基因预测中外显子分值的转化

多数基因预测算法都会给出预测外显子的分值,但这些分值大多没有意义,很难给使用者带来有用的信息.作者以基因预测程序Fgenes为例,分别用4种方法将外显子的分值转换成概率分值,通过计算机模拟,4种方法都取得了良好的效果,其中以局部多项式估计效果最好.     

平行机上有优先约束的工件的排序问题

考虑有优先约束的单位工件在m台同型机上的排序问题，目标函数是使工件的完工时间之和最少，当机器的台数不确定时这个问题已经得到了解决．该文中指出当机器的台数确定为m(m≥3)时该问题是NP-完备的。     

起重机箱形主梁的概率断裂力学分析

本文将概率断裂力学应用于起重机箱形主梁的疲劳寿命分析中．介绍了概率断裂力学的统计基础、内容和方法，用实例计算了起重机箱形主梁的可靠度。     

An Evolutionary Real-Time 3D Route Planner for Aircraft

Abstract: A novel evolutionary route planner for aircraft is proposed in this paper. In the new planner, individual candi-dates are evaluated with respect to the workspace, thus the computation of the configuration space is not required. By usingproblem-specific chromosome structure and genetic operators, the routes are generated in real time, with different missionconstraints such as minimum route leg length and flying altitude, maximum turning angle, maximum climbing/diving an-gle and route distance constraint taken into account.     

基于Hopfield网络单元机组负荷多变量模型算法控制

电厂负荷系统是一个有耦合和约束的多变量对象。模型算法控制(MAC)是预测控制的一种，在惯性迟延对象的控制中有很好的应用。该文提出用Hopfield网络来求解具有约束的多变量负荷系统的控制输入。仿真表明，该方法算法简单、鲁棒性强、控制效果理想。     

概率型算子在L_p(p≥1)空间中的渐近表示

研究概率型算子的渐近性质,建立了渐近公式发展了 R.A.Khan的结果.     

概率度量空间的致紧性和连通性

本文对概率度量空间上的概率致紧性及概率致紧集的连通性作了数量刻丽,给出了 Menger 空间的点集为概率致紧集的一个充要条件及概率致紧为连通集的充要条件。     

A Heuristic Algorithm for QoS Multicast Routing

In recent years, QoS multicast routing has continued to be a very important research topic in the areas of networks. This paper presents a heuristic algorithm for the QoS multicast routing (HAQMR). This heuristic algorithm deals with delay and bandwidth constraints and has low cost. The HAQMR attempts to significantly reduce the overhead for constructing a multicast tree, the proof for correctness of the HAQMR is given, and the performance of the HAQMR is evaluated by simulations. The study shows that HAQMR provides an available approach to QoS multicast routing.     

THE OPTIMALITY CONDITIONS OF D-PSEUDOCONVEX PROGRAMMING WITH OPERATOR CONSTRAINTS

The problem of D-pseudoconvex programming with operator con-straints is discussed in this paper.Some new optimality conditions are presentedand an error made by V.Barbu in [4] is pointed out.