INL based dynamic performance estimation for ADC BIST

In the design of railway bridges, it is necessary to be able to predict their dynamic behavior under a moving train load so as to avoid a resonance state from repetitive moving axle forces with uniform intervals. According to design trends, newly developed girder bridges weigh less and have longer spans. Since the dynamic interaction between bridge superstructures and passing trains is one of the critical issues concerning such railway bridges that are designed with greater flexibility, it is very important to evaluate the modal parameters of newly designed PSC girders before carrying out dynamic analyses. In this paper, a full scale incrementally prestressed 25-meter long concrete girder was fabricated as a test specimen and modal testing was performed at every prestressing stage in order to evaluate the modal parameters, including the natural frequency and the modal damping ratio. Young's modulus was also obtained from the global stiffness of the test specimen. During the modal testing, a digitally controlled vibration exciter and an impact hammer were applied in order to obtain precise frequency response functions, and the modal parameters were evaluated at various construction stages. With the availability of reliable properties from the modal experiments, dynamic performance estimation of a PSC girder railway bridge during the passage of a moving train can be carried out.

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On dynamic performance estimation of fault-prone Infrastructure-as-a-Service clouds

International Journal of Distributed Sensor Networks ◽

10.1177/1550147717718514 ◽

2017 ◽

Vol 13 (7) ◽

pp. 155014771771851 ◽

Cited By ~ 3

Author(s):

Wanbo Zheng ◽

Yuandou Wang ◽

Yunni Xia ◽

Quanwang Wu ◽

Lei Wu ◽

...

Keyword(s):

Cloud Computing ◽

Analytical Approach ◽

Virtual Machines ◽

Dynamic Performance ◽

Rejection Rate ◽

Performance Estimation ◽

Variable Load ◽

Infrastructure As A Service ◽

Trend Prediction ◽

Prediction Approach

The cloud computing paradigm enables elastic resources to be scaled at run time satisfy customers’ demand. Cloud computing provisions on-demand service to users based on a pay-as-you-go manner. This novel paradigm enables cloud users or tenant users to afford computational resources in the form of virtual machines as utilities, just like electricity, instead of paying for and building computing infrastructures by their own. Performance usually specified through service level agreement performance commitment of clouds is one of key research challenges and draws great research interests. Thus, performance issues of cloud infrastructures have been receiving considerable interest by both researchers and practitioners as a prominent activity for improving cloud quality. This work develops an analytical approach to dynamic performance modeling and trend prediction of fault-prone Infrastructure-as-a-Service clouds. The proposed analytical approach is based on a time-series and stochastic-process-based model. It is capable of predicting the expected system responsiveness and request rejection rate under variable load intensities, fault frequencies, multiplexing abilities, and instantiation processing times. A comparative study between theoretical and measured performance results through a real-world campus cloud is carried out to prove the correctness and accuracy of the proposed prediction approach.

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