reduction of approximately 42.5%. Under the evaluated conditions, the proposed method effectively mitigates point-cloud deformation and registration instability caused by robot swaying and slow-scanning sonar, while confidence weighting is retained as an auxiliary robustness mechanism for handling low-confidence correspondences. Full article

Renewable-dominated power systems need operational digital twins that do more than mirror assets: they must convert streaming evidence into state awareness and secure control actions. This paper presents DT-Grid, a digital twin (DT) framework for real-time state awareness and operational optimization. DT-Grid treats the twin as an evidential control layer with four coupled functions: a topology and data twin, robust temporal state assimilation, confidence-envelope construction, and rolling optimal power flow (OPF). The state-awareness module solves a Huber-weighted, temporally regularized estimation problem and exposes residual information to the optimization layer. The dispatch layer then uses this evidence as a security margin while scheduling conventional generation, renewable acceptance, and corrective actions. We evaluated the framework on the PGLib IEEE 118-bus benchmark driven by Open Power System Data Germany load, wind, and solar profiles over 365 operating points and three measurement seeds. DT-Grid reduced injection root mean square error (RMSE) from 464.6 MW under static weighted least squares (WLS) to 217.0 MW, improved bad-data F1 from 0.140 to 0.149, and reduced the realized overload proxy by 97.9% compared with persistence-driven OPF. The results indicate that state evidence is most valuable when it is carried into dispatch constraints, while pure temporal smoothing can still produce lower phase-angle error in some operating points. Full article

A strategy combining imprinting with anode oxidation is proposed for preparing an aluminum template with a negative compound-eye array. Injection compression molding with the aluminum template mounted on the mold cavity surface is applied to fabricate polystyrene replicas with a biomimetic compound-eye array on their surfaces. It is demonstrated that orderly microlenses and dense nanopillars with average diameters of approximately 225 μm and 63 nm, respectively, are formed on the polystyrene replicas. The polystyrene replica surfaces with the compound-eye array exhibit both hydrophobicity, with a water contact angle of 151 ± 2° and a rolling angle of 4 ± 1°, and excellent antireflectivity, showing an average reflectance of approximately 4% across the 400–1000 nm wavelength range. The microlens and nanopillar structures on the PS replicas are therefore key to achieving both hydrophobicity and antireflectivity simultaneously. The proposed fast mass-replication approach, which combines imprinting, anode oxidation, and injection compression molding, offers an efficient route for producing bioinspired compound-eye arrays. This strategy shows potential for applications in optoelectronics, photovoltaics, and self-cleaning optical surfaces. Full article

Smart sensor networks for environmental monitoring require accurate and continuous estimation of key variables such as temperature, humidity, and wind speed; however, physical sensor deployments are frequently limited by high costs, hardware failures, and data quality degradation, while existing virtual sensor approaches rely on single-model architectures that lack explicit uncertainty modeling and fail to capture the complex non-linear dynamics of real-world IoT time-series data. This paper proposes VAS-DPFF, a virtual augmented sensor framework based on deterministic and probabilistic feature fusion, which contributes a principled integration of well-established deterministic and probabilistic techniques within a unified AIoT-compatible virtual sensing architecture. The framework integrates: (i) a deterministic pipeline comprising temporal encoding, rolling statistics, and mutual information-based feature selection; (ii) a probabilistic pipeline employing Bayesian Ridge Regression (BRR) and Gaussian Process Regression (GPR) to generate uncertainty-aware synthetic features; and (iii) an early feature-level fusion strategy feeding an XGBoost regression model augmented with Gaussian noise injection. Experiments on 84,582 time-series records from a nine-station IoT environmental monitoring network in Gwacheon City, South Korea, demonstrate strong multi-target prediction performance: temperature RMSE = 0.811 °C, R² = 0.973; humidity RMSE $=4.113$%, R² = 0.964; and wind speed RMSE $=0.602$ m/s, R² = 0.798, representing RMSE reductions of 61.2%, 60.7%, and 62.3% over the existing method, respectively. Comprehensive ablation studies, sensitivity analysis, and augmentation validation confirm that the proposed integration of deterministic and probabilistic features yields consistent and practically valuable improvements in virtual sensing performance suitable for AIoT-enabled smart sensor network deployments across multiple environmental monitoring targets. Full article

In the context of the “dual-carbon” target, the large-scale integration of renewable energy sources leads to an increased risk of transient voltage instability at the high voltage direct current (HVDC) transmission receiving end. The HVDC transmission system possesses fast and accurate power regulation capability. After a fault occurs near the inverter station, reducing the DC current enables the reactive power from the compensation devices to be released and injected into the receiving-end power grid, thereby providing emergency voltage support for the receiving-end grid. To reduce control costs, an optimization model constrained by transient voltage violation is established, and the DC current modulation is acquired via an online solution. To maintain system stability and meet the requirements of online applications, it is crucial to rapidly solve the optimization model based on the grid operating mode and contingency information to update the emergency control strategy table in the special protection system (SPS). Conventional global orthogonal collocation (GOC) and adaptive orthogonal collocation (AOC)-based solution methods transform the optimization model in the continuous time domain into a nonlinear programming (NLP) problem for solution, which addresses the low efficiency of traditional rolling optimization. However, the GOC- and AOC-based solution methods improve the discretization accuracy of the model by pursuing global uniform densification of collocation points, making it difficult to balance solution accuracy and solution efficiency. To this end, this paper proposes an efficient interval partition dynamic adaptive orthogonal collocation (IP-DAOC)-based solution method. Firstly, the overall optimization time window is interval-partitioned into multiple initial intervals, and an interval-partitioned transient voltage stability emergency control optimization model is established. Furthermore, the interval length and the number of collocation points are dynamically adjusted according to the curvature of interpolation polynomials at collocation points in different intervals. Finally, after interval adjustment, the dynamic equations discretized in adjacent intervals are made continuous by reconstructing the differential matrix. This solution method reduces the total number of collocation points, thereby decreasing the scale of the NLP problem and narrowing the search space, significantly improving solution efficiency while ensuring solution accuracy. To verify the effectiveness of the proposed solution method, simulations are carried out on a modified IEEE 14-bus system. The results are compared with those of the traditional GOC- and AOC-based solution methods, which further demonstrate the superiority of the proposed solution method. Full article

This paper proposes a reinforcement learning-assisted active disturbance rejection control (ADRC) framework for a nonlinear quadrotor unmanned aerial vehicle (UAV). Conventional ADRC controllers are designed for the quadrotor altitude and attitude channels. To evaluate robustness under disturbance-intensive conditions, a composite external disturbance is injected into the roll-channel dynamics. A Deep Deterministic Policy Gradient (DDPG)-based adaptive tuning mechanism is integrated into the roll-channel ADRC for the nonlinear state error feedback (NLSEF) gain adaptation, while fixed-parameter ADRC is retained for the remaining three channels. Without requiring system linearization and prior knowledge of disturbance models, the reinforcement learning agent learns an optimal gain adaptation policy directly through interaction with the nonlinear roll subsystem. Quantitative simulations demonstrate superior roll-axis disturbance rejection, leading to 90% faster settling time, the root mean square (RMS) control effort being reduced by 5.1%, and a 7.6% peak input suppression compared to conventional ADRC. The learning-based adaptation maintains comparable tracking accuracy across all channels while significantly improving transient recovery and control smoothness in the most disturbance-sensitive axis, validating selective reinforcement learning integration for robust nonlinear quadrotor flight control. Full article

High-performance full-color displays and white lighting require stable and efficient red, green, and blue emitters; however, they are often limited by wide bandgaps, imbalanced carrier injection/transport, complex device structures, and high material costs. To address these challenges, we designed and synthesized a multifunctional deep-blue molecule (PPI-F-PO) integrating a phenanthroimidazole moiety, a 9,9-diphenylfluorene unit, and a phosphine oxide group. The twisted structure of fluorene, featuring a sp³-hybridized carbon, effectively suppresses conjugation extension and aggregation-caused quenching, whereas the electron-withdrawing phosphine oxide group enhances electron transport. Consequently, it exhibits good thermal stability, high solid-state photoluminescence quantum yield (58.8%), and high triplet energy (E_T = 2.54 eV). Non-doped blue OLEDs based on this emitter achieve a maximum external quantum efficiency (EQE) of 2.52% with deep-blue CIE coordinates of (0.16, 0.06). Moreover, using this material as a host, green and orange-red phosphorescent OLEDs exhibit maximum EQEs of 15.4% and 9.7%, respectively, along with low efficiency roll-off. This work demonstrates that a bipolar deep-blue emitter with high triplet energy can act both as a high-efficiency standalone emitter and as a universal host for lower-energy phosphors, thereby simplifying device architecture and reducing material costs for full-color OLEDs. Full article

Rolling bearings are crucial components in CNC machine tool spindles, and their health condition directly affects machining precision and operational reliability. To address the significant challenges of bearing fault diagnosis in industrial environments, this paper proposes an adaptive shapelet-based deep learning model for bearing fault diagnosis. The proposed model integrates three key components: (1) an adaptive multi-scale shapelet extraction module for discriminative pattern learning, (2) a gated parallel CNN with depthwise separable convolutions for multi-scale spatial feature extraction, (3) an enhanced bidirectional long short-term memory network with residual connections for temporal dependency modeling. A composite loss function combining cross-entropy, supervised contrastive learning, and multi-scale consistency regularization is employed for training. To simulate real-world industrial noise conditions, Gaussian, uniform, and impulse noise were injected into the signals. Experiments conducted on the CWRU and IMS datasets demonstrate that, compared with state-of-the-art methods, the proposed approach achieves stronger noise robustness, higher fault classification accuracy, and more stable performance under severe noise contamination. Full article

Perovskite quantum dots (PeQDs) are highly promising luminescent materials for next-generation displays owing to their excellent optoelectronic properties, such as narrow emission linewidth, high photoluminescence quantum yield, tunable bandgap, and solution processability. Blue-emitting PeQDs are particularly crucial for realizing full-color displays with high color purity. This review systematically summarizes synthesis strategies for blue-emitting PeQDs and their recent advances in perovskite light-emitting diodes (PeLEDs). We first introduce the working principles of PeLEDs and detail three primary approaches to achieving blue emission through mixed-halide engineering, quasi-two-dimensional structure construction via A-site cation substitution, and quantum size effect utilization. We then review mainstream synthesis methods, including hot-injection, ligand-assisted reprecipitation, and post-synthetic anion exchange, discussing their respective advantages and limitations. Key device optimization strategies are also outlined, covering surface passivation, core–shell structures, interface engineering, and light outcoupling enhancement. Finally, we address current challenges in material stability, efficiency roll-off, and charge imbalance and provide an overview of future research directions for high-performance blue PeLEDs based on PeQDs. Full article

High-speed jet-in-crossflow (JICF) configurations are central to several aerospace applications, including turbine-blade film cooling, thrust vectoring, and fuel or hydrogen injection in combusting or reacting flows. This study employs high-fidelity direct numerical simulations (DNS) to investigate the dynamics of a supersonic jet (Mach 3.73) interacting with a subsonic crossflow (Mach 0.8) at low Reynolds numbers. Three momentum-flux ratios (J = 2.8, 5.6, and 10.2) are considered, capturing a broad range of jet–crossflow interaction regimes. Turbulent inflow conditions are generated using the Dynamic Multiscale Approach (DMA), ensuring physically consistent boundary-layer turbulence and accurate representation of jet–crossflow interactions. Modal decomposition via proper orthogonal decomposition (POD) and spectral POD (SPOD) is used to identify the dominant spatial and spectral features of the flow. Across the three configurations, near-wall mean shear enhances small-scale turbulence, while increasing J intensifies jet penetration and vortex dynamics, producing broadband spectral gains. Downstream of the jet injection, the spectra broadly preserve the expected standard pressure and velocity scaling across the frequency range, except at high frequencies. POD reveals coherent vortical structures associated with shear-layer roll-up, jet flapping, and counter-rotating vortex pair (CVP) formation, with increasing spatial organization at higher momentum ratios. Further, POD reveals a shift in dominant structures: shear-layer roll-up governs the leading mode at high J, whereas CVP and jet–wall interactions dominate at lower J. Spectral POD identifies global plume oscillations whose Strouhal number rises with J, reflecting a transition from slow, wall-controlled flapping to faster, jet-dominated dynamics. Overall, the results demonstrate that the momentum-flux ratio (J) regulates not only jet penetration and mixing but also the hierarchy and characteristic frequencies of coherent vortical, thermal, and pressure and acoustic structures. The predominance of shear-layer roll-up over counter-rotating vortex pair (CVP) dynamics at high J, the systematic upward shift of plume-oscillation frequencies, and the strong analogy with low-frequency shock–boundary-layer interaction (SBLI) dynamics collectively provide new mechanistic insight into the unsteady behavior of supersonic jet-in-crossflow flows. Full article

The novel industrial trial is conducted to investigate the effect of argon injection into the down-leg of the RH degasser on the inclusion removal. The ‘cold steel plate dipping’ is used to take samples of molten steel and argon bubbles from the RH ladle. The industrial CT detection and electron microscope observation are applied to analyze the bubble characteristics. The results show that the size of bubbles generated by argon injection in the down-leg ranges from 7 to 1430 μm. Among them, the number density of bubbles with a diameter of 60 μm is the largest, reaching 0.1 per mm³. After adopting the down-leg argon injection technology, the average oxygen activity at the end of the RH process decreases by 2.35 ppm, and the surface defects of cold-rolled sheets of all grades are reduced. Based on the theoretical analysis of bubble collision and adhesion to inclusions, the small-sized bubbles have a relatively high capture probability for inclusions smaller than 10 μm. Comprehensively analyzing the experimental results, it is found that the down-leg argon injection technology has an obvious effect on removing inclusions. Full article

This study introduces a sensor-centric cybersecurity framework for railway infrastructure that extends Failure Mode and Effects Analysis (FMEA) from traditional reliability evaluation into the domain of cyber-induced failures affecting data integrity, availability and authenticity. The contribution lies in bridging regulatory obligations of the NIS2 Directive with field-layer monitoring by enabling risk indicators to evolve dynamically rather than remain static documentation artefacts. The approach is demonstrated using a scenario-based dataset collected from approximately 250 trackside, rolling-stock, environmental and power-monitoring sensors deployed over a 25 km operational segment, with representative anomalies generated through controlled spoofing, replay and injection conditions. Risk was evaluated using RPN scores derived from Severity–Occurrence–Detectability scales, while anomaly-detection performance was observed through detection-latency variation, changes in RPN distribution, and qualitative responsiveness of timestamp-based alerts. Instead of presenting a fixed benchmark, the results show how evidence from real sensor streams can recalibrate O and D factors in near-real-time and reduce undetected exposure windows, enabling measurable compliance documentation aligned with NIS2 Article 21. The findings confirm that coupling FMEA with streaming telemetry creates a verifiable risk-evaluation loop and supports a transition toward continuous, evidence-driven cybersecurity governance in railway systems. Full article