加油机器人操作臂设计及运动学研究

为减少污染和节省人力,使用加油机器人代替人力完成加油工作成为未来发展的必然趋势。为此,专门设计一种加油机器人操作臂的结构形式,在D-H坐标系中建立四自由度加油机器人操作臂的运动学模型,采用变换矩阵方法求解该操作臂正、逆运动学解析表达式,并运用Matlab软件的机器人工具箱对该操作臂正、逆运动学方程进行仿真验证。研究结果证明正、逆方程运动学方程解的正确性以及设计方法的可行性。     

Sequential learning versus no learning in Bayesian regression models

We consider two regression models: linear and logistic. The dependent variable is observed periodically and in each period a Bayesian formulation is used to generate updated forecasts of the dependent variable as new data is observed. One would expect that including new data in the Bayesian updates results in improved forecasts over not including the new data. Our findings indicate that this is not always true. We show there exists a subset of the independent variable space that we call the “region of no learning.” If the independent variable values for a given period in the future are in this region, then the forecast does not change with any new data. Moreover, if the independent variable values are in a neighborhood of the region of no learning, then there may be little benefit to wait for the new data and update the forecast. We propose a statistical approach to characterize this neighborhood which we call the “region of little learning.” Our results provide insights into the trade‐offs that exist in situations when the decision maker has an incentive to make an early decision based on an early forecast versus waiting to make a later decision based on an updated forecast. © 2014 Wiley Periodicals, Inc. Naval Research Logistics 61: 532–548, 2014     

靶场试验遥测数据实时处理软件技术

新研弹药产品靶场试验,被试产品往往装有遥测设备,对试验全过程进行测量和监控,被测参数通过遥测发射设备发送至地面接收站,实时处理遥测数据,发现异常,为保证试验安全、现场指挥决策和后期技术分析提供科学依据。本文提出了遥测数据实时处理的一般要求及实现的一般过程和方法,并对关键算法进行了描述。     

On the effectiveness of top‐down strategy for forecasting autoregressive demands

We investigate the relative effectiveness of top‐down versus bottom‐up strategies for forecasting the demand of an item that belongs to a product family. The demand for each item in the family is assumed to follow a first‐order univariate autoregressive process. Under the top‐down strategy, the aggregate demand is forecasted by using the historical data of the family demand. The demand forecast for the items is then derived by proportional allocation of the aggregate forecast. Under the bottom‐up strategy, the demand forecast for each item is directly obtained by using the historical demand data of the particular item. In both strategies, the forecasting technique used is exponential smoothing. We analytically evaluate the condition under which one forecasting strategy is preferred over the other when the lag‐1 autocorrelation of the demand time series for all the items is identical. We show that when the lag‐1 autocorrelation is smaller than or equal to 1/3, the maximum difference in the performance of the two forecasting strategies is only 1%. However, if the lag‐1 autocorrelation of the demand for at least one of the items is greater than 1/3, then the bottom‐up strategy consistently outperforms the top‐down strategy, irrespective of the items' proportion in the family and the coefficient of correlation between the item demands. A simulation study reveals that the analytical findings hold even when the lag‐1 autocorrelation of the demand processes is not identical. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2007.     

Stochastically minimizing the makespan in flow shops

In this article, we are concerned with scheduling stochastic jobs in a flowshop with m machines and zero intermediate storage. We assume that there are n - 2 identically distributed and 2 fast stochastic jobs. Roughly, the main result states that the makespan is stochastically minimized by placing one of the fast jobs first and the other last.