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).     

Polynomial filtering for nonlinear stochastic systems with state‐ and disturbance‐dependent noises

This article is concerned with the polynomial filtering problem for a class of nonlinear stochastic systems governed by the Itô differential equation. The system under investigation involves polynomial nonlinearities, unknown‐but‐bounded disturbances, and state‐ and disturbance‐dependent noises ((x,d)‐dependent noises for short). By expanding the polynomial nonlinear functions in Taylor series around the state estimate, a new polynomial filter design method is developed with hope to reduce the conservatism of the existing results. In virtue of stochastic analysis and inequality technique, sufficient conditions in terms of parameter‐dependent linear matrix inequalities (PDLMIs) are derived to guarantee that the estimation error system is input‐to‐state stable in probability. Moreover, the desired polynomial matrix can be obtained by solving the PDLMIs via the sum‐of‐squares approach. The effectiveness and applicability of the proposed method are illustrated by two numerical examples with one concerning the permanent magnet synchronous motor.     

基于数值微分核脉冲信号数字处理方法

核反应堆核测量系统测量探测器输出的核脉冲信号，该信号后沿拖尾很长，在计数率较高时容易产生信号堆积和基线漂移等问题，导致源区计数率测量上限仅能达到105 Hz左右。文中基于数值微分方法，采用数字处理技术，在时域上分析了核脉冲信号经过前置放大、信号成形、低通滤波和脉冲甄别后的输出，并利用探测器实测信号进行了仿真。仿真结果表明，基于数值微分的数字处理方法可以实现相邻0.4 μs脉冲信号的识别和测量，将源区测量计数率上限提升到2×106 Hz以上。      

API X52管线钢与Corus RQT 701超高强钢的焊接工艺

针对超高强钢在机械行业的广泛使用,焊接接头易出现淬硬脆化、裂纹等缺陷的情况,采用焊条电弧焊(SMAW)和药芯气体保护焊(FCAW-GS)组合的焊接方法,对API X52管线钢与Corus RQT 701超高强钢的焊接进行了工艺试验,并进行常规力学性能试验。结果表明,通过合理匹配焊接工艺参数,严格控制低热输入量及层间温度,实现了小熔合比的异种钢焊接,且焊接接头具有良好的力学性能,达到了标准和规格书的要求;异种高强钢焊接,采用SMAW打底焊接和FCAWGS填充并盖面的方法,焊接效率较单纯采用焊条电弧焊工艺提高2倍以上。     

Adaptive finite‐time formation tracking control for multiple nonholonomic UAV system with uncertainties and quantized input

An adaptive finite‐time formation tracking control approach is proposed for multiple unmanned aerial vehicle (UAV) system with quantized input signals in this paper. The UAVs are described by nonholonomic kinematic model and autopilot model with uncertainties. An enhanced hysteretic quantizer is introduced to avoid chattering, and some restrictions are released by using a new quantization decomposition method. Based on backstepping technique and finite‐time Lyapunov stability theory, the adaptive finite‐time controller is designed for the trajectory tracking of the multi‐UAV formation. The nonholonomic constraints are solved by a transverse function. A transformation is introduced to the control input signals to eliminate the quantization effect. Stability analysis proves that the tracking errors can converge to a small neighborhood of the origin within finite time and all the closed‐loop signals are semiglobally finite‐time bounded. The effectiveness of the proposed control approach is validated by simulation and experiment.     

Full and reduced-order H∞ filtering of Takagi–Sugeno fuzzy time-varying delay systems: Input–output approach

This article addresses the issue of delay-dependent H_∞ filtering design for TakagiŮSugeno fuzzy time-varying delay systems using the input–output approach. A three-term approximation model has been used to transform the original system into two interconnected subsystems. Since the nonquadratic Lyapunov–Krasovskii functional requires to deal with the membership function's (MF) time derivative, upper-bound inequalities have been added to the obtained conditions. Based on the scaled small gain theorem, nonquadratic Lyapunov–Krasovskii functional approach and considering the bounds of the MF time-derivative, the H_∞ full- and reduced-order filters are designed and then formulated in terms of linear matrix inequalities. Finally, illustrative examples are presented to demonstrate the validity of the proposed methods.     

数字化核电厂班组SSA测度方法与实验研究

情景意识(SA)问题在数字化核电厂中更为突出。为识别班组共享情景意识（SSA）水平及行为形成因子（PSF）对班组SSA的影响，基于情景意识全面评估技术（SAGAT）建立一种班组SSA测量和计算方法并进行模拟机实验。结果表明，个体SA（ISA）水平与SSA水平有关，ISA级别越高，SSA级别越高；ISA和班组SSA都受到PSF的影响，PSF的状态水平越高，ISA和SSA的水平越高；对于不同的实验场景，操纵员的ISA水平和班组SSA水平不同，这表明风险场景的症状越明显及知识经验水平越好，ISA和SSA水平就越高。最后，通过被试的自评价识别当前PSF的状态水平，主要不良的PSF是班组沟通和合作水平、压力水平、人-机界面等。这些评估结果为提高数字化核电厂人因可靠性和安全水平提供了理论支持。     

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。     

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.     

Extended-state-observer-based dynamic surface control of flexible-joint robot systems with input saturation

This article presents an extended-state-observer-based dynamic surface control approach for flexible-joint robot systems with asymmetric input saturation and large unknown dynamic knowledge. Traditional controllers for flexible-joint robot systems usually use approximation technology to deal with unknown dynamics knowledge. Unlike the traditional control algorithm, this article utilizes an extended state observer to estimate the unknown dynamics. For the closed-loop system, the delay strategy handles the time-scale separation issue, the filtering system overcomes the “explosion of differentiation” caused by the repeated differentiation of auxiliary control signals, and the mean-value-theorem solves the input saturation problem of the actuator. The stability analysis implies that estimation errors of extended state observers (ESOs) and other state variables are semiglobally uniformly ultimately bounded. Compared with fuzzy control algorithms, the novel ESO-based dynamic surface control approach not only omits online learning time but also uses only a few control parameters to obtain satisfactory tracking performance. Finally, a comparison simulation experiment is provided to illustrate the effectiveness of the gained conclusions.