SRS: Spatial-tagged radio-mapping system combining LiDAR and mobile-phone data for indoor location-based services

One of the most challenging issues in radio received signal strength (RSS)-based localization systems is the generation and distribution of a radio map with a coordinate system linked with spatial information in a large indoor space. This study proposes a novel spatial-tagged radio-mapping system (SRS) that effectively combines the heterogeneous properties of LiDAR and mobile phones to simultaneously perform both spatial and radio mappings. The SRS consists of synchronization, localization, and map building processes, and enables real-time spatial and radio mapping. In the synchronization process, the distance range, motion data, and radio signals obtained through the LiDAR and mobile phone are collected in nodal units according to the sensing time. In the localization process, a feature variance filter is used to control the number of features generated from LiDAR and estimate the positions at which the nodes are generated in real time according to the motion data and radio signals. In map building, the estimated positions of the nodes are used to extract spatial and radio maps by using a unified location coordinate system. To ensure mobility, the SRS is manufactured in the form of a backpack supporting LiDAR and a mobile phone; the usefulness of the system is experimentally verified. The experiments are performed in a large indoor shopping mall with a complex structure. The experimental results demonstrated that a common coordinate system could be used to build spatial and radio maps with high accuracy and efficiency in real time. In addition, the field applicability of the SRS to location-based services is experimentally verified by applying the constructed radio map to well-known fingerprinting algorithms using the heterogeneous mobile phones.     

5G核心网业务模型的智能化预测研究

摘 要：核心网业务模型的建立是5G网络容量规划和网络建设的基础，通过现有方法得到的理论业务模型是静态不可变的且与实际网络存在偏离。为了克服现有5G核心网业务模型与现网模型适配性较差以及规划设备无法满足用户实际业务需求的问题，提出了一种长短期记忆（long short-term memory，LSTM）网络与卷积LSTM （convolution LSTM，ConvLSTM）网络双通道融合的 5G 核心网业务模型预测方法。该方法基于人工智能（artificial intelligence，AI）技术以实现高质量的核心网业务模型的智能预测，形成数据反馈闭环，实现网络自优化调整，助力网络智能化建设。     

面向老龄化社会的产品服务系统设计赋能

目的 针对面向老龄化社会的产品及产品服务系统设计,将赋能的设计理念和价值引入其中,探索设计结果提升老年人能动性和参与性的赋能品质,为设计师开展设计赋能实践提供参考.方法 以设计教学中的学生设计方案作为研究材料,邀请设计研究者参与工作坊,对设计结果的赋能属性进行分析,并通过聚类获得类别化的设计属性,产生了能描述赋能品质的属性词汇表.结果 总结了包含5组形容词组的设计赋能品质集,这一集合包含"顺应性"和"激励性"两个面向.结论 研究结果为设计师进行老龄化设计提供了知识参考,有助于提升设计师的赋能意识.揭示了设计赋能充满矛盾性的品质,提出面向老龄化的设计赋能需要在"顺应"和"激励"之间找到平衡.     

高分子材料导热性能影响因素研究进展

介绍了高分子材料导热性能影响因素研究进展，重点阐释了聚合物基体的结构特性（链结构、分子间相互作用、取向、结晶度等）、导热填料（种类、含量、形态、尺寸等）以及制备方法等对高分子材料导热性能的影响。     

Impact of shape representation schemes used in discrete element modelling of particle packing

In different computer models, shape is represented using different methodologies, to varying degrees of precision. This paper examines two approaches to shape representation, and their effects on accuracy in the context of cylindrical particle packing. Two discrete element method (DEM) based software packages are used. A X-ray CT scan of a packed bed provides the experimental measurements for comparison. Eight sphere-composite representations of the same cylindrical pellet were tested. Two of these gave results that quantitatively follow experimental measurements. A range of factors that in theory could affect accuracy of the simulation results are examined, including edge roundedness, surface roughness and restitutional behaviour as a function of sphere-composite representations. The conclusion is that, for packing at least, matching the object's overall shape and dimensions is not enough. Only when a high enough resolution is applied to corners and edges, could the sphere-composite approach possibly match the experimental data quantitatively.     

A self‐scalable load injection service

Load testing of applications is an important and costly activity for software provider companies. Classical solutions are very difficult to set up statically, and their cost is prohibitive in terms of both human and hardware resources. Virtualized cloud computing platforms provide new opportunities for stressing an application's scalability, by providing a large range of flexible and less expensive (pay‐per‐use model) computation units. On the basis of these advantages, load testing solutions could be provided on demand in the cloud. This paper describes a Benchmark‐as‐a‐Service solution that automatically scales the load injection platform and facilitates its setup according to load profiles. Our approach is based on: (i) virtualization of the benchmarking platform to create self‐scaling injectors; (ii) online calibration to characterize the injector's capacity and impact on the benched application; and (iii) a provisioning solution to appropriately scale the load injection platform ahead of time. We also report experiments on a benchmark illustrating the benefits of this system in terms of cost and resource reductions. Copyright © 2013 John Wiley & Sons, Ltd.     

Intelligent Information‐Centric Networking routing scheme under imprecise information

Information‐Centric Networking (ICN) has been accepted to overcome some weaknesses of the current Internet architecture, showing that “what is being exchanged” is more important than “who are exchanging information.” Given the inadequate considerations on Quality of Service (QoS) and energy saving in ICN routing, we propose in this paper a routing algorithm to enhance the two aspects. At first, on one hand, Cauchy distribution is used as a fuzzy model to evaluate users' QoS requirements, such as bandwidth, delay, and error rate; on the other hand, we formulate energy saving problem to evaluate the green quality of routing algorithm. Then, we design a link selection approach by considering QoS and energy saving, which belongs to a multi‐objective decision problem resolved by intelligent drops algorithm. Finally, we implement the proposed algorithm and compare it with the famous adaptive forwarding mechanism in terms of some significant metrics, and the experimental results reveal that the proposed algorithm is more efficient.     

Crystallization of progesterone polymorphs using polymer-induced heteronucleation (PIHn) method

Progesterone is a natural hormone steroid used in humans for several treatments and in livestock for artificial insemination, which exhibits two polymorphic forms at ambient conditions: form 1 and form 2. Form 2 is metastable and more soluble than form 1; however, it is not suitable to use as powder raw material because it transforms into form 1 by the effects of grinding. A polymorphic screening of progesterone based on polymer-induced heteronucleation method was performed as an alternative to prepare the metastable form. Polyvinyl alcohol, hydroxypropyl methylcellulose (HPMC), dextran, gelatin, polyisoprene (PI) and acrylonitrile-butadiene (NBR) copolymer were used. Crystals were prepared from 0.5, 10 and 40?mg/mL solutions in acetone at room temperature by solvent evaporation. The samples were characterized by X-ray powder diffraction, differential scanning calorimetry (DSC), scanning electron microcopy and attenuated total reflectance infrared Fourier transform spectroscopy. Form 1 was nucleated from 40?mg/mL solutions on the six polymers and from 10?mg/mL solutions on PI and NBR. The mixture of form 1 and form 2 was obtained from 10?mg/mL solution on HPMC, dextran and gelatin and from 0.5?mg/mL solution crystallizations. Therefore, the polymeric devices, which crystallized the metastable and more soluble polymorph (2) of progesterone, would be a promissory alternative for the pharmaceutical applications.     

深度学习识别光网络单元故障的设计与应用

为解决依赖装维上门鉴别光网络单元故障带来的不便,可以从机器视觉入手实现自动化故障识别。近年,ImageNet挑战赛的成功推动了物体识别技术的跨越式发展,特别是基于卷积的深度学习技术在视觉识别方面已经达到人类水平,为光网络单元故障的自动识别提供了技术基础。文章对识别光网络单元的工作状态进行了研究,将设备工作状态分为7个场景,提出了利用手机APP采集图片识别故障的解决方案并投入了实际生产;重点阐述了深度学习模块的设计与实现,提出一种通过算法整合的方式综合运用物体检测和图像分类算法,分3阶段逐步求精,解决了图片过滤,光网络单元型号和状态识别等问题,实现了基于计算机视觉自动识别光网络单元故障。从数据上看产品的端到端准确率超过84%,识别速度达到10 FPS,月均提供服务超过1万人次,在减少用户等待的同时节约了人力资源。     

Effects of sintering in a high magnetic field on properties of vitrified bond and vitrified CBN composites

Vitrified bond CBN grinding wheels are being widely used due to their superior performance. Also, advantages of vitrified grinding wheels are high elastic modulus, stable chemical property, and low thermal expansion coefficient. Brittleness and low strength are key factors restricting the development of vitrified bond CBN grinding wheels. In this paper, the sintering in a high magnetic field was innovatively introduced into the manufacturing of vitrified bond CBN grinding wheels, and the effects of sintering in a high magnetic field on properties on vitrified bond and vitrified CBN composites were systematically investigated. Vitrified bond was characterized using three-point bending, scanning electron microscopy, X-ray diffraction. It was observed that microstructure of vitrified bond could be changed, grain orientation could be controlled and average grain size could be decreased in a high magnetic field, while vitrified bond strength could be simultaneously improved. High quality vitrified bond could be obtained by appropriately adjusting the strength and direction of high magnetic field. Results demonstrated that vitrified bond properties were improved when the magnetic field strength was 6?T. In order to highlight the high magnetic field effect on the vitrified CBN composites, the ordinary CBN abrasives and nickel plated CBN abrasives were used respectively. Microstructures, bending strengths of vitrified CBN composites were compared in different high magnetic fields. When the magnetic field strength was appropriate (less than 6?T), the binding characteristic of vitrified bond CBN composites with nickel plated CBN abrasives was greatly improved. The highest bending strength value of vitrified CBN composites was 79.5?MPa in 6?T high magnetic field.