Search Results (6,398)

This paper presents a metrology-oriented review of vision-based displacement and deformation measurement for civil structural health monitoring (SHM), with an emphasis on field robustness and uncertainty quantification (UQ). The review focuses on image- and video-based methods that convert visual information into quantitative physical measurements, such as displacement, strain, or derived dynamic indicators. The literature is organized according to the main stages of the measurement chain: image formation, image-plane motion estimation, and geometric conversion to metric motion. Within this framework, measurement pipelines are interpreted through three levels of geometric mapping, namely, a scalar scale-factor model, a planar homography-based model, and a full Jacobian-based model. The review synthesizes major method families, including marker-based and markerless tracking, feature-based tracking, optical flow, digital image correlation (DIC), phase-based motion magnification, edge-based estimators, fixed- and moving-camera configurations, UAV-based acquisition with ego-motion compensation, hybrid vision–sensor fusion, and deep-learning-enhanced pipelines. A structured taxonomy of uncertainty sources is then presented along the processing chain, covering camera geometry and calibration, imaging noise and blur, quantization, timing and synchronization, environmental disturbances, optical turbulence and heat haze, platform motion, algorithmic failure modes, and reference-sensor uncertainty. The paper also compares UQ practices, including GUM-aligned analytical propagation, Monte Carlo methods, DIC-specific error budgets, bootstrap and resampling strategies, and probabilistic deep learning. The main contribution of this review is to connect computer-vision-based displacement pipelines with metrological requirements by explicitly linking measurement models, uncertainty sources, UQ methods, and field-validation evidence within a unified framework. A practical uncertainty-budget template is compiled to support traceable reporting across different pipelines and deployment scenarios. The paper concludes with prioritized research gaps and future directions, including standardized benchmarks and datasets, traceable UQ for moving-camera systems, multi-sensor fusion with end-to-end uncertainty propagation, long-term drift characterization, optical-turbulence and adverse-weather modeling, validated subpixel limits at extreme range, probabilistic deep learning–metrology integration, and standardized reporting practices. Full article

In window tracking mode, stray light and detector readout noise can submerge star spot signals in star sensor images. The resulting degradation reduces centroid extraction accuracy and may even cause extraction failure, thereby preventing precise attitude determination. This study uses the self-supervised spatiotemporal denoising model ASTERIS as the baseline. ASTERIS integrates 3D spatiotemporal inputs with a global attention mechanism for joint noise modeling, thereby providing stronger denoising and restoration capability than conventional methods such as multi-frame stacking. However, ASTERIS lacks adaptive compensation for subpixel jitter in on-orbit star images and has difficulty preserving the high-frequency morphology of star spots, affecting denoising performance and centroiding accuracy. To address these limitations, this study introduces two improvements: First, frame-by-frame spatial deformable convolution is incorporated into the decoder upsampling stage to adaptively compensate for subpixel offsets, actively suppress background noise, and lower the parameter count. Second, a complex-valued frequency domain loss with a high-frequency weighted mask is designed to jointly constrain the amplitude and phase spectra, thereby preserving high-frequency star spot details. Experimental results show that, for star images with extremely low signal-to-noise ratios, the proposed method improves the peak signal-to-noise ratio by approximately 17.8 dB and reduces the centroid localization error to approximately 0.1 pixels. This performance is substantially better than that of the original ASTERIS model, which improves the peak signal-to-noise ratio by approximately 9.5 dB and yields an error of approximately 0.4 pixels, and the multi-frame stacking method, which improves the peak signal-to-noise ratio by approximately 6.0 dB and yields an error of approximately 0.5 pixels. Under the simulated strong noise conditions considered in this study, the proposed method achieves effective centroid extraction, demonstrating its potential for on-orbit star sensor data processing. Future work will further address its engineering deployment. Full article

This study aimed to assess the remineralization efficacy of NaF gel enriched with hydroxyapatite nanoparticles (HANPs) and bioactive glass nanoparticles (BAGNPs), with and without adjunctive Er, Cr: YSGG laser irradiation (ECL; 0.5 W, 5 Hz, 20 mJ/pulse, 60 µs pulse duration, water–air spray), on artificially demineralized enamel by evaluating enamel microhardness (MH), resin tag length (RTL), and shear bond strength (SBS) of pit and fissure sealants (PFSs). A total of 168 extracted human third molars free from cracks, fractures, erosion, enamel hypoplasia, surface irregularities, and any history of prior chemical or fluoride treatment were included in the study. All samples underwent continuous immersion in a demineralizing solution until specific DIAGNOdent values of 10-25 were achieved. Samples were randomly allocated into six groups (n = 28): Group 1 (untreated control), Group 2 (NaF gel), Group 3 (NaF + HANPs), Group 4 (NaF + BAGNPs), Group 5 (NaF + HANPs-ECL), and Group 6 (NaF + BAGNPs-ECL). Enamel MH was assessed using a Vickers MH tester (n = 8). RTL was evaluated using scanning electron microscopy (SEM) (n = 8). SBS was measured using a universal testing machine (n = 12), followed by failure mode analysis. Data were analyzed using ANOVA and Tukey’s post hoc test (p < 0.05). Group 5 (NaF + HANPs-ECL) exhibited the highest values for MH (366.20 ± 26.11 HV), RTL (70.34 ± 2.57 µm), and SBS (13.67 ± 0.35 MPa), whereas the untreated control group exhibited the lowest values for all the outcomes. Groups 1 and 2 demonstrated comparable RTL and SBS values (p > 0.05). The remaining groups exhibited significantly different MH, RTL, and SBS values (p < 0.05). The ECL-assisted nanoparticle-integrated NaF gel significantly enhanced enamel MH, RTL, and shear SBS of PFS compared to NaF gel alone. HANPs demonstrated superior remineralization outcomes compared to BAGNPs across all tested parameters. The present findings support the adjunctive use of laser activation with nanoparticle-modified NaF gel as a promising strategy for optimizing sealant performance on demineralized enamel. Full article

Typhoon-eye sensing places unmanned aerial vehicle (UAV) swarms in a setting where the wind field that carries the target signal also displaces aircraft, drains energy, weakens links, and increases failure risk. A rule that improves only routing or only motion can therefore move the swarm into another failure mode. This paper proposes a risk-driven cross-layer coordination scheme for such missions. A bounded risk index, computed from isolation, connectivity loss, and wind intensity, acts as a supervisory variable for multi-hop reachability maintenance, isolated-node recovery, and layered altitude adaptation. For evaluation, graph reachability is separated from useful data return through a degraded multi-hop aggregation model that includes distance loss, wind-dependent reliability, rain-induced packet loss, relay forwarding loss, and mothership collection capacity. The simulator combines a bounded Holland-type storm field, stochastic turbulence, nonlinear propulsion energy consumption, and wind-dependent structural failure. Against three literature-inspired baselines, two AI-inspired comparators, and six ablation variants, the method keeps a balanced profile across connectivity, isolation, wind exposure, data collection, and survival. In 30-run steady-state robustness tests under heavy-rain attenuation, the full strategy showed clear gains over routing-only and multi-agent reinforcement learning (MARL)-routing comparators in connectivity and isolation, but did not uniformly dominate topology reconstruction or the multi-agent deep deterministic policy gradient–artificial potential field (MADDPG-APF) recovery comparator. The results indicate that, in storm-dominated swarm sensing, resilience comes mainly from coordinating exposure reduction with topology stabilization, rather than from optimizing a single layer. Full article

Additively manufactured composite architected discs offer a potential route for producing lightweight semi-finished blanks that can subsequently be shaped by conventional forming processes. However, the relationship between infill architecture, feature size, and deep-drawing formability remains poorly understood. This study investigates the deep-drawing response of material-extruded short-fibre-reinforced polymer composite discs by combining experimental tests and finite element simulations. Four infill strategies, namely perforated body, re-entrant, square and triangular, were first compared at drawing depths of 10 and 20 mm. The perforated body and re-entrant geometries were successfully formed at 10 mm, whereas only the perforated body withstood 20 mm without macroscopic failure. A second campaign focused on perforated discs with hole diameters of 2.5, 5, 7.5 and 10 mm. All configurations were drawable at 10 mm, while the 2.5 mm case failed at 20 mm. Statistical analysis confirmed that hole diameter significantly affected both retained cup height and side-hole aspect ratio. At 20 mm, larger holes reduced local ovalization but increased elastic recovery, leading to lower retained cup height. FEM simulations were used as an interpretative first-order model. They supported the experimental trends by comparing deformation modes, tensile/compressive stress redistribution, forming energy and strain localization. The results show that the formability of architected composite blanks is governed not only by material volume or porosity but by the ability of the internal architecture to accommodate deformation through a suitable balance between local stiffness and geometric compliance. These findings provide design-oriented guidelines for the development of additively manufactured architected blanks intended for hybrid additive–forming manufacturing routes. Full article

Failure Mode and Effect Analysis (FMEA) is a widely applied preventive risk assessment approach to enhance product reliability and safety, yet its structural validity is frequently questioned. Existing improvement models generally overlook the interrelationships among failure modes and suffer from high uncertainty and instability in opinion aggregation, risk factor weight allocation, and Risk Priority Number (RPN) computation. To bridge these gaps, this study proposes an integrated decision-making model. First, the Decision Making and Trial Evaluation Laboratory (DEMATEL) method is employed to analyze interactions among failure modes, constructing an influential network diagram to identify critical items. Second, a Rough Dombi Aggregator is applied for opinion aggregation, minimizing data loss and handling uncertainties from experts’ diverse backgrounds. Third, the Full Consistency Method (FUCOM) is utilized to determine the relative weights of risk factors. Finally, four weighted aggregation methods are developed to calculate RPNs, mitigating the instability common in traditional methods. This vehicle power system case study, alongside model comparison and sensitivity analysis, demonstrates the model’s effectiveness and robustness. The results indicate that across 9 different weight fluctuation scenarios, the core high-risk item “FM₇: Generator coil burned out due to short circuit” consistently ranks 1st. This highlights the exceptional stability of the proposed model in overcoming evaluation fluctuations. Ultimately, this integrated framework not only enhances the accuracy and robustness of failure mode prioritization but also serves as a valuable practical reference for engineers formulating preventive maintenance strategies under limited resources. Full article

Sandstone–marble waste powder concrete composite structures serve as common load-bearing systems in tunnels, underground caverns, and similar engineering projects, where the interface roughness characteristics directly govern their overall stability and service safety. To investigate the influence of interface roughness on the failure behavior of the composite, four groups of sandstone–concrete composite specimens made with marble waste powder concrete were prefabricated with different joint roughness coefficients (JRC = 0, 7.84, 17.99, 20.79). The concrete matrix was prepared with marble waste powder incorporated at 25 wt% of the total binder, corresponding to 20.45 wt% of the total mixture, and the water-to-binder ratio was 0.20. Uniaxial compression tests were conducted with synchronous acoustic emission (AE) and digital image correlation (DIC) monitoring to examine the roughness-dependent mechanical response, energy evolution, damage activity, and strain localization of the composites. The results show that the peak stress and elastic modulus of the composite increase continuously with increasing JRC. When JRC increases from 0 to 20.79, the peak stress increases by 170.3% and the elastic modulus increases by 201.1%. The energy evolution mechanism transitions from progressive damage with gradual energy dissipation at low roughness to a three-stage mode at high roughness, characterized by initial frictional energy dissipation, intermediate energy storage, and rapid elastic energy release and dissipated energy increase near failure. DIC results further reveal that increasing interface roughness suppresses interfacial shear slip and promotes tensile-dominated strain localization, whereas excessive roughness may induce local stress concentration around asperities and increase the tendency toward abrupt post-peak instability, the failure mode changes from mixed tensile–shear failure with obvious interfacial slip to tensile-dominated failure. Full article

Deep Neural Networks (DNNs) are increasingly being used in the design of industrial safety-critical autonomous applications such as autonomous vehicles, industrial robotics, and medical instrumentation and control systems. Ensuring reliable and robust operation of the DNN-based safety-critical systems is challenging because of the complex structure of DNN hardware accelerators utilized for inference that are susceptible to the effects of multi-faults, common-cause fault models, data uncertainties, and unpredictable erroneous behavior. Additionally, transient, permanent, and timing faults affect the accelerator design of processing elements, memory arrays, and datapaths, propagate through DNN computations, and potentially can cause catastrophic failures at the system level. The objective of this survey paper is to systematically evaluate the state-of-the-art fault-tolerant DNN accelerator architectures with particular emphasis on their applicability to safety-critical autonomous systems in industry. The survey investigates architectural perspective, fault modeling, and platform-level trade-offs, runtime resilience, validation practices, and certification readiness, following a PRISMA methodology with evidence-driven synthesis and unbiased study selection. Database searches across IEEE Xplore, Scopus, and Web of Science identified 200 records, of which 82 studies were included based on predefined inclusion and exclusion criteria emphasizing industrial safety-critical relevance, fault modeling at the hardware level, and the implementation at the architectural level. The results indicate that there was a clear shift from traditional redundancy-based approaches to cross-layer and adaptive approaches that provide better trade-offs between performance, reliability, and hardware overhead. The current studies presented are based on simplified fault models, incomplete validation- procedures, and limited consideration of system-level and certification needs, which often do not consider critical failure modes such as Silent Data Corruption (SDC). This has resulted in a significant gap between research-level solutions and industrial deployment requirements. This survey underscores the need for scalable, integrated, and certification-aware design approaches to help connect fault modeling, architectural resilience, validation, and safety assurance to develop reliable and deployable DNN accelerator systems for next-generation industrial safety-critical autonomous applications. Full article

This study examines likelihood-based estimation of the joint survival function $S(t_1,t_2)=Pr\{T^{\left(1\right)}>t_1,T^{\left(2\right)}>t_2\}$ for systems with two competing failure modes observed under right censoring. Rather than introducing a new distributional family, the study compares established dependence mechanisms within a common observed-data framework. Exponential and Weibull margins are combined with three types of dependence: Archimedean copulas, represented by the Gumbel and Clayton families; shared gamma frailty, used to model latent measurement-level heterogeneity; and Marshall–Olkin extensions, used to represent common shocks and simultaneous failures. The same observation scheme, likelihood construction, censoring design, and performance criteria are used across models. Model performance is evaluated through Monte Carlo simulation using bias, integrated mean squared error, and empirical coverage, and the workflow is illustrated with the Device G reliability data. The results show that ignoring dependence can distort joint survival estimates, especially under moderate or high censoring. They also show that copula, frailty, and Marshall–Olkin specifications can lead to different reliability assessments because they encode different stochastic mechanisms. The estimation workflow includes multi-start optimization and diagnostics for boundary solutions, Hessian stability, and irregular likelihood behavior. Full article

Modeling a user’s evolving goals, values, and affect over time is central to perspective-aware AI, yet progress is bottlenecked by the lack of longitudinal data with ground-truth labels for the latent identity state. We introduce PAiNT (Perspective-Aware AI Identity and Narrative Toolkit), a generative framework that simulates long-horizon persona trajectories and emits corresponding multimodal artifacts with ontology-aligned labels of the latent identity state that produced them. PAiNT decouples identity dynamics from artifact generation via a typed Persona Matrix and Situation Graph, coordinated through a multi-agent loop with validation-gated transitions and bounded-window history conditioning. Across four personality archetypes, four backbone LLMs, and three architectural ablations, evaluated with a nine-metric suite calibrated on published longitudinal data, we find that (i) persona initialization produces a durable identity signal that persists above stochastic event noise; (ii) multi-agent orchestration and history conditioning govern distinct quality dimensions, with removal of either causing different failure modes; and (iii) a coherence frontier constrains the trade-off between temporal resolution and horizon, with substantial penalties at daily granularity. We release PAiNT and PAi-Bench, a human-validated benchmark of 1200 labeled multimodal artifacts. Full article

This paper investigates the seismic performance of precast concrete shear-wall subassemblies connected by post-cast ultra-high performance concrete (UHPC) zones and short lap-spliced reinforcement with a lap length of $10d$, where d denotes the diameter of the reinforcement bar. Seven quasi-static cyclic tests were conducted, including one cast-in-place control specimen, five specimens with horizontal UHPC back-cast joints at the wall base, and one exploratory specimen with both horizontal and vertical UHPC back-cast joints. The variables considered were the joint arrangement and the axial compression ratio. The specimens with horizontal joints generally exhibited compression-flexure-dominated damage, and the crushing zone shifted from the wall-footing interface to the ordinary concrete immediately above the UHPC back-cast zone. The specimen with the vertical joint (TW6) exhibited bending-shear damage, accompanied by limited in-plane lateral slip at the beam–wall joint and shear damage of several vertical bars. Specimen TW2, with an axial compression ratio of 0.30, was identified as a construction-quality-sensitive case because an insufficient local UHPC cover caused splitting damage and reduced hysteretic stability. The strain measurements indicate that, within the limits of the present instrumentation, the 10d lap in the UHPC zone provided effective stress transfer in the tested specimens; however, direct interface-slip and bond-slip tests are still required for generalized design verification. Under an axial compression ratio of 0.20, TW1 and TW6 showed comparable seismic indices to the cast-in-place specimen, but the conclusions are limited to the tested configurations. All specimens reached ultimate drift ratios greater than 1/100, and their seismic performance is discussed together with failure mode, stiffness degradation, energy dissipation, and connection reliability. Full article

With the advancement of marine engineering, coral concrete—comprising coral coarse aggregate, coral sand, and seawater—has garnered increasing research interest. To further investigate its compressive mechanical behavior under axial and lateral biaxial stress states, a total of 9 prismatic specimens under axial loading and 45 cubic specimens under biaxial loading were prepared, encompassing three strength grades (C20, C30, and C40) and five lateral stress ratios (0, 0.25, 0.5, 0.75, and 0.9). The failure modes and corresponding axial and biaxial stress–strain curves were meticulously recorded. The axial mechanical response was systematically analyzed, leading to the establishment of a compressive damage constitutive model based on the Weibull distribution. Additionally, the influence of the lateral stress ratio on both peak stress and peak strain was examined, and multiple biaxial failure criteria were formulated. Experimental results reveal that the failure modes of coral concrete specimens are analogous to those of natural coarse aggregate concrete and are significantly affected by the lateral stress ratio. Specifically, an increase in the lateral stress ratio results in higher peak stress, while the absolute value of peak strain exhibits a linear variation. Finally, the proposed axial damage constitutive model and the biaxial failure criteria are rigorously validated against the experimental data. Full article

Integrated sizing and shape optimization of structural layouts often encounters inherent computational difficulties under nonlinear structural responses and transient buckling criteria. These challenges primarily stem from disjointed sub-problems and localized numerical constraints. This work proposes an integrated optimization methodology utilizing a unified flexible multibody dynamics (FMBD) architecture, with the globally convergent method of moving asymptotes (GCMMA) serving as the mathematical programming solver. By leveraging time-domain dynamic relaxation, complex structural phenomena—including localized post-buckling trajectories and structure-mechanism structural transitions—are mapped into standard kinematic displacement bounds, which are subsequently resolved via the gradient-based solver. Comparative analyses against classic static benchmarks demonstrate that the method’s dynamic and geometric nonlinear characteristics allow the design to naturally circumvent various failure modes associated with ideal results under actual loading, yielding outcomes that better align with engineering requirements. Furthermore, the use of displacement constraints avoids the overly restrictive limitations that buckling criteria often impose on the design space; the ability to simultaneously accommodate and rapidly implement both structural and mechanical configurations expands the optimization space, resulting in significantly lighter structures and mechanisms. This method offers a versatile, stable, and complementary computational pathway for the conceptual design and early-stage exploration of integrated size and shape optimization for structures and mechanisms. Full article

One of the most important aspects of reliability engineering is modeling the failure behavior of devices, which can exhibit monotonic or non-monotonic patterns and depend explicitly on design, internal components, and operating conditions, often leading to multiple failure modes. Methodologies exist to address these scenarios; however, many lack the flexibility to capture diverse behaviors throughout the device’s lifespan. Given this, this paper presents a novel reliability model based on the Competitive Risk (CR) framework. This model comprises a minimal variable derived from the Perks Risk Model Type I (PRMTI) to capture risk-rate patterns of different shapes. Unlike existing CR and additive models, the proposed approach effectively handles failure-time data with increasing, bathtub, inverted, or modified risk rates. Relevant mathematical properties for reliability scenarios are presented and analyzed. Furthermore, two approaches for parameter estimation are offered: the maximum likelihood method (MLE) and Bayesian inference (BaI) using the Hamiltonian Monte Carlo (HMC) method. Finally, to verify the proposed methodology, two case studies and a comparative analysis are presented, in which the PRMTI is tested against six other methodologies with similar properties. The results show that the PRMTI outperforms empirical calculations, offering greater agreement and more accurate predictions of failure probabilities. These findings highlight the model’s versatility and accuracy in representing complex failure mechanisms in reliability studies. Full article

Sensor networks increasingly combine exposed sensing nodes, optical communication, photonic hardware, near-sensor inference, and distributed infrastructure monitoring. This changes the security problem from protecting packets alone to establishing device provenance, measurement integrity, link confidentiality and availability, trustworthy inference, physical situational awareness, lifecycle control, and governance. This structured review with documented scoping searches examines security by light: mechanisms in which optical or photonic phenomena directly realize, constrain, compute, or observe a security-relevant function. The review synthesizes screened evidence across photonic roots of trust, visible-light communication and LiFi security, photonic intelligence, reservoir and chaotic photonics, and distributed photonic sensing infrastructure. Searches across arXiv, IEEE Xplore, ACM Digital Library, and Scopus yielded 228 deduplicated candidate records, of which 187 were retained as core evidence and eight as contextual evidence. To avoid overstating heterogeneous photonic work, retained records were separated into direct security evidence, security-adjacent capability evidence, background/framework evidence, and excluded records. The central result is architectural: light-enabled mechanisms are most defensible when they provide explicit, confidence-rated evidence to conventional security engineering. In this paper, confidence-rated evidence means evidence whose security interpretation is tied to a stated asset, adversary or failure mode, evidence role, validation setting, robustness limits, deployment fit, and reproducibility condition. This avoids treating optical novelty, spatial confinement, analog complexity, or high-dimensional dynamics as assurance by themselves. The paper develops an auditable taxonomy, evidence appraisal rubric, mechanism-family synthesis, integration architecture, maturity analysis, and research agenda for incorporating light-enabled mechanisms into secure sensor-networked systems. Full article