Tubular structural composites are widely used in applications where high stiffness and low
weight are required. This work proposes the study and evaluation of the influence of …

Deep neural networks (DNNs) have greatly impacted numerous fields over the past decade.
Yet despite exhibiting superb performance over many problems, their black-box nature still …

This investigation delves into the dynamics of buckling, modal, and static analyses within
delaminated composite cylinders, integral to sectors such as aerospace, automotive, naval …

Deep neural networks that play a pivotal role in fields such as images, text, and audio are
vulnerable to adversarial attacks. In current textual adversarial attacks, the vast majority are …

Numerous works study black-box attacks on image classifiers, where adversaries generate
adversarial examples against unknown target models without having access to their internal …

Generative adversarial networks face harsh opposition between training data and model
performance. In facing insufficient training data, the training process is extremely unstable …

The excellent image-generation ability of generative adversarial networks (GANs) has been
widely used. However, training a GAN requires large-scale data support, which hinders in …

The effectiveness of a wind turbine elastic support in reducing vibrations significantly
impacts the unit's lifespan. During the structural design process, it is necessary to consider …

Decision-based black-box attacks can craft adversarial examples by only querying the target
model for hard-label predictions. However, most existing methods are not efficient when …

With the rapid development of deep learning-based detection algorithms, deep learning is
widely used in the field of infrared small target detection. However, well-designed …