scholarly journals Acoustic Structure Inverse Design and Optimization Using Deep Learning

2021 ◽  
Author(s):  
Xuecong Sun ◽  
Han Jia ◽  
Yuzhen Yang ◽  
Han Zhao ◽  
Yafeng Bi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Acoustic Waves ◽  
Design Method ◽  
Helmholtz Resonator ◽  
Structure Design ◽  
Acoustic Structure ◽  
Design And Optimization ◽  
Wide Range ◽  
Potential Applications ◽  
Coupling Parameters

Abstract From ancient to modern times, acoustic structures have been used to control the propagation of acoustic waves. However, the design of acoustic structures has remained a time-consuming and computational resource-consuming iterative process. In recent years, deep learning has attracted unprecedented attention for its ability to tackle hard problems with large datasets, achieving state-of-the-art results in various tasks. In this work, an acoustic structure design method is proposed based on deep learning. Taking the design of multiorder Helmholtz resonator as an example, we experimentally demonstrate the effectiveness of the proposed method. Our method is not only able to give a very accurate prediction of the geometry of acoustic structures with multiple strong-coupling parameters, but also capable of improving the performance of evolutionary approaches in optimization for a desired property. Compared with the conventional numerical methods, our method is more efficient, universal and automatic, and it has a wide range of potential applications, such as speech enhancement, sound absorption and insulation.

Stress Field Guided Lattice Structure Design Based on Hexahedral Mesh

2019 ◽  
Author(s):  
Lingyun Liu ◽  
Yizhou Liao ◽  
Shuming Gao
Keyword(s):  
Stress Field ◽  
Lattice Structure ◽  
Design Method ◽  
Structure Design ◽  
Complex Structures ◽  
Hexahedral Mesh ◽  
Principal Stress Direction ◽  
Lattice Design ◽  
Solid Models ◽  
Wide Range

Abstract Lattice structures are promising for a wide range of applications. The development of additive manufacturing (AM) technology has made it possible to manufacture complex structures. However, designing the optimal lattices of complex solid models efficiently and automatically remains a challenge. Thus, we propose a novel stress-field-guided lattice design method to improve the mechanical properties of a lattice structure. Stress field is used to make the boundary struts of each cell of a lattice structure aligning to the principal stress direction while remaining conformal. Hierarchical cell templates are designed to reduce the computational burden of the cell optimization of a lattice structure. The proposed method is verified experimentally, and the experimental results prove the efficiency and validity of the proposed method.

Deep Neural Networks for Hierarchical Taxonomic Fossil Classification of Carbonate Skeletal Grains

2021 ◽  
Author(s):  
Sidhant Idgunji ◽  
Madison Ho ◽  
Jonathan L. Payne ◽  
Daniel Lehrmann ◽  
Michele Morsilli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Training Dataset ◽  
Single Class ◽  
Deep Learning Algorithm ◽  
Human Ability ◽  
Wide Range ◽  
Potential Applications ◽  
Animal Phyla

<p>The growing digitization of fossil images has vastly improved and broadened the potential application of big data and machine learning, particularly computer vision, in paleontology. Recent studies show that machine learning is capable of approaching human abilities of classifying images, and with the increase in computational power and visual data, it stands to reason that it can match human ability but at much greater efficiency in the near future. Here we demonstrate this potential of using deep learning to identify skeletal grains at different levels of the Linnaean taxonomic hierarchy. Our approach was two-pronged. First, we built a database of skeletal grain images spanning a wide range of animal phyla and classes and used this database to train the model. We used a Python-based method to automate image recognition and extraction from published sources. Second, we developed a deep learning algorithm that can attach multiple labels to a single image. Conventionally, deep learning is used to predict a single class from an image; here, we adopted a Branch Convolutional Neural Network (B-CNN) technique to classify multiple taxonomic levels for a single skeletal grain image. Using this method, we achieved over 90% accuracy for both the coarse, phylum-level recognition and the fine, class-level recognition across diverse skeletal grains (6 phyla and 15 classes). Furthermore, we found that image augmentation improves the overall accuracy. This tool has potential applications in geology ranging from biostratigraphy to paleo-bathymetry, paleoecology, and microfacies analysis. Further improvement of the algorithm and expansion of the training dataset will continue to narrow the efficiency gap between human expertise and machine learning.</p>

scholarly journals Creative design for sandwich structures: A review

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142092132
Author(s):  
Yixiong Feng ◽  
Hao Qiu ◽  
Yicong Gao ◽  
Hao Zheng ◽  
Jianrong Tan
Keyword(s):  
High Performance ◽  
Sandwich Structures ◽  
Design Method ◽  
Material Design ◽  
Structure Design ◽  
Core Structure ◽  
Structure Evolution ◽  
Creative Design ◽  
Potential Applications ◽  
Sandwich Core

Sandwich structures are important innovative multifunctional structures with the advantages of low density and high performance. Creative design for sandwich structures is a design process based on sandwich core structure evolution mechanisms, material design method, and panel (including core structure and facing sheets) performance prediction model. The review outlines recent research efforts on creative design for sandwich structures with different core constructions such as corrugated core, honeycomb core, foam core, truss core, and folded cores. The topics discussed in this review article include aspects of sandwich core structure design, material design, and mechanical properties, and panel performance and damage. In addition, examples of engineering applications of sandwich structures are discussed. Further research directions and potential applications are summarized.

Highly Directional Acoustic Waves Generated by a Horned Parametric Acoustic Array Loudspeaker

2018 ◽  
Vol 141 (1) ◽  
Author(s):  
L. H. Tong ◽  
S. K. Lai ◽  
J. W. Yan ◽  
C. Li
Keyword(s):  
Acoustic Waves ◽  
Sound Pressure ◽  
Low Frequencies ◽  
Equivalent Radius ◽  
Design And Optimization ◽  
Wide Range ◽  
Acoustic Array ◽  
Parametric Array ◽  
Overall Efficiency ◽  
Acoustic Horns

Acoustic horns can enhance the overall efficiency of loudspeakers to emanate highly directional acoustic waves. In this work, a theoretical model is developed to predict difference frequency acoustic fields generated by a parametric array loudspeaker (PAL) with a flared horn. Based on this model, analytical solutions are obtained for exponentially horned PALs. A numerical analysis on the performance of horned PALs subject to various horn parameters (i.e., horn length and flare constant) is implemented. To compare with nonhorned parametric acoustic array (PAA) devices, it is able to generate highly directional acoustic wave beams for a wide range of difference frequencies, in which the generated sound pressure levels at low frequencies can be significantly enhanced. In addition, the equivalent radius of a nonhorned emitter that matches the directivity achieved by a horned one is also quantitatively investigated. The present research will provide useful guidelines for the design and optimization of horned parametric array equipment.

Design and optimization of nonuniform cellular structures

2017 ◽  
Vol 232 (7) ◽  
pp. 1280-1293 ◽  
Author(s):  
Xin Jin ◽  
Guo-Xi Li ◽  
Meng Zhang
Keyword(s):  
Topology Optimization ◽  
Cellular Structure ◽  
Mechanical Performance ◽  
Design Method ◽  
Lightweight Design ◽  
Structure Design ◽  
Cellular Structures ◽  
Manufacturing Constraints ◽  
Element Analysis ◽  
Design And Optimization

Topology optimization and cellular structure infilling are two important approaches to achieve a lightweight design while meeting the relevant mechanical property requirements. In this work, we present a density-variable cellular structure design method combined with topology optimization while ensuring the manufacturability. The effective mechanical properties are reported as functions of the relative density to combine cellular structures with the topology optimization model. The manufacturing constraints are analyzed and expressed in topology optimization. In addition, density-variable cellular structures are rapidly modeled by mapping the topology optimization results to the relative densities of cells and via the use of user-defined features. It is shown by means of finite element analysis that the proposed design approach can improve the mechanical performance compared to the uniform cellular structure under the same weight reduction. And the choice of cell size for higher stiffness of the designed structure varies with different values of manufacturing constraints.

scholarly journals A cyclical deep learning based framework for simultaneous inverse and forward design of nanophotonic metasurfaces

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Abhishek Mall ◽  
Abhijeet Patil ◽  
Amit Sethi ◽  
Anshuman Kumar
Keyword(s):  
Genetic Algorithm ◽  
Deep Learning ◽  
Design Space ◽  
Optical Response ◽  
Inverse Design ◽  
Electromagnetic Response ◽  
Design And Optimization ◽  
Half Wave ◽  
Wide Range ◽  
Optimization Mechanism

Abstract The conventional approach to nanophotonic metasurface design and optimization for a targeted electromagnetic response involves exploring large geometry and material spaces. This is a highly iterative process based on trial and error, which is computationally costly and time consuming. Moreover, the non-uniqueness of structural designs and high non-linearity between electromagnetic response and design makes this problem challenging. To model this unintuitive relationship between electromagnetic response and metasurface structural design as a probability distribution in the design space, we introduce a framework for inverse design of nanophotonic metasurfaces based on cyclical deep learning (DL). The proposed framework performs inverse design and optimization mechanism for the generation of meta-atoms and meta-molecules as metasurface units based on DL models and genetic algorithm. The framework includes consecutive DL models that emulate both numerical electromagnetic simulation and iterative processes of optimization, and generate optimized structural designs while simultaneously performing forward and inverse design tasks. A selection and evaluation of generated structural designs is performed by the genetic algorithm to construct a desired optical response and design space that mimics real world responses. Importantly, our cyclical generation framework also explores the space of new metasurface topologies. As an example application of the utility of our proposed architecture, we demonstrate the inverse design of gap-plasmon based half-wave plate metasurface for user-defined optical response. Our proposed technique can be easily generalized for designing nanophtonic metasurfaces for a wide range of targeted optical response.

scholarly journals Nano-imprinted subwavelength gratings as polarizing beamsplitters

2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Julian Wüster ◽  
Yannick Bourgin ◽  
Patrick Feßer ◽  
Arne Behrens ◽  
Stefan Sinzinger
Keyword(s):  
Nanoimprint Lithography ◽  
Optical Measurement ◽  
Design Method ◽  
Optical Systems ◽  
Surface Metrology ◽  
Measurement Systems ◽  
Common Path ◽  
Dielectric Coatings ◽  
Subwavelength Gratings ◽  
Wide Range

AbstractPolarizing beamsplitters have numerous applications in optical systems, such as systems for freeform surface metrology. They are classically manufactured from birefringent materials or with stacks of dielectric coatings. We present a binary subwavelength-structured form-birefringent diffraction grating, which acts as a polarizing beamsplitter for a wide range of incidence angles −30∘…+30∘. We refine the general design method for such hybrid gratings. We furthermore demonstrate the manufacturing steps with Soft-UV-Nanoimprint-Lithography, as well as the experimental verification, that the structure reliably acts as a polarizing beamsplitter. The experimental results show a contrast in efficiency for TE- and TM-polarization of up to 1:18 in the first order, and 34:1 in the zeroth order. The grating potentially enables us to realize integrated compact optical measurement systems, such as common-path interferometers.

scholarly journals A Generalization Performance Study Using Deep Learning Networks in Embedded Systems

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1031
Author(s):  
Joseba Gorospe ◽  
Rubén Mulero ◽  
Olatz Arbelaitz ◽  
Javier Muguerza ◽  
Miguel Ángel Antón
Keyword(s):  
Deep Learning ◽  
Embedded Systems ◽  
Embedded System ◽  
General Purpose ◽  
Learning Networks ◽  
Performance Study ◽  
Learning Techniques ◽  
Wide Range ◽  
Learning Architectures

Deep learning techniques are being increasingly used in the scientific community as a consequence of the high computational capacity of current systems and the increase in the amount of data available as a result of the digitalisation of society in general and the industrial world in particular. In addition, the immersion of the field of edge computing, which focuses on integrating artificial intelligence as close as possible to the client, makes it possible to implement systems that act in real time without the need to transfer all of the data to centralised servers. The combination of these two concepts can lead to systems with the capacity to make correct decisions and act based on them immediately and in situ. Despite this, the low capacity of embedded systems greatly hinders this integration, so the possibility of being able to integrate them into a wide range of micro-controllers can be a great advantage. This paper contributes with the generation of an environment based on Mbed OS and TensorFlow Lite to be embedded in any general purpose embedded system, allowing the introduction of deep learning architectures. The experiments herein prove that the proposed system is competitive if compared to other commercial systems.

scholarly journals Data augmentation for computed tomography angiography via synthetic image generation and neural domain adaptation

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Malte Seemann ◽  
Lennart Bargsten ◽  
Alexander Schlaefer
Keyword(s):  
Computed Tomography ◽  
Neural Networks ◽  
Deep Learning ◽  
Medical Imaging ◽  
Computed Tomography Angiography ◽  
Data Augmentation ◽  
Domain Adaptation ◽  
Synthetic Image ◽  
Wide Range ◽  
The Impact

AbstractDeep learning methods produce promising results when applied to a wide range of medical imaging tasks, including segmentation of artery lumen in computed tomography angiography (CTA) data. However, to perform sufficiently, neural networks have to be trained on large amounts of high quality annotated data. In the realm of medical imaging, annotations are not only quite scarce but also often not entirely reliable. To tackle both challenges, we developed a two-step approach for generating realistic synthetic CTA data for the purpose of data augmentation. In the first step moderately realistic images are generated in a purely numerical fashion. In the second step these images are improved by applying neural domain adaptation. We evaluated the impact of synthetic data on lumen segmentation via convolutional neural networks (CNNs) by comparing resulting performances. Improvements of up to 5% in terms of Dice coefficient and 20% for Hausdorff distance represent a proof of concept that the proposed augmentation procedure can be used to enhance deep learning-based segmentation for artery lumen in CTA images.

scholarly journals Uncertainty-Aware Deep Learning-Based Cardiac Arrhythmias Classification Model of Electrocardiogram Signals

Computers ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 82
Author(s):  
Ahmad O. Aseeri
Keyword(s):  
Deep Learning ◽  
Cardiac Arrhythmias ◽  
Large Scale ◽  
Clinical Decision Making ◽  
Probabilistic Approach ◽  
Classification Model ◽  
Gating Mechanism ◽  
Uncertainty Estimates ◽  
Wide Range

Deep Learning-based methods have emerged to be one of the most effective and practical solutions in a wide range of medical problems, including the diagnosis of cardiac arrhythmias. A critical step to a precocious diagnosis in many heart dysfunctions diseases starts with the accurate detection and classification of cardiac arrhythmias, which can be achieved via electrocardiograms (ECGs). Motivated by the desire to enhance conventional clinical methods in diagnosing cardiac arrhythmias, we introduce an uncertainty-aware deep learning-based predictive model design for accurate large-scale classification of cardiac arrhythmias successfully trained and evaluated using three benchmark medical datasets. In addition, considering that the quantification of uncertainty estimates is vital for clinical decision-making, our method incorporates a probabilistic approach to capture the model’s uncertainty using a Bayesian-based approximation method without introducing additional parameters or significant changes to the network’s architecture. Although many arrhythmias classification solutions with various ECG feature engineering techniques have been reported in the literature, the introduced AI-based probabilistic-enabled method in this paper outperforms the results of existing methods in outstanding multiclass classification results that manifest F1 scores of 98.62% and 96.73% with (MIT-BIH) dataset of 20 annotations, and 99.23% and 96.94% with (INCART) dataset of eight annotations, and 97.25% and 96.73% with (BIDMC) dataset of six annotations, for the deep ensemble and probabilistic mode, respectively. We demonstrate our method’s high-performing and statistical reliability results in numerical experiments on the language modeling using the gating mechanism of Recurrent Neural Networks.

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