scholarly journals Aladin performance validation

2019 ◽  
Author(s):  
Frédéric Fabre
Keyword(s):  
Performance Validation

scholarly journals Falcon 4 performance validation by single event analysis

2021 ◽  
Vol 27 (S1) ◽  
pp. 1338-1339
Author(s):  
Jeroen Keizer ◽  
Gerald van Hoften ◽  
Jaap Mulder ◽  
Gijs van Duinen
Keyword(s):  
Event Analysis ◽  
Single Event ◽  
Performance Validation

Importance of incorporating quantitative imaging biomarker technical performance characteristics when estimating treatment effects

2021 ◽  
pp. 174077452098193
Author(s):  
Nancy A Obuchowski ◽  
Erick M Remer ◽  
Ken Sakaie ◽  
Erika Schneider ◽  
Robert J Fox ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Confidence Intervals ◽  
Treatment Effect ◽  
Quantitative Imaging ◽  
Imaging Biomarker ◽  
Imaging Biomarkers ◽  
Measurement Bias ◽  
Technical Performance ◽  
Quantitative Imaging Biomarker ◽  
Performance Validation

Background/aims Quantitative imaging biomarkers have the potential to detect change in disease early and noninvasively, providing information about the diagnosis and prognosis of a patient, aiding in monitoring disease, and informing when therapy is effective. In clinical trials testing new therapies, there has been a tendency to ignore the variability and bias in quantitative imaging biomarker measurements. Unfortunately, this can lead to underpowered studies and incorrect estimates of the treatment effect. We illustrate the problem when non-constant measurement bias is ignored and show how treatment effect estimates can be corrected. Methods Monte Carlo simulation was used to assess the coverage of 95% confidence intervals for the treatment effect when non-constant bias is ignored versus when the bias is corrected for. Three examples are presented to illustrate the methods: doubling times of lung nodules, rates of change in brain atrophy in progressive multiple sclerosis clinical trials, and changes in proton-density fat fraction in trials for patients with nonalcoholic fatty liver disease. Results Incorrectly assuming that the measurement bias is constant leads to 95% confidence intervals for the treatment effect with reduced coverage (<95%); the coverage is especially reduced when the quantitative imaging biomarker measurements have good precision and/or there is a large treatment effect. Estimates of the measurement bias from technical performance validation studies can be used to correct the confidence intervals for the treatment effect. Conclusion Technical performance validation studies of quantitative imaging biomarkers are needed to supplement clinical trial data to provide unbiased estimates of the treatment effect.

Performance validation of step-isothermal calorimeters

2006 ◽  
Vol 83 (2) ◽  
pp. 331-334 ◽  
Author(s):  
B A Finnin ◽  
M A A O'Neill ◽  
S Gaisford ◽  
A E Beezer ◽  
J Hadgraft ◽  
...  
Keyword(s):  
Performance Validation

scholarly journals Review of Design Elements within Power Infrastructure Cyber–Physical Test Beds as Threat Analysis Environments

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1409
Author(s):  
Bjorn Vaagensmith ◽  
Vivek Kumar Singh ◽  
Robert Ivans ◽  
Daniel L. Marino ◽  
Chathurika S. Wickramasinghe ◽  
...  
Keyword(s):  
User Interfaces ◽  
Fault Injection ◽  
Modern Society ◽  
Physical Test ◽  
Design Elements ◽  
Physical Systems ◽  
Threat Analysis ◽  
Model Based Testing ◽  
Potential Applications ◽  
Performance Validation

Cyber–physical systems (CPSs) are an integral part of modern society; thus, enhancing these systems’ reliability and resilience is paramount. Cyber–physical testbeds (CPTs) are a safe way to test and explore the interplay between the cyber and physical domains and to cost-effectively enhance the reliability and resilience of CPSs. Here a review of CPT elements, broken down into physical components (simulators, emulators, and physical hardware), soft components (communication protocols, network timing protocols), and user interfaces (visualization-dashboard design considerations) is presented. Various methods used to validate CPS performance are reviewed and evaluated for potential applications in CPT performance validation. Last, initial simulated results for a CPT design, based on the IEEE 33 bus system, are presented, along with a brief discussion on how model-based testing and fault–injection-based testing (using scaling and ramp-type attacks) may be used to help validate CPT performance.

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