Block-free optical quantum Banyan network based on quantum state fusion and fission

Optical switch fabric plays an important role in building multiple-user optical quantum communication networks.Owing to its self-routing property and low complexity, a banyan network is widely used for building switch fabric. While,there is no efficient way to remove internal blocking in a banyan network in a classical way, quantum state fusion, by which the two-dimensional internal quantum states of two photons could be combined into a four-dimensional internal state of a single photon, makes it possible to solve this problem. In this paper, we convert the output mode of quantum state fusion from spatial-polarization mode into time-polarization mode. By combining modified quantum state fusion and quantum state fission with quantum Fredkin gate, we propose a practical scheme to build an optical quantum switch unit which is block free. The scheme can be extended to building more complex units, four of which are shown in this paper.

A Robust Interacting Multisensor State Fusion Based on Adaptive Outlier Controlling Multirate Model

An adaptive outlier controlling multirate model based on Hong’s multirate kinetic model was represented in order to resist the outliers and utilize their useful information. Wavelet transform was introduced to detect and control the outliers. The multirate information extraction and the controlling of outliers were properly integrated to establish an adaptive outlier controlling multirate model. The proposed model was applied to multisensor state fusion with interacting multiple model (IMM), and a robust interacting multisensor state fusion algorithm was established based on adaptive outlier controlling multirate model. The Monte-Carlo simulation shows that it could improve the accuracy of fusion estimation by 70% compared to Hong’s algorithm and at least 14% to Xiao’s algorithm.

Preparation of entangledW states based on the cavity QED system

We present a qubit-loss-free(QLF)fusion scheme for generating large-scale atom W states in cavity quantum electrodynamics(QED)system.Compared to the most current fusion schemes which are conditioned on the case where one particle can be extracted from each initial W state to the fusion process,our scheme will access one or two particles from each W state.Based on the atom–cavity-field detuned interaction,three jWin+m+t states can be generated from the jWin,jWim,and jWit states with the help of two auxiliary atoms,and three jWin+m+t+q states can be generated from jWin,jWim,jWit,and a jWiq state with the help of three auxiliary atoms.Comparing the numerical simulations of the resource cost of fusing three small-size W states based on the previous schemes,our fusion scheme seems to be more efficient.This QLF fusion scheme can be generalized to the case of fusing k different or identical particle W states.Furthermore,with no qubit loss,it greatly reduces the number of fusion steps and prepares W states with larger particle numbers.

A Model of Federated Evidence Fusion for Real-time Urban Traffic State Estimation

In order to make full use of heterogeneous multi-sensor data to serve urban intelligent transportation systems, a real-time urban traffic state fusion model was proposed, named federated evidence fusion model. The model improves conventional D-S evidence theory in temporal domain, such that it can satisfy the requirement of real-time processing and utilize traffic detection information more efficaciously. The model frame and computational procedures are given. In addition, a generalized reliability weight matrix of evidence is also presented to increase the accuracy of estimation. After that, a simulation test is presented to explain the advantage of the proposed method in comparison with conventional D-S evidence theory. Besides, the validity of the model is proven by the use of the data of loop detectors and GPS probe vehicles collected from an urban link in Shanghai. Results of the experiment show that the proposed approach can well embody and track traffic state at character level in real-time conditions.

A Way to Realize Controlled Nuclear Fusion by γ-Laser or γ-Ray

A way is proposed to realize controllable-nuclear fusion by γ-laser or γ-ray and ordinary laser with their certain frequencies and large enough intensities to irradiate a target ball. The function of ordinary laser is to heat the target nuclei and to realize the inertial confinement for the target nuclei. The target nuclei absorbing γ-photons will be in a certain excited state. The scattering cross-sections will be larger and the ignition temperature will be lower to realize fusion of the nuclei in their excited states than those of the nuclei in their ground states. In contrast with the nuclei applied in conventional fusion, e.g., deutons and tritons, according to the way, the nuclei applied to fusion should have the following characters: the nuclei have their excited states, one of the excited states has higher energy and longer lifetime, and the masses of the nuclei are lesser. Thus, the Lawson conditions can more easily be realized so that the controllable nuclear fusion is possibly realized by the way.

基于改进ESKF的植保无人机时延位姿补偿算法

为解决全球导航卫星系统和惯性测量单元融合时间不同步问题,提高植保无人机位姿估计精度,本文根据植保无人机大惯性、强振动的特性提出一种基于改进误差状态卡尔曼的时延位姿补偿算法。首先对名义状态变量线性预测,引入渐消因子提高强振动环境下的系统稳定性;接着采用互补滤波对角速度补偿,对姿态误差状态变量修正;最后结合测量的延迟时间,使用互补滤波外推数据,提高大惯性特性下的速度位置精度。实验结果表明,相较于误差状态卡尔曼算法,横滚角和俯仰角均方根误差减少0.2669°和0.2414°,偏航角均方根误差减少0.0764°;正常航迹植保作业下,东北天方向速度均方根误差减少0.2105、0.1849、0.2388 m/s;东北天方向位置均方根误差分别减少0.21、0.19、0.23 m,有效提高位姿估计精度。

多传感器信息融合的刀具磨损状态智能监测系统

为了提高数控机床刀具磨损状态智能监测的可靠性,提出一种基于多传感器信息融合的刀具磨损状态智能监测方法及系统。利用多种传感器分别采集刀具加工过程中的机床变频器输入电流信号、工件三向振动信号和声信号,然后对采集到的信号进行时域、频域和时频域处理分析。系统自动识别提取出其中与刀具磨损程度相关性较高的敏感特征变量,并利用马氏距离法对敏感特征向量进行分析计算,确定刀具不同状态下的阈值,并据此判断刀具的磨损状态。最后基于上述原理利用QT开发平台研发一套完整的数控机床刀具磨损状态智能监测系统。试验结果表明,该系统能够实时准确地监测出刀具的磨损状态。

Thermonuclear Plasma Steady States Generation

This report is a systematic and complemented summary of the earlier published works by the authors [1-4]. The concept of gravitational radiation as a radiation of one level with the electromagnetic radiation is based on theoretically proved and experimentally confirmed fact of existence of?electron’s stationary states in own gravitational field, characterized by gravitational constant K = 1042 G (G—Newtonian gravitational constant) and by irremovable space-time curvature. The received results strictly correspond to principles of the relativistic theory of gravitation and the quantum mechanics. The given work contributes into further elaboration of the findings considering their application to dense high-temperature plasma of multiple-charge ions. This is due to quantitative character of electron gravitational radiation spectrum such that amplification of gravitational radiation may take place only in multiple-charge ion high-temperature plasma.

考虑多信息融合的风电机组状态评估与预测系统开发

为降低风电机组故障发生概率,提高其可靠性,该文利用数据采集与监控系统(supervisory control and data acquisition,SCADA)检测获得的风电机组运行状态数据,通过研究多指标融合状态评价模型及其预测算法,解决风电机组状态参数评估与预测难题。结合SCADA系统结构,设计并规划风电机组状态参数评估与预测系统架构与功能;利用输出功率波动、风能利用率以及开机运行比率3项参数,基于阈值法,建立风电机组状态退化评价指标模型,通过主成分分析法对3个评估标准进行权重计算,并将各指标进行信息融合,综合反应风电机组运行状态;设计Convolutional Neural Network-Long Short-Term Memory风电机组状态预测模型,实施风电场运行状态参数预测;开发风电机组状态评估与预测系统软件,验证所提方法的有效性。

基于多源信息融合的船舶电气设备状态识别方法

为可靠掌握船舶电气设备状态,保证设备的运行安全,提出多源信息融合的船舶电气设备状态识别方法。采用时间序列模型检测并修正船舶电气设备多源历史数据中的连续异常数据和独立异常数据;基于联合卡尔曼滤波算法融合修正后的电气设备多源历史数据,依据融合后的多源数据训练谱聚类和深度神经网络,构建船舶电气设备状态识别网络模型,结合电气设备的实时运行数据,识别船舶电气设备状态。测试结果显示,该方法能够确定数据中的连续异常数据和独立异常数据,并且完成所有异常数据的修正,保证数据的完整性;离散度结果均在0.016以下;能够完成电气设备正常状态、异常状态以及紧急状态的识别,最小均方根误差值均在0.0044以下,识别效果良好。

基于多特征增强融合的交流接触器状态表征

准确掌握交流接触器的运行状态是实现交流接触器智能化的基础,为此提出了一种基于多特征增强融合的状态表征方法。首先针对交流接触器特征参数非线性、非平稳、随机变化、趋势性不明显但符合一定统计特性的特点,利用Wasserstein概率距离进行特征变换得到趋势性较强的初态特征;然后以多属性初态特征参数为驱动,考虑参数间强耦合的特点,利用自编码器神经网络进行特征压缩提取,剔除冗余信息,保留有用信息;在此基础上,以压缩提取特征为输入,通过自组织映射神经网络来实现多维特征的竞争性融合输出,得到交流接触器运行状态的综合健康指标,实现交流接触器状态的定量表征。最后,结合实测数据验证了所提方法的有效性,并与其他2种方法相比较,结果表明:所提方法得到的健康指标趋势性、单调性和鲁棒性相比其他2种方法分别至少提高了4%、24%和5%,研究可为下一步研究交流接触器的精准控制和智能化提供参考。

基于改进PSO优化的RBF火灾预测系统

针对系统预测火灾状态不准确,导致火情变大造成人民群众生命和财产损失的问题,本文提出了一种基于改进粒子群优化的径向基神经网络多传感器数据融合算法的火灾状态预测系统。以温度、烟雾浓度、一氧化碳浓度为输入,以无火、阴燃火、明火的概率为输出,为了避免输出产生偏差,模糊推理系统对神经网络系统的输出做补偿。由于粒子群算法存在容易陷入局部最优的缺陷,采用一种非线性动态自适应惯性权重的改进粒子群优化算法(IPSO)。仿真实验表明,改进后的系统,以明火为例的平均绝对百分比误差达到0.169、均方根误差达到0.0021、平均绝对误差达到0.031。

基于知识与AW-ESN融合的烧结过程FeO含量预测

氧化亚铁(FeO)含量是衡量烧结矿强度和还原性的重要指标,烧结过程FeO含量的实时准确预测对于提升烧结质量、优化烧结工艺具有重要意义.然而烧结过程热状态参数缺失、过程参数波动频繁给FeO含量的高精度预测带来巨大的挑战,为此,提出一种基于知识与变权重回声状态网络融合(Fusion of data-knowledge and adaptive weight echo state network, DK-AWESN)的烧结过程FeO含量预测方法.首先,针对烧结过程热状态参数缺失的问题,建立烧结料层最高温度分布模型,实现基于料层温度分布特征的FeO含量等级划分;其次,针对烧结过程参数波动频繁的问题,提出基于核函数高维映射的多尺度数据配准方法,有效抑制离群点的影响,提升建模数据的质量;最后,针对烧结过程数据驱动模型缺乏机理认知致使模型预测精度不高的问题,将过程数据中提取得到的FeO含量等级知识与AW-ESN (Adaptive weight echo state network)结合,建立DK-AWESN模型,有效提升复杂工况下FeO含量的预测精度.现场工业数据试验表明,所提方法能实时准确地预测烧结过程FeO含量,为烧结过程的智能化调控提供实时有效的FeO含量反馈信息.

基于等效电路模型融合电化学原理的锂离子电池荷电状态估计

准确高效地评估锂离子电池荷电状态(SOC)是确保电动汽车和储能设备性能和安全的关键。等效电路模型被认为是描述锂离子电池内部复杂反应过程的一种有效方法。针对基于等效电路模型的SOC估计准确性与复杂性难以权衡的问题,本研究采用一阶RC模型作为基础,为了提高整个SOC区间的模型性能表现,通过电化学原理对模型进行优化,通过在一阶RC模型的OCV模块上添加反映电池内部固相扩散过程的改进误差项,在保证较低的计算复杂性的前提下,减小了等效电路模型与更准确的机理模型之间存在的误差。然后基于倍率测试以及脉冲测试数据对电池进行参数辨识,以粒子群算法为基础通过参数解耦的方式降低了参数辨识的复杂度、提升了辨识准确度;同时基于小倍率测试的开路电压(OCV)数据采用多项式方法进行OCV-SOC曲线拟合。随后基于模型参数辨识结果开展SOC估计研究,针对常规卡尔曼滤波准确度不足的问题,在无迹卡尔曼滤波基础上结合加权滑动窗口的思想以提升SOC估计的精确性和鲁棒性,并基于UDDS和DST动态工况测试数据进行算法验证,最终估计效果相对于传统方法呈现出优异的精度与鲁棒性,并且可以在初始SOC有较大偏差时快速收敛至准确值。

基于惯性数据融合的串联机械臂运动状态估计方法

在串联机械臂运动跟踪中,将惯性测量单元(IMU)的运动信息映射到机械臂运动链上,针对IMU测量时的安装误差和随机噪声会影响到测量信息准确性的问题,提出了一种基于IMU数据融合的运动状态估计方法。该方法首先对传感器与旋转关节坐标系进行校准,再通过融合IMU中陀螺仪和加速计信息,建立了基于扩展卡尔曼滤波(EKF)算法的运动姿态最优估计模型。与机械臂实际输出的角度信息相比,所提方法的平均角度和位姿均方根误差(RMSE)为0.19°和0.45 mm,均优于单目相机惯性传感器联合标定方法。实验结果表明:该方法能够精确地估计机械臂运动状态,并且具有精度更高且运动空间不受限制的优点。

一种用于多域对话状态追踪的知识增强与自注意力引导的图神经网络

对话状态追踪是对话系统的重要组成部分,旨在从用户与系统的对话中跟踪用户意图,其通常表示为槽位-槽值对序列.近年来,深度神经网络模型在对话状态追踪问题上取得了较大进展.然而,现有模型在槽位相关性建模方面还存在可拓展性差与易引入噪声等问题.针对上述问题,本文提出了一种知识增强与自注意力引导的图神经网络KESA-GNN(Knowledge-Enhanced&Self-Attention Guided Graph Neural Network).首先,KESA-GNN通过外部知识嵌入增强槽的语义表征提升多头自注意力机制对槽位间相关性的辨别能力.其次,为了精确建模槽位间的诸如共指、共现等相关性,提出了一种自注意力引导的图神经网络建模槽位相关性.该网络采用多头注意力机制获得槽位间的注意力矩阵以及槽位表征,通过Max-N Relation算法获得注意力矩阵中强相关关系集,将稠密的注意力矩阵稀疏化,从而引导图神经网络中强相关槽位间的信息传播,降低无关槽位的噪声影响.最后,KESA-GNN采用门控融合机制过滤槽位多头注意力和图神经网络输出的槽位表征,从而获取更准确的槽位表征向量,进一步提升了KESA-GNN的鲁棒性.在多域对话数据集上的实验结果表明,KESA-GNN模型的性能优于最新的基线模型.

多源量测环境下计及时延融合的配电网区间状态估计

配电网运行的不确定性和量测误差的未知但有界性使得精准感知系统状态难以实现。为此,文中考虑多源量测环境提出了一种计及时延融合的配电网区间状态估计方法。首先,扩展微型同步相量测量装置的量测范围,增加高精度量测冗余,提出多源量测数据融合策略实现量测数据在采样时刻上的同步。其次,基于区间数建立计及量测不确定性的区间状态估计模型,并线性化区间量测函数以降低模型复杂度。然后,提出结合区间约束传播的改进Krawczyk-Moore(KM)算法求解区间状态估计模型,采用改进的KM算子设定合理的初始解区间,引入区间约束传播缓解KM算法区间过度扩张的问题。基于IEEE 33节点和IEEE 118节点配电系统的仿真结果表明,所提方法在保证区间完备性的同时有效降低了估计区间的保守性。

误差状态卡尔曼滤波的视觉惯性自适应融合定位方法研究

头盔瞄准具(HMS)是近年来新一代战斗机飞行员的辅助瞄准设备,能够帮助飞行员增强战场态势感知能力,对敌方目标进行快速、精准打击。其能正常工作的关键是获取飞行员头部相对于运动飞机的姿态参数。本文结合头盔瞄准具这一应用场景研究了视觉组合姿态测量关键技术。视觉惯性组合定位能够实现目标位姿测量方法的优势互补,而由于标称噪声矩阵无法绝对准确预测,融合算法的鲁棒性、精度有待进一步提升。针对这一问题,本文提出一种误差状态卡尔曼滤波框架下基于变分贝叶斯推断的视觉惯性自适应融合方法。首先,对于过程噪声使用逆威沙特(Wishart)分布进行建模,之后通过引入隐变量分解一步预测协方差,并结合变分贝叶斯推断实现了对过程噪声协方差矩阵的在线估计。试验证明,在复杂运动及标称噪声协方差矩阵偏移较大的测量条件下,所提位姿测量算法具有较高的精度与鲁棒性,能够完成对靶标的快速、高精度跟踪。

主动配电网态势感知技术研究综述与展望

随着分布式电源以及多元化负荷大规模接入,传统无源配电网逐步转化为有源配电网,配电网的故障种类愈发多样化,工作环境、工作状况与拓扑结构日趋复杂。主动配电网更需通过精进、高效的态势感知技术提高系统运行决策的及时性和准确性,准确预测系统潜在风险。文中阐述了主动配电网中态势感知技术的意义与概念,构建了其基本构架,并对态势觉察、态势理解与态势预测的研究进程、研究难点和未来研究方向进行了详细梳理与总结。

融合相机和激光雷达的小天体运动状态估计方法

针对单一视觉传感器难以实现小天体运动状态估计的问题,提出了一种融合相机和激光雷达的小天体运动状态估计方法。首先,建立融合相机和激光雷达测量模型,通过跟踪带有深度信息的图像特征点,利用扩展卡尔曼滤波器估计小天体的自旋角速度、自旋轴方向、位置和速度;其次,设计了特征融合矩阵,可实现对图像特征点、点云、融合特征点的实时更新;最后,分析本文算法的有效性以及特征点数量、观测高度、噪声大小对算法的影响。仿真结果表明,本文算法的精度和收敛速度均优于基于单一传感器的小天体运动状态估计方法。