A Framework of Dynamic Data Driven Digital Twin for Complex Engineering Products: the Example of Aircraft Engine Health Management

Digital twin is a vital enabling technology for smart manufacturing in the era of Industry 4.0. Digital twin effectively replicates its physical asset enabling easy visualization, smart decision-making and cognitive capability in the system. In this paper, a framework of dynamic data driven digital twin for complex engineering products was proposed. To illustrate the proposed framework, an example of health management on aircraft engines was studied. This framework models the digital twin by extracting information from the various sensors and Industry Internet of Things (IIoT) monitoring the remaining useful life (RUL) of an engine in both cyber and physical domains. Then, with sensor measurements selected from linear degradation models, a long short-term memory (LSTM) neural network is proposed to dynamically update the digital twin, which can estimate the most up-to-date RUL of the physical aircraft engine. Through comparison with other machine learning algorithms, including similarity based linear regression and feed forward neural network, on RUL modelling, this LSTM based dynamical data driven digital twin provides a promising tool to accurately replicate the health status of aircraft engines. This digital twin based RUL technique can also be extended for health management and remote operation of manufacturing systems

Zinc ferrite based gas sensors: A review

Flammable, explosive and toxic gases, such as hydrogen, hydrogen sulfide and volatile organic compounds vapor, are major threats to the ecological environment safety and human health. Among the available technologies, gas sensing is a vital component, and has been widely studied in literature for early detection and warning. As a metal oxide semiconductor, zinc ferrite (ZnFe2O4) represents a kind of promising gas sensing material with a spinel structure, which also shows a fine gas sensing performance to reducing gases. Due to its great potentials and widespread applications, this article is intended to provide a review on the latest development in zinc ferrite based gas sensors. We first discuss the general gas sensing mechanism of ZnFe2O4 sensor. This is followed by a review of the recent progress about zinc ferrite based gas sensors from several aspects: different micro-morphology, element doping and heterostructure materials. In the end, we propose that combining ZnFe2O4 which provides unique microstructure (such as the multi-layer porous shells hollow structure), with the semiconductors such as graphene, which provide excellent physical properties. It is expected that the mentioned composites contribute to improving selectivity, long-term stability, and other sensing performance of sensors at room or low temperature

Learning to reconstruct and understand indoor scenes from sparse views

This paper proposes a new method for simultaneous 3D reconstruction and semantic segmentation for indoor scenes. Unlike existing methods that require recording a video using a color camera and/or a depth camera, our method only needs a small number of (e.g., 3~5) color images from uncalibrated sparse views, which significantly simplifies data acquisition and broadens applicable scenarios. To achieve promising 3D reconstruction from sparse views with limited overlap, our method first recovers the depth map and semantic information for each view, and then fuses the depth maps into a 3D scene. To this end, we design an iterative deep architecture, named IterNet, to estimate the depth map and semantic segmentation alternately. To obtain accurate alignment between views with limited overlap, we further propose a joint global and local registration method to reconstruct a 3D scene with semantic information. We also make available a new indoor synthetic dataset, containing photorealistic high-resolution RGB images, accurate depth maps and pixel-level semantic labels for thousands of complex layouts. Experimental results on public datasets and our dataset demonstrate that our method achieves more accurate depth estimation, smaller semantic segmentation errors, and better 3D reconstruction results over state-of-the-art methods

Genomics of Divergence along a Continuum of Parapatric Population Differentiation

MM received funding from the Max Planck innovation funds for this project. PGDF was supported by a Marie Curie European Reintegration Grant (proposal nr 270891). CE was supported by German Science Foundation grants (DFG, EI 841/4-1 and EI 841/6-1)

Evidence for Positive Selection on a Number of MicroRNA Regulatory Interactions during Recent Human Evolution

MicroRNA (miRNA)–mediated gene regulation is of critical functional importance in animals and is thought to be largely constrained during evolution. However, little is known regarding evolutionary changes of the miRNA network and their role in human evolution. Here we show that a number of miRNA binding sites display high levels of population differentiation in humans and thus are likely targets of local adaptation. In a subset we demonstrate that allelic differences modulate miRNA regulation in mammalian cells, including an interaction between miR-155 and TYRP1, an important melanosomal enzyme associated with human pigmentary differences. We identify alternate alleles of TYRP1 that induce or disrupt miR-155 regulation and demonstrate that these alleles are selected with different modes among human populations, causing a strong negative correlation between the frequency of miR-155 regulation of TYRP1 in human populations and their latitude of residence. We propose that local adaptation of microRNA regulation acts as a rheostat to optimize TYRP1 expression in response to differential UV radiation. Our findings illustrate the evolutionary plasticity of the microRNA regulatory network in recent human evolution

Analytic modeling of tunnel Field-Effect-Transistors and experimental investigation of GaN High-Electron-Mobility-Transistors

High density and lower power drive the aggressive scaling down of CMOS transistors. Yet, the scaling of Si bulk MOSFETs are approaching physical limits, suffering from poor electrostatic control due to short channel effects, gate leakage current caused by gate oxide tunneling, and most importantly the non-scaled supply voltage imposed by thermionic emission limitation. Tunnel FETs (TFETs) based on band-to-band tunneling current injection mechanism, have emerged as promising candidates to deliver steep turn-off slopes, thus enables a sharp reduction of supply voltage to below 0.5 V. This dissertation is primarily devoted to develop an accurate analytic model for TFETs with a double-gate structure, providing physical insights to the design principles. At the core of the model is a gate-controlled channel potential that satisfies the source and drain boundary conditions. The potential is of an exponential profile with a characteristic scale length given by the device thickness. Both the source-to-channel tunneling and source-to-drain tunneling are developed and included in the model. It has been verified by numerical simulations for a wide range of bandgaps and channel lengths. Also incorporated in the model are the short-channel effect, source doping effect, ambipolar effect, and de-bias of gate voltage by channel charge. Based on these, the guidelines for scaling TFETs to sub-10-nm channel lengths are brought forth. The model is continuous, physical and predictive in the sense that there is no need for ad hoc fitting parameters. For high-power and high-frequency applications, GaN high-electron-mobility-transistors (HEMTs) stand out as promising candidate devices for achieving high breakdown voltage, high output current and high transconductance characteristics. Yet, the performance of GaN HEMTs suffers from mobility degradation due to poor thermal dissipation of conventional epitaxial substrates. This dissertation also experimentally demonstrates the GaN HEMTs fabricated on diamond substrate with extraordinary thermal management capability. The self-heating induced current droop is effectively absent in the saturated Ids-Vds characteristics of the resulting devices, thus paving the way for enhancing the energy conversion efficiency

Feature Recognition of Froth Images Based on Energy Distribution Characteristics

This paper proposes a determining algorithm for froth image features based on the amplitude spectrum energy statistics by applying Fast Fourier Transformation to analyze the energy distribution of various-sized froth. The proposed algorithm has been used to do a froth feature analysis of the froth images from the alumina flotation processing site, and the results show that the consistency rate reaches 98.1 % and the usability rate 94.2 %; with its good robustness and high efficiency, the algorithm is quite suitable for flotation processing state recognition