Search Results (31,261)

Metal–polymer hybrid joints are gaining importance as they combine high structural rigidity with a low weight. Additive manufacturing processes such as the laser powder bed fusion process (L-PBF) enable the production of complex metallic lattice structures that allow for form-fitting force transmission between the metal and polymer as mechanical interlock elements. In this work, metal–polymer hybrid compounds with additively manufactured transition zones are systematically investigated and mechanically evaluated. Three different lattice geometries (z4A, z8A, z8V) were fabricated from maraging steel (1.2709) using L-PBF and then hybridised with injection moulding using polypropylene (PP C7069-100NA). Mechanical characterisation was performed by tensile tests according to DIN EN ISO 527, in combination with statistical analyses and an analytical serial three-spring model to determine the homogenised elasticity modulus of the transition zone. The results show significant geometry-related differences in tensile strength, maximum force, and effective stiffness. The A-shaped transition zone geometry (z4A) achieves the highest mechanical performance and up to 82% of the tensile strength of the pure polymer, while the V-shaped transition zone geometry (z8V) has significantly lower load-bearing capacities. Variance analysis shows a dominant geometric influence with effect strength of η² ≈ 0.99. The analytically predicted stiffness values match the experimental results within 5–10%. This work demonstrates a reproducible, simulation-sparse approach to the analysis and design of metal–polymer hybrid connections. Full article

Accurate meteorological visibility estimation is critical to the safety and reliability of transportation and environmental monitoring systems. Despite the prevalence of deep learning, models often struggle with the non-linear visual degradation caused by varying atmospheric conditions and a scarcity of instrument-calibrated datasets. This study makes two primary contributions. First, we introduce the Hong Kong Chu Hai College Visibility Dataset (HKCHC-VD) comprising 11,148 high-resolution images paired with precise visibility measurements from a Biral SWS-100 sensor. Second, we propose a Range-Aware Attention Framework (RAT-Attn), an adaptive attention mechanism that translates classical range-specific atmospheric modeling into differentiable deep learning operations. This is a domain-specific architectural optimization that integrates a dual-backbone architecture (CNN and Vision Transformer) with a learnable threshold mechanism. This design enables the model to dynamically prioritize spatial and channel-wise features based on estimated visibility intervals, specifically targeting the non-linear visual degradation unique to fog and haze. Experimental results demonstrate that our proposed approach outperforms existing baselines, including VisNet and landmark ANN-based methods. The ResNet + ViT (spatial-threshold) variant achieves the most balanced performance, recording a Mean Squared Error (MSE) of 5.87 km², a Mean Absolute Error (MAE) of 1.65 km, and a classification accuracy of 87.07%. In critical low-visibility conditions (0 to 10 km), the framework reduces regression error by over 75% compared to the baselines. These results confirm that range-aware adaptive feature fusion is essential for robust meteorological estimation in real-world environments. Full article

Tightly coupled GNSS/INS is a widely adopted architecture for UAVs and ground vehicles. In this study, a Kalman-filter-based fusion framework integrates inertial data with satellite observables, including pseudorange and Doppler-derived range rate, to sustain precise navigation when GNSS quality degrades. A key bottleneck is that many pipelines rely on fixed or overly simplified measurement-noise covariance models, which cannot track the nonstationary statistics of real observations. To address this issue, we develop an adaptive covariance estimator built on a Transformer enhanced with three modules: a Linear-Attention layer, a Residual Sparse Denoising Autoencoder (R-SDAE), and a lightweight residual channel-attention block (LRCAM). The estimator predicts the measurement-noise covariance online. R-SDAE distills sparse, outlier-resistant features from noisy ephemeris; LRCAM reweights informative channels via residual gating; and Linear Attention preserves long-range spatiotemporal dependencies while reducing attention cost from O(N²) to O(N). A predictive factor further modulates the covariance for improved efficiency and adaptability. Experimental results on real road-test data show that the proposed method achieves sub-meter positioning accuracy in open-sky conditions and preserves meter-level accuracy with improved robustness under GNSS-degraded urban scenarios, outperforming the compared adaptive-filtering baselines and neural covariance estimators and thereby demonstrating superior positioning accuracy and stability. Full article

Wheeled bipedal robots are promising for industrial mobility because they combine tight turning, agile balancing, and efficient rolling. Their inherently unstable and underactuated dynamics make reliable reference tracking challenging, particularly in the presence of sustained external disturbances and modeling errors. This paper presents a systematic modeling and control study using a three-degrees-of-freedom sagittal plane representation derived from the original six-degrees-of-freedom dynamics. Two linear tracking controllers are designed and compared: a full state feedback tracking controller and a linear quadratic servo controller with integral action. Practical performance is validated through real-time hardware in the loop simulation, where the controller runs on embedded hardware and the plant is executed on a real-time target including discrete time-sampling effects and analog input output communication noise associated with signal transmission. The results show that both controllers achieve stabilization, while the comparative HILS results reveal a trade-off rather than a uniformly superior controller. The full state feedback controller often yields lower finite-horizon position tracking errors, whereas the linear quadratic servo controller provides tighter body-pitch regulation and the more reliable removal of steady-state offset under sustained constant disturbances. These results demonstrate the feasibility of optimal servo control on cost-effective embedded platforms and indicate that controller selection should depend on the desired balance, considering tracking accuracy, disturbance rejection, convergence behavior, and actuator usage. Full article

The large-scale integration of renewable energy sources and power-electronic-interfaced devices has significantly weakened transient support capability and disturbance tolerance, posing new challenges to the secure and stable operation of modern power systems. Conventional stability analysis methods suffer from high computational burden, long execution time, and limited adaptability to diverse operating scenarios. The rapid development of artificial intelligence (AI) provides effective technical support for fast and accurate assessment of power-system security and stability. This paper presents a comprehensive review of AI-based methods and the interpretability for transient stability assessment (TSA) in modern power systems. First, an intelligent TSA framework is introduced, consisting of three key stages: sample construction and enhancement, intelligent algorithms and learning mechanisms, and model training and interpretability. Subsequently, existing methods for data augmentation, intelligent algorithms, learning mechanisms, and interpretability analysis are systematically reviewed, and the corresponding application scene, technical superiority and limitations are discussed. Finally, from a knowledge–data fusion perspective, four representative integration paradigms combining mechanism-based models and data-driven approaches are summarized, and the application prospects in power-system stability analysis are discussed. Full article

Silicon carbide (SiC) and SiC fiber-reinforced SiC matrix composites (SiC/SiC) are receiving renewed attention for use in next-generation fusion reactors due to their ability to withstand extreme conditions, including high temperatures, neutron irradiation, and plasma interactions. General Atomics Electromagnetic Systems (GA-EMS) has demonstrated significant progress in scaling up the fabrication of SiC/SiC, achieving high mechanical uniformity and meeting dimensional requirements in components up to 12 feet in length. Key developments are discussed including scale-up of the chemical vapor infiltration (CVI) process from lab-scale to full sized parts, high-dose (100 dpa) irradiation testing, nuclear-grade ceramic joining technologies, and production-focused quality control with the collective aim to establish SiC/SiC as a reliable solution for structural and functional components in fusion systems. Beyond manufacturing, the paper addresses supply chain barriers, particularly the limited availability and high cost of nuclear-grade SiC fiber. GA-EMS is developing a novel SiC fiber production method based on a thermochemical cure step that is anticipated to reduce costs compared to traditional approaches. Additionally, advancements in engineered SiC materials, such as SiC foams and tungsten-graded SiC composites, are discussed as promising solutions for specific fusion reactor components. Full article

Shop-floor weld inspection may appear to be a solved problem until a camera is deployed near a galvanized-sheet MIG welding line. The seam reflects light, the texture changes from frame to frame, and the defects of interest are often small and visually subtle. Additionally, the hardware near the line is rarely a data-center GPU. With those constraints in mind, this paper presents YOLO-MIG, a compact detector built on YOLOv10n for weld-seam inspection in practical production conditions. We make three focused changes to the baseline: a C2f-EMSCP backbone block to better preserve weak defect cues with modest parameter growth, a BiFPN neck to keep small-target information alive during feature fusion, and a C2fCIB head to clean up predictions that otherwise get distracted by seam edges and illumination artifacts. On a workshop-collected dataset containing 326 original images, with the training subset expanded through augmentation to 2608 labeled samples in total, YOLO-MIG achieves 98.4% mAP@0.5 and 56.29% mAP@0.5:0.95 on the test set while remaining lightweight (1.83 M parameters, 3.87 MB FP16 weights). Compared with YOLOv10n, the proposed model improves mAP@0.5 by 9.36 points and mAP@0.5:0.95 by 4.89 points, while reducing parameters, GFLOPs, and model size by 43.4%, 19.9%, and 29.9%, respectively. The results suggest that YOLO-MIG is not only accurate but also realistic to deploy at the edge for intelligent weld quality control. Full article

To address the limitations of traditional forest above-ground biomass (AGB) retrieval methods—namely, the restricted accuracy of physical models and the limited generalization ability of purely data-driven models—this study proposes a differentiable physical modeling (DPM) approach for forest AGB estimation. The method adopts the water cloud model (WCM) as a physics-based framework, grounded in radiative transfer theory, and integrates C-band synthetic aperture radar (SAR) data with multispectral imagery. Within the PyTorch tensor computation framework, automatic differentiation (AD) is employed to seamlessly couple the WCM with the deep fully connected neural network (DFCNN), enabling a differentiable implementation of the WCM. Using mean squared error (MSE) as the loss function, the neural network parameters are optimized through backpropagation and gradient descent, thereby constructing an end-to-end trainable DPM model that effectively retrieves forest AGB while preserving physical interpretability and generalization capability. To validate the proposed method, two representative test sites were selected: Simao in Pu’er, Yunnan Province, and Genhe in Inner Mongolia. GF-3 PolSAR and RADARSAT-2 data were used to extract backscattering coefficients and compute the radar vegetation index (RVI), while Landsat 8 OLI imagery was employed to calculate the normalized difference vegetation index (NDVI), difference vegetation index (DVI), and soil-adjusted vegetation index (SAVI). These datasets, together with ASTER GDEM, field-measured biomass, and other relevant datasets, were integrated to construct a multisource dataset combining remote sensing and ground observations. The performance of the DPM model was then compared with the traditional WCM and several data-driven models, including the fully connected neural network (FNN), generalized regression neural network (GRNN), RF, and Adaptive Boosting (AdaBoost). The results indicate that the DPM model achieved R² = 0.60, RMSE = 24.23 Mg/ha, Bias = 0.4 Mg/ha, and ubRMSE = 22.43 Mg/ha in Simao, and R² = 0.48, RMSE = 33.29 Mg/ha, Bias = 0.87 Mg/ha, and ubRMSE = 33.28 Mg/ha in Genhe, demonstrating consistently better performance than both the WCM and all tested data-driven models. The DPM model demonstrated consistent performance across ecologically contrasting forest regions. It alleviated the systematic overestimation bias of purely data-driven models and overcame the limitations in predictive accuracy resulting from the simplified structure of the WCM. The differentiability of the WCM enables the loss function errors to be backpropagated through the neural network, thereby allowing the optimization of the physical model parameters. Overall, the DPM framework integrates the advantages of both physical models and data-driven approaches, providing an estimation method with acceptable accuracy for forest AGB retrieval. It also offers theoretical and practical insights for the integration of deep learning and physical knowledge in other research fields. Full article

Background/Objectives: Lumbar interbody fusion (LIF) is widely utilized to treat multilevel degenerative lumbar spine pathologies. This systematic review aimed to comprehensively review lateral and posterior multilevel LIF procedures and their clinical and radiographic outcomes. Methods: Following the PRISMA guidelines, a search of PubMed, Embase, Web of Science, and Cochrane identified eligible studies. Patient demographics, as well as clinical and radiographic outcomes were collected. Risk of bias was assessed using the MINORS criteria, while randomized trials were evaluated using the RoB-2 tool. An extensive subgroup analysis was completed when that was possible. Results: A total of 45 studies were included consisting of 5623 patients. The pooled outcomes indicated that TLIF demonstrated the lowest operative duration (198.7 ± 77.83 min) and LOS (5.09 ± 2.5 days), alongside favorable ODI (33.68 ± 6.43), VAS leg pain (5.39 ± 0.66), and VAS back pain (4.67 ± 0.79) score gains. Comparative evidence found that LLIF and OLIF provided advantageous radiographic improvement to the posterior approaches. Comparative evidence on techniques challenged the use of autogenous bone within PLIF, PEEK over HA/PA66 cages, and found no advantages in unilateral decompression within TLIF. There was minimal clinical difference in evidence assessing MIS (minimally invasive) vs. open-TLIF or unilateral vs. bilateral pedicle screw fixation (PSF). Conclusions: This is the first systematic review of the multilevel LIF literature, revealing that while pooled data favored TLIF, a publication bias was detected, and comparative evidence reported advantages for lateral and oblique approaches. Given the lack of conclusive evidence, robust study designs are needed to guide clinical decision-making for multilevel lumbar pathology. Full article

With the proliferation of online examination systems, protecting academic integrity and reliably detecting cheating have become significant research problems. Current AI-based online monitoring systems can achieve high accuracy by analyzing visual behavioral cues; however, their often black-box nature limits their explainability, reliability, and legal compliance (e.g., GDPR). In contrast, while rule-based approaches are interpretable, they are insufficient for generalizing complex and ambiguous human behaviors. This study proposes an explainable neuro-symbolic framework combining data-driven learning with symbolic reasoning for cheating detection in online exams. The proposed framework comprises three main layers: a neural perceptron layer that generates a suspicious behavior score; a symbolic reasoning layer comprising ANFIS and ILP methods to increase explainability and manage ambiguity; and a neuro-symbolic fusion layer that integrates these two layers. The success of the proposed framework for plagiarism detection was evaluated using a dataset containing visual–behavioral features such as gaze behavior, head pose, hand-object interaction, and device usage, along with the XGBoost method at the neural perceptron layer. Experimental results show that the proposed approach achieves high detection success and supports decision-making using logical rules, thereby reducing false positives. In this respect, the study offers an ethical, transparent, and reliable solution for online exam security. Full article

A Cu-containing FeCrMnNiAl multi-principal element alloy was processed by laser-based and electron beam-based powder bed fusion (PBF-LB/M and PBF-EB/M) to investigate processing–microstructure–property relationships. In focus were alloy variants with a relatively high Cu content. Two PBF-LB/M scan strategies, employing a Gaussian beam with and without a re-scan with a laser featuring a flat-top profile, were compared to PBF-EB/M processing, followed by heat-treatments between 300 °C and 1000 °C. The phase constitution, elemental partitioning and grain boundary characteristics were analyzed by X-ray diffraction, electron backscatter diffraction and energy-dispersive X-ray spectroscopy. Mechanical behavior was assessed by hardness and tensile testing. Both manufacturing routes promoted the evolution of stable multi-phase microstructures composed of face-centered-cubic (FCC)- and body-centered-cubic (BCC)-type phases across all heat-treatment conditions. PBF-LB/M processing resulted in finer, dendritic microstructures and suppressed formation of a Cu-rich FCC phase due to higher cooling rates, whereas PBF-EB/M promoted the evolution of Cu-rich FCC segregates and equiaxed grain morphologies. Heat-treatment above 700 °C led to recrystallization, accompanied by an increase of the FCC phase fraction, grain coarsening, and recovery. At lower heat-treatment temperatures, the changes in microstructure are different. Here, it is assumed that small, non-clustered Cu-rich precipitates formed at the grain and sub-grain boundaries, although this assumption is only based on the assessment of the mechanical properties. The size of these precipitates is below the resolution limit of the techniques applied for analysis in the present work. Additional structures seen within the Cu-rich areas of PBF-EB/M-manufactured samples treated at lower temperatures also seem to have an influence on the hardness and yield strength. All of the conditions investigated exhibited pronounced brittleness, limiting reliable tensile property evaluation and indicating the need for further optimization of processing strategies and microstructural control for high-Cu-fraction-containing multi-principal element alloys. Full article

Reliable process monitoring and quality evaluation for resistance spot welding (RSW) have become more important now than ever. An ultrasonic probe embedded into welding electrodes has enabled the acquisition of data about molten pool formation throughout welding, but automation of high-performance ultrasonic data analyses is still necessary to fully realize a monitoring system. This work proposes a two-stage deep learning (DL) approach for automated ultrasonic data analysis for RSW processing monitoring. The first stage conducts semantic segmentation on ultrasonic M-scan welding process signatures, yielding masks for identified molten pool and stack regions from which weld penetration measurements can be directly extracted, as well as expulsion occurrences throughout welding. From input images and segmentation outputs, the second stage directly estimates resultant weld nugget diameters using an additional neural network. Both stages leveraged architectures based on TransUNet, mixing elements of both convolutional neural networks (CNN) and vision transformers, and the effect of cross-attention for stack-up sheet thickness data fusion was investigated via an ablation study. Additionally, in the diameter estimation stage, the ablation study included alternative feature extraction architectures in the network and investigated the provision of M-scans to the model alongside segmentation masks. In both cases, cross-attention was determined to improve performance, and in the case of diameter estimation, providing M-scans as input was found to be beneficial in general. With cross-attention, the segmentation approach yielded a mean intersection over union (IoU) of 0.942 on molten pool, stack, and expulsion regions in the M-scans with 13.4 ms inference time. With cross-attention, diameter estimates yielded a mean absolute error of 0.432 mm with 4.3 ms inference time, representing a significant improvement over algorithmic approaches based on ultrasonic time of flight. Additionally, the approach attained >90% probability of detection (POD) at 0.830 mm below the acceptable diameter threshold and <10% probability of false alarm (PFA) at 0.828 mm above the threshold. These results demonstrate a novel production-ready application of DL in ultrasonic nondestructive evaluation (NDE) and pave the way for zero-defect RSW manufacturing. Full article

Space-based multi-target passive tracking is critical for space situational awareness, but faces severe challenges due to the limited field-of-view (FoV) and directional ambiguity of onboard sensors. These constraints often lead to target loss, poor observability, and decreased estimation accuracy. To address these issues, different fusion architectures have been explored. While centralized measurement-level fusion offers superior accuracy for estimating target states, distributed estimation-level fusion provides greater reliability for estimating the number of targets. To adaptively leverage these two complementary strengths, a dynamic hierarchical fusion method through real-time optimization of the fusion topology is proposed. Specifically, at each decision epoch, sensor nodes are dynamically partitioned into local fusion nodes (LFNs) and detection-only nodes (DONs). Each LFN receives measurements from selected DONs and executes an iterated-correction Gaussian-mixture probability hypothesis density filter. Subsequently, LFNs share and fuse their estimates using the intensity-dependent arithmetic average fusion. This dynamic process is achieved by applying a sensor management scheme based on partially observable Markov decision process (POMDP). To ensure accurate cardinality estimation, the reward function in POMDP utilizes the posterior expected number of targets. The resultant optimization is efficiently solved using a binary particle swarm optimization algorithm. Numerical and hardware-in-the-loop simulations demonstrate the effectiveness of the proposed method in balancing the accuracy of target number and state estimation. Full article

Engineering drawings are fundamental to industries such as oil and gas, construction, and manufacturing. However, current practices relying on manual design or rigid parametric templates often suffer from inefficiency and layout inconsistencies. To address these issues, the layout task is formulated as the Orthogonal Rectangle Packing Problem with Multiple Configurations and Complex Constraints (ORPPMC). The Deep Reinforcement Learning for Multi-Configuration Drawing Layout (DRL-MCDL) framework is proposed, which integrates the Pointer Network for Drawing Element Sequencing (PN-DES) with the Target-Type-Matching-based Multi-Pattern Positioning Strategy (TTM-MPPS). Within this framework, PN-DES employs deep reinforcement learning and feature fusion to combine element attributes with layout configurations for optimal sequence inference, while TTM-MPPS performs precise positioning in accordance with industrial rules to ensure strict adherence to aesthetic requirements. Ablation experiments validate the contribution of each module. Experimental results on real-world engineering drawings demonstrate that DRL-MCDL achieves a Feasibility Rate (FR) exceeding 98.5% on standard instances (12–40 elements), significantly outperforming traditional methods. Furthermore, it maintains a high inference efficiency with an Average Time (AT) of less than 0.3 s, striking an optimal balance between layout quality and computational speed. Full article

Wearable fall detection systems face a fundamental challenge: while gyroscope data provide valuable orientation cues, naively combining raw gyroscope and accelerometer signals can degrade performance due to noise contamination. To overcome this challenge, we present a dual-stream transformer architecture that incorporates (i) Kalman-based sensor fusion to convert noisy gyroscope angular velocities into stable orientation estimates (roll, pitch, yaw), maintaining an internal state of body pose, and (ii) processing accelerometer and orientation streams in separate encoder pathways before fusion to prevent cross-modal interference. Our architecture further integrates Squeeze-and-Excitation channel attention and Temporal Attention Pooling to focus on fall-critical temporal patterns. Evaluated on the SmartFallMM dataset using 21-fold leave-one-subject-out cross-validation, the dual-stream Kalman transformer achieves 91.10% F1, outperforming single-stream Kalman transformers (89.80% F1) by 1.30% and single-stream baseline transformers (88.96% F1) by 2.14%. We further evaluate the model in real time using a watch-based SmartFall App on five participants, maintaining an average F1 score of 83% and an accuracy of 90%. These results indicate robust performance in both offline and real-world deployment settings, establishing a new state-of-the-art for inertial-measurement-unit-based fall detection on commodity smartwatch devices. Full article