Data reduction strategies.

Based on the variety of methods available for gathering data for the aircraft health status, the challenge is to reduce the overall amount of data in a trackable and safe manner to ensure that the remaining data are characteristic of the current aircraft status. This chapter will cover available data reduction strategies for this task and discuss the data intensity of the SHM methods of Chaps. 5 to 8 and established approaches to deal with the acquired data. This includes aspects of algorithms and legal issues arising in this context

An overview on structural health monitoring: From the current state-of-the-art to new bio-inspired sensing paradigms

In the last decades, the field of structural health monitoring (SHM) has grown exponentially. Yet, several technical constraints persist, which are preventing full realization of its potential. To upgrade current state-of-the-art technologies, researchers have started to look at nature’s creations giving rise to a new field called ‘biomimetics’, which operates across the border between living and non-living systems. The highly optimised and time-tested performance of biological assemblies keeps on inspiring the development of bio-inspired artificial counterparts that can potentially outperform conventional systems. After a critical appraisal on the current status of SHM, this paper presents a review of selected works related to neural, cochlea and immune-inspired algorithms implemented in the field of SHM, including a brief survey of the advancements of bio-inspired sensor technology for the purpose of SHM. In parallel to this engineering progress, a more in-depth understanding of the most suitable biological patterns to be transferred into multimodal SHM systems is fundamental to foster new scientific breakthroughs. Hence, grounded in the dissection of three selected human biological systems, a framework for new bio-inspired sensing paradigms aimed at guiding the identification of tailored attributes to transplant from nature to SHM is outlined.info:eu-repo/semantics/acceptedVersio

ESA - RESGROW: Epansion of the Market for EO Based Information Services in Renewable Energy - Biomass Energy sector

Biomass energy is of growing importance as it is widely recognised, both scientifically and politically, that the increase of atmospheric CO2 has led to an enhanced efficiency of the greenhouse effect and, as such, warrants concern for climate change. It is accepted (IPCC 2011 and just recently in the draft version of the IPCC 2013 report) that climate change is partly induced by humans notably by using fossil fuels. For reducing the use of oil or coal, biomass energy is receiving more and more attention as an additional energy source available regionally in large parts of the world. Effective management of renewable energy resources is critical for the European and the global energy supply system. The future contribution of bioenergy to the energy supply strongly depends on its availability, in other words the biomass potential. Biomass potentials are currently mainly assessed on a national to regional or on a global level, with the bulk biomass potential allocated to the whole country. With certain biomass fractions being of low energy density, transport distances and thus their spatial distribution are crucial economic and ecological factors. For other biomass fractions a super-regional or global market is envisaged. Thus spatial information on biomass potentials is vital for the further expansion of bioenergy use. This study, which is an updated version of a study carried out in 2007 in frame of the ENVISOLAR project, analyses the potential use of Earth Observation data as input for biomass models in order to assessment and manage of the biomass energy resources especially biomass potentials of agricultural and forest areas with high spatial resolution (typical 1km x 1km). In addition to a sorrow review of recent developments in data availability and approaches in comparison to its 2007’ version, this study also includes a review on approaches to directly correlate remote sensing data with biomass estimations. An overview of existing biomass models is given covering models using remote sensing data as input as well as models using only meteorological and/or management data as input. It covers the full life cycle from the planning stage to plant management and operations (Figure 1). Several groups of stakeholders were identified

Using a Multiobjective Approach to Balance Mission and Network Goals within a Delay Tolerant Network Topology

This thesis investigates how to incorporate aspects of an Air Tasking Order (ATO), a Communications Tasking Order (CTO), and a Network Tasking Order (NTO) within a cognitive network framework. This was done in an effort to aid the commander and or network operator by providing automation for battlespace management to improve response time and potential inconsistent problem resolution. In particular, autonomous weapon systems such as unmanned aerial vehicles (UAVs) were the focus of this research This work implemented a simple cognitive process by incorporating aspects of behavior based robotic control principles to solve the multi-objective optimization problem of balancing both network and mission goals. The cognitive process consisted of both a multi-move look ahead component, in which the future outcomes of decisions were estimated, and a subsumption decision making architecture in which these decision-outcome pairs were selected so they co-optimized the dual goals. This was tested within a novel Air force mission scenario consisting of a UAV surveillance mission within a delay tolerant network (DTN) topology. This scenario used a team of small scale UAVs (operating as a team but each running the cognitive process independently) to balance the mission goal of maintaining maximum overall UAV time-on-target and the network goal of minimizing the packet end-to-end delays experienced within the DTN. The testing was accomplished within a MATLAB discrete event simulation. The results indicated that this proposed approach could successfully simultaneously improve both goals as the network goal improved 52% and the mission goal improved by approximately 6%

Prediction and optimization techniques for performance enhancement of vehicular ad-hoc networks

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