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1.
死亡风险预测指根据病人临床体征监测数据来预测未来一段时间的死亡风险。对于ICU病患,通过死亡风险预测可以有针对性地对病人做出临床诊断,以及合理安排有限的医疗资源。基于临床使用的MEWS和Glasgow昏迷评分量表,针对ICU病人临床监测的17项生理参数,提出一种基于多通道的ICU脑血管疾病死亡风险预测模型。引入多通道概念应用于BiLSTM模型,用于突出每个生理参数对死亡风险预测的作用。采用Attention机制用于提高模型预测精度。实验数据来自MIMIC [Ⅲ]数据库,从中提取3?080位脑血管疾病患者的16?260条记录用于此次研究,除了六组超参数实验之外,将所提模型与LSTM、Multichannel-BiLSTM、逻辑回归(logistic regression)和支持向量机(support vector machine, SVM)四种模型进行了对比分析,准确率Accuracy、灵敏度Sensitive、特异性Specificity、AUC-ROC和AUC-PRC作为评价指标,实验结果表明,所提模型性能优于其他模型,AUC值达到94.3%。 相似文献
2.
为了建立适用于书画打印宣纸印刷质量的预测模型,本研究测量了14种书画打印宣纸的粗糙度、白度、不透明度、定量、光泽度和针对宣纸特别设定的帘纹深浅以及帘纹疏密度等表面物理参量,并在相同条件下,使用喷墨打印设备输出并测量印品色度值,利用总变差模型构建去除帘纹色差的测定方法,得到与人眼视觉特征相符的色差。运用GRNN广义回归神经网络结合书画打印宣纸表面物理参量与宣纸去帘纹后的色差值,建立预测模型。结果表明,该模型能够在仅测量书画打印宣纸表面物理参量的情况下,便能较为准确地预测书画打印宣纸印刷质量,为书画打印宣纸印刷前的选纸工作提供指导依据。 相似文献
3.
Ting Cui Ling Xu Feng Ding Ahmed Alsaedi Tasawar Hayat 《International Journal of Adaptive Control and Signal Processing》2020,34(11):1658-1676
Parameter estimation plays an important role in the field of system control. This article is concerned with the parameter estimation methods for multivariable systems in the state-space form. For the sake of solving the identification complexity caused by a large number of parameters in multivariable systems, we decompose the original multivariable system into some subsystems containing fewer parameters and study identification algorithms to estimate the parameters of each subsystem. By taking the maximum likelihood criterion function as the fitness function of the differential evolution algorithm, we present a maximum likelihood-based differential evolution (ML-DE) algorithm for parameter estimation. To improve the parameter estimation accuracy, we introduce the adaptive mutation factor and the adaptive crossover factor into the ML-DE algorithm and propose a maximum likelihood-based adaptive differential evolution algorithm. The simulation study indicates the efficiency of the proposed algorithms. 相似文献
4.
Somnath Karmakar 《Polycyclic Aromatic Compounds》2019,39(2):159-171
Eigensolutions of {X( = C,B,N),Y( = C,B,N)}-cyclacene graphs with next nearest neighbor (nnn) interactions have been obtained in analytical forms by adapting n-fold rotational symmetry followed by two-fold rotational symmetry (or a plane of symmetry). Expressions of eigensolution indicate the subspectral relationship among such cyclacenes with an even number of hexagonal rings e.g., eigenvalues of {X,Y}-di-cyclacene are found in the eigenspectra of all such even cyclacenes. Total π-electron energies and highest occupied molecular orbital and lowest unoccupied molecular orbital (HOMO–LUMO) gaps are calculated using the analytical expressions obtained and are found to vary negligibly with the variation of nnn interactions in such cyclacenes. Total π-electron energy is found to increase due to increase in restriction intensity of nnn interactions, whereas the HOMO–LUMO gap of polyacenecs having the even number of hexagonal rings and with one electron at each site (atom) decreases with increase in the restriction intensity since such systems contain degenerate half-filled HOMO (bonding or nonbonding) that are much more vulnerable for perturbations imposed through nnn interactions. 相似文献
5.
6.
为了开发β受体阻断剂新药(S)-噻吗洛尔半水合物,采用3-吗啉-4-氯-1,2,5-噻二唑为起始原料,经水解反应得到中间体1(3-吗啉-4-羟基-1,2,5-噻二唑)。中间体1与R-环氧氯丙烷发生醚化反应,经后处理及重结晶得到中间体2 {(R)-4-[4-(环氧乙烷-2-基甲氧基)-1,2,5-噻二唑-3-基]吗啉}。中间体2经胺化反应、马来酸成盐及重结晶得到(S)-马来酸噻吗洛尔。(S)-马来酸噻吗洛尔经游离、纯水转晶得到符合药典标准的(S)-噻吗洛尔半水合物,总收率14.05%且e.e.值为99.66%。最终成品经IR、1H-NMR、13C-NMR、MS、TGA、DSC表征,并优化各步反应条件。结果表明:以三乙胺为醚化反应缚酸剂75 ℃反应最佳;以乙醇为胺化反应溶剂46 ℃反应16 h最佳;S-噻吗洛尔的转晶拆分以水作溶剂,比传统不对称合成工艺安全稳定,操作简单,适合工业化生产。 相似文献
7.
本文提出了将智能全站仪这种精密测量仪器广泛应用于田赛项目测量中,并结合其他科技元素用于解决田赛项目中跳远、投掷等项目的测量及数据管理问题,实现实时、自动、准确地裁定和报告比赛成绩。 相似文献
8.
The main purpose of this paper is to survey some recent progresses on control theory for stochastic distributed parameter systems, i.e., systems governed by stochastic differential equations in infinite dimensions, typically by stochastic partial differential equations. We will explain the new phenomenon and difficulties in the study of controllability and optimal control problems for one dimensional stochastic parabolic equations and stochastic hyperbolic equations. In particular, we shall see that both the formulation of corresponding stochastic control problems and the tools to solve them may differ considerably from their deterministic/finite-dimensional counterparts. More importantly, one has to develop new tools, say, the stochastic transposition method introduced in our previous works, to solve some problems in this field. 相似文献
9.
AbstractModel order reduction is a common practice to reduce large order systems so that their simulation and control become easy. Nonlinearity aware trajectory piecewise linear is a variation of trajectory piecewise linearization technique of order reduction that is used to reduce nonlinear systems. With this scheme, the reduced approximation of the system is generated by weighted sum of the linearized and reduced sub-models obtained at certain linearization points on the system trajectory. This scheme uses dynamically inspired weight assignment that makes the approximation nonlinearity aware. Just as weight assignment, the process of linearization points selection is also important for generating faithful approximations. This article uses a global maximum error controller based linearization points selection scheme according to which a state is chosen as a linearization point if the error between a current reduced model and the full order nonlinear system reaches a maximum value. A combination that not only selects linearization points based on an error controller but also assigns dynamic inspired weights is shown in this article. The proposed scheme generates approximations with higher accuracies. This is demonstrated by applying the proposed method to some benchmark nonlinear circuits including RC ladder network and inverter chain circuit and comparing the results with the conventional schemes. 相似文献
10.
目前网络上的服装图像数量增长迅猛,对于大量服装图像实现智能分类的需求日益增加。将基于区域的全卷积网络(Region-Based Fully Convolutional Networks,R-FCN)引入到服装图像识别中,针对服装图像分类中网络训练时间长、形变服装图像识别率低的问题,提出一种新颖的改进框架HSR-FCN。新框架将R-FCN中的区域建议网络和HyperNet网络相融合,改变图片特征学习方式,使得HSR-FCN可以在更短的训练时间内达到更高的准确率。在模型中引入了空间转换网络,对输入服装图像和特征图进行了空间变换及对齐,加强了对多角度服装和形变服装的特征学习。实验结果表明,改进后的HSR-FCN模型有效地加强了对形变服装图像的学习,且在训练时间更短的情况下,比原来的网络模型R-FCN平均准确率提高了大约3个百分点,达到96.69%。 相似文献