A Fuzzy-Pod Based Estimation of Unsteady Mixed Convection in a Partition Located Cavity with Inlet and Outlet Ports

2015 ◽  
Vol 12 (01) ◽  
pp. 1350107 ◽  
Author(s):  
Fatih Selimefendigil ◽  
Hakan F. Öztop
Keyword(s):  
Mixed Convection ◽  
Strouhal Number ◽  
Computationally Efficient ◽  
Validation Data ◽  
Data Set ◽  
Square Enclosure ◽  
Unsteady Mixed Convection ◽  
Novel Approach ◽  
Fuzzy Clustering Method ◽  
Proper Orthogonal

In the present study, a novel approach based on Proper Orthogonal Decomposition (POD) and fuzzy clustering method is utilized to predict the flow field and heat transfer for the unsteady mixed convection in a square enclosure with two ventilation ports. An adiabatic thin fin is placed on the bottom wall of the cavity and all walls of the enclosure are kept at constant temperature. An oscillating velocity is imposed at the inlet port for a range of Strouhal numbers between 0.1 and 1. Reduced order models of the system are obtained with fuzzy-POD approach for Richardson number of 1 and 100. The estimation data set is obtained for Strouhal numbers 0.1 and 0.5, and the validation data set is obtained for Strouhal number of 0.25. A comparison of the modal coefficients obtained from the proposed approach compares well with the modal coefficients obtained by projecting the CFD data at Strouhal number of 0.25 onto the POD modes. The proposed approach is computationally efficient and the problem of numerical instability in the computation with the conventional Galerkin-POD approach can be circumvented.

scholarly journals Thresholded Partial Least Squares: Fast Construction of Interpretable Whole-brain Decoders

2021 ◽  
Author(s):  
Sangil Lee ◽  
Eric T. Bradlow ◽  
Joseph W. Kable
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Brain Activity ◽  
Mental States ◽  
Computationally Efficient ◽  
Activity Data ◽  
Data Set ◽  
Whole Brain ◽  
Novel Approach ◽  
Brain Data

AbstractRecent neuroimaging research has shown that it is possible to decode mental states and predict future consumer behavior from brain activity data (a time-series of images). However, the unique characteristics (and high dimensionality) of neuroimaging data, coupled with a need for neuroscientifically interpretable models, has largely discouraged the use of the entire brain’s data as predictors. Instead, most neuroscientific research uses “regionalized” (partial-brain) data to reduce the computational burden and to improve interpretability (i.e., localizability of signal), at the cost of losing potential information. Here we propose a novel approach that can build whole-brain neural decoders (using the entire data set and capitalizing on the full correlational structure) that are both interpretable and computationally efficient. We exploit analytical properties of the partial least squares algorithm to build a regularized regression model with variable selection that boasts (in contrast to most statistical methods) a unique ‘fit-once-tune-later’ approach where users need to fit the model only once and can choose the best tuning parameters post-hoc. We demonstrate its efficacy in a large neuroimaging dataset against off-the-shelf prediction methods and show that our new method scales exceptionally with increasing data size, yields more interpretable results, and uses less computational memory, while retaining high predictive power.

scholarly journals Comments on “A Parallel Mixture of SVMs for Very Large Scale Problems”

2004 ◽  
Vol 16 (7) ◽  
pp. 1345-1351 ◽  
Author(s):  
Xiaomei Liu ◽  
Lawrence O. Hall ◽  
Kevin W. Bowyer
Keyword(s):  
Large Scale ◽  
Forest Cover ◽  
Large Data ◽  
Support Vector ◽  
Data Sets ◽  
Computationally Efficient ◽  
Data Set ◽  
Class Prediction ◽  
Novel Approach ◽  
Vector Machines

Collobert, Bengio, and Bengio (2002) recently introduced a novel approach to using a neural network to provide a class prediction from an ensemble of support vector machines (SVMs). This approach has the advantage that the required computation scales well to very large data sets. Experiments on the Forest Cover data set show that this parallel mixture is more accurate than a single SVM, with 90.72% accuracy reported on an independent test set. Although this accuracy is impressive, their article does not consider alternative types of classifiers. We show that a simple ensemble of decision trees results in a higher accuracy, 94.75%, and is computationally efficient. This result is somewhat surprising and illustrates the general value of experimental comparisons using different types of classifiers.

scholarly journals Development and temporal external validation of a simple risk score tool for prediction of outcomes after severe head injury based on admission characteristics from level-1 trauma centre of India using retrospectively collected data

BMJ Open ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. e040778
Author(s):  
Vineet Kumar Kamal ◽  
Ravindra Mohan Pandey ◽  
Deepak Agrawal
Keyword(s):  
Hospital Mortality ◽  
External Validation ◽  
Trauma Centre ◽  
Unfavourable Outcome ◽  
Motor Score ◽  
Validation Data ◽  
Data Set ◽  
Development Data ◽  
Level 1 ◽  
Pupillary Reactivity

ObjectiveTo develop and validate a simple risk scores chart to estimate the probability of poor outcomes in patients with severe head injury (HI).DesignRetrospective.SettingLevel-1, government-funded trauma centre, India.ParticipantsPatients with severe HI admitted to the neurosurgery intensive care unit during 19 May 2010–31 December 2011 (n=946) for the model development and further, data from same centre with same inclusion criteria from 1 January 2012 to 31 July 2012 (n=284) for the external validation of the model.Outcome(s)In-hospital mortality and unfavourable outcome at 6 months.ResultsA total of 39.5% and 70.7% had in-hospital mortality and unfavourable outcome, respectively, in the development data set. The multivariable logistic regression analysis of routinely collected admission characteristics revealed that for in-hospital mortality, age (51–60, >60 years), motor score (1, 2, 4), pupillary reactivity (none), presence of hypotension, basal cistern effaced, traumatic subarachnoid haemorrhage/intraventricular haematoma and for unfavourable outcome, age (41–50, 51–60, >60 years), motor score (1–4), pupillary reactivity (none, one), unequal limb movement, presence of hypotension were the independent predictors as its 95% confidence interval (CI) of odds ratio (OR)_did not contain one. The discriminative ability (area under the receiver operating characteristic curve (95% CI)) of the score chart for in-hospital mortality and 6 months outcome was excellent in the development data set (0.890 (0.867 to 912) and 0.894 (0.869 to 0.918), respectively), internal validation data set using bootstrap resampling method (0.889 (0.867 to 909) and 0.893 (0.867 to 0.915), respectively) and external validation data set (0.871 (0.825 to 916) and 0.887 (0.842 to 0.932), respectively). Calibration showed good agreement between observed outcome rates and predicted risks in development and external validation data set (p>0.05).ConclusionFor clinical decision making, we can use of these score charts in predicting outcomes in new patients with severe HI in India and similar settings.

scholarly journals Dynamic pricing with demand disaggregation for hotel revenue management

2021 ◽  
Author(s):  
Andrei M. Bandalouski ◽  
Natalja G. Egorova ◽  
Mikhail Y. Kovalyov ◽  
Erwin Pesch ◽  
S. Armagan Tarim
Keyword(s):  
Dynamic Pricing ◽  
Programming Model ◽  
Computer Experiments ◽  
Price Policy ◽  
Linear Constraints ◽  
Economic Consequences ◽  
Elastic Model ◽  
Computationally Efficient ◽  
Data Set ◽  
Dynamic Price

AbstractIn this paper we present a novel approach to the dynamic pricing problem for hotel businesses. It includes disaggregation of the demand into several categories, forecasting, elastic demand simulation, and a mathematical programming model with concave quadratic objective function and linear constraints for dynamic price optimization. The approach is computationally efficient and easy to implement. In computer experiments with a hotel data set, the hotel revenue is increased by about 6% on average in comparison with the actual revenue gained in a past period, where the fixed price policy was employed, subject to an assumption that the demand can deviate from the suggested elastic model. The approach and the developed software can be a useful tool for small hotels recovering from the economic consequences of the COVID-19 pandemic.

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