In structural simulation and design,an accurate computational model directly determines the effectiveness of performance evaluation.To establish a high-fidelity dynamic model of a complex assembled structure,a Hierarc...In structural simulation and design,an accurate computational model directly determines the effectiveness of performance evaluation.To establish a high-fidelity dynamic model of a complex assembled structure,a Hierarchical Model Updating Strategy(HMUS)is developed for Finite Element(FE)model updating with regard to uncorrelated modes.The principle of HMUS is first elaborated by integrating hierarchical modeling concept,model updating technology with proper uncorrelated mode treatment,and parametric modeling.In the developed strategy,the correct correlated mode pairs amongst the uncorrelated modes are identified by an error minimization procedure.The proposed updating technique is validated by the dynamic FE model updating of a simple fixed–fixed beam.The proposed HMUS is then applied to the FE model updating of an aeroengine stator system(casings)to demonstrate its effectiveness.Our studies reveal that(A)parametric modeling technique is able to build an efficient equivalent model by simplifying complex structure in geometry while ensuring the consistency of mechanical characteristics;(B)the developed model updating technique efficiently processes the uncorrelated modes and precisely identifies correct Correlated Mode Pairs(CMPs)between FE model and experiment;(C)the proposed HMUS is accurate and efficient in the FE model updating of complex assembled structures such as aeroengine casings with large-scale model,complex geometry,high-nonlinearity and numerous parameters;(D)it is appropriate to update a complex structural FE model parameterized.The efforts of this study provide an efficient updating strategy for the dynamic model updating of complex assembled structures with experimental test data,which is promising to promote the precision and feasibility of simulation-based design optimization and performance evaluation of complex structures.展开更多
In this paper, a numerical method for correlation sensitivity analysis of a nonlinear random vibration system is presented. Based on the first passage failure model, the probability perturbation method is employed to ...In this paper, a numerical method for correlation sensitivity analysis of a nonlinear random vibration system is presented. Based on the first passage failure model, the probability perturbation method is employed to determine the statistical characteristics of failure modes and the correlation between them. The sensitivity of correlation between failure modes with respect to random parameters characterizing the uncertainty of the hysteretic loop is discussed. In a numerical example, a two-DOF shear structure with uncertain hysteretic restoring force is considered. The statistical characteristics of response, failure modes and the sensitivity of random hysteretic loop parameters are provided, and also compared with a Monte Carlo simulation.展开更多
Design of video encoders involves implementation of fast mode decision(FMD) algorithm to reduce computation complexity while maintaining the performance of the coding. Although H.264/scalable video coding(SVC) achieve...Design of video encoders involves implementation of fast mode decision(FMD) algorithm to reduce computation complexity while maintaining the performance of the coding. Although H.264/scalable video coding(SVC) achieves high scalability and coding efficiency, it also has high complexity in implementing its exhaustive computation. In this paper, a novel algorithm is proposed to reduce the redundant candidate modes by making use of the correlation among layers. A desired mode list is created based on the probability to be the best mode for each block in base layer and a candidate mode selection in the enhancement layer by the correlations of modes among reference frame and current frame. Our algorithm is implemented in joint scalable video model(JSVM)9.19.15 reference software and the performance is evaluated based on the average encoding time, peak signal to noise ration(PSNR)and bit rate. The experimental results show 41.89% improvement in encoding time with minimal loss of 0.02 dB in PSNR and 0.05%increase in bit rate.展开更多
Current integration methods for single-cell RNA sequencing(scRNA-seq)data and spatial transcriptomics(ST)data are typically designed for specific tasks,such as deconvolution of cell types or spatial distribution predi...Current integration methods for single-cell RNA sequencing(scRNA-seq)data and spatial transcriptomics(ST)data are typically designed for specific tasks,such as deconvolution of cell types or spatial distribution prediction of RNA transcripts.These methods usually only offer a partial analysis of ST data,neglecting the complex relationship between spatial expression patterns underlying cell-type specificity and intercellular cross-talk.Here,we present eMCI,an explainable multimodal correlation integration model based on deep neural network framework.eMCI leverages the fusion of scRNA-seq and ST data using different spot–cell correlations to integrate multiple synthetic analysis tasks of ST data at cellular level.First,eMCI can achieve better or comparable accuracy in cell-type classification and deconvolution according to wide evaluations and comparisons with state-of-the-art methods on both simulated and real ST datasets.Second,eMCI can identify key components across spatial domains responsible for different cell types and elucidate the spatial expression patterns underlying cell-type specificity and intercellular communication,by employing an attribution algorithm to dissect the visual input.Especially,eMCI has been applied to 3 cross-species datasets,including zebrafish melanomas,soybean nodule maturation,and human embryonic lung,which accurately and efficiently estimate per-spot cell composition and infer proximal and distal cellular interactions within the spatial and temporal context.In summary,eMCI serves as an integrative analytical framework to better resolve the spatial transcriptome based on existing single-cell datasets and elucidate proximal and distal intercellular signal transduction mechanisms over spatial domains without requirement of biological prior reference.This approach is expected to facilitate the discovery of spatial expression patterns of potential biomolecules with cell type and cell–cell communication specificity.展开更多
For accurate Finite Element(FE)modeling for the structural dynamics of aeroengine casings,Parametric Modeling-based Model Updating Strategy(PM-MUS)is proposed based on efficient FE parametric modeling and model updati...For accurate Finite Element(FE)modeling for the structural dynamics of aeroengine casings,Parametric Modeling-based Model Updating Strategy(PM-MUS)is proposed based on efficient FE parametric modeling and model updating techniques regarding uncorrelated/correlated mode shapes.Casings structure is parametrically modeled by simplifying initial structural FE model and equivalently simulating mechanical characteristics.Uncorrelated modes between FE model and experiment are reasonably handled by adopting an objective function to recognize correct correlated modes pairs.The parametrized FE model is updated to effectively describe structural dynamic characteristics in respect of testing data.The model updating technology is firstly validated by the detailed FE model updating of one fixed–fixed beam structure in light of correlated/uncorrelated mode shapes and measured mode data.The PM-MUS is applied to the FE parametrized model updating of an aeroengine stator system(casings)which is constructed by the proposed parametric modeling approach.As revealed in this study,(A)the updated models by the proposed updating strategy and dynamic test data is accurate,and(B)the uncorrelated modes like close modes can be effectively handled and precisely identify the FE model mode associated the corresponding experimental mode,and(C)parametric modeling can enhance the dynamic modeling updating of complex structure in the accuracy of mode matching.The efforts of this study provide an efficient dynamic model updating strategy(PM-MUS)for aeroengine casings by parametric modeling and experimental test data regarding uncorrelated modes.展开更多
This paper proposes a new signal noise level estimation approach by local regions. The estimated noise variance is applied as the threshold for an improved empirical mode decomposition（EMD） based signal denoising me...This paper proposes a new signal noise level estimation approach by local regions. The estimated noise variance is applied as the threshold for an improved empirical mode decomposition（EMD） based signal denoising method. The proposed estimation method can effectively extract the candidate regions for the noise level estimation by measuring the correlation coefficient between noisy signal and a Gaussian filtered signal. For the improved EMD based method, the situation of decomposed intrinsic mode function（IMFs） which contains noise and signal simultaneously are taken into account. Experimental results from two simulated signals and an X-ray pulsar signal demonstrate that the proposed method can achieve better performance than the conventional EMD and wavelet transform（WT） based denoising methods.展开更多
基金co-supported by National Natural Science Foundation of China(No.51975124)Shanghai International Cooperation Project of One Belt and One Road of China(No.20110741700)Major Research Special Project of Aeroengine and Gas Turbine of China(No.J2019-IV-0016)。
文摘In structural simulation and design,an accurate computational model directly determines the effectiveness of performance evaluation.To establish a high-fidelity dynamic model of a complex assembled structure,a Hierarchical Model Updating Strategy(HMUS)is developed for Finite Element(FE)model updating with regard to uncorrelated modes.The principle of HMUS is first elaborated by integrating hierarchical modeling concept,model updating technology with proper uncorrelated mode treatment,and parametric modeling.In the developed strategy,the correct correlated mode pairs amongst the uncorrelated modes are identified by an error minimization procedure.The proposed updating technique is validated by the dynamic FE model updating of a simple fixed–fixed beam.The proposed HMUS is then applied to the FE model updating of an aeroengine stator system(casings)to demonstrate its effectiveness.Our studies reveal that(A)parametric modeling technique is able to build an efficient equivalent model by simplifying complex structure in geometry while ensuring the consistency of mechanical characteristics;(B)the developed model updating technique efficiently processes the uncorrelated modes and precisely identifies correct Correlated Mode Pairs(CMPs)between FE model and experiment;(C)the proposed HMUS is accurate and efficient in the FE model updating of complex assembled structures such as aeroengine casings with large-scale model,complex geometry,high-nonlinearity and numerous parameters;(D)it is appropriate to update a complex structural FE model parameterized.The efforts of this study provide an efficient updating strategy for the dynamic model updating of complex assembled structures with experimental test data,which is promising to promote the precision and feasibility of simulation-based design optimization and performance evaluation of complex structures.
基金National Natural Science Foundation of ChinaUnder Grant No: 50535010
文摘In this paper, a numerical method for correlation sensitivity analysis of a nonlinear random vibration system is presented. Based on the first passage failure model, the probability perturbation method is employed to determine the statistical characteristics of failure modes and the correlation between them. The sensitivity of correlation between failure modes with respect to random parameters characterizing the uncertainty of the hysteretic loop is discussed. In a numerical example, a two-DOF shear structure with uncertain hysteretic restoring force is considered. The statistical characteristics of response, failure modes and the sensitivity of random hysteretic loop parameters are provided, and also compared with a Monte Carlo simulation.
文摘Design of video encoders involves implementation of fast mode decision(FMD) algorithm to reduce computation complexity while maintaining the performance of the coding. Although H.264/scalable video coding(SVC) achieves high scalability and coding efficiency, it also has high complexity in implementing its exhaustive computation. In this paper, a novel algorithm is proposed to reduce the redundant candidate modes by making use of the correlation among layers. A desired mode list is created based on the probability to be the best mode for each block in base layer and a candidate mode selection in the enhancement layer by the correlations of modes among reference frame and current frame. Our algorithm is implemented in joint scalable video model(JSVM)9.19.15 reference software and the performance is evaluated based on the average encoding time, peak signal to noise ration(PSNR)and bit rate. The experimental results show 41.89% improvement in encoding time with minimal loss of 0.02 dB in PSNR and 0.05%increase in bit rate.
基金supported by the National Key R&D Program of China(Nos.2023YFF1204700 and 2022YFF1202100)the National Natural Science Foundation of China(Nos.12371485,62201150,T2341022,62172164,12322119,and 12271180)the Natural Science Foundation of Guangdong Province of China(Nos.2022A1515110759,2023A1515110558,and 2024A1515011797).
文摘Current integration methods for single-cell RNA sequencing(scRNA-seq)data and spatial transcriptomics(ST)data are typically designed for specific tasks,such as deconvolution of cell types or spatial distribution prediction of RNA transcripts.These methods usually only offer a partial analysis of ST data,neglecting the complex relationship between spatial expression patterns underlying cell-type specificity and intercellular cross-talk.Here,we present eMCI,an explainable multimodal correlation integration model based on deep neural network framework.eMCI leverages the fusion of scRNA-seq and ST data using different spot–cell correlations to integrate multiple synthetic analysis tasks of ST data at cellular level.First,eMCI can achieve better or comparable accuracy in cell-type classification and deconvolution according to wide evaluations and comparisons with state-of-the-art methods on both simulated and real ST datasets.Second,eMCI can identify key components across spatial domains responsible for different cell types and elucidate the spatial expression patterns underlying cell-type specificity and intercellular communication,by employing an attribution algorithm to dissect the visual input.Especially,eMCI has been applied to 3 cross-species datasets,including zebrafish melanomas,soybean nodule maturation,and human embryonic lung,which accurately and efficiently estimate per-spot cell composition and infer proximal and distal cellular interactions within the spatial and temporal context.In summary,eMCI serves as an integrative analytical framework to better resolve the spatial transcriptome based on existing single-cell datasets and elucidate proximal and distal intercellular signal transduction mechanisms over spatial domains without requirement of biological prior reference.This approach is expected to facilitate the discovery of spatial expression patterns of potential biomolecules with cell type and cell–cell communication specificity.
基金co-supported by National Natural Science Foundation of China(Nos.51975124 and 51675179)Shanghai International Cooperation Project of One Belt and One Road of China(No.20110741700)Research Startup Fund of Fudan University(No.FDU38341)。
文摘For accurate Finite Element(FE)modeling for the structural dynamics of aeroengine casings,Parametric Modeling-based Model Updating Strategy(PM-MUS)is proposed based on efficient FE parametric modeling and model updating techniques regarding uncorrelated/correlated mode shapes.Casings structure is parametrically modeled by simplifying initial structural FE model and equivalently simulating mechanical characteristics.Uncorrelated modes between FE model and experiment are reasonably handled by adopting an objective function to recognize correct correlated modes pairs.The parametrized FE model is updated to effectively describe structural dynamic characteristics in respect of testing data.The model updating technology is firstly validated by the detailed FE model updating of one fixed–fixed beam structure in light of correlated/uncorrelated mode shapes and measured mode data.The PM-MUS is applied to the FE parametrized model updating of an aeroengine stator system(casings)which is constructed by the proposed parametric modeling approach.As revealed in this study,(A)the updated models by the proposed updating strategy and dynamic test data is accurate,and(B)the uncorrelated modes like close modes can be effectively handled and precisely identify the FE model mode associated the corresponding experimental mode,and(C)parametric modeling can enhance the dynamic modeling updating of complex structure in the accuracy of mode matching.The efforts of this study provide an efficient dynamic model updating strategy(PM-MUS)for aeroengine casings by parametric modeling and experimental test data regarding uncorrelated modes.
基金supported by the China Aerospace Science and Technology Corporation’s Aerospace Science and Technology Innovation Fund Project(casc2013086)CAST Innovation Fund Project(cast2012028)
文摘This paper proposes a new signal noise level estimation approach by local regions. The estimated noise variance is applied as the threshold for an improved empirical mode decomposition（EMD） based signal denoising method. The proposed estimation method can effectively extract the candidate regions for the noise level estimation by measuring the correlation coefficient between noisy signal and a Gaussian filtered signal. For the improved EMD based method, the situation of decomposed intrinsic mode function（IMFs） which contains noise and signal simultaneously are taken into account. Experimental results from two simulated signals and an X-ray pulsar signal demonstrate that the proposed method can achieve better performance than the conventional EMD and wavelet transform（WT） based denoising methods.
