Assessing Operator Strategies for Adjusting Replan Alerts in Controlling Multiple Unmanned Vehicles

url is for conference abstract.This study examined the impact of allowing an operator to adjust the rate of prompts to view automation-generated plans on operator performance and workload when supervising a decentralized network of heterogeneous unmanned vehicles. Background: Future unmanned vehicles systems will invert the operator-to-vehicle ratio so that one operator can control multiple vehicles with different capabilities, connected through a decentralized network. A previous experiment showed that higher rates of replan prompting led to higher workload and lower system performance. Poor performance was associated with a lack of operator consensus for when to accept the automation’s suggested prompts for new plan consideration. Method: Three initial rates of replanning were tested on an existing, multiple unmanned vehicle simulation environment that leverages decentralized algorithms for vehicle routing and task allocation, in conjunction with human supervision. Operators were provided with the ability to adjust the rate of replanning. Results: The majority of the operators chose to adjust the rate at which they were prompted to replan. Operators favored particular replan intervals, no matter which initial replan interval they started at. It was found that different initial replan intervals produced differences in mission performance. In addition, increasing amounts of replanning caused the system to destroy more targets but do a poorer job at tracking targets. Conclusion: Operators have preferences for the rate at which they prefer to view automation-generated plans. Allowing operators to institute these preferences influenced the overall mission performance. Further research is necessary to determine the full impact of the operators’ strategies for changing the replan intervals on net mission performance. Application: Future unmanned vehicles systems designs should incorporate the flexibility to allow operators to adjust the frequency at which the automation generates new plans for approval.Aurora Flight Sciences Corp.United States. Office of Naval Researc

Exponential Decay of Correlations for Strongly Coupled Toom Probabilistic Cellular Automata

We investigate the low-noise regime of a large class of probabilistic cellular automata, including the North-East-Center model of Toom. They are defined as stochastic perturbations of cellular automata belonging to the category of monotonic binary tessellations and possessing a property of erosion. We prove, for a set of initial conditions, exponential convergence of the induced processes toward an extremal invariant measure with a highly predominant spin value. We also show that this invariant measure presents exponential decay of correlations in space and in time and is therefore strongly mixing.Comment: 21 pages, 0 figure, revised version including a generalization to a larger class of models, structure of the arguments unchanged, minor changes suggested by reviewers, added reference

Examples of user algorithms implementing ARAIM techniques for integrity performance prediction, procedures development and pre-flight operations

Advanced Receiver Autonomous Integrity Monitoring (ARAIM) is a new Aircraft Based Augmentation System (ABAS) technique, firstly presented in the two reports of the GNSS Evolutionary Architecture Study (GEAS). The ARAIM technique offers the opportunity to enable GNSS receivers to serve as a primary means of navigation, worldwide, for precision approach down to LPV-200 operation, while at the same time potentially reducing the support which has to be provided by Ground and Satellite Based Augmented Systems (GBAS and SBAS)

Selection of DNA nanoparticles with preferential binding to aggregated protein target.

High affinity and specificity are considered essential for affinity reagents and molecularly-targeted therapeutics, such as monoclonal antibodies. However, life's own molecular and cellular machinery consists of lower affinity, highly multivalent interactions that are metastable, but easily reversible or displaceable. With this inspiration, we have developed a DNA-based reagent platform that uses massive avidity to achieve stable, but reversible specific recognition of polyvalent targets. We have previously selected these DNA reagents, termed DeNAno, against various cells and now we demonstrate that DeNAno specific for protein targets can also be selected. DeNAno were selected against streptavidin-, rituximab- and bevacizumab-coated beads. Binding was stable for weeks and unaffected by the presence of soluble target proteins, yet readily competed by natural or synthetic ligands of the target proteins. Thus DeNAno particles are a novel biomolecular recognition agent whose orthogonal use of avidity over affinity results in uniquely stable yet reversible binding interactions

Integration of an ARAIM algorithm in the development of an instrument approach procedure and for pre-flight operational briefing

Advanced Receiver Autonomous Integrity Monitoring (ARAIM) offers the opportunity to enable Global Navigation Satellite System (GNSS) receivers to serve as a primary means of navigation, worldwide, for precision approach down to Localizer Performance with Vertical guidance (LPV-200) operation. Previous produced works analysed the performance of this new technique, clearly showing the potential of ARAIM architectures to provide the Required Navigation Performance (RNP) for LPV 200. However, almost all of the studies were performed with respect to fixed points on a grid on the Earth’s surface, with full view of the sky, evaluating ARAIM performance from a geometrical point of view and using nominal performance in simulated scenarios lasting several days. In our previous work we presented the ARAIM performance in simulated operational configurations. Attitude changes from manoeuvers, obscuration by the aircraft body and shadowing from the surrounding environment could all affect the incoming signal from the GNSS constellations, leading to configurations that could adversely affect the real performance. In this paper, we continue the previous work. The new proposed algorithm integrates ARAIM performance prediction capability, considering the attitude and terrain shadowing effects, in two different scenarios: In the design of instrument approach procedures. The algorithm could be used to improve the procedure of the development of new instrument approaches, reducing time, effort and costs. In the aircraft Flight Management Systems. The algorithm could support the pilots in the pre-flight briefing, highlighting possible integrity outage in advance and allowing them to select a different approach or making them aware of the need to utilise additional positioning systems. Increased awareness and better pre-flight planning could ultimately improve the safety of flights and contribute to the safe introduction of GNSS as a viable positioning method for instrument approach

ProbCD: enrichment analysis accounting for categorization uncertainty

As in many other areas of science, systems biology makes extensive use of statistical association and significance estimates in contingency tables, a type of categorical data analysis known in this field as enrichment (also over-representation or enhancement) analysis. In spite of efforts to create probabilistic annotations, especially in the Gene Ontology context, or to deal with uncertainty in high throughput-based datasets, current enrichment methods largely ignore this probabilistic information since they are mainly based on variants of the Fisher Exact Test. We developed an open-source R package to deal with probabilistic categorical data analysis, ProbCD, that does not require a static contingency table. The contingency table for
the enrichment problem is built using the expectation of a Bernoulli Scheme stochastic process given the categorization probabilities. An on-line interface was created to allow usage by non-programmers and is available at: http://xerad.systemsbiology.net/ProbCD/. We present an analysis framework and software tools to address the issue of uncertainty in categorical data analysis. In particular, concerning the enrichment analysis, ProbCD can accommodate: (i) the stochastic nature of the high-throughput experimental techniques and (ii) probabilistic gene annotation