Quantum Superimposing Multiple Anti-Cloning Machine

We prove that the nonorthogonal states randomly selected from a set can evolve into a linear superposition of multiple copies of anti-cloned state (an orthogonal state along with the original) with failure branch if and only if the input states are linearly independent. We derive a bound on the success probabilities of our machine. We show that probabilistic anti-cloning and multiple anti-cloning machines are special cases of our machine. The results for a single input state are also generalized into the case of several input copies of a state.     

Machine Learning for Many-Body Localization Transition

We employ the methods of machine learning to study the many-body localization(MBL) transition in a 1D random spin system. By using the raw energy spectrum without pre-processing as training data, it is shown that the MBL transition point is correctly predicted by the machine. The structure of the neural network reveals the nature of this dynamical phase transition that involves all energy levels, while the bandwidth of the spectrum and nearest level spacing are the two dominant patterns and the latter stands out to classify phases. We further use a comparative unsupervised learning method, i.e., principal component analysis, to confirm these results.     

Machine Learning and Micromagnetic Studies of Magnetization Switching

Magnetization switching is one of the most fundamental topics in the field of magnetism.Machine learning(ML)models of random forest(RF),support vector machine(SVM),deep neural network(DNN)methods are built and trained to classify the magnetization reversal and non-reversal cases of single-domain particle,and the classification performances are evaluated by comparison with micromagnetic simulations.The results show that the ML models have achieved great accuracy and the DNN model reaches the best area under curve(AUC)of 0.997,even with a small training dataset,and RF and SVM models have lower AUCs of 0.964 and 0.836,respectively.This work validates the potential of ML applications in studies of magnetization switching and provides the benchmark for further ML studies in magnetization switching.     

基于机器视觉的西林瓶尺寸检测

在西林瓶生产过程中,尺寸是一项重要的产品质量判断标准,与传统的西林瓶尺寸人工检测方法相比,基于机器视觉的自动检测具有巨大优越性。为实现西林瓶尺寸的检测,提出了一种基于机器视觉的西林瓶尺寸检测方案,设计了系统的图像采集和背光源照明方案,通过中值滤波对图像进行去噪,利用对图像像素点的运算算法,对图像的灰度进行了校正变换,增强图像的对比度,采用Canny算子成功提取西林瓶边缘,在HALCON平台下实现了西林瓶尺寸测量。设定系统标定方法并选取15个2mL样品西林瓶进行测试,结果表明,该方法对西林瓶尺寸检测快速准确,边缘量化精度达到了亚像素级别,检测精度为0.02mm,满足西林瓶生产的参数测量精度要求,为工业生产产品尺寸的自动检测提供了一种有效的新途径。     

基于快速压缩机的2-戊酮点火过程研究

酮类燃料可作为燃料添加剂和示踪剂在内燃机中使用,然而其化学反应动力学机理仍缺乏研究。本项工作利用快速压缩机实验平台测量了2-戊酮当量比为0.5和1.0,温度890~1130 K的条件下的点火延迟时间,并结合快速采样系统利用气相色谱对2-戊酮点火延迟过程中的7种中间组分进行了定性及定量测量。类比丁酮和1-戊烯的相关反应,构建了2-戊酮的详细化学反应动力学模型,与实验数据相对比,指出了模型优化的方向,为2-戊酮的进一步研究提供了基础.     

基于机器视觉的细水雾液滴尺寸测量与分析

为了满足科研与工程中对细水雾液滴尺寸测量的高精度低成本要求,对雾滴尺寸的机器视觉测量方法进行了深入研究.在自行建立的高压喷雾系统与雾滴采集装置上对细水雾液滴进行了采样,用显微镜及其CCD相机对雾滴样本进行了图像采集,用图像处理软件对采集的雾滴图像进行了处理与分析,测量并统计了5966个雾滴,得到了雾滴尺寸的频谱分布和累积分布以及雾滴平均直径和特征直径,将测量结果与相位多普勒粒子分析仪(PDPA)的测量结果进行了比较.结果表明,机器视觉方法町测量的最小雾滴直径约4.39 μm;机器视觉测量结果与PDPA测最结果相当接近,两种方法测得的细水雾液滴平均直径和特征直径的相对误差均在5%以内,雾滴尺寸均匀度指数的相对误差为0.27%.     

基于支持向量机的非线性荧光光谱的识别

提出将支持向量机网络应用于含不同浓度杂质气体的非线性荧光光谱的识别。由于原始光谱数据的光谱通道数目很大,首先用小波变换去噪压缩,然后采用主成分分析方法对光谱信息进行连续两次的特征提取。在保持原光谱数据主要信息基本不变的情况下,将数据维数由3979压缩到514(小波变换)并提取9个主成分。这样,不仅减少了网络的输入维数,而且加快了网络的训练速度。实验结果表明,无论对训练样本还是未学习过的测试样本,其正确识别率均可达到100%。网络的训练和测试速度较快,可以更有效地应用于大气杂质气体的实时监测。     

基于支持向量机的舰船图像识别

支持向量机(SVM)是一种基于超平面分类的新的学习方法,具有很强的泛化能力。研究了支持向量机的学习机理,以及实现支持向量机的序贯最小优化算法(SMO),并用来对舰船图像进行识别。首先将待识别目标进行二维小波分解,获取不同尺度下的小波系数,然后对其进行主元分析,得到的主元分量作为支持向量机的特征量输入。实验结果表明,该方法具有良好的分类性能。     

虚拟机在舰艇编队一体化防御系统中的应用

面对信息化、远程精确打击的空袭作战,必须构建网络化、一体化的防空作战体系;主要针对舰艇编队一体化防御系统中无线网络的不可靠性,设计了基于嵌入式虚拟机的架构,提出了任务的动态分配和移植算法;首先根据作战环境中网络的拓扑条件,虚拟机动态的将控制任务分配到可靠的无线节点中,并实时监测网络状态,当网络结构发生变化时,将底层不可靠无线节点中的控制任务移植到可靠的无线节点中;通过这种方法可以解决作战环境恶劣和无线网络本身的不可靠引起的控制的不稳定性问题,确保防御任务的顺利进行;仿真结果表明,当编队一体化防御系统的链路发生故障时,此方法能够在不改变原有控制算法的基础上,迅速重建控制构架,不仅适用于舰艇编队一体化防御系统这种对时间要求和安全性要求高的系统,而且对基于无线网络的控制系统也有一定的借鉴意义。     

Machine learning identification of symmetrized base states of Rydberg atoms

Studying the complex quantum dynamics of interacting many-body systems is one of the most challenging areas in modern physics. Here, we use machine learning (ML) models to identify the symmetrized base states of interacting Rydberg atoms of various atom numbers (up to six) and geometric configurations. To obtain the data set for training the ML classifiers, we generate Rydberg excitation probability profiles that simulate experimental data by utilizing Lindblad equations that incorporate laser intensities and phase noise. Then, we classify the data sets using support vector machines (SVMs) and random forest classifiers (RFCs). With these ML models, we achieve high accuracy of up to 100% for data sets containing only a few hundred samples, especially for the closed atom configurations such as the pentagonal (five atoms) and hexagonal (six atoms) systems. The results demonstrate that computationally cost-effective ML models can be used in the identification of Rydberg atom configurations.     

Experimental Study of Sheared Poloidal Flows in a Linear Plasma Machine

The radial profiles of fluctuations have been investigated in the Santander Linear Plasma Machine (SLPM). Experimental evidence of significant poloidal and E×B sheared flow has been observed in the plasma periphery. The resulting shearing rate of poloidal flow is comparable to the inverse of the correlation time of fluctuations, and the radial correlation is significantly reduced in the shear layer location. The possible role of flows driven by fluctuations and electron temperature gradients to explain these results is at present under investigation.     

Phase Space Prediction of Chaotic Time Series with Nu-Support Vector Machine Regression

A new class of support vector machine, nu-support vector machine, is discussed which can handle both classification and regression. We focus on nu-support vector machine regression and use it for phase space prediction of chaotic time series. The effectiveness of the method is demonstrated by applying it to the Hénon map. This study also compares nu-support vector machine with back propagation (BP) networks in order to better evaluate the performance of the proposed methods. The experimental results show that the nu-support vector machine regression obtains lower root mean squared error than the BP networks and provides an accurate chaotic time series prediction. These results can be attributable to the fact that nu-support vector machine implements the structural risk minimization principle and this leads to better generalization than the BP networks.     

光栅刻划机直线度误差补偿的研究

中阶梯光栅是一种特殊的衍射光栅, 它以高的衍射级次和大的衍射角来工作, 具有高分辨率、全波闪耀等特性。已广泛应用于高端光谱仪器之中,极大地促进了航天航空、天文、医疗、军事、环境等尖端科技的发展。但是专业的刻划系统需要定制,价格昂贵。使用已成熟的超精密加工设备来加工中阶梯光栅,可以大大降低中阶梯光栅母版的制备成本。超精密单点金刚石车床制备中阶梯光栅时,系统直线度不好,存在较大的累积误差,导致中阶梯光栅衍射波前较差,达不到制备要求。为了减小超精密单点金刚石车床固有的直线度误差,对超精密单点金刚石车床进行了误差补偿。首先,以累积误差曲线为依据进行第一次补偿。实验结果表明,当补偿系数为0.75～0.85时,此时衍射波前的PV(峰谷值)值在约400 nm,一次直线度补偿效果到达极限。然后,以闪耀级的衍射波前曲线为依据进行第二次直线度补偿,二次补偿后的衍射波前PV值为约83 nm。补偿后的结果表明衍射波前得到大幅改善,有利于提高所制备光栅的质量,在光栅实际刻划中具有指导作用。     

提高三坐标测量机应用精度的分析与计算

三坐标测量机对空间任意直线测量时 ,如果将其标准尺用线性球列代替 ,使线性球列空间化 ,再通过特定的分析和处理 ,求得空间线段的测量误差 ,实现对三坐标测量机的精度检定。文中对该检定方法进行了深入细致的精度分析与计算 ,检定不确定度评定结果表明 :该检定方法符合坐标测量机检定规程的要求 ,并且具有较高的检定精度 ,不失为有关系统提供了一种可操作性强、应用成本低的好方法     

基于计算机视觉的切膜机薄膜表面缺陷检测系统的设计

在切膜机的生产线上,需要对薄膜进行不同大小的切割;然而,实际生产中的薄膜的表面会存在各种缺陷,需要对其进行缺陷检测,检测内容主要包括划痕、污渍等;针对传统人工检测方法的低效率性,提出基于计算机视觉的自动检测设计方案,同时给出一种改进的适用于缺陷检测的背景差法;实验验证了方法的有效性和检测算法的可行性,实现了薄膜缺陷的快速检测,提高了生产率。     

Federated Quantum Machine Learning

Distributed training across several quantum computers could significantly improve the training time and if we could share the learned model, not the data, it could potentially improve the data privacy as the training would happen where the data is located. One of the potential schemes to achieve this property is the federated learning (FL), which consists of several clients or local nodes learning on their own data and a central node to aggregate the models collected from those local nodes. However, to the best of our knowledge, no work has been done in quantum machine learning (QML) in federation setting yet. In this work, we present the federated training on hybrid quantum-classical machine learning models although our framework could be generalized to pure quantum machine learning model. Specifically, we consider the quantum neural network (QNN) coupled with classical pre-trained convolutional model. Our distributed federated learning scheme demonstrated almost the same level of trained model accuracies and yet significantly faster distributed training. It demonstrates a promising future research direction for scaling and privacy aspects.     

武威重离子治癌装置高能束线的准直安装

武威重离子治癌装置高能线爬升段上下跨度约为18m,由于超大超重元件的悬空安装和狭小的安装空间及通视条件不足,使得就位时磁铁的位置及角度与地面做标定时不同,给准直安装工作带来了挑战。借助于激光跟踪仪和三维控制网,通过多重坐标转换,探索了一种新的方法,消除了爬升段磁铁调节时角度带来的偏差,有效提高了准直安装的工作效率,使得最后所有的磁铁安装各向安装误差均控制在0.10mm以内,其结果优于该装置的精度要求,为整体安装进度提前提供了有力保证。     

基于角度优化的鲁棒极端学习机算法

针对数据集样本中带有噪声和离群点问题,提出了一种基于角度优化的鲁棒极端学习机算法。该方法利用鲁棒激活函数角度优化的原则,首先降低了离群点对分类算法的影响,从而保持数据样本的全局结构信息,达到更好的去噪效果。其次,有效的避免隐层节点输出矩阵求解不准的问题,进一步增强极端学习机的泛化性能。通过应用在普遍图像数据库上的实验结果表明,这种提出的算法与其他算法相比具有更强的鲁棒性和较高的识别率。