Zobrazeno 1 - 10
of 3 048
pro vyhledávání: '"POTTER, MICHAEL"'
Autor:
Potter, Michael, Tang, Shuo, Ghanem, Paul, Stojanovic, Milica, Closas, Pau, Akcakaya, Murat, Wright, Ben, Necsoiu, Marius, Erdogmus, Deniz, Everett, Michael, Imbiriba, Tales
Continuously optimizing sensor placement is essential for precise target localization in various military and civilian applications. While information theory has shown promise in optimizing sensor placement, many studies oversimplify sensor measureme
Externí odkaz:
http://arxiv.org/abs/2405.18999
In response to the critical need for effective reconnaissance in disaster scenarios, this research article presents the design and implementation of a complete autonomous robot system using the Turtlebot3 with Robotic Operating System (ROS) Noetic. U
Externí odkaz:
http://arxiv.org/abs/2404.13767
Autor:
Potter, Michael, Akcakaya, Murat, Necsoiu, Marius, Schirner, Gunar, Erdogmus, Deniz, Imbiriba, Tales
Radar Automated Target Recognition (RATR) for Unmanned Aerial Vehicles (UAVs) involves transmitting Electromagnetic Waves (EMWs) and performing target type recognition on the received radar echo, crucial for defense and aerospace applications. Previo
Externí odkaz:
http://arxiv.org/abs/2402.17987
We empirically show that process-based Parallelism speeds up the Genetic Algorithm (GA) for Feature Selection (FS) 2x to 25x, while additionally increasing the Machine Learning (ML) model performance on metrics such as F1-score, Accuracy, and Receive
Externí odkaz:
http://arxiv.org/abs/2401.10846
Survival Analysis (SA) models the time until an event occurs, with applications in fields like medicine, defense, finance, and aerospace. Recent research indicates that Neural Networks (NNs) can effectively capture complex data patterns in SA, wherea
Externí odkaz:
http://arxiv.org/abs/2312.16019
Autor:
Potter, Michael, Jun, Miru
We implement a Bayesian inference process for Neural Networks to model the time to failure of highly reliable weapon systems with interval-censored data and time-varying covariates. We analyze and benchmark our approach, LaplaceNN, on synthetic and r
Externí odkaz:
http://arxiv.org/abs/2312.10494
Autor:
Potter, Michael, Cheng, Benny
We propose to integrate weapon system features (such as weapon system manufacturer, deployment time and location, storage time and location, etc.) into a parameterized Cox-Weibull [1] reliability model via a neural network, like DeepSurv [2], to impr
Externí odkaz:
http://arxiv.org/abs/2301.01850