Dimensional Parameters Research Articles

Impact resistance analysis of a dual-constituent negative Poisson’s ratio lattice metamaterial with tailorable coefficient of thermal expansion

The combination of parameters such as negative Poisson’s ratio (NPR) and negative coefficient of thermal expansion (NCTE) can result in novel materials with specialized functions to meet the requirements of multifunctional and multipurpose devices. However, the additional structural design to meet NCTE requires a re-examination of its impact resistance. In this paper, the deformation modes and impact resistance of dual-constituent re-entrant hexagonal lattice metamaterial (DRHLM) with both NPR and NCTE are investigated under different impact velocities, and the nominal stress–strain curves and energy absorption characteristics of DRHLM structures with different arch heights, chord lengths, and angles of the bottom edge ribs and the diagonal ribs are discussed. The results show that the bi-material bending ribs affect the energy absorption of the structure, and in most cases the DRHLM structure has a better energy absorption capacity than the traditional concave hexagonal structure, but too much or too little curvature of the bi-material portion reduces the energy absorption characteristics of the DRHLM structure. In addition, the increase in the pinch angle of the ribs leads to the weakening of the negative Poisson’s ratio effect, which is also detrimental to the energy absorption of the structure. The work done can provide guidance for finding the dimensional parameters of DRHLM structures with optimal energy absorption performance.

To the question of construction of welding inverter sources with a high power factor

Purpose. Design a power-factor-corrected, energy- and cost-efficient switchmode power supply for welding. Methodology. It is proposed to use the principle of direct conversion and to develop the "hybrid" power circuit consisting of a main power path using direct conversion and an auxiliary power path having a converter with an intermediate DC link with relatively low stored energy. Since a load to the welding power supply is the welding arc, its instantaneous power fluctuations during the mains voltage period essentially do not influence the welding process due to the thermal constant of the weld pool molten metal. Effective use of this features of the welding arc allows to optimize the power processing, reduce the amount of energy stored in the reactive elements of the circuit and improve the economical, mass and dimensional parameters of the supplies. Findings. A new power factor corrected switchmode power supply topology is proposed utilizing beforementioned principles of power processing. The algorithm for calculating the switching frequency of supply power switches is given. To confirm the adequacy of the developed provisions, an experimental sample of the power supply was created. Oscillograms of mains voltage and source current consumption and oscillograms in output current limitation mode are obtained. The results of experiments showed that the supply has a power factor > 0.94 in a wide range of powers. Originality. A distinctive feature of the supply is the absence of additional inductive components in the power path, a lower capacitance of the DC-link bulk capacitor and a simplified scheme for limiting its inrush current during initial charging. The supply has an increased open circuit voltage, independent of the mains voltage, which allows for easy arc ignition during manual arc welding. Practical value. Due to the increased power factor, the current drawn from the mains is 30 % to 45 % lower than that of widespread inverter supplies without a power factor corrector. These features allow to simultaneously use more power supply units with the same mains current load, and/or to provide stable welding in conditions of a "weak" mains.

Comparison of threshold algorithms for automatic processing of fat crystal microscopic images based on ImageJ

AbstractMicroscopic image analysis is a crucial tool in fat crystallization research, enabling the analysis of crystal size, network structure, fractal dimension and other parameters through binarization. It is essential to seek an appropriate thresholding algorithm to binarize fat crystal images, which plays a vital role in image segmentation. In this article, the effectiveness of 17 thresholding algorithms such as Default, Mean, IsoData, Otsu, Li and Triangle were analyzed in processing fat crystal images with different shapes, background colors and image intensities. This was expected to discover a stable and objective thresholding algorithm for the binarization of fat crystal images. The performance evaluation was conducted according to the peak signal noise ratio (PSNR), structural similarity index (SSIM) and region non‐uniformity (RNU) parameter. Moreover, the comparative analysis of crystal size error, crystal area fraction and intraclass correlation coefficients (ICC) for fractal dimension values would provide a foundation for the selection of thresholding techniques for fat crystal network images. The results indicated that the Default algorithm exhibited remarkable robustness and applicability with high‐quality and stable outputs in fat crystal image processing.

Assessing input parameter hyperspace and parameter identifiability in a cardiovascular system model via sensitivity analysis

We aim to clarify our understanding of the process of state-space model input parameter identification, known, within the clinical context, as model personalisation. To do so, we apply reference sensitivity and identifiability techniques to a lumped parameter, single ventricle representation of the systemic circulation, chosen in view of its relative simplicity and prior art. We attempt to quantify the reliability of input parameter identifiability through the lens of 4 clinically relevant measurements and the attendant difficulty in personalising the model. In turn, this that we extend existing methods which combine both parameter influence and orthogonality, to global sensitivities. By examining different parameter sensitivity evaluation methodologies, we investigate the stability of optimal parameter subsets which are commonly used to aid clinical investigations. In order to perform the personalisation process, one must understand the complexity of the high dimensional input parameter hyperspace associated with this class of model. By utilising Sobol indices, we propose a domain-agnostic and intuitive approach. This involves varying the bounds of the input parameter space relative to the model’s base state. These investigations yield a pseudo-mapping of the input hyperspace, cementing our understanding of the role of identifiable input parameters in the state-space model. Our findings suggest a novel global methodology for input parameter identifiability and input hyperspace mapping, providing valuable insights into solving the personalisation process.

Research on Settlement and Section Optimization of Cemented Sand and Gravel (CSG) Dam Based on BP Neural Network

In order to predict the settlement and compressive stress of the cemented sand and gravel (CSG) dam, and optimize its section design, relying on a CSG dam in the design phase, using finite element software ANSYS, the influence of the dam’s own geometric dimensions and the material parameters of the overburden, including upstream and downstream slope coefficients of the first and the second stage of the dam body, the elastic modulus and the Poisson’s ratio of the overburden on the dam’s settlement and compressive stress are studied. An orthogonal experiment with six factors and three levels is conducted for a grey relational analysis of the dam’s maximum settlement and maximum compressive stress separately on these six parameters. Based on the BP neural network, the six selected factors are used as input layers for the neural network prediction model, and the maximum settlement and compressive stress of the dam are taken as the result to be output. The mapping relationship between the geometric dimensions of the dam body and the maximum settlement and the maximum compressive stress in the trained prediction model is combined with the global optimization tool Pattern Search in the MATLAB toolbox to optimize the section design of the dam. The results reveal that the six selected factors have a high correlation degree with the dam’s maximum settlement and maximum compressive stress. In dimension parameters, the downstream slope coefficient of the second stage of the dam has the greatest impact on the maximum settlement, with a grey correlation degree of 0.7367, and the upstream slope coefficient of the second stage of the dam has the greatest impact on the maximum compressive stress, with a grey correlation degree of 0.7012. The influence of the elastic modulus of the overburden on the maximum settlement and maximum compressive stress of the dam body is greater than its Poisson’s ratio. The BP neural network is applicable for predicting the dam’s settlement based on geometric dimension parameters of the dam and material parameters of the surrounding environment, with R2 reaching 0.9996 and RMSE only 0.0109 cm. Based on the optimization method combined with BP neural network, the material consumption is saved by 11.83%, the maximum settlement is reduced by 2.6%, the maximum compressive stress is reduced by 37.35%, and the optimization time is shortened by 40.92%, compared to the traditional method. The findings have certain reference value for site selection, dimension design, overburden treatment, and design optimization of CSG dams.

Optimization and test of a ring-ring typed atmospheric pressure plasma jet for optical fabrication

In recent years, the atmospheric pressure plasma jet (APPJ) has gained considerable attention in the field of optical fabrication because the chemical etching approach offers the advantage of avoiding mechanical damage to workpieces. This paper presents the development of an atmospheric pressure plasma machining system with a ring-ring typed APPJ, which exhibits several favorable characteristics. It has a relatively low processing temperature, no electrode corrosion, no plasma contamination, and a long discharge plume, etc. Optimization of the dimensional parameters and analysis of reactive gas flow rate are conducted through experimental investigations. Comparative experiments are carried out with the commonly used needle-ring typed APPJ, revealing significant improvements of the ring-ring typed APPJ, i.e., a fourfold increase in machining distance, a tenfold increase in machining efficiency, and a 58% enhancement in the stability of the material volumetric removal rate. Furthermore, on-line temperature monitoring of the footprint has been achieved due to the specific traits of the extended discharge plume. Fabrication experiments are conducted to demonstrate the significant correlation between the temperature and material removal rate, which shows the possibility of improving the accuracy of optical fabrication based on the ring-ring typed APPJ.

Compliance and Kinetostatics of a Novel 2PRS-2PSS Compliant Parallel Micromanipulator: Modeling and Analysis.

A novel 2PRS-2PSS (P represents the prismatic pair, R represents the revolute hinge, S represents the spherical hinge) compliant parallel micromanipulator with two translational DOFs and two rotational DOFs is presented, and its compliance model and kinetostatic model are sequentially developed and analyzed. Initially, an analytical model used to describe the compliance of this micromanipulator was developed using the compliance matrix method (CMM). Through a comparison with finite element analysis, the accuracy of this analytical model is confirmed, and the influence of various dimensional and structural parameters on the compliance behavior is investigated. Subsequently, the micromanipulator is treated as an equivalent spring system, allowing for the derivation of its governing equation based on the established compliance model. From this equation, a kinetostatic model relating input forces to output displacements is derived. Validation of this model is performed by comparing analytical results with finite element simulations under specific motion trajectories, revealing a maximum relative error of 6.18%. This close agreement verifies the accuracy of the kinetostatic model. Finally, the impact of the parameters of the flexure hinge on the mapping matrix is examined to offer insights into minimizing undesired displacements, providing valuable guidance for optimizing the micromanipulator's performance.

Topological dyonic black holes of massive gravity with generalized quasitopological electromagnetism

In this paper we investigate new dyonic black holes of massive gravity sourced by generalized quasitopological electromagnetism in arbitrary dimensions. We begin by deriving the exact solution to the field equations defining these black holes and look at how graviton’s mass, dimensionality parameter, and quasitopological electromagnetic field affect the horizon structure of anti-de Sitter dyonic black holes. We also explore the asymptotic behaviour of the curvature invariants at both the origin and infinity to analyze the geometric structure of the resultant black holes. We also compute the conserved and thermodynamic quantities of these dyonic black holes with the help of established techniques and known formulas. After investigating the relevancy of first law, we look at how various parameters influence the local thermodynamic stability of resultant black hole solution. We also examine how thermal fluctuations affect the local stability of dyonic black holes in massive gravity. Finally, we study the shadow cast of the black hole.

Investigation on the rock-breaking performance of specially-shaped PDC cutter under confining pressure conditions

Cutter shape has a significant influence on the rock-breaking performance of drill bit. Compared with conventional Polycrystalline Diamond Compact (PDC) cutter, the emergence of specially-shaped PDC cutters (SSPCs) with different geometric shapes is of great benefit to solve the problems of low rate of penetration (ROP) and rock-breaking efficiency in deep hard and hot dry rock formations. However, the high confining pressure and high in-situ stress (HCHS) have a significant influence on the rock-breaking performance of PDC cutter which cannot be ignored. Given that the above facts, this paper selects five kinds of PDC cutters with different cutter shapes, and implements a series of cutting experiments of granite under confining pressure conditions (CPCs) and atmospheric pressure condition (APC) using a true triaxial rock cutting device. The influence of cutter profile on the rock-breaking performance of PDC cutter was studied from the perspective of cutting force, mechanical specific energy (MSE), cuttings, coarseness index (CI) and fractal dimension. The results show that the rock breaking under CPC is more difficult than that under APC, resulting in the increased cutting force and MSE. Moreover, the cuttings obtained under CPC are smaller in size and the cutting grooves are smoother. Cutter shape has a great influence on the rock-breaking efficiency of PDC cutter, the rock-breaking efficiency of axe-shaped cutter is higher, while that of stinger cutter is lower. Moreover, PDC cutters with different shapes have different sensitivities to confining pressure, and stinger cutter has the lowest sensitivity to confining pressure, in other words, confining pressure has little influence on the rock breaking process of stinger cutter. What is more, the distribution of cuttings is characterized by the fractal dimension and particle size distribution parameter from the perspective of cuttings morphology and size respectively, and they present completely opposite changing trends. The obtained results are of great significance to the optimization of cutter profile, the design of bit cutter arrangement and the drilling of deep formation.

Nonlinear stability of ES-FG porous sandwich cylindrical shells subjected to axial compression in thermal environment

Stability analysis of cylindrical shells subjected to axial compression is a common problem in engineering applications. The phenomenon of instability can easily appear with thin cylindrical shells. Therefore, to increase the load-carrying capacity of this type of structure, the cylinders are increased in thickness by adding a porous core layer and reinforcing orthogonal stiffeners. In this study, the nonlinear buckling and postbuckling behavior of functionally graded (FG) porous sandwich cylindrical shells with orthogonal eccentrically stiffeners (ES) are investigated via analytical approach. The FG porous sandwich cylindrical shells are rested on Pasternak elastic foundations and subjected to axial compression load in a thermal environment. The reinforcement of the shells is achieved through closely spaced stringers and rings, with the material properties of both the shell and stiffeners graded continuously in the thickness direction. The governing equations are derived based on the Donnell’s shell theory with von Karman geometrical nonlinearity and the smeared stiffeners technique. The Galerkin procedure is employed to ascertain the critical load and post-buckling response, employing a three-term deflection solution. The study analyzed the effects of parameters such as porosity coefficient, material, temperature, dimensional parameters, stiffener, and foundation characteristics.

Advancements in stress‐based multiaxial fatigue prediction: A data‐driven approach and a new criterion

AbstractA highly accurate iteration of the Liu & Zenner multiaxial fatigue criterion is presented and validated against the FatLim dataset. The proposed criterion modifies the mean stress parameters of the original criterion while keeping the amplitude parameters unchanged. The investigation of the best‐performing stress amplitude parameters revealed that existing multiaxial fatigue criteria that rely on dimensional weight parameters exhibit a notable prediction bias. This bias leads to inaccurate fatigue assessments. The paper sheds light on the extent of this bias and its implications for common engineering practices. By introducing the refined criterion, the study addresses these limitations and provides a more reliable framework for multiaxial fatigue analysis. The comprehensive validation against the FatLim dataset demonstrates the superiority of the proposed criterion in accurately predicting fatigue behavior under diverse loading conditions. The findings underscore the significance of considering both amplitude and mean stress parameters in multiaxial fatigue criteria, offering valuable insights for enhancing the accuracy of fatigue assessments in engineering applications.

Preparation and Mechanical Properties of Core-Shell PS&CeO2 Composite Abrasive Particles.

Core-shell composite abrasive particles are a topic of great interest in surface finishing. It is important to explore the preparation technology and performance parameters associated with them. In this paper, a core-shell composite abrasive particle made of polystyrene and cerium oxide (PS&CeO2, CSPC), which is rigid on the outside and flexible on the inside, is proposed. The microstructure, physical phase characteristics, and mechanical properties of the inner core and composite abrasive particles are investigated. PS microspheres and CSPC composite abrasive particles with different structural features were prepared through a series of experiments, morphological observations, and physical and chemical characterization experiments. Their microstructures and physical phase properties were investigated. The indentation load curves of the PS microspheres and CSPC composite abrasive samples were measured by using an atomic force microscope. The analysis focused on the effects of various dimensional and structural parameters on the modulus of elasticity of both PS microspheres and CSPC composite abrasive particles. The analysis shows that the experimentally prepared PS microspheres have good dispersion, a smooth surface, and a uniform particle size distribution. The prepared CSPC composite abrasive particles are regular spheres with rough, rice-like surfaces, low modulus of elasticity, and overall nonrigid and soft elastic properties. The results of this paper can provide a guide for the preparation technology, performance regulation, and application of polymer microspheres and core-shell composite abrasive particles in CMP.

Stability of a realistic astrophysical pulsar and its mass–radius relation in higher-order curvature gravity

The objective of this research is to explore compact celestial objects while considering the framework of an extended gravitational theory known as R+f(G) gravity. The notations R and G denote the Ricci scalar and the Gauss–Bonnet invariant, respectively. Radio pulsars, which are neutron stars with masses greater than 1.8 times that of the Sun (M⊙), provide exceptional opportunities for delving into fundamental physics in extraordinary environments unparalleled in the observable universe and surpassing the capabilities of experiments conducted on Earth. Through the utilization of both the linear and quadratic expressions of the function f(G)=α1G2, where α1 (with dimensional units of [length6]) are incorporated, we have achieved an accurate analytical solution for anisotropic perfect-fluid spheres in a state of hydrostatic equilibrium. By integrating the dimensional parameters α1 and the compactness factor, defined as C=2GMRc2, we showcase our capacity to encompass and depict all physical characteristics within the stellar structure. We illustrate that the model is capable of producing a stable arrangement for the pulsar PSR J0740+6620, encompassing its geometric and physical properties. We illustrate that by utilizing the quadratic form of G in the R+f(G) framework, the Krori-Barua ansatz establishes semi-analytical relationships between radial pressure (pr), tangential pressure (pt), and density (ρ). Remarkably, in the context of the positive/negative α1 quadratic R+f(G) gravity framework, the maximum compactness is inherently restricted to values fail below the Buchdahl limit when the surface density exceeds 4×1014 g/cm3. This differs from General Relativity (GR) in which the compactness is unlikely to approach the black hole limit. The model predicts a core density that is multiple times greater than the saturation nuclear density, represented as ρnuc=4×1014 g/cm3, and a surface density ρs that surpasses the nuclear saturation density. We offer the mass–radius diagram associated with the boundary density we have determined, and this diagram has been shown to be consistent with other observational discoveries.

Подводные резервуары для хранения нефти: массогабаритные параметры и конструктивные особенности

The extensive exploration and development of shallow water oil fields on the continental shelf require the search for new sources of hydrocarbon reserves and movement towards the deep-water or Arctic areas. Today, the development of technologies enables oil production in complex conditions of the Arctic and deep-water shelf. Such production, however, is associated with high technological and environmental risks, which necessitates the development of innovative technologies and means to mitigate environmental risks. One of the solutions is the application of underwater oil storage facilities that have gained popularity abroad. The article provides an analysis of the existing designs of underwater tanks. The main factors affecting the selection of appropriate mass and dimensional parameters for underwater storage facilities are listed, including operational conditions, stability and strength, the technology of tank and membrane manufacturing, transportation and installation on the seabed, safety and environmental requirements, and economic factors. The calculations of mass and overall dimensions of the considered structures help to determine preferable capacities and evaluate the impact of flexible membranes on the mass and dimensional characteristics of oil storage tanks. Studies have shown that using membranes reduces the wall thickness, overall dimensions, and mass of the structure. The main factors contributing to accidents during the operation of subsea oil storage facilities have been identified, along with safety barriers aimed to mitigate the negative impact on the flora and fauna of the surrounding water area. Further investigation of the issue of wax deposition in underwater tanks that occurs when the temperature of crude oil drops due to heat loss in the surrounding seawater is planned. The deposited wax can either remain in the crude oil or settle as a layer adhering to the internal walls of the tank.

Unipolar optical transitions in nanoclusters of ellipsoidal geometry

The quantum states of an ellipsoidal nanocluster of a heterophase system InAs/GaAs are studied using an exact analytical approach, in contrast to the generally accepted theoretical model based on the adiabatic approximation. It is shown that the spectrum of a nanoobject is formed from local groups, consisting of discrete levels, separated by terahertz frequency intervals. A double random degeneration of certain spectrum states is revealed. Allowed mid-infrared (IR) optical transitions between different spectral states are analyzed. The role of dimensional parameters and features of the shape of a nanoobject in the characteristics of unipolar transitions is assessed. The absorption spectrum for the transition in the lower part of the substructure spectrum is calculated.

Analyzing the nonlinear torsional buckling and post-buckling of ES-FG porous cylindrical shells in thermal environment using FSDT in terms of the displacement components

The main aim of this paper is to investigate the nonlinear buckling and post-buckling of eccentrically stiffened sandwich functionally graded porous (FGP) cylindrical shells surrounded by elastic foundations in thermal environments and under torsional load by analytical approach in terms of the displacement components. The shells are reinforced by eccentric rings and stringers attached to the inside and material properties of face sheets and stiffeners are assumed to be continuously graded in the thickness direction. The sandwich cylindrical shell is composed of FG porous core and two FG layer coating. Based on the first order shear deformation theory (FSDT) with von Kármán geometrical nonlinearity and smeared stiffeners technique, the governing equations are derived. Using Galerkin method, the closed form to find critical torsional load and post-buckling load-deflection curves are obtained. The effects of porosity parameters, the thickness of the porous core, temperature, stiffener, foundation, material and dimensional parameters are analyzed.

On the symmetries of singular limits of spacetimes

We consider spacetime metrics with a given (but quite generic) dependence on a dimensionful parameter such that in the 0 and ∞ limits of that parameter the metric becomes singular. We study the isometry groups of the original spacetime metrics and of the singular metrics that arise in the limits and the corresponding symmetries of the motion of p-branes evolving in them, showing how the Killing vectors and their Lie algebras can be found in general. We illustrate our general results with several examples which include limits of anti-de Sitter spacetime in which the holographic screen is one of the singular metrics and of pp-waves.

Cross-shaped nanoaperture nanoantennas inside plasmonic nanorings for large SERS enhancement and multiple hotspots

We have designed a novel nanostructure consisting of a cross-shaped nanoaperture nanoantenna inside plasmonic nanorings for achieving very large values of electric field enhancement, as well as large theoretical surface-enhanced Raman scattering (SERS) enhancement factor, towards the center of the nanostructure. In this work, we employed Finite-difference-time-domain (FDTD) numerical modeling to simulate the plasmonic (gold) nanostructures present on silica substrates. We found that the nanostructures being proposed by us show very high localized electric field enhancements as well as multiple hotspots in which the electric field is enhanced and localized. We observed that these hotspots have large electric field enhancements (and therefore large theoretical SERS enhancement factors) at more than one wavelength. Thus, the proposed nanostructure can be used to achieve a multiple wavelength SERS response. The electric field enhancements and the resonance wavelengths of nanostructures can be tuned in the visible and the NIR region by modifying the nanostructure dimensions like the gap between the tips in the central nanoaperture structure, height of nanostructure, and tip angle variation. It is observed that as the number of gold nanorings increase, the electric field enhancement (as well as the theoretical SERS enhancement factor) also increase due to the focusing of light towards the center of nanostructure, and after the addition of a few rings, the electric field enhancement becomes almost constant. We also studied the polarization dependence of the nanostructure by varying the angle of polarization of the incident light to check the variation of the electric field of the nanostructure, and observed that the proposed nanostructures did not have much polarization dependence. Moreover, due to the symmetric nature of the plasmonic nanostructure, the position of the hotspot region shifts to the adjacent corner on rotating the incident field polarization. We optimized all the dimensional parameters to get the best possible theoretical SERS enhancement factor of ∼ 1010. Moreover, we simulated a periodic array of these plasmonic nanostructures on the silica substrates, having equal periodicity in X and Y directions, and achieved a theoretical SERS enhancement factor of ∼ 1011.

A Study on Dimensionality Reduction and Parameters for Hyperspectral Imagery Based on Manifold Learning.

With the rapid advancement of remote-sensing technology, the spectral information obtained from hyperspectral remote-sensing imagery has become increasingly rich, facilitating detailed spectral analysis of Earth's surface objects. However, the abundance of spectral information presents certain challenges for data processing, such as the "curse of dimensionality" leading to the "Hughes phenomenon", "strong correlation" due to high resolution, and "nonlinear characteristics" caused by varying surface reflectances. Consequently, dimensionality reduction of hyperspectral data emerges as a critical task. This paper begins by elucidating the principles and processes of hyperspectral image dimensionality reduction based on manifold theory and learning methods, in light of the nonlinear structures and features present in hyperspectral remote-sensing data, and formulates a dimensionality reduction process based on manifold learning. Subsequently, this study explores the capabilities of feature extraction and low-dimensional embedding for hyperspectral imagery using manifold learning approaches, including principal components analysis (PCA), multidimensional scaling (MDS), and linear discriminant analysis (LDA) for linear methods; and isometric mapping (Isomap), locally linear embedding (LLE), Laplacian eigenmaps (LE), Hessian locally linear embedding (HLLE), local tangent space alignment (LTSA), and maximum variance unfolding (MVU) for nonlinear methods, based on the Indian Pines hyperspectral dataset and Pavia University dataset. Furthermore, the paper investigates the optimal neighborhood computation time and overall algorithm runtime for feature extraction in hyperspectral imagery, varying by the choice of neighborhood k and intrinsic dimensionality d values across different manifold learning methods. Based on the outcomes of feature extraction, the study examines the classification experiments of various manifold learning methods, comparing and analyzing the variations in classification accuracy and Kappa coefficient with different selections of neighborhood k and intrinsic dimensionality d values. Building on this, the impact of selecting different bandwidths t for the Gaussian kernel in the LE method and different Lagrange multipliers λ for the MVU method on classification accuracy, given varying choices of neighborhood k and intrinsic dimensionality d, is explored. Through these experiments, the paper investigates the capability and effectiveness of different manifold learning methods in feature extraction and dimensionality reduction within hyperspectral imagery, as influenced by the selection of neighborhood k and intrinsic dimensionality d values, identifying the optimal neighborhood k and intrinsic dimensionality d value for each method. A comparison of classification accuracies reveals that the LTSA method yields superior classification results compared to other manifold learning approaches. The study demonstrates the advantages of manifold learning methods in processing hyperspectral image data, providing an experimental reference for subsequent research on hyperspectral image dimensionality reduction using manifold learning methods.

Magnetic field detection utilizing soft magnetic ribbons and a rectangular solenoid

An effective approach to high-sensitivity magnetic field detection under low-frequency excitation by soft magnetic ribbons and a rectangular solenoid is proposed and certificated. The solenoid wound by nonmagnetic copper wire is located at the geometric center region of the soft magnetic ribbons that act as the magnetic core. The proposed magnetic sensor utilizes the nonlinear magnetization of soft magnetic ribbons and the magneto-inductive effect and proximity effect of the solenoid, exhibiting significant impedance variation at a relatively low frequency. The impedance ratio and impedance sensitivity of the prototype reaches the maximum value of 5630% and 570% Oe−1 at 500 kHz, which is far superior to the conventional giant magneto-impedance (GMI) magnetic sensor and planar coil laminated GMI magnetic sensor. The investigation indicates the fabricated magnetic sensor with optimal dimension parameters can provide a sensitivity of 3329 mV Oe−1 at 500 kHz. More importantly, the proposed prototype is particularly suitable for micromachining, providing the possibility for manufacturing high-sensitivity micro-magnetic sensors.