速变信号的小波分析应用研究

遥测速变参数处理方法大多基于经典分析方法,文中采用现代时频分析方法对遥测动态环境参数进行分析,对比算法在瞬态信号成分检测方面的优势,小波变换比傅立叶变换更适合分析非平稳信号,尤其是含瞬态或突变成分的信号.     

关于值分布论中一个著名猜测的推广

运用Nevanlinna的亚纯函数理论方法,研究了超越亚纯函数的值分布理论,获得了如下的结论:设f为超越亚纯函数,c为f的不恒等于0的的小函数,则当n≥3时,fnf′-c有无穷多个零点;若附加条件f只有有限多个级≤2的零点,则对一切正整数n,fnf′-c都有无穷多个零点.因而对Chiang Y M的问题作出了部分回答.     

What you should know about queueing models to set staffing requirements in service systems

One traditional application of queueing models is to help set staffing requirements in service systems, but the way to do so is not entirely straightforward, largely because demand in service systems typically varies greatly by the time of day. This article discusses ways—old and new—to cope with that time‐varying demand. © 2007 Wiley Periodicals, Inc. Naval Research Logistics, 2007     

基于贝叶斯方法的指数寿命型产品可靠度评估方法

以经典法为推理基础,利用无验前信息时验前分布密度的假设方式及共轭分布函数的传递性,推导出指数寿命型产品在不同试验方案下可靠度R的多层贝叶斯评估方法。使得在利用贝叶斯方法对指数寿命型产品进行评估时,更具科学性和合理性。     

极值指数之推广矩估计量的渐近正态性

首先将推广矩估计量代换为一种新的估计量,然后研究由该估计量引起的一种与尾经验过程有关的函数的弱收敛问题,最后得到与推广矩估计量有关的一随机过程的弱收敛函数,同时也就得到了推广矩估计量的渐近分布,因而证明了推广估计量的渐近正态性.     

基于径向基神经网络的水下振动物体辐射噪声级别分类研究

运用径向基神经网络,利用水下振动物体内表面加速度信号对其辐射噪声级别进行分类,达到判断其声隐身性的目的.该方法的运算量较传统方法大大降低,极大地提高了计算速度.实例表明,该方法能较准确地对水下振动物体辐射声场声压级别进行分类,进而对其推广应用于潜艇提供了较好的依据.     

基于虚拟检测函数下的IMM-UKF机动目标跟踪

为了有效提高对机动目标的跟踪效果,将无迹卡尔曼滤波(UKF)引入到交互多模型(IMM)算法框架内,加强状态估计精度;引入强跟踪滤波器(STF)到UKF算法中,避免对强机动目标的过大时间延迟和跟踪性能差的缺点;提出虚拟检测函数法,在跟踪过程中自适应调整"当前"统计模型的机动参数,加大模型集与目标真实运动模式匹配概率。仿真结果验证了改进算法的有效性。     

基于相关性的OFDM盲识别与参数估计方法

针对非协作通信领域OFDM信号识别难的问题,提出一种基于信号循环前缀相关性的盲识别和时域参数估计算法。通过对OFDM信号循环自相关函数进行分析,在时域上求出延时自相关运算函数的波形,利用循环前缀的相关波峰的特性,以区别OFDM信号和单载波信号,实现对OFDM信号的盲识别。在此基础上,通过采用多个OFDM信号构造循环前缀相关性的累加函数所形成的波峰完成对符号和循环前缀长度等参数的估计。     

泵喷推进器线谱非定常推力预报方法与试验验证

为预报泵喷推进器转子与周期性前导叶尾流互作用线谱非定常推力,忽略泵喷推进器转子叶片厚度,将泵喷转子简化为环形叶栅,根据片条理论,在半径r处截取泵喷转子分段,忽略流场参数沿分段径向的变化,从而可将该环形叶栅分段视为平面叶栅,在平面叶栅与简谐波互作用的基础上考虑周期性进流,推导得到前导叶分段与转子分段互作用线谱非定常激振力,转子分段周向积分得到非定常推力线谱预报公式,通过数值和试验方法验证公式的有效性。开展设计参数影响分析,得到当前导叶-转子间距与前导叶弦长的比值大于1时,转子-后导叶间距对转子单个叶片的激振力线谱推力几乎不存在影响。     

Predicting older-donor kidneys' post-transplant renal function using pre-transplant data

This paper provides a methodology for predicting post-transplant kidney function, that is, the 1-year post-transplant estimated Glomerular Filtration Rate (eGFR-1) for each donor-candidate pair. We apply customized machine-learning algorithms to pre-transplant donor and recipient data to determine the probability of achieving an eGFR-1 of at least 30 ml/min. This threshold was chosen because there is insufficient survival benefit if the kidney fails to generate an eGFR-1 ≥ 30 ml/min. For some donor-candidate pairs, the developed algorithm provides highly accurate predictions. For others, limitations of previous transplants' data results in noisier predictions. However, because the same kidney is offered to many candidates, we identify those pairs for whom the predictions are highly accurate. Out of 6977 discarded older-donor kidneys that were a match with at least one transplanted kidney, 5282 had one or more identified candidate, who were offered that kidney, did not accept any other offer, and would have had ≥80% chance of achieving eGFR-1 ≥ 30 ml/min, had the kidney been transplanted. We also show that transplants with ≥80% chance of achieving eGFR-1 ≥ 30 ml/min and that survive 1 year have higher 10-year death-censored graft survival probabilities than all older-donor transplants that survive 1 year (73.61% vs. 70.48%, respectively).