Low‐memory iterative density fitting

A new low‐memory modification of the density fitting approximation based on a combination of a continuous fast multipole method (CFMM) and a preconditioned conjugate gradient solver is presented. Iterative conjugate gradient solver uses preconditioners formed from blocks of the Coulomb metric matrix that decrease the number of iterations needed for convergence by up to one order of magnitude. The matrix‐vector products needed within the iterative algorithm are calculated using CFMM, which evaluates them with the linear scaling memory requirements only. Compared with the standard density fitting implementation, up to 15‐fold reduction of the memory requirements is achieved for the most efficient preconditioner at a cost of only 25% increase in computational time. The potential of the method is demonstrated by performing density functional theory calculations for zeolite fragment with 2592 atoms and 121,248 auxiliary basis functions on a single 12‐core CPU workstation. © 2015 Wiley Periodicals, Inc.     

A consensus successive projections algorithm – multiple linear regression method for analyzing near infrared spectra

The successive projections algorithm (SPA) is widely used to select variables for multiple linear regression (MLR) modeling. However, SPA used only once may not obtain all the useful information of the full spectra, because the number of selected variables cannot exceed the number of calibration samples in the SPA algorithm. Therefore, the SPA-MLR method risks the loss of useful information. To make a full use of the useful information in the spectra, a new method named “consensus SPA-MLR” (C-SPA-MLR) is proposed herein. This method is the combination of consensus strategy and SPA-MLR method. In the C-SPA-MLR method, SPA-MLR is used to construct member models with different subsets of variables, which are selected from the remaining variables iteratively. A consensus prediction is obtained by combining the predictions of the member models. The proposed method is evaluated by analyzing the near infrared (NIR) spectra of corn and diesel. The results of C-SPA-MLR method showed a better prediction performance compared with the SPA-MLR and full-spectra PLS methods. Moreover, these results could serve as a reference for combination the consensus strategy and other variable selection methods when analyzing NIR spectra and other spectroscopic techniques.     

Development of models for prediction of the antioxidant activity of derivatives of natural compounds

Antioxidants are important for maintaining the appropriate balance between oxidizing and reducing species in the body and thus preventing oxidative stress. Many natural compounds are being screened for their possible antioxidant activity. It was found that a mushroom pigment Norbadione A, which is a pulvinic acid derivative, shows an antioxidant activity; the same was found for other pulvinic acid derivatives and structurally related coumarines. Based on the results of in vitro studies performed on these compounds as a part of this study quantitative structure–activity relationship (QSAR) predictive models were constructed using multiple linear regression, counter-propagation artificial neural networks and support vector regression (SVR). The models have been developed in accordance with current QSAR guidelines, including the assessment of the models applicability domains. A new approach for the graphical evaluation of the applicability domain for SVR models is suggested. The developed models show sufficient predictive abilities for the screening of virtual libraries for new potential antioxidants.     

人造麝香的危害性及其残留检测方法研究进展

综述了近年来人造麝香的危害性及其残留检测方法的研究进展。对化妆品、水、大气、土壤和药物中人造麝香的前处理方法和检测方法进行了比较,同时对此领域的发展方向进行了展望(引用文献31篇)。     

基于内源性致香物质和化学计量学的烟草感官评价研究

采用主成分分析法结合遗传算法和神经网络,建立了基于烟草内源性致香物质的感官质量评价预测模型。利用气相色谱-质谱(GC-MS)技术对超临界萃取-分子蒸馏所得烟草精油中的内源性致香组分进行定性定量分析,汇总各类致香指标后,对其进行主成分分析;以提取所得5个主成分的得分作为输入变量,感官评吸分数作为输出变量,分别使用标准BP神经网络和遗传算法(GA)优化的BP神经网络建立预测模型。对比实验结果表明,GA优化后的模型预测效果更优,其预测值与实验值间的相关系数为0.96,预测均方根误差为1.81,说明GA-BP模型具有更好的拟合能力和预测能力,该模型能有效地预测烟草精油的感官品质。     

基于高通量解析算法的复杂样品重叠气相色谱-质谱信号的快速分析

化学计量学算法为重叠气相色谱-质谱(GC-MS)信号的解析提供了有效手段,但其在计算过程中一般需要将数据进行分段处理,然后只对信号的某些区间进行解析,难以实现真正意义上的高通量分析。该文结合移动窗口目标转换因子分析(MWTTFA)和非负免疫算法(NNIA),建立了一种高通量解析方法。首先,根据所有可能存在的目标组分的标准质谱信息,利用MWTTFA检验复杂信号中存在的组分,并确定目标组分的质谱信息和洗脱时间区域。以得到的质谱信息作为后续计算的输入值,利用NNIA解析得到相应的色谱信息。采用快速升温程序对17种和42种农药混合标准样品的GC-MS信号进行分析,利用所建立的方法可在10 min内得到全部组分的色谱和质谱信息。     

Computational method for optimal control of switched systems with input and state constraints

In this paper, we consider an optimal control problem of switched systems with input and state constraints. Since the complexity of such constraint and switching laws, it is difficult to solve the problem using standard optimization techniques. In addition, although conjugate gradient algorithms are very useful for solving nonlinear optimization problem, in practical implementations, the existing Wolfe condition may never be satisfied due to the existence of numerical errors. And the mode insertion technique only leads to suboptimal solutions, due to only certain mode insertions being considered. Thus, based on an improved conjugate gradient algorithm and a discrete filled function method, an improved bi-level algorithm is proposed to solve this optimization problem. Convergence results indicate that the proposed algorithm is globally convergent. Three numerical examples are solved to illustrate the proposed algorithm converges faster and yields a better cost function value than existing bi-level algorithms.     

卫星运动预测及规避的建模与求解

近年来,随着各国侦查卫星系统的不断完善,有效根据观测数据预测卫星的轨迹,同时建立适当的规避防范措施变得尤为重要·通过研究地面监测站监测的卫星数据及地理信息,提出了一种基于三维坐标系变换的卫星轨迹求解模型,具体建立模型的方法为:建立4种不同的坐标系,通过卫星坐标在4种坐标系中的变换,将地面检测站检测到的卫星数据进行处理,拟合出卫星的六个运动参数,得出卫星运动轨迹方程,并较为精准的预测卫星被观测站观测到的情况,以及任意时刻星下点的经纬度坐标.针对卫星的运动情况,为满足我方军事目标的运动需求,根据新疆地图建立军队行军路线的有向图,然后,在考虑卫星规避的情况下对每条路线进行模拟分析,最终,通过每条路线模拟分析的结果进行比对,得出最优的行军路线.     

循环取货模式下的多车场协同运输问题研究

分析循环取货模式和协同运输问题的关联性,提出了供需节点分离下的多车场一体化协同运输路线优化问题,考虑运输价值和运输成本,引入节点-弧流量的概念,通过比较流量大小确定节点集合,构建了问题的多供应点、多需求点运输模型.考虑取货的单向性和送货的闭合性,构造了求解模型的两阶段算法,运用动态规划的递推解法确定取货最优路线,然后基于余弦定理的几何法求解出发点和返回点不相同的送货路径优化问题,最后通过算例分析,说明了模型的合理性和算法的有效性.     

结合àtrous算法与Retinex算法的医学图像增强

提出结合àtrous算法与Retinex算法的医学图像增强.方法在二进小波理论基础上,利用推广的二维àtrous算法对医学图像进行处理,对经过多级分解后的医学图像的低频利用连续非线性函数增强,而对分解后的多个高频采用改进的Retinex算法进行增强;最后,对增强后的低频和高频进行重构.经过反复仿真试验结果表明:方法在去除噪声的同时更好地保留了图像边缘特征,增强了图像细节信息和凸显图像的轮廓,并克服对比度下降的不足,较好地去除光晕现象,视觉效果好,层次感更丰富.