Digital Twin Technology

Digital twin technology is considered to be the core technology of realizing Cyber-Physical System (CPS). It is the simulation technology that integrates multidisciplinary, multiphysical quantity, multiscale and multi probability by making full use of physical model, sensor update, operation history and other data. It is the mapping technology for the whole lifecycle process of physical equipment in virtual space. It is the basic technology of Industrial 4.0. This chapter mainly introduces: (1) the generation of digital twin technology; (2) the definition and characteristics of digital twin technology; (3) the relationship between digital twin and digital thread; (4) the implementation of the product digital twin model; and (5) the research progress and application of digital twin research

Complex Method Mixed with PSO Applying to Optimization Design of Bridge Crane Girder

In engineer design, basic complex method has not enough global search ability for the nonlinear optimization problem, so it mixed with particle swarm optimization (PSO) has been presented in the paper,that is the optimal particle evaluated from fitness function of particle swarm displacement complex vertex in order to realize optimal principle of the largest complex central distance.This method is applied to optimization design problems of box girder of bridge crane with constraint conditions.At first a mathematical model of the girder optimization has been set up,in which box girder cross section area of bridge crane is taken as the objective function, and its four sizes parameters as design variables, girder mechanics performance, manufacturing process, border sizes and so on requirements as constraint conditions. Then complex method mixed with PSO is used to solve optimization design problem of cane box girder from constrained optimization studying approach, and its optimal results have achieved the goal of lightweight design and reducing the crane manufacturing cost . The method is reliable, practical and efficient by the practical engineer calculation and comparative analysis with basic complex method

Regularized kernel function parameter of KPCA using WPSO-FDA for feature extraction and fault recognition of gearbox

Gearbox is subject to damage or malfunctions by complicated factors such as installation position and operation condition, meanwhile, accompanied by some nonlinear behaviors, which increase the difficulty of fault diagnosis and identification. Kernel principal component analysis (KPCA) is a commonly used method to realize nonlinear mapping via kernel function for feature extraction. However, choosing an appropriate kernel function and the proper setting of its parameter are decisive to obtain a high performance of the kernel methods. In this paper, we present a novel approach combining PSO and KPCA to enhance the fault classification performance. The standard particle swarm optimization (WPSO) was used to regularize kernel function parameter of KPCA instead of the empirical value. In particular, in view of the thought of Fisher Discriminate Analysis (FDA) in pattern recognition, the optimal mathematical model of kernel parameter was constructed, and its global optimal solution was searched by WPSO. The effectiveness of the method was proven using the Iris data set classification and gearbox faults classification. In the process, gearbox fault experiments were carried out, and the vibration signals in different conditions have been tested and processed, and the fault feature parameters were extracted. At last the analysis results of gearbox fault recognition was obtained by KPCA and compared with PCA. The results show that the separability of failure patterns in the feature space is improved after kernel parameter optimized by WPSO-FDA. The problems of single failure and compound fault recognition have been effectively solved by the optimized KPCA

Stack bound inference for abstract java bytecode

10.1109/TASE.2010.24Proceedings - 2010 4th International Symposium on Theoretical Aspects of Software Engineering, TASE 201057-6

Water promoted photocatalytic Cβ-O bonds hydrogenolysis in lignin model compounds and lignin biomass conversion to aromatic monomers

Photocatalysis has proved its potential in cleaving the Cβ-O linkages between the natural aromatic units in lignin biomass and converting abundant lignin biomass to valuable aromatic monomer products. However, the slow reaction rate and low selectivity for aromatic monomers still hinder its future industrial implementation. To address these challenges in photocatalytic Cβ-O bond fragmentation, a Zn/S rich phase zinc indium sulfide photocatalyst was developed to promote hydrogenolysis of Cβ-O linkages in lignin. In this work, water is for the first time, used as the hydrogen donor and can significantly promote the photocatalytic process by eliminating the limitation of protons supply. The reaction selectivity for aromatic monomers increased by 170% and PP-ol conversion rate raised by 58% comparing to the reaction condition without water. Notably, complete conversion of lignin model compounds with an expectational improved reaction rate and over 90% selectivity for aromatic monomers have been achieved in this study. The isotopic labeling experiments and kinetic isotope effects (KIE) measurements also indicate that the dissociation of the O–H bond in water which provides protons to the Cβ-O bond hydrogenolysis process is a critical step to this reaction. Mechanistic studies reveal that the dehydrogenated radical intermediates are initially generated by the oxidation of photogenerated holes, and the protons generated from photocatalytic water splitting are superior in facilitating the subsequently hydrogenolysis process of Cβ-O bonds. This study provides a new and effective strategy to promote the cleavage of Cβ-O linkages and is helpful for the future development of photocatalytic lignin valorization

Auto-Parallelizing Large Models with Rhino: A Systematic Approach on Production AI Platform

We present Rhino, a system for accelerating tensor programs with automatic parallelization on AI platform for real production environment. It transforms a tensor program written for a single device into an equivalent distributed program that is capable of scaling up to thousands of devices with no user configuration. Rhino firstly works on a semantically independent intermediate representation of tensor programs, which facilitates its generalization to unprecedented applications. Additionally, it implements a task-oriented controller and a distributed runtime for optimal performance. Rhino explores on a complete and systematic parallelization strategy space that comprises all the paradigms commonly employed in deep learning (DL), in addition to strided partitioning and pipeline parallelism on non-linear models. Aiming to efficiently search for a near-optimal parallel execution plan, our analysis of production clusters reveals general heuristics to speed up the strategy search. On top of it, two optimization levels are designed to offer users flexible trade-offs between the search time and strategy quality. Our experiments demonstrate that Rhino can not only re-discover the expert-crafted strategies of classic, research and production DL models, but also identify novel parallelization strategies which surpass existing systems for novel models

Functional analysis of the GbDWARF14 gene associated with branching development in cotton

Plant architecture, including branching pattern, is an important agronomic trait of cotton crops. In recent years, strigolactones (SLs) have been considered important plant hormones that regulate branch development. In some species such as Arabidopsis, DWARF14 is an unconventional receptor that plays an important role in the SL signaling pathway. However, studies on SL receptors in cotton are still lacking. Here, we cloned and analysed the structure of the GbD14 gene in Gossypium barbadense and found that it contains the domains necessary for a SL receptor. The GbD14 gene was expressed primarily in the roots, leaves and vascular bundles, and the GbD14 protein was determined via GFP to localize to the cytoplasm and nucleus. Gene expression analysis revealed that the GbD14 gene not only responded to SL signals but also was differentially expressed between cotton plants whose types of branching differed. In particular, GbD14 was expressed mainly in the axillary buds of normal-branching cotton, while it was expressed the most in the leaves of nulliplex-branch cotton. In cotton, the GbD14 gene can be induced by SL and other plant hormones, such as indoleacetic acid, abscisic acid, and jasmonic acid. Compared with wild-type Arabidopsis, GbD14-overexpressing Arabidopsis responded more rapidly to SL signals. Moreover, we also found that GbD14 can rescue the multi-branched phenotype of Arabidopsis Atd14 mutants. Our results indicate that the function of GbD14 is similar to that of AtD14, and GbD14 may be a receptor for SL in cotton and involved in regulating branch development. This research provides a theoretical basis for a profound understanding of the molecular mechanism of branch development and ideal plant architecture for cotton breeding improvements

Protection Evaluation of a Five-Gene-Deleted African Swine Fever Virus Vaccine Candidate Against Homologous Challenge

African swine fever virus (ASFV) represents a serious threat to the global swine industry, and there are no safe or commercially available vaccines. Previous studies have demonstrated that inactivated vaccines do not provide sufficient protection against ASFV and that attenuated vaccines are effective, but raise safety concerns. Here, we first constructed a deletion mutant in which EP153R and EP402R gene clusters were knocked out. Based on the deletion mutant, a further deletion from the MGF_360-12L, MGF_360-13L to MGF_360-14L genes was obtained. The five-genes knockout virus was designated as ASFV-ΔECM3. To investigate the efficacy and safety of the ASFV-ΔECM3 virus as a vaccine candidate, the evaluation of the virus was subsequently carried out in pigs. The results showed that the ASFV-ΔECM3 virus could induce homologous protection against the parental isolate, and no significant clinical signs or viremia were observed. These results show that the contiguous deletion mutant, ASFV-ΔECM3 encompassing the EP153R/EP402R and MGF_360-12L/13L/14L genes, could be a potential live-attenuated vaccine candidate for the prevention of ASFV infection