Geometric Regions Research Articles

Physiological definition for region of interest selection in electrical impedance tomography data: description and validation of a novel method

Objective. Geometrical region of interest (ROI) selection in electrical impedance tomography (EIT) monitoring may lack sensitivity to subtle changes in ventilation distribution. Therefore, we demonstrate a new physiological method for ROI definition. This is relevant when using ROIs to compute subsequent EIT-parameters, such as the ventral-to-dorsal ratio during a positive end-expiratory pressure (PEEP) trial.Approach.Our physiological approach divides an EIT image to ensure exactly 50% tidal impedance variation in the ventral and dorsal region. To demonstrate the effects of our new method, EIT measurements during a decremental PEEP trial in 49 mechanically ventilated ICU-patients were used. We compared the center of ventilation (CoV), a robust parameter for changes in ventro-dorsal ventilation distribution, to our physiological ROI selection method and different commonly used ROI selection methods. Moreover, we determined the impact of different ROI selection methods on the PEEP level corresponding to a ventral-to-dorsal ratio closest to 1.Main results.The division line separating the ventral and dorsal ROI was closer to the CoV for our new physiological method for ROI selection compared to geometrical ROI definition. Moreover, the PEEP level corresponding to a ventral-to-dorsal ratio of 1 is strongly influenced by the chosen ROI selection method, which could have a profound clinical impact; the within-subject range of PEEP level was 6.2 cmH2O depending on the chosen ROI selection method.Significance.Our novel physiological method for ROI definition is sensitive to subtle ventilation-induced changes in regional impedance (i.e. due to (de)recruitment) during mechanical ventilation, similar to the CoV.

Bioinspired deformation computational design method for muscle-driven soft robots using MPM.

In order to adapt to complex and changing environments, animals have a wide variety of locomotor forms, which has inspired the investigation of their deformation and driving mechanisms. In this paper, we propose a computational design method for muscle-driven soft robots to satisfy desired deformations, aiming to mimic the deformation behavior of muscle-driven animals in nature. By inputting an initial shape and a desired shape, this paper generates the ideal muscle-driven layout for the soft robot so that it can closely realize the desired deformation. The material point method (MPM) is utilized to simulate the soft medium so as to achieve the effect of coupling and coordinated deformation of arbitrary shapes. The method efficiently searches for muscle layouts corresponding to various deformations and realizes the deformation behaviors of a variety of bio-inspired robots, including soft robots such as bionic snakes, frogs, and human faces. Experimental results show that for both the bionic snake and frog soft robots, the overlap of the geometric contour regions between the actual and simulated deformations is more than 90%, which validates the effectiveness of the method. In addition, the global muscle distributions of the bionic snake and human face soft robots during motion are generated and validated by effective simulation.

Study on Optimization Method for InSAR Baseline Considering Changes in Vegetation Coverage.

Time-series Interferometric Synthetic Aperture Radar (InSAR) technology, renowned for its high-precision, wide coverage, and all-weather capabilities, has become an essential tool for Earth observation. However, the quality of the interferometric baseline network significantly influences the monitoring accuracy of InSAR technology. Therefore, optimizing the interferometric baseline is crucial for enhancing InSAR's monitoring accuracy. Surface vegetation changes can disrupt the coherence between SAR images, introducing incoherent noise into interferograms and reducing InSAR's monitoring accuracy. To address this issue, we propose and validate an optimization method for the InSAR baseline that considers changes in vegetation coverage (OM-InSAR-BCCVC) in the Yuanmou dry-hot valley. Initially, based on the imaging times of SAR image pairs, we categorize all interferometric image pairs into those captured during months of high vegetation coverage and those from months of low vegetation coverage. We then remove the image pairs with coherence coefficients below the category average. Using the Small Baseline Subset InSAR (SBAS-InSAR) technique, we retrieve surface deformation information in the Yuanmou dry-hot valley. Landslide identification is subsequently verified using optical remote sensing images. The results show that significant seasonal changes in vegetation coverage in the Yuanmou dry-hot valley lead to noticeable seasonal variations in InSAR coherence, with the lowest coherence in July, August, and September, and the highest in January, February, and December. The average coherence threshold method is limited in this context, resulting in discontinuities in the interferometric baseline network. Compared with methods without baseline optimization, the interferometric map ratio improved by 17.5% overall after applying the OM-InSAR-BCCVC method, and the overall inversion error RMSE decreased by 0.5 rad. From January 2021 to May 2023, the radar line of sight (LOS) surface deformation rate in the Yuanmou dry-hot valley, obtained after atmospheric correction by GACOS, baseline optimization, and geometric distortion region masking, ranged from -73.87 mm/year to 127.35 mm/year. We identified fifteen landslides and potential landslide sites, primarily located in the northern part of the Yuanmou dry-hot valley, with maximum subsidence exceeding 100 mm at two notable points. The OM-InSAR-BCCVC method effectively reduces incoherent noise caused by vegetation coverage changes, thereby improving the monitoring accuracy of InSAR.

A multi-domain model for microcirculation in optic nerve: Blood flow and oxygen transport

Microcirculation of blood and transport of oxygen play important roles in the biological function of the optic nerve and its diseases. This work develops a multi-domain model for the optic nerve, that includes important biological structures and various physical mechanisms in blood flow and oxygen delivery. The two vascular networks are treated as five domains in the same geometric region, with various exchanges among them (such as Darcy’s law for fluid flow) and with the tissue domain (such as water leak, diffusion). The numerical results of the coupled model for a uniform case of vasculature distribution show mechanisms and scales consistent with literature and intuition. The effects of various important model parameters (relevant to pathological conditions) are investigated to provide insights into the possible implications. The vasculature distribution (resting volume fractions here) has significant impacts on the blood circulation and could lead to insufficient blood supply in certain local regions and thereby affect the delivery of oxygen. The water leak across the capillary wall will have nontrivial effects after the leak coefficients pass a threshold. The pulsatile arterial pressure leads to expected pulsatile patterns and stable spatial profiles, and the uniform case is almost the averaged version of pulsatile case. The effects of viscosity, the stiffness of blood vessel wall, oxygen demand, etc. have also been analyzed. The framework can be extended to include ionic transport or to study the retina when more biological structural information is available.

Coverage Path Planning with Adaptive Hyperbolic Grid for Step-Stare Imaging System

Step-stare imaging systems are widely used in aerospace optical remote sensing. In order to achieve fast scanning of the target region, efficient coverage path planning (CPP) is a key challenge. However, traditional CPP methods are mostly designed for fixed cameras and disregard the irregular shape of the sensor’s projection caused by the step-stare rotational motion. To address this problem, this paper proposes an efficient, seamless CPP method with an adaptive hyperbolic grid. First, we convert the coverage problem in Euclidean space to a tiling problem in spherical space. A spherical approximate tiling method based on a zonal isosceles trapezoid is developed to construct a seamless hyperbolic grid. Then, we present a dual-caliper optimization algorithm to further compress the grid and improve the coverage efficiency. Finally, both boustrophedon and branch-and-bound approaches are utilized to generate rotation paths for different scanning scenarios. Experiments were conducted on a custom dataset consisting of 800 diverse geometric regions (including 2 geometry types and 40 samples for 10 groups). The proposed method demonstrates comparable performance of closed-form path length relative to that of a heuristic optimization method while significantly improving real-time capabilities by a minimum factor of 2464. Furthermore, in comparison to traditional rule-based methods, our approach has been shown to reduce the rotational path length by at least 27.29% and 16.71% in circle and convex polygon groups, respectively, indicating a significant improvement in planning efficiency.

Preliminary Failure Analyses of Loaded Hot Water Bottles

Hot water bottles are widely utilised for their therapeutic advantages, such as relieving muscle tension and imparting warmth. However, the increasing frequency and potential risks associated with bursting or failure necessitate a detailed examination of the contributing factors as their failure is not fully understood in a scientific manner. With the apparent lack of analysis of hot water bottles in the literature, this study employs, for the first time, a dual methodology involving finite-element (FE) analysis conducted in ABAQUS and experimental validation to systematically investigate the underlying mechanisms leading to failure incidents. Through FE modelling and analysis, the stress and strain distribution within typical hot water bottles is modelled under compression loading conditions, facilitating the identification of vulnerable areas prone to failure. Experimental validation encompasses uniaxial loading compression tests on distinct specimens, generating load–displacement curves that elucidate material responses to compressive forces and highlight variations in load-bearing capacities. The study explores diverse failure modes, attributing them to stress concentration at geometric transitions and contact regions. Stress–strain curves contribute valuable insights into material characteristics, with ultimate stress values as crucial indicators of resistance to deformation and rupture. The FE analysis simulation results visualise deformation patterns and stress concentration zones. The findings illustrate that the highest stress concentration areas exist in the internal boundary of hot water bottles near the neck and cap region. This is experimentally confirmed through the bursting failures of four samples, with three failures occurring in this specific region. The findings support the guidance that users should avoid sleeping with a hot water bottle as it may fail under compression if they lay on top of it. Meanwhile, this result guides manufacturers to strengthen the weak areas of hot water bottles around the nicks and edges. This study significantly enhances our understanding of hot water bottle mechanics, thereby guiding design practice to improve overall performance and user safety. In summary, hot water bottles are commonly used but have not been investigated scientifically regarding external loading conditions and their related failure, as the current study has achieved. Identifying the weak points through experiment and simulation directs manufacturers towards required improvements in particular regions, such as the bottleneck and edge reinforcement during the design and manufacturing phases.

Crater Triangle Matching Algorithm Based on Fused Geometric and Regional Features

Craters are regarded as significant navigation landmarks during the descent and landing process in small body exploration missions for their universality. Recognizing and matching craters is a crucial prerequisite for visual and LIDAR-based navigation tasks. Compared to traditional algorithms, deep learning-based crater detection algorithms can achieve a higher recognition rate. However, matching crater detection results under various image transformations still poses challenges. To address the problem, a composite feature-matching algorithm that combines geometric descriptors and region descriptors (extracting normalized region pixel gradient features as feature vectors) is proposed. First, the geometric configuration map is constructed based on the crater detection results. Then, geometric descriptors and region descriptors are established within each feature primitive of the map. Subsequently, taking the salience of geometric features into consideration, composite feature descriptors with scale, rotation, and illumination invariance are generated through fusion geometric and region descriptors. Finally, descriptor matching is accomplished by computing the relative distances between descriptors and adhering to the nearest neighbor principle. Experimental results show that the composite feature descriptor proposed in this paper has better matching performance than only using shape descriptors or region descriptors, and can achieve a more than 90% correct matching rate, which can provide technical support for the small body visual navigation task.

Geometric modeling of phase ordering for the isotropic–smectic A phase transition

BackgroundLiquid crystal (LC) mesophases have an orientational and positional order that can be found in both synthetic and biological materials. These orders are maintained until some parameter, mainly the temperature or concentration, is changed, inducing a phase transition. Among these transitions, a special sequence of mesophases has been observed, in which priority is given to the direct smectic liquid crystal transition. The description of these transitions is carried out using the Landau–de Gennes (LdG) model, which correlates the free energy of the system with the orientational and positional order.MethodologyThis work explored the direct isotropic-to-smectic A transition studying the free energy landscape constructed with the LdG model and its relation to three curve families: (I) level-set curves, steepest descent, and critical points; (II) lines of curvature (LOC) and geodesics, which are directly connected to the principal curvatures; and (III) the Casorati curvature and shape coefficient that describe the local surface geometries resemblance (sphere, cylinder, and saddle).ResultsThe experimental data on 12-cyanobiphenyl were used to study the three curve families. The presence of unstable nematic and metastable plastic crystal information was found to add information to the already developed smectic A phase diagram. The lines of curvature and geodesics were calculated and laid out on the energy landscape, which highlighted the energetic pathways connecting critical points. The Casorati curvature and shape coefficient were computed, and in addition to the previous family, they framed a geometric region that describes the phase transition zone.Conclusion and significanceA direct link between the energy landscape’s topological geometry, phase transitions, and relevant critical points was established. The shape coefficient delineates a stability zone in which the phase transition develops. The methodology significantly reduces the impact of unknown parametric data. Symmetry breaking with two order parameters (OPs) may lead to novel phase transformation kinetics and droplets with partially ordered surface structures.

[formula omitted] sampled-data load frequency control with time-varying delays considering delay compensation

Time delays in load frequency control (LFC) systems may deteriorate the control performance or cause instability. How to mitigate such adverse effects of delays in a simple but effective way is worth investigating and motivates this work. For sampled-data LFC systems with time-varying delays, this paper introduces two delay compensation techniques, namely linear and division-based methods, aimed at enhancing the robustness of LFC. A stability criterion based on H∞ is derived to ensure LFC system stability under delay compensation. Building upon this criterion, we propose an H∞ controller optimization method to tune delay compensation controller gains offline by searching for a minimum H∞ disturbance attenuation level and effectively segmenting the geometric region formed by the delays. The effectiveness of the proposed delay compensation techniques and the controller design method is verified via numerical examples on an LFC system with both generation unit and energy storage participation.

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.

Discrimination of Plant Structures in 3D Point Cloud Through Back-Projection of Labels Derived from 2D Semantic Segmentation

In the decommissioning of the Fukushima Daiichi Nuclear Power Station, radiation dose calculations necessitate a 3D model of the workspace are performed to determine suitable measures for reducing exposure. However, the construction of a 3D model from a 3D point cloud is a costly endeavor. To separate the geometrical shape regions on 3D point cloud, we are developing a structure discrimination method using 3D and 2D deep learning to contribute to the advancement of 3D modeling automation technology. In this paper, we present a method for transferring and fusing labels to handle 2D prediction labels in 3D space. We propose an exhaustive label fusion method designed for plant facilities with intricate structures. Through evaluation on a mock-up plant dataset, we confirmed the method’s effective performance.

Artificial neural network-based optimization studies for efficient energy harvesting from auxetic laminated composite smart beam

In the present study, a smart laminated composite beam with auxetic configurations is considered to improve its energy harvesting capabilities. For this purpose, three different cases of auxetic geometries are considered on the cantilever laminated composite beam near the fixed end, and the piezoelectric patches are attached in bimorph configuration over this auxetic region. The coupled structural and piezoelectric equations are developed based on beam element theory. The principle of minimum potential energy is implemented to solve the equations of motion. Effective Young’s modulus and Poisson’s ratio of the auxetic geometric region are utilized in the beam model, and the results are validated with three-dimensional finite element solution. An experimental study is carried out to validate the finite element studies. The energy harvesting capability of laminated composite beam has significantly improved by using reentrant auxetic geometry. In addition to auxetic geometry, auxetic cubical inclusions are also considered to study their effect on energy harvesting potential. On validating the results obtained from auxetic geometry and auxetic cubical inclusion cases, some parametric studies are conducted to know the influence of auxetic inclusion dimensions, ply orientations, and thickness ratio on the voltage energy output. It is found that with an increase in the length and thickness of the auxetic geometry, the cross-ply oriented auxetic energy harvester’s output potential raised considerably. To obtain the optimum parameters required to enhance average output power and minimize the energy loss factor, grey-relational analysis tool, and artificial neural network-based optimization schemes are implemented. The energy harvesting system with the optimized parameters has shown about 60% improvement in the average power output and conversion efficiency.

Automated deviation-aware landmark selection for freeform product accuracy qualification in 3D printing

Landmarks are essential in non-rigid shape registration for identifying the correspondence between designs and actual products. In 3D printing, manual selection of landmarks becomes labor-intensive, due to complex product geometries and their non-uniform shape deviations. Automatic selection, however, has to pinpoint landmarks indicative of geometric regions prone to deviations for accuracy qualification. Existing automatic landmarking methods often generate clustered and redundant landmarks for prominent features with high curvatures, compromising the balance between global and local registration errors. To address these issues, we propose an automatic landmark selection method through deviation-aware segmentation and landmarking. As opposed to segmentation for semantic feature identification, deviation-aware segmentation partitions a freeform product for high-curvature region identification. Prone to deviation, these regions are generated through curvature-sensitive remeshing to extract vertices of high curvature and automatic clustering of vertices based on vertex density. Within each segment or high-curvature region, a curvature-weighted function is tailored for the Gaussian process landmarking to sequentially select landmarks with the highest local curvatures. Furthermore, we propose a new evaluation criterion to assess the effectiveness of selected landmarks through registration. The proposed approach is tested through automatic landmarking of printed dental models.

Free‐form reinforced concrete, prestressed concrete, and composite components: Calculation of cross‐section values

AbstractA resource‐efficient use of concrete as a construction material can be achieved by adapting the individual shape of a component under consideration to the stresses that occur and by arranging composite construction materials (e.g., reinforcing steel, prestressing steel, or structural steel) in suitable areas of the component. Due to the advancing digitalization in the construction industry, for example in the context of Building Information Modeling, computer‐aided 3D modeling methods are increasingly being used in the planning of structures. These allow engineers to design components in free form. In reinforced concrete, prestressed concrete, and composite construction, the design of such components is currently still associated with great effort. In the context of the development of a practical method for the calculation of free‐form concrete components, this paper presents a CAD‐integrated method for the calculation of cross‐section values. Cross‐section values are required as an essential calculation basis when real, three‐dimensional structural components are treated using simplified calculation theories, such as the beam theory. In this paper, the mathematical and numerical fundamentals of a method for the calculation of cross‐section values of free‐form concrete, reinforced concrete, prestressed concrete, and compound components are presented. The calculation method is based on flat geometric regions described by Non‐uniform Rational B‐Spline tensor product surfaces, which can be extracted from solid models, for example.

A straightforward spectral emissivity estimating method based on constructing random rough surfaces

Spectral emissivity is an essential and sensitive parameter to characterize the radiative capacity of the solid surface in scientific and engineering applications, which would be non-negligibly affected by surface morphology. However, there is a lack of assessment of the effect of roughness on emissivity and a straightforward method for estimating the emissivity of rough surfaces. This paper established an estimating method based on constructing random rough surfaces to predict rough surface (Geometric region) emissivity for metal solids. Based on this method, the emissivity of ideal gray and non-gray body surfaces was calculated and analyzed. The calculated and measured spectral emissivities of GH3044, K465, DD6, and TC4 alloys with different roughness were compared. The results show that the emissivity increases with the roughness degree, and the enhancement effect weakens with the increase of roughness or emissivity due to the existing limit (emissivity ε = 1.0). At the same time, the roughness would not change the overall spectral distribution characteristics but may attenuate the local features of the spectral emissivity. The estimated results are in good agreement with the experimental data for the above alloys’ rough surfaces. This study provides a new reliable approach to obtaining the spectral emissivity of rough surfaces. This approach is especially beneficial for measuring objects in extreme environments where emissivity is difficult to obtain. Meanwhile, this study promotes an understanding of surface morphology’s effect mechanism on emissivity.

Research on landscape elements in urban tunnel sections under driving conditions

Traditional urban tunnel landscape design focuses on static conditions and neglects the influence of dynamic visual perception differences on the landscape effect that drivers experience under actual driving conditions. To optimize the design of tunnel landscapes both qualitatively and quantitatively, it is necessary to extract and analyze the landscape elements present in urban tunnel sections while considering dynamic visual perception. In the present study, a geometric visual region model was established by introducing the concept of equivalent radius based on the relationship between speed and visual changes. Three typical categories of tunnel landscape materials were collected using field surveys and video shooting methods. Geometric characteristics of landscape elements were extracted from pixelated dynamic visual images, resulting in evaluation indicators for these elements, and the element grades were determined using the Schroeder method. An analysis was conducted to identify the main landscape elements and their distinct characteristics in urban tunnels. Critical components that were examined included tunnel ceilings, sidewalls, road surfaces, and pedestrian walkway object marking. The distribution of these elements in terms of pixel area ratios was determined to be 29.7% for tunnel ceilings, 27.2% for sidewalls, 19.6% for road surfaces, and 4.9% for pedestrian walkway object marking. Optimizing these designs requires controlling unity and contrast in colour design as well as hierarchy, rhythm, proportionality, and unity in pattern design. The results from the field investigation, pixel extraction, and correlation coefficient analysis provide strong evidence supporting the correlation between the main landscape elements identified by the theoretical model. In fact, the correlation exceeds 50%, indicating a significant relationship between these elements. These findings validate the reasonableness of the theoretical model in achieving quantitative design for significant landscape areas and provide a solid foundation for establishing an evaluation index system for urban tunnel landscapes.

Integration of Polynomials Times Double Step Function in Quadrilateral Domains for XFEM Analysis

The numerical integration of discontinuous functions is an abiding problem addressed by various authors. This subject gained even more attention in the context of the extended finite element method (XFEM), in which the exact integration of discontinuous functions is crucial to obtaining reliable results. In this scope, equivalent polynomials represent an effective method to circumvent the problem while exploiting the standard Gauss quadrature rule to exactly integrate polynomials times step function. Certain scenarios, however, might require the integration of polynomials times two step functions (i.e., problems in which branching cracks, kinking cracks or crack junctions within a single finite element occur). In this context, the use of equivalent polynomials has been investigated by the authors, and an algorithm to exactly integrate arbitrary polynomials times two Heaviside step functions in quadrilateral domains has been developed and is presented in this paper. Moreover, the algorithm has also been implemented into a software library (DD_EQP) to prove its precision and effectiveness and also the proposed method’s ease of implementation into any existing computational software or framework. The presented algorithm is the first step towards the numerical integration of an arbitrary number of discontinuities in quadrilateral domains. Both the algorithm and the library have a wide application range, in addition to fracture mechanics, from mathematical computing of complex geometric regions, to computer graphics and computational mechanics.

Acoustic scattering properties of seagrass: In/ex-situ measurements of Posidonia oceanica

The marine prairies are paramount to the marine ecosystem playing a crucial role in various ways. Owing to the global atmospheric events inducing hydrospheric changes, marine seaweeds have been negatively affected and are vulnerable. Conventional methods (SCUBA), which were previously used to collect seagrasses, have become a destructive method for deriving unrecoverable damages for their stocks and have been replaced with remote sensing methods. Considering the advantages of the acoustic methods, two different in/ex-situ experiments were conducted to ground-truth the common seagrass, P. oceanica, in time and space of the Turkish Mediterranean water in 2011-2012 using a scientific echosounder with a split beam transducer operated at a frequency of 206 kHz. After the separation of the seagrass from spurious scatterers, the acoustic parameters (Sa: area backscattering strength, Sv: volume backscattering strength, and TS: Target Strength) were correlated and regressed with the biometric variables (Leaf Area Index, biomass, volume, length, width, diameter, or thickness) of different parts (leaf, rhizome, and sheath) of the seagrass. Estimation of biometrics by acoustic methods has been considered a challenge up to now. Statistical relationships between biometrics and acoustics were precisely examined for the species. The relationships were linearly established in the acoustic geometric region. There was a seasonal difference in the relationships. Their rhizomes and sheaths were considered unstable non-linear parts and remained unexplained for the acoustic response. Acoustic response to the leaf density (d, g cm-3), which was a distinguished component in the reflection, was found to be dependent on the seasonal biological activities of P. oceanica. Posidonia, which has a d greater than 1 g/cm3, produced the geometric region. The present study was the first attempt to establish the relationships of the seagrass under protection, and can decrease usage of destructive methods for future studies.

Quadrilateral Mesh Generation Method Based on Convolutional Neural Network

The frame field distributed inside the model region characterizes the singular structure features inside the model. These singular structures can be used to decompose the model region into multiple quadrilateral structures, thereby generating a block-structured quadrilateral mesh. For the generation of block-structured quadrilateral mesh for two-dimensional geometric models, a convolutional neural network model is proposed to identify the singular structure inside the model contained in the frame field. By training the network model with a large number of model region decomposition data obtained in advance, the model can identify the vectors of the frame field in the region located in the segmentation field. Then, the segmentation streamline is constructed from the annotation. Based on this, the geometric region is decomposed into several small regions, regions which are then discretized with quadrilateral mesh elements. Finally, through two geometric models, it is verified that the convolutional neural network model proposed in this study can effectively identify the singular structure inside the model to realize the model region decomposition and block-structured mesh generation.

A dual-gradient underwater meta-auricle for broadband sound signal enhancement

Perceiving the long-distance or weaker sounds is an important technical indicator for the development of acoustic detection equipment such as sonar. Especially, acoustic signal enhancement of free-field and long-distance application condition close to that in the lake or ocean environment is still challenging. To this end, we propose a dual-gradient artificial meta-auricle, which comprehensively utilizes the wavefront reflection phase modulation and geometrical concave reflection mechanisms, and could greatly enhance the underwater sound signal in a broadband frequency range. As the reflection phase of each metasurface unit from the center to the outermost side is gradient distributed, the reflected sound waves would converge on the central axis; while the height of each unit is incremental distributed from the center to the outside and forms a concave surface, so that the reflected sound waves could converge at the geometric center. In this way, in the geometric center region, the two focusing mechanisms are superimposed with each other synergistically. Therefore, the focusing amplitude could be effectively enhanced, and the working frequency band would be greatly broadened. This reflective acoustic metalens, similar to the auricle of an animal, enables signal enhancement over a wide frequency band, both in waveguide and in free-field conditions.