Comparison of burning velocity differences in a numerical explosion simulator

A verification study of the two flame models in the numerical explosion simulation tool FLACS has been conducted. The beta flame model, and the SIF flame model has been tested in a 1D-channel at different time step sizes. Methods for measuring the flame speed has been discussed, and a best practice method has been chosen. The 1D-channel has been tested for closed end ignition and open end ignition. The possible introduction of a Fourier number as a stability criteria is also discussed

Contrast-Phys: Unsupervised Video-based Remote Physiological Measurement via Spatiotemporal Contrast

Video-based remote physiological measurement utilizes face videos to measure the blood volume change signal, which is also called remote photoplethysmography (rPPG). Supervised methods for rPPG measurements achieve state-of-the-art performance. However, supervised rPPG methods require face videos and ground truth physiological signals for model training. In this paper, we propose an unsupervised rPPG measurement method that does not require ground truth signals for training. We use a 3DCNN model to generate multiple rPPG signals from each video in different spatiotemporal locations and train the model with a contrastive loss where rPPG signals from the same video are pulled together while those from different videos are pushed away. We test on five public datasets, including RGB videos and NIR videos. The results show that our method outperforms the previous unsupervised baseline and achieves accuracies very close to the current best supervised rPPG methods on all five datasets. Furthermore, we also demonstrate that our approach can run at a much faster speed and is more robust to noises than the previous unsupervised baseline. Our code is available at https://github.com/zhaodongsun/contrast-phys.Comment: accepted to ECCV 202

Fast k-means based on KNN Graph

In the era of big data, k-means clustering has been widely adopted as a basic processing tool in various contexts. However, its computational cost could be prohibitively high as the data size and the cluster number are large. It is well known that the processing bottleneck of k-means lies in the operation of seeking closest centroid in each iteration. In this paper, a novel solution towards the scalability issue of k-means is presented. In the proposal, k-means is supported by an approximate k-nearest neighbors graph. In the k-means iteration, each data sample is only compared to clusters that its nearest neighbors reside. Since the number of nearest neighbors we consider is much less than k, the processing cost in this step becomes minor and irrelevant to k. The processing bottleneck is therefore overcome. The most interesting thing is that k-nearest neighbor graph is constructed by iteratively calling the fast $k$-means itself. Comparing with existing fast k-means variants, the proposed algorithm achieves hundreds to thousands times speed-up while maintaining high clustering quality. As it is tested on 10 million 512-dimensional data, it takes only 5.2 hours to produce 1 million clusters. In contrast, to fulfill the same scale of clustering, it would take 3 years for traditional k-means

Hydrodynamics of an oscillating articulated eel-like structure

This study examines the hydrodynamic performance of a highly simplified eel-like structure consisting of three articulated segments with the two aft segments oscillating. A physical model was built and tested to determine the forces developed with the model stationary, to find the self-propulsion speed, and to explore the effect on hydrodynamic performance of different swimming patterns. It was found that hydrodynamic performance increases with increasing oscillation frequency; the highest forces when stationary, and the highest self-propulsion speeds were produced by swimming patterns in which the amplitude in the aft segment is larger than that in the forward segment, and in which the motion of the aft segment lags the forward segment. A simple semi-empirical model based on Morison’s equation was implemented to predict the hydrodynamic forces. This was shown to predict mean thrust well in cases in which the aft segment oscillates in phase with the forward segment, but less reliably when the phase difference between the segments increases. Force time histories are generally not well-predicted using this approach. Nonetheless, self-propulsion speeds are predicted within 30% in all cases examined

Identifying factors associated with sedentary time after stroke. Secondary analysis of pooled data from nine primary studies.

<p><b>Background</b>: High levels of sedentary time increases the risk of cardiovascular disease, including recurrent stroke.</p> <p><b>Objective</b>: This study aimed to identify factors associated with high sedentary time in community-dwelling people with stroke.</p> <p><b>Methods</b>: For this data pooling study, authors of published and ongoing trials that collected sedentary time data, using the activPAL monitor, in community-dwelling people with stroke were invited to contribute their raw data. The data was reprocessed, algorithms were created to identify sleep-wake time and determine the percentage of waking hours spent sedentary. We explored demographic and stroke-related factors associated with total sedentary time and time in uninterrupted sedentary bouts using unique, both univariable and multivariable, regression analyses.</p> <p><b>Results</b>: The 274 included participants were from Australia, Canada, and the United Kingdom, and spent, on average, 69% (SD 12.4) of their waking hours sedentary. Of the demographic and stroke-related factors, slower walking speeds were significantly and independently associated with a higher percentage of waking hours spent sedentary (p = 0.001) and uninterrupted sedentary bouts of <i>>30</i> and <i>>60 min</i> (p = 0.001 and p = 0.004, respectively). Regression models explained 11–19% of the variance in total sedentary time and time in prolonged sedentary bouts.</p> <p><b>Conclusion</b>: We found that variability in sedentary time of people with stroke was largely unaccounted for by demographic and stroke-related variables. Behavioral and environmental factors are likely to play an important role in sedentary behavior after stroke. Further work is required to develop and test effective interventions to address sedentary behavior after stroke.</p

Rotary Position Sensors Comparative study of different rotary position sensors for electrical machines used in an hybrid electric vehicle application

Today, many projects about Electric Vehicles (EVs) and Hybrid Electric Vehicles (HEVs) are in progress within the automotive industry. Fuel-efficiency and reduction of carbon dioxide emissions from vehicles are the main targets. This thesis is within in one of these projects that is called electric All Wheel Drive(eAWD) at BorgWarner TorqTransfer Systems AB. A key parameter to perform an accurate and efficient control of an electric machine is the position sensor. The sensor measures the angular position of the rotor shaft and there are several ways and techniques to do this. This thesis aims to compare different common position sensors and identify ”new” sensor techniques by performing a literature study, model and simulate sensors and test an electric machine with different sensors implemented. Various enhancement methods to improve the position information and prediction are also evaluated. The electric motor prototype used in the eAWD project has different position sensors implemented and these are simulated in Matlab/Simulink together with the system model of the electric machine and control system. Tests are also performed and compared to the simulation results. The results show on best performance when using the resolver as position sensor. The Hall-effect sensor can be improved with an observer, but the observer is not suitable for this specific type of Torque Vectoring (TV) application. The Hall-effect sensor has a speed dependent torque ripple that leads to harmonics at frequencies that relates to the speed of the unit which may causes problems, such as mechanical resonances in the system. There are several ”new” sensor techniques based on the theory of eddy-currents that may be of interest since they are said to be more optimized for EV and HEV applications