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.     

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。     

基于信号增强的缓慢泄漏检测方法

目前管道泄漏检测方法可有效检测突发泄漏，对于缓慢泄漏则存在检测灵敏度低、定位不准确等问题。基于此，提出了一种基于信号增强的缓慢泄漏检测方法。通过信号压缩（抽取及移位）克服缓慢泄漏压力信号下降平缓的缺点；根据声波信号具有波形尖锐突出、对突发泄漏敏感的优点，通过建立以压力为输入、虚拟声波为输出的声波信号变送器模型，将压力信号转换为声波信号，克服了泄漏压力信号容易被淹没在管道压力波动及背景噪声中的缺点，实现了缓慢泄漏信号的增强；利用临近插值方法重构虚拟声波信号，基于延时互相关分析实现了缓慢泄漏的准确定位。实验结果表明，该方法具有显著的信号增强效果和定位精度，实现了缓慢泄漏的准确检测。     

敷设某种吸声材料的声诱饵简化模型隔离度仿真计算

随着水声电子对抗技术的不断发展,对于声诱饵系统边收边发技术的需求日益增强。实现边收边发技术的难点之一在于如何增大声诱饵系统发射端与接收端的隔离度。由于高分子吸声材料对声能的吸收作用,可以考虑采用在声诱饵接收端敷设吸声材料的方式,对声诱饵系统的隔离度进行改善。利用有限元计算软件对简化的声诱饵模型的隔离度进行数值仿真计算,分析声诱饵模型接收端敷设不同厚度的吸声材料时,对声诱饵系统隔离度的影响。计算结果表明,在声诱饵接收端敷设吸声材料时,可以增大声诱饵系统发射端与接收端的隔离度;当吸声材料达到一定厚度以后,对声诱饵系统隔离度的影响几乎不变。     

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.     

Stretchable Composite Acoustic Transducer for Wearable Monitoring of Vital Signs

A highly flexible, stretchable, and mechanically robust low‐cost soft composite consisting of silicone polymers and water (or hydrogels) is reported. When combined with conventional acoustic transducers, the materials reported enable high performance real‐time monitoring of heart and respiratory patterns over layers of clothing (or furry skin of animals) without the need for direct contact with the skin. The approach enables an entirely new method of fabrication that involves encapsulation of water and hydrogels with silicones and exploits the ability of sound waves to travel through the body. The system proposed outperforms commercial, metal‐based stethoscopes for the auscultation of the heart when worn over clothing and is less susceptible to motion artefacts. The system both with human and furry animal subjects (i.e., dogs), primarily focusing on monitoring the heart, is tested; however, initial results on monitoring breathing are also presented. This work is especially important because it is the first demonstration of a stretchable sensor that is suitable for use with furry animals and does not require shaving of the animal for data acquisition.     

面向长距离量子通信的双光子关联成像技术

量子时域鬼成像利用光子对的时间-频率关联性实现了50km光纤链路上的图像传送,有望应用于量子通信的协议中。然而,现有方案的成像质量较差,成像速度较慢,因此其应用性在一定程度上受到了限制。文章对已有成像方案进行了较大的改进,利用可编程滤波器数字设定待成像信息,进而实现了二维图像信息的自动化实时传送。实验结果证明,改进方案的成像质量和速度均有显著改善,有利于推动量子关联成像在量子通信领域的应用。