Towards Real-Time, On-Board, Hardware-Supported Sensor And Software Health Management For Unmanned Aerial Systems, Johann M. Schumann, Kristin Y. Rozier, Thomas Reinbacher, Ole J. Mengshoel, Timmy Mbaya, Corey Ippolito
Jun 2015
Towards Real-Time, On-Board, Hardware-Supported Sensor And Software Health Management For Unmanned Aerial Systems, Johann M. Schumann, Kristin Y. Rozier, Thomas Reinbacher, Ole J. Mengshoel, Timmy Mbaya, Corey Ippolito
Ole J Mengshoel
For unmanned aerial systems (UAS) to be successfully deployed and integrated within the national airspace, it is imperative that they possess the capability to effectively complete their missions without compromising the safety of other aircraft, as well as persons and property on the ground. This necessity creates a natural requirement for UAS that can respond to uncertain environmental conditions and emergent failures in real-time, with robustness and resilience close enough to those of manned systems. We introduce a system that meets this requirement with the design of a real-time onboard system health management (SHM) capability to continuously monitor sensors, software, …
Towards Real-Time, On-Board, Hardware-Supported Sensor And Software Health Management For Unmanned Aerial Systems, Johann Schumann, Kristin Y. Rozier, Thomas Reinbacher, Ole J. Mengshoel, Timmy Mbaya, Corey Ippolito
Sep 2013
Towards Real-Time, On-Board, Hardware-Supported Sensor And Software Health Management For Unmanned Aerial Systems, Johann Schumann, Kristin Y. Rozier, Thomas Reinbacher, Ole J. Mengshoel, Timmy Mbaya, Corey Ippolito
Ole J Mengshoel
Unmanned aerial systems (UASs) can only be deployed if they can effectively complete their missions and respond to failures and uncertain environmental conditions while maintaining safety with respect to other aircraft as well as humans and property on the ground. In this paper, we design a real-time, on-board system health management (SHM) capability to continuously monitor sensors, software, and hardware components for detection and diagnosis of failures and violations of safety or performance rules during the flight of a UAS. Our approach to SHM is three-pronged, providing: (1) real-time monitoring of sensor and/or software signals; (2) signal analysis, preprocessing, and …
Optimizing Parallel Belief Propagation In Junction Trees Using Regression, Lu Zheng, Ole J. Mengshoel
Jul 2013
Optimizing Parallel Belief Propagation In Junction Trees Using Regression, Lu Zheng, Ole J. Mengshoel
Ole J Mengshoel
The junction tree approach, with applications in artificial intelligence, computer vision, machine learning, and statistics, is often used for computing posterior distributions in probabilistic graphical models. One of the key challenges associated with junction trees is computational, and several parallel computing technologies - including many-core processors - have been investigated to meet this challenge. Many-core processors (including GPUs) are now programmable, unfortunately their complexities make it hard to manually tune their parameters in order to optimize software performance. In this paper, we investigate a machine learning approach to minimize the execution time of parallel junction tree algorithms implemented on a …
Exploring Multiple Dimensions Of Parallelism In Junction Tree Message Passing, Lu Zheng, Ole J. Mengshoel
Jun 2013
Exploring Multiple Dimensions Of Parallelism In Junction Tree Message Passing, Lu Zheng, Ole J. Mengshoel
Ole J Mengshoel
Belief propagation over junction trees is known to be computationally challenging in the general case. One way of addressing this computational challenge is to use node-level parallel computing, and parallelize the computation associated with each separator potential table cell. However, this approach is not efficient for junction trees that mainly contain small separators. In this paper, we analyze this problem, and address it by studying a new dimension of node-level parallelism, namely arithmetic parallelism. In addition, on the graph level, we use a clique merging technique to further adapt junction trees to parallel computing platforms. We apply our parallel approach …
Scaling Bayesian Network Parameter Learning With Expectation Maximization Using Mapreduce, Erik B. Reed, Ole J. Mengshoel
Nov 2012
Scaling Bayesian Network Parameter Learning With Expectation Maximization Using Mapreduce, Erik B. Reed, Ole J. Mengshoel
Ole J Mengshoel
Bayesian network (BN) parameter learning from incomplete data can be a computationally expensive task for incomplete data. Applying the EM algorithm to learn BN parameters is unfortunately susceptible to local optima and prone to premature convergence. We develop and experiment with two methods for improving EM parameter learning by using MapReduce: Age-Layered Expectation Maximization (ALEM) and Multiple Expectation Maximization (MEM). Leveraging MapReduce for distributed machine learning, these algorithms (i) operate on a (potentially large) population of BNs and (ii) partition the data set as is traditionally done with MapReduce machine learning. For example, we achieved gains using the Hadoop implementation …
Mapreduce For Bayesian Network Parameter Learning Using The Em Algorithm, Aniruddha Basak, Irina Brinster, Ole J. Mengshoel
Nov 2012
Mapreduce For Bayesian Network Parameter Learning Using The Em Algorithm, Aniruddha Basak, Irina Brinster, Ole J. Mengshoel
Ole J Mengshoel
This work applies the distributed computing framework MapReduce to Bayesian network parameter learning from incomplete data. We formulate the classical Expectation Maximization (EM) algorithm within the MapReduce framework. Analytically and experimentally we analyze the speed-up that can be obtained by means of MapReduce. We present details of the MapReduce formulation of EM, report speed-ups versus the sequential case, and carefully compare various Hadoop cluster configurations in experiments with Bayesian networks of different sizes and structures.
Accelerating Bayesian Network Parameter Learning Using Hadoop And Mapreduce, Aniruddha Basak, Irina Brinster, Xianheng Ma, Ole J. Mengshoel
Aug 2012
Accelerating Bayesian Network Parameter Learning Using Hadoop And Mapreduce, Aniruddha Basak, Irina Brinster, Xianheng Ma, Ole J. Mengshoel
Ole J Mengshoel
Learning conditional probability tables of large Bayesian Networks (BNs) with hidden nodes using the Expectation Maximization algorithm is heavily computationally intensive. There are at least two bottlenecks, namely the potentially huge data set size and the requirement for computation and memory resources. This work applies the distributed computing framework MapReduce to Bayesian parameter learning from complete and incomplete data. We formulate both traditional parameter learning (complete data) and the classical Expectation Maximization algorithm (incomplete data) within the MapReduce framework. Analytically and experimentally we analyze the speed-up that can be obtained by means of MapReduce. We present the details of our …
Belief Propagation By Message Passing In Junction Trees: Computing Each Message Faster Using Gpu Parallelization, Lu Zheng, Ole J. Mengshoel, Jike Chong
Jun 2011
Belief Propagation By Message Passing In Junction Trees: Computing Each Message Faster Using Gpu Parallelization, Lu Zheng, Ole J. Mengshoel, Jike Chong
Ole J Mengshoel
Compiling Bayesian networks (BNs) to junction trees and performing belief propagation over them is among the most prominent approaches to computing posteriors in BNs. However, belief propagation over junction tree is known to be computationally intensive in the general case. Its complexity may increase dramatically with the connectivity and state space cardinality of Bayesian network nodes. In this paper, we address this computational challenge using GPU parallelization. We develop data structures and algorithms that extend existing junction tree techniques, and specifically develop a novel approach to computing each belief propagation message in parallel. We implement our approach on an NVIDIA …
