Results 111 to 120 of about 804,833 (294)
Quality and Reliability Engineering International, EarlyView.ABSTRACT This study examines how Lean Six Sigma (LSS) currently functions in the U.S. textile and apparel industry after years of adoption and use. Moving beyond isolated project‐level evidence, this study investigates whether LSS has been implemented as an organizational capability and integrated into the corporate culture, adapted to textile‐specific
Alireza Vahedi Fakhr +2 more
wiley +1 more source
Integrating Image Segmentation and Deep Learning to Improve Radio Frequency Propagation Models
International Journal of Satellite Communications and Networking, EarlyView.ABSTRACT This paper proposes a multi‐sensor approach to improve radio frequency (RF) propagation models, which play a key role in the rapidly expanding field of connected vehicle technology. Focusing on the 1‐ to 20‐GHz frequency range, which is critical for both satellite‐to‐vehicle and base station‐to‐vehicle communications, our study introduces a ...
Jonathan Israel +2 more
wiley +1 more source
Defensive Distillation is Not Robust to Adversarial Examples
CoRR, 2016 We show that defensive distillation is not secure: it is no more resistant to targeted misclassification attacks than unprotected neural networks.
Nicholas Carlini, David A. Wagner 0001
openaire +3 more sources
Does innovation success need advocacy? Stakeholder involvement in firm innovation
Strategic Management Journal, EarlyView.Abstract Research Summary Prior research documents that firms often collaborate with advocacy groups to mitigate stakeholder contention. Integrating stakeholder theory into a performance model of collaborative innovation, we propose an overlooked explanation for such collaborations: to enhance the adoption of firms' innovations.
Paolo Carioli +3 more
wiley +1 more source
IEEE AccessSignificant advances have been made in recent years in improving the robustness of deep neural networks, particularly under adversarial machine learning scenarios where the data has been contaminated to fool networks into making undesirable predictions ...
Hossein Aboutalebi +3 more
doaj +1 more source
Moving target defense for adaptive adversaries
2013 IEEE International Conference on Intelligence and Security Informatics, 2013 Machine learning (ML) plays a central role in the solution of many security problems, for example enabling malicious and innocent activities to be rapidly and accurately distinguished and appropriate actions to be taken. Unfortunately, a standard assumption in ML - that the training and test data are identically distributed - is typically violated in ...
Richard Colbaugh, Kristin Glass
openaire +2 more sources
Quantum Machine Learning for Cybersecurity: A Taxonomy and Future Directions
Software: Practice and Experience, EarlyView.ABSTRACT Context The rapid increase in cyber threats, coupled with increasingly sophisticated attack strategies and the exponential growth of data in recent years, has exposed significant limitations in classical machine learning, rule‐based, and signature‐based defense mechanisms.
Siva Sai +5 more
wiley +1 more source
Adversarial Defense Method Based on Latent Representation Guidance for Remote Sensing Image Scene Classification. [PDF]
Entropy (Basel), 2023 Da Q +6 more
europepmc +1 more source
Research Progress on Thermal Runaway and Thermal Management of Lithium‐Ion Batteries
Safety Science and Technology, EarlyView.This review summarizes thermal runaway mechanisms, propagation pathways, mitigation strategies, and intelligent thermal management technologies for lithium‐ion batteries, highlighting the transition from passive heat dissipation to integrated safety regulation through advanced cooling, barrier materials, sensing, machine learning, and digital‐twin ...
Songrong Li +4 more
wiley +1 more source
Dictionary Learning Based Scheme for Adversarial Defense in Continuous-Variable Quantum Key Distribution. [PDF]
Entropy (Basel), 2023 Li S +5 more
europepmc +1 more source