Search Results (1,915)

This study proposes a novel framework to enhance inner race fault features in servo motor bearings by acquiring rotary encoder-derived instantaneous angular speed (IAS) signals, which are obtained from a servo motor encoder without requiring additional external sensors. However, such signals are often obscured by strong periodic interferences from motor pole-pair and shaft rotation order components. To address this issue, three key improvements are introduced within the cyclic blind deconvolution (CYCBD) framework: (1) a comb-notch filtering strategy based on rotation domain synchronous averaging (RDA) to suppress dominant periodic interferences; (2) an adaptive fault order estimation method using the autocorrelation of the squared envelope spectrum (SES) for robust localization of the true fault modulation order; and (3) an improved envelope harmonic product (IEHP), based on the geometric mean of harmonics, which optimizes the deconvolution filter length. These combined enhancements enable the proposed improved CYCBD (ICYCBD) method to accurately extract weak fault-induced cyclic impulses under complex interference conditions. Experimental validation on a test rig demonstrates the effectiveness of the approach in enhancing and extracting the fault-related features associated with the inner race defect. Full article

Hyperspectral imaging (HSI) systems often suffer from complex noise degradation during the imaging process, significantly impacting downstream applications. Deep learning-based methods, though effective, rely on impractical paired training data, while traditional model-based methods require manually tuned hyperparameters and lack generalization. To address these issues, we propose SS³L (Self-Supervised Spectral-Spatial Subspace Learning), a novel HSI denoising framework that requires neither paired data nor manual tuning. Specifically, we introduce a self-supervised spectral–spatial paradigm that learns noisy features from noisy data, rather than paired training data, based on spatial geometric symmetry and spectral local consistency constraints. To avoid manual hyperparameter tuning, we propose an adaptive rank subspace representation and a loss function designed based on the collaborative integration of spectral and spatial losses via noise-aware spectral-spatial weighting, guided by the estimated noise intensity. These components jointly enable a dynamic trade-off between detail preservation and noise reduction under varying noise levels. The proposed SS³L embeds noise-adaptive subspace representations into the dynamic spectral–spatial hybrid loss-constrained network, enabling cross-sensor denoising through prior-informed self-supervision. Experimental results demonstrate that SS³L effectively removes noise while preserving both structural fidelity and spectral accuracy under diverse noise conditions. Full article

Malay songket motifs are a vital component of Malaysia’s intangible cultural heritage, characterized by intricate visual designs and deep cultural symbolism. However, the practical digital preservation and retrieval of these motifs present challenges, particularly due to the rotational variations typical in textile imagery. This study introduces a novel Content-Based Image Retrieval (CBIR) model that integrates Principal Component Analysis (PCA) for feature extraction and Quadratic Geometric Distance (QGD) for measuring similarity. To evaluate the model’s performance, a curated dataset comprising 413 original images and 4956 synthetically rotated songket motif images was utilized. The retrieval system featured metadata-driven preprocessing, dimensionality reduction, and multi-angle similarity assessment to address the issue of rotational invariance comprehensively. Quantitative evaluations using precision, recall, and F-measure metrics demonstrated that the proposed PCAQGD + Rotation technique achieved a mean F-measure of 59.72%, surpassing four benchmark retrieval methods. These findings confirm the model’s capability to accurately retrieve relevant motifs across varying orientations, thus supporting cultural heritage preservation efforts. The integration of PCA and QGD techniques effectively narrows the semantic gap between machine perception and human interpretation of motif designs. Future research should focus on expanding motif datasets and incorporating deep learning approaches to enhance retrieval precision, scalability, and applicability within larger national heritage repositories. Full article

Cement-based materials are essential construction components, yet their complex microstructures critically govern mechanical performance and durability. This study investigates the micro-textural characteristics and mechanical properties of cement paste modified with grinding aids (triethanolamine, TEA; maleic acid triethanolamine ester, MGA) and polycarboxylate-based superplasticizer (PCA). Moving beyond qualitative SEM limitations, we employ advanced image-based quantitative techniques: grayscale-based texture analysis for statistical evaluation and fractal dimension analysis for geometric quantification of microstructural irregularity. Results demonstrate that grinding aids enhance particle dispersion and reduce agglomeration, resulting in a more uniform micro-texture characterized by lower grayscale variability and reduced fractal dimensions. PCA superplasticizers further significantly enhance fluidity and compressive strength. The optimal formulation (MGA + PCA) achieved a 20% increase in 28-day compressive strength compared to control samples. The fractal dimension D_B exhibits a positive correlation with compressive strength, while energy and correlation values show a negative correlation; in contrast, entropy and contrast values demonstrate a positive correlation. This research advances quantitative microstructure characterization in cementitious materials, offering insights for tailored additive formulations to enhance sustainability and efficiency in concrete production. Full article

In the manufacturing of micro-scale components, geometric dimensional accuracy and product quality are critical factors that directly influence both production costs and efficiency. To meet the growing demands in this field, micro-extrusion technology has been developed and extensively applied, particularly in mass and bulk production. This technology is considered an optimal solution for improving dimensional accuracy, enhancing mechanical properties, increasing production efficiency, and reducing costs compared to traditional methods, while also aligning with the current trends of modern industrial development. This study investigates the influence of temperature and friction on forming force, formability, and product quality during the micro-extrusion process. A combined approach of simulation and experimentation was utilized to form cross-shaped coupling components using pure copper as the material. The results indicate a significant relationship between temperature, friction coefficient, and forming force. Furthermore, 550 °C is identified as the most suitable temperature for hot forming, providing a balance between force reduction and product quality. These insights enhance the predictability and control of the micro-extrusion process and contribute to reducing production defects. Ultimately, the findings support wider implementation of micro-extrusion in the manufacturing of high-accuracy small-scale parts and align with modern trends emphasizing miniaturization, automation, and cost efficiency. Full article

Background: The anatomical variability of the nasal cavity affects intranasal drug delivery, especially to the olfactory region for nose-to-brain treatments. While previous studies used average models or 2D measurements to account for inter-individual variability, 3D shape variation of the region crossed by drug particles that target the olfactory area, namely the region of interest (ROI), remains unexplored to our knowledge. Methods: A geometric morphometric analysis was performed on the ROI of 151 unilateral nasal cavities from the CT scans of 78 patients. Ten fixed landmarks and 200 sliding semi-landmarks were digitized, using Viewbox 4.0, and standardized via Generalized Procrustes Analysis. Shape variability was analyzed through Principal Component Analysis. Morphological clusters were identified using Hierarchical Clustering on Principal Components, and characterized with MANOVA, ANOVA, and Tukey tests. Results: Validation tests confirmed the method’s reliability. Three morphological clusters were identified. Variations were significant in the X and Y axes, and minimal in Z. Cluster 1 had a broader anterior cavity with shallower turbinate onset, likely improving olfactory accessibility. Cluster 3 was narrower with deeper turbinates, potentially limiting olfactory accessibility. Cluster 2 was intermediate. Notably, 31.5% of patients had at least one cavity in cluster 1. Conclusion: Three distinct morphotypes of the region of the nasal cavity that potentially influence accessibility were identified. These findings will guide future computational fluid dynamics studies for optimizing nasal drug targeting and represent a practical step toward tailoring nose-to-brain drug delivery strategies in alignment with the principles of personalized medicine. Full article

Early identification of mine water inrush types and determination of water sources are prerequisites for water disaster monitoring and early warning. A mine water source identification model is proposed to improve the accuracy of water source prediction based on Kernel Principal Component Analysis (KPCA) and Support Vector Machine (SVM) models optimized by the Improved Sparrow Search Algorithm (ISSA). Nine conventional hydrochemical indicators are selected, including Ca²⁺, Mg²⁺, Na⁺+K⁺, HCO₃⁻, Cl⁻, SO₄²⁻, total hardness, alkalinity, and pH. KPCA can realize the dimensionality reduction to eliminate the redundancy of information between discriminant indices, simplify the model structure, and enhance the calculation speed of the predicted model. The penalty factor C and kernel parameter g of the SVM model are optimized by the Sparrow Search Algorithm (SSA). In addition, comparative analysis with the SVM, SSA-SVM, and ISSA-SVM models demonstrates that the KPCA and ISSA significantly enhance the classification performance of the SVM model. The KPCA-ISSA-SVM model outperforms three contrastive models in terms of accuracy, precision, recall, Kappa coefficient, Matthews Correlation Coefficient, and geometric mean values of 90.75%, 0.90, 0.88, 0.89, 0.87, and 0.89, respectively. These outcomes underscore the superior performance of the KPCA-ISSA-SVM hybrid model and its potential for effectively identifying mine water sources. This research can serve to identify the mine water sources. Full article

Global Navigation Satellite Systems (GNSSs) are widely used in many applications but can be out of use in some critical conditions. Hybrid pseudolite systems utilize ground and aero stations as pseudolites to provide positioning signals for users within the covered area. The positioning accuracy is an important performance parameter for the pseudolite system and is decided by the layout of the pseudolites. This paper proposes a layout optimization method based on an Incremental Geometric Dilution of Precision (IGDOP) model. The IGDOP considers the GDOP value into two parts. One is the fixed part corresponding to the ground stations, and the other is the varying part related to the movable aero pseudolite stations. Thus, when the aero pseudolites’ position changes, the new GDOP value could be obtained only by calculating the varying part. Then, a Monte-Carlo Genetic Algorithm (MC-GA) is proposed for the IGDOP calculation for a minimum value. This algorithm comprises two main components: first, it leverages the random sampling capability of the Monte-Carlo Algorithm to provide sample points that satisfy the sample space for the subsequent Genetic Algorithm, which serve as individuals of the initial population; subsequently, it searches for the minimum value of IGDOP via the Genetic Algorithm and determines the optimized layout of the hybrid pseudolite system. Simulations are carried out using a hybrid pseudolite system with four fixed stations and n movable stations. The results validate the developed IGDOP model and show that the approach enables scalable optimization of n − 1 movable stations via four fixed stations, providing an efficient, low-complexity solution to the system layout optimization. Full article

Diffractive sail components may be used in part or whole for in-space propulsion and attitude control. A sun-facing hybrid diffractive solar sail having reflective front facets and transmissive side facets is described. This hybrid design seeks to minimize the undesirable scattering from side facets. Predictions of radiation pressure are compared for analytical geometrical optics and numerical finite difference time domain approaches. Our calculations across a spectral irradiance band from 0.5 to 3 μm suggest the transverse force in a sun facing configuration reaches 48% when the refractive index of the sail material is 1.5. Diffraction measurements at a representative optical wavelength of 633 nm support our predictions. Full article

While material extrusion via fused filament fabrication (FFF) offers design flexibility and rapid prototyping, its practical use in engineering is limited by mechanical challenges, including residual stresses, geometric distortions, and potential interlayer debonding. These issues arise from the dynamic thermal profiles during FFF, including temperature gradients, non-uniform hardening, and rapid thermal cycling, which lead to uneven internal stress development depending on fabrication parameters and object topology. These problems can compromise the structural integrity and mechanical properties of FFF parts, especially when the load-bearing capacity and geometric accuracy are critical. This study focuses on polylactic acid (PLA) due to its widespread application in engineering. It introduces a computational framework for coupled thermo-mechanical simulations of the FFF process using ABAQUS (Version 2020) finite element software. A key innovation is an automated subroutine that converts G-code into a time-resolved event series for finite element activation. The simulation framework explicitly models the sequential stages of printing, cooling, and detachment, enabling prediction of adhesive loss and post-process warpage. A transient thermal model evaluates the temperature distribution during FFF, providing boundary conditions for a mechanical simulation that predicts residual stresses and warping. Uniquely, the proposed model incorporates the detachment stage, enabling a more realistic and experimentally validated prediction of warpage and residual stress release in FFF-fabricated components. Although the average deviation between predicted and measured displacements is about 10.6%, the simulation adequately reflects the spatial distribution and magnitude of warpage, confirming its practical usefulness for process optimization and design validation. Full article

The study of qualitative morphological variation is essential for taxonomists and professionals involved in the identification and diagnosis of species of agricultural importance. This becomes particularly critical when quarantine decisions depend on the accurate identification of species belonging to highly diverse genera, poorly reviewed taxonomic groups, or sets of morphologically similar species that lack comprehensive identification keys. Geometric morphometrics has proven to be a powerful tool for resolving taxonomic uncertainties and distinguishing economically significant pest insects, even in the absence of formal taxonomic keys. In this study, we applied geometric morphometrics to analyze pronotum shape variation across 11 species of the genus Acanthocephala, representing nearly half of the currently recognized diversity in the genus, including several species of quarantine relevance to the United States. Our results indicate that principal component analysis accounted for 67% of the total shape variation and identified shape patterns that are useful for distinguishing between several species. Discriminate analysis further supported the differentiation among species, with significant differences confirmed through Mahalanobis distances. Although some species exhibited morphological overlaps, particularly among closely related taxa, most comparisons yielded statistically significant results. These findings demonstrate that the shape of the pronotum is a reliable and informative characteristic for species delimitation within the Acanthocephala group. We propose the use of geometric morphometrics as a reproducible, cost-effective, and robust method for species-level identification in taxonomically complex groups, which has valuable applications in quarantine inspection, pest monitoring, and agricultural biosecurity. Full article

Information geometry provides a data-informed geometric lens for understanding data or physical systems, treating data or physical states as points on statistical manifolds endowed with information metrics, such as the Fisher information. Building on this foundation, we develop a robust mathematical framework for analyzing data residing on Riemannian manifolds, integrating geometric insights into information-theoretic principles to reveal how information is structured by curvature and nonlinear manifold geometry. Central to our approach are tools that respect intrinsic geometry: gradient flow lines, exponential and logarithmic maps, and kernel-based principal component analysis. These ingredients enable faithful, low-dimensional representations and insightful visualization of complex data, capturing both local and global relationships that are critical for interpreting physical phenomena, ranging from microscopic to cosmological scales. This framework may elucidate how information manifests in physical systems and how informational principles may constrain or shape dynamical laws. Ultimately, this could lead to groundbreaking discoveries and significant advancements that reshape our understanding of reality itself. Full article

The corrosion of welded joints creates widespread issues for the ocean engineering, petrochemical, and nuclear power industries. Geometric discontinuity of the weld reinforcement height plays an important role in weld corrosion, but the mechanism is still unclear. The corrosion behavior of flat and convex SA106B welded joints is investigated at different flow velocities by experiments and simulation. The damage components of the material and geometric discontinuity are quantified. Electrochemical measurements, morphology observations, and flow field simulations are conducted. The results show that the corrosion of the welded joints is influenced by mass transfer and galvanic corrosion. The corrosion of the welded joints is aggravated by geometric discontinuity and increased flow velocity. The damage component introduced by the material of the welded joint decreases with increasing flow velocity, and the maximum value is 91.56% at 0.5 m/s. The damage component introduced by the geometry of the weld reinforcement height increases with increasing flow velocity, reaching up to 45.77% at 6.9 m/s. The corrosion mechanism is also discussed. Full article

This study investigates the development of axial force in micro anti-slide piles under soil movement during slope stabilization. Axial force arises from two primary mechanisms: axial soil displacement ($z_{\mathrm{s}}$) and pile kinematics. The former plays a dominant role, producing either tensile or compressive axial force depending on the direction of $z_{\mathrm{s}}$, while the kinematically induced component remains consistently tensile. A sliding angle of $\alpha =5°$ represents an approximate transition point where these two effects balance each other. Furthermore, the two mechanisms exhibit distinct mobilization behaviors: $z_{\mathrm{s}}$-induced axial force mobilizes earlier than both bending moment and shear force, whereas kinematically induced axial force mobilizes significantly later. The study reveals two distinct pile–soil interaction mechanisms depending on proximity to the slip surface: away from the slip surface, axial soil resistance is governed by rigid cross-section translation, whereas near the slip surface, rotation-dominated displacement accompanied by soil–pile separation introduces significant complexity in predicting both the magnitude and direction of axial friction. A hyperbolic formulation was adopted to model both the lateral soil resistance relative to lateral pile–soil displacement (p-y behavior) and the axial frictional resistance relative to axial pile–soil displacement (t-z behavior). Soil resistance equations were derived to explicitly incorporate the effects of cross-sectional rotation and pile–soil separation. A novel beam-spring finite element method (BSFEM) that incorporates both geometric and material nonlinearities of the pile behavior was developed, using a soil displacement-driven solution algorithm. Validation against both numerical simulations and field monitoring data from an engineering application demonstrates the model’s effectiveness in capturing the distribution and evolution of axial deformation and axial force in micropiles under varying soil movement conditions. Full article

Thorough investigation into Tesla valve (TV) design was conducted across a large design of experiments (DOE) consisting of four varying geometric parameters and six different Reynolds number regimes in order to develop an optimized pressure drop device utilizing machine learning (ML) methods. A non-standard TV design was geometrically parameterized, and an automation suite was created to cycle through numerous combinations of parameters. Data were collected from completed computational fluid dynamics (CFD) simulations. TV designs were tested in the restricted flow direction for overall differential pressure, and overall minimum pressure with consideration to the onset of cavitation. Qualitative observations were made on the effects of each geometric parameter on the overall valve performance, and particular parameters showed greater influence on the pressure drop compared to classically optimized parameters used in previous TV studies. The overall minimum pressure demonstrated required system pressure for a valve to be utilized such that onset to cavitation would not occur. Data were utilized to train an ML model, and an optimized geometry was selected for maximized pressure drop. Multiple optimization efforts were made to meet design pressure drop goals versus traditional diodicity metrics, and two geometries were selected to develop a final design tool for overall pressure drop component development. Future work includes experimental validation of the large dataset, as well as further validation of the design tool for use in industry. Full article