Deployment of a cloud pipeline for real-time visual inspection using fast streaming high-definition images

We investigate the challenges of building an end-to-end cloud pipeline for real-time intelligent visual inspection system for use in automotive manufacturing. Current methods of visual detection in automotive assembly are highly labor intensive, and thus prone to errors. An automated process is sought that can operate within the real-time constraints of the assembly line and can reduce errors. Components of the cloud pipeline include capture of a large set of high-definition images from a camera setup at the assembly location, transfer and storage of the images as needed, execution of object detection, and notification to a human operator when a fault is detected. The end-to-end execution must complete within a fixed time frame before the next car arrives in the assembly line. In this article, we report the design, development, and experimental evaluation of the tradeoffs of performance, accuracy, and scalability for a cloud system.     

Optimizing the Optoelectronic Properties of Face-On Oriented Poly(3,4-Ethylenedioxythiophene) via Water-Assisted Oxidative Chemical Vapor Deposition

Engineering the texture and nanostructure to improve the electrical conductivity of semicrystalline conjugated polymers must address the rate-limiting step for charge carrier transport. In highly face-on orientation, the charge transport between chains within a crystallite becomes rate-limiting, which is highly sensitive to the π–π stacking distance and interchain charge transfer integral. Here, face-on oriented semicrystalline poly(3,4-ethylenedioxythiophene) (PEDOT) thin films are grown via water-assisted (W-A) oxidative chemical vapor deposition (oCVD). Combining W-A with the volatile oxidant, antimony pentachloride, yields an optimized electrical conductivity of 7520  ±  240 S cm⁻¹, a record for PEDOT thin films. Systematic control of π–π stacking distance from 3.50 Å down to 3.43 Å yields an electrical conductivity enhancement of ≈ 1140%. The highest electrical conductivity also corresponds to minimum in Urbach energy of 205 meV, indicating superior morphological order. The figure of merit for transparent conductors, σ_dc/σ_op, reaches a maximum value of 94, which is 1.9 × and 6.7 × higher than oCVD PEDOT grown without W-A and utilizing vanadium oxytrichloride and iron chloride oxidizing agents, respectively. The W-A oCVD is single-step all-dry process and provides conformal coverage, allowing direct growth on mechanical flexible, rough, and structured surfaces without the need for complex and costly transfer steps.     

急性炎性疾病患者乳酸林格氏液液体动力学

目的:探索炎性疾病患者的乳酸林格氏液（Ringer's lactate,RL）液体动力学特征以及炎性生物标记物是否可以作为协变量影响RL分布和排泄。方法:本研究为前瞻性队列研究。选择40例美国麻醉医师分级（ASA）I-II级，腹腔镜下择期胆囊切除术（胆囊炎组，n=20）或者腹腔镜下急诊阑尾切除术（阑尾炎组，n=20）。所有患者麻醉诱导前开始输注RL，按15 mL/kg，35 min内输毕。采用酶联免疫（enzyme-linked immunosorbent assay,ELISA）方法测定血浆炎症（TNF-α,IL-10和CRP）或者内皮损伤生物标记物（syndecan-1,SDC-1）；利用血红蛋白（Hb）稀释-时间曲线和尿量，使用Phoenix软件，采用非线性混合效应模型分析计算RL液体动力学参数和协变量的影响。结果:与胆囊炎组相比，阑尾炎组RL从组织间隙到血浆的转运速率常数（k21）显著降低（14×10-3min-1 versus 35×10-3min-1;P=0.012）。阑尾炎组C反应蛋白（CRP）升高［中位数38.1(1.8-143.6) μg/mL versus 1.3(0.1-159.0) μg/mL;P<0.001］；与清醒状态相比，麻醉期间（输液开始后30～45 min），液体从中央室中到外周室的转运速率常数（k12）显著增加（57×10-3min-1 versus 32×10-3min-1；P<0.01）。清除速率常数（k10）降低90%（0.6×10-3min-1 versus 5.3×10-3min-1;P<0.001）。无论在清醒状态还是麻醉状态下低血压均能降低液体清除；炎症或者内膜损伤的生物标记物不能作为显著影响RL液体动力学参数的协变量。结论:阑尾炎或者胆囊炎患者术前输入液体后“炎症反应的生物标记物”不是RL的液体动力学的协变量，但是两组患者中，全身麻醉期间输入液体的清除率下降。     

FRAME BUILDING IN BERMUDA: ENGLISH CARPENTRY GONE NATIVE

Early-modern timber framing and associated finish vary significantly among the countless places settled by Europeans and Africans in the age of exploration. Most of these dots on maps of the western hemisphere remain essentially unstudied, as evidence for their vernacular architecture slips away. This is of more than local interest because the material offers opportunities to investigate how related populations share and alter cultural traits, and how the traits evolve in response to degrees of immigration, value of labour, environmental conditions and trade. This paper focuses on one of the dots, presenting new evidence for frame construction on the small British island of Bermuda, and addresses its role in the population’s economic evolution.     

Low-Loss Organic Hyperbolic Materials in the Visible Spectral Range: A Joint Experimental and First-Principles Study

Hyperbolic media strengthen numerous attractive applications in optics such as super-resolution imaging, enhanced spontaneous emission, and nanoscale waveguiding. Natural hyperbolic materials exist at visible frequencies; however, implementations of these materials suffer substantial compromises resulting from the high loss in the currently available candidates. Here, the first experimental and theoretical investigation of regioregular poly(3-alkylthiophenes) (rr-P3ATs), a naturally low-loss organic hyperbolic material (OHM) in the visible frequency range, is shown. These hyperbolic properties arise from a highly ordered structure of layered electron-rich conjugated thiophene ring backbones separated by insulating alkyl side chains. The optical and electronic properties of the rr-P3AT can be tuned by controlling the degree of crystallinity and alkyl side chain length. First-principles calculations support the experimental observations, which result from the rr-P3AT's structural and optical anisotropy. Conveniently, rr-P3AT-based OHMs are facile to fabricate, flexible, and biocompatible, which may lead to tremendous new opportunities in a wide range of applications.     

Knowledge-Based Virtual Modeling and Simulation of Manufacturing Processes for Industry 4.0

Abstract

Industry 4.0 aims at providing a digital representation of a production landscape, but the challenges in building, maintaining, optimizing, and evolving digital models in inter-organizational production chains have not been identified yet in a systematic manner. In this paper, various Industry 4.0 research and technical challenges are addressed, and their present scenario is discussed. Moreover, in this article, the novel concept of developing experience-based virtual models of engineering entities, process, and the factory is presented. These models of production units, processes, and procedures are accomplished by virtual engineering object (VEO), virtual engineering process (VEP), and virtual engineering factory (VEF), using the knowledge representation technique of Decisional DNA. This blend of the virtual and physical domains permits monitoring of systems and analysis of data to foresee problems before they occur, develop new opportunities, prevent downtime, and even plan for the future by using simulations. Furthermore, the proposed virtual model concept not only has the capability of Query Processing and Data Integration for Industrial Data but also real-time visualization of data stream processing.     

Experience-Based Cognition for Driving Behavioral Fingerprint Extraction

Abstract

With the rapid progress of information technologies, cars have been made increasingly intelligent. This allows cars to act as cognitive agents, i.e., to acquire knowledge and understanding of the driving habits and behavioral characteristics of drivers (i.e., driving behavioral fingerprint) through experience. Such knowledge can be then reused to facilitate the interaction between a car and its driver, and to develop better and safer car controls. In this paper, we propose a novel approach to extract the driver’s driving behavioral fingerprints based on our conceptual framework Experience-Oriented Intelligent Things (EOIT). EOIT is a learning system that has the potential to enable Internet of Cognitive Things (IoCT) where knowledge can be extracted from experience, stored, evolved, shared, and reused aiming for cognition and thus intelligent functionality of things. By catching driving data, this approach helps cars to collect the driver’s pedal and steering operations and store them as experience; eventually, it uses obtained experience for the driver’s driving behavioral fingerprint extraction. The initial experimental implementation is presented in the paper to demonstrate our idea, and the test results show that it outperforms the Deep Learning approaches (i.e., deep fully connected neural networks and recurrent neural networks/Long Short-Term Memory networks).