Output-relevant Variational autoencoder for Just-in-time soft sensor modeling with missing data

Main challenges for developing data-based models lie in the existence of high-dimensional and possibly missing observations that exist in stored data from industry process. Variational autoencoder (VAE) as one of the deep learning methods has been applied for extracting useful information or features from high-dimensional dataset. Considering that existing VAE is unsupervised, an output-relevant VAE is proposed for extracting output-relevant features in this work. By using correlation between process variables, different weight is correspondingly assigned to each input variable. With symmetric Kullback–Leibler (SKL) divergence, the similarity is evaluated between the stored samples and a query sample. According to the values of the SKL divergence, data relevant for modeling are selected. Subsequently, Gaussian process regression (GPR) is utilized to establish a model between the input and the corresponding output at the query sample. In addition, owing to the common existence of missing data in output data set, the parameters and missing data in the GPR are estimated simultaneously. A practical debutanizer industrial process is utilized to illustrate the effectiveness of the proposed method.     

Mixture ratio modeling of dynamic systems

Any knowledge extraction relies (possibly implicitly) on a hypothesis about the modelled-data dependence. The extracted knowledge ultimately serves to a decision-making (DM). DM always faces uncertainty and this makes probabilistic modelling adequate. The inspected black-box modeling deals with “universal” approximators of the relevant probabilistic model. Finite mixtures with components in the exponential family are often exploited. Their attractiveness stems from their flexibility, the cluster interpretability of components and the existence of algorithms for processing high-dimensional data streams. They are even used in dynamic cases with mutually dependent data records while regression and auto-regression mixture components serve to the dependence modeling. These dynamic models, however, mostly assume data-independent component weights, that is, memoryless transitions between dynamic mixture components. Such mixtures are not universal approximators of dynamic probabilistic models. Formally, this follows from the fact that the set of finite probabilistic mixtures is not closed with respect to the conditioning, which is the key estimation and predictive operation. The paper overcomes this drawback by using ratios of finite mixtures as universally approximating dynamic parametric models. The paper motivates them, elaborates their approximate Bayesian recursive estimation and reveals their application potential.     

Efficient Low Dissipative High Order Schemes for Multiscale MHD Flows, II: Minimization of ∇·B Numerical Error

An adaptive numerical dissipation control in a class of high order filter methods for compressible MHD equations is systematically discussed. The filter schemes consist of a divergence-free preserving high order spatial base scheme with a filter approach which can be divergence-free preserving depending on the type of filter operator being used, the method of applying the filter step, and the type of flow problem to be considered. Some of these filter variants provide a natural and efficient way for the minimization of the divergence of the magnetic field (∇·B) numerical error in the sense that commonly used divergence cleaning is not required. Numerical experiments presented emphasize the performance of the ∇·B numerical error. Many levels of grid refinement and detailed comparison of the filter methods with several commonly used compressible MHD shock-capturing schemes will be illustratedA condensed version appears in the Proceedings of the International Conference on High Performance Scientific Computing, March 10-14, 2003, Hanoi, Vietnam. This is a revised version of a longer internal report, Feb. 19, 2004. The longer internal report was published as a RIACS Technical Report TR03.10, July 2003, NASA Ames Research Center     

麻醉剂与二氧化碳的红外线监测系统：控制与信息处理

本文阐述了麻醉剂(氨氟醚或异氟醚)与二氧化碳浓度监测系统的微机控制与信息处理子系统.该子系统完成传感器输出电信号与气体分压强的转换、误差的自动校正、非正常情况的报警、吸入与呼出麻醉剂分压及二氧化碳呼气末分压的自动搜索、呼吸频率的检测等任务,使整个监测系统的功能扩展,性能明显提高.     

Intramolecular crosslinking of poly(vinyl alcohol)

Poly(vinyl alcohol) is crosslinked in dilute solution (c=0.1 wt%) with glutaraldehyde. The reaction product is characterized by viscometry and gel permeation chromatography (g.p.c.). The intrinsic viscosity decreases with increasing degree of crosslinking and does not depend on temperature. G.p.c. reveals that the reaction product is not homogeneous, but consists of a mixture of particles with different sizes, possibly both intra- and intermolecularly crosslinked molecules. The intramolecularly crosslinked molecules are smaller in size than the initial polymer molecules and their size depends on the degree of crosslinking. They possess a narrow particle size distribution even if the initial polymer sample had a broad molecular weight distribution.     

交联透明质酸的降解及其释药性能

用己二酸二酰肼（ADH）对透明质酸（HA）进行化学修饰，制备交联透明质酸（HA-ADH）薄膜。粘度法测试表明HA-ADH是一种可降解的生物材料，并且与HA相比，HA．ADH的降解速率减慢。在此基础上，研究了以疏水性的替硝唑（TDZ）和亲水性的头孢唑啉钠（CEZ）为模拟药物的HA-ADH药物载体薄膜的释药性能。紫外-可见（UV-Vis）吸收光谱检测表明，HA-ADH是一种疏水性药物TDZ的优良缓释制荆，这是由于TDZ的疏水性和HA-ADH薄膜的缓慢溶胀和降解性能的结合而得到的，药物的释放主要受扩散机制控制。     

压电超声微电机混合结构定子的固有频率特性分析

压电超声微电机是依靠定子振动变换能的功率器件，定子性能将直接关系到微电机能否实现，本文采用Ｒａｙｌｅｉｇｈ－Ｒｉｔｚ方法来分析机械中经常采用的多层膜结构（混合结构）定子的固有振动特性，推导中考虑了平面应力和弯曲应力对固有频率的影响，模拟计算和实验结果符合得较好，此外还讨论了定子有关参数对固有频率的影响。     

激光二极管线阵光束变换研究

推导出激光二极管线阵光束通过透镜系统的光强分布表达式，分析了出射光束的传输特性，设计一种凹柱透镜的光束变换系统得到一维均匀光强分布。此外，给出扩展角宽度的计算公式。     

高温稀土永磁Sm2(Co,Cu,Fe,Zr)17 的制备和性能

制备了高温稀土永磁材料Sm(Coba1Fe0．26Cu0．05Zr0．026)7．0，研究了磁性能与工艺条件的关系．结果表明：提高烧结温度可使材料的Br和(BH)max增大，但是使Hci降低；适当提高真空预烧温度，可使材料在较低烧结温度下致密化，具有较高的Hci和(BH)max和温度稳定性．真空预烧温度过高使性能的急剧降低，其主要原因是Sm的析出．在最佳工艺条件下材料的磁性能参数分别为：Br1．08T3Hci2286kA／m，Hcb932kA／m，(BH)max220．8kJ／m^3；β20-200℃为-0．19％／℃．