Process Feature Change Recognition Based on Model Performance Monitoring and Adaptive Model Correction for the Gold Cyanidation Leaching Process

Harnessing the power of multicore platforms is challenging due to the additional levels of parallelism present. In this paper we use the NAS Parallel Benchmarks to study three programming models, MPI, OpenMP and PGAS to understand their performance and memory usage characteristics on current multicore architectures. To understand these characteristics we use the Integrated Performance Monitoring tool and other ways to measure communication versus computation time, as well as the fraction of the run time spent in OpenMP. The benchmarks are run on two different Cray XT5 systems and an Infiniband cluster. Our results show that in general the three programming models exhibit very similar performance characteristics. In a few cases, OpenMP is significantly faster because it explicitly avoids communication. For these particular cases, we were able to re-write the UPC versions and achieve equal performance to OpenMP. Using OpenMP was also the most advantageous in terms of memory usage. Also we compare performance differences between the two Cray systems, which have quad-core and hex-core processors. We show that at scale the performance is almost always slower on the hex-core system because of increased contention for network resources.

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Application of Improved Extended Kalman Filter for Adaptive Model of Aircraft Engine

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.709.180 ◽

2014 ◽

Vol 709 ◽

pp. 180-185

Author(s):

Gu Ting Zhou ◽

San Mai Su

Keyword(s):

Kalman Filter ◽

Fault Diagnosis ◽

Extended Kalman Filter ◽

Performance Monitoring ◽

Aircraft Engine ◽

Filter Method ◽

Adaptive Model ◽

Nominal Model ◽

Simulation Results ◽

Kalman Filter Method

Adaptive model is the basis of engine fault diagnosis, performance monitoring, engine control, etc. This paper presents an improved kalman filter method which uses engine measurable parameters deviation to estimate the degradation parameters to correct the nominal model, and the acquisition and application of multiple kalman filter gain matrices in the whole flight envelope is analyzed. Simulation is carried out taking a civil engine as simulation object, the simulation results show that the method used in this paper to establish unmeasured parameters adaptive model can get the engine parameters better.

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An approac h for adaptive model performance validation within digital twinning

International Journal of Computational Methods and Experimental Measurements ◽

10.2495/cmem-v9-n3-213-225 ◽

2021 ◽

Vol 9 (3) ◽

pp. 213-225

Author(s):

Madhu Sudan Sapkota ◽

Edward Apeh ◽

Mark Hadfield ◽

Roya Haratian ◽

Robert Adey ◽

...

Keyword(s):

Model Performance ◽

Adaptive Model ◽

Performance Validation

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Vision based Iterative Learning Control of a MEMS micropositioning stage with intersample estimation and adaptive model correction

Proceedings of the 2011 American Control Conference ◽

10.1109/acc.2011.5991120 ◽

2011 ◽

Cited By ~ 2

Author(s):

Patrick J. White ◽

Douglas A. Bristow

Keyword(s):

Iterative Learning Control ◽

Learning Control ◽

Iterative Learning ◽

Adaptive Model ◽

Model Correction

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Predictors of Purchasing a Hearing Aid After an Evaluation Period: A Prospective Study in Dutch Older Hearing Aid Candidates

American Journal of Audiology ◽

10.1044/2019_aja-heal18-18-0163 ◽

2019 ◽

Vol 28 (3S) ◽

pp. 802-805 ◽

Cited By ~ 1

Author(s):

Marieke Pronk ◽

Janine F. J. Meijerink ◽

Sophia E. Kramer ◽

Martijn W. Heymans ◽

Jana Besser

Keyword(s):

Help Seeking ◽

Hearing Aid ◽

Model Performance ◽

Area Under The Curve ◽

Secondary Data ◽

Support Program ◽

Evaluation Period ◽

A Prospective Study ◽

Study Participants ◽

Baseline Covariates

Purpose The current study aimed to identify factors that distinguish between older (50+ years) hearing aid (HA) candidates who do and do not purchase HAs after having gone through an HA evaluation period (HAEP). Method Secondary data analysis of the SUpport PRogram trial was performed ( n = 267 older, 1st-time HA candidates). All SUpport PRogram participants started an HAEP shortly after study enrollment. Decision to purchase an HA by the end of the HAEP was the outcome of interest of the current study. Participants' baseline covariates (22 in total) were included as candidate predictors. Multivariable logistic regression modeling (backward selection and reclassification tables) was used. Results Of all candidate predictors, only pure-tone average (average of 1, 2, and 4 kHz) hearing loss emerged as a significant predictor (odds ratio = 1.03, 95% confidence interval [1.03, 1.17]). Model performance was weak (Nagelkerke R 2 = .04, area under the curve = 0.61). Conclusions These data suggest that, once HA candidates have decided to enter an HAEP, factors measured early in the help-seeking journey do not predict well who will and will not purchase an HA. Instead, factors that act during the HAEP may hold this predictive value. This should be examined.

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Constructing and Deconstructing Concepts

Experimental Psychology (formerly Zeitschrift für Experimentelle Psychologie) ◽

10.1027/1618-3169/a000337 ◽

2016 ◽

Vol 63 (5) ◽

pp. 249-262 ◽

Cited By ~ 2

Author(s):

Charles A. Doan ◽

Ronaldo Vigo

Keyword(s):

Information Theory ◽

Model Performance ◽

Unsupervised Classification ◽

Family Resemblance ◽

Category Membership ◽

Formal Models ◽

Human Beings ◽

One Dimensional ◽

Relational Patterns ◽

Optimal Classification

Abstract. Several empirical investigations have explored whether observers prefer to sort sets of multidimensional stimuli into groups by employing one-dimensional or family-resemblance strategies. Although one-dimensional sorting strategies have been the prevalent finding for these unsupervised classification paradigms, several researchers have provided evidence that the choice of strategy may depend on the particular demands of the task. To account for this disparity, we propose that observers extract relational patterns from stimulus sets that facilitate the development of optimal classification strategies for relegating category membership. We conducted a novel constrained categorization experiment to empirically test this hypothesis by instructing participants to either add or remove objects from presented categorical stimuli. We employed generalized representational information theory (GRIT; Vigo, 2011b , 2013a , 2014 ) and its associated formal models to predict and explain how human beings chose to modify these categorical stimuli. Additionally, we compared model performance to predictions made by a leading prototypicality measure in the literature.

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