Using Dempster–Shafer theory and real options theory to assess competing strategies for implementing IT infrastructures: A case study

Hilhorst, Cokky A. R.; Ribbers, P.M.A.; Heck, Eric van; Smits, Martin

doi:10.1016/j.dss.2008.07.006

Cited by 21 publications

(18 citation statements)

References 29 publications

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“…The evidence‐based way is adopted to describe and handle the uncertainty. The fusion algorithm is used to draw the maximum likelihood conclusion from the fuzzy information . In DST, the judgment is based on the frame of discernment.…”

Section: Multisource Information Fusion Methods For Structural Behaviomentioning

confidence: 99%

“…The multisource information fusion for dam safety has three units, namely, information acquisition unit, information fusion unit, and information storage unit . The information acquisition unit is used to collect the prototypical monitoring data, the data on monitoring model, and the empirical knowledge.…”

Section: Multisource Information Fusion‐based Diagnosing Framework Ofmentioning

confidence: 99%

“…The multisource information fusion for dam safety has three units, namely, information acquisition unit, information fusion unit, and information storage unit. [17][18][19][20] The information acquisition unit is used to collect the prototypical monitoring data, the data on monitoring model, and the empirical knowledge. The information fusion unit can be divided into three levels, namely, data fusion, feature fusion, and decision fusion, as shown in Figure 2.…”

Section: Multisource Information Fusion-based Diagnosing Framework mentioning

confidence: 99%

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Multisource information fusion-based approach diagnosing structural behavior of dam engineering

Wen

Sun³

et al. 2017

Struct Control Health Monit

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Summary Considering the effect of many factors on structural behavior, the diagnosing problem on structural behavior of dam engineering is investigated. Dempster–Shafer theory of evidence (DST) and set pair theory (SPT) are combined to fuse the multisource space–time information on dam safety. The structural behavior of dam engineering is identified and its development is forecasted by implementing the information fusions with 3 levels, namely, data fusion, feature fusion, and decision fusion. First, a multisource information fusion system on dam safety is built and an information fusion‐based flowchart is presented to diagnose the dam structural behavior. Second, the batch estimation algorithm is adopted to implement the data fusion on same type information. Third, DST and SPT are applied to the decision fusion on heterogeneous information of dam safety. The index connection number and partial connection number in SPT are introduced to determine the basic probability assignment in DST and identify the dam structural behavior, respectively. Last, one actual dam undergoing structural reinforcements is taken as an example. Its structural behavior is diagnosed, and the reinforcement effect is assessed. It is indicated that the proposed approach is more suitable to be used to evaluate the safety status and reinforcement effect of dangerous dam.

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Section: Multisource Information Fusion Methods For Structural Behaviomentioning

confidence: 99%

Section: Multisource Information Fusion‐based Diagnosing Framework Ofmentioning

confidence: 99%

Section: Multisource Information Fusion-based Diagnosing Framework mentioning

confidence: 99%

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Multisource information fusion-based approach diagnosing structural behavior of dam engineering

Wen

Sun³

et al. 2017

Struct Control Health Monit

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“…In general, with ȣ in the same frame of discernment, the combining results of n basic probability assignment values ଵ ǡ ଶ ǡ ‫ڮ‬ ǡ ୬ are as follows [11,12]:…”

Section: Multi-sensors Data Fusion Based On D-s Evidence Theorymentioning

confidence: 99%

Feature extraction and multi-sensor data fusion in monitoring and pre-warning system for security of pipeline based on multi-seismic sensors

Sun

Jin-quan²,

Wang³

2012

2012 IEEE International Conference on Mechatronics and Automation

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In recent years, aiming at the increasingly serious pipeline damages because of the artificial factors, we investigate the characteristic of seismic signals and develop a monitoring and pre-warning system for security of pipelines based on multi-seismic sensors. There are many sensors and processing modules to acquire the seismic signals generated by the ground targets. If these modules all transmit the original data to host machine, it will jam the information channel. So in this system, the seismic signals are processed and extracted the features in respective modules. The non-stationary signal analysis method based on empirical mode decomposition is used to process the seismic signals and acquire some intrinsic mode functions. The target feature vectors were composed of the normalized kurtosis extracted from the decomposition results. The single sensor's judgment was made by the main normalized kurtosis values in the important decomposed frequency bands. Then the features vectors are transmitted to the host machine which will finish the pattern recognition, multi-sensor data fusion and target classification analysis. It will greatly reduce the dimension of feature vectors, the data amount and complexity. This paper will mainly introduce the feature extraction and multi-sensor data fusion. This D-S evidence reasoning was to fuse the recognition results for improving the target recognition accuracy. Then the last judgment was made. The processing methods above were proved effective by the experiment data analysis.

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“…The ER approach provides a new avenue for handling various types of uncertainties in a unified format that includes precise data, the absence of data, incomplete data and probability uncertainty. It has been applied to decision problems in many areas, such as engineering design (Chin, Yang, Guo, & Lam, 2009;Yang, Xu, Xie, & Maddulapalli, 2011), environmental impact assessment (Wang, Yang, & Xu, 2006), business management (Hilhorst, Ribbers, van Heck, & Smits, 2008), and group decision-making (Fu & Yang, 2010).…”

Section: Introductionmentioning

confidence: 99%

A group decision-making approach based on evidential reasoning for multiple criteria sorting problem with uncertainty

Liu

Liao

Yang

2015

European Journal of Operational Research

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Using Dempster–Shafer theory and real options theory to assess competing strategies for implementing IT infrastructures: A case study

Cited by 21 publications

References 29 publications

Multisource information fusion-based approach diagnosing structural behavior of dam engineering

Multisource information fusion-based approach diagnosing structural behavior of dam engineering

Feature extraction and multi-sensor data fusion in monitoring and pre-warning system for security of pipeline based on multi-seismic sensors

A group decision-making approach based on evidential reasoning for multiple criteria sorting problem with uncertainty

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