Results 61 to 70 of about 79,918 (254)

Deep Learning‐Assisted Design of Mechanical Metamaterials

Advanced Intelligent Discovery, EarlyView.
This review examines the role of data‐driven deep learning methodologies in advancing mechanical metamaterial design, focusing on the specific methodologies, applications, challenges, and outlooks of this field. Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have ...
Zisheng Zong, Pu Wen, Zhiping Chai, Dunxuan Wu, Han Ding, Zhigang Wu   +5 more
wiley   +1 more source

Exploring Synergy of Denoising and Distillation: Novel Method for Efficient Adversarial Defense

Applied Sciences
Escalating advancements in artificial intelligence (AI) has prompted significant security concerns, especially with its increasing commercialization. This necessitates research on safety measures to securely utilize AI models.
Inpyo Hong, Sokjoon Lee
doaj   +1 more source

Adversarial Robust Aerial Image Recognition Based on Reactive-Proactive Defense Framework with Deep Ensembles

Remote Sensing, 2023
As a safety-related application, visual systems based on deep neural networks (DNNs) in modern unmanned aerial vehicles (UAVs) show adversarial vulnerability when performing real-time inference.
Zihao Lu, Hao Sun, Kefeng Ji, Gangyao Kuang   +3 more
doaj   +1 more source

Detecting adversarial examples with inductive Venn-ABERS predictors [PDF]

, 2019
Inductive Venn-ABERS predictors (IVAPs) are a type of probabilistic predictors with the theoretical guarantee that their predictions are perfectly calibrated.
Goossens, Bart, Peck, Jonathan, Saeys, Yvan   +2 more
core   +1 more source

Predicting Performance of Hall Effect Ion Source Using Machine Learning

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
This study introduces HallNN, a machine learning tool for predicting Hall effect ion source performance using a neural network ensemble trained on data generated from numerical simulations. HallNN provides faster and more accurate predictions than numerical methods and traditional scaling laws, making it valuable for designing and optimizing Hall ...
Jaehong Park, Guentae Doh, Dongho Lee, Youngho Kim, Changmin Shin, Su‐Jin Shin, Young‐Chul Ghim, Sanghoo Park, Wonho Choe   +8 more
wiley   +1 more source

Care and COVID 19: Lessons for liberals and neoliberals

Child &Family Social Work, EarlyView., 2023
Abstract Within the liberal political traditions, care is regarded as a private matter, a problem of ethics rather than justice. Social justice is framed as an issue of economics (re/distribution), culture (recognition) and/or politics (representation).
Kathleen Lynch
wiley   +1 more source

Stylized Pairing for Robust Adversarial Defense

Applied Sciences, 2022
Recent studies show that deep neural networks (DNNs)-based object recognition algorithms overly rely on object textures rather than global object shapes, and DNNs are also vulnerable to human-less perceptible adversarial perturbations. Based on these two
Dejian Guan, Wentao Zhao, Xiao Liu
doaj   +1 more source

MAD: Meta Adversarial Defense Benchmark

, 2023
Adversarial training (AT) is a prominent technique employed by deep learning models to defend against adversarial attacks, and to some extent, enhance model robustness. However, there are three main drawbacks of the existing AT-based defense methods: expensive computational cost, low generalization ability, and the dilemma between the original model ...
Peng, X., Zhou, D., Sun, G., Shi, J., Wu, L.   +4 more
openaire   +2 more sources

Roadmap on Artificial Intelligence‐Augmented Additive Manufacturing

Advanced Intelligent Systems, EarlyView.
This Roadmap outlines the transformative role of artificial intelligence‐augmented additive manufacturing, highlighting advances in design, monitoring, and product development. By integrating tools such as generative design, computer vision, digital twins, and closed‐loop control, it presents pathways toward smart, scalable, and autonomous additive ...
Ali Zolfagharian, Liuchao Jin, Qi Ge, Wei‐Hsin Liao, Andrés Díaz Lantada, Francisco Franco Martínez, Tianyu Zhang, Tao Liu, Charlie C. L. Wang, Mohammad Hossein Mosallanejad, Reza Ghanavati, Abdollah Saboori, Alejandro De Blas De Miguel, William Solórzano‐Requejo, Yi Cai, Xiangyang Dong, Huangyi Qu, Najmeh Samadiani, Guangyan Huang, Austin Downey, Yanzhou Fu, Lang Yuan, Tsz‐Kwan (Glory) Lee, Arbind Agrahari Baniya, Eisha Waseem, Abdul Rahman Sani, Abbas Z. Kouzani, Yijia Wu, Markus P. Nemitz, Masoud Shirzad, Dageon Oh, Seung Yun Nam, Amedeo Franco Bonatti, Irene Chiesa, Gabriele Maria Fortunato, Giovanni Vozzi, Carmelo De Maria, Mahdi Bodaghi   +37 more
wiley   +1 more source

A divide-and-conquer reconstruction method for defending against adversarial example attacks

Visual Intelligence
In recent years, defending against adversarial examples has gained significant importance, leading to a growing body of research in this area. Among these studies, pre-processing defense approaches have emerged as a prominent research direction. However,
Xiyao Liu, Jiaxin Hu, Qingying Yang, Ming Jiang, Jianbiao He, Hui Fang   +5 more
doaj   +1 more source