Search Results (950)

Visual object tracking finds extensive application in real-time video analysis on edge devices, yet faces dual challenges: decreased speed due to limited computational resources and weak anti-disturbance capability in complex scenarios. This paper proposes the Hybrid Closed-Loop Tracker (HCLT) to enhance both speed and robustness of embedded visual tracking. HCLT integrates high-precision and high-speed trackers to make real-time performance controllable, while a Kalman filter is employed for state observation and feedback. Within this closed-loop framework, we introduce motion and feature point information as supplementary states and further design mechanisms for adaptive search region adjustment and tracking recovery. Our methods effectively mitigate the impact of external disturbances. Experimental results demonstrate that HCLT further improves both speed and robustness on the basis of high-performance trackers, achieving high tracking accuracy across multiple public benchmark datasets. It demonstrates excellent anti-disturbance performance, particularly in challenging scenarios such as blur and occlusions, while maintaining frame rates exceeding 35 frames per second (FPS) at 720p resolution when deployed on an RK3588 embedded device, thus representing a significant improvement over deep neural network trackers. Full article

This article settles a new multi-fault-tolerant control problem of an Euler–Bernoulli beam system (EBBS) in the existence of multiplicative faults, additive faults, and control-matched disturbances simultaneously, using the direct adaptive learning control technique. Such an EBBS can be employed to model the vibration of flexible vehicles and flexible manipulators in the actual engineering control. An original hierarchical adaptive boundary control strategy is developed to compensate for multiplicative and additive faults and to reject control-matched disturbances. The classical Lyapunov direct method, together with the variation of Wirtinger’s inequality is utilized to demonstrate the closed-loop system performance. The modified ${\mathcal{C}}_0$-semigroup frame is exploited to certify the well-posedness of its solution under the designed controller. Simulation study on a simply supported beam is demonstrated to verify the validity of the evolved multi-fault-tolerant boundary control algorithm. Full article

To address the challenge of exorbitant computational costs in the reliability analysis of complex steel structures, which stems from the impact of multiple sources of uncertainty throughout their entire lifecycle, this paper presents a comparative evaluation of the explicit reconstruction of the Limit State Function (LSF) using SVM combined with Hyperparameter Optimization (HPO) for structural reliability analysis under constrained computational budgets. Although traditional Monte Carlo simulation (MCS) exhibits high accuracy, it requires a substantial number of finite element calculations, rendering it difficult to satisfy the efficiency requirements of engineering projects. Conversely, the first-order and second-order reliability methods (FORM/SORM) offer high computational efficiency but rely on explicit limit state functions, posing challenges for their direct application to complex structural systems. Thus, this study initially acquires response samples of the structure under various combinations of random variables through a limited number of finite element analyses (FEA). Subsequently, it employs an SVM to develop a highly accurate equivalent explicit limit state function, which serves as a substitute for the original implicit limit state function. Finally, it integrates Monte Carlo simulation to efficiently evaluate the structure’s failure probability and reliability index. Meanwhile, to tackle the problem of SVM model performance being highly susceptible to hyperparameters, this study presents a comparative analysis of four strategies: Bayesian Optimization (BO), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Random Search (RS), aiming to identify the optimal parameter combination and improve the model’s generalization capability. Through verification with four progressive examples, including linear, nonlinear, truss, and multistory frame structures, the results demonstrate that the proposed method can accurately characterize the nonlinearity of structural responses. The obtained failure probabilities and reliability indices are in close agreement with those obtained from the direct Monte Carlo simulation (MCS) and existing research. Moreover, while maintaining computational accuracy, the method significantly reduces computational costs, thereby providing an efficient and practical solution for structural reliability analysis in engineering practice. Full article

Narrative extraction algorithms construct storylines by finding coherent paths through document collections. The Narrative Trails algorithm frames this as maximum-capacity path optimization, where path quality depends on a coherence function measuring document relationships. We introduce quantum kernels as coherence functions for narrative extraction—to the best of our knowledge, the first systematic characterisation of quantum kernel methods for storyline extraction—and compare them against classical baselines on two corpora using a multi-seed protocol. The sweep covers 93 method evaluations (54 quantum kernels across three encoder families—R_Y+CNOT-ring, IQP/ZZ-feature-map, and a projected quantum kernel—and 39 classical kernels—cosine, RBF, and the cluster-aware Narrative Trails baseline). On 11,215 human navigation paths from Wikispeedia, evaluation metrics divide into two clusters that disagree with each other: alignment-based metrics (length-normalised DTW and per-step DTW similarity) favour methods that produce long alignment-rich paths, while set-overlap metrics (Jaccard and F1) favour methods that produce shorter paths with higher article overlap. On LLM-judged coherence for Cuban news storylines, evaluated under a 12-method × 5-seed × 30-endpoint-pair × 2-judge design (Claude Sonnet 4.5 and GPT-4o, both at $T=0$ via structured tool calling), the cluster-aware classical baseline is the top method in terms of mean overall coherence; the 5-method quantum-kernel pool and the 7-method classical-kernel pool on matched projection input show no significant differences after Holm correction. Cross-task analysis reveals that LLM coherence rank correlates with alignment-cluster Wikispeedia metrics (Spearman $\rho \approx +0.70$) and anti-correlates with overlap-cluster metrics ($\rho \approx -0.62$). A closed-form theoretical analysis shows that the depth-1 R_Y+CNOT-ring kernel reduces to a classical product-of-cosines kernel order equivalent to RBF, explaining the absence of empirical separation at low depth; deeper encoders break the cancellation but exponentially concentrate kernel values, eroding inter-pair distinguishability. Our results characterise quantum coherence kernels as competitive with classical kernels on the same projected input rather than decisively superior, with the cluster-aware classical baseline retaining a modest advantage attributable to its explicit topical structure. Full article

Structural reanalysis involves repeated evaluation of structural responses under iterative design changes. It is a major computational burden in structural optimization, sensitivity analysis, and health monitoring. The three-layer architecture, which comprises the stiffness, displacement, and force layers, is motivated by the governing structural mechanics relationship $F=K·U$, which establishes stiffness and displacement as natural intermediate quantities for predicting internal forces. This physics-informed hierarchy reduces dependence on large training datasets while preserving predictive accuracy across all response quantities. The framework predicts member-level stiffness statistics, nodal displacements, and internal forces through three sequential layers: stiffness, displacement, and force. Each layer enriches the feature set of the layer above. Sensitivity-based secondary inputs are derived analytically from closed-form expressions relating cross-sectional dimensions to stiffness and displacement changes. This embeds structural mechanics knowledge directly into the feature engineering process without additional analyses. Member stiffness matrices are recovered as submatrices of the global stiffness matrix, encoding inter-member structural context into each member’s representation. The framework is implemented on a six-floor, three-bay reinforced concrete frame of 42 members. Training uses 1890 data points from 45 finite element iterations. The Random Forest model achieves R² scores of 0.99, 0.98, and 0.91 for axial force, shear force, and bending moment, respectively, on unseen validation data. Once trained on 45 FE iterations, the framework evaluates any number of candidate cross-sectional configurations in a single batch inference pass, enabling a shift from sequential solver-driven reanalysis to model-driven batch optimization. The proposed framework offers a scalable, interpretable, and physics-consistent alternative to both classical reanalysis methods and purely data-driven models, with direct applicability to structural size optimization and structural health monitoring workflows. Full article

Permanent-magnet spherical motors (PMSpMs) possess three-degree-of-freedom omnidirectional motion characteristics, and rotor attitude estimation (RAE) is essential for closed-loop control. This article proposes a visual RAE method for spherical motors using a Kalman filter and geometric constraint Transformer (GK-TransT). An RAE system was equipped with a monocular area scan camera with a visual feature component (VFC) mounted on the bottom of the rotor. In the proposed GK-TransT algorithm, the Kalman filter is used to enhance the robustness and accuracy of the TransT tracker. To verify the algorithm, a tracking comparison was conducted among the GK-TransT, original TransT, KCF, and CSRT algorithms. The results indicate that the tracking precisions of the proposed GK-TransT algorithm for the main and auxiliary feature points reach 90.9% and 94.4%, respectively, with an average processing speed of 61.23 FPS and a single-frame latency of 16.33 ms. Considering the tracking precision, real-time performance, and robustness under occlusion and motion blur conditions, the GK-TransT algorithm is more applicable for the RAE of the PMSpM. In addition, an RAE test bench was developed, and the GK-TransT-based method and a micro-electro-mechanical system (MEMS) sensor were compared. The physical ground truth of a hydraulic rotary table was used as the benchmark. The comparison results indicate that the GK-TransT-based method achieves a higher accuracy than the MEMS method. Finally, the practicability of the proposed method is proved. Full article

This paper advances a theoretical argument that generative AI is accelerating the democratization of computational thinking and, in turn, reshaping education, professional practice, and the nature of computing itself. Traditionally, computational thinking has been closely tied to learning to program, thereby limiting who could effectively employ it. The emergence of large language models (LLMs) challenges this linkage by decoupling many forms of computational problem solving from direct programming. In response to this shift, the paper explores the implications for curriculum design and workforce roles through a theoretical and interpretive lens. Drawing on prior literature, historical context, and illustrative examples from recent scholarship and practice, we develop a conceptual account of AI-augmented computing. We argue that LLMs lower barriers to entry by abstracting away much of manual coding and reallocating effort toward problem framing, prompt engineering, oversight, and validation. We further argue that this transition is redistributing computational skills across disciplines, positioning prompt engineering as an emerging engineering practice, and increasing pressure on universities to redesign curricula around AI literacy, fluency, and mastery. Full article

Unmanned Aerial Vehicle (UAV), particularly rotary-wing platforms such as quadcopters and octocopters, has evolved from controlled remote sensing platforms into autonomous agents capable of active task execution. This evolution from collect-then-analyze workflows to closed-loop perception, reasoning, and action signifies a paradigm shift toward Embodied AI, unlocking opportunities for the low-altitude economy. However, current research on UAV Embodied AI (UAV-EAI) often implicitly frames the field as a direct extension of indoor robotics or autonomous driving, which overlooks the fundamental distinctions of aerial agents. To bridge this gap, we introduce a comparative framework contrasting UAV-EAI with Indoor-EAI and Autonomous Driving Embodied AI (AD-EAI). By systematically decomposing the domain into nine key dimensions, we (i) analyze core tasks such as perception, localization, and exploration; (ii) review enabling infrastructure, including simulators and datasets; and (iii) categorize modeling methods ranging from physics-centric control to cognition-centric models. Our analysis demonstrates that the convergence of 6-DoF motion space, kilometer-scale unstructured environments, and stringent on-device constraints establishes a research regime qualitatively different from ground-based agents. These factors significantly impede the migration of existing VLM/LLM-based embodied systems for UAVs. Finally, we summarize open challenges and outline promising directions for the next generation of UAV-EAI. Full article

The conservation and restoration of architectural heritage face dual challenges from natural erosion and human interference, necessitating the adoption of efficient and non-contact digital technologies to achieve sustainable preservation. Virtual reality (VR) technology, with its advantages of immersion, interactivity, and visualization, provides a novel technological pathway for digital documentation, conservation decision-making, and public presentation of architectural heritage. Taking the Fuliang Red Pagoda in Jingdezhen, Jiangxi Province, as the research object, this study constructs a high-precision digital reconstruction and VR interactive application workflow based on the integration of terrestrial laser scanning and close-range photogrammetry. Through point cloud denoising, Iterative Closest Point (ICP) registration, and Poisson surface reconstruction algorithms, a refined three-dimensional model of the pagoda is achieved, and an immersive VR system is developed with functions including component information query, virtual restoration scheme switching, and interactive exploration. The results demonstrate that this technical workflow not only enables non-contact digital archiving of the Fuliang Red Pagoda but also provides a visual decision-support tool for conservation interventions. Under full-scene operation, the system achieves an average rendering frame rate of 92 FPS and maintains motion-to-photon latency below 20 ms, ensuring good real-time performance and interaction stability. The findings indicate that VR-based digital technologies can enhance the scientific rigor of conservation planning and promote public engagement while adhering to the principles of authenticity and minimum intervention. This study provides a replicable technical pathway and practical reference for high-precision digital reconstruction and sustainable conservation of historic buildings. Full article

Video synthetic aperture radar (ViSAR) provides continuous multiframe images while maintaining high resolution and has become an important tool for complex scene surveillance and moving target tracking. ViSAR imaging is susceptible to interframe drift caused by motion errors, which severely degrades video stability. When registering long time series of real airborne video SAR images, conventional image registration based on Normalized Cross-Correlation (NCC) is affected by several factors, including platform residual motion errors, approximations in the imaging geometry, interpolation resampling, and SAR speckle noise. As a result, noticeable interframe jitter persists in the registered sequence, and the stabilization accuracy is insufficient to meet high-precision image stabilization requirements. To address these issues, this paper proposes an image stabilization method for airborne video SAR based on a joint Singer-random walk model. Firstly, with the first frame selected as the reference, subpixel drift measurements in the azimuth and range directions are extracted from continuous frames via NCC-based registration. Subsequently, the true drift is modeled as a two-dimensional Singer process and the systematic bias as a random walk process, yielding a joint state space model that comprises displacement, velocity, acceleration, and bias components. On this basis, a Kalman filter and a Rauch–Tung–Striebel (RTS) fixed-interval smoother are applied to perform temporal filtering and trajectory smoothing on the drift measurements, thereby producing smooth two-dimensional drift estimates that closely approximate the actual drift trajectory. Finally, the smoothed drift trajectory is used to perform frame-by-frame subpixel drift correction on the original image sequence, achieving high-precision interframe stabilization of the ViSAR imagery. The results of real data processing demonstrate that the proposed method can effectively improve the consistency and scene stability of ViSAR multi-frame imaging. Full article

A central open question in automotive intrusion detection is not merely whether relational representations of Controller Area Network (CAN) traffic improve performance, but which aspects of CAN traffic structure transfer robustly across attacks and which do not transfer across vehicle platforms, and why. To investigate this question systematically, we develop a lightweight intrusion-detection framework combining statistical traffic descriptors, structural identifier transition features, and graph topology representations extracted from sliding windows of CAN frames. Because CAN is a broadcast-only bus with no request–response mechanism, each ECU independently transmits its identifiers at fixed periodic rates; accordingly, the structural and graph-based features capture the temporal scheduling regularity of identifier broadcasts, not directed inter-ECU communication dependencies. Stress-testing the framework under cross-attack and cross-dataset transfer reveals a clear four-level hierarchy: (1) statistical features collapse under cross-attack transfer (ROC-AUC as low as 0.009), failing to generalise beyond the attack type seen during training; (2) structural transition features are the most robust form of representation, maintaining high cross-attack performance (ROC-AUC > 0.999) across all evaluated scenarios within the same vehicle platform; (3) graph topology features are scenario-dependent, achieving high robustness in DoS-trained scenarios but producing sub-random results in Fuzzy-trained scenarios, exposing a sensitivity to injection density profiles; and (4) the hybrid combination provides the strongest overall operational package, consistently across four classifiers. Cross-dataset transfer to the ROAD dataset reveals the precise boundary conditions of transferability: structural representations transfer only when an attack perturbs identifier transition regularity (correlated signal attacks, ROC-AUC = 0.81–0.83), while attacks that affect only payload semantics (speedometer) or exploit identifier–space novelty (fuzzing) lie outside the detection scope of transition-based features, regardless of the vehicle platform. A vehicle-specific calibration experiment further shows that the correlated-attack generalization gap can be closed with as little as 10% of target-vehicle normal traffic, whereas speedometer attacks remain structurally invisible by design. A key contribution of this work is therefore a transparent approach for identifying when relational CAN representations transfer and when they do not—a finding that is more scientifically valuable than a uniformly optimistic performance claim and which provides concrete guidance for practitioners designing cross-platform automotive IDS. Full article

Macrosteles fascifrons, a representative aster leafhopper frequently detected in rice-growing environments, is an economically significant insect that inhabits rice fields and plays a role in the ecology of crop pests and disease transmission. To expand the understanding of viral diversity associated with the aster leafhopper, we analyzed its virome using deep transcriptome sequencing. In addition to several previously reported viruses, we identified two previously unreported RNA viruses, tentatively designated as Macrosteles fascifrons hepe-like virus 1 (MfHV1) and Macrosteles fascifrons rhabdovirus 1 (MfRV1). The complete genome sequences of both genomes were obtained using overlapping RT-PCR and rapid amplification of cDNA ends. Excluding the poly(A) tail, the genome of MfHV1 is 6688 nucleotides in length and exhibits a genomic organization characteristic of the family Hepeviridae, comprising three major open reading frames (ORFs) that encode a putative nonstructural polyprotein, a capsid protein, and a small accessory protein. The ORF encoding the capsid protein partially overlaps with the ORF encoding the small accessory protein, a genomic feature commonly observed in hepe-like viruses. The genome of MfRV1 is 14,984 nucleotides in length and displays the canonical genomic organization of the family Rhabdoviridae. An additional accessory ORF was identified between the putative M and G genes. Phylogenetic analysis based on polyprotein sequences placed MfHV1 within the Hepeviridae, most closely related to insect-associated hepe-like viruses, whereas MfRV1 clustered within the subfamily Deltarhabdovirinae. According to ICTV guidelines, virus classification is based on a combination of sequence divergence, phylogenetic relationships, and genome organization. MfHV1 and MfRV1 share low amino acid sequence identities with known viruses (maximum 36.07% for the MfHV1 polyprotein and 47.7% for the MfRV1 RNA-dependent RNA polymerase). Based on sequence divergence, genome organization, and phylogenetic placement, these viruses are classified as putative novel members of their respective families. This study expands the diversity of virus-associated sequences detected in M. fascifrons and provides additional genomic resources for understanding insect-associated RNA viruses. Full article

This article examines how LGBTQ+ allyship is made, felt, and negotiated within a secondary school workshop using creative, participatory methods. Drawing on affect theory (see Sara Ahmed) and feminist new materialist scholarship (see Barad, Renold, among others), the paper analyses a collaborative collage activity centered on Pride flags and symbolic materials. Rather than treating allyship as a fixed identity or a knowledge-based achievement, the study explores how it emerges relationally through encounters with materials, symbols, bodies, and digital technologies. Through close analysis of moments of uncertainty, affective attachment, cutting and shattering of symbols, and the collective naming of the final artwork, the article traces how not-knowing, pleasure, confusion, and togetherness function as generative forces for allyship. The workshop is framed as a propositional research-creation space in which phones, Google searches, bunting, scissors, and book references intra-act with young peoples’ lived experiences, redistributing epistemic authority and unsettling school-based expectations of correct knowledge. The findings contribute to existing research on LGBTQ+ inclusion and allyship in schools by shifting focus from identity labels and institutional frameworks toward the affective, material, and speculative processes through which allyship is assembled in the moment. In doing so, the paper offers an alternative conceptualisation of allyship as relational practice rather than static position, with implications for creative pedagogy and inclusive educational research. Full article

This study aims to improve the accuracy of cruise-phase power consumption prediction for multirotor unmanned aerial vehicles operating under varying wind conditions. Existing parametric energy models typically retain the wind velocity vector in the ground or inertial reference frame, and this representation does not distinguish between axial drag contributions along the fuselage and lateral attitude-correction contributions perpendicular to it. The proposed framework addresses this limitation through a physics-informed coordinate transformation that projects the measured wind vector into the body frame of the aircraft using quaternion-derived heading angles, yielding separate axial and lateral wind components. These components enter the power model as two additional predictors that augment the induced-power baseline, with the axial term following a cubic airspeed–power relationship consistent with parasitic drag formulations and the lateral term following a quadratic relationship consistent with attitude-correction mechanics. The framework is validated on a publicly available flight dataset, which comprises 188 flights of a DJI Matrice 100 quadcopter across payloads of 0 to 0.75 kg, ground speeds of 4 to 12 m/s, and altitudes of 25 to 100 m. Compared with the induced-power baseline, the proposed model reduces the root mean square error by 15.9% and the mean squared error by 29.7% during the cruise phase. The improvement is larger when wind speeds exceed 6 m/s, a regime in which the baseline residuals increase while the proposed model retains a comparatively stable error profile. Residual analysis indicates that baseline errors follow an approximately quadratic trend relative to the axial and lateral wind components, consistent with established parasitic-power and attitude-correction formulations. The closed-form structure of the proposed model is compatible with onboard execution on flight controllers, which suggests a feasible pathway toward its use as the power-prediction module within downstream range-estimation and energy-reserve sizing routines. Full article

Power electronic systems are often engineered through a sequential–iterative workflow in which hardware parameters are initially sized from steady-state, ripple, thermal, and electromagnetic-compatibility constraints, and controllers are subsequently tuned to satisfy dynamic and closed-loop performance requirements. While converters are inherently designed for closed-loop operation, increasing power density, uncertainty, and distributed interaction make the underlying design process resemble a strategic interplay among multiple decision-makers, including hardware designers, control algorithms, loads, disturbances, and manufacturing constraints. This paper develops a unifying game-theoretic perspective on the optimal design and control of power electronic systems. Classical concepts—such as robust control, worst-case design, droop-based load sharing, and tolerance allocation—are reinterpreted as equilibrium solutions of zero-sum, Stackelberg, non-cooperative, or cooperative games. Beyond a conceptual taxonomy, two illustrative simulation case studies are provided: (i) a Stackelberg hardware–controller co-design of a buck converter, demonstrating simultaneous passive-component reduction and improved transient performance relative to a conservative sequential design; and (ii) a droop-controlled parallel-converter example contrasting Nash and cooperative equilibria, explicitly quantifying trade-offs between bus-voltage regulation, current-sharing fairness, and conduction losses. By framing power electronic design and control as interacting strategic processes rather than isolated optimization stages, the paper aims to show that game theory can serve as a structured and practically interpretable framework for distributed and uncertainty-aware power electronic systems. Full article