Artificial Immune System for Unmanned Aerial Vehicle Abnormal Condition Detection and Identification

A detection and identification scheme for abnormal conditions was developed for an unmanned aerial vehicle (UAV) based on the artificial immune system (AIS) paradigm. This technique involves establishing a body of data to represent normal conditions referred to as “self” and differentiating these conditions from abnormal conditions, referred to as “non-self”. Data collected from simulation of the UAV attempting to autonomously fly a pre-decided trajectory were used to develop and test a scheme that was able to detect and identify aircraft sensor and actuator faults. These faults included aerodynamic control surface locks and damages and angular rate sensor biases. The method used to create the AIS is known as the partition of the universe approach. This approach differs from standard clustering approaches because the universe is divided into uniform partition clusters rather than clustering data using some clustering algorithm. It is simpler and requires less computational resources. This will be the first time that this approach has been applied for use in aerospace engineering. Data collected from nominal flights were used to define self partitions, and the non-self partitions were defined implicitly. The creation scheme is also discussed, involving all software used for simulation, as well as the process of creating the self and the logic behind the detection and identification schemes. The detection scheme was evaluated based on detection rate, detection time, and false alarms for flights under both normal and abnormal conditions. The failure identification scheme was assessed in terms of identification rate and time. Investigation of the proposed technique showed promising results for the cases explored with comparable performance with respect to clustering-based approaches and motivates further research and extension of the proposed methodology toward a more complete health management system

Integrated Immunity-based Methodology for UAV Monitoring and Control

A general integrated and comprehensive health management framework based on the artificial immune system (AIS) paradigm is formulated and an automated system is developed and tested through simulation for the detection, identification, evaluation, and accommodation (DIEA) of abnormal conditions (ACs) on an unmanned aerial vehicle (UAV). The proposed methodology involves the establishment of a body of data to represent the function of the vehicle under nominal conditions, called the self, and differentiating this operation from that of the vehicle under an abnormal condition, referred to as the non-self. Data collected from simulations of the selected UAV autonomously flying a set of prescribed trajectories were used to develop and test novel schemes that are capable of addressing the AC-DIEA of sensor and actuator faults on a UAV. While the specific dynamic system used here is a UAV, the proposed framework and methodology is general enough to be adapted and applied to any complex dynamic system. The ACs considered within this effort included aerodynamic control surface locks and damage and angular rate sensor biases. The general framework for the comprehensive health management system comprises a novel complete integration of the AC-DIEA process with focus on the transition between the four different phases. The hierarchical multiself (HMS) strategy is used in conjunction with several biomimetic mechanisms to address the various steps in each phase. The partition of the universe approach is used as the basis of the AIS generation and the binary detection phase. The HMS approach is augmented by a mechanism inspired by the antigen presenting cells of the adaptive immune system for performing AC identification. The evaluation and accommodation phases are the most challenging phases of the AC-DIEA process due to the complexity and diversity of the ACs and the multidimensionality of the AIS. Therefore, the evaluation phase is divided into three separate steps: the qualitative evaluation, direct quantitative evaluation, and the indirect quantitative evaluation, where the type, severity, and effects of the AC are determined, respectively. The integration of the accommodation phase is based on a modular process, namely the strategic decision making, tactical decision marking, and execution modules. These modules are designed by the testing of several approaches for integrating the accommodation phase, which are specialized based on the type of AC being addressed. These approaches include redefining of the mission, adjustment or shifting of the control laws, or adjusting the sensor outputs. Adjustments of the mission include redefining of the trajectory to remove maneuvers which are no longer possible, while adjusting of the control laws includes modifying gains involved in determination of commanded control surface deflections. Analysis of the transition between phases includes a discussion of results for integrated example cases where the proposed AC-DIEA process is applied. The cases considered show the validity of the integrated AC-DIEA system and specific accommodation approaches by an improvement in flight performance through metrics that capture trajectory tracking errors and control activity differences between nominal, abnormal, and accommodated cases

Some exact non-vacuum Bianchi VI0 and VII0 instantons

We report some new exact instantons in general relativity. These solutions are K\"ahler and fall into the symmetry classes of Bianchi types VI0 and VII0, with matter content of a stiff fluid. The qualitative behaviour of the solutions is presented, and we compare it to the known results of the corresponding self-dual Bianchi solutions. We also give axisymmetric Bianchi VII0 solutions with an electromagnetic field.Comment: latex, 15 pages with 3 eps figure

The influence of collective neutrino oscillations on a supernova r-process

Recently, it has been demonstrated that neutrinos in a supernova oscillate collectively. This process occurs much deeper than the conventional matter-induced MSW effect and hence may have an impact on nucleosynthesis. In this paper we explore the effects of collective neutrino oscillations on the r-process, using representative late-time neutrino spectra and outflow models. We find that accurate modeling of the collective oscillations is essential for this analysis. As an illustration, the often-used "single-angle" approximation makes grossly inaccurate predictions for the yields in our setup. With the proper multiangle treatment, the effect of the oscillations is found to be less dramatic, but still significant. Since the oscillation patterns are sensitive to the details of the emitted fluxes and the sign of the neutrino mass hierarchy, so are the r-process yields. The magnitude of the effect also depends sensitively on the astrophysical conditions - in particular on the interplay between the time when nuclei begin to exist in significant numbers and the time when the collective oscillation begins. A more definitive understanding of the astrophysical conditions, and accurate modeling of the collective oscillations for those conditions, is necessary.Comment: 27 pages, 10 figure

Observation of laser pulse propagation in optical fibers with a SPAD camera

Recording processes and events that occur on sub-nanosecond timescales poses a difficult challenge. Conventional ultrafast imaging techniques often rely on long data collection times, which can be due to limited device sensitivity and/or the requirement of scanning the detection system to form an image. In this work, we use a single-photon avalanche detector array camera with pico-second timing accuracy to detect photons scattered by the cladding in optical fibers. We use this method to film supercontinuum generation and track a GHz pulse train in optical fibers. We also show how the limited spatial resolution of the array can be improved with computational imaging. The single-photon sensitivity of the camera and the absence of scanning the detection system results in short total acquisition times, as low as a few seconds depending on light levels. Our results allow us to calculate the group index of different wavelength bands within the supercontinuum generation process. This technology can be applied to a range of applications, e.g., the characterization of ultrafast processes, time-resolved fluorescence imaging, three-dimensional depth imaging, and tracking hidden objects around a corner. © The Author(s) 20171541sciescopu

Migratory Connectivity of the Monarch Butterfly (Danaus plexippus): Patterns of Spring Re-Colonization in Eastern North America

Each year, millions of monarch butterflies (Danaus plexippus) migrate up to 3000 km from their overwintering grounds in central Mexico to breed in eastern North America. Malcolm et al. (1993) articulated two non-mutually exclusive hypotheses to explain how Monarchs re-colonize North America each spring. The ‘successive brood’ hypothesis proposes that monarchs migrate from Mexico to the Gulf Coast, lay eggs and die, leaving northern re-colonization of the breeding range to subsequent generations. The ‘single sweep’ hypothesis proposes that overwintering monarchs continue to migrate northward after arriving on the Gulf coast and may reach the northern portion of the breeding range, laying eggs along the way. To examine these hypotheses, we sampled monarchs throughout the northern breeding range and combined stable-hydrogen isotopes (δD) to estimate natal origin with wing wear scores to differentiate between individuals born in the current vs. previous year. Similar to Malcolm et al. (1993), we found that the majority of the northern breeding range was re-colonized by the first generation of monarchs (90%). We also estimated that a small number of individuals (10%) originated directly from Mexico and, therefore adopted a sweep strategy. Contrary to Malcolm et al. (1993), we found that 62% of monarchs sampled in the Great Lakes originated from the Central U.S., suggesting that this region is important for sustaining production in the northern breeding areas. Our results provide new evidence of re-colonization patterns in monarchs and contribute important information towards identifying productive breeding regions of this unique migratory insect

Multiple populations in globular clusters. Lessons learned from the Milky Way globular clusters

Recent progress in studies of globular clusters has shown that they are not simple stellar populations, being rather made of multiple generations. Evidence stems both from photometry and spectroscopy. A new paradigm is then arising for the formation of massive star clusters, which includes several episodes of star formation. While this provides an explanation for several features of globular clusters, including the second parameter problem, it also opens new perspectives about the relation between globular clusters and the halo of our Galaxy, and by extension of all populations with a high specific frequency of globular clusters, such as, e.g., giant elliptical galaxies. We review progress in this area, focusing on the most recent studies. Several points remain to be properly understood, in particular those concerning the nature of the polluters producing the abundance pattern in the clusters and the typical timescale, the range of cluster masses where this phenomenon is active, and the relation between globular clusters and other satellites of our Galaxy.Comment: In press (The Astronomy and Astrophysics Review

Transcriptional Subtyping and CD8 Immunohistochemistry Identifies Patients With Stage II and III Colorectal Cancer With Poor Prognosis Who Benefit From Adjuvant Chemotherapy

This work was funded by Cancer Research UK: C212/A13721 and C11884/A24387Peer reviewedPublisher PD