Poly[[[μ3-N′-(carboxymethyl)ethylene­di­amine-N,N,N′-triacetato]dysprosium(III)] trihydrate]

In the title coordination polymer, {[Dy(C10H13N2O8)]·3H2O}n, the dysprosium(III) ion is coordinated by two N atoms and six O atoms from three different (carb­oxy­meth­yl)ethyl­ene­diamine­triacetate ligands in a distorted square-anti­prismatic geometry. The ligands connect the metal atoms, forming layers parallel to the ab plane. O—H⋯O hydrogen bonds further assemble adjacent layers into a three-dimensional supra­molecular network

Research on the technology of sealing disk-baffle integrated structure design

Sealing disk-baffle integrated structure has been widely applied in modern advanced aero-engines. In this paper, the features of typical sealing disk-baffle integrated structure were analyzed in order to explore the feasible direction of the structure design and optimization. The finite element models were analyzed for the CFM56 series aero-engines [1], and the stress was computed under the different temperature fields, operating speeds and non-linear contacting. The design technologies were summarized for the sealing disk-baffle integrated structure. The results provided bases and references for structure design and engineering applications of the sealing disk-baffle integrated structure

Anderson Accelerated Gauss-Newton-guided deep learning for nonlinear inverse problems with Application to Electrical Impedance Tomography

Physics-guided deep learning is an important prevalent research topic in scientific machine learning, which has tremendous potential in various complex applications including science and engineering. In these applications, data is expensive to acquire and high accuracy is required for making decisions. In this work, we introduce an efficient physics-guided deep learning framework for the variational modeling of nonlinear inverse problems, which is then applied to solve an electrical impedance tomography (EIT) inverse problem. The framework is achieved by unrolling the proposed Anderson accelerated Gauss-Newton (GNAA) algorithm into an end-to-end deep learning method. Firstly, we show the convergence of the GNAA algorithm in both cases: Anderson depth is equal to one and Anderson depth is greater than one. Then, we propose three types of strategies by combining the complementary strengths of GNAA and deep learning: GNAA of learned regularization (GNAA-LRNet), where the singular values of the regularization matrix are learned by a deep neural network; GNAA of learned proximity (GNAA-LPNet), where the regularization proximal operator is learned by using a deep neural network; GNAA of plug-and-play method (GNAA-PnPNet) where the regularization proximal operator is replaced by a pre-trained deep denoisers. Lastly, we present some numerical experiments to illustrate that the proposed approaches greatly improve the convergence rate and the quality of inverse solutions

HEAT TRANSFER MODELING AND APPLICATION OF GAS-LIQUID TWO-PHASE FLOW IN PARTIALLY-BURIED PIPELINE

Abstract For the deepwater partially-buried pipeline transporting wet natural gas, a mechanistic heat transfer model is developed, and the temperature profile behaviors and corrosion resistant alloy (CRA) length of deepwater pipelines of Liwan3-1 gas field are studied by numerical simulation. The result shows that for a critical temperature of 23 °C, all production flowlines should be used CRA, and the CRA length of the tieback pipeline is recommended to be 1.5 km. There are significant differences of temperature drop between the mechanistic and linear models

Learning Dense UV Completion for Human Mesh Recovery

Human mesh reconstruction from a single image is challenging in the presence of occlusion, which can be caused by self, objects, or other humans. Existing methods either fail to separate human features accurately or lack proper supervision for feature completion. In this paper, we propose Dense Inpainting Human Mesh Recovery (DIMR), a two-stage method that leverages dense correspondence maps to handle occlusion. Our method utilizes a dense correspondence map to separate visible human features and completes human features on a structured UV map dense human with an attention-based feature completion module. We also design a feature inpainting training procedure that guides the network to learn from unoccluded features. We evaluate our method on several datasets and demonstrate its superior performance under heavily occluded scenarios compared to other methods. Extensive experiments show that our method obviously outperforms prior SOTA methods on heavily occluded images and achieves comparable results on the standard benchmarks (3DPW)

A Practical Method of Coverage Assessment and Measurement for Digital Terrestrial Television Broadcasting

This paper specifies the objective assessment and measurement method for signals coverage quality of single transmitter and outdoor fixed reception of digital terrestrial television broadcasting system, wherein transmission system of single frequency network is used to convert the input data stream to output RF signal. Any equivalent measurement to guarantee the same measurement uncertainty can also be adopted