Out-of-distribution detection for deep neural networks with isolation forest and local outlier factor

S Luan, Z Gu, LB Freidovich, L Jiang, Q Zhao - IEEE Access, 2021 - ieeexplore.ieee.org
Deep Neural Networks (DNNs) are extensively deployed in today's safety-critical
autonomous systems thanks to their excellent performance. However, they are known to …

Learning-based optoelectronically innervated tactile finger for rigid-soft interactive grasping

L Yang, X Han, W Guo, F Wan, J Pan… - IEEE Robotics and …, 2021 - ieeexplore.ieee.org
This letter presents a novel design of a soft tactile finger with omni-directional adaptation
using multi-channel optical fibers for rigid-soft interactive grasping. Machine learning …

Cross-layer adaptation with safety-assured proactive task job skipping

Z Wang, C Huang, H Kim, W Li, Q Zhu - ACM Transactions on Embedded …, 2021 - dl.acm.org
During the operation of many real-time safety-critical systems, there are often strong needs
for adapting to a dynamic environment or evolving mission objectives, eg, increasing …

Bounding perception neural network uncertainty for safe control of autonomous systems

Z Wang, C Huang, Y Wang, C Hobbs… - … , Automation & Test …, 2021 - ieeexplore.ieee.org
Future autonomous systems will rely on advanced sensors and deep neural networks for
perceiving the environment, and then utilize the perceived information for system planning …

[PDF][PDF] Wip: End-to-end analysis of adversarial attacks to automated lane centering systems

H Liang, R Jiao, T Sato, J Shen, QA Chen… - Workshop on Automotive …, 2021 - par.nsf.gov
Machine learning techniques, particularly those based on deep neural networks (DNNs), are
widely adopted in the development of advanced driver-assistance systems (ADAS) and …

Co-Design of Resilient Timing-Constrained Cyber-Physical Systems

H Liang - 2021 - search.proquest.com
Abstract Cyber-physical systems (CPS), as a multidisciplinary area, have been widely
adopted in our daily life and attract experts from various fields. CPS aims to achieve real …