248 research outputs found
Color 3D Printing: Theory, Method, and Application
Our research team proposes a colored manufacturing technology with a layer-by-layer printing process. Using digital inkjet printing in layer-by-layer printing color graphics, a further low-cost color 3D Printing (3DP) technology can be developed. It can provide an integrated way to prototype and reproduce 3D objects, from concept to design and manufacturing. Ultimately, with fast graphics printing method, it guarantees a feasible way to further promote cultural and creative products
Enhancing Performance of Machine Learning-Based Modeling of Electromagnetic Structures
The machine learning (ML)-based modeling of electromagnetic (EM) structures involves the development of a surrogate model that approximates the relationship between EM geometries and responses, such as S 11 , gain, etc. The performance of the surrogate model is mainly affected by the simulation data for training. Normally, the training data is collected by uniformly sweeping the geometric parameters. Restricted by the computation power, only a limited parameter space can be sampled. The trained surrogate model behaves well within the sampling range but deteriorates as the parameter range extends. In this paper, we expand the predictable parameter range of an ML model with the same simulation expense by optimizing the data acquisition strategy. This approach leads to the proposed model demonstrating higher accuracy within an extended parameter space than conventional models, while the simulation consumption remains the same. We present an application example to validate its effectiveness. The proposed modified ML-based design method can potentially improve the performance of surrogate models in real-world applications
A Joint Learning and Communications Framework for Federated Learning over Wireless Networks
In this paper, the problem of training federated learning (FL) algorithms
over a realistic wireless network is studied. In particular, in the considered
model, wireless users execute an FL algorithm while training their local FL
models using their own data and transmitting the trained local FL models to a
base station (BS) that will generate a global FL model and send it back to the
users. Since all training parameters are transmitted over wireless links, the
quality of the training will be affected by wireless factors such as packet
errors and the availability of wireless resources. Meanwhile, due to the
limited wireless bandwidth, the BS must select an appropriate subset of users
to execute the FL algorithm so as to build a global FL model accurately. This
joint learning, wireless resource allocation, and user selection problem is
formulated as an optimization problem whose goal is to minimize an FL loss
function that captures the performance of the FL algorithm. To address this
problem, a closed-form expression for the expected convergence rate of the FL
algorithm is first derived to quantify the impact of wireless factors on FL.
Then, based on the expected convergence rate of the FL algorithm, the optimal
transmit power for each user is derived, under a given user selection and
uplink resource block (RB) allocation scheme. Finally, the user selection and
uplink RB allocation is optimized so as to minimize the FL loss function.
Simulation results show that the proposed joint federated learning and
communication framework can reduce the FL loss function value by up to 10% and
16%, respectively, compared to: 1) An optimal user selection algorithm with
random resource allocation and 2) a standard FL algorithm with random user
selection and resource allocation.Comment: This paper has been accepted by IEEE Transactions on Wireless
Communication
Fast and Automatic 3D Modeling of Antenna Structure Using CNN-LSTM Network for Efficient Data Generation
Deep learning-assisted antenna design methods such as surrogate models have
gained significant popularity in recent years due to their potential to greatly
increase design efficiencies by replacing the time-consuming full-wave
electromagnetic (EM) simulations. However, a large number of training data with
sufficiently diverse and representative samples (antenna structure parameters,
scattering properties, etc.) is mandatory for these methods to ensure good
performance. Traditional antenna modeling methods relying on manual model
construction and modification are time-consuming and cannot meet the
requirement of efficient training data acquisition. In this study, we proposed
a deep learning-assisted and image-based intelligent modeling approach for
accelerating the data acquisition of antenna samples with different physical
structures. Specifically, our method only needs an image of the antenna
structure, usually available in scientific publications, as the input while the
corresponding modeling codes (VBA language) are generated automatically. The
proposed model mainly consists of two parts: Convolutional Neural Network (CNN)
and Long Short Term Memory (LSTM) networks. The former is used for capturing
features of antenna structure images and the latter is employed to generate the
modeling codes. Through training, the proposed model can achieve fast and
automatic data acquisition of antenna physical structures based on antenna
images. Experiment results show that the proposed method achieves a significant
speed enhancement than the manual modeling approach. This approach lays the
foundation for efficient data acquisition needed to build robust surrogate
models in the future
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