High repetition rate sealed CO2 TEA lasers using heterogeneous catalysts

The significant operational advantages offered by CO2 lasers, operating in the 10.6 micron region of the spectrum, over current solid state lasers, emitting in the near IR region, have prompted increased interest in the development of compact, reliable, rugged CO2 laser sources. Perhaps the most critical aspect associated with achieving a laser compatible with military use is the development of lasers which require no gas replenishment. Sealed, single shot, CO2 TEA lasers have been available for a number of years. Stark et al were first to demonstrate reliable sealed operation in single shot CO2 TEA lasers in 1975 using gas catalysis. GEC Avionics reported the compact, environmentally qualified, MKIII CO2 TEA laser with a pulse life of greater than 10 to the 6th power pulses in 1980. A sealed laser lifetime of greater than 10 to the 6th power pulses is acceptable for single shot cases, such as direct detection rangefinders for tank laser sights. However, in many other applications, such as tracking of fast moving targets, it is essential that a repetition rate of typically 30Hz to 100Hz is employed. In such cases, a pulse lifetime of 10 to the 6th power pulses is no longer sufficient and a minimum pulse lifetime 10 to the 7th power pulses is essential to ensure a useful service life. In 1983 Stark el al described a sealed, 100Hz CO2 TEA laser, with a life of greater than 2.6 x 10 to the 6th power, which employed heterogeneous catalysis. Following this pioneering work, GEC Avionics has been engaged in the development of sealed high repetition rate lasers with a pulse lifetime of 20 million pulses

Smoothed particle magnetohydrodynamic simulations of protostellar outflows with misaligned magnetic field and rotation axes

We have developed a modified form of the equations of smoothed particle magnetohydrodynamics which are stable in the presence of very steep density gradients. Using this formalism, we have performed simulations of the collapse of magnetised molecular cloud cores to form protostars and drive outflows. Our stable formalism allows for smaller sink particles (< 5 AU) than used previously and the investigation of the effect of varying the angle, {\theta}, between the initial field axis and the rotation axis. The nature of the outflows depends strongly on this angle: jet-like outflows are not produced at all when {\theta} > 30{\deg}, and a collimated outflow is not sustained when {\theta} > 10{\deg}. No substantial outflows of any kind are produced when {\theta} > 60{\deg}. This may place constraints on the geometry of the magnetic field in molecular clouds where bipolar outflows are seen.Comment: Accepted for publication in MNRAS, 13 pages, 14 figures. Animations can be found at http://www.astro.ex.ac.uk/people/blewis/research/outflows_misaligned_fields.htm

Head-up transition behavior of pilots with and without head-up display in simulated low-visibility approaches

To quantify head-up transition behavior with and without a flightpath type head-up display, eight rated B-727 pilots each flew 31 manual and coupled approaches in a simulator with B-727 dynamics and collimated model board external scene. Data were also obtained on the roll played by the head-up display in the coupled-to-manual transition. Various wind shears, low visibilities, and ceilings were tested along with unexpected misalignment between the runway and head-up display symbology. The symbolic format used was a conformal scene. Every pilot except one stayed head-up, flying with the display after descending below the ceiling. Without the display and as altitude decreased, the number of lookups from the instrument panel decreased and the duration of each one increased. No large differences in mean number or duration of transitions up or down were found during the head-up display runs comparing the no-misalignment with the lateral instrument landing system offset misalignment runs. The head-up display led to fewer transitions after the pilot made a decision to land or execute a missed approach. Without the display, pilots generally waited until they had descended below the ceiling to look outside the first time, but with it several pilots looked down at their panel at relatively high altitudes (if they looked down at all). Manual takeover of control was rapid and smooth both with and without the display which permitted smoother engine power changes

Late-Time Behavior of Stellar Collapse and Explosions: I. Linearized Perturbations

Problem with the figures should be corrected. Apparently a broken uuencoder was the cause.Comment: 16pp, RevTex, 6 figures (included), NSF-ITP-93-8

Evaluation of FIDC system

A fuel vapor injector/igniter system was evaluated for its effect on automobile engine performance, fuel economy, and exhaust emissions. Improved fuel economy and emissions, found during the single cylinder tests were not realized with a multicylinder engine. Multicylinder engine tests were conducted to compare the system with both a stock and modified stock configuration. A comparison of cylinder-to-cylinder equivalence ratio distribution was also obtained from the multicylinder engine tests. The multicylinder engine was installed in a vehicle was tested on a chassis dynamometer to compare the system with stock and modified stock configurations. The fuel vapor injector/igniter system (FIDC) configuration demonstrated approximately five percent improved fuel economy over the stock configuration, but the modified stock configuration demonstrated approximately twelve percent improved fuel economy. The hydrocarbon emissions were approximately two-hundred-thirty percent higher with the FIDC system than with the stock configuration. Both the FIDC system and the modified stock configuration adversely affected driveability. The FIDC system demonstrated a modest fuel savings, but with the penalty of increased emissions, and loss of driveability

Introducing Fuzzy Layers for Deep Learning

Many state-of-the-art technologies developed in recent years have been influenced by machine learning to some extent. Most popular at the time of this writing are artificial intelligence methodologies that fall under the umbrella of deep learning. Deep learning has been shown across many applications to be extremely powerful and capable of handling problems that possess great complexity and difficulty. In this work, we introduce a new layer to deep learning: the fuzzy layer. Traditionally, the network architecture of neural networks is composed of an input layer, some combination of hidden layers, and an output layer. We propose the introduction of fuzzy layers into the deep learning architecture to exploit the powerful aggregation properties expressed through fuzzy methodologies, such as the Choquet and Sugueno fuzzy integrals. To date, fuzzy approaches taken to deep learning have been through the application of various fusion strategies at the decision level to aggregate outputs from state-of-the-art pre-trained models, e.g., AlexNet, VGG16, GoogLeNet, Inception-v3, ResNet-18, etc. While these strategies have been shown to improve accuracy performance for image classification tasks, none have explored the use of fuzzified intermediate, or hidden, layers. Herein, we present a new deep learning strategy that incorporates fuzzy strategies into the deep learning architecture focused on the application of semantic segmentation using per-pixel classification. Experiments are conducted on a benchmark data set as well as a data set collected via an unmanned aerial system at a U.S. Army test site for the task of automatic road segmentation, and preliminary results are promising.Comment: 6 pages, 4 figures, published in 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE

Vehicle test report: Battronic pickup truck

An electric pickup truck was tested to characterize certain parameters and to provide baseline data that can be used for the comparison of improved batteries that may be incorporated into the vehicle at a later time. The vehicle tests were concentrated on the electrical drive subsystem; i.e., the batteries, controller, and motor. The tests included coastdowns to characterize the road load and range evaluations for both cyclic and constant speed conditions. A qualitative evaluation of the vehicle's performance was made by comparing its constant speed range performance with other vehicles

Kernel Matrix-Based Heuristic Multiple Kernel Learning

Kernel theory is a demonstrated tool that has made its way into nearly all areas of machine learning. However, a serious limitation of kernel methods is knowing which kernel is needed in practice. Multiple kernel learning (MKL) is an attempt to learn a new tailored kernel through the aggregation of a set of valid known kernels. There are generally three approaches to MKL: fixed rules, heuristics, and optimization. Optimization is the most popular; however, a shortcoming of most optimization approaches is that they are tightly coupled with the underlying objective function and overfitting occurs. Herein, we take a different approach to MKL. Specifically, we explore different divergence measures on the values in the kernel matrices and in the reproducing kernel Hilbert space (RKHS). Experiments on benchmark datasets and a computer vision feature learning task in explosive hazard detection demonstrate the effectiveness and generalizability of our proposed methods

The resurrection of group selection as a theory of human cooperation

Two books edited by members of the MacArthur Norms and Preferences Network (an interdisciplinary group, mainly anthropologists and economists) are reviewed here. These books in large part reflect a renewed interest in group selection that has occurred among these researchers: they promote the theory that human cooperative behavior evolved via selective processes which favored biological and/or cultural group-level adaptations as opposed to individual-level adaptations. In support of this theory, an impressive collection of cross-cultural data are presented which suggest that participants in experimental economic games often do not behave as self-interested income maximizers; this lack of self-interest is regarded as evidence of group selection. In this review, problems with these data and with the theory are discussed. On the data side, it is argued that even if a behavior seems individually-maladaptive in a game context, there is no reason to believe that it would have been that way in ancestral contexts, since the environments of experimental games do not at all resemble those in which ancestral humans would have interacted cooperatively. And on the theory side, it is argued that it is premature to invoke group selection in order to explain human cooperation, because more parsimonious individual-level theories have not yet been exhausted. In summary, these books represent ambitious interdisciplinary contributions on an important topic, and they include unique and useful data; however, they do not make a convincing case that the evolution of human cooperation required group selection