Mathematical Basis of Sensor Fusion in Intrusion Detection Systems

Thomas, Ciza

doi:10.5772/9965

Cited by 2 publications

(2 citation statements)

References 15 publications

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“…The interested reader is referred to literature [2,36,37,38] for further information on basic principles of IFU and their applications. Comprehensive studies on contemporary research on IFU are found in [39,40].…”

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

View full text Add to dashboard Cite

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“…Since each type of sensor provides different features under various environmental conditions, the performance of an application depends highly on selecting a sensor or multi-sensor combination. Therefore, the selection of appropriate sensors has become one of the most significant factors for high-performance vision systems [ 2 , 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

3D Multi-Spectrum Sensor System with Face Recognition

Kim

et al. 2013

Sensors

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This paper presents a novel three-dimensional (3D) multi-spectrum sensor system, which combines a 3D depth sensor and multiple optical sensors for different wavelengths. Various image sensors, such as visible, infrared (IR) and 3D sensors, have been introduced into the commercial market. Since each sensor has its own advantages under various environmental conditions, the performance of an application depends highly on selecting the correct sensor or combination of sensors. In this paper, a sensor system, which we will refer to as a 3D multi-spectrum sensor system, which comprises three types of sensors, visible, thermal-IR and time-of-flight (ToF), is proposed. Since the proposed system integrates information from each sensor into one calibrated framework, the optimal sensor combination for an application can be easily selected, taking into account all combinations of sensors information. To demonstrate the effectiveness of the proposed system, a face recognition system with light and pose variation is designed. With the proposed sensor system, the optimal sensor combination, which provides new effectively fused features for a face recognition system, is obtained.

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Mathematical Basis of Sensor Fusion in Intrusion Detection Systems

Cited by 2 publications

References 15 publications

Information Fusion of Conflicting Input Data

Information Fusion of Conflicting Input Data

3D Multi-Spectrum Sensor System with Face Recognition

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