Performance Validation of Engine Starter Utilizing Feed Screw Mechanism

2016 ◽  
Author(s):  
Isamu Shiotsu ◽  
Kisaburo Hayakawa ◽  
Hiroyuki Nishizawa
Keyword(s):  
Feed Screw ◽  
Screw Mechanism ◽  
Performance Validation

Three-Fingered Robot Hand with Gripping Force Generating Mechanism Using Small Gas Springs - Mechanical Design and Basic Experiments –

2019 ◽  
Vol 31 (1) ◽  
pp. 118-134 ◽  
Author(s):  
Junya Tanaka ◽  
Keyword(s):  
Power Supply ◽  
Mechanical Design ◽  
Robot Hand ◽  
Single Motor ◽  
Feed Screw ◽  
Screw Mechanism ◽  
Gripping Force ◽  
Basic Experiments ◽  
Flexion And Extension

This paper presents the mechanical design of a new three-fingered robot hand for a robot designed to handle tableware. The finger mechanism has three joints and consists of a pair of fourbar linkage mechanisms, one small gas spring, and one feed screw mechanism. As the feed screw moves, the finger mechanism performs flexion and extension operations with its joints interlocked. The gas spring generates gripping force, which is adjusted at the position of the moving part moved by the feed screw. Therefore, the three-fingered robot hand can open and close synchronously, powered by a single motor in the base of the hand. The hand grips with mechanical flexibility. In addition, it can maintain its grip with no power supply. Tests show that the hand can successfully perform the movements required to grasp various kinds of tableware.

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.

Precision loss of ball screw mechanism under sliding-rolling mixed motion behavior

2021 ◽  
Vol 28 (5) ◽  
pp. 1357-1376
Author(s):  
Bao-bao Qi ◽  
Qiang Cheng ◽  
Shun-lei Li ◽  
Zhi-feng Liu ◽  
Cong-bin Yang
Keyword(s):  
Ball Screw ◽  
Screw Mechanism ◽  
Motion Behavior
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