Quantum algorithm for neighborhood preserving embedding

Neighborhood preserving embedding (NPE) is an important linear dimensionality reduction technique that aims at preserving the local manifold structure. NPE contains three steps, i.e., finding the nearest neighbors of each data point, constructing the weight matrix, and obtaining the transformation matrix. Liang et al. proposed a variational quantum algorithm (VQA) for NPE [Phys. Rev. A 101 032323 (2020)]. The algorithm consists of three quantum sub-algorithms, corresponding to the three steps of NPE, and was expected to have an exponential speedup on the dimensionality n. However, the algorithm has two disadvantages: (i) It is not known how to efficiently obtain the input of the third sub-algorithm from the output of the second one. (ii) Its complexity cannot be rigorously analyzed because the third sub-algorithm in it is a VQA. In this paper, we propose a complete quantum algorithm for NPE, in which we redesign the three sub-algorithms and give a rigorous complexity analysis. It is shown that our algorithm can achieve a polynomial speedup on the number of data points m and an exponential speedup on the dimensionality n under certain conditions over the classical NPE algorithm, and achieve a significant speedup compared to Liang et al.'s algorithm even without considering the complexity of the VQA.     

五分类血液分析仪的智能光学系统设计

目前国内血液分析仪的白细胞五分类大多以硬件方式实现,且存在硬件结构复杂,制作成本高和过度依赖某些精密部件等问题。为简化五分类仪器的系统结构,提出了一种用于白细胞五分类的智能光学系统,该系统以全光学技术作为白细胞检测方式,采用VC6.0作为软件开发平台,建立了RBF神经网络的白细胞五分类识别算法模型,整个细胞识别和分类过程完全由软件实现,从而降低硬件复杂程度,减小了外界干扰因素的影响。实验结果：样机对LYM、MON、NEU、EOS、BAS的测试相对偏差分别为1.43%、4.41%、3.92%、2.94%、11.1%,满足了国家标准中的性能要求,故仪器整体的分类结果比较理想。结论：本文提出的智能光学系统具有性能稳定可靠、抗干扰能力强的特点。     

一种基于并行混沌和SFLA的风电电网无功优化模型设计

针对已有方法在解决电网无功优化时,由于系统的无功不足和电网电压的不稳定,容易过早收敛到局部最优解的缺点,设计了一种基于并行混沌和混合蛙跳算法 (Shuffle Frog Leaping Algorithm, SFLA)的电网无功优化模型。首先,建立了最小化有功网损、最大化静态电压稳定裕度和最大化无功补偿单位投资收益的多目标数学优化模型,然后,对经典的SFLA进行改进,通过引入精英协同进化机制和划分种群的方式实现并行寻优,从而增加个体的多样性和加快最优解的求取速度,在不同种群中设计不同的适应度函数和个体更新进化方法。为了使得算法的初始解分布更为均匀,引入用混沌机制来对种群进行初始化,最后,对基于并行混沌和SFLA的总体算法进行了设计和分析。在Matlab环境下进行实验,实验结果表明文中方法得到的优化结果具有电网有功损耗小、单位投资收益高和静态电压稳定裕度大的优点,具有较强的可行性和适应性。     

碳减排下的汽车零部件循环取货路径优化研究

针对能源消耗严重和环境污染双重压力下的现象,提出对汽车企业零部件循环取货路径的优化建议.将CO2的排放量与满足汽车制造企业的生产需要相结合,根据货运车辆的碳排放需要选择合适的碳排放计算模型,分别建立不考虑碳排放的循环取货路径模型和考虑碳排放的循环取货路径模型,利用遗传算法及基本原理对所建模型进行算法设计,并运用Matl...     

A New Strategy in Boosting Information Spread

Finding a seed set to propagate more information within a specific budget is defined as the influence maximization (IM) problem. The traditional IM model contains two cardinal aspects: (i) the influence propagation model and (ii) effective/efficient seed-seeking algorithms. However, most of models only consider one kind of node (i.e., influential nodes), ignoring the role of other nodes (e.g., boosting nodes) in the spreading process, which are irrational. Specifically, in the real-world propagation scenario, the boosting nodes always improve the spread of influence from the initial activated seeds, which is an efficient and cost-economic measure. In this paper, we consider the realistic budgeted influence maximization (RBIM) problem, which contains two kind of nodes to improve the diffusion of influence. Facing the newly modified objective function, we propose a novel B-degree discount algorithm to solve it. The novel B-degree discount algorithm which adopts the cost-economic boosting nodes to retweet the influence from the predecessor nodes can greatly reduce the cost, and performs better than other state-of-the-art algorithms in both effect and efficiency on RBIM problem solving.     

调剖措施效果的模糊理论评价

针对以往对油田调剖措施效果的生产动态资料、水驱曲线、监测资料和经济效益等单因素评价方法结论不够全面的缺点,把它们指标细分,分别对其进行模糊评判,再采用层次分析法确定指标权重,应用模糊综合评价方法对调剖措施效果进行全面的评价,形成一套调剖效果评价标准.用该方法对北三东区块进行评价,得到结果表明,调剖措施模糊综合评价结论客观地反映调剖措施的效果,避免了单因素评价的片面性,全方面反映各指标水平,使措施效果评价系统化,定量化以及标准化.     

Numerical analysis of conservative unstructured discretisations for low Mach flows

Unstructured meshes allow easily representing complex geometries and to refine in regions of interest without adding control volumes in unnecessary regions. However, numerical schemes used on unstructured grids have to be properly defined in order to minimise numerical errors. An assessment of a low Mach algorithm for laminar and turbulent flows on unstructured meshes using collocated and staggered formulations is presented. For staggered formulations using cell‐centred velocity reconstructions, the standard first‐order method is shown to be inaccurate in low Mach flows on unstructured grids. A recently proposed least squares procedure for incompressible flows is extended to the low Mach regime and shown to significantly improve the behaviour of the algorithm. Regarding collocated discretisations, the odd–even pressure decoupling is handled through a kinetic energy conserving flux interpolation scheme. This approach is shown to efficiently handle variable‐density flows. Besides, different face interpolations schemes for unstructured meshes are analysed. A kinetic energy‐preserving scheme is applied to the momentum equations, namely, the symmetry‐preserving scheme. Furthermore, a new approach to define the far‐neighbouring nodes of the quadratic upstream interpolation for convective kinematics scheme is presented and analysed. The method is suitable for both structured and unstructured grids, either uniform or not. The proposed algorithm and the spatial schemes are assessed against a function reconstruction, a differentially heated cavity and a turbulent self‐igniting diffusion flame. It is shown that the proposed algorithm accurately represents unsteady variable‐density flows. Furthermore, the quadratic upstream interpolation for convective kinematics scheme shows close to second‐order behaviour on unstructured meshes, and the symmetry‐preserving is reliably used in all computations. Copyright © 2016 John Wiley & Sons, Ltd.     

摇摆显示装置图形界面的设计与实现

设计了一种新型＂助降灯臂＂的图形界面,它通过2台大屏幕显示器来中心对称地显示角度指针光带,从而精确地指示横摇角度.设计在基于TMS320C6416双通道视频显示系统的基础上进行,采用了硬件上实现效率较高的改进的Bresenham多点直线生成算法,用C语言分别对左向和右向指针屏幕进行了编制了相应的直线生成程序,并给出了结果,实现了横摇指示的可视化.     

一种改进型遗传算法的网格工作流调度研究

用有向无环图表示的网格工作流调度问题是一种典型的NP-完全问题,因而,有效的调度算法是必不可少的。为解决这一问题,提出了一种改进型的遗传算法。运用适应度差的染色体与最优个体进行二级优先杂交和变异,不仅保障了种群的多样性,也提高了种群的收敛速度。采用Gridsim工具进行模拟后,证实该算法较标准的遗传算法更适用、更有效。     

优化MGM(1,n)模型及其应用研究

从最小二乘估计的适用条件出发,在建立MGM(1,n)模型前,先采用BoxCox变换对原始数据进行变换,以解决或缓解误差项的正态性偏离问题.首先,以模型拟合平均误差最小化为目标,建立非线性优化模型,在参数的置信区间内,应用PSO(Particle Swarm Optimization)算法求最优解.然后,应用求解得到的参数,建立优化MGM(1,n)模型.在实例分析中,将优化MGM(1,n)模型应用于深基坑围护结构变形预测,实验结果表明通过对原始数据做合适的Box-Cox变换,能够有效提高模型的拟合及预测精度,拓广多变量灰色预测模型的适用范围.