An artificial intelligence-based structural health monitoring system for aging aircraft

To reduce operating expenses, airlines are now using the existing fleets of commercial aircraft well beyond their originally anticipated service lives. The repair and maintenance of these 'aging aircraft' has therefore become a critical safety issue, both to the airlines and the Federal Aviation Administration. This paper presents the results of an innovative research program to develop a structural monitoring system that will be used to evaluate the integrity of in-service aerospace structural components. Currently in the final phase of its development, this monitoring system will indicate when repair or maintenance of a damaged structural component is necessary

The potential for inter-row cultivation in organic pulse production

Non-Peer Reviewe

Data-efficient Neuroevolution with Kernel-Based Surrogate Models

Surrogate-assistance approaches have long been used in computationally expensive domains to improve the data-efficiency of optimization algorithms. Neuroevolution, however, has so far resisted the application of these techniques because it requires the surrogate model to make fitness predictions based on variable topologies, instead of a vector of parameters. Our main insight is that we can sidestep this problem by using kernel-based surrogate models, which require only the definition of a distance measure between individuals. Our second insight is that the well-established Neuroevolution of Augmenting Topologies (NEAT) algorithm provides a computationally efficient distance measure between dissimilar networks in the form of "compatibility distance", initially designed to maintain topological diversity. Combining these two ideas, we introduce a surrogate-assisted neuroevolution algorithm that combines NEAT and a surrogate model built using a compatibility distance kernel. We demonstrate the data-efficiency of this new algorithm on the low dimensional cart-pole swing-up problem, as well as the higher dimensional half-cheetah running task. In both tasks the surrogate-assisted variant achieves the same or better results with several times fewer function evaluations as the original NEAT.Comment: In GECCO 201

Multiprotein DNA looping

DNA looping plays a fundamental role in a wide variety of biological processes, providing the backbone for long range interactions on DNA. Here we develop the first model for DNA looping by an arbitrarily large number of proteins and solve it analytically in the case of identical binding. We uncover a switch-like transition between looped and unlooped phases and identify the key parameters that control this transition. Our results establish the basis for the quantitative understanding of fundamental cellular processes like DNA recombination, gene silencing, and telomere maintenance.Comment: 11 pages, 4 figure

A Generalized Preferential Attachment Model for Business Firms Growth Rates: II. Mathematical Treatment

We present a preferential attachment growth model to obtain the distribution $P(K)$ of number of units $K$ in the classes which may represent business firms or other socio-economic entities. We found that $P(K)$ is described in its central part by a power law with an exponent $\phi=2+b/(1-b)$ which depends on the probability of entry of new classes, $b$. In a particular problem of city population this distribution is equivalent to the well known Zipf law. In the absence of the new classes entry, the distribution $P(K)$ is exponential. Using analytical form of $P(K)$ and assuming proportional growth for units, we derive $P(g)$, the distribution of business firm growth rates. The model predicts that $P(g)$ has a Laplacian cusp in the central part and asymptotic power-law tails with an exponent $\zeta=3$. We test the analytical expressions derived using heuristic arguments by simulations. The model might also explain the size-variance relationship of the firm growth rates.Comment: 19 pages 6 figures Applications of Physics in Financial Analysis, APFA

Towards hardware acceleration of neuroevolution for multimedia processing applications on mobile devices

This paper addresses the problem of accelerating large artificial neural networks (ANN), whose topology and weights can evolve via the use of a genetic algorithm. The proposed digital hardware architecture is capable of processing any evolved network topology, whilst at the same time providing a good trade off between throughput, area and power consumption. The latter is vital for a longer battery life on mobile devices. The architecture uses multiple parallel arithmetic units in each processing element (PE). Memory partitioning and data caching are used to minimise the effects of PE pipeline stalling. A first order minimax polynomial approximation scheme, tuned via a genetic algorithm, is used for the activation function generator. Efficient arithmetic circuitry, which leverages modified Booth recoding, column compressors and carry save adders, is adopted throughout the design

50 nm GaAs mHEMTs and MMICs for ultra-low power distributed sensor network applications

We report well-scaled 50 nm GaAs metamorphic HEMTs (mHEMTs) with DC power consumption in the range 1-150 ΜW/Μ demonstrating f<sub>T</sub> of 30-400 GHz. These metrics enable the realisation of ultra-low power (<500 ΜW) radio transceivers for autonomous distributed sensor network applications

Universal scaling behavior at the upper critical dimension of non-equilibrium continuous phase transitions

In this work we analyze the universal scaling functions and the critical exponents at the upper critical dimension of a continuous phase transition. The consideration of the universal scaling behavior yields a decisive check of the value of the upper critical dimension. We apply our method to a non-equilibrium continuous phase transition. But focusing on the equation of state of the phase transition it is easy to extend our analysis to all equilibrium and non-equilibrium phase transitions observed numerically or experimentally.Comment: 4 pages, 3 figure

Nuclear thermal propulsion transportation systems for lunar/Mars exploration

Nuclear thermal propulsion technology development is underway at NASA and DoE for Space Exploration Initiative (SEI) missions to Mars, with initial near-earth flights to validate flight readiness. Several reactor concepts are being considered for these missions, and important selection criteria will be evaluated before final selection of a system. These criteria include: safety and reliability, technical risk, cost, and performance, in that order. Of the concepts evaluated to date, the Nuclear Engine for Rocket Vehicle Applications (NERVA) derivative (NDR) is the only concept that has demonstrated full power, life, and performance in actual reactor tests. Other concepts will require significant design work and must demonstrate proof-of-concept. Technical risk, and hence, development cost should therefore be lowest for the concept, and the NDR concept is currently being considered for the initial SEI missions. As lighter weight, higher performance systems are developed and validated, including appropriate safety and astronaut-rating requirements, they will be considered to support future SEI application. A space transportation system using a modular nuclear thermal rocket (NTR) system for lunar and Mars missions is expected to result in significant life cycle cost savings. Finally, several key issues remain for NTR's, including public acceptance and operational issues. Nonetheless, NTR's are believed to be the 'next generation' of space propulsion systems - the key to space exploration