Image Classifier Based Generative Method for Planar Antenna Design
- URL: http://arxiv.org/abs/2401.06149v1
- Date: Sat, 16 Dec 2023 19:43:05 GMT
- Title: Image Classifier Based Generative Method for Planar Antenna Design
- Authors: Yang Zhong, Weiping Dou, Andrew Cohen, Dia'a Bisharat, Yuandong Tian,
Jiang Zhu, Qing Huo Liu
- Abstract summary: We propose a method to extend the antenna design on printed circuit boards (PCBs) for more engineers of interest.
By taking two separate steps to decide their geometric dimensions and positions, antenna prototypes can be facilitated with no experience required.
- Score: 33.121376096111355
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: To extend the antenna design on printed circuit boards (PCBs) for more
engineers of interest, we propose a simple method that models PCB antennas with
a few basic components. By taking two separate steps to decide their geometric
dimensions and positions, antenna prototypes can be facilitated with no
experience required. Random sampling statistics relate to the quality of
dimensions are used in selecting among dimension candidates. A novel
image-based classifier using a convolutional neural network (CNN) is introduced
to further determine the positions of these fixed-dimension components. Two
examples from wearable products have been chosen to examine the entire
workflow. Their final designs are realistic and their performance metrics are
not inferior to the ones designed by experienced engineers.
Related papers
- Compositional Generative Inverse Design [69.22782875567547]
Inverse design, where we seek to design input variables in order to optimize an underlying objective function, is an important problem.
We show that by instead optimizing over the learned energy function captured by the diffusion model, we can avoid such adversarial examples.
In an N-body interaction task and a challenging 2D multi-airfoil design task, we demonstrate that by composing the learned diffusion model at test time, our method allows us to design initial states and boundary shapes.
arXiv Detail & Related papers (2024-01-24T01:33:39Z) - SQuADDS: A validated design database and simulation workflow for superconducting qubit design [2.394350905741035]
We present an open-source database of superconducting device designs that may be used as starting point for customized quantum solvers.
We present a robust for achieving high accuracy on design simulations.
Our database includes a front-end interface that allows users to generate bestguess'' designs based on desired circuit parameters.
arXiv Detail & Related papers (2023-12-20T23:31:53Z) - Engineering the Neural Collapse Geometry of Supervised-Contrastive Loss [28.529476019629097]
Supervised-contrastive loss (SCL) is an alternative to cross-entropy (CE) for classification tasks.
We propose methods to engineer the geometry of learnt feature embeddings by modifying the contrastive loss.
arXiv Detail & Related papers (2023-10-02T04:23:17Z) - One-Dimensional Deep Image Prior for Curve Fitting of S-Parameters from
Electromagnetic Solvers [57.441926088870325]
Deep Image Prior (DIP) is a technique that optimized the weights of a randomly-d convolutional neural network to fit a signal from noisy or under-determined measurements.
Relative to publicly available implementations of Vector Fitting (VF), our method shows superior performance on nearly all test examples.
arXiv Detail & Related papers (2023-06-06T20:28:37Z) - ShipHullGAN: A generic parametric modeller for ship hull design using
deep convolutional generative model [0.0]
We introduce ShipHullGAN, a generic parametric modeller built using deep convolutional generative adversarial networks (GANs)
At a high level, the new model intends to address the current conservatism in the parametric ship design paradigm.
We trained ShipHullGAN on a large dataset of 52,591 textitphysically validated designs from a wide range of existing ship types.
arXiv Detail & Related papers (2023-04-29T09:31:20Z) - Design Space Exploration and Explanation via Conditional Variational
Autoencoders in Meta-model-based Conceptual Design of Pedestrian Bridges [52.77024349608834]
This paper provides a performance-driven design exploration framework to augment the human designer through a Conditional Variational Autoencoder (CVAE)
The CVAE is trained on 18'000 synthetically generated instances of a pedestrian bridge in Switzerland.
arXiv Detail & Related papers (2022-11-29T17:28:31Z) - Rethinking Semantic Segmentation: A Prototype View [126.59244185849838]
We present a nonparametric semantic segmentation model based on non-learnable prototypes.
Our framework yields compelling results over several datasets.
We expect this work will provoke a rethink of the current de facto semantic segmentation model design.
arXiv Detail & Related papers (2022-03-28T21:15:32Z) - A Machine Learning Generative Method for Automating Antenna Design and
Optimization [9.438718097561061]
We introduce a flexible geometric scheme with the concept of mesh network that can form any arbitrary shape by connecting different nodes.
For a dual resonance antenna design with wide bandwidth, our proposed method is in par with Trust Region Framework and much better than the other mature machine learning algorithms.
arXiv Detail & Related papers (2022-02-28T21:30:37Z) - HyperHyperNetworks for the Design of Antenna Arrays [91.3755431537592]
We present deep learning methods for the design of arrays and single instances of small antennas.
In the case of a single antenna, the solution is based on a composite neural network that combines a simulation network, a hypernetwork, and a refinement network.
The learning objective is based on measuring the similarity of the obtained radiation pattern to the desired one.
arXiv Detail & Related papers (2021-05-09T05:21:28Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.