Ultrasonic tomographic velocimeter for visualization of axial flow fields in pipes

An ultrasonic tomographic velocimeter to provide quantitative images of axial flow fields in pipes is developed and presented in this work. To detect the flow in various directions and positions, a novel transducer configuration strategy is proposed. All-in-one transducers are mounted in two sectional planes of the pipe. In each plane, N transducers are equally spaced along the circumference. Overlapped propagation paths are introduced by the configuration strategy, and the influence of the vortex flow can be eliminated theoretically by averaging the line velocities of the overlapped paths. To achieve a fast detection speed, the projection data is collected via an electrical scan in a fan-beam mode. After rearrangement and interpolation of the projection data, the parallel beam filtered back projection (FBP) algorithm is implemented to reconstruct the axial flow field. Numerical simulations with the theoretical velocity profiles were performed. The compensation method for the vortex flow is proved to be effective and necessary, and the number of transducers required for reconstruction of common flow profiles was estimated. Accordingly, an ultrasonic tomographic velocimeter consisting of 2×12 transducers was fabricated. Experiments were conducted in the straight pipe and downstream of a single bend pipe and compared with the computational fluid dynamics (CFD) simulation results. As demonstrated, the ultrasonic tomographic velocimeter was capable of visualizing both symmetric and asymmetric axial flow fields with high reliability.     

Modelling of chromatic contrast for retrieval of wallpaper images

Colour remains one of the key factors in presenting an object and, consequently, has been widely applied in retrieval of images based on their visual contents. However, a colour appearance changes with the change of viewing surroundings, the phenomenon that has not been paid attention yet while performing colour‐based image retrieval. To comprehend this effect, in this article, a chromatic contrast model, CAMcc, is developed for the application of retrieval of colour intensive images, cementing the gap that most of existing colour models lack to fill by taking simultaneous colour contrast into account. Subsequently, the model is applied to the retrieval task on a collection of museum wallpapers of colour‐rich images. In comparison with current popular colour models including CIECAM02, HSI and RGB, with respect to both foreground and background colours, CAMcc appears to outperform the others with retrieved results being closer to query images. In addition, CAMcc focuses more on foreground colours, especially by maintaining the balance between both foreground and background colours, while the rest of existing models take on dominant colours that are perceived the most, usually background tones. Significantly, the contribution of the investigation lies in not only the improvement of the accuracy of colour‐based image retrieval but also the development of colour contrast model that warrants an important place in colour and computer vision theory, leading to deciphering the insight of this age‐old topic of chromatic contrast in colour science. © 2014 Wiley Periodicals, Inc. Col Res Appl, 40, 361–373, 2015     

影像设备OM测试法解读(能源之星2.0版)

美国能源之星对影像设备能效测试2.0版操作模式功率法(OM法)主要反应被测样品在完成准备状态、睡眠状态、低耗能状态或关闭状态的能耗情况。分析了OM法的各测试项目以及相应指标,归纳总结了该标准规范在实施中应注意的问题,对影像设备附加功能进行了详细说明。     

Evaluating the commercial airliner cabin environment with different air distribution systems

Ventilation systems for commercial airliner cabins are important in reducing contaminant transport and maintaining thermal comfort. To evaluate the performance of a personalized displacement ventilation system, a conventional displacement ventilation system, and a mixing ventilation system, this study first used the Wells‐Riley equation integrated with CFD to obtain the SARS quanta value based on a specific SARS outbreak on a flight. This investigation then compared the three ventilation systems in a seven‐row section of a fully occupied, economy‐class cabin in Boeing 737 and Boeing 767 airplanes. The SARS quanta generation rate obtained for the index patient could be used in future studies. For all the assumed source locations, the passengers’ infection risk by air in the two planes was the highest with the mixing ventilation system, while the conventional displacement ventilation system produced the lowest risk. The personalized ventilation system performed the best in maintaining cabin thermal comfort and can also reduce the infection risk. This system is recommended for airplane cabins.     

Slag eye formation in single and dual bottom purged industrial steelmaking ladles

ABSTRACT

Argon gas is often injected from the bottom of the ladle during steel refining operations. The injected gas interacts with the liquid (metal and slag) bath and enhances the momentum, heat, and mass transfer rate in the melt. However, during these gas–liquid interactions, an opening of the slag layer called slag eye is formed, which exposes the molten metal surface to the atmosphere, which is generally undesirable. In the current work, a transient, three-dimensional mathematical model is used to study the turbulent gas–liquid interactions in single as well as dual bottom blown industrial steelmaking ladles. A Coupled Level Set Volume of Fluid (CLSVOF) model is used for tracking the steel-argon, steel-slag, and argon-slag interfaces, from which the slag-eye area has been predicted. It is found that the inlet gas purging rate, melt height, slag layer thickness, angular and radial positions of the gas inlets affect the slag opening area. Non-dimensional empirical correlations are proposed to predict the slag opening area in both single as well as dual purged ladles, using non-linear regression analysis.     

An Artificial Skyrmion Platform with Robust Tunability in Synthetic Antiferromagnetic Multilayers

Magnetic skyrmions are topologically nontrivial spin structures, and their existence in ferromagnetically coupled multilayers has been widely reported with a disordered arrangement. Here, a nucleation scenario of ordered skyrmions in nanostructured synthetic antiferromagnetic (SAF) multilayers is proposed and experimentally demonstrated using direct magnetization imaging, indirect magnetometer and magnetoresistance measurement, and micromagnetic simulation. Instead of relying on Dzyaloshinskii–Moriya interaction, the antiferromagnetic interlayer exchange coupling in the SAF multilayers fulfills the role of nucleation and stabilization of skyrmions. The robustness of the proposed skyrmion nucleation scenario is examined against temperature from 4.5 to 300 K and device size from 400 to 1200 nm. Interestingly, these synthetic skyrmions still behave well with a size less than 100 nm. The higher stability than generic magnetic domains can be attributed to topological protection. The results thus provide an artificial skyrmion platform to meet the functional needs of high density and designable arrangement in magnonic and spintronic applications.     

R2D2: A scalable deep learning toolkit for medical imaging segmentation

Deep learning has gained a significant popularity in recent years thanks to its tremendous success across a wide range of relevant fields of applications, including medical image analysis domain in particular. Although convolutional neural networks (CNNs) based medical applications have been providing powerful solutions and revolutionizing medicine, efficiently training of CNNs models is a tedious and challenging task. It is a computationally intensive process taking long time and rare system resources, which represents a significant hindrance to scientific research progress. In order to address this challenge, we propose in this article, R2D2, a scalable intuitive deep learning toolkit for medical imaging semantic segmentation. To the best of our knowledge, the present work is the first that aims to tackle this issue by offering a novel distributed versions of two well-known and widely used CNN segmentation architectures [ie, fully convolutional network (FCN) and U-Net]. We introduce the design and the core building blocks of R2D2. We further present and analyze its experimental evaluation results on two different concrete medical imaging segmentation use cases. R2D2 achieves up to 17.5× and 10.4× speedup than single-node based training of U-Net and FCN, respectively, with a negligible, though still unexpected segmentation accuracy loss. R2D2 offers not only an empirical evidence and investigates in-depth the latest published works but also it facilitates and significantly reduces the effort required by researchers to quickly prototype and easily discover cutting-edge CNN configurations and architectures.