Search Results (1,405)

Light field displays offer promising autostereoscopic 3D visualization with continuous parallax, full-color reproduction, and natural depth cues. In practice, ray-tracing simulation is often employed to evaluate the effective viewing zone of light field systems. However, the actual viewing zone in conventional light field systems is significantly smaller than the simulated value, severely limited by narrow viewing angles due to crosstalk from adjacent elemental images. This study proposes an isolated microlens array (i-MLA) architecture incorporating a custom directional mesh (DM)—a 3D-printed light-blocking barrier grid with tapered pitch—to physically isolate each lenslet and completely suppress crosstalk. Combined with optimized extended coding pitch for a target viewing distance, ray-tracing simulations and experiments on a 13.3-inch 4K display with a 7 mm and 5.4 mm pitch MLA demonstrate dramatic improvement. The conventional light field system provides only a 3.4° margin, which is below the minimum angular separation required for binocular viewing, whereas the i-MLA system achieves a 7.4° margin—twice that of the conventional system. Compared with conventional systems, the i-MLA architecture does not increase overall volume; it simply replaces the glass gap with a single, simple optical element to achieve a wider viewing angle while preserving the compact form factor. Full article

With the rapid development of offshore wind power, the fractional frequency offshore wind power system based on the modular multilevel matrix converter (M3C) faces severe frequency stability challenges due to the reduced inertia under high wind power penetration. This paper focuses on its frequency control and proposes a set of coordinated strategies. Modified frequency regulation schemes for wind turbines (WTs) under different operating states avoid secondary frequency drop (SFD) and accelerate rotor speed recovery. A coordinated power allocation strategy combining energy storage (ES) and automatic generation control (AGC) suppresses wind-induced power fluctuations, with a reducing pitch angle variation method to extend WTs’ life. Meanwhile, an adaptive virtual inertia control strategy for M3C enhances sustained inertia support. A coordinated frequency control scheme between wind farm, M3C, and ES is further constructed to achieve faster and better frequency stabilization under wind and load variations. Simulation results under a 10.5 MW load disturbance show that, compared with the uncontrolled scheme, the proposed scheme raises the frequency nadir from 49.01 Hz to 49.67 Hz, limits the maximum rate of change of frequency (ROCOF) to 0.583 Hz/s with a 49.8% reduction, fully eliminates SFD, and provides theoretical support for the stable grid integration of fractional frequency offshore wind power. Full article

This study aims to enhance the precision of humanoid robots in imitating complex human “walking–grasping” coordinated movements. Addressing limitations in sample efficiency and reward function design in Generative Adversarial Imitation Learning (GAIL), we propose the Similarity Reward-Augmented Generative Adversarial Imitation Learning (SRA-GAIL) framework. The method integrates plantar thin-film resistive pressure sensors to measure the real-time pressure distribution at four key points on both feet, combined with roll/pitch angle data acquired from JY901S inertial measurement units (IMUs). A Lagrangian constraint optimization strategy is employed to achieve gait stability control based on the zero moment point (ZMP). Simultaneously, a visual similarity evaluation module is established using human demonstration trajectories captured by a Logitech C920E camera, augmented by grip force feedback from flexible thin-film pressure sensors on the hands. This enables the design of a multimodal sensor-fused similarity reward function. By incorporating Lagrangian constraint optimization and a maximum entropy reinforcement learning framework, Similarity Reward-Augmented Generative Adversarial Imitation Learning synchronously optimizes gait stability control—guided by zero moment point (ZMP) and roll/pitch data—and vision-based trajectory similarity evaluation. These components address motion stability constraints and trajectory similarity metrics, respectively, generating biomechanically plausible gait strategies. A spatiotemporal attention mechanism parses human motion trajectory features to drive the end-effector for high-precision trajectory tracking. To validate the proposed method, an imitation learning experimental system was constructed on a physical XIAOLI humanoid robot platform, integrating inertial measurement units (IMUs), plantar pressure sensors, and a vision system. Quantitative evaluations were conducted across multiple dimensions, including robot platform analysis, walking stability, object grasping success rates, and end-effector trajectory similarity. The results demonstrate that, compared to Generative Adversarial Imitation Learning (GAIL) and behavioral cloning, Similarity Reward-Augmented Generative Adversarial Imitation Learning achieves a stable object grasping success rate of 93.7% in complex environments, with a 23.8% improvement in sample efficiency. The method maintains a 96.5% compliance rate for zero moment point (ZMP) trajectories within the support polygon, significantly outperforming baseline approaches. This effectively addresses the bottleneck in robot policies adapting to dynamic changes in real-world environments. Full article

As floating offshore wind turbines (FOWTs) scale towards 10 MW+ capacities, suppressing wave-induced rotational resonance becomes critical for system survivability. This study introduces an optimized, highly symmetrical four-float semi-submersible platform, explicitly tailored to support the DTU 10 MW wind turbine and paired with an orthogonal four-point mooring system. Using three-dimensional linear potential flow theory via ANSYS AQWA, comprehensive frequency- and time-domain hydrodynamic evaluations were conducted. To address the inherent limitations of inviscid potential flow assumptions, an empirical added-damping method was implemented. Quantitative results demonstrate a drastic reduction in motion responses: the peak Response Amplitude Operator (RAO) for heave decreased by 68.6% (from 1.945 m/m to 0.610 m/m). Most notably, the peak RAOs for the critical rotational degrees of freedom—pitch and roll—were reduced by over 92% (from 2.080 °/m and 2.216 °/m to ~0.168 °/m, respectively). Ultimately, compared to traditional asymmetric three-float concepts, this novel symmetric omnidirectional layout provides a more uniform restoring stiffness. The resulting suppression of pitch and roll resonance results in a profound reduction in tower-base bending moments and gyroscopic loads, thereby significantly enhancing the dynamic stability, safety margins, and fatigue life of the 10 MW FOWT under extreme survival sea states. Full article

The control of Twin-Rotor Multi-Input Multi-Output (TRMS) systems presents a significant challenge due to high nonlinearity, strong aerodynamic cross-coupling, and the inevitable discrepancies between theoretical models and physical plants. This paper first exposes the instability of conventional Backstepping control under real hardware conditions, where unmodeled dynamics and parametric uncertainties drive the yaw subsystem into divergent oscillation, then proposes and experimentally validates two advanced architectures to overcome this limitation. The first is an online adaptive Backstepping gain-tuning scheme based on a novel Rate-Constrained Sequential Quantum Black Hole Optimization (RS-QBHO) algorithm. The second is a Hybrid Backstepping–Sliding Mode Control (BSMC) architecture that integrates structural disturbance rejection directly into the recursive design. Both schemes are formally verified via Lyapunov stability analysis and validated on a physical TRMS rig under identical hardware-in-the-loop conditions. Experimental results confirm that while the standard Backstepping controller failed in the yaw axis with an RMSE of 2.5624 rad, both proposed methods achieved stabilization. The QBHO-tuned controller yielded RMSE values of 0.0799 rad for pitch and 0.2305 rad for yaw, while the BSMC strategy proved superior, achieving 0.0682 rad and 0.1858 rad, respectively. These findings demonstrate that while meta-heuristic optimization effectively compensates for parametric mismatches, the passive disturbance rejection of the sliding mode term offers a more effective solution for mitigating unmodeled aerodynamic dynamics in MIMO flight platforms. Full article

This study presents the design, simulation, and experimental validation of a novel sector-shaped thread rolling machine aimed at improving forming efficiency, structural compactness, and process controllability compared with conventional linear thread rolling systems. A systematic engineering framework integrating mechanism design, curved-die implementation, motion control, finite-element simulation, and experimental verification is established. DEFORM-3D simulations are performed to investigate the effects of friction coefficient and die spacing on material flow and thread profile formation, and the results are used to guide machine construction and parameter optimization. Experimental results demonstrate that the proposed mechanism can simultaneously form four screws within a single rotation cycle, significantly enhancing production efficiency. Under optimized parameters, the relative errors of pitch diameter and helix angle are maintained within 5%, showing good agreement with simulation predictions. The findings confirm the feasibility, controllability, and stable forming capability of the proposed system, providing a practical and efficient solution for next-generation compact and high-productivity thread rolling equipment. Full article

This article is an in-depth analysis of the performance and efficiency of various control systems used in quadrotor unmanned aerial vehicles (UAVs). The study is focused on the comparison of three main control approaches, including Sliding Mode Control (SMC), Fuzzy Logic Control (FLC), and an extended version of Sliding Mode Control with the use of the Gray Wolf Optimizer (SMC-GWO), as well as a supportive validation model the Genetic Algorithm (SMC-GA). Based on the Newton–Euler formulation, the mathematical model of a quadrotor has been developed to provide a true picture of the dynamic behavior of the quadrotor. The model was then implemented in MATLAB/Simulink 2025b to test the performance of the system in its nominal and perturbed conditions. The findings have shown that the hybrid SMC-GWO controller has significant improvement in response speed, accuracy, and stability compared to the other controllers. Precisely, the SMC-GWO demonstrated 78.46 percent decrease in rise time and 23.40 percent decrease in settling time compared to the traditional SMC, as well as a nearly negligible steady-state error (SSE = 0.0008) in the roll channel. The proposed controller in the pitch channel reduced the rise time by 93.65 percent and the settling time by 20.22 percent, with a much smoother and more stable tracking and an effectively negligible steady-state error (SSE = 0.0001). The hybrid controller in the yaw channel had a 77.94 percent better rise time and 23.16 percent better settling time, resulting in a steady-state error of 0.0022. In relation to altitude control, SMC -GWO decreased the rise time by 91.87 percent and settling time by 25.04 percent over classical SMC, yet the steady-state error was almost zero. Under constant, time-varying actuator disturbances, the SMC-GWO controller also demonstrated better system stabilization and trajectory-tracking behavior than both SMC and FLC, as well as slightly better behavior than SMC-GA in the presence of faults and disturbances. These results verify that a UAV control framework based on the combination of the Gray Wolf Optimizer and Sliding Mode Control is more resilient, quick, and significantly more precise. Full article

Under turbulent wind conditions, rapid wind speed fluctuations can markedly increase the fatigue loads borne by wind turbine blades and towers. In practice, conventional PID pitch control based on speed feedback often struggles to deliver satisfactory load mitigation, mainly because the wind turbine system is highly nonlinear, strongly coupled, and subject to time-delay effects. To overcome these limitations, this paper proposes a load-predictive pitch control strategy built on a Long Short-Term Memory (LSTM) network. Specifically, the LSTM model is first employed to predict the hub-fixed tilt and yaw moments ahead of time. These predicted values are then introduced as feedforward signals and combined with the conventional speed-based pitch control signal as well as a proportional feedback term. After that, the inverse Coleman transformation is used to generate the individual pitch commands for each blade. To verify the effectiveness of the proposed method, co-simulations were carried out in FAST and MATLAB/Simulink on a 5000 KW distributed pitch-controlled wind turbine under IEC Kaimal spectrum wind conditions, with a mean wind speed of 18 m/s and Class B turbulence intensity. The results show that the LSTM prediction model achieves an R² of 0.998 on the test dataset, with an RMSE as low as 0.0051. Compared with the conventional pitch-based power control strategy, the proposed approach maintains the same average power output while significantly reducing fatigue loads, thereby contributing to a longer service life for the wind turbine. Full article

The squat is a fundamental multi-joint movement widely studied in strength training and biomechanics. While numerous experimental and computational studies have examined squat kinematics and joint loading, the mechanisms governing how squat technique adapts to increasing external load remain insufficiently understood. In particular, inverse-dynamics-based approaches often overlook explicit constraints imposed by limited joint moment capacity. This study presents a computational framework for predicting load-dependent adaptations of squat posture. The human body was represented as a multi-segment rigid-body system, with joints modeled as nonlinear rotational elements with bounded moment capacity. A reference squat trajectory was first generated kinematically, and a constrained optimization procedure was then applied at each motion frame to determine a mechanically admissible posture under increasing barbell load. The results show that higher loads lead to systematic posture adaptations, including increased torso inclination and redistribution of rotational demand from the knee toward the hip joint. For the highest load, peak torso pitch increased from 30° to over 40°, while joint utilization exceeded unity, indicating the onset of yielding. These findings identify joint moment capacity as a key constraint governing squat technique and demonstrate the potential of the proposed framework for predictive biomechanical analysis. Full article

A growing number of studies have highlighted the various sensory interactions involved in the musical experience, as relationships between music and dimensions of taste, olfaction, sound, and visual qualities, such as associations between pitch and the size of images or objects, spatial location and frequency, and instrumental timbres and visual shapes. These studies share the premise that the way we relate to the musical phenomenon, whether in the processes of production, perception, or understanding, emerges from an integrated and intrinsically multisensory perceptual event. Nevertheless, because music is present daily in everyday life and because this experience is inherently subjective, such interactions tend to occur so naturally and seem so obvious that they have been relegated to common sense. On the other hand, evidence indicates that sensory interactions constitute a fundamental ancestral mechanism for cognitive and neuronal development governed by non-arbitrary tendencies, multiple variables, and patterns of predictability. The novel contribution of this review is to advance a dynamic theoretical model of multisensory musical experience that takes crossmodal correspondences as its central organising axis, articulated through three structuring principles (universality, congruence effect, hierarchical tendency) and their interaction with musical organisation, cognitive structure, and the sensory systems mobilised by music. A future research agenda is also proposed to broaden and deepen investigations in the field of music psychology and human development. Full article

This study investigates the aerodynamic, aeroacoustic, and psychoacoustic behaviour of a side-by-side twin-propeller Unmanned Aerial Vehicle (UAV) system operating under both static and forward-flight conditions, with particular focus on the effects of asynchronous rotational speeds. Experiments were conducted using two identical five-bladed constant pitch propellers with a diameter of 9 in (228.6 mm) and a pitch to diameter ratio of 1. Rotational speed differences between 0 and 300 rpm were examined in 50 rpm increments at inflow velocities of 0 m/s, 14 m/s and 24 m/s. The results show that variations in rotational speed have a significant influence on both acoustic levels and perceived annoyance. Asynchronous operation causes the dominant tonal peak at the blade passing frequency to split into two components, reducing tonal reinforcement. This produces noise level reductions of approximately 2 dB in static and high advance ratio conditions, increasing to about 5 dB reduction at low advance ratios. Psychoacoustic metrics show greater sensitivity to tonal structure than to overall sound pressure level, with annoyance reductions of about 5% in static conditions and up to 15% at low advance ratios. A modest aerodynamic penalty of about 5% at $\Delta N=50$ rpm is observed, increasing with larger speed mismatches. Full article

Ultrahigh lateral resolution (UHLR) optical coherence tomography (OCT) technology, also called optical coherence microscopy (OCM), has gained popularity, especially in the field of biomedical imaging. In these systems, high numerical aperture (NA) Microscope objectives (MO) are employed in OCM systems to offer better than 3 µm lateral resolution. However, in the implemented broadband OCM configuration, the use of complex multi-element microscope objectives can reduce the detected returned signal compared with a simpler imaging lens configuration. This reduction in detected returned signals can become an important practical limitation in many OCM applications, particularly for biomedical imaging when high imaging speed is crucial. This study investigates whether a single off-the-shelf lens can provide a practical alternative to conventional MOs, achieving higher throughput while maintaining reasonable spatial resolution. We systematically evaluated 14 commercial lenses using Zemax OpticStudio simulations, identifying an aspherized achromatic lens (Edmund Optics #85302) that best met these key criteria. To validate its feasibility for OCM, performance was tested in both Full-Field Time-Domain OCM (FF-TD-OCM) and Line-Field Spectral-Domain OCM (LF-SD-OCM) configurations. Using a broadband composite Superluminescent Diode (SLD) source (750–920 nm), we quantified the resolvable features, axial resolution, and overall light transmission. The validated system demonstrated near-diffraction-limited performance. In the LF-SD-OCM setup, it successfully resolved features as fine as Group 8, Element 6, corresponding to a 2.2 µm line pair pitch (~1.1 µm line width) and achieved a 2.86 µm axial resolution in air. A through-focus comparison further showed practically useful contrast retention around focus. Additional imaging of onion epidermal tissue and ex vivo porcine corneal tissue demonstrated that the proposed lens could provide interpretable structural images on representative biological samples. Under the tested LF-SD-OCM detection configuration, the selected lens delivered approximately 2.0 dB higher returned signal than the Mitutoyo MY10X-823 objective according to 1.59× larger received signal. Full article

Pitch canker caused by the fungus Fusarium circinatum is a destructive disease that affects pines in Europe, South Africa, and North America, particularly along the southeastern and western coasts of the United States. This study systematically elucidated the function of the Leucine-rich repeat (LRR) protein FcLRR1 in the pine pitch canker pathogen Fusarium circinatum. A total of 13 LRR proteins were identified via bioinformatic analysis. Using a gene knockout system, we demonstrated that deletion of FcLRR1 significantly impaired vegetative growth, conidiation, and conidium germination; led to a complete loss of macroconidia production; and drastically reduced abiotic stress tolerance and virulence. Transcriptome profiling revealed 612 downregulated genes, which were significantly enriched in pathways such as starch and sucrose metabolism, indicating that FcLRR1 modulated energy supply and pathogenicity through carbon source utilization. Through genome-wide protein structure modeling and yeast two-hybrid assays, we identified and validated the interaction between FcLRR1 and ALG-11, among other candidate proteins, further supporting its involvement in carbon metabolism, cell wall integrity, and pathogenesis. This study represents the first functional characterization of an LRR-containing protein in a forest pathogenic fungus and provides a foundational basis for developing targeted disease control strategies. Full article

Contemporary Music Information Retrieval (MIR) and Natural Language Processing (NLP) systems are increasingly applied to diverse musical traditions, yet they are largely grounded in Western musical and linguistic assumptions. This study examines whether commonly used MIR features and multilingual NLP models adequately represent the acoustic, linguistic, and cultural structures of Maskandi music in comparison to Western music and identifies where representational gaps and biases arise. A multidimensional framework was employed, comprising acoustic and structural MIR analysis, linguistic and semantic lyrical analysis, and bias analysis. A curated dataset of 60 recordings and corresponding lyrics was analysed using rhythm and beat features, pitch contour measures, structural self-similarity, timbre embeddings, semantic similarity, lexical diversity, metaphor density, topic modelling, multilingual embeddings, and dataset-level audits. The results reveal systematic representational failures: beat tracking showed lower median IOI coefficient of variation for Maskandi (0.028) versus Western music (0.040, p = 0.0199) yet exhibited greater algorithmic instability, tempo averaged 131.16 BPM versus 111.69 BPM (p = 0.000262), pitch glide proportions were significantly higher in Maskandi (0.34 vs. 0.16), on-beat energy ratios differed substantially (2.26 vs. 1.19, p < 0.0000007), semantic similarity revealed high intra-genre coherence for Maskandi (0.73) versus Western (0.25), metaphor density approached zero in Maskandi versus up to 7 per 100 words in Western lyrics, topic modeling produced two compact clusters for Maskandi versus 6 dispersed clusters for Western, timbre embeddings achieved a 0.405 silhouette score, dataset audits revealed 0% Maskandi representation across seven major MIR corpora with African traditions comprising <3%. The study concludes that statistical separability does not imply representational adequacy and highlights the need for culturally grounded MIR and NLP representations to support diverse musical traditions. Full article

Automated pig-welfare monitoring needs scalable, non-invasive signals that work across ages and individuals. A key methodological contribution of this study is the use of subject-wise validation, which ensures generalization to unseen animals and prevents inflated accuracy caused by growth-related and individual ‘voice’ differences. Vocalizations can help, but growth and individual “voice” differences can confound distress patterns and overstate accuracy without subject-wise validation. In our study, we explicitly accounted for individual variability by including animal identity as a random effect in mixed models and by using grouped cross-validation, where models were tested only on pigs not seen during training. This approach ensures that the reported accuracy reflects generalization across different individuals rather than memorization of specific vocal signatures. We analyzed 2221 vocal samples from 40 pigs (20 males, 20 females) recorded across four growth phases (farrowing, nursery, growing, finishing) under six conditions (pain, hunger, thirst, cold stress, heat stress, normal). Acoustic features extracted in Praat included energy, duration, intensity, pitch, and formants (F1–F4). Using blockwise variance decomposition, we quantified contributions of distress exposure, growth phase, and sex, and estimated the additional variance explained by animal identity. Distress exposure dominated intensity and spectral traits, particularly Formant 2, whereas the growth phase produced systematic shifts in duration and pitch. Animal identity added a modest but consistent increment in explained variance (~+0.02–0.03 R² beyond sex, phase, and distress). For prediction, we used 5-fold cross-validation grouped by animal. A Random Forest achieved a modest balanced accuracy of 0.609 and macro-F1 of 0.597; pain was most separable (recall 0.825), while other states showed moderate recall, indicating overlap. These results support hierarchical acoustic encoding of distress and establish a benchmark for precision welfare monitoring. Furthermore, they highlight that resolving complex physiological overlaps, such as heat stress and resource competition, requires a shift from unimodal acoustic models to multimodal Precision Livestock Farming (PLF) systems that integrate bioacoustics with continuous environmental and behavioral data streams. Full article