提升机综合后备保护装置减速点的设置方法

依次给出了提升机综合后备保护装置减速点到停车位距离的粗略计算公式、某段(点)到停车位的安全制动距离计算公式、某段(点)到停车位的超速持续判断安全制动距离的计算公式及减速点到停车位的超速持续判断安全制动距离的计算公式,在井口下与井口相距该安全制动距离的点即设置为减速点。该方法适用于提升机电控系统减速点随载重量自动调整时后备保护减速点的设置。应用结果表明,根据该方法设置的提升机综合保护装置减速点可保证在提升机电控系统减速点失效后使提升容器在井口以下安全停车,可靠性高。     

光伏电池最大功率点跟踪方法比较

根据太阳能光伏电池的工程数学模型,在Matlab环境下建立了光伏电池仿真模型,分析了光照强度和温度变化对光伏电池输出特性的影响。针对扰动观察法采用固定的扰动步长而难以获得较高跟踪精度和响应速度的问题,提出了一种基于变步长的改进的扰动观察法,并通过对光伏电池控制系统进行仿真,比较了这2种最大功率点跟踪方法的仿真曲线。结果表明,采用改进的扰动观察法的光伏电池控制系统能更快速跟踪最大功率点,且在最大功率点处稳定性较好。     

改进型双馈风力发电机最大功率点跟踪策略

针对传统双馈风力发电机最大功率点跟踪极值搜索策略将正弦信号作为搜索信号而很难将正弦信号从总的输出信号中区分出来的问题,提出了一种利用风湍流作为极值搜索信号的改进型最大功率点跟踪策略。该改进策略对叠加风湍流的叶尖速比和功率系数进行傅里叶变换,获得相位差信息,从而确定叶尖速比变化方向,使双馈风力发电机达到最佳运行工作点。仿真结果表明,该改进策略可控制风力发电机转速较好地跟踪风速变化,实现了额定风速以下运行区域的最大风能捕获。     

摄影测量中一种非编码点的自动匹配方法

在极限约束法基础上提出了一种非编码点匹配的改进算法,首先采用极限约束法得到候选匹配对应点,然后按编码点位置将图像Delaunay三角化,缩小匹配范用。在对应Delaunay三角形内确定匹配点,若在三角形范围内仍不能唯一的确定匹配点,则通过交比不变性来实现对应点的匹配。本方法不限制标志点的粘贴位置,满足摄影测量中实际应用要求。     

集成一体化智能熔点仪的设计

介绍由微控制器（单片机）、温度传感器、加热系统、搅拌系统、键盘和LCD显示等组成的集成一体化智能熔．最仪的工作原理,阐述其硬件电路结构和软件程序设计,并进行反复实验。实验结果表明：该仪器控温精度达到4-0．2℃,线性升温误差不超过0．5％,各项指标符合国家药典标准,具有硬件结构简单、软件算法科学的特点,能够低成本、高精度地实现对温度的控制。     

基于移动最小二乘曲面的点云拼接算法

针对现有点云拼接方法受被测零件复杂外形和测量噪声影响的问题,提出基于移动最小二乘( Moving Least-Squares,MLS)曲面的点云拼接算法.该算法利用一种极值投影方法来有效计算点到MLS曲面的对应点,并结合迭代最近点(Iterative Closest Point,ICP)算法将各个视角的点云统一到该ML...     

基于平面矢量场的可视化方法的研究

平面矢量场可视化是科学计算可视化的重要研究内容。矢量场以直观的图形图像显示场的运动,透过抽象数据有效洞察其内涵本质和变化规律,广泛应用于计算流体力学、航空动力学、大气物理和气象分析等领域。对平面矢量场进行可视化的方法有很多种,常见的有点图标法、流线法、线积分卷积法和拓扑分析法等。论文主要研究常见的可视化方法,分析它们的性能,并通过实际的例子,对具体的可视化结果图像进行比较。通过该研究,能够给人们在选择可视化方法时提供依据。     

基于模式搜索的连续函数寻优

模式搜索方法是求解最优化问题的一种直接搜索方法,它只根据函数值信息来进行求优。但模式搜索对起始点非常敏感,对好的起始点,模式搜索可以搜索到全局最优点,但对某些起始点,模式搜索只能搜索到局部极值点。本文提出基于模式搜索的一个新方法,实验证明,它能搜索到全局最优。     

Fault tolerant multiple event detection in a wireless sensor network

With an increasing acceptance of Wireless Sensor Networks (WSNs), the health of individual sensor is becoming critical in identifying important events in the region of interest. One of the key challenges in detecting event in a WSN is how to detect it accurately transmitting minimum information providing sufficient details about the event. At the same time, it is also important to devise a strategy to handle multiple events occurring simultaneously. In this paper, we propose a Polynomial-based scheme that addresses these problems of Event Region Detection (PERD) by having a aggregation tree of sensor nodes. We employ a data aggregation scheme, TREG (proposed in our earlier work) to perform function approximation of the event using a multivariate polynomial regression. Only coefficients of the polynomial (P$P$) are passed instead of aggregated data. PERD includes two components: event recognition and event report with boundary detection. This can be performed for multiple simultaneously occurring events. We also identify faulty sensor(s) using the aggregation tree. Performing further mathematical operations on the calculated P$P$ can identify the maximum (max) and minimum (min) values of the sensed attribute and their locations. Therefore, if any sensor reports a data value outside the [min, max] range, it can be identified as a faulty sensor. Since PERD is implemented over a polynomial tree on a WSN in a distributed manner, it is easily scalable and computation overhead is marginal. Results reveal that event(s) can be detected by PERD with error in detection remaining almost constant achieving a percentage error within a threshold of 10%$10\%$ with increase in communication range. Results also show that a faulty sensor can be detected with an average accuracy of 94%$94\%$ and it increases with increase in node density.     

Optimized scenario for rainfall forecasting using genetic algorithm coupled with artificial neural network

Rainfall forecasting plays many important role in water resources studies such as river training works and design of flood warning systems. Recent advancement in artificial intelligence and in particular techniques aimed at converting input to output for highly nonlinear, non-convex and dimensionalized processes such as rainfall field, provide an alternative approach for developing rainfall forecasting model. Artificial neural networks (ANNs), which perform a nonlinear mapping between inputs and outputs, are such a technique. Current literatures on artificial neural networks show that the selection of network architecture and its efficient training procedure are major obstacles for their daily usage. In this paper, feed-forward type networks will be developed to simulate the rainfall field and a so-called back propagation (BP) algorithm coupled with genetic algorithm (GA) will be used to train and optimize the networks. The technique will be implemented to forecast rainfall for a number of times using rainfall hyetograph of recording rain gauges in the Upper Parramatta catchment in the western suburbs of Sydney, Australia. Results of the study showed the structuring of ANN network with the input parameter selection, when coupled with GA, performed better compared to similar work of using ANN alone.