A robust queueing network analyzer based on indices of dispersion

We develop a robust queueing network analyzer algorithm to approximate the steady-state performance of a single-class open queueing network of single-server queues with Markovian routing. The algorithm allows nonrenewal external arrival processes, general service-time distributions and customer feedback. The algorithm is based on a decomposition approximation, where each flow is partially characterized by its rate and a continuous function that measures the stochastic variability over time. This function is a scaled version of the variance-time curve, called the index of dispersion for counts (IDC). The required IDC functions for the external arrival processes can be calculated from the model primitives or estimated from data. Approximations for the IDC functions of the internal flows are calculated by solving a set of linear equations. The theoretical basis is provided by heavy-traffic limits for the flows established in our previous papers. A robust queueing technique is used to generate approximations of the mean steady-state performance at each queue from the IDC of the total arrival flow and the service specification at that queue. The algorithm's effectiveness is supported by extensive simulation studies.     

Establishment,simulation and verification of firepower safety control model

On a narrow warship platform, the coordinated use of shipborne weapon systems may cause firepower conflicts, which seriously endangers the ship safety. Meanwhile, with directed-energy weapons mounted on ships, firepower conflicts between weapons become a “high probability event”. Aiming at the problem of firepower safety control, based on the research about the collision probability model of air crafts and space targets and according to the cone of fire model of conventional weapons and directed-energy weapons, this paper solved the firepower conflict probabilities between conventional weapons as well as between conventional weapons and directed-energy weapons respectively using the methods of probability theory, and established the firepower safety control model. Then the calculation of firepower conflict probability was carried out using the dimensionality reduction method based on the equivalent conversion of polar coordinates and the power series method based on Laplace transform. The simulation results revealed that the proposed model and calculation methods are effective and reliable, which can provide theoretical basis and technical support for resolution of firepower conflicts between weapons.     

三维介质体的时域散射分析

用时间递推(marching-on in-time,MOT)方法求解了电磁场时域耦合积分方程,计算了均匀介质体的表面等效电流和表面等效磁流,得到时域散射远场并给出了详细推导过程。举例比较了将时域散射用Fourier变化后在频域的RCS和频域直接求得的RCS,以说明该算法的正确性。     

瞬态高强加热条件下含湿多孔介质传热传质模型研究

提出了瞬态高强加热下含湿多孔介质传热传质的新模型,模型包括的水分种类齐全、水分迁移机制全面,假设条件相对较少,考虑了非Fourier传热效应和非Fick传质效应。新模型通过具体的分区分析得到简化并更加实用。     

FBN和JF402在泡沫洗消剂中的展开作用及其对泡沫性能的影响

研究了FBN、JF402 2种碳氟表面活性剂与发泡剂配伍后形成的水溶液在油面上的铺展及泡沫性能（发泡力和稳定性）。结果表明FBN、JF402的加入可提高体系的铺展能力。FBN的铺展性能好于JF402,但JF402泡沫性能好于FBN。     

快速传递对准的抗野值方法研究

快速传递对准对滤波精度与收敛速度有着严格的要求,而测量野点的存在会严重破坏传递对准的精度。提出了一种新的抗野值滤波方法,在线取测量信号的一个小区间,采用正向对称延拓的方法构成新序列并对其进行小波阈值去噪,检测出野点,用前几个周期的信号构造新的测量值进行Kalman滤波,有效地剔除了野点的影响,滤波系统保持了很好的精度,满足了传递对准的要求。     

构造二维正交紧支撑线性相位小波滤波器的递推算法

根据二维小波滤波器参数化理论,提出了一个具有可操作性的二维小波滤波器递推算法。用最简形式刻画了4阶标准正交中心对称矩阵,并证明了根据算法产生的线性相位滤波器的一个重要性质。     

无监督特征学习的人体活动识别

针对人的局限性可能会导致在提取特征中丢失重要信息,从而影响最终的识别效果问题,提出无监督特征学习技术的惯性传感器特征提取方法。其核心思想是使用无监督特征学习方法学习多个特征映射,再将所有特征映射拼接起来形成最终的特征计算方法。其优点是不会造成重要信息的损失,而且可以显著减少所使用的无监督特征学习模型的规模。为了验证所提出的特征提取方法在活动识别中的有效性,运用一个公开的活动识别数据集,使用三种常用无监督模型进行特征提取,并使用支持向量机进行活动识别。实验结果表明,特征提取方法取得了良好的效果,与其他方法相比具有一定的优势。     

GPS接收机模糊搜索粗时定位方法

由于难以获得免时定位算法所需的接收机概略位置,提出模糊搜索的粗时定位方法。为减小搜索空间,在位同步后优选几何精度因子值较好的4颗卫星通过遍历伪距模糊的方式进行位置估计,并基于伪距残余均方根最小准则选出最合理的位置估算结果。根据概略位置恢复所有卫星的完整伪距。利用5状态的免时定位算法估计接收机位置。使用11个国际卫星导航服务观测站的观测数据对算法进行验证,结果表明,在概略位置未知情况下该方法能够实现粗时定位,且定位精度与直接使用完整伪距的精时最小二乘定位精度相当。     

混合气体识别的响应等效性与瞬态信号正交分解模型

针对目前混合气体识别大多采用传感器稳态响应信号、基于线性混合假设或大量样本学习,而瞬态响应信号特征分析主要应用非正交分解的问题,提出一种基于响应等效性与瞬态信号正交分解的混合气体识别模型。分析金属氧化物半导体传感器对混合气体的响应特性,建立基于气体响应成分等效性假设的气体非线性混合模型,在此基础上,提出并应用一种新的正交基函数——扩展类Legendre正交基,对气体传感器瞬态响应信号进行分解;通过对正交分解系数与气体浓度的回归分析,验证二者之间的指数型关联关系,并以正交分解系数为特征参数,利用气体非线性等效混合模型对混合气体分解与辨识。实验结果表明,尽管这种混合气体识别模型仅用单一气体检测的先验知识,对混合气体的识别误差仍可达到15%以内。