A model-based and simulation-driven methodology for design of haptic devices

High precision and reliable haptic devices are highly complex products. The complexity that has to be carefully treated in the design process is largely due to the multi-criteria and conflicting character of the functional and performance requirements. These requirements include high stiffness, large workspace, high manipulability, small inertia, low friction, high transparency, as well as cost constraints. The requirements are a basis for creating and assessing design concepts. Concept evaluation relies to a large extent on a systematic usage of kinematic, dynamic, stiffness, friction, and control models. The design process can benefit from a model-based and simulation-driven approach, where one starts from an abstract top-level model that is extended via stepwise refinements and design space exploration into a detailed and integrated systems model that can be physically realized. Such an approach is presented, put in context of the V-model, and evaluated through a test case where a haptic device, based on a Stewart platform, is designed and realized. It can be concluded, based on simulation and experimental results that the performance of this deterministically optimized haptic device satisfies the stated user requirements. Experiences from this case indicate that the methodology is capable of supporting effective and efficient development of high performing haptic devices. However, more test cases are needed to further validate the presented methodology.     

MB-GNG: Addressing drawbacks in multi-objective optimization estimation of distribution algorithms

We examine the model-building issue related to multi-objective estimation of distribution algorithms (MOEDAs) and show that some of their, as yet overlooked, characteristics render most current MOEDAs unviable when addressing optimization problems with many objectives. We propose a novel model-building growing neural gas (MB-GNG) network that is specially devised for properly dealing with that issue and therefore yields a better performance. Experiments are conducted in order to show from an empirical point of view the advantages of the new algorithm.     

Spectral estimation of the Lévy density in partially observed affine models

The problem of estimating the Lévy density of a partially observed multidimensional affine process from low-frequency and mixed-frequency data is considered. The estimation methodology is based on the log-affine representation of the conditional characteristic function of an affine process and local linear smoothing in time. We derive almost sure uniform rates of convergence for the estimated Lévy density both in mixed-frequency and low-frequency setups and prove that these rates are optimal in the minimax sense. Finally, the performance of the estimation algorithms is illustrated in the case of the Bates stochastic volatility model.     

888nm半导体抽运的60W皮秒激光振荡器模型

为了建立一个基于888nm半导体抽运的高平均功率和高效率皮秒激光振荡器的理论模型,采用模拟计算方法和谐振腔理论、ABCD定律、自洽条件以及连续被动锁模条件,利用激光晶体Nd:YVO4对波长888nm半导体抽运源的吸收特性和元件的相关参量、合适的腔模参量、实现稳定锁模的参量进行了理论分析和计算,并通过以上研究和模拟计算得到了皮秒振荡器模型的相关数据。结果表明,在120W的抽运功率下,激光器可以输出约61.5W的皮秒激光,光光转换效率51.3%。这一理论模型的建立对高平均功率和高效率皮秒激光振荡器的实验研究起着指导作用。     

锥面共形阵列信源方位和极化参数的联合估计算法

该文用交叉电偶极子对在锥面共形载体表面构造极化敏感阵列,在建立其快拍数据模型的基础上实现了信源方位和极化参数的联合估计。算法首先通过合理的矩阵变换将阵列流形中的信源方位和极化信息去耦合,然后分别根据秩损理论和旋转不变子空间思想对其进行估计,最后通过一种轮换比对配对方法实现信源方位和极化参数的联合估计。Monte-Carlo仿真实验表明,所提算法可以很好地解决锥面共形阵列的多参数联合估计问题。     

一种新的基于UMVE和OS的恒虚警检测算法

为了充分利用参考单元所提供的信息,减少恒虚警损失,该文基于无偏最小方差估计(UMVE)方法和有序统计(OS)方法,提出了一种新的恒虚警检测器(MOSUM-CFAR)。它的前沿和后沿滑窗分别采用UMVE和OS方法产生两个局部估计,再对二者求和得到背景功率水平估计。在Swerling II型目标假设下,文中推导出MOSUM-CFAR在均匀背景下虚警概率Pfa和检测概率Pd及多目标环境下检测概率Pd的解析表达式,并与其它方案作了比较。分析结果表明MOSUM-CFAR在均匀背景和多目标环境下均具有相当好的检测性能。     

DSSS信号的扩频序列估计

提出了一个在非协作通信中对直接扩频序列(Direct Spread Spectrum Sequence,DSSS)信号的扩频序列进行估计的简单而有效的算法。在算法中我们提出了两个测度函数,一个用于搜索扩频序列在接收信号中的起始位置,另一个用于序列中的逐码片(chip)估计。理论分析和计算机仿真表明了此算法的有效性。     

基于规划路径约束的机器人定位方法

路径规划是为机器人生成可行驶路径以实现循迹的过程。因此,机器人的位置应该位于或靠近规划的行驶路径。从而,路径规划可为机器人定位产生重要的约束。该文提出一种规划路径约束的位置概率图 (PI-LPM)模型,该模型通过概率来表征机器人在整个地图范围内所处的位置的可能性。其中,模型中概率密度函数是通过核密度估计 (KDE)方法从表征规划路径的所有数据点生成。在所提出的PI-LPM模型基础上,提出一种规划路径约束的机器人定位新算法 (RL-PPC)来提高机器人定位精度。在该方法中,应用粒子滤波算法来融合所提出的PI-LPM模型和已有的传感器定位方法。融合过程中,从PI-LPM模型中计算得到的概率是分配粒子权重的一个重要因素。实验中分别利用仿真数据和真实数据对所提出的模型与算法进行验证。实验结果表明,所提RL-PPC算法可有效融合PI-LPM模型与主流的定位系统(如GPS和LiDAR定位系统),并显著提高机器人定位的整体性能。     

单观测器无源定位误差下界的仿真分析

单观测器无源定位所能达到的最优定位精度与观测器的运动速度、运动轨迹、测量精度和测量速率密切相关。该文采用仿真方法,分析了观测器运动轨迹、运动速度、测量精度和测量速率等因素对测角无源定位误差Cramer-Rao下界的影响,比较了三种形式观测器轨迹的定位误差下界,得出的结果对确定具体定位与跟踪系统战术指标、以及对滤波算法的选用具有实用价值。     

A pilot-assisted non-Cyclic Prefixed MIMO-SCFDE system

A non-Cyclic Prefixed Multiple-Input Multiple-Output Single-Carrier Frequency-Domain Equalization (non-CP MIMO-SCFDE) system based on a recursive algorithm of Joint Channel Es- timation and Data Detection (recursive-JCEDD) is proposed in this paper. Unlike the traditional CP MIMO-SCFDE system, the transmitted block of the proposed system is designed in the way that block-type pilot sequences and Single-Carrier (SC) information sequences have been arranged alter- nately without any cyclic prefix before each ...