[HTML][HTML] Lean neural networks for autonomous radar waveform design
In recent years, neural networks have exploded in popularity, revolutionizing the domains of
computer vision, natural language processing, and autonomous systems. This is due to …
computer vision, natural language processing, and autonomous systems. This is due to …
Artificial neural networks and data science
T Bihl, WA Young II, A Moyer, S Frimel - Encyclopedia of Data …, 2023 - igi-global.com
Artificial neural networks (ANNs) consist of a family of techniques that are commonly
employed to recognize and interpret patterns in big data that are used in prediction …
employed to recognize and interpret patterns in big data that are used in prediction …
Failure mode effect classification for power electronics converters operating in a grid-connected system
Power electronic interfaces are the key aspects for achieving efficient grid integration for
various distributed generation applications. As these interfaces continue to increase, their …
various distributed generation applications. As these interfaces continue to increase, their …
Integration of Computer Vision with Analogical Reasoning for Characterizing Unknowns
Current state-of-the-art artificial intelligence struggles with accurate interpretation of out-of-
library (OOL) objects. One method proposed remedy is analogical reasoning (AR), which …
library (OOL) objects. One method proposed remedy is analogical reasoning (AR), which …
[PDF][PDF] A preliminary look at heuristic analysis for assessing artificial intelligence explainability
Artificial Intelligence and Machine Learning (AI/ML) models are increasingly criticized for
their “black-box” nature. Therefore, eXplainable AI (XAI) approaches to extract human …
their “black-box” nature. Therefore, eXplainable AI (XAI) approaches to extract human …
Amygdala Modeling with Context and Motivation Using Spiking Neural Networks for Robotics Applications
MA Zeglen - 2022 - corescholar.libraries.wright.edu
Cognitive capabilities for robotic applications are furthered by developing an artificial
amygdala that mimics biology. The amygdala portion of the brain is commonly understood to …
amygdala that mimics biology. The amygdala portion of the brain is commonly understood to …
Symbols to represent AI systems
TD Hawkes, TJ Bihl - NAECON 2021-IEEE National Aerospace …, 2021 - ieeexplore.ieee.org
As autonomous systems and artificial intelligence (AI) components are developed, confusion
often abounds about what these components are and do during operations. Often this is a …
often abounds about what these components are and do during operations. Often this is a …
Bayesian augmentation of deep learning to improve video classification
Traditional automated video classification methods lack measures of uncertainty, meaning
the network is unable to identify those cases in which its predictions are made with …
the network is unable to identify those cases in which its predictions are made with …
Exploring spiking neural networks (SNN) for low Size, Weight, and Power (SWaP) benefits
Size, Weight, and Power (SWaP) concerns are growing as artificial intelligence (AI) use
spreads in edge applications. AI algorithms, such as artificial neural networks (ANNs), have …
spreads in edge applications. AI algorithms, such as artificial neural networks (ANNs), have …
Assessing Multi-Agent Reinforcement Learning Algorithms for Autonomous Sensor Resource Management
Unmanned aerial vehicles (UAVs) have applications in search and rescue operations and
such operations could be more efficient by using appropriate artificial intelligence (AI) to …
such operations could be more efficient by using appropriate artificial intelligence (AI) to …