罗坡坝水电站泄水建筑物设计及试验研究

罗坡坝水电站拱坝设置有三个溢流表孔,中间孔高程低于两侧的孔,三个泄水孔设有差动式挑坎。通过模型试验表明,不发生危及岸坡稳定的泄流雾化问题,此外,适当控制泄流水舌射程可以减少坝下冲坑深度,消能效果良好。     

Km高温抗蚀耐磨涂料在省煤器中的应用

锅炉省煤器磨损一直是困绕锅炉稳定运行的主要因素之一。本介绍了Km高温抗蚀耐磨涂料的性能、特点，以及在实际生产中的使用情况，通过实践充分肯定了Km高温抗蚀耐磨涂料的可行性、可靠性和经济性。     

谈谈职业教育的大众性

对职业教育的大众性问题进行了论述、实证分析和理论、政策、改革等思考。     

氢化物发生原子吸收光谱法测定金属锌中的痕量汞

采用氢化物发生原子吸收光谱法测定金属锌中的痕量汞，研究了金属锌消解的最佳条件，本法简便、快速、重现性好，准确度高，灵敏度为0．73μg/L，检测限为0．22μg/L，相对标准偏差为3．1％，回收率为96％-102％。     

基于ANSYS的铸嘴流体耦合场三维有限元仿真分析

作者用大型有限元分析软件ANSYS对铸嘴内稳态层流铝熔体进行仿真分析。实现了三维建模。解决了三维热一流耦合求解的问题，得到铝流体的速度、压力及温度的分布图。分析了铸嘴出口速度和温度的分布规律及其对连续铸轧的影响。     

High-cycle fatigue characterization of titanium 5Al-2.5Sn alloy

High-cycle fatigue behaviour of titanium 5Al-2.5Sn alloy at room temperature has been studied. S-N curve characterization is performed at different stress ratios ranging from 0 to 0.9 on a subsized fatigue specimen. Both two-stress and three-stress level tests are conducted at different stress ratios to study the cumulative fatigue damage. Life prediction techniques of linear damage rule, double linear damage rule and damage curve approaches are applied, and results are compared with the experimental data. The agreement between prediction and experiment is found to be excellent.     

手写体汉字识别问题综论

本文在讨论了人对汉字的认知心理试验研究结果之后,认为人类的汉字识别是经由整字属性及分层结构的多种途径复合而成的。这些途径中的大多数都可以在手写体汉字的机器识别中模仿运用。由此可把汉字的机器识别方法归纳为整模式法和子模式法两个大类,对它们的有利方面和困难方面进行了讨论;对影响其性能的因素进行了分析;对识别率以及它与识别字集大小和后处理等的关系提出了评估的问题。     

Kernel-based least squares policy iteration for reinforcement learning.

In this paper, we present a kernel-based least squares policy iteration (KLSPI) algorithm for reinforcement learning (RL) in large or continuous state spaces, which can be used to realize adaptive feedback control of uncertain dynamic systems. By using KLSPI, near-optimal control policies can be obtained without much a priori knowledge on dynamic models of control plants. In KLSPI, Mercer kernels are used in the policy evaluation of a policy iteration process, where a new kernel-based least squares temporal-difference algorithm called KLSTD-Q is proposed for efficient policy evaluation. To keep the sparsity and improve the generalization ability of KLSTD-Q solutions, a kernel sparsification procedure based on approximate linear dependency (ALD) is performed. Compared to the previous works on approximate RL methods, KLSPI makes two progresses to eliminate the main difficulties of existing results. One is the better convergence and (near) optimality guarantee by using the KLSTD-Q algorithm for policy evaluation with high precision. The other is the automatic feature selection using the ALD-based kernel sparsification. Therefore, the KLSPI algorithm provides a general RL method with generalization performance and convergence guarantee for large-scale Markov decision problems (MDPs). Experimental results on a typical RL task for a stochastic chain problem demonstrate that KLSPI can consistently achieve better learning efficiency and policy quality than the previous least squares policy iteration (LSPI) algorithm. Furthermore, the KLSPI method was also evaluated on two nonlinear feedback control problems, including a ship heading control problem and the swing up control of a double-link underactuated pendulum called acrobot. Simulation results illustrate that the proposed method can optimize controller performance using little a priori information of uncertain dynamic systems. It is also demonstrated that KLSPI can be applied to online learning control by incorporating an initial controller to ensure online performance.