Search Results (567)

Unbalance diagnosis for non-constant speed systems is challenging because the 1X fundamental rotational harmonic magnitude, commonly used as an unbalance indicator, depends on shaft rotational speed. This dependency makes it difficult to separate speed effects from unbalance effects. It has been shown that 1X magnitudes become speed-invariant if they are normalized with respect to the rotational speed in power four for variable-speed wind turbines. However, the applicability of this diagnostic technology to dual-speed machines remains unclear. This study experimentally investigates unbalance diagnosis technologies for dual-speed wind turbines, for which speed-dependent interference is present. Vibration data are collected from the main bearings of two dual-speed wind turbines. Novel residual-based, speed-invariant unbalance diagnostic technology is proposed. The experimental results show consistent statistical distributions of the new diagnosis indicator across low and high-speed operating regimes. These findings confirm the suitability of the proposed technology for unbalance diagnosis for dual-speed rotating machinery. Full article

The geometric signatures of macroscopic interfaces in the two-dimensional critical Ising model strictly adhere to Schramm–Loewner Evolution (SLE) theory. In this study, we propose a physics-driven generative approach using Super-Resolution Generative Adversarial Networks (SRGANs) to approximate the inverse coarse-graining operation to generate larger configurations. From the perspective of Geometric Deep Learning (GDL), we leverage the geometric priors of Convolutional Neural Networks (CNNs)—specifically their translational and rotational symmetries—to effectively encode the universal physical laws of the Ising Hamiltonian. This inductive bias allows the model to be trained on small scales yet be generalized to large-scale systems ($2048 \times 2048$) while preserving physical conservation. To accommodate spin discreteness, we employ an L1-based loss function to maintain domain wall sharpness. SLE analysis and long-range correlation functions confirm that the model reproduces critical dynamics and conformal invariance, successfully serving as a physics-preserving inverse coarse-graining transformation framework. Full article

Daily, monthly, and annual multi-product sales records are stored in databases, but due to the massive amounts of data, they are not used for decision-making when updating product catalogs. Meanwhile, the use of artificial intelligence in business is increasing across all sectors of the economy. Large-scale data handling can be achieved using artificial intelligence techniques. Specifically, ABC inventory classification currently employs artificial intelligence techniques, including neural networks, fuzzy systems, and genetic algorithms. However, a state-of-the-art review has not found any research using vision techniques to classify ABC inventories. To address this gap, this research presents a novel approach to the intelligent classification of a company’s multiple products, using ABC. Recent vision system research often uses the Otsu method or its variants to determine the optimum threshold for binary image segmentation. Unlike this approach, our research does not use a single threshold value; instead, it uses the full binary frequency histogram as an image representation. From this, eight invariant characteristics are extracted from translation, rotation, and scale. The results show that the classification is accurate, clear, and simple as a decision-making tool. The proposed method is general and can be used in any production sector and at any enterprise size. Full article

Hyperspectral imagery (HSI) offers rich spectral signatures and fine-grained spatial structures for remote sensing, but practical HSI classification is often constrained by scarce labels and complex geometric disturbances, including translation, rotation, scaling, and shear. Existing deep models are typically developed under Euclidean assumptions and rely on data-hungry training pipelines, which makes them brittle in the few-shot regime. To address this challenge, we propose EMNet, a Lie-group-based Equivariant Manifold Network for few-shot HSI classification that explicitly encodes geometric invariance and improves discriminative accuracy. EMNet couples an SE(2)-based Equivariance-Guided Module (EGM) to enforce equivariance to translations and rotations with an affine Lie-group-based Characteristic Filtering Convolution (CFC) that models scaling and shearing on the feature manifold while adaptively suppressing redundant responses. Extensive experiments on WHU-Hi-HongHu, Houston2013, and Indian Pines demonstrate state-of-the-art performance with competitive complexity, achieving OAs of 95.77% (50 samples/class), 97.37% (50 samples/class), and 96.09% (5% labeled samples), respectively, and yielding up to +3.34% OA, +6.01% AA, and +4.14% Kappa over the strong DGPF-RENet baseline. Under a stricter 25-samples-per-class protocol with 10 repeated random hold-out splits, EMNet consistently improves the mean accuracy while exhibiting lower variance, indicating better stability to sampling uncertainty. On the city-scale Xiongan New Area dataset with extreme long-tail imbalance (1580 × 3750 pixels, 256 bands, and 5.925 M labeled pixels), EMNet further boosts OA from 85.89% to 93.77% under the 1% labeled-sample protocol, highlighting robust generalization for large-area mapping. Beyond point estimates, we report mean ± SD/SE across repeated splits and provide rigorous statistical validation by computing Yule’s Q statistic for class-wise behavior similarity, performing the Friedman test with Nemenyi post hoc comparisons for multi-method ranking significance, and presenting 95% confidence intervals together with Cohen’s d effect sizes to quantify practical improvement. Full article

Local Lorentz Invariance is one of the fundamental postulates of General Relativity, making its experimental verification of paramount importance. Given that various frontier theoretical models predict potential symmetry breaking, the Standard Model Extension framework has been established to systematically study such phenomena. Within the Standard Model Extension gravitational sector, the high-order Lorentz-violating terms with mass dimension $d=6$ exhibit a rapid signal decay with distance, providing a distinct detection advantage in short-range gravity experiments. This work is dedicated to optimizing the testing schemes for $d=6$ Lorentz-violating coefficients. Based on a high-precision torsion balance platform, we propose a novel scheme featuring a comb-stripe design. The improvements are twofold: first, the spatial orientation of the experimental apparatus is optimized to leverage the modulation effects of the Earth’s rotation, thereby enhancing the capability to distinguish and constrain different violation parameters; second, the test and source masses are reconfigured into specifically designed stripe patterns to significantly amplify the fringe-field signals sensitive to Lorentz-violating effects. This paper systematically elaborates on the theoretical foundation and design principles of the new scheme. By performing a detailed comparison of the constraint potentials of various stripe configurations, the five-stripe geometry is identified as the optimal experimental configuration. This study provides a new experimental methodology for exploring physics beyond the Standard Model at higher levels of precision. Full article

To address the problem of fault diagnosis for rotating machinery under complex operating conditions in real industrial systems, most existing domain generalization methods fail to sufficiently consider inter-class feature structures when learning domain-invariant representations. This limitation often leads to degraded diagnostic performance in cross-domain scenarios, particularly under class imbalance or significant operating condition variations. Moreover, existing feature extraction networks specifically designed for rotating machinery are often inadequate for fault diagnosis tasks under variable operating conditions. To overcome these challenges, this paper proposes a domain-invariant fault feature representation learning framework for multi-source domain generalization. Specifically, we design a mechanism-aware multi-branch feature extraction network inspired by excitation–modulation mechanisms of fault generation, which captures fault-sensitive characteristics from both time-domain and frequency-domain perspectives. In addition, a class-conditional feature alignment strategy based on ICM (Independent Causal Mechanism) mixing is introduced to enhance cross-domain consistency. Through feature structure regularization, discriminative information across categories is effectively preserved under domain shifts. Extensive experimental results demonstrate that the proposed method significantly improves diagnostic performance and generalization ability on the CWRU bearing dataset as well as the HUST bearing and gearbox datasets. Notably, when the number of source domains increases, the proposed framework exhibits superior training efficiency. Full article

We propose a comprehensive rotation-minimizing (RM) Darboux framework for the study of curve theory and relativistic ruled surfaces in Minkowski three-space ${\mathbb{E}}_1^3$. The construction merges the adaptability of the classical Darboux frame to surface geometry with the reduced rotational behavior characteristic of RM frames, yielding a natural geometric description of curves in a Lorentzian environment. For unit speed non-null curves, the governing equations of the RM Darboux frame are derived, and precise connections between the RM curvature functions and the classical Frenet and Darboux invariants are obtained, thereby elucidating the geometric significance of RM curvatures in Lorentzian geometry. Within this setting, multiple classes of ruled surfaces are generated using RM Darboux frame vector fields. Necessary and sufficient conditions for developability, minimality, and flatness are formulated exclusively in terms of RM curvature quantities. The role of the causal character of the generating curve is analyzed in detail, revealing distinct geometric behaviors for space-like and time-like cases. These findings indicate that the RM Darboux framework constitutes a flexible and effective approach for modeling curve-induced surface geometries in Minkowski space, with potential relevance to relativistic kinematics, world sheet constructions, and geometric problems arising in mathematical physics. Full article

Gibson’s concept of optic flow established that perception is grounded in lawful structure generated by action. However, no formal mechanical framework has described the invariant structure of action-generated kinesthetic information during skilled manipulation. This study introduces haptic flow as a screw-theoretic invariant defined by the coupled rotational–translational organization of a body–object system. Motion capture data from a two-case comparison (one proficient and one novice golfer) were analyzed using instantaneous screw axes (ISA), pitch evolution, and cylindroid geometry derived from a linear line-complex formulation. The proficient golfer exhibited (1) progressive convergence of ISAs toward a coherent bundle, (2) stabilization of screw pitch through impact, and (3) co-cylindrical alignment of harmonic screws consistent with inertial–restoring conjugacy. In contrast, the novice golfer showed fragmented ISA organization and elevated pitch variability. These differences were descriptive rather than inferential and do not imply population-level generalization. The findings suggest that skilled manipulation is characterized by stabilization of symmetry-bearing screw invariants rather than by independent joint control. Interpreted ecologically, haptic flow is proposed as a mechanically specified candidate invariant generated by lawful body–object coupling. The present study establishes a geometric framework for quantifying such invariants while identifying the need for cross-task and perceptual validation. Full article

Significant nonlinear radiation differences and weak texture differences exist between multimodal weak texture remote sensing images (MWTRSIs). When using traditional methods to match MWTRSIs, the low distinguishability of descriptors in weak texture regions results in poor matching performance. A robust matching method is proposed based on normalized structural feature transform (NSFT), which can extract spatial structural features of images while mitigating nonlinear radiation differences between weak texture regions. First, the bilateral filter is used to transform the weak texture remote sensing image into a normalized image, which not only greatly weakens the nonlinear radiation difference but also retains most of the structural information. Then, the UC-KAZE detector is designed to extract many evenly distributed feature points on the normalized image. Subsequently, a multimodal weak texture feature descriptor with rotation invariance is designed based on the self-similarity of the weak texture image. Finally, the initial correspondences are constructed by bilateral matching, and the mismatches are removed by the fast sample consensus (FSC) algorithm. We perform comparison experiments on eight types of MWTRSIs. The results show that the proposed method has good scale and rotation invariance and good resistance to nonlinear radiation differences and weak texture differences. Full article

Optical and synthetic aperture radar (SAR) image registration faces challenges from nonlinear radiometric distortions and geometric deformations caused by different imaging mechanisms. This paper proposes a coarse-to-fine registration algorithm integrating geographic information constraints with cross-modal feature consistency mapping. The coarse stage employs imaging geometry-based coordinate transformation with airborne navigation data to eliminate scale and rotation differences. The fine stage constructs a multi-scale phase congruency-based feature response aggregation model combined with rotation-invariant descriptors and global-to-local search for sub-pixel alignment. Experiments on integrated airborne optical/SAR datasets demonstrate superior performance with an average RMSE of 2.00 pixels, outperforming both traditional handcrafted methods (3MRS, OS-SIFT, POS-GIFT, GLS-MIFT) and state-of-the-art deep learning approaches (SuperGlue, LoFTR, ReDFeat, SAROptNet) while reducing execution time by 37.0% compared with the best-performing baseline. The proposed coarse registration also serves as an effective preprocessing module that improves SuperGlue’s matching rate by 167% and LoFTR’s by 109%, with a hybrid refinement strategy achieving 1.95 pixels RMSE. The method demonstrates robust performance under challenging conditions, enabling real-time UAV-based multi-sensor fusion applications. Full article

Accurate derivative information is central to sensitivity analysis and optimization, yet standard finite differences can lose many digits when the step size is small because of subtractive cancellation. Complex-step differentiation largely resolves this issue for first derivatives, but robust second derivatives and mixed partials remain delicate: several practical complex-step variants for $f^{″}$ still subtract nearly equal quantities, and quaternion-step rules are often presented as separate constructions. We develop a unified slice-based framework that extracts first and second derivatives from a single evaluation by projecting algebraic coefficients in commutative subalgebras of the complexified quaternions. First, we formulate a directional quaternion-steprule parameterized by an arbitrary unit pure quaternion u and provide an explicit projection operator that makes the underlying complex slice ${\mathbb{C}}_u\simeq \mathbb{C}$ transparent; the resulting first-derivative formula is rotation invariant and recovers classical j-step and planar $(j,k)$-step rules as special cases. Second, we construct a bicomplex double-step calculus in the commuting imaginary units $\mathrm{i}$ and u and show that one evaluation at $z+(\mathrm{i}+u)h$ separates derivative information into distinct coefficients, with the $\mathrm{i}u$-component equal to $h^2{f}^{″}\left(z\right)+\mathcal{O}\left(h^4\right)$, giving a subtraction-free $\mathcal{O}\left(h^2\right)$ approximation of $f^{″}$. For bivariate analytic functions we additionally derive one-shot identities for $f_x$, $f_y$, and $f_{xy}$ from $f(x+uh,y+\mathrm{i}h)$ and supply practical extraction identities, step-size guidance for $h^2$-scaled coefficients, and branch-consistency diagnostics for non-entire functions. The “cancellation-free” property here refers to avoiding the subtraction of nearly equal real quantities at the level of the differentiation formula; in floating-point arithmetic, coefficient extraction and the $1/h^2$ scaling for second-order quantities still interact with roundoff, and we quantify the resulting stable regimes numerically. Full article

Loop closure detection remains a critical challenge in LiDAR-based SLAM, particularly for achieving robust place recognition in environments with rotational and translational variations. To extract more concise environmental representations from point clouds and improve extraction efficiency, this paper proposes a novel composite descriptor—the vertical feature and circle combined (VCC) descriptor, a novel 3D local descriptor designed for efficient and rotation-invariant place recognition. The VCC descriptor captures environmental structure by extracting vertical features from voxelized point clouds and encoding them into circular arc-based histograms, ensuring robustness to viewpoint changes. Under the same hardware, experiments conducted on different datasets demonstrate that the proposed algorithm significantly improves both feature representation efficiency and loop closure recognition performance when compared with the other descriptors, completing loop closure retrieval within 30 ms, which satisfies real-time operation requirements. The results confirm that VCC provides a compact, efficient, and rotation-invariant representation suitable for LiDAR-based SLAM systems. Full article

This research presents a novel thermomechanical model of a rotatable spherical shell characterized by changing thermal conductivity, situated within the framework of the Moore–Gibson–Thompson (MGT) theorem of generalized thermoelasticity. The governing differential equations in the Laplace transform domain, utilizing non-dimensional variables, have been applied to a thermoelastic, isotropic, homogeneous spherical shell subjected to ramp-type thermal loading. The numerical distributions of temperature increase, volumetric strain, and invariant average stress are illustrated in figures for varying values of thermal conductivity, ramp-time heat, rotation speed, and Moore–Gibson–Thompson relaxation time, and are analyzed. The variable thermal conductivity impacts all analyzed functions and substantially modifies the behaviour of the thermomechanical spherical shell. The ramp-time heat, rotational speed, and relaxation time of the Moore–Gibson–Thompson parameters substantially influence the distributions of temperature increase, volumetric strain, and invariant stress. Full article

Underwater optical images often exhibit severe color distortion, weak texture, and uneven illumination due to light absorption and scattering in water. These issues result in unstable feature detection and inaccurate image registration. To address these challenges, this paper proposes an underwater image stitching method that integrates ORB (Oriented FAST and Rotated BRIEF) feature extraction with a fixed-ratio constraint matching strategy. First, lightweight color and contrast enhancement techniques are employed to restore color balance and improve local texture visibility. Then, ORB descriptors are extracted and matched via a KNN (K-Nearest Neighbors) nearest-neighbor search, and Lowe’s ratio test is applied to eliminate false matches caused by weak texture similarity. Finally, the geometric transformation between image frames is estimated by incorporating robust optimization, ensuring stable homography computation. Experimental results on real underwater datasets show that the proposed method significantly improves stitching continuity and structural consistency, achieving 40–120% improvements in SSIM (Structural Similarity Index) and PSNR (peak signal-to-noise ratio) over conventional Harris–ORB + KNN, SIFT (scale-invariant feature transform) + BF (brute force), SIFT + KNN, and AKAZE (accelerated KAZE) + BF methods while maintaining processing times within one second. These results indicate that the proposed method is well-suited for real-time underwater environment perception and panoramic mapping on low-cost, micro-sized underwater robotic platforms. Full article

This study aimed to adapt and validate the Polish version of the Perceived Future Employability Scale (PFES) and verify its factor structure among university students. Drawing on Social Cognitive Career Theory and the concept of possible selves, this study analysed how students perceive their future employment opportunities. This research was conducted among 408 students (61.0% female, 39.0% male; age: M = 20.97, SD = 2.68) at Maria Curie-Skłodowska University. Exploratory factor analysis using Principal Axis Factoring with Oblimin rotation revealed a six-factor structure explaining 63.74% of total variance. Based on stringent psychometric criteria (primary loadings ≥0.50, cross-loadings <0.30), six items exhibiting weak or problematic loadings were systematically removed, yielding a refined 18-item version that maintains all 6 theoretical dimensions while improving model fit. Confirmatory factor analysis demonstrated excellent fit using DWLS estimation (CFI = 0.996, RMSEA = 0.053) and acceptable fit with ML estimation (CFI = 0.958, RMSEA = 0.062). Reliability analysis demonstrated good-to-excellent internal consistency (α = 0.756–0.903; ω = 0.754–0.893) and adequate convergent validity (AVE = 0.612–0.785). Full measurement invariance across gender was established. The final Polish PFES comprises six dimensions: perceived future network, perceived expected experiences, perceived future personal characteristics, anticipated reputation of educational institution, perceived future labour market knowledge, and perceived future skills. The PFES provides a psychometrically sound tool for career development research and interventions supporting UN Sustainable Development Goals 4 and 8. Full article