APPLICATION OF VECTOR TO SMALL DEFORMATION

ＡＰＰＬＩＣＡＴＩＯＮ　ＯＦ　ＶＥＣＴＯＲ　ＴＯ　ＳＭＡＬＬ　ＤＥＦＯＲＭＡＴＩＯＮＺｈａｎｇＳｈｅｎｘｕｅ（张慎学）（ＲｅｃｅｉｖｅｄＤｅｃ．１６，１９９４，Ｍａｙ２８，１９９５，ＣｏｍｍｕｎｉｃａｔｅｄｂｙＣｈｉｅｎＷｅｉｚａｎｇ）Ａｂｓｔｒａｃ...     

一种低精度惯性测量单元的精确标定技术

低精度惯性测量单元的温度特性和非线性严重，为补偿光纤陀螺的温度特性和非线性，通过高低温、多速率的标定实验研究了光纤陀螺输出电压与温度、转速的关系，采用零偏和标度因数统一标定的思想提出了光纤陀螺分段模型；为补偿MEMS加速度计的温度特^陛，通过高低温位置实验研究了加速度计输出电压与温度、输入加速度的关系，提出了加速度计分段模型。采用逐步线性回归对以上模型进行了简化。实时补偿效果表明，当温度从-30℃到60℃变化时，在±60（°）／s转速内角速度误差基本小于0．02（°）／s，加速度误差小于0．005g．     

Qsar Models for Predicting the Acute Toxicity of Selected Organic Chemicals with Diverse Structures to Aquatic Non-Vertebrates and Humans

Abstract

The linear and non-linear relationships of acute toxicity (as determined on five aquatic non-vertebrates and humans) to molecular structure have been investigated on 38 structurally-diverse chemicals. The compounds selected are the organic chemicals from the 50 priority chemicals prescribed by the Multicentre Evaluation of In Vitro Cytotoxicity (MEIC) programme. The models used for the evaluations are the best combination of physico-chemical properties that could be obtained so far for each organism, using the partial least squares projection to latent structures (PLS) regression method and backpropagated neural networks (BPN). Non-linear models, whether derived from PLS regression or backpropagated neural networks, appear to be better than linear models for describing the relationship between acute toxicity and molecular structure. BPN models, in turn, outperform non-linear models obtained from PLS regression. The predictive power of BPN models for the crustacean test species are better than the model for humans (based on human lethal concentration). The physico-chemical properties found to be important to predict both human acute toxicity and the toxicity to aquatic non-vertebrates are the n?octanol water partition coefficient (Pow) and heat of formation (HF). Aside from the two former properties, the contribution of parameters that reflect size and electronic properties of the molecule to the model is also high, but the type of physico-chemical properties differs from one model to another. In all of the best BPN models, some of the principal component analysis (PCA) scores of the ¹³C-NMR spectrum, with electron withdrawing/accepting capacity (LUMO, HOMO and IP) are molecular size/volume (VDW or MS1) parameters are relevant. The chemical deviating from the QSAR models include non-pesticides as well as some of the pesticides tested. The latter type of chemical fits in a number of the QSAR models. Outliers for one species may be different from those of other test organisms.     

高光谱影像的鲜桃可溶性固形物含量预测模型

可溶性固形物含量(SSC)是决定鲜桃风味和品质的重要成分。高光谱影像的特征提取为无损检测可溶性固形物含量提供了数据基础和方法路径。先前的研究表明,基于多光谱、荧光谱、近红外光谱、电子鼻的水果内部品质评估取得较好的结果。但是,由于缺少多特征融合,从而限制了水果品质的精准估测。为此,提出了一种基于堆栈自动编码器-粒子群优化支持向量回归(SAE-PSO-SVR)模型预测鲜桃可溶性固形物含量。首先,利用高光谱影像提取光谱信息、空间信息及空-谱融合信息。其次,设置普适性堆栈自动编码器(SAE)提取光谱信息、空间信息及空-谱融合信息的深层特征。最后,将深层特征作为粒子群优化支持向量回归(PSO-SVR)模型的输入数据进行鲜桃可溶性固形物含量的预测。其中,对于光谱信息作为输入的SAE模型,设计了453-300-200-100-40, 453-350-250-150-50, 453-350-250-100-60的三个隐含层结构。对于空间信息作为输入的SAE模型,设计了894-700-500-300-50, 894-650-350-200-80, 894-800-700-500-100的三个隐含层结构。对于融合信息作为输入的SAE模型,设计了1347-800-400-200-40, 1347-750-550-400-100, 1347-700-500-360-150的三个隐含层结构。实验结果表明,对于输入数据分别为光谱信息、空间信息及融合信息的SAE模型,结构为453-300-200-100-40, 894-800-700-500-100和1347-750-550-400-100的模型效果较好,而且基于融合信息的模型预测精度明显优于基于光谱信息或者图像信息的模型。为了验证模型的普适性,利用结构为1347-750-550-400-100的SAE模型提取融合信息的深层特征估测不同品种鲜桃的可溶性固形物含量并进行可视化。结果表明,基于结构为1237-650-310-130的SAE-PSO-SVR模型预测效果最好(R2=0.873 3, RMSE=0.645 1)。因此,所提出的SAE-PSO-SVR模型提高了鲜桃可溶性固形物含量的估计精度,为鲜桃的其他成分检测提供了技术支撑。     

Variable Selection and Regularization in Quantile Regression via Minimum Covariance Determinant Based Weights

The importance of variable selection and regularization procedures in multiple regression analysis cannot be overemphasized. These procedures are adversely affected by predictor space data aberrations as well as outliers in the response space. To counter the latter, robust statistical procedures such as quantile regression which generalizes the well-known least absolute deviation procedure to all quantile levels have been proposed in the literature. Quantile regression is robust to response variable outliers but very susceptible to outliers in the predictor space (high leverage points) which may alter the eigen-structure of the predictor matrix. High leverage points that alter the eigen-structure of the predictor matrix by creating or hiding collinearity are referred to as collinearity influential points. In this paper, we suggest generalizing the penalized weighted least absolute deviation to all quantile levels, i.e., to penalized weighted quantile regression using the RIDGE, LASSO, and elastic net penalties as a remedy against collinearity influential points and high leverage points in general. To maintain robustness, we make use of very robust weights based on the computationally intensive high breakdown minimum covariance determinant. Simulations and applications to well-known data sets from the literature show an improvement in variable selection and regularization due to the robust weighting formulation.     

Intelligent Fault Identification for Rolling Bearings Fusing Average Refined Composite Multiscale Dispersion Entropy-Assisted Feature Extraction and SVM with Multi-Strategy Enhanced Swarm Optimization

Rolling bearings act as key parts in many items of mechanical equipment and any abnormality will affect the normal operation of the entire apparatus. To diagnose the faults of rolling bearings effectively, a novel fault identification method is proposed by merging variational mode decomposition (VMD), average refined composite multiscale dispersion entropy (ARCMDE) and support vector machine (SVM) optimized by multistrategy enhanced swarm optimization in this paper. Firstly, the vibration signals are decomposed into different series of intrinsic mode functions (IMFs) based on VMD with the center frequency observation method. Subsequently, the proposed ARCMDE, fusing the superiorities of DE and average refined composite multiscale procedure, is employed to enhance the ability of the multiscale fault-feature extraction from the IMFs. Afterwards, grey wolf optimization (GWO), enhanced by multistrategy including levy flight, cosine factor and polynomial mutation strategies (LCPGWO), is proposed to optimize the penalty factor C and kernel parameter g of SVM. Then, the optimized SVM model is trained to identify the fault type of samples based on features extracted by ARCMDE. Finally, the application experiment and contrastive analysis verify the effectiveness of the proposed VMD-ARCMDE-LCPGWO-SVM method.     

Polarized photoluminescence spectroscopy in WS2, WSe2 atomic layers and heterostructures by cylindrical vector beams

Due to the large exciton binding energy, two-dimensional (2D) transition metal dichalcogenides (TMDCs) provide an ideal platform for studying excitonic states and related photonics and optoelectronics. Polarization states lead to distinct light-matter interactions which are of great importance for device applications. In this work, we study polarized photoluminescence spectra from intralayer exciton and indirect exciton in WS₂ and WSe₂ atomic layers, and interlayer exciton in WS₂/WSe₂ heterostructures by radially and azimuthally polarized cylindrical vector laser beams. We demonstrated the same in-plane and out-of-plane polarization behavior from the intralayer and indirect exciton. Moreover, with these two laser modes, we obtained interlayer exciton in WS₂/WSe₂ heterostructures with stronger out-of-plane polarization, due to the formation of vertical electric dipole moment.     

含参广义集值强向量平衡问题的稳定性

本文借助集合极限的性质和弱f-性假设证明了含参广义集值强向量平衡问题解集映射的下半连续性,其方法不同于最近文献(Zhao,2016和Meng,2018).此外,建立了含参广义集值强向量平衡问题解集连通性的充分条件,并举例验证了所得结果的正确性.本文得结果推广和改进了已有文献(Gong,2008,Xu,2009,Chen,2010,Xu,2013和Zhao,2013)中相应结果.     

Well‐posedness of fractional integro‐differential equations in vector‐valued functional spaces

We study the well‐posedness of the fractional differential equations with infinite delay on Lebesgue–Bochner spaces and Besov spaces , where A and B are closed linear operators on a Banach space X satisfying ,  and . Under suitable assumptions on the kernels a and b, we completely characterize the well‐posedness of in the above vector‐valued function spaces on by using known operator‐valued Fourier multiplier theorems. We also give concrete examples where our abstract results may be applied.