Improving correlation accuracy of crashworthiness applications by combining the CORA and MADM methods

With the increasing use of Computer Aided Engineering, it has become vital to be able to evaluate the accuracy of numerical models. This research poses the problem of selection of the most accurate and relevant correlation solution to a set of corridor variations. Specific methods such as CORA, widely accepted in industry, are developed to objectively evaluate the correlation between monotonic functions, while the Minimum Area Discrepancy Method, or MADM, is the only method to address the correlation of non-injective mathematical variations, usually related to force/acceleration versus displacement problems. Often, it is not possible to differentiate objectively various solutions proposed by CORA, which this paper proposes to answer. This research is original, as it proposes a new innovative correlation optimisation framework, which can select the best CORA solution by including MADM as a subsequent process. The paper and the methods are rigorous, having used an industry standard driver airbag computer model, built virtual test corridors and compared the relationship between different CORA and MADM ratings from 100 Latin Hypercube samples. For the same CORA value of ‘1’ (perfect correlation), MADM was capable to objectively differentiate between 13 of them and provide the best correlation possible. The paper has recommended the MADM settings n = 1; m = 2 or n = 3; m = 2 for a congruent relationship with CORA. As MADM is performed subsequently, this new framework can be implemented in already existing industrial processes and provide automotive manufacturers and Original Equipment Manufacturers (OEM) with a new tool to generate more accurate computer models.

Heating Homes with Servers: Workload Scheduling for Heat Reuse in Distributed Data Centers

Data centers consume lots of energy to execute their computational workload and generate heat that is mostly wasted. In this paper, we address this problem by considering heat reuse in the case of a distributed data center that features IT equipment (i.e., servers) installed in residential homes to be used as a primary source of heat. We propose a workload scheduling solution for distributed data centers based on a constraint satisfaction model to optimally allocate workload on servers to reach and maintain the desired home temperature setpoint by reusing residual heat. We have defined two models to correlate the heat demand with the amount of workload to be executed by the servers: a mathematical model derived from thermodynamic laws calibrated with monitored data and a machine learning model able to predict the amount of workload to be executed by a server to reach a desired ambient temperature setpoint. The proposed solution was validated using the monitored data of an operational distributed data center. The server heat and power demand mathematical model achieve a correlation accuracy of 11.98% while in the case of machine learning models, the best correlation accuracy of 4.74% is obtained for a Gradient Boosting Regressor algorithm. Also, our solution manages to distribute the workload so that the temperature setpoint is met in a reasonable time, while the server power demand is accurately following the heat demand.

Towards Bengali Word Embedding: Corpus Creation, Intrinsic and Extrinsic Evaluations

Distributional word vector representation orword embedding has become an essential ingredient in many natural language processing (NLP) tasks such as machine translation, document classification, information retrieval andquestion answering. Investigation of embedding model helps to reduce the feature space and improves textual semantic as well as syntactic relations.This paper presents three embedding techniques (such as Word2Vec, GloVe, and FastText) with different hyperparameters implemented on a Bengali corpusconsists of180 million words. The performance of the embedding techniques is evaluated with extrinsic and intrinsic ways. Extrinsic performance evaluated by text classification, which achieved a maximum of 96.48% accuracy. Intrinsic performance evaluatedby word similarity (e.g., semantic, syntactic and relatedness) and analogy tasks. The maximum Pearson (ˆr) correlation accuracy of 60.66% (Ssˆr) achieved for semantic similarities and 71.64% (Syˆr) for syntactic similarities whereas the relatedness obtained 79.80% (Rsˆr). The semantic word analogy tasks achieved 44.00% of accuracy while syntactic word analogy tasks obtained 36.00%

Non-Contact Respiratory Rate Monitoring with raybaby in an NICU: An Observational Study

Objectives: This study aimed at evaluating the reliability of respiratory rate obtained by a non-contact technology with respect to a medically validated monitor among preterm babies. Design: This observational study compared the respiratory rates from raybaby’s non-contact technology and FDA approved Earlysense unit for the same instants of time through 760 hours of monitoring. 18 preterm babies in the NICU of a paediatric specialty hospital in India were considered for the study. The raybaby device was installed in front of the incubator and the contact-free FDA approved device was placed below the mattress of the incubator. The Respiratory Rate monitored was displayed on the device’s monitoring screen. Respiratory rates from both devices were compared to calculate the agreement between the values. Correlation, Accuracy, Hit Percentage and Fit Curves for the non-contact technology of raybaby with respect to the clinically certified device. Results: With 760 hours of monitoring, 37404 breathing instances were analysed. This yielded an accuracy of 98%. 95% of the data points fell within the +/- 5 units error range which is usually followed by medical devices. Conclusions: Raybaby uses a non-contact technology for monitoring Respiratory Rate. The average breathing rate observed was 33 to 43 breaths per minute, which falls within the breathing range of 30-60 breaths per minute. From the 37404 data points analysed, raybaby® establishes further proof for the breathing range and trend found in babies. The accuracy of non-contact technology for respiratory monitoring establishes great potential for making health monitoring less intrusive and efficient for use. This renders the technology as a hopeful tool for respiratory monitoring to deploy at observation units during the pandemic.

On the Use of Cost-Effective Valid-Time-Shifting (VTS) Method to Increase Ensemble Size in the GFS Hybrid 4DEnVar System

Valid-time-shifting (VTS) ensembles, either in the form of full ensemble members (VTSM) or ensemble perturbations (VTSP), were investigated as inexpensive means to increase ensemble size in the NCEP Global Forecast System (GFS) hybrid four-dimensional ensemble–variational (4DEnVar) data assimilation system. VTSM is designed to sample timing and/or phase errors, while VTSP can eliminate spurious covariances through temporal smoothing. When applying a shifting time interval (τ = 1, 2, or 3 h), VTSM and VTSP triple the baseline background ensemble size from 80 (ENS80) to 240 (ENS240) in the EnVar variational update, where the overall cost is only increased by 23%–27%, depending on the selected τ. Experiments during a 10-week summer period show the best-performing VTSP with τ = 2 h improves global temperature and wind forecasts out to 5 days over ENS80. This could be attributed to the improved background ensemble distribution, ensemble correlation accuracy, and increased effective rank in the populated background ensemble. VTSM generally degrades global forecasts in the troposphere. Improved global forecasts above 100 hPa by VTSM may benefit from the increased spread that alleviates the underdispersiveness of the original background ensemble at such levels. Both VTSM and VTSP improve tropical cyclone track forecasts over ENS80. Although VTSM and VTSP are much less expensive than directly running a 240-member background ensemble, owing to the improved ensemble covariances, the best-performing VTSP with τ = 1 h performs comparably or only slightly worse than ENS240. The best-performing VTSM with τ = 3 h even shows more accurate track forecasts than ENS240, likely contributed to by its better sampling of timing and/or phase errors for cases with small ensemble track spread.

ACCURACY ASSESSMENT OF PROFESSIONAL GRADE UNMANNED SYSTEMS FOR HIGH PRECISION AIRBORNE MAPPING

Recently, sophisticated multi-sensor systems have been implemented on-board modern Unmanned Aerial Systems. This allows for producing a variety of mapping products for different mapping applications. The resulting accuracies match the traditional well engineered manned systems. This paper presents the results of a geometric accuracy assessment project for unmanned systems equipped with multi-sensor systems for direct georeferencing purposes. There are a number of parameters that either individually or collectively affect the quality and accuracy of a final airborne mapping product. This paper focuses on identifying and explaining these parameters and their mutual interaction and correlation. Accuracy Assessment of the final ground object positioning accuracy is presented through real-world 8 flight missions that were flown in Quebec, Canada. The achievable precision of map production is addressed in some detail.

Development and Comparison of a Heat-Transfer Correlation for Supercritical Refrigerants

The objective of this paper is to act as a collection of multiple different heat-transfer correlations and to check their accuracy when compared to experimental data obtained in supercritical-pressure refrigerants (R-22 and R-134a). This paper is also intended to collect as much relevant data of heat transfer in supercritical refrigerants as possible for future research. The experimental data have been retrieved from graphs within a wide range of operating parameters. This study is in support of potential use of supercritical refrigerants as modeling fluids instead of supercritical water. The use of refrigerants as modelling fluids instead of water will allow to decrease costs and technical difficulties during experiments at supercritical pressures and widen operating ranges, because the critical parameters of refrigerants are significantly lower than those of water. The research was completed by collecting graphed data from several different experimental series using both R-22 and R-134a data. The advantage of comparing different refrigerants for determining correlation accuracy is to increase the predictability for other potential experiments using refrigerants. All data are taken from bare-tube experiments to produce a relative baseline for heat-transfer characteristics. These experiments have been performed within the following range: Inner tube diameter ranging between 4.4 mm to 13 mm, pressure ranging between 4.3 MPa to 5.5 MPa, and at a number of various mass and heat fluxes. Sixteen potential heat-transfer correlations have been selected and used in this assessment. The correlation by Watts and Chou [1] and Cheng et al. [2] were shown to have the lowest root-mean-square error. Other correlations with the reasonable accuracy include Mokry et al. [3] and Swenson et al. [4] correlations. However, it was decided to develop a new correlation based on these refrigerant data in an attempt to increase the prediction accuracy. Therefore, based on the Mokry et al. [3] correlation a modified correlation was developed, which generalized the experimental Freon data with higher accuracy than the know correlations. This correlation is intended to create a basis for further study on the use of refrigerants as modeling fluids. While Freon has similar properties to water at supercritical conditions, the different molecular properties causes factors to affect each fluid differently. For refrigerants at supercritical conditions, the factors that seem to have the most effect are the dynamic viscosity and density of a fluid.