An efficient, dynamically adaptive method to tolerate transient faults in multi-core systems

2011 ◽  
Author(s):  
Hananeh Aliee ◽  
Hamid R. Zarandi
Keyword(s):  
Adaptive Method ◽  
Transient Faults

Finite element adaptive method for hypersonic thermochemical nonequilibrium flows

AIAA Journal ◽  
1997 ◽  
Vol 35 ◽  
pp. 1294-1302
Author(s):  
Djaffar Ait-Ali-Yahia ◽  
Wagdi G. Habashi
Keyword(s):  
Finite Element ◽  
Adaptive Method ◽  
Nonequilibrium Flows

New self-adaptive method for image denoising based on sparse decomposition and clustering

2013 ◽  
Vol 33 (2) ◽  
pp. 476-479
Author(s):  
Yali WEI ◽  
Xianbin WEN ◽  
Yongliao ZOU ◽  
Yongchun ZHENG
Keyword(s):  
Image Denoising ◽  
Adaptive Method ◽  
Sparse Decomposition ◽  
Self Adaptive

Adaptive method for SNR estimation in speech signal

1996 ◽  
Vol 32 (5) ◽  
pp. 421 ◽  
Author(s):  
J. Rodriguez ◽  
F. Rios ◽  
R. Escaño-Quero ◽  
J.F. Martin
Keyword(s):  
Speech Signal ◽  
Adaptive Method ◽  
Snr Estimation

279 WACSAW: A Statistical and Individually Adaptive Method to Determine Sleep and Wakefulness from Activity

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A111-A112
Author(s):  
Austin Vandegriffe ◽  
V A Samaranayake ◽  
Matthew Thimgan
Keyword(s):  
Time Series ◽  
Optimal Transport ◽  
Activity Patterns ◽  
Adaptive Method ◽  
Wasserstein Distance ◽  
Activity Data ◽  
Commercial Grade ◽  
Movement Data ◽  
The Difference ◽  
Levene Test

Abstract Introduction Technological innovations have broadened the type and amount of activity data that can be captured in the home and under normal living conditions. Yet, converting naturalistic activity patterns into sleep and wakefulness states has remained a challenge. Despite the successes of current algorithms, they do not fill all actigraphy needs. We have developed a novel statistical approach to determine sleep and wakefulness times, called the Wasserstein Algorithm for Classifying Sleep and Wakefulness (WACSAW), and validated the algorithm in a small cohort of healthy participants. Methods WACSAW functional routines: 1) Conversion of the triaxial movement data into a univariate time series; 2) Construction of a Wasserstein weighted sum (WSS) time series by measuring the Wasserstein distance between equidistant distributions of movement data before and after the time-point of interest; 3) Segmenting the time series by identifying changepoints based on the behavior of the WSS series; 4) Merging segments deemed similar by the Levene test; 5) Comparing segments by optimal transport methodology to determine the difference from a flat, invariant distribution at zero. The resulting histogram can be used to determine sleep and wakefulness parameters around a threshold determined for each individual based on histogram properties. To validate the algorithm, participants wore the GENEActiv and a commercial grade actigraphy watch for 48 hours. The accuracy of WACSAW was compared to a detailed activity log and benchmarked against the results of the output from commercial wrist actigraph. Results WACSAW performed with an average accuracy, sensitivity, and specificity of >95% compared to detailed activity logs in 10 healthy-sleeping individuals of mixed sexes and ages. We then compared WACSAW’s performance against a common wrist-worn, commercial sleep monitor. WACSAW outperformed the commercial grade system in each participant compared to activity logs and the variability between subjects was cut substantially. Conclusion The performance of WACSAW demonstrates good results in a small test cohort. In addition, WACSAW is 1) open-source, 2) individually adaptive, 3) indicates individual reliability, 4) based on the activity data stream, and 5) requires little human intervention. WACSAW is worthy of validating against polysomnography and in patients with sleep disorders to determine its overall effectiveness. Support (if any):

Pilot Tests of FasTR Method for Locating Transient Faults in Medium Voltage Underground Power Networks

2020 ◽  
pp. 1-1
Author(s):  
Moussa Kafal ◽  
Nicolas Gregis ◽  
Jaume Benoit ◽  
Nicolas Ravot ◽  
Clara Lagomarsini ◽  
...  
Keyword(s):  
Transient Faults ◽  
Power Networks ◽  
Medium Voltage ◽  
Pilot Tests
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