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Physical Sciences and Mathematics Commons™
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Full-Text Articles in Physical Sciences and Mathematics
Time-Of-Flight Emission Profiles Of The Entire Plume Using Fast Imaging During Pulsed Laser Deposition Of Yba2Cu3O7−X, Carl J. Druffner, Glen P. Perram, Rand R. Biggers
Time-Of-Flight Emission Profiles Of The Entire Plume Using Fast Imaging During Pulsed Laser Deposition Of Yba2Cu3O7−X, Carl J. Druffner, Glen P. Perram, Rand R. Biggers
Faculty Publications
Emission time-of-flight (TOF) profiles have been obtained using gated imagery to further the process control during the pulsed laser deposition of the high temperature superconductor, YBa2Cu3O7−x. An intensified charge coupled device array was used to obtain a sequence of plume images at 10ns temporal resolution and 0.2mm spatial resolution. Plume imagery is transformed to TOF profiles and pulse-to-pulse variations removed using physically based smoothing techniques. Comparison with non-imaging sensors establishes excellent agreement, with systematic uncertainties in streaming speed and temperatures of less than 15% and 8%, respectively. The resulting streaming speeds of 0.4–1.2×10 …
Cooperative Reinforcement Learning Using An Expert-Measuring Weighted Strategy With Wolf, Kevin Cousin, Gilbert L. Peterson
Cooperative Reinforcement Learning Using An Expert-Measuring Weighted Strategy With Wolf, Kevin Cousin, Gilbert L. Peterson
Faculty Publications
Gradient descent learning algorithms have proven effective in solving mixed strategy games. The policy hill climbing (PHC) variants of WoLF (Win or Learn Fast) and PDWoLF (Policy Dynamics based WoLF) have both shown rapid convergence to equilibrium solutions by increasing the accuracy of their gradient parameters over standard Q-learning. Likewise, cooperative learning techniques using weighted strategy sharing (WSS) and expertness measurements improve agent performance when multiple agents are solving a common goal. By combining these cooperative techniques with fast gradient descent learning, an agent’s performance converges to a solution at an even faster rate. This statement is verified in a …
A Comparison Of Generalizability For Anomaly Detection, Gilbert L. Peterson, Robert F. Mills, Brent T. Mcbride, Wesley T. Allred
A Comparison Of Generalizability For Anomaly Detection, Gilbert L. Peterson, Robert F. Mills, Brent T. Mcbride, Wesley T. Allred
Faculty Publications
In security-related areas there is concern over the novel “zeroday” attack that penetrates system defenses and wreaks havoc. The best methods for countering these threats are recognizing “non-self” as in an Artificial Immune System or recognizing “self” through clustering. For either case, the concern remains that something that looks similar to self could be missed. Given this situation one could logically assume that a tighter fit to self rather than generalizability is important for false positive reduction in this type of learning problem. This article shows that a tight fit, although important, does not supersede having some model generality. This …
An Evolutionary Algorithm To Generate Hyper-Ellipsoid Detectors For Negative Selection, Joseph M. Shapiro, Gary B. Lamont, Gilbert L. Peterson
An Evolutionary Algorithm To Generate Hyper-Ellipsoid Detectors For Negative Selection, Joseph M. Shapiro, Gary B. Lamont, Gilbert L. Peterson
Faculty Publications
This paper introduces hyper-ellipsoids as an improvement to hyper-spheres as intrusion detectors in a negative selection problem within an artificial immune system. Since hyper-spheres are a specialization of hyper-ellipsoids, hyper-ellipsoids retain the benefits of hyper-spheres. However, hyper-ellipsoids are much more flexible, mostly in that they can be stretched and reoriented. The viability of using hyper-ellipsoids is established using several pedagogical problems. We conjecture that fewer hyper-ellipsoids than hyper-spheres are needed to achieve similar coverage of nonself space in a negative selection problem. Experimentation validates this conjecture. In pedagogical benchmark problems, the number of hyper-ellipsoids to achieve good results is significantly …
A New Blind Method For Detecting Novel Steganography, Brent T. Mcbride, Gilbert L. Peterson, Steven C. Gustafson
A New Blind Method For Detecting Novel Steganography, Brent T. Mcbride, Gilbert L. Peterson, Steven C. Gustafson
Faculty Publications
Steganography is the art of hiding a message in plain sight. Modern steganographic tools that conceal data in innocuous-looking digital image files are widely available. The use of such tools by terrorists, hostile states, criminal organizations, etc., to camouflage the planning and coordination of their illicit activities poses a serious challenge. Most steganography detection tools rely on signatures that describe particular steganography programs. Signature-based classifiers offer strong detection capabilities against known threats, but they suffer from an inability to detect previously unseen forms of steganography. Novel steganography detection requires an anomaly-based classifier. This paper describes and demonstrates a blind classification …