Engineering topics

Kangas, Lars J

Publications and source records attributed to Kangas, Lars J.

3 records

DOE OSTI2012

In Silico Identification Software (ISIS): A Machine Learning Approach to Tandem Mass Spectral Identification of Lipids

Liquid chromatography-mass spectrometry-based metabolomics has gained importance in the life sciences, yet it is not supported by software tools for high throughput identification of metabolites based on their fragmentation spectra. An algorithm (ISIS: in silico identification software) and its implementation are presented and show great promise in generating in silico spectra of lipids for the purpose of structural identification. Instead of using chemical reaction rate equations or rules-based fragmentation libraries, the algorithm uses machine learning to find accurate bond cleavage rates in a mass spectrometer employing collision-induced dissocia-tion tandem mass spectrometry. A preliminary test of the algorithm with 45 lipids from a subset of lipid classes shows both high sensitivity and specificity.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2011

Evaluating In-Clique and Topological Parallelism Strategies for Junction Tree-Based Bayesian Inference Algorithm on the Cray XMT

Long viewed as a strong statistical inference technique, Bayesian networks have emerged to be an important class of applications for high-performance computing. We have applied an architecture-conscious approach to parallelizing the Lauritzen-Spiegelhalter Junction Tree algorithm for exact inferencing in Bayesian networks. In optimizing the Junction Tree algorithm, we have implemented both in-clique and topological parallelism strategies to best leverage the fine-grained synchronization and massive-scale multithreading of the Cray XMT architecture. Two topological techniques were developed to parallelize the evidence propagation process through the Bayesian network. One technique involves performing intelligent scheduling of junction tree nodes based on its topology and relative size. The second technique involves decomposing the junction tree into a much finer tree-like representation to offer much more opportunities for parallelism. We evaluate these optimizations on five different Bayesian networks and report our findings and observations. Another important contribution of this paper is to demonstrate the application of massive-scale multithreading for load balancing and use of implicit parallelism-based compiler optimizations in designing scalable inferencing algorithms.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2010

Fault Detection in Distributed Climate Sensor Networks using Dynamic Bayesian Networks

The Atmospheric Radiation Measurement program operated by U.S. Department of Energy is one of the largest climate research programs dedicated to the collection of long-term continuous measurements of cloud properties and other key components of the earth’s climate system. Given the critical role that collected ARM data plays in the analysis of atmospheric processes and conditions and in the enhancement and evaluation of global climate models, the production and distribution of high-quality data is one of ARM’s primary mission objectives. Fault detection in ARM’s distributed sensor network is one critical ingredient towards maintaining high quality and useful data. We are modeling ARM’s distributed sensor network as a dynamic Bayesian network where key measurements are mapped to Bayesian network variables. We then define the conditional dependencies between variables by discovering highly correlated variable pairs from historical data. The resultant dynamic Bayesian network provides an automated approach to identifying whether certain sensors are malfunctioning or failing in the distributed sensor network. A potential fault or failure is detected when an observed measurement is not consistent with its expected measurement and the observed measurements of other related sensors in the Bayesian network. We present some of our experiences and promising results with the fault detection dynamic Bayesian network.

54 ENVIRONMENTAL SCIENCES↗