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).     

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.     

具有自动装卸搬运机器人的制造单元建模与分析

针对具有自动装卸搬运机器人的制造单元，着重考虑其双重资源约束的特点，提出了该系统的有限缓冲开排队网的建模方法。根据工件的加工、装卸与搬运等工艺流程，定义该系统的排队网模型的各种状态，基于连续时间马尔可夫链，采用精确解法对该模型进行分析求解，获得系统一系列的稳态性能指标。为了验证排队网建模方法的有效性，建立其对应的仿真模型，分别采用排队网模型的数值计算与仿真模型的仿真统计2种方法求出系统的性能指标进行对比，并基于排队网模型对该制造单元的系统性能进行分析，为该类制造单元的资源配置优化和设施布局优化等问题提供重要的理论支撑。     

两种多层穿梭车系统的性能比较分析

物流配送中心订单响应速度的加快,对订单拣选系统提出了更高的要求。多层穿梭车系统因其柔性强、效率高等特点,近年来得到了广泛应用。多层穿梭车系统具有两种形式--基于转载车和基于环形输送线,二者均可实现货物的快速拣选,但在系统吞吐量和订单完成周期方面存在差异,致使企业决策者难以进行系统选择。基于上述原因,本文比较两种多层穿梭车系统的性能差异。建立两个系统的开环排队网络模型,然后求解系统吞吐量、订单完成周期,通过仿真验证了排队论模型的准确性,通过试验对两种系统的性能进行对比分析。结果表明,系统吞吐量与层数、巷道数相关,层数越多,巷道数越少,转载车系统相对于环线系统优势越显著;订单完成周期与层数、订单到达率相关,层数越多,订单到达率越低,转载车系统相对于环线系统越具优势。     

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。     

行业视角下中国对外贸易隐含碳排放研究

贸易隐含碳测算是调整进出口贸易结构、提高资源利用率、实现低碳经济的重要依据。基于多区域投入产出法，构建行业层面的双边贸易隐含碳测算模型，采用1997—2011年WIOD提供的相关数据，测算中国与美国、德国、日本、韩国、澳大利亚、俄罗斯、印度、巴西8个主要贸易国双边贸易隐含碳净排放量。研究表明：在中国与美国、德国、日本、韩国、澳大利亚的双边贸易中，主要为行业隐含碳净出口；在与俄罗斯、印度、巴西的双边贸易中，行业隐含碳净出口和净进口现象并存。通常情况下，中国采取以碳排放换取贸易顺差、以贸易逆差换取碳减排的贸易策略。为了减少隐含碳排放，中国应该推行碳排放交易市场，整顿高污染行业，优化进出口贸易结构，鼓励增加高碳行业的进口量和低碳行业的出口量。     

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。     

生物氧化预处理中进气量实时预测数学模型研究

精准预测生物氧化预处理中的进气量对提高黄金提取率和节能降耗具有重要意义。以气体管流连续性方程和运动方程为控制方程，采用Preissmann隐格式法作为差分方法。同时，根据集合卡尔曼滤波(Ensemble Kalman filter，EnKF)算法原理，构造进气量、压强的状态空间模型。结果表明，基于气体管流控制方程建立的进气量模型预测结果与实际进气量观测值具有较好的一致性；与传统静态预测方法相比，EnKF同化方法引入实时观测值和模型参数的更新，有效提高了进气量的预测精度，其平均绝对误差、平均相对误差和均方根误差有明显的降低。可见，基于气体管流控制方程建立的预测模型结合EnKF同化方法是提高生物氧化槽进气量预测精度的有效手段。