A Dead Reckoning Calibration Scheme Based on Optimization with an Adaptive Quantum-Inspired Evolutionary Algorithm for Vehicle Self-Localization

Parameter calibration is critical for self-localization based on dead reckoning in the control of intelligent vehicles such as autonomous driving. Most traditional calibration methods for robotics control based on dead reckoning rely on data collection with specially designed paths. For the calibration of parameters in the control of intelligent vehicles, the design of such paths is considered impossible due to the complexity of road conditions. To solve this problem, an optimization-based dead reckoning calibration scheme is introduced in this research using the differential global positioning system to obtain the actual positions of the intelligent vehicle. In this scheme, the difference between the positions obtained through dead reckoning and the positions obtained through the differential global positioning system is selected as the optimization objective function to be minimized. An adaptive quantum-inspired evolutionary algorithm is developed to improve the quality and efficiency of optimization. Experiments with an intelligent vehicle were also conducted to demonstrate the effectiveness of the developed calibration scheme. In addition, the newly introduced adaptive quantum-inspired evolutionary algorithm is compared with the classic genetic algorithm and the classic quantum-inspired evolutionary algorithm using eight benchmark test functions considering computation quality and efficiency.     

智能仪器课程教学中的运算放大器

本文总结了主流运算放大器尤其是程控增益放大器、仪用放大器和隔离放大器的主要特点,包括型号、增益范围、增益编程或设置方式及主要生产厂家等,通过对这些特点的教学或学习,帮助学生了解有关器件的主要类型、特征,为有关教学和应用提供有价值的参考。     

中国移动广西公司智能网定位分析研究

本文通过对中国移动广西公司智能网系统现状的发展趋势进行调查研究,对智能网与BOSS的计费方式进行了比较,对智能网与BOSS融合发展的方向进行了探讨,最终给出中国移动广西公司智能网发展的定位。     

Improved Variational Mode Decomposition and CNN for Intelligent Rotating Machinery Fault Diagnosis

This paper proposes an intelligent diagnosis method for rotating machinery faults based on improved variational mode decomposition (IVMD) and CNN to process the rotating machinery non-stationary signal. Firstly, to solve the problem of time-domain feature extraction for fault diagnosis, this paper proposes an improved variational mode decomposition method with automatic optimization of the number of modes. This method overcomes the problems of the traditional VMD method, in that each parameter is set by experience and is greatly influenced by subjective experience. Secondly, the decomposed signal components are analyzed by correlation, and then high correlated components with the original signal are selected to reconstruct the original signal. The continuous wavelet transform (CWT) is employed to extract the two-dimensional time–frequency domain feature map of the fault signal. Finally, the deep learning method is used to construct a convolutional neural network. After feature extraction, the two-dimensional time-frequency image is applied to the neural network to identify fault features. Experiments verify that the proposed method can adapt to rotating machinery faults in complex environments and has a high recognition rate.     

苯乙烯/马来酸酐共聚物智能水凝胶的制备与性能

合成了苯乙烯/马来酸酐共聚物接枝聚乙二醇400(PEG400)水凝胶.研究了环境pH、温度、离子强度对该凝胶溶涨性能的影响,并对凝胶的动态溶涨行为进行了分析.结果显示该水凝胶对环境pH、温度、离子强度等变化均作出体积相转变响应,表现出了智能高分子凝胶的特性.     

基于长短时预测的基站节能策略

随着移动通信技术的发展和5G商用建设的加快,5G功耗将继续大幅度提升运营成本。如何在保障业务体验及设备安全的基础上实现节能最大化,始终是产业界研究的焦点之一。针对网络结构复杂、站型丰富等挑战,提出了以“感知、预测、分析、决策”AI技术为核心的节能策略生成、闭环安全保障技术。基于离线数据,验证了预测技术效果,达到了基站节能误关率低于2%、召回率不低于84%的效果。进一步的实践应用效果证明,在保障网络质量稳定的前提下,能够有效挖掘更多节能空间和节能时长,显著提升节能量,达到降本增效的目的。     

数据与模型协同驱动的智能光网络架构与关键技术

网络的规模升级和超大连接、超高带宽、超低时延应用的不断深化,对光传输网络资源利用和网络差异化服务提出了更高要求,使得传统模型驱动下的网络形态和配置方式面临挑战。基于数据与模型协同驱动思想,提出“3层3循环”架构及其“3可功能”特征的智能光网络技术方案,并对智能化实现技术展开研究,通过开发设计的智能传输网络平台对所提算法的性能进行测试,经验证,数据与模型协同驱动的智能光网络传输性能得到有效提升,为实现网络智能化提供了理论技术支撑。     

一种基于模糊理论的雷达与视频融合交通目标跟踪方法

为了解决单传感器在道路监测中存在的局限性以及交通环境多样化等问题,基于模糊集理论的一种目标决策标准对传统的数据融合算法进行改进。该方法利用决策层模糊集理论的多传感器融合算法对交通目标进行跟踪,采用Z型隶属函数用于决策分支的优化。与传统的算法相比,改进后的数据融合算法对雷达航迹冗余、视频干扰和目标分割等问题都有所改善,并且进一步提高了跟踪精度。收集了大量的实际交通数据来验证雷达和视频跟踪算法的有效性,实验结果表明多传感器融合算法优于单传感器的目标跟踪算法。     

智能反射面辅助的毫米波MIMO系统波束成形设计

智能反射面(Intelligent Reflecting Surface, IRS),也被称为可重构智能表面(Reconfigurable Intelligent Surface, RIS)或大型智能表面(Large Intelligent Surface, LIS),通过重新配置无线通信传输环境,可以有效提高通信质量。考虑IRS辅助的毫米波多输入多输出(Multiple-Input Multiple-Output, MIMO)下行系统,通过采用流形优化(Manifold Optimization, MO)技术处理,协同优化IRS的无源波束成形、功率分配、预编码矩阵以及合并矩阵,以最大化多用户和速率。由于利用流形优化算法设计的最佳目标函数是非凸的,因此利用毫米波级联信道的内在结构,将非凸问题转换为更加容易处理的问题。仿真结果表明,所采用的MO方案能获得比最小均方误差(Minimum Mean Square Error, MMSE)方案以及随机相位方案更好的和速率性能。     

可重构智能表面辅助的无源波束成形和信号检测

针对基于空间调制的可重构智能表面(Reconfigurable Intelligent Surface,RIS)联合无源波束成形和信号检测系统中无源波束成形的非凸二次约束二次规划问题,提出了基于连续凸逼近(Successive Convex Approximation,SCA)和优化最小化(Majorization-inimization,MM)的低复杂度算法。针对用户信息的双线性检测问题,提出自适应阻尼因子双线性广义近似消息传递(Bilinear Generalized Approximate Message Passing,BiG睞MP)算法,在保证误码率性能的同时算法具有更快的收敛速度与更低的复杂度。针对仅有0和1组成的可重构智能表面信息检测,提出基于广义近似消息传递(Generalized Approximate Message Passing,GAMP)算法的改进算法,在计算从检查节点到变量节点的消息时算法不使用GAMP算法中近似值,而直接计算信息。仿真结果表明,在降低误码率性能方面改进的GAMP算法比GAMP实现了4 dB的信噪比增益。