高灰度级TFT-LCD显示系统的实现

提出了一种以FPGA为核心控制器的高灰度级TFT-LCD显示系统的设计方案。该系统采用幅值-帧速率控制调制-空间混合灰度调制方法,首先将10 bit的R、G、B信号分割成高8 bit数据和低2 bit数据,低2 bit数据经过空间处理后平均分到4个相邻的8 bit数据上,然后把这8 bit数据继续分割成高6 bit数据和低2 bit数据,其中高6 bit数据用于幅值调制,低2 bit数据用于帧速率控制调制,从而实现1 024个灰度等级显示。试验结果表明,该系统具有良好的显示效果和较高的显示灰度等级。     

Augmented Lyapunov functional approach for stability of neutral systems with mixed delays

This paper is concerned with the neutral‐delay‐dependent and discrete‐delay‐dependent stability for uncertain neutral systems with mixed delays and norm‐bounded uncertainties. Through constructing a new augmented Lyapunov‐Krasovskii functional and proving its positive definiteness, introducing some slack matrices and using integral inequality, the improved delay‐dependent stability criteria are derived in terms of linear matrix inequalities. Numerical examples are given to illustrate the significant improvement on the conservatism of the delay bound over some existing results. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Continuous process control using neural networks

We present some adaptive control strategies based on neural networks that can be used for designing controllers for continuous process control problems. Specifically, a learning algorithm has been formulated based on reinforcement learning, a weakly supervised learning technique, to solve set-point control and control scheduling for continuous processes where the process cannot be modeled easily. It is shown how reinforcement learning can be used to learn the control strategy adaptively based on exploration of the control space without making assumptions about the process model. A new learning scheme, handicapped learning, was developed to learn a control schedule that specifies a schedule of set points. Applications studied include the control of a nonisothermal continuously stirred tank reactor at its unstable state and the learning of the daily time-temperature schedule for an environment controller. Experimental results demonstrate good learning performance, indicating that the learning algorithm can be used for solving transient startup and boundary value control problems.     

固氟氯化铵焙烧法从包头稀土矿中回收稀土的动力学

研究了固氟氯化铵焙烧法分解包头混合型稀土精矿回收稀土的动力学.结果表明:固氟焙砂的氯化反应过程符合Bagdasarym提出的区域反应速率模型,氯化反应过程经过了两个串联反应步骤,且反应生成物的核按一个方向长大;反应速率方程遵从Erofeev方程的串联反应步骤模型,反应表观活化能Ea为36.831 KJ·mol-1,过程控制步骤是内扩散控制.     

基于改进深度强化学习的智能微电网群控制优化方法

针对微电网群控制的经济效益、负荷波动以及碳排放问题,提出一种基于改进深度强化学习的智能微电网群运行优化方法。首先,计及分布式电源、电动汽车及负荷特性,提出微电网的系统模型。然后,针对微电网群的运行特点,提出4个系统优化目标和5个约束条件,并且引入分时电价机制调控负荷运行。最后,利用改进深度强化学习算法对微电网群进行优化,合理调控多种能源协同出力,调整负荷状态,实现电网经济运行。仿真结果表明了所提方法的有效性,与其他方法相比,其收益较高且碳排放量较小,可实现系统的经济环保运行。     

基于改进指针网络的卫星对地观测任务规划方法

针对卫星观测任务规划问题约束复杂、求解空间大和输入任务序列长度不固定的特点,使用深度强化学习（DRL）方法对卫星观测任务规划问题进行求解. 综合考虑时间窗口约束、任务间转移机动时间和卫星电量、存储约束,对卫星观测任务规划问题进行建模. 基于指针网络（PN）的运行机制建立序列决策算法模型,使用Mask向量来考虑卫星观测任务规划问题中的各类约束,并通过Actor Critic强化学习算法对模型进行训练,以获得最大的收益率. 借鉴多头注意力（MHA）机制的思想对PN进行改进,提出多头注意力指针网络（MHA-PN）算法. 根据实验结果可以看出,MHA-PN算法显著提高了模型的训练速度和泛化性能,训练好的MHA-PN算法模型可以直接对输入序列进行端到端的推理,避免传统启发式算法迭代求解的过程,具有较高的求解效率.     

高温混合障碍空间中的移动机器人路径规划

为了解决高温场景中移动机器人全局路径规划所面临的安全与效率问题,提出高温热源虚拟障碍的定义,建立混合障碍空间模型,将高温场景中的路径规划问题转化为高温混合障碍空间中考虑路径温度代价和长度代价的多目标优化问题. 改进NSGA-Ⅱ算法,通过选取优秀非可行解扩展种群,提高了种群多样性和进化效率,提出新的交叉和变异概率计算方法. 根据种群进化进程和个体代价函数值调整概率,实现了种群前期搜索能力和后期收敛性的平衡. 仿真所得的最优路径结果表明,该改进算法的路径长度代价虽然比原算法和其他改进算法略有增加,但温度代价大幅降低,更有效地避免了陷入局部最优.     

密集观测场景下的敏捷成像卫星任务规划方法

针对密集观测场景下敏捷成像卫星任务规划问题求解空间大、输入任务序列较长的特点,综合考虑时间窗口约束、任务转移时卫星姿态调整时间、存储约束和电量约束,对敏捷成像卫星任务规划问题进行建模. 提出融合IndRNN和Pointer Networks的算法模型（Ind-PN）对敏捷成像卫星任务规划问题进行求解,使用多层的IndRNN结构作为算法模型的解码器. 基于Pointer Networks机制对输入任务序列进行选择,使用Mask向量考虑敏捷成像卫星任务规划问题中的各类约束. 基于Actor Critic强化学习算法对算法模型进行训练,以获得最大的观测收益率. 实验结果表明,对于密集观测场景下的任务规划,Ind-PN算法的收敛速度更快,可以获得更高的观测收益率.     

采用碳化钨堆焊工艺强化45钢模具表面的研究

介绍了碳化钨条氧—乙炔火焰耐磨堆焊技术在模具表面强化中应用的工艺方法 ,同时提出了普通碳钢 +焊接表面强化部分代替模具钢的方法 ,该方法在生产实践中取得了满意的效果。     

Reaction process of monazite and bastnaesite mixed rare earth minerals calcined by CaO-NaCl-CaCl2

The decomposition reactions of monazite and bastnaesite mixed rare earth minerals calcined by CaO-NaCl-CaCl2 were studied by means of TG-DTA and XRD. The results show that the process of the minerals decomposed by CaO involves two steps. The first step occurs in the temperature range of 425-540 ℃, and the main reactions are bastnaesite decomposition, i.e. REOF reacts with CaO to produce RE2O3 and CaF2, and Ce2O3 is oxidized to CeO2. During this step, CaCO3 is formed at about 500 ℃. The second step takes place in the temperature range of 610-700 ℃, and the reactions are monazite decomposition into RE2O3, Ca5F（PO4）3 and Ca3（PO4）2 by CaO and CaF2. In this process, the decomposition ability is improved because CaO from CaCO3 decomposing has high chemical activity. In calcining process, the new formed Ca5F（PO4）3 restrains fluorine that can escape in form of gaseous compound. The decomposition ratio of the mixed rare earth minerals reaches 90.8% at 700 ℃.