Convolutional Neural Network Auto Encoder Channel Estimation Algorithm in MIMO-OFDM System

Higher transmission rate is one of the technological features of prominently used wireless communication namely Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO–OFDM). One among an effective solution for channel estimation in wireless communication system, specifically in different environments is Deep Learning (DL) method. This research greatly utilizes channel estimator on the basis of Convolutional Neural Network Auto Encoder (CNNAE) classifier for MIMO-OFDM systems. A CNNAE classifier is one among Deep Learning (DL) algorithm, in which video signal is fed as input by allotting significant learnable weights and biases in various aspects/objects for video signal and capable of differentiating from one another. Improved performances are achieved by using CNNAE based channel estimation, in which extension is done for channel selection as well as achieve enhanced performances numerically, when compared with conventional estimators in quite a lot of scenarios. Considering reduction in number of parameters involved and re-usability of weights, CNNAE based channel estimation is quite suitable and properly fits to the video signal. CNNAE classifier weights updation are done with minimized Signal to Noise Ratio (SNR), Bit Error Rate (BER) and Mean Square Error (MSE).     

冰下单载波水声通信试验研究

北极地区战略地位的提高促进了极区各种应用的快速发展，冰下水声通信成为其中一个热点问题。为了研究冰下单载波水声通信性能，在黄海冰区开展冰下试验。重点分析了内嵌锁相环的多通道判决反馈均衡(Multi-channelDecision Feedback Equalization, M-DFE)技术在冰下水声通信中抗多途时延的能力，研究比较了改进比例归一化最小均方(Improved Proportional normalized Least Mean Square, IPNLMS)算法和递归最小二乘(Recursive Least Squares,RLS)算法的均衡性能。试验结果表明IPNLMS和RLS都能克服信道多途效应带来的影响。在牺牲计算复杂度的前提下，RLS具有更好的均衡效果，M-DFE在冰下环境中能够有效消除码间干扰。     

Adaptive constraint control for flexible manipulator systems modeled by partial differential equations with dead-zone input

In this article, based on partial differential equations (PDEs), the flexible manipulator system with both dead-zone input and state constraints is studied. The dynamic model of the flexible manipulator system is described by PDEs. The parameters of the dead-zone input are unknown, and the state constraint problem is also considered. An adaptive approach is proposed to offset the effects caused by dead-zone input. Thus, to guarantee that all states remain within their respective constraint regions, the boundary control law based on the barrier Lyapunov function is given, and an adaptive controller is designed. According to the Lyapunov analysis method, the control method is given to ensure that all signals of the closed-loop system are bounded and all states satisfy the constraint conditions. Finally, simulation results show the effectiveness of the proposed control method in this article.     

网络化雷达现状及技术发展趋势

随着新的作战对象和作战样式的出现,以及作战环境更加复杂,仅依靠单个雷达节点的能力已经越来越难以满足新的作战需求。文中针对网络化雷达这一新体制雷达开展研究,通过分析国外网络化雷达系统发展现状、趋势及关键技术,总结对我国网络化雷达技术发展的经验启示,为我国网络化雷达系统发展提供技术参考。     

基于改进卷积神经网络的单幅图像超分辨率重建方法

对于重建图像存在的边缘失真和纹理细节信息模糊的问题，提出一种基于改进卷积神经网络（CNN）的图像超分辨率重建方法。首先在底层特征提取层以三种插值方法和五种锐化方法进行多种预处理操作，并将只进行一次插值操作的图像和先进行一次插值后进行一次锐化的图像合并排列成三维矩阵；然后在非线性映射层将预处理后构成的三维特征映射作为深层残差网络的多通道输入，以获取更深层次的纹理细节信息；最后在重建层为减少图像重建时间在网络结构中引入亚像素卷积来完成图像重建操作。在多个常用数据集上的实验结果表明，与经典方法相比，所提方法重建图像的纹理细节信息和高频信息能得到更好的恢复，峰值信噪比（PSNR）平均增加0.23 dB，结构相似性（SSIM）平均增加0.0066。在保证图像重建时间的前提下，所提方法更好地保持重建图像的纹理细节并减少图像边缘失真，提升重建图像的性能。     

催化剂用高温焙烧炉系统的设计开发

针对某厂催化剂的焙烧特性,研制出新型的多通道燃气式高温焙烧炉,并对其系统进行优化。实现了焙烧系统的精确控温和安全保护,可解决原有的辊道窑产能低、能耗高、设备运行成本高等问题。单台设备产能提高3倍,能耗降低20%,催化剂制造成本明显降低,达到增产提质的目的。     

Reduced-order multiple observer for Takagi–Sugeno systems with unknown inputs

The paper discusses a niche area problem — the design of reduced-order multiple observers which can achieve the finite-time state reconstruction for Takagi–Sugeno multiple models with unknown inputs. The new reduced-order multiple observer obtained in this paper is only the second ever designed, the author of this paper continuing his work started one year ago with the introduction of the reduced-order multiple observer concept. The obtaining of the new reduced-order multiple observer is achieved by combining two classical reduced-order observers for linear time-invariant multivariable systems with unknown inputs and a full-order multiple observer for Takagi–Sugeno systems. The main innovative idea behind the design of the reduced-order multiple observer for Takagi–Sugeno systems described by the multiple models is the split of the multiple model into two subsystems: an unknown-input-free subsystem and an unknown-input-depending subsystem. The unknown-input-free subsystem is brought to the form of a standard multiple model and, in this situation, any algorithm to design full-order multiple observers can be used in the design of reduced-order multiple observers. The steps of the design procedure have been summarized and software implemented; then, the validation of the suggested algorithm has been done for two concrete cases associated to the motion of an aircraft during its landing     

Integral barrier Lyapunov function-based adaptive fuzzy output feedback control for nonlinear delayed systems with time-varying full-state constraints

In this article, an adaptive fuzzy output feedback control method is presented for nonlinear time-delay systems with time-varying full state constraints and input saturation. To overcome the problem of time-varying constraints, the integral barrier Lyapunov functions (IBLFs) integrating with dynamic surface control (DSC) are applied for the first time to keep the state from violating constraints. The effects of unknown time delays can be removed by using designed Lyapunov-Krasovskii functions (LKFs). An auxiliary design system is introduced to solve the problem of input saturation. The unknown nonlinear functions are approximated by the fuzzy logic systems (FLS), and the unmeasured states are estimated by a designed fuzzy observer. The novel controller can guarantee that all signals remain semiglobally uniformly ultimately bounded and satisfactory tracking performance is achieved. Finally, two simulation examples illustrate the effectiveness of the presented control methods.     

商用车输入轴生产线智能化建设

根据用户要求,从空间布局和网络拓扑方面介绍了输入轴智能化生产线的建设过程,详述各环节的工作任务和动作机能,给出了主要零/部件的结构图样。生产线投用后,成效显著。对智能化制造商具有指导意义,对商用车用户具有借鉴意义。     

多孔道底吹喷枪搅拌的数值模拟与强化机理分析

底吹富氧搅拌是熔池熔炼的一种关键强化技术，对品位低、杂质多的矿产资源具有很好适应性，底吹喷枪是其关键工艺设备。本文将数值模拟与数理统计相结合，建立了多孔道底吹喷枪强化搅拌的三维数学模型，并通过水力学模型实验对数学模型进行了验证。针对模型计算结果，以气泡上升时间、液体的气含率方差和平均湍流强度等为评价标准，对直管及多孔道底吹喷枪搅拌过程的混合均匀度进行对比研究。结果表明：多孔道喷枪喷吹方式在提高熔体搅动能的同时能使中上部熔体的混合更为充分，并在生产实践中取得了良好的应用效果。