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Full-Text Articles in Engineering
Accuracy And Multi-Core Performance Of Machine Learning Algorithms For Handwritten Character Recognition, Sumod Mohan
Accuracy And Multi-Core Performance Of Machine Learning Algorithms For Handwritten Character Recognition, Sumod Mohan
All Theses
There have been considerable developments in the quest for intelligent machines since the beginning of the cybernetics revolution and the advent of computers. In the last two decades with the onset of the internet the developments have been extensive. This quest for building intelligent machines have led into research on the working of human brain, which has in turn led to the development of pattern recognition models which take inspiration in their structure and performance from biological neural networks. Research in creating intelligent systems poses two main problems. The first one is to develop algorithms which can generalize and predict …
Developing Large-Scale Bayesian Networks By Composition: Fault Diagnosis Of Electrical Power Systems In Aircraft And Spacecraft, Ole J. Mengshoel, Scott Poll, Tolga Kurtoglu
Developing Large-Scale Bayesian Networks By Composition: Fault Diagnosis Of Electrical Power Systems In Aircraft And Spacecraft, Ole J. Mengshoel, Scott Poll, Tolga Kurtoglu
Ole J Mengshoel
In this paper, we investigate the use of Bayesian networks to construct large-scale diagnostic systems. In particular, we consider the development of large-scale Bayesian networks by composition. This compositional approach reflects how (often redundant) subsystems are architected to form systems such as electrical power systems. We develop high-level specifiations, Bayesian networks, clique trees, and arithmetic circuits representing 24 different electrical power systems. The largest among these 24 Bayesian networks contains over 1,000 random variables. Another BN represents the real-world electrical power system ADAPT, which is representative of electrical power systems deployed in aerospace vehicles. In addition to demonstrating the scalability …
Diagnosis And Reconfiguration Using Bayesian Networks: An Electrical Power System Case Study, W. Bradley Knox, Ole J. Mengshoel
Diagnosis And Reconfiguration Using Bayesian Networks: An Electrical Power System Case Study, W. Bradley Knox, Ole J. Mengshoel
Ole J Mengshoel
Automated diagnosis and reconfiguration are important computational techniques that aim to minimize human intervention in autonomous systems. In this paper, we develop novel techniques and models in the context of diagnosis and reconfiguration reasoning using causal Bayesian networks (BNs). We take as starting point a successful diagnostic approach, using a static BN developed for a real-world electrical power system. We discuss in this paper the extension of this diagnostic approach along two dimensions, namely: (i) from a static BN to a dynamic BN; and (ii) from a diagnostic task to a reconfiguration task.
More specifically, we discuss the auto-generation of …
The Diagnostic Challenge Competition: Probabilistic Techniques For Fault Diagnosis In Electrical Power Systems, Brian W. Ricks, Ole J. Mengshoel
The Diagnostic Challenge Competition: Probabilistic Techniques For Fault Diagnosis In Electrical Power Systems, Brian W. Ricks, Ole J. Mengshoel
Ole J Mengshoel
Constraint Handling Using Tournament Selection: Abductive Inference In Partly Deterministic Bayesian Network, Severino F. Galan, Ole J. Mengshoel
Constraint Handling Using Tournament Selection: Abductive Inference In Partly Deterministic Bayesian Network, Severino F. Galan, Ole J. Mengshoel
Ole J Mengshoel
Constraints occur in many application areas of interest to evolutionary computation. The area considered here is Bayesian networks (BNs), which is a probability-based method for representing and reasoning with uncertain knowledge. This work deals with constraints in BNs and investigates how tournament selection can be adapted to better process such constraints in the context of abductive inference. Abductive inference in BNs consists of finding the most probable explanation given some evidence. Since exact abductive inference is NP-hard, several approximate approaches to this inference task have been developed. One of them applies evolutionary techniques in order to find optimal or close-to-optimal …
Methods For Probabilistic Fault Diagnosis: An Electrical Power System Case Study, Brian Ricks, Ole J. Mengshoel
Methods For Probabilistic Fault Diagnosis: An Electrical Power System Case Study, Brian Ricks, Ole J. Mengshoel
Ole J Mengshoel
Health management systems that more accurately and quickly diagnose faults that may occur in different technical systems on-board a vehicle will play a key role in the success of future NASA missions. We discuss in this paper the diagnosis of abrupt continuous (or parametric) faults within the context of probabilistic graphical models, more specifically Bayesian networks that are compiled to arithmetic circuits. This paper extends our previous research, within the same probabilistic setting, on diagnosis of abrupt discrete faults. Our approach and diagnostic algorithm ProDiagnose are domain-independent; however we use an electrical power system testbed called ADAPT as a case …