Solar ponds in alkaline lake and oil well regions

Solar ponds are probably the simplest technology available for the useful conversion of solar energy. The basic technology is proven. Solar ponds have been shown to be technically feasible and economically viable for many applications, particularly for thermal use. The electrical conversion and use of solar energy via solar ponds is still questionable, in general, for economic viability. By putting the untapped sources together in the South Plains region, it looks promising economically both for thermal and electrical conversions and applications. There are a number of alkaline lake basins randomly scattered in the South Plains region of the U.S.A. In that area, there are thousands of crude oil producing wells that produce brine in abundance. The selection of suitable alkaline lake basins as a solar pond site and as depository sites of brine from oil wells and the using of this brine and salty water from alkaline lakes makes the solar pond economically viable for both thermal and electrical demands in the area.     

粉浆浇注制备铁基梯度复合材料

利用粉浆浇注-熔渗法制备Fe-WC-Cu复合材料,研究球磨时间、pH值和(NaPO3)6含量对粉浆流动性的影响.利用浇注方向上等距离位置硬度的变化,定性地研究WC的分布,并用金相结果进行佐证.结果表明:球磨时间为24 h,pH值为11,(NaPO3)6含量为0.5%时,粉浆具有最好的流动性;粉末装载量(质量分数)为75%时,样品具有最高的生坯密度;WC沿粉末沉降方向呈现明显的梯度分布,重力场和Clogging效应引起的颗粒偏析是导致梯度分布的原因;加入(NaPO3)6能提高浆料稳定性,阻止WC梯度的形成.     

Effect of Y2O3 Content on Microstructure of Gradient Bioceramic Composite Coating Produced by Wide-Band Laser Cladding

Hydroxyapatite (HA) ,a kind of bioactive materi-al , has wide application prospect in hard tissue re-placemen and repairment due to its si milar chemicalcomposition and crystallographic structure to that ofbone mineral[1 ,2]. Since HA has lowstrength (50 …     

液压系统高效冷却净化一体化技术研究

实验研究了管壳式热交换器壳侧流动和多孔介质强化传热技术 ,实验证实 ,壳侧采用螺旋板和多孔介质具有很好的强化传热效果 ,螺旋板的最佳角度为 40°,多孔介质的最佳孔隙率为 0 .985。同时研究了高梯度磁净化技术 ,探讨了冷却净化一体化的兼容性和互惠性。研制了一种管壳式冷却净化器 ,测试表明 ,管壳式冷却净化器对 10 μm以上的颗粒的过滤效率为84%,比一般磁过滤器效率高 2 .1倍 ;壳侧对流换热系数比现有冷却器高19.7%。     

Fast parallel Preconditioned Conjugate Gradient algorithms for robot manipulator dynamics simulation

In this paper fast parallel Preconditioned Conjugate Gradient (PCG) algorithms for robot manipulator forward dynamics, or dynamic simulation, problem are presented. By exploiting the inherent structure of the forward dynamics problem, suitable preconditioners are devised to accelerate the iterations. Also, based on the choice of preconditioners, a modified dynamic formulation is used to speedup both serial and parallel computation of each iteration. The implementation of the parallel algorithms on two interconnected processor arrays is discussed and their computation and communication complexities are analyzed. The simulation results for a Puma Arm are presented to illustrate the effectiveness of the proposed preconditioners. With a faster convergence due to preconditioning and a faster computation of iterations due to parallelization, the developed parallel PCG algorithms represent the fastest alternative for parallel computation of the problem withO(n) processors.     

A design and analysis of objective function-based unsupervised neural networks for fuzzy clustering

Fuzzy clustering has played an important role in solving many problems. In this paper, we design an unsupervised neural network model based on a fuzzy objective function, called OFUNN. The learning rule for the OFUNN model is a result of the formal derivation by the gradient descent method of a fuzzy objective function. The performance of the cluster analysis algorithm is often evaluated by counting the number of crisp clustering errors. However, the number of clustering errors alone is not a reliable and consistent measure for the performance of clustering, especially in the case of input data with fuzzy boundaries. We introduce two measures to evaluate the performance of the fuzzy clustering algorithm. The clustering results on three data sets, Iris data and two artificial data sets, are analyzed using the proposed measures. They show that OFUNN is very competitive in terms of speed and accuracy compared to the fuzzy c-means algorithm.     

一种降低电离层梯度影响的Hatch滤波算法

目前,地基增强系统（Ground-Based Augment System,GBAS）中的主要问题是：尽管使用载波平滑降低了伪距噪,利用差分技术消除了用户和基准站间的大部分共同误差,从而减少了差分改正值中的误差。但是电离层时间梯度和空间梯度于这两个过程中产生了额外的误差,阻碍了差分改正值质量的进一步提高。基于对电离层时间和空间梯度引起的额外误差的分析,并考虑平滑伪距噪声,文章提出了一种新的Hatch滤波方法,该滤波器的时间常数是随卫星仰角、电离层时间和空间梯度以及用户与参考站之间的距离而实时变化的。该方法降低了差分改正值误差,提高了定位精度。并且通过差分定位仿真并对比传统的单频Hatch滤波方法,发现使用该方法后,减小了差分校正值的误差。     

基于中值滤波和梯度锐化的边缘检测

实现一种基于中值滤波和梯度锐化的边缘检测方法。首先采用既能过滤噪声又能保护边缘信息的中值滤波对图像进行平滑处理。然后采用梯度锐化加强边缘像素强度,通过简单的二值化获得图像的初始边缘,系统采用连通区域标记的方法去除位于图像封闭边界内的残留黑块。最后通过实验结果与人工提取的结果比较,进行误差分析,找出本文算法的不足之处,以便对该算法进行改进。实践证明,本文的边缘检测方法对含有噪声点且灰度分布比较均匀的图像有很好的效果。     

基于横纵向联合控制的多目标优化车辆跟驰研究

为解决车辆在拥堵环境中因车速波动较大所带来的跟驰平稳性较差、跟踪无效或不安全等问题,提出了基于车辆模型和深度强化学习的多目标优化跟驰方案。首先基于车辆横纵向动力学建立车辆跟驰模型,然后根据车间距误差、速度误差、横向偏差及相对偏航角等,利用深度确定性策略梯度算法得到跟驰车的加速度和转向角,以更平稳安全地控制跟驰车辆。经NGSIM公开驾驶数据集进行测试与验证,该方案可有效地提升跟驰车辆的稳定、舒适与安全性,对保证交通安全和提升道路通行能力具有重要意义。     

基于人类视觉模型的各向异性扩散滤波方法

针对各向异性扩散模型中扩散系数的梯度阈值确定问题进行了研究,提出了一种基于人类视觉模型的各向异性扩散滤波器。首先在PM方程的基础上研究了各向异性扩散系数中梯度阈值的取值问题。根据仿生学原理,分析了韦伯比曲线,将图像根据背景亮度划分为不同的区域,分别采用不同的公式计算梯度阈值。然后讨论了改进后算法在8邻域内的离散实现问题。仿真结果表明,与传统的恒常梯度阈值扩散模型相比,改进后的算法在有效保留图像重要信息的同时对噪声的抑制效果更为理想。