Homotopy continuation enhanced branch and bound algorithms for strongly nonconvex mixed-integer nonlinear optimization

Large-scale strongly nonlinear and nonconvex mixed-integer nonlinear programming (MINLP) models frequently appear in optimization-based process synthesis, integration, intensification, and process control. However, they are usually difficult to solve by existing algorithms within acceptable time. In this study, we propose two robust homotopy continuation enhanced branch and bound (HCBB) algorithms (denoted as HCBB-FP and HCBB-RB) where the homotopy continuation method is employed to gradually approach the optimum of the NLP subproblem at a node from the solution at its parent node. A variable step length is adapted to effectively balance feasibility and computational efficiency. The computational results from solving four existing process synthesis problems demonstrate that the proposed HCBB algorithms can find the same optimal solution from different initial points, while the existing MINLP algorithms fail or find much worse solutions. In addition, HCBB-RB is superior to HCBB-FP due to much lower computational effort required for the same locally optimal solution.     

Multiple Empirical Kernel Learning with Majority Projection for imbalanced problems

Traditional Multiple Empirical Kernel Learning (MEKL) expands the expressions of the sample and brings better classification ability by using different empirical kernels to map the original data space into multiple kernel spaces. To make MEKL suit for the imbalanced problems, this paper introduces a weight matrix and a regularization term into MEKL. The weight matrix assigns high misclassification cost to the minority samples to balanced misclassification cost between minority and majority class. The regularization term named Majority Projection (MP) is used to make the classification hyperplane fit the distribution shape of majority samples and enlarge the between-class distance of minority and majority class. The contributions of this work are: (i) assigning high cost to minority samples to deal with imbalanced problems, (ii) introducing a new regularization term to concern the property of data distribution, (iii) and modifying the original PAC-Bayes bound to test the error upper bound of MEKL-MP. Through analyzing the experimental results, the proposed MEKL-MP is well suited to the imbalanced problems and has lower generalization risk in accordance with the value of PAC-Bayes bound.     

M-estimator based Chinese Remainder Theorem with few remainders using a Kroenecker product based mapping vector

The Chinese Remainder Theorem (CRT) explains how to estimate an integer-valued number from the knowledge of the remainders obtained by dividing such unknown integer by co-prime integers. As an algebraic theorem, CRT is the basis for several techniques concerning data processing. For instance, considering a single-tone signal whose frequency value is above the sampling rate, the respective peak in the DFT informs the impinging frequency value modulo the sampling rate. CRT is nevertheless sensitive to errors in the remainders, and many efforts have been developed in order to improve its robustness. In this paper, we propose a technique to estimate real-valued numbers by means of CRT, employing for this goal a Kroenecker based M-Estimation (ME), specially suitable for CRT systems with low number of remainders. Since ME schemes are in general computationally expensive, we propose a mapping vector obtained via Kroenecker products which considerably reduces the computational complexity. Furthermore, our proposed technique enhances the probability of estimating an unknown number accurately even when the errors in the remainders surpass 1/4 of the greatest common divisor of all moduli. We also provide a version of the mapping vectors based on tensorial n-mode products, delivering in the end the same information of the original method. Our approach outperforms the state-of-the-art CRT methods not only in terms of percentage of successful estimations but also in terms of smaller average error.     

基于分支定界法的相控阵雷达事件调度算法

为解决相控阵雷达事件调度问题中调度成功率、时间偏移率与算法时效性三者之间的矛盾，本文提出一种基于分支定界法的调度算法；该算法首先在现有调度结果上调取所有可执行事件，即获得若干"节点"，多步递推后由多"层"节点形成若干条备选"分支"，扩大解空间范围，提高算法性能；然后通过"删减"操作删减掉收益较低分支，将解空间控制在一适当范围内，减小搜索盲目性，降低算法计算量.仿真实验表明，与基于综合优先级算法的调度结果相比，利用本文算法调度时调度成功率提升了52%；与基于时间指针算法的调度结果相比，本身算法时间偏移率降低了61%；与基于遗传算法的调度结果相比，本文算法调度耗时仅为前者1~2%.     

可溶态和膜结合态马铃薯多酚氧化酶的性质对比

以大西洋马铃薯为原料,制备可溶态(s PPO)和膜结合态(mPPO)多酚氧化酶,并对两种粗酶液的酶学性质进行研究。结果表明,s PPO最适反应温度为35℃,最适p H值为7,mPPO最适反应温度为30℃,最适pH值为7。以焦性没食子酸、邻苯二酚为底物时,sPPO的米氏常数(K_m)分别为10.04 mmol/L和24.00 mmol/L,最大反应速度(V_(max))值分别为443 U/(mL·min)和965U/(m L·min),mPPO的米氏常数(K_m)分别为4.98 mmol/L和48.04 mmol/L,最大反应速度(V_(max))值分别为299 U/(mL·min)和912U/(m L·min),且对焦性没食子酸的催化效果优于邻苯二酚。6种抑制剂对比发现,Na_2SO_3对sPPO活性的抑制效果最好,Vc对mPPO活性的抑制效果最好。sPPO和mPPO的酶学特性有一定的差异,对马铃薯制品加工过程中酶促褐变的控制具有指导意义。     

基于Bootstrap方法的密封寿命可靠性评估

针对高可靠、长寿命密封件在小样本试验下的可靠性寿命评估需求,提出一种基于虚拟增广样本和Bootstrap方法的密封寿命小样本数据可靠性评估方法。并以轴用阶梯圈为例,进行寿命可靠性评估。首先通过虚拟增广法将轴用阶梯圈台架寿命试验数据样本数增广至10个,使得样本数满足Bootstrap方法的适用条件;然后再利用Bootstrap方法得到轴用阶梯圈的寿命可靠性评估结果。     

致密砂岩气藏地层水可动性及其影响因素研究

为研究致密砂岩气藏储层物性参数与地层水可动能力间的关系,明确不同物性储层的地层水可动性,通过核磁共振方法测量岩心孔隙大小分布,进行了X射线全岩衍射分析,提出了一种采用离心法与核磁共振相结合方式划分地层水可动程度的新方法,并分析了孔隙度、渗透率、孔隙结构、岩石矿物组成与地层水可动性之间的关系。结果表明,地层水可动或不可动无绝对界限,储层孔隙结构决定地层水可动性;矿物组成对地层水可动性影响不大。研究认为,地层水根据可动性可分为束缚水、可动束缚水、可动水,但束缚水饱和度、可动束缚水饱和度、可动水饱和度的划分不是绝对的,通过实验方法可得到其近似值。研究结果对致密砂岩气藏划分开发层系,减小地层产水可能性具有指导意义。     

一款紧凑增强型可变阻抗电磁带隙结构

基于传统AI-EBG结构,提出了一种小尺寸的增强型电磁带隙结构,实现了从0.5~9.4 GHz的宽频带-40 dB噪声抑制深度,且下截止频率减少到数百MHz,可有效抑制多层PCB板间地弹噪声。文中同时研究了EBG结构在高速电路应用时的信号完整性问题,使用差分信号方案可改善信号完整性。     

Adaptive robust boundary control of shaft vibrations under perturbations with unknown upper bounds

This paper presents robust and adaptive boundary control designs to stabilize the two‐dimensional vibration of hybrid shaft model. The hybrid shaft is mathematically represented by a set of partial differential equations, governing the shaft vibrations, coupled to ordinary differential equations, describing rigid body spinning and dynamic boundary conditions. The control objective is to stabilize the transverse vibrations of the perturbed shaft while regulating the spinning rate. To achieve this, the paper first establishes robust boundary control laws that fulfil the control objective in the presence of modeling uncertainties and external disturbances operating over the shaft domain and boundary. Lyapunov‐based analyses show that the proposed robust control exponentially stabilizes the shaft with vanishing distributive perturbations, while assuring ultimately bounded vibrations in the case of nonvanishing perturbations. Then, adaptive control philosophy is utilized to achieve redesigned robust controllers that only use online adaptation of control gains without acquiring the knowledge of bounds on perturbations, as well as dynamic parameters. An advantage of this design is avoiding an overconservative robust control law, which may induce poor stability and chattering in tackling system perturbations with unknown upper bounds. Simulations through finite element method illustrate the results. Copyright © 2014 John Wiley & Sons, Ltd.