Non‐greedy heuristics and augmented neural networks for the open‐shop scheduling problem

In this paper we propose some non‐greedy heuristics and develop an Augmented‐Neural‐Network (AugNN) formulation for solving the classical open‐shop scheduling problem (OSSP). AugNN is a neural network based meta‐heuristic approach that allows integration of domain‐specific knowledge. The OSSP is framed as a neural network with multiple layers of jobs and machines. Input, output and activation functions are designed to enforce the problem constraints and embed known heuristics to generate a good feasible solution fast. Suitable learning strategies are applied to obtain better neighborhood solutions iteratively. The new heuristics and the AugNN formulation are tested on several benchmark problem instances in the literature and on some new problem instances generated in this study. The results are very competitive with other meta‐heuristic approaches, both in terms of solution quality and computational times. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005.     

Approximation of multiclass queueing networks with highly variable arrivals under deterministic routing

In this paper, we present the heavy‐traffic bottleneck phenomenon under multiclass deterministic routing and discuss how it can be addressed by decomposition approximation. Examples show that Bitran and Tirupati's method and Whitt's enhancements for deterministic routing may not properly account for this phenomenon. We propose refinements to these methods based on Whitt's variability functions. Results of numerical experiments on simple networks and semiconductor manufacturing process show significant improvement in the approximation of expected waiting time at bottleneck stations. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005.     

基于ANN模式识别的某型导弹测试车配电系统故障诊断

人工神经网络诊断特点与基于模式识别的诊断特点非常相似。将ANN模式识别技术应用于某型导弹测试车配电系统故障诊断。根据测试车配电系统的故障特点,设计ANN为4层BP网络,具有9个输入、10个输出,两个隐含层神经元数目分别为9和6。测试结果表明该方法能有效诊断测试车配电系统故障。     

二值化联合变换相关器识别复杂背景目标时的阈值选取

讨论了利用二值化联合变换相关器对复杂背景目标进行相关识别的阈值选取问题。由于不同阈值对二值化联合变换相关器识别结果有很大的影响,阈值过高,会损失过多的联合变换功率谱中的有效成份,使相关峰减弱;阈值过低,会使联合功率谱中的噪声放大,相关峰可能被淹没在噪声之中。因而存在一个最佳阈值。实验表明,最佳阈值位于联合功率谱最大灰度值的40%～50%之间,在此阈值范围内,相关峰值较高,噪声也得到有效抑制。     

小波变换在过零调制信号检测中的应用研究

将小波分析理论应用于配电网络过零调制信号的检测中,解决了在强噪声、低信噪比的配电网络中通信误码率高的难题,提高了系统的信噪比。给出了小波变换Mallat算法的分解和重构公式。信号仿真表明,应用该方法可以很好地从复杂环境下提取有用信号的特征。     

基于NNLI技术的齿轮箱故障诊断

将NNLI技术应用到齿轮箱的故障诊断中,提出了基于NNLI的特征提取方法,并将该方法与神经网络结合起来,进一步提出基于NNLI特征提取的神经网络故障诊断方法,给出了两种不同的网络分类器,通过齿轮箱故障诊断实例验证了该方法的有效性。     

陆军诸兵种合同作战兰切斯特方程的弹药消耗预测研究

描述了诸兵种合同作战的兰切斯特方程及其矩阵解,提出了一种通过兵力损耗换算弹药损耗的新思路,分析和确定了用该方法预测弹药消耗的相关参数,最后运用Matlab进行仿真计算,预测出了武器装备战损情况和弹药消耗情况。     

基于BP神经网络带调压器直流发电机特性的辨识

为使坦克直流发电机的调压特性实验能够在计算机上仿真进行,节约教学实验成本,可利用神经网络对直流发电机的调压特性进行辨识,得到了比手工绘制精度更高的特性曲线.因此,调压特性的神经网络辨识法,有利于直流发电机特性实验教学.     

地形通视性算法改进

为提高地形上两点间的通视性查询效率,满足仿真系统实时性要求,对通视性算法进行改进,并将改进算法与现有算法进行试验比较,结果表明改进算法的精度和计算效率是满足仿真需要的。     

基于图元识别的OCR文本图像倾斜矫正快速算法

提出了一种基于文本图元识别以跟踪字符中心线的高精度矫正OCR图像倾斜的快速算法,该算法思想虽然简单,却具有高效和高精度的特点,同时还具有高可靠性和良好的抗噪特征.实验表明,该方法完全满足实时应用的需要.