Search Results (2,232)

This study aims to explore how the functional characteristics of virtual models in autonomous driving systems influence users’ psychological needs, thereby shaping driving habits and continued usage intentions. As autonomous driving systems evolve toward learning-based intelligence, human–computer interaction interfaces play a crucial role in shaping driver behavior and sustained system adoption. However, prior research has primarily focused on trust and intention to use, with less attention paid to how motivational and system-feature factors jointly influence driving habits and continuous use intentions. Building upon self-determination theory (SDT), this study constructs an extended framework for human–AI co-driving behavior, examining the impact of three intrinsic psychological drivers (perceived autonomy importance, self-efficacy, and identification) on driving habits and continuous use intentions, while also considering the visual factors of virtual models and nine technical feature factors. Using online questionnaires, 614 valid samples were collected and empirically tested using PLS-SEM and IPMA. Results indicate that the perceived importance of autonomy significantly positively influences both driving habits and continuous use intentions; self-efficacy, identification, and visual factors did not show significant effects at either stage. Among the technical feature factors, data acquisition and feedback, intelligent driving modes, and risk perception capabilities significantly enhanced driving habits and continuous use intentions, achieving an optimal “high importance–high performance” match in the IPMA matrix. IPMA further revealed that perceived autonomy importance was associated with a “high importance–low performance” mismatch in the driving habit formation stage, representing a critical shortfall requiring urgent optimization; intelligent driving modes and data acquisition and feedback, conversely, demonstrated both high importance and high performance in driving continuous use intentions. These findings extend the application of self-determination theory to autonomous driving scenarios into a unified framework integrating motivational and system-feature factors, providing empirical evidence for phased optimization of human–AI co-driving system design and evaluation. Full article

Objective feedback is important for improving welding training and reducing dependence on experienced instructors. This study proposes an image-based sensing framework for extracting motion and geometric features from videos acquired using a simple visualization system mounted inside a welding helmet during semi-automatic gas metal arc welding (GMAW) and manual gas tungsten arc welding (GTAW). The welding videos were acquired in real time, whereas the subsequent image-processing and quantitative feature-extraction procedures were performed offline. This visualization system consists of an instructor-side unit and a welder-side unit equipped with a prototype compact camera. Welding images were trimmed to 288 × 288 pixels and classified into eight regions for GMAW and six regions for GTAW using a U-Net-based semantic segmentation model. The segmented regions in GMAW included the arc, molten pool, groove, torch, wire, overlap regions, and background, whereas those in GTAW included the arc, bead, groove, electrode, filler wire, and background. Groove edges were detected using Canny edge detection and the Hough transform. The wire-tip position in GMAW and the electrode-tip position in GTAW were estimated using the arc centroid as an image-based positional proxy and normalized by the detected groove width to obtain normalized wire-tip and electrode-tip motion, respectively. In addition, the molten-pool width in GMAW and the bead width in GTAW were extracted and normalized using the same procedure to obtain groove-normalized geometric features. For the GMAW dataset, the segmentation model achieved a validation accuracy of 95.2% and a validation mean intersection over union (mIoU) of approximately 67.1%. For the GTAW dataset, the corresponding validation accuracy and validation mIoU were 96.6% and approximately 85.9%, respectively. The results showed that the proposed framework can successfully extract normalized wire-tip motion and molten-pool width in GMAW, as well as normalized electrode-tip motion and bead width in GTAW, from welding videos acquired from different welders. These findings demonstrate the potential of the proposed framework to extract quantitative motion and geometric features from welding videos, providing useful data for future welding training support and welding behavior analysis. Full article

High-precision urban 3D localization is a critical foundation for vehicular Internet of Things (IoT) applications, yet conventional Global Navigation Satellite System (GNSS)-based localization is vulnerable to signal obstruction, multipath effects, and non-line-of-sight propagation in complex environments. To improve localization reliability, this paper proposes a consistency-guided quantitative–qualitative fusion (CG–QQF) framework with vision-based terrain constraints. The framework integrates heterogeneous quantitative sensors, including an absolute positioning source, inertial measurement unit (IMU), wheel encoders, and a steering angle sensor, for continuous metric state estimation, while a monocular camera provides qualitative terrain-slope information. Rather than treating visual perception as a direct metric observation, the proposed method introduces it as a conditional structural constraint that is activated only when it is consistent with the quantitative estimate, thereby regularizing the localization solution and suppressing vertical drift. Although ultra-wideband (UWB) positioning is adopted as the absolute positioning source in the experimental platform, it serves as a generic positioning module and can be replaced by GNSS-based techniques such as real-time kinematic (RTK) and precise point positioning (PPP). In an indoor scaled proof-of-concept experiment over a controlled four-lap dataset, CG–QQF achieves a 3D RMSE of 0.0575 m and a vertical MAE of 0.0042 m. Its 3D RMSE is approximately 4.0% lower than quantitative sensor fusion (QSF), 37.9% lower than absolute-positioning/inertial fusion (ABS–INS), and 49.9% lower than vision-assisted quantitative fusion (VA–QF). These results demonstrate that consistency-triggered qualitative constraints can complement metric sensor fusion without directly introducing uncertain visual measurements, providing a practical mechanism for improving the robustness and vertical stability of heterogeneous localization systems. Full article

Airborne dust plumes are difficult to localize in underground mine-face video because the scene is dark, illumination moves with machinery, and dust is confused with lamp bloom, reflective steel, and water-spray aerosol. We present DustVeil, a label-free two-stage system for image-space plume localization. Its software teacher combines background-referenced veiling (C1), local texture decay (C2), and absolute dark-channel response (C3) with a probabilistic soft-OR, then applies glare and chroma gates. The teacher returns a dimensionless response map in [0, 1], a binary plume mask and the corresponding image-area ratio; it does not estimate dust concentration, particle-size distribution, respirable exposure, or hazard categories. Teacher outputs from 342 frames in 114 clips/24 sessions supervise a 0.47 M parameter TinyU-Net. Evaluation uses a 144-image synthetic calibration set and a 72-frame real test set drawn from 72 clips in 18 sessions, with all roles separated at clip and session levels. Thresholds are selected only on synthetic masks and frozen before real scoring. After replacing per-image score normalization with fixed baseline-normal calibration and using reference implementations of the anomaly methods, DustVeil obtains IoU/F1 of 0.366/0.500 and the lowest clean-frame false-positive area (3.7% versus 13.9–59.9%). A separate water-spray set quantifies visual specificity. TinyU-Net runs at 610 FPS for network-only inference and 233 FPS aggregate in the measured six-stream decode-to-mask pipeline; optical flow is excluded from these figures. The validated scope is six fixed visible-light RGB cameras with camera-specific unlabelled calibration at one site, rather than concentration monitoring or camera-disjoint deployment. Full article

Archaeological sites with limited surface visibility and low spatial legibility often depend on presentation systems to communicate historical functions and heritage meaning. Using the Site of Xuanquan Posthouse as a case study, this study examined how visual attention and visitor perceptions jointly inform the interpretive effectiveness of such low-visibility archaeological sites. A complementary two-stage quantitative design was adopted. First, 20 adults viewed 12 static presentation images, and visual attention was compared across eight categories of information. Second, 252 valid questionnaires were analyzed using partial least squares structural equation modeling (PLS-SEM) and importance–performance map analysis (IPMA). Archaeological remains, reconstructed architecture or models, and digital media generally showed shorter time-to-first-fixation values and greater cumulative visual attention, whereas explanatory text, diagrams and maps, and bamboo and wooden slip documents were less visually prominent. Interpretation quality, perceived authenticity, and exhibition experience quality were all positively associated with perceived heritage understanding and perceived interpretation effectiveness, with heritage understanding playing a central mediating role. The findings indicate that effective presentation should connect visually prominent entry points with explanatory, spatial, and evidentiary information so that visitors can progress from recognizing physical remains to understanding their historical functions and heritage significance. Full article

UAV visual localization aims to utilize real-time ground observation imagery captured by drone platforms to retrieve the most relevant images from a large-scale satellite remote sensing image database and thereby estimate their corresponding geographic coordinates. It is a critical task in autonomous navigation of unmanned systems, emergency reconnaissance, and low-altitude remote sensing applications. Existing UAV visual localization methods typically rely on global feature similarity to rank candidate satellite tiles and directly adopt the center of the Top-1 tile as the localization result, leading to unstable rankings and coordinate estimation errors in continuous area localization tasks. To address these issues, this paper proposes a token-level local matching reranking method and a neighborhood-consistent position fusion method for UAV visual localization. First, a global search efficiently retrieves a Top-K candidate set from a large-scale reference database. Second, a token-level local matching reranking module is introduced, which utilizes local token interactions, neighborhood geometric priors, and candidate relationship modeling to perform fine-grained reranking and score calibration of high-confidence candidates, thereby enhancing the reliability of the top candidates’ rankings. Finally, a neighborhood-consistent position fusion strategy is proposed, which adaptively fuses and predicts position coordinates by jointly utilizing the spatial distribution and confidence relationships of multiple candidate satellite tiles to mitigate the discretization errors caused by center-based localization using a single tile. Experimental results on the GTA-UAV and UAV-VisLoc datasets demonstrate that the proposed method effectively improves candidate ranking quality and reduces meter-level localization errors under same-area settings. Compared with the Global Retrieval baseline, over five independent runs under the GTA-UAV same-area setting, the proposed method achieves an average Recall@1 (R@1) gain of 2.89 percentage points and reduces the average Dis@1 localization error by 60.95 m. In a single-seed evaluation under the UAV-VisLoc same-area setting, it also improves R@1 by 3.03 percentage points and reduces Dis@1 localization error by 39.53 m. Full article

Moon and Mars mission architectures are shifting from short stays to longer surface stays in isolated, confined and extreme (ICE) conditions, where small crews live almost entirely inside pressurized habitats. As transit durations increase and lunar outposts evolve into more permanent bases, crews are exposed for longer periods to environmental hazards and non-terrestrial gravity that disrupt usual sensorimotor patterns. In this context, the habitat becomes the primary interface between human bodies and extreme environments, shaping how inhabitants perceive, move, orient themselves and sustain everyday routines away from Earth. This article develops a neuroarchitecture integrative model for indoor living in lunar and Martian habitats, treating space habitat resilience as a cognitive and experiential property of the human–habitat system. The model connects advances in space architecture and planetary science research with person–environment theories to show how interior form and indoor environmental quality (IEQ) influence attention, emotional regulation and social functioning under confinement. It distinguishes a macro scale, where planetary constraints compress human experience into Built Environments in Extreme Environments (BEXEs), from a micro scale, where habitability is organized into four functional clusters (somatic, operational, psychosocial and ludic-recreational). Conventional IEQ assessment addresses a small set of generic dimensions applicable to any building; here, these are reorganized into twelve cluster-specific dimensions, three per cluster, calibrated for confinement and for the absence of an accessible exterior. Focusing on room shape and proportions, degrees of enclosure and visual order as key interior variables, the model positions the habitat as an active co-regulator of cognition and argues for design agendas that move beyond minimum safety and volume standards toward evidence-informed cognitive habitability in emerging off-Earth settlements. Full article

This study investigates the correlations between the parameters of the Hysteresis-Attrition-Friction model (HAF), hysteresis (h), attrition (a), and friction coefficient (ν), under different surface conditions and contact pressures. The HAF model, a sigmoidal model developed in-house, provides a novel approach for characterizing tribological behaviour through its three key parameters (h, a, ν). The analysis employed a bootstrapping method to generate numerous parameter samples, allowing for the estimation of joint distributions and the identification of correlations between parameter pairs. The model here is applied to data from experiments conducted using both smooth and rough surface configurations at three distinct contact pressures: 1.2 GPa, 1.6 GPa, and 1.9 GPa. The results revealed a positive correlation between hysteresis and attrition across both surface types, indicating that higher energy dissipation through hysteresis is associated with increased friction. Conversely, negative correlations were found between hysteresis and friction coefficient, and between attrition and friction, suggesting a trade-off between energy dissipation and friction. The bivariate kernel density plots further highlighted these patterns, helping to visualize the complex relationships within the model. The results emphasize the importance of considering surface conditions and pressure when applying the HAF model to predict performance in tribological systems. Full article

Manual soldering requires phase-dependent coordination of posture, hand movement, tool position, and visual attention. We propose a depth-camera framework that estimates the work phase and detects deviations using three-dimensional upper-body and hand features, task-related object positions, and gaze-related approximation features derived from facial orientation and head posture. The system estimates three predefined phases: preparation, active soldering, and cleanup. Windows with insufficient phase confidence are assigned to Uncertain Phase and routed to review rather than treated as deviation labels. In the evaluation, normal trials showed stable process sequences, whereas trials with scripted simulated unsafe-like movements produced local increases in the deviation score and review-required intervals associated with reduced phase-estimation reliability. These findings suggest that the framework may support retrospective safety-related assessment and the identification of process-inconsistent operations in seated manual soldering under controlled laboratory conditions. The bio-inspired contribution is a functional abstraction of phase-dependent perceptual-motor coordination into context-dependent engineering reference patterns and an uncertainty-aware review mechanism. Full article

Grain warehouses are critical nodes in food storage, circulation, and reserve systems, and their safe operation is relevant to occupational protection, reserve stability, and food-system resilience. However, unsafe warehouse behaviors are still mainly identified through manual patrols and fragmented inspections, making continuous observation, cross-site coordination, and early intervention difficult. To address this problem, this study proposes a grain warehouse safety governance framework with federated visual intelligence. The framework combines a real-time visual perception module, data-local federated learning, and a city-level digital supervision platform. Drawing on the Wenzhou grain reserve application scenario, the study defines twelve visually observable risk categories, including personal protective equipment (PPE) violations, and links artificial intelligence (AI) recognition with multi-source inspection, spatial mapping, warning generation, collaborative verification, work approval, closed-loop rectification, and large-model-assisted analysis. A four-client technical pilot compares isolated local, centralized, Federated Averaging (FedAvg), and Federated Proximal Optimization (FedProx) training across three selected recognition tasks. The task-specific results provide quantitative evidence from held-out warehouse-level validation and an intermittent-client stress test. The best held-out weighted F1-score (F1) values were 0.7712 for crossing over a conveyor, 0.5891 for passing under a conveyor, and 0.8481 for missing five-point safety harness. In the one-small-client-absent stress test, FedProx achieved weighted F1 scores of 0.5701, 0.4324, and 0.8064 for the same tasks, respectively. The framework translates visual detections into warning outputs, management indicators, and accountable safety workflows under data-governance constraints. It positions computer vision (CV) as an automated observation mechanism for responsible AI governance and embeds visual intelligence into a human-confirmed, data-local, and auditable safety-management workflow for sustainable food-storage infrastructure. Full article

A rare carved lacquer (tixi) plate recovered from the Southern Song Dynasty Nanhai No. 1 shipwreck was examined to reconstruct its coating stratigraphy, raw materials, and manufacturing sequence. Detached fragments collected before conservation treatment were investigated by cross-sectional optical microscopy, micro-Raman spectroscopy, thermally assisted hydrolysis–methylation pyrolysis–gas chromatography/mass spectrometry (THM-Py-GC/MS), scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDS), and wood-anatomical microscopy. The polished cross-section contains nine lacquer layers with a combined thickness of 387.3 μm above a heterogeneous ground, yielding ten visually distinguishable strata. Alternating dark, red, and yellow layers establish the technological basis for the carved polychrome effect. Raman bands identify cinnabar (HgS) in the red layer and orpiment (As₂S₃) in the yellow layer. THM-Py-GC/MS detected homologous alkenes, alkanes, and alkylbenzenes diagnostic of Chinese lacquer derived from Toxicodendron vernicifluum. Monocarboxylic acids were present, whereas no clear dicarboxylic-acid markers of a drying oil were observed; because the object was waterlogged and degraded, this absence is treated as a lack of positive evidence rather than proof that oil was never used. The ground is enriched in Ca and P and is therefore consistent with a bone-ash-based filler, although phase-specific confirmation remains necessary. Wood anatomy identifies the substrate as Chinese fir (Cunninghamia lanceolata, Cupressaceae). Together, the results document an organic–inorganic multilayer coating system and provide material evidence for Southern Song carved-lacquer technology, while defining conservation risks associated with a waterlogged wooden core and light-sensitive pigments. The marine archaeological context and the support-to-surface, layer-resolved design distinguish this case from most previous studies of Song-dynasty lacquerware. Beyond technological reconstruction, the findings identify conservation priorities for waterlogged wood, the wood–ground–lacquer interface, and light-sensitive pigmented layers, and provide a transferable evidence framework for comparative research on archaeological lacquer. Full article

Navigating large, unfamiliar campuses can be challenging for visitors, even with campus maps available. To address this issue, this study proposes an intelligent, autonomous campus navigation system to help users reach their destinations efficiently. The proposed system integrates computer vision, LiDAR, speech recognition, global positioning, and path-planning technologies to provide accurate, user-friendly guidance in complex environments. A YOLO-based neural network performs real-time building recognition, while a speech recognition module interprets users’ spoken destination requests and commands. GPS data are mapped to campus map coordinates to improve localization accuracy, and Dijkstra’s algorithm computes optimal navigation paths. All components are integrated into a graphical user interface that provides real-time visual feedback, including recognized building names and current location. Experimental results demonstrate that the proposed system achieves reliable building recognition, accurate speech understanding, and effective route planning, significantly reducing navigation time for users unfamiliar with the campus. The proposed framework not only enhances smart campus navigation but also shows strong potential for extension to other large-scale environments such as hospitals and shopping malls. Full article

Wearable electrocardiographic technologies have become increasingly important for continuous cardiac monitoring; however, most existing portable systems are limited by the number of recorded leads or provide only basic signal acquisition without advanced automated analysis. This study presents a third-generation wearable twelve-lead electrocardiographic platform developed for multilead ECG acquisition and automated spatial ECG analysis. Compared with the previous generation, the hardware modification primarily consists of architectural consolidation: functions previously distributed across an STM32 microcontroller and separate wireless communication modules are integrated into a single ESP32-S3-based architecture, while the ECG acquisition principle, ten-electrode configuration, and sampling rate remain unchanged. The main methodological contribution of the present work is the software pipeline for lead-specific ST₈₀ measurement and analysis of ST-segment deviations across anatomically contiguous leads. The system uses an ADS1298 analog front-end for synchronized multichannel ECG acquisition. The host software performs digital preprocessing, R-peak detection, ECG feature extraction, twelve-lead reconstruction, lead-specific ST₈₀ measurement, contiguous-lead analysis, and generation of a preliminary computer-assisted ECG report. The developed platform was clinically evaluated using sequential recordings acquired with the proposed system and a reference clinical electrocardiograph. Quantitative comparison of automated PR, QRS, QT, and QTc measurements in 30 paired recordings demonstrated positive correlations with the reference BTL Flexi 12 ECG (r = 0.756–0.820, all p < 0.001), with mean absolute errors ranging from 2.53 ms for QRS duration to 10.40 ms for the QT interval. The system successfully recorded diagnostically interpretable twelve-lead ECGs in all participants and produced stable signal quality suitable for clinical assessment. The software automatically identified ECG waves and intervals, reconstructed twelve-lead recordings, evaluated ST-segment deviations across individual leads, and localized ischemia-related changes according to standard anatomical lead groups. Integration of signal acquisition, processing, visualization, and automated interpretation into a single telemedicine-oriented platform reduced hardware complexity while maintaining reliable multichannel ECG monitoring. The proposed wearable platform demonstrates the feasibility of combining compact embedded hardware with automated multilead ECG analysis for remote cardiovascular monitoring. The presented architecture provides a practical foundation for telemedicine applications and may support earlier recognition of clinically significant electrocardiographic abnormalities during ambulatory monitoring. Full article

Bubble columns are widely used in gas–liquid processes, yet predicting bubble hydrodynamics remains challenging because of the coupled effects of operating conditions and wall confinement. This study experimentally investigates the influence of confinement, gas flow rate, axial position, and liquid flow configuration on bubble size, rise velocity, and shape in rectangular bubble columns using high-speed visualization and shadowgraphy measurements. Experiments are performed at three confinement ratios, $\lambda =3.7$, $\lambda =11$, and $\lambda =18.5$. The results show that confinement strongly modifies bubble formation, growth, velocity, and shape. Under strong confinement, larger bubbles, higher aspect ratios, and significant axial increases in bubble size are observed, indicating continued bubble enlargement along the column height. Bubble rise velocity and dimensionless velocity are also strongly affected by confinement, whereas liquid flow configuration mainly influences bubble velocity under weak confinement. Furthermore, visual observations reveal the onset of transient heterogeneous flow structures under strong confinement despite conventional flow-regime maps predicting homogeneous flow. Existing aspect-ratio correlations reproduce the general trend but do not fully account for confinement effects. These findings demonstrate that confinement is a governing parameter in rectangular bubble columns and should be explicitly considered in future hydrodynamic and mass transfer models for confined gas–liquid systems. Full article

The relevance of this study stems from the need for a rational allocation of limited financial resources under conditions of intense economic confrontation. The central research question is as follows: how can resources be allocated optimally and zones of guaranteed survival be mathematically justified in a continuous conflict characterized by bilinear dynamics and nonmeasurable shock controls? To address this question, we propose a new computational approach based on a differential game of quality. Unlike existing models focused primarily on abstract equilibrium analysis, the present study applies a limit-transition method for multistep positional strategies to a macroeconomic survival problem. The computational experiments explicitly construct and visualize the preference regions, i.e., the guaranteed-survival regions, of the opposing parties. Sensitivity analysis reveals an important pattern: a linear increase in the parameter $\mathsf{\alpha }$ by 10% expands the system’s survival region by 14%. In addition, the proposed semi-implicit method reduces the computational time by 35–40% while preserving numerical stability. The practical significance of this study lies in establishing an algorithmic foundation for decision support systems (DSS) that can model the boundaries of economic resilience and synthesize optimal strategies for keeping the system within a safe region. Full article