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).     

PID Control of Planar Nonlinear Uncertain Systems in the Presence of Actuator Saturation

This paper investigates PID control design for a class of planar nonlinear uncertain systems in the presence of actuator saturation. Based on the bounds on the growth rates of the nonlinear uncertain function in the system model, the system is placed in a linear differential inclusion. Each vertex system of the linear differential inclusion is a linear system subject to actuator saturation. By placing the saturated PID control into a convex hull formed by the PID controller and an auxiliary linear feedback law, we establish conditions under which an ellipsoid is contractively invariant and hence is an estimate of the domain of attraction of the equilibrium point of the closed-loop system. The equilibrium point corresponds to the desired set point for the system output. Thus, the location of the equilibrium point and the size of the domain of attraction determine, respectively, the set point that the output can achieve and the range of initial conditions from which this set point can be reached. Based on these conditions, the feasible set points can be determined and the design of the PID control law that stabilizes the nonlinear uncertain system at a feasible set point with a large domain of attraction can then be formulated and solved as a constrained optimization problem with constraints in the form of linear matrix inequalities (LMIs). Application of the proposed design to a magnetic suspension system illustrates the design process and the performance of the resulting PID control law.      

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.     

能源土边界面模型的应力更新算法及算例分析

水合物的存在会显著影响能源土的刚度、峰值强度与剪胀性。针对已有能源土模型的不足，结合边界面模型的建模思想，构建一个新的能源土边界面模型，模型参数较少，能够恰当反映能源土的应力-应变关系。计算能源土变形问题的核心在于正确积分塑性本构方程，应用完全隐式回退Euler算法，建立模型应力及塑性内变量的更新公式，并给出显式的一致性切线模量表达式。基于ABAQUS软件提供的二次开发接口，编写模型的用户材料子程序，应用已有试验数据验证程序正确性。最后应用开发的子程序对能源土的平面应变试验进行模拟，分析水合物饱和度对剪切带倾角与孔隙比的影响。     

A robust quantized consensus protocol for discrete-time multi-agent systems with additive noise

This paper investigates the consensus problem of multiple discrete-time integrator agents with communication constraints and additive process noise. It proposes a protocol to achieve the approximate consensus of agents over inter-agent communication networks with finite bit rates. Under that protocol, dynamic encoding and decoding algorithms are implemented for each pair of neighboring agents to transmit quantized states at a finite bit rate. With received quantized states of neighboring agents, the control input of each agent is locally computed. Particularly input saturation is introduced into the local controllers of agents and places both lower and upper bounds on the local control inputs of agents. These control input bounds can be known in advance and greatly enhance the robustness of the consensus protocol. Under the proposed protocol, the approximate consensus can be guaranteed at any finite bit rate to encode the states of agents. It is shown that even a single bit per time step is enough for the desired approximate consensus. The additive process noise does not increase the bit rate required for that approximate consensus. Moreover, the proposed consensus protocol can be designed with only an upper bound on the number of agents and is more robust than some previous consensus protocols which may require the global information of the inter-agent network topology, such as the second largest eigenvalue of the Laplacian matrix. Even when some communication links are broken due to communication failure or some nodes leave, the same set of consensus parameters can still robustly guarantee the expected approximate consensus. Simulations are conducted to illustrate the effectiveness of the proposed quantized consensus protocol.     

多层疏松砂岩气藏水平井出水机理及防控对策——以柴达木盆地台南气田为例

柴达木盆地台南气田水平井出水已成为制约该气田稳产的主要因素。为破解上述难题,基于核磁共振测井解释技术,对储层束缚水饱和度分布特征进行研究,并结合该气田水平井投产初期的生产情况,确定投产即产出层内水的储层特征参数;通过岩心气驱水实验对束缚水产出的影响因素进行研究,通过隔夹层击穿实验开展隔夹层封隔能力的影响因素研究;采用数值模拟技术研究水平井在不同日产气量下,水平段长度对井底压力的影响;在此基础上,提出了该气田水平井出水防控对策。研究结果表明:①钻遇可动水饱和度大于7.2%、含气饱和度小于63.5%储层的水平井投产初期即会产出地层水;②泥质含量越低、生产压差越大越有利于束缚水的采出,平面非均质性较强的储层束缚水的产出是一个持续的过程;③隔夹层的封隔能力随夹层的垂向渗透率、含水饱和度减小而增强,随泥质含量、夹层厚度增大而增强;④泥质含量大于90%的Ⅰ类泥岩的突破压力约为4 MPa,泥质含量介于80%～90%的Ⅱ类泥岩的突破压力约为2MPa,泥质含量介于60%～80%的Ⅲ类泥岩的突破压力约为1.5MPa。结论认为,通过增加水平段长度和控制生产压差可以延缓边水侵入,延长气井无水采气期;水平井生产初期应均衡采气,生产过程中应加强动态监测,出水后应及时排水,以提高气井的累计产气量。     

考虑饱和度影响的红壤土抗剪强度模型及应用

鉴于降雨入渗过程中土体饱和度变化对库区边坡稳定性的影响,基于Bishop非饱和土抗剪强度理论和van Genuchten土水特征曲线模型,建立了直接考虑饱和度影响的多参数土体抗剪强度理论模型,并采用三峡库区红壤土设计了土水特征曲线试验和三轴剪切试验,获得了基质吸力和抗剪强度随饱和度变化的试验结果,进而验证了模型的合理性。结果表明,模型预测值与试验实测值的吻合度较好,建立的模型能较好地预测三峡库区红壤土的抗剪强度。进一步分析可知,红壤土抗剪强度指标——粘聚力和内摩擦角均随饱和度的增加呈非线性递减;在净总应力一定的情况下,红壤土抗剪强度随饱和度的增加呈对数关系递减。     

亲油型聚结滤芯饱和度预测模型研究

纤维聚结滤芯广泛用于压缩空气净化、发动机曲轴箱通风、加工和切割等一系列工艺过程中，用于除去气流中的液体气溶胶颗粒。由于聚结滤芯饱和度对于过滤效率及阻力具有重要影响，因此建立饱和度与滤材参数及操作条件之间的关系将有助于优化滤芯结构并提高过滤性能。目前实际工业用聚结滤芯通常由多层微米级玻璃纤维材料组成，然而现有计算模型无法用于此类滤芯的饱和度预测。因此，本文基于多种常用亲油型聚结滤芯压降及饱和度实验测试结果，根据“跳跃-通道”模型及毛细管理论建立了新的饱和度预测模型。通过与大量已发表文献数据对比发现，当饱和值大于0.2时，预测值与实验结果吻合度较好，相对偏差≤20%。随着饱和度的降低，滤芯润湿区域和非润湿区域之间界限逐渐明显，此时无需对毛细管半径进行修正。然而，新模型仍然要依靠压降测量值进行计算，这一问题需在后续工作中加以解决。     

低黏度聚α-烯烃合成油加氢精制催化剂的制备及性能评价

在氧化铝载体制备过程中添加硼元素进行改性，将所得改性载体通过等体积浸渍法浸渍钨、钼、镍金属溶液，制得低黏度聚α-烯烃合成油加氢精制催化剂，采用XRD、H₂-TPR等手段对改性载体和加氢精制催化剂进行表征。结果表明，加氢精制催化剂载体通过硼改性，可以降低活性金属组分与载体的相互作用，催化剂的酸性大大增强，同时还能引入B酸。在反应温度为240 ℃、氢分压为4.5 MPa、体积空速为0.2 h^-1、氢油体积比为300∶1的条件下，考察改性加氢精制催化剂应用于PAO4加氢的芳烃饱和性能，并与未改性催化剂进行对比，结果表明，改性前后的加氢精制催化剂均可有效改善产品的颜色，但改性加氢精制催化剂的芳烃饱和性能远远高于未改性催化剂。改性加氢精制催化剂稳定性评价结果表明，该催化剂具有良好的活性稳定性，能够满足工业应用的要求。