Inverse identification of constitutive parameters from heat source fields: A local approach applied to hyperelasticity

In this paper, a new inverse identification method of constitutive parameters is developed from full kinematic and thermal field measurements. It consists in reconstructing the heat source field from two different approaches by using the heat diffusion equation. The first one requires the temperature field measurement and the value of the thermophysical parameters. The second one is based on the kinematic field measurement and the choice of a thermo-hyperelastic model that contains the parameters to be identified. The identification is carried out at the local scale, ie, at any point of the heat source field, without using the boundary conditions. In the present work, the method is applied to the challenging case of hyperelasticity from a heterogeneous test. Due to large deformations undergone by the rubber specimen tested, a motion compensation technique is developed to plot the kinematic and the thermal fields at the same points before reconstructing the heterogeneous heat source field. In the present case, the constitutive parameter of the Neo-Hookean model has been identified, and its distribution has been characterized with respect to the strain state at the surface of a cross-shaped specimen.     

[数字孪生驱动的智能制造]基于文献计量的数字孪生研究进展分析

伴随物联网技术、云计算及机器学习等新一代信息技术的迅速发展，数字孪生技术已逐渐成为新的研究热点。基于文献计量法，对1994年以来全球486篇数字孪生相关论文的研究领域、国家与地区、论文发布期刊、关键词、研究作者及高被引论文等模块展开详细分析。结果表明，数字孪生作为一个新的研究课题，其技术挖掘性强，已在制造工程、计算机科学及电子工程等领域得到了广泛应用。在智能制造刚性需求的驱动下，数字孪生技术在未来具有非常好的理论研究和技术应用前景。     

Deep learning methods for fingerprint-based indoor positioning: a review

ABSTRACT

Outdoor positioning systems based on the Global Navigation Satellite System have several shortcomings that have deemed their use for indoor positioning impractical. Location fingerprinting, which utilizes machine learning, has emerged as a viable method and solution for indoor positioning due to its simple concept and accurate performance. In the past, shallow learning algorithms were traditionally used in location fingerprinting. Recently, the research community started utilizing deep learning methods for fingerprinting after witnessing the great success and superiority these methods have over traditional/shallow machine learning algorithms. This paper provides a comprehensive review of deep learning methods in indoor positioning. First, the advantages and disadvantages of various fingerprint types for indoor positioning are discussed. The solutions proposed in the literature are then analyzed, categorized, and compared against various performance evaluation metrics. Since data is key in fingerprinting, a detailed review of publicly available indoor positioning datasets is presented. While incorporating deep learning into fingerprinting has resulted in significant improvements, doing so, has also introduced new challenges. These challenges along with the common implementation pitfalls are discussed. Finally, the paper is concluded with some remarks as well as future research trends.     

Application of the novel Droplet digital PCR technology for identification of meat species

The authenticity and traceability of meat products are issues of primary importance to ensure food safety. Unfortunately, food adulteration (e.g. the addition of inexpensive cuts to minced meat products) and mislabelling (e.g. the inclusion of meat from species other than those declared) happens frequently worldwide. The aim of this study was to apply a droplet digital PCR assay for the detection and quantification (copies μL⁻¹) of the beef, pork, horse, sheep, chicken and turkey in meat products. The analysis conducted on commercial meat showed the presence of traces of DNA from other animal species than those declared. We show that the method is highly sensitive, specific and accurate (accuracy = 100%). This method could be adopted by competent food safety authorities to verify compliance with the labelling of meat products and to ensure quality and safety throughout the meat supply chain, from primary production to consumption.     

云边协同助力行业数字化转型

随着边缘计算的快速发展,边缘计算在各行业中的应用也越来越广泛.分析了云边协同在典型场景下的应用需求和业务模式,对云边协同在不同行业场景下的重要作用进行了介绍,最后给出了云边协同未来的发展趋势.     

基于津沽文化脉络的文创产品创新发展策略研究

类比故宫博物院等优秀文创产品产业化发展思路对津沽文化脉络进行文化传承元素的有效梳理，结合产业化发展的活态传承与保护形式，以文创产品设计为研究载体，带动城市文化创新发展与文化自信，间接促进社会经济发展。通过总结天津非物质文化遗产的特性和产业化发展现状，对比分析不同领域文创产品设计案例。以市场需求定位设计层级，建立在设计层级基础上的产品创新设计理念，从升级保护技术的角度拓展产业化发展思路，结合数字化营销模式，适应现今智能化时代需求。总结了非遗文创产品在层级设计指导下的历史与文化，以及在现代与未来概念下的文化传承创新发展方向。进一步带动了天津文化产业发展，促进文化产业的良性循环，为天津传统文化产业化发展提出了切实可行的创新理念。     

Damage evolution around white etching layer during uniaxial loading

Rolling contact fatigue cracks and thermally induced defects are common problems in the railway industry especially as demands for increasing loads, speeds, and safety continue to rise. Often, the two types of defects are found together in the field, however, whether one causes the other to occur is not completely agreed upon. The effect of thermal damage, in the form of a martensite spot on pearlitic steel test bars, on the fatigue life in uniaxial low cycle fatigue experiments was investigated by the authors. However, the focus of the current work was to characterize the damage evolution from the low cycle fatigue (LCF) tests and correlate the crack initiation and propagation with the initial thermal damage. Residual stress measurements, digital image correlation, and X‐ray tomography were used to characterize the effects of the thermal damage before, during, and after fatigue testing, respectively. It was found that the thermal damage causes strain accumulation and crack initiation at the interface between the two materials. The strain evolution was visualized using digital image correlation (DIC), clearly showing the strain concentrations at the top and bottom of the white etching layers (WEL), where the residual stresses are also most tensile. X‐ray tomography confirmed the planar crack growth from the martensite spot.     

Yellow resistant photosensitive resin for digital light processing 3D printing

Digital light processing (DLP) has been studied and developed in the field of three-dimensional (3D) printing in recent years due to its fast curing rate and high resolution. To reduce the cost and viscosity of the resin system, the aromatic polyurethane acrylates (PUAs) were used as oligomer. The matrix resin called PUH₂ consists of oligomers (PUA, bisphenol A polyoxyethylene ether dimethyl acrylate) and active diluents (hydroxyethyl acrylate, hydroxyethyl methacrylate). However, the photosensitive resin containing aromatic isocyanate groups was easily yellowed under ultraviolet light. In this article, we developed a resin for DLP 3D printing with yellowing resistance, excellent mechanical properties and high heat resistance. The optimal ratio of 3DP-PUH₂ resin was PUH₂/TPO/RYOJI-292/dye/nanosilica = 100/5/0.4/0.01/0.1, and its viscosity was 500 cp, which is suitable for DLP 3D printing. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020 , 137, 48369.     

Graph-based video fingerprinting using double optimal projection

A double optimal projection method that involves projections for intra-cluster and inter-cluster dimensionality reduction are proposed for video fingerprinting. The video is initially set as a graph with frames as its vertices in a high-dimensional space. A similarity measure that can compute the weights of the edges is then proposed. Subsequently, the video frames are partitioned into different clusters based on the graph model. Double optimal projection is used to explore the optimal mapping points in a low-dimensional space to reduce the video dimensions. The statistics and geometrical fingerprints are generated to determine whether a query video is copied from one of the videos in the database. During matching, the video can be roughly matched by utilizing the statistics fingerprint. Further matching is thereafter performed in the corresponding group using geometrical fingerprints. Experimental results show the good performance of the proposed video fingerprinting method in robustness and discrimination.