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Traditional fault diagnosis and prognosis (FDP) approaches are based on periodic sampling, i.e., samples are taken and algorithms are executed both in a periodic manner. As the volume of sensor data and complexity of algorithms keep increasing, the bottleneck of FDP is mainly the limited computational resources, which is particularly true for distributed applications where FDP functions are deployed...
In trustable networks, the process that nodes submit service satisfaction degree to form reputation of service provider has uncertainties of fuzziness and randomness. A cloud theory based model is proposed for the computation of node reputation. Weights of service satisfaction degrees in the model include an attenuation coefficient part and a certainty degree part. The certainty degree part is obtained...
The process of human's blink expressing deep information of mind has uncertainties of fuzziness and randomness. A cloud theory-based method is proposed to realize uncertainty control of virtual human's blink. Eyes' maximal open angle cloud and blink interval cloud are designed. A cloud-based blink control algorithm is proposed. Results of comparing it with certainty method show that the proposed algorithm...
An entropy gain based overall trust degree (OTD) computation model is proposed for large scale trusted networks. To overcome subjectivity in trust decision, attributes' entropies and their gains are adopted to set attribute weights in the OTD fusion model. Attribute weights are dynamically adjusted by refreshing sample set and attribute entropy gains, to adapt to variations of network entities. Under...
Current DTD (Direct Trust Degree) model for trusted networks cannot keep sensitive to variations of interaction results between nodes. To describe the direct trust relationship scientifically, a sensitive DTD model is proposed. It includes a stable part and a sensitive part. The stable part uses a window to obtain DTD from many history direct interaction proofs. To describe the attenuation feature...
Prognosis is a fundamental enabling technique for condition-based maintenance (CBM) systems and prognostics and health management (PHM) systems and therefore, plays a critical role in the successful deployment of these systems. The purpose of prognosis is to predict the remaining useful life of a system/subsystem or a component when a fault is detected. Although different prognostic algorithms have...
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