A residual a posteriori error estimate for partition of unity finite elements for three-dimensional transient heat diffusion problems using multiple global enrichment functions

In this article, a study of residual based a posteriori error estimation is presented for the partition of unity finite element method (PUFEM) for three-dimensional (3D) transient heat diffusion problems. The proposed error estimate is independent of the heuristically selected enrichment functions and provides a useful and reliable upper bound for the discretization errors of the PUFEM solutions. Numerical results show that the presented error estimate efficiently captures the effect of h-refinement and q-refinement on the performance of PUFEM solutions. It also efficiently reflects the effect of ill-conditioning of the stiffness matrix that is typically experienced in the partition of unity based finite element methods. For a problem with a known exact solution, the error estimate is shown to capture the same solution trends as obtained by the classical L₂ norm error. For problems with no known analytical solutions, the proposed estimate is shown to be used as a reliable and efficient tool to predict the numerical errors in the PUFEM solutions of 3D transient heat diffusion problems.     

Automated industrial IoT-device integration using the OpenPnP reference architecture

Distributed control systems are currently evolving towards industrial Internet of Things (IoT) systems communicating fully using Internet protocols. This creates opportunities for streamlining costly commissioning processes, which today require substantial manual work for installing, configuring, and integrating thousands of actuators and sensors. The vision of “plug-and-produce” control systems has been pursued for more than 15 years, but existing approaches fell short regarding configuration tasks and vendor neutrality. This paper introduces the standards-based IoT reference architecture OpenPnP, which allows largely automating the configuration and integration tasks of industrial commissioning processes. The architecture includes a number of design and technology decisions and the required implementation can be scaled down to resource-constrained industrial devices. This paper demonstrates how OpenPnP can reduce configuration and integration efforts up to 90% in typical settings, while potentially scaling well up to tens of thousands of communicated signals. Practitioners can orient their implementations towards OpenPnP, therefore potentially enabling “plug-and-produce” in many thousands of control systems.     

Tackling heterogeneous concept drift with the Self-Adjusting Memory (SAM)

Instance matching is the problem of determining whether two instances describe the same real-world entity or not. Instance matching plays a key role in data integration and data cleansing, especially for building a knowledge base. For example, we can regard each article in encyclopedias as an instance, and a group of articles which refers to the same real-world object as an entity. Therefore, articles about Washington should be distinguished and grouped into different entities such as Washington, D.C (the capital of the USA), George Washington (first president of the USA), Washington (a state of the USA), Washington (a village in West Sussex, England), Washington F.C. (a football club based in Washington, Tyne and Wear, England), Washington, D.C. (a novel). In this paper, we proposed a novel instance matching approach Active Instance Matching with Pairwise Constraints, which can bring the human into the loop of instance matching. The proposed approach can generate candidate pairs in advance to reduce the computational complexity, and then iteratively select the most informative pairs according to the uncertainty, influence, connectivity and diversity of pairs. We evaluated our approach one two publicly available datasets AMINER and WIDE-NUS and then applied our approach to the two large-scale real-world datasets, Baidu Baike and Hudong Baike, to build a Chinese knowledge base. The experiments and practice illustrate the effectiveness of our approach.     

Intelligence by mechanics

Research on the biomechanics of animal and human locomotion provides insight into basic principles of locomotion and respective implications for construction and control. Nearly elastic operation of the leg is necessary to reproduce the basic dynamics in walking and running. Elastic leg operation can be modelled with a spring-mass model. This model can be used as a template with respect to both gaits in the construction and control of legged machines. With respect to the segmented leg, the humanoid arrangement saves energy and ensures structural stability. With the quasi-elastic operation the leg inherits the property of self-stability, i.e. the ability to stabilize a system in the presence of disturbances without sensing the disturbance or its direct effects. Self-stability can be conserved in the presence of musculature with its crucial damping property. To ensure secure foothold visco-elastic suspended muscles serve as shock absorbers. Experiments with technically implemented leg models, which explore some of these principles, are promising.     

Nondestructive mechanical release of ordered polymer microfiber arrays from porous templates

The fabrication of one-dimensional (1D) nanostructures and microstructures inside the pores of porous templates is intensively investigated. The release of these structures is commonly accomplished by etching and destroying the templates. The 1D nanostructures and microstructures tend to condense because of the occurrence of capillary forces during drying of the specimens. It is shown that highly ordered arrays of polymer microfibers can be easily detached from silanized porous templates by mechanical lift-off. This procedure leaves the templates intact, thus allowing their recycling, and does not involve the use of solutions or solvents, thus circumventing condensation. Therefore, mechanical lift-off may enable the up-scaling of template-based approaches to the fabrication of highly ordered assemblies of 1D nanostructures and microstructures.     

Enthalpy and heat capacity of LiAlO2 between 298 and 1700 K by drop calorimetry

The enthalpy of γ-LiAlO₂ was measured between 403 and 1673 K by isothermal drop calorimetry. The smoothed enthalpy curve between 298 and 1700 K results in H⁰(T) − H⁰(298 K)=−37 396 + 93.143 · T + 0.00557 · T² + 2 725 221 · T⁻¹ J/mol. The standard deviation is 2.2%. The heat capacity was derived by differentiation of the enthalpy curve. The value extrapolated to 298 K is C_p,298=(65.8 ± 2.0) J/K mol.     

Performance of near-infrared reflectance spectroscopy (NIRS) in routine analysis of C18 unsaturated fatty acids in intact rapeseed

This study was aimed to evaluate the performance of near-infrared reflectance spectroscopy (NIRS) in the analysis of the oil composition for fatty acids like oleic (C18:1), linoleic (C18:2) and linolenic (C18:3) in zero-erucic acid rapeseed (Brassica napus L.). Intact-seed samples of 1094 lines from a breeding programme for high-oleic acid rapeseed were analyzed by both NIRS and gas chromatography (GC). Previously developed calibration equations were initially used for NIRS analyses. The accuracy of NIRS was considerably improved by including some samples of the actual breeding population into the original calibration set and developing new calibration equations. The inclusion of twenty randomly selected samples led to a reduction of the standard error of performance (SEP) from 2.6% to 1.9% for oleic, from 3.8% to 2.0% for linoleic, and from 1.1% to 0.9% for linolenic acid. The application of the new equations to the remaining population of 1074 samples resulted in coefficients of correlation between NIRS and GC values of 0.95 for oleic, 0.92 for linoleic, and 0.90 for linolenic acid. Furthermore, the effectiveness of a selection for high oleic, high linoleic, or low linolenic acid content based on NIRS data was demonstrated. The results of this study will help potential users to choose the optimal selection strategy in routine analysis of C18 unsaturated fatty acids by NIRS within a breeding programme.     

Retrospective mapping of burnt areas in Central Siberia using a modification of the normalised difference water index

The boreal forest contains almost half the total carbon pool of world forest ecosystems, and so has a very significant role in global biogeochemical cycles. The flux of greenhouse gases in and out of these forests is influenced strongly by disturbances such as diseases, logging and predominantly fire. It is important to quantify these disturbances to enable the modelling of major greenhouse gases. However, because of the remoteness and vastness of the boreal forest, little data is available on the type, extent, frequency and severity of these disturbances in Siberia. For burnt areas, two of the more responsive wavelengths are the short wave infra-red (SWIR) and the near infra-red (NIR). These produce a vegetation index, the normalised difference SWIR (NDSWIR) capable of detecting retrospective disturbances. Here we combine the NDSWIR from MODIS imagery acquired in the summer of 2003 with thermal anomaly data from 1992 to 2003 to detect and date areas which burnt at some point between 1992 and 2003. The semi-automated method is called SWIR and Thermal ANomalies for Detecting Disturbances (STANDD) and is complemented by an Normalised Difference Vegetation Index (NDVI) differencing method using MODIS 2002 and 2003 imagery to ensure reliable detection of area burnt in the year of image acquisition (i.e. 2003). The area of this study covers approximately 3 million km² stretching from Lake Baikal in the south to the Laptev Sea in the north, above the Arctic Circle. Landsat ETM+ images were used to validate the shape and areal extent of the burnt areas resulting in an 81% overall accuracy with a kappa coefficient of agreement of 0.63.