Dimensional Domain Research Articles

Multi-channel Fused Vision Transformer Network for Bearing Fault Diagnosis under Different Working Conditions

Abstract Many of the current fault diagnosis methods rely on time-domain signals. While the richest information are contained in these signals, their complexity poses challenges to network learning and limits the ability to fully characterize them. To address these issues, a novel Multi-channel Fused Vision Transformer Network (MFVTN) is proposed in this paper. Firstly, the Overlapping Patch Embedding (OPE) module is introduced to overlap the time-domain map with edge information, preserving the global continuous features of the time-domain map and adding positional encoding for sorting. This integration helps the Vision Transformer (ViT) merge detailed features and construct the global mapping. Secondly, multiple dimensional time domain signal features are extracted and fused in parallel, enabling multi-domain fault diagnosis of bearings. In order to enhance the network ability to extract domain-invariant features, an adversarial training strategy combined with Wasserstein distance is utilized. The results demonstrate that the diagnostic accuracy of the proposed MFVTN can reach 98.2%.

Superior Piezoelectric Performance in Textured CaBi2Nb2O9 Ferroelectric Ceramics through Rare-Earth Gadolinium Doping and Spark Plasma Sintering.

High-performance piezoelectric ceramics with excellent thermal stability are crucial for high-temperature piezoelectric sensor applications. However, conventional fabrication processes offer limited enhancements in piezoelectric performance. In this study, we achieved a significant breakthrough in the piezoelectric performance of highly textured CaBi2Nb2O9 (CBN) ceramics by incorporating rare-earth gadolinium doping and utilizing spark plasma sintering. The resulting Ca0.97Gd0.03Bi2Nb2O9 (CBN-3Gd) ceramics exhibited superior piezoelectric properties, with a high piezoelectric constant d33 of 26 pC/N and a high Curie temperature TC of 946 °C. We employed piezoresponse force microscopy (PFM) to observe the morphology and dimensions of the ferroelectric domains, revealing a rod-shaped 3D domain configuration. This configuration facilitated polarization rotation in the textured ceramics, as analyzed using X-ray photoelectron spectroscopy (XPS) and polarization-electric field (P-E) hysteresis loops. Furthermore, the textured CBN-3Gd ceramics demonstrated exceptional thermal stability and reliability. The piezoelectric constant d33 decreased by only 11.8% over a temperature range of room temperature to 500 °C, and the DC electrical resistivity remained at 6.7 × 105 Ω cm at 600 °C. This work not only highlights the great potential of textured CBN-based ceramics for high-temperature piezoelectric sensors but also provides a viable strategy for enhancing the performance of piezoelectric materials with large aspect ratio micromorphology.

Particle resolved DNS and URANS simulations of turbulent heat transfer in packed structures

An accurate prediction of turbulent heat transfer in packed-bed reactors is fundamental for the design and performance of such reactors. Various classes of Reynolds-Averaged Navier-Stokes models are available in the literature for predicting turbulent heat transfer via closing the Reynolds stress and Turbulent Heat Flux terms. In this work, we investigate the accuracy of representative unsteady RANS turbulence models in modeling heat transfer characteristics in packed-bed reactors. For this accuracy assessment, the performance of the examined unsteady RANS models is compared with particle-resolved Direct Numerical Simulations including heat transfer. The simulations are conducted in a three dimensional periodic domain consisting of randomly arranged cylindrical particles for Reynolds number Rep=1000. The extracted mean quantities (e.g. velocity and temperature), turbulent heat flux components and turbulent kinetic energy are used as a reference for the RANS models' accuracy assessment. The SST k−ω and Spalart-Allmaras models performed well in predicting mean fields. However, all models tended to mispredicted the turbulent quantities including the turbulent viscosity and heat flux vector. The thermal field calculations rely on the turbulent viscosity based Simple Gradient Diffusion Hypothesis for modeling turbulent heat flux, which introduce challenges in capturing the coupling between thermal and turbulent fields.

Functional Data Analysis: An Introduction and Recent Developments.

Functional data analysis (FDA) is a statistical framework that allows for the analysis of curves, images, or functions on higher dimensional domains. The goals of FDA, such as descriptive analyses, classification, and regression, are generally the same as for statistical analyses of scalar-valued or multivariate data, but FDA brings additional challenges due to the high- and infinite dimensionality of observations and parameters, respectively. This paper provides an introduction to FDA, including a description of the most common statistical analysis techniques, their respective software implementations, and some recent developments in the field. The paper covers fundamental concepts such as descriptives and outliers, smoothing, amplitude and phase variation, and functional principal component analysis. It also discusses functional regression, statistical inference with functional data, functional classification and clustering, and machine learning approaches for functional data analysis. The methods discussed in this paper are widely applicable in fields such as medicine, biophysics, neuroscience, and chemistry and are increasingly relevant due to the widespread use of technologies that allow for the collection of functional data. Sparse functional data methods are also relevant for longitudinal data analysis. All presented methods are demonstrated using available software in R by analyzing a dataset on human motion and motor control. To facilitate the understanding of the methods, their implementation, and hands-on application, the code for these practical examples is made available through a code and data supplement and on GitHub.

Research on the Design of Academic Evaluation Standards of E-commerce Professional Group in Secondary Vocational Schools based on the Integration of Post-course Competition Certificate: Taking Short Video Courses as an Example

Given the subjective and one-sided problems existing in the current evaluation system of e-commerce courses in secondary vocational schools, this paper puts forward the implementation path of “four in one.” Through the integration of big data technology, knowledge graph technology and the PGSD ability analysis model, the evaluation graph is constructed, aiming at achieving comprehensiveness, adaptability, professionalism, scientific and objectivity of the evaluation. The evaluation graph includes a three-tier structure of evaluation system, knowledge domain and skill dimension, and systematically distributes weights to realize the development of multi-dimensional evaluation and personalized learning path.

Air vehicle system health and asset management: modeling, simulation, and decision support

AbstractEffective Asset Management (AM) is essential to achieve operational excellence in the context of large complex Cyber-Physical Systems (CPSs). It involves coordinated activities across all life-cycle stages, including information and service exchange within and between adjacent air operations domains, for effective air operations and collaboration. For enterprises and military air operations, AM presuppose information and service exchange between the adjacent domains of air operations. Important aspects of aviation AM are that CPSs include Integrated Vehicle Health Management (IVHM), CPS models, and Digital Twin (DT), as central concepts used to predict and optimize asset performance. This article provides an overview of the aviation AM phenomenology, environment, and challenges, and how they may impact envisioned realisations of effective AM solutions for support to enterprise air vehicle operations. The article serves as an extension of the paper Cyber-physical Asset Management of Air Vehicle Systems presented at the International Congress and Workshop of Industrial AI and Maintenance (Candell et al., in Proceedings of the IAI2023–7th international congress and workshop on industrial AI and eMaintenance, Luleå, Sweden, 2023). A comprehensive approach is presented, comprising interdependent dimensions of the problem domain, and how they may be integrated into a framework and a platform concept addressing aviation AM needs.

A novel method for solving the seismic response of non-horizontally layered half-space

In this paper, a novel method is developed to solve the free-field motion of the non-horizontally layered half-space subjected to seismic excitation in the time domain. The total wave motions are decomposed into a known and an unknown wave motion. Making use of the fact that the nodal forces at nodes in half-space resulted from the two motions will be zeros, the scattering problem resulted from the seismic excitation is transformed into a radiation problem. The radiation damping of the unbounded layered foundation in the time domain is expressed by the acceleration unit-impulse response matrix obtained using the scaling surface based Scaled Boundary Finite Element Method (SBFEM). In the numerical examples, firstly, the accuracy of the scaling surfaced based SBFEM in simulating the radiation damping is demonstrated by surface excitations in the layered half-space. Then, a time-domain analysis of the free-field motion of a horizontally layered half-space is studied to verify the accuracy and validity of the proposed method. Finally, a study of the free-field motion of non-horizontally layered half-space is investigated, and the results show that increasing the dimensions of the computational domain can significantly improve the accuracy.

Application of deep learning reduced-order modeling for single-phase flow in faulted porous media

AbstractOur research is positioned within the framework of subsurface resource utilization for sustainable economies. We concentrate on modeling the underground single-phase fluid flow affected by geological faults using numerical simulations. The study of such flows is characterized by strong uncertainites in the data defing the problem due to the difficulty of taking precise measurements in the subsoil. We aim to demonstrate the feasibility of a reduced order model that is both reliable and computationally efficient, thereby facilitating the incorporation of uncertainties. We account for the uncertainities of the properties of the rock and the geometry of the fault. The latter is achieved by using a radial basis function mesh deformation method. This approach benefits from a mixed-dimensional framework to model the rock matrix and faults as n and $${n-1}$$ n - 1 dimensional domains, allowing for non-conforming meshes. Our primary focus is on a reduced-order model capable of reproducing flow variables across the entire domain. We utilize the Deep Learning Reduced Order Model (DL-ROM), a nonintrusive neural network-based technique, and we compare it against the traditional Proper Orthogonal Decomposition (POD) method across various scenarios. The most relevant contributions of this work are: the proof of concept of the use of neural network for reduced order models for subsoil flow, dealing with non-affine problems and mixed dimensional domain. Additionally, we generalize an existing mesh deformation method for discontinuous deformation maps. Our analysis highlights the capability of reduced order model, highlighting DL-ROM’s capacity to expedite complex analyses with promising accuracy and efficiency, making multi-query analyses with various quantities of interest affordable.

Enhancing quantitative 1H NMR model generalizability on honey from different years through partial least squares subspace and optimal transport based unsupervised domain adaptation

Honey is a nourishing and natural food product that is widely favored by a diverse group of consumers. Proton Nuclear Magnetic Resonance (1H NMR) is a powerful tool for quantitative analysis of honey and plays a crucial role in ensuring its quality. The 1H NMR technique necessitates the utilization of multivariate calibration models to facilitate the quantitative analysis of key compounds present in honey. However, maintaining consistent measurement conditions across different years is scarcely possible, which can significantly impact the distribution of training and test spectra, ultimately leading to reduced performance of predictive models. Unsupervised domain adaptation (UDA) methods have gained considerable attention for their ability to match distribution differences between the labeled source spectra and the unlabeled target spectra without costly annotation. To enhance the quantitative model generalizability on honey from different years, we propose a UDA method known as partial least squares subspace and optimal transport-based UDA (PLSS-OT-UDA). This approach eliminates distribution differences between the source subspace and target subspace via partial least squares (PLS) dimensionality reduction and OT. Firstly, the optimal latent variable weight matrix from the source domain (i.e., labeled 1H NMR data in 2017) is extracted with PLS. Next, the dimension of both source and target domains (i.e., unlabeled 1H NMR data in 2018) is reduced and their corresponding subspaces are obtained with weight matrix of the source domain. Finally, OT is then employed to align the distribution of the source and target domains within the subspace. Experimental results on the honey dataset demonstrate that the PLSS-OT-UDA outperforms traditional methods, including transfer component analysis (TCA), optimal transport for domain adaptation (OTDA), domain adaptation based on principal component analysis and optimal transport (PCA-OTDA), and subspace alignment (SA), with respect to generalization performance on three components: baume degree, sugar content, and water content.

Controlling Structure and Morphology of MoS2 via Sulfur Precursor for Optimized Pseudocapacitive Lithium Intercalation Hosts

AbstractMolybdenum disulfide (MoS2)‐based electrode materials can exhibit a pseudocapacitive charge storage mechanism induced by nanosized dimension of the crystalline domains, which is why control over material structure via synthesis conditions is of significance. In this study, we investigate how the use of different sulfide precursors, specifically thiourea (TU), thioacetamide (TAA), and L‐cysteine (LC), during the hydrothermal synthesis of MoS2, affects its physicochemical, and consequently, electrochemical properties. The three materials obtained exhibit distinct morphologies, ranging from micron‐sized architectures (MoS2 TU), to nanosized flakes (MoS2 TAA and LC). While all three synthesized samples exhibit pseudocapacitive Li+ intercalation properties, the capacity retention of the latter two consisting of nanosized flakes is further improved at high cycling rates. The individual charge storage properties are analyzed by operando X‐ray diffraction, dilatometry, and 3D Bode analysis, revealing a correlation between the morphology, porosity, and the electrochemical intercalation behavior of the obtained electrode materials. The results demonstrate a facile strategy to control MoS2 structure and related functionality by choice of hydrothermal synthesis precursors.

A novel efficient RFEM for reliability analysis and design of multi-line dynamically installed anchor for floating offshore wind turbines

A novel multi-line dynamically installed anchor was previously proposed by the authors to allow for the mooring of multiple floating offshore wind turbine, resulting in a significant reduction in the total number and cost of anchors required for floating wind farms. Considering the spatial variability of soil properties and the uncertainty of environmental loads, the present study performs reliability analysis and design of the multi-line dynamically installed anchor. Firstly, a strategy to repeatedly use fundamental random variables is proposed and validated for reducing the number of random variables used in Karhunen-Loève expansion in the simulation of random field of soil properties when the ratio of the soil domain dimension to the scale of fluctuation is large. Then, the efficiency, accuracy, and robustness of the RFEM (random finite element method) combined with K-MCS (Kriging model and Monte Carlo simulation) based on the proposed strategy are validated through examples of random capacity of foundations. Next, the random capacities and probabilistic VHMT failure envelopes of the multi-line dynamically installed anchor in spatially variable soil are investigated. Finally, the reliability design of multi-line dynamically installed anchors is conducted and compared with that of multi-line pile anchors, in which both the spatial variability of soil and the uncertainty of loads are condidered. The results show that the costs for multi-line dynamically installed anchors are obviously less than those of multi-line pile anchors.

No bulk thermal currents in massive Dirac fermions

We calculate the energy current flowing in the bulk of a (2+1)-dimensional system of massive Dirac fermions and along a (1+1)-dimensional domain wall generated by flipping the sign of the particle mass. We show that, at low temperatures and in the long-wavelength limit, the system does not support a bulk thermal Hall current proportional to the temperature gradient. The only such contribution is due to states localized at the domain wall. This puts an end to a controversy existing in the literature and amends previous results obtained via first-order perturbation calculations. Published by the American Physical Society 2024

Numerical analysis of tulip flame by dynamic mode decomposition

Inversion of a premixed flame front to the shape of a tulip flower during the last stage of its propagation in a channel or tube of aspect ratio (AR) greater than two has intrigued the scientific community for decades. In the present work, the tulip inversion of hydrogen-air premixed flame is simulated numerically in two dimensional domains of AR 7.32, and 3.66. Dynamic Mode Decomposition is carried out on the two-dimensional pressure distribution obtained from the numerical simulation to understand the reasons behind the flame inversion. A plot of real and imaginary components of the eigenvalues over a unit circle clearly showed the presence of unstable acoustic modes during the tulip inversion for the domain of AR 7.32. The tulip inversion is not observed for the domain of AR 3.66. The distribution of the power and frequency of various modes are also plotted to identify the unstable modes. The present study shows generation of additional pressure waves during the tulip inversion process, which in term creates a velocity field that facilitates the flame inversion process.

Exploring Emotional Intelligence and College Adjustment in Final-Year College Students

This study aims to analyze the relationship between the dimensions of college adjustment and domains of emotional intelligence among final-year students (7th semester) with the Merdeka Belajar Kampus Merdeka (MBKM) curriculum. This research used a correlational method with a non-experimental quantitative approach. The study respondents were final-year students of the Faculty of Psychology, Padjadjaran University, who participated in MBKM activities outside the university during the sixth semester and resumed regular classes during the seventh semester. Among 72 eligible students, 67 participated in the study. The instruments used to measure college adjustment and emotional intelligence were the translated version of Student Adaptation to College Questionnaire (SACQ) and an instrument based on Goleman's emotional intelligence theory, respectively. Correlation tests were conducted using the Pearson Product Moment. The findings revealed a significant positive correlation between college adjustment and emotional intelligence (r = 0.410, p < 0.05). Furthermore, the analysis proved correlations among certain dimensions of college adjustment and domains of emotional intelligence. The implications highlight the role of MBKM activities in enhancing students' skills to support their adaptation and emotional intelligence.

A Secure and Interpretable AI for Smart Healthcare System: A Case Study on Epilepsy Diagnosis Using EEG Signals.

The efficient patient-independent and interpretable framework for electroencephalogram (EEG) epileptic seizure detection (ESD) has informative challenges due to the complex pattern of EEG nature. Automated detection of ES is crucial, while Explainable Artificial Intelligence (XAI) is urgently needed to justify the model detection of epileptic seizures in clinical applications. Therefore, this study implements an XAI-based computer-aided ES detection system (XAI-CAESDs), comprising three major modules, including of feature engineering module, a seizure detection module, and an explainable decision-making process module in a smart healthcare system. To ensure the privacy and security of biomedical EEG data, the blockchain is employed. Initially, the Butterworth filter eliminates various artifacts, and the Dual-Tree Complex Wavelet Transform (DTCWT) decomposes EEG signals, extracting real and imaginary eigenvalue features using frequency domain (FD), time domain (TD) linear feature, and Fractal Dimension (FD) of non-linear features. The best features are selected by using Correlation Coefficients (CC) and Distance Correlation (DC). The selected features are fed into the Stacking Ensemble Classifiers (SEC) for EEG ES detection. Further, the Shapley Additive Explanations (SHAP) method of XAI is implemented to facilitate the interpretation of predictions made by the proposed approach, enabling medical experts to make accurate and understandable decisions. The proposed Stacking Ensemble Classifiers (SEC) in XAI-CAESDs have demonstrated 2% best average accuracy, recall, specificity, and F1-score using the University of California, Irvine, Bonn University, and Boston Children's Hospital-MIT EEG data sets. The proposed framework enhances decision-making and the diagnosis process using biomedical EEG signals and ensures data security in smart healthcare systems.

AN ANALYSIS OF CYLINDRICAL BORIS SOLVER FOR TYPICAL PARAMETERS OF TWO-DIMENSIONAL PENNING ION SOURCE SIMULATION

The cylindrical Boris solver is analyzed for typical two-dimensional Penning ion source simulation parameters. The analysis comprises the solver's accuracy and stability, especially for the latter simulation stages, typically after about 30 μs. The simulation is done for two cases; the first is a gyration simulation with a homogenous magnetic field, and the second uses the same setup as the Penning simulation. Several investigated quantities to determine the error are the radial position, axial position, and velocity magnitude (or kinetic energy). The error is calculated by comparing the result with the reference result from the exact solver with an incredibly small time step width, dt = 10-15 s. The result shows a discrepancy between cylindrical and cartesian Boris solvers. The velocity magnitude of the particle decays as time goes on for the cylindrical Boris solver, especially when the particle is close to the z-axis, an error not found on the cartesian solver. For typical Penning simulation parameters, the trajectory of individual particles is way off the reference trajectory. However, the mean position is relatively close to the reference compared to the dimension of the simulation domain. The kinetic energy is also relatively accurate, with a similar slow decay related to the deteriorating non-axial velocity components previously observed in the first case. Thus, for the simultaneous simulation of millions of particles, there should not be any significant observable difference in actual Penning simulation compared to Penning simulation with reference time step width.

A global space–time Trefftz DG scheme for the time-dependent isotropic elastic wave equations

In this paper we are concerned with Trefftz discretizations of the time-dependent linear elastic wave equation in arbitrary space dimensional domains Ω⊂Rd(d∈N). We propose a Trefftz discontinuous Galerkin method, construct Trefftz basis functions and prove that the corresponding approximate solutions possess optimal-order error estimates with respect to the meshwidth. A space–time domain decomposition preconditioner is introduced for the system generated by the proposed variational formula. Besides, we propose the global Trefftz DG method combined with local DG methods to solve the nonhomogeneous model. The numerical results verify the validity of the theoretical results, and show that the resulting approximate solutions possess high accuracy, and the proposed preconditioner is effective.

A (2+1)-dimensional domain wall at one-loop

We consider the domain wall in the (2+1)-dimensional ϕ4 double well model, created by extending the ϕ4 kink in an additional infinite direction. Classically, the tension is m3/3λ where λ is the coupling and m is the meson mass. At order O(λ0) all ultraviolet divergences can be removed by normal ordering, less trivial divergences arrive only at the next order. This allows us to easily quantize the domain wall, working at order O(λ0). We calculate the leading quantum correction to its tension as a two-dimensional integral over a function which is determined analytically. This integral is performed numerically, resulting in −0.0866m2. This correction has previously been computed twice in the literature, and the results of these two computations disagreed. Our result agrees with and so confirms that of Jaimunga, Semenoff and Zarembo. We also find, at this order, the excitation spectrum and a general expression for the one-loop tensions of domain walls in other scalar models.

Subjective sleep more predictive of global cognitive function than objective sleep in older adults: A specification curve analysis

ObjectivesSleep is associated with cognitive function in older adults. In the current study, we examined this relationship from subjective and objective perspectives, and determined the robustness and dimensional specificity of the associations using a comprehensive modelling approach. MethodsMultiple dimensions of subjective (sleep quality and daytime sleepiness) and objective sleep (sleep stages, sleep parameters, sleep spindles, and slow oscillations), as well as subjectively reported and objectively measured cognitive function were collected from 55 older adults. Specification curve analysis was used to examine the robustness of correlations for the effects of sleep on cognitive function. ResultsRobust associations were found between sleep and objectively measured cognitive function, but not with subjective cognitive complaints. In addition, subjective sleep showed robust and consistent associations with global cognitive function, whereas objective sleep showed a more domain-specific association with episodic memory. Specifically, subjective sleep quality and daytime sleepiness correlated with global cognitive function, and objective sleep parameters correlated with episodic memory. ConclusionsOverall, associations between sleep and cognitive function in older adults depend on how they are measured and which specific dimensions of sleep and domains of cognitive function are considered. It highlights the importance of focusing on specific associations to ameliorate the detrimental effects of sleep disturbance on cognitive function in later life.

Partial trace ideals, torsion and canonical module

For any finitely generated module M with non-zero rank over a commutative one dimensional Noetherian local domain, the numerical invariant h(M) was introduced and studied in [25]. We establish a bound on it which helps capture information about the torsion submodule of M when M has rank one and generalizes the discussion in [25]. We further study bounds and properties of h(M) in the case when M is the canonical module ωR. This in turn helps in answering a question of S. Greco and then provides classifications in the Gorenstein, almost Gorenstein and far-flung Gorenstein setups.