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.     

射频激励扩散型冷却阵列波导CO2激光器技术

本文介绍了目前国内外研究者们提出的各种射频激励相干阵列波导ＣＯ２激光器技术，讨论了射频激励相干阵列波导ＣＯ２激光器技术的一个研究方向。     

抽油机球铁齿轮的化学成分及生产工艺

为提高抽油机减速器齿轮铸件的机械性能，提出以Sn代替Mo作为合金元素，并通过试验确定了合理的Si、Cu、Sn的加入量。指出Si加入量应控制在1．9％－2．1％范围内，同时加入0．36％～0．9％的Cu和0．025％～0．07％的Sn，可保证球铁的强度和延伸率都满足QT700－2的要求。鉴于试件与实际铸件的表面硬度有一定的差值，为提高铸件表面硬度，进行了余热正火试验，并给出了铸件在铸型内的时间－冷却温度曲线。最后根据球铁化学成分的选择结果，进行了齿轮生产试验。     

人造导星技术及导星用激光系统

论述了自适应光学系统对人造导星的需要;简单介绍了两类激光导星;重点描述了销激光导星的产生机理及可用于销激光导星的一些激光系统.     

高灵敏度荧光显微镜及其在细胞光敏效应研究中的应用

研制了一种可探测极微弱细胞荧光图象的高灵敏度荧光显微镜，在仪器中使用了一个高质量的亮度增益为５０，０００的象增强器对细胞的荧光图象进行光能量放大，因而这种荧光显微镜可探测到普通荧光显微镜不能观察的微弱荧光图象。由于仪器中光源的输出强度很小，避免了长时间观察对活细胞的光损伤，并可观察到荧光效率低的荧光物质的分布图象。探测系统中用计算机采集图象数据，同时对数据进行必要处理，以提高图象的质量。这种高灵敏度的荧光显微镜可用于分析细胞中荧光物质的分布，以及细胞动态生理过程的研究。使用此仪器已直接观察到光敏竹红菌甲素在Ｈｅｌａ细胞（人体宫颈癌细胞）中的分布，细胞受光照射后的光损伤现象及加入三种细胞保护剂后获得不同程度保护的细胞状态。     

Frequency Dependence of Fatigue Life and Internal Heating of a Fiber-Reinforced/Ceramic-Matrix Composite

The influence of loading frequency on the fatigue life and internal (frictional) heating of unidirectional SiC-fiber/ calcium aluminosilicate-matrix composites was investigated at room temperature. Specimens were subjected to tension–tension fatigue at sinusoidal loading frequencies from 25 to 350 Hz and maximum fatigue stresses of 180 to 240 MPa. The key findings of the study were that (1) fatigue life decreased sharply as the loading frequency was increased, (2) for all loading frequencies, fatigue failures occurred at stress levels that were significantly below the monotonic proportional limit stress of ∼285 MPa, and (3) pronounced internal heating occurred during fatigue, with the surface temperature of the fatigue specimens increasing by 160 K during 350-Hz fatigue at a peak stress of 240 MPa.     

大碎石柔性基层沥青性能分析

用沥青大碎石柔性基层对旧路面进行补强，可以有效地提高路面的使用性能，减少反射裂缝的产生，延长道路的使用寿命。对沥青大碎石柔性基层用加德士AH-70、SBS改性沥青和MAC改性沥青分别进行试验，通过试验结果和经济性能对比分析，找到适合此结构的最佳沥青。     

Multistage amplifier topologies with nested Gm-Ccompensation

This paper presents a multistage amplifier for low-voltage applications (<2 V). The amplifier consists of simple (noncascode) low gain stages and is stabilized using a nested transconductance-capacitance compensation (NGCC) scheme. The resulting topology is similar to the well known nested Miller compensation (NMC) multistage amplifier, except that the proposed topology contains extra G _m feedforward stages which are used to enhance the amplifier performance. The NGCC simplifies the transfer function of the proposed multistage amplifier which, in turn, simplifies its stability conditions. A comparison between the NGCC and NMC shows that the NGCC has wider bandwidth and is easier to stabilize. A four-stage NGCC amplifier has been fabricated using a 2-μm CMOS process and is tested using a ±1.0 V power supply. A dc gain of 100 dB has been measured. A gain bandwidth product of 1 MHz with 58° of phase margin and power of 1.4 mW can be achieved. The op amp occupies an active area of 0.22 mm². Step response shows that the op amp is stable     

Boundary Constrained Swept Surfaces for Modelling and Animation

Due to their simplicity and intuitiveness, swept surfaces are widely used in many surface modelling applications. In this paper, we present a versatile swept surface technique called the boundary constrained swept surfaces. The most distinct feature is its ability to satisfy boundary constraints, including the shape and tangent conditions at the boundaries of a swept surface. This permits significantly varying surfaces to be both modelled and smoothly assembled, leading to the construction of complex objects. The representation, similar to an ordinary swept surface, is analytical in nature and thus it is light in storage cost and numerically very stable to compute. We also introduce a number of useful shape manipulation tools, such as sculpting forces, to deform a surface both locally and globally. In addition to being a complementary method to the mainstream surface modelling and deformation techniques, we have found it very effective in automatically rebuilding existing complex models. Model reconstruction is arguably one of the most laborious and expensive tasks in modelling complex animated characters. We demonstrate how our technique can be used to automate this process.     

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.