Integrated Sensor Systems and Data Fusion for Homeland Protection

Farina, A.; Ortenzi, L.; Ristić, Branko; Skvortsov, Alex

doi:10.1016/b978-0-12-396500-4.00022-3

Cited by 7 publications

(3 citation statements)

References 144 publications

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“…All living biological systems constantly make intuitive and subconscious use of IFU, which protects themselves from danger and guarantees survival [27,28]. Mercier et al showed that humans react slower towards an external stimulus acquired by one sense (audio or visual) than if these stimuli appear combined (audio-visual) [29].…”

Section: Related Workmentioning

confidence: 99%

Information Fusion of Conflicting Input Data

Mönks

Dörksen

Lohweg

et al. 2016

Sensors

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Sensors, and also actuators or external sources such as databases, serve as data sources in order to realise condition monitoring of industrial applications or the acquisition of characteristic parameters like production speed or reject rate. Modern facilities create such a large amount of complex data that a machine operator is unable to comprehend and process the information contained in the data. Thus, information fusion mechanisms gain increasing importance. Besides the management of large amounts of data, further challenges towards the fusion algorithms arise from epistemic uncertainties (incomplete knowledge) in the input signals as well as conflicts between them. These aspects must be considered during information processing to obtain reliable results, which are in accordance with the real world. The analysis of the scientific state of the art shows that current solutions fulfil said requirements at most only partly. This article proposes the multilayered information fusion system MACRO (multilayer attribute-based conflict-reducing observation) employing the μBalTLCS (fuzzified balanced two-layer conflict solving) fusion algorithm to reduce the impact of conflicts on the fusion result. The performance of the contribution is shown by its evaluation in the scope of a machine condition monitoring application under laboratory conditions. Here, the MACRO system yields the best results compared to state-of-the-art fusion mechanisms. The utilised data is published and freely accessible.

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Section: Related Workmentioning

confidence: 99%

Information Fusion of Conflicting Input Data

Mönks

Dörksen

Lohweg

et al. 2016

Sensors

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“…Detection of hazardous tracer releases is of great significance for many fields of science and technology spanning from technological and environmental monitoring to defence and security systems (see Lien et al (2010), Dekker and Skvortsov (2009) and references therein). This topic is still an area of active research and there is a vast amount of literature devoted to this subject (see Farina et al (2013), Rajasegarar et al (2013), Mendis et al (2012) and references therein).…”

Section: Introductionmentioning

confidence: 99%

Statistics of chemical tracer concentration in a multi-compartment structure measured with a sensor network

Karunasekera

Skvortsov

Gunatilaka

et al. 2013

Piantadosi, J., Anderssen, R.S. And Boland J. (Eds) MODSIM2013, 20th International Congress on Modelling and Simulation

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An understanding of the statistics of concentrations of chemical tracers dispersing through multicompartment structures such as buildings, ships, and aircraft is important in many applications (e.g., industrial, military, and ecological). Such an understanding can be useful for early detection of hazardous releases, source backtracking, and assessment of their impact on engineered structures (e.g., buildings, aircraft, ships). It may be also important for design of monitoring sensor systems. We present the statistics of chemical tracer concentrations obtained by analysing recent experimental data obtained in a study of tracer dispersion in a complex structure. The experiment involved the release of a tracer (dyed salt solution) from a point source placed inside a multi-compartment structure (Fig. 1) embedded in a water tank and measuring the salt concentration at different locations within the compartments using a network of conductivity sensors. We show that moments of measured concentration at a given sensor depend on the Euclidean distance between the source and the sensor, resembling the behaviour observed in advection-diffusion transport in porous media. (a) (b) (c) Figure 1. The experimental setup: (a) Image of the multi-compartment structure used for the experiment; (b) the flow path in the OPEN configuration; and, (c) the flow path in the CLOSE configuration.

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“…Reference [1] gives us the advantages and disadvantages of the radar tracking systems. In recent years, with the increasing development of the neural network method, because of its large-scale parallel processing, distributed information memory, better adaptability and self-organizing ability, as well as the learning association, tolerance etc., it is possible for us to give play to the advantages and solve the problem of the radar information processing.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Application for Netted Radar Information Processing Systems

2013

AMM

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Integrated Sensor Systems and Data Fusion for Homeland Protection

Cited by 7 publications

References 144 publications

Information Fusion of Conflicting Input Data

Information Fusion of Conflicting Input Data

Statistics of chemical tracer concentration in a multi-compartment structure measured with a sensor network

Neural Network Application for Netted Radar Information Processing Systems

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