Gait Generation and Stabilization for Nearly Passive Dynamic Walking Using Auto‐distributed Impulses

We propose a state feedback control design via linearization for flexible walking on flat ground. First, we generate nearly passive limit cycles, being stable or not, using impulsive toe‐off actuations. The term ‘nearly passive’ means that the dynamics is completely passive almost everywhere except at the toe‐off moment. A feature of our gait generation method is that walking gaits are characterized only by amounts of supplied energy, and we observe that other variables, including input torques, are auto‐balanced via our method. After gait generation, we design a feedback controller considering robustness and input saturation. As a result, each limit cycle can be matched with its respective controller classified only by energy levels. We have verified that walking speeds monotonically increase by adding more energy, and the ankle joint plays a significant role in compass‐gait walking. Finally, instead of applying impulsive torques, we discuss a practical issue regarding realistic control inputs that ensure stable gait transitions as energy levels are elevated.     

Multi layer ELM-RBF for multi-label learning

Many neural network methods such as ML-RBF and BP-MLL have been used for multi-label classification. Recently, extreme learning machine (ELM) is used as the basic elements to handle multi-label classification problem because of its fast training time. Extreme learning machine based auto encoder (ELM-AE) is a novel method of neural network which can reproduce the input signal as well as auto encoder, but it can not solve the over-fitting problem in neural networks elegantly. Introducing weight uncertainty into ELM-AE, we can treat the input weights as random variables following Gaussian distribution and propose weight uncertainty ELM-AE (WuELM-AE). In this paper, a neural network named multi layer ELM-RBF for multi-label learning (ML-ELM-RBF) is proposed. It is derived from radial basis function for multi-label learning (ML-RBF) and WuELM-AE. ML-ELM-RBF firstly stacks WuELM-AE to create a deep network, and then it conducts clustering analysis on samples features of each possible class to compose the last hidden layer. ML-ELM-RBF has achieved satisfactory results on single-label and multi-label data sets. Experimental results show that WuELM-AE and ML-ELM-RBF are effective learning algorithms.     

基于Map-Reduce并行构架和AE的大数据指纹监控识别算法

传统的指纹识别监控方法在应用到以海量、在线和增量为特征的大数据环境中时,不能自动高效地进行识别;由此设计了一种基于Map-Reduce并行框架和AE的大数据智能监控指纹识别算法;首先,设计了基于AE和BP神经网络的识别模型,采用AE进行指纹图像自动特征提取,然后提出了一种基于比较差异算法对AE进行参数初始化的训练算法,采用BP神经网络进行具体识别;最后,将识别过程分解为Map函数和Reduce函数;在Map-Reduce并行框架下对FVC2004中的指纹数据库进行实验,实验结果表明了文中方法能自动、高效地进行指纹识别。     

Dynamic isotropic design of a class of Gough-Stewart parallel manipulators lying on a circular hyperboloid of one sheet

This paper proposes an optimal design method based on dynamic isotropy conditions of a generalized natural frequency matrix in task space for a standard Gough-Stewart parallel manipulator (GSPM). Firstly, the dynamic isotropy conditions are discussed and presented. Next, it is proved that standard GSPMs lie on a circular hyperboloid of one sheet. Further, we present a method to use a circular hyperboloid to construct 3-3-, 6-3- and 6-6-type standard GSPMs. Moreover, such novel definition is used to examine various dynamic isotropy conditions as functions of the coefficients of a hyperboloid. An optimal design method is also proposed accordingly. Finally, a numerical example is given to illustrate the design procedures. The results are very close to the complete dynamic isotropy.     

Developing a Higher-Cycled Product Design CAE Model： The Evolution of Automotive Product Design and CAE

To help corporations survive amidst worldwide quality competition, the authors have focused on the strategic development of a higher-cycled product design CAE model employing a highly reliable CAE analysis technology component model. Their efforts are part of principle-based research aimed at evolving product design and CAE development processes to ensure better quality assurance. To satisfy the requirements of developing and producing high quality products while also reducing costs and shortening development times, the effectiveness of this model was verified by successfully applying it to the technological problems of loosening bolts and other product design bottlenecks at auto manufacturers.     

测量机器人在三角网测量中的应用分析

简述了TCA2003测量机器人全圆观测法机载软件的组成与特点。以陈村水电站大坝的三角网观测为应用实例,基于对其测量平差成果的精度及测量时间分析,将其与传统人工观测的效率进行比较,证明了测量机器人测量的优越性。指出了一些仪器使用及机载软件存在的问题,对自动目标识别（ATR）仪器机载软件的应用开发具有指导作用。     

智能停车场管理系统的探讨

针对传统停车场存在着收费管理混乱及整体运作效率低等问题,设计了一种基于RFID的智能停车场管理系统。介绍了系统的基本功能、总体结构,并对RFID技术、车位引导技术和车牌自动识别技术进行了详细的探讨。     

NGN业务系统中异构数据库中间件的设计和实现

针对下一代网络(NGN)业务开发过程中业务提供商需要和电信运营商的各种异构数据库进行通信的情况,设计和实现一种基于异构的数据库中间件。采用分层结构和多线程的方式,并对数据访问请求进行统一调度排队,从而有效地提高数据访问的安全性、灵活性和可扩展性。目前该中间件已经大量地应用于在线的NGN业务系统中。     

Accurate evaluation of Green’s functions in a layered medium by SDP-FLAM

Based on local Taylor expansions on the complex plane, a method for fast locating all modes (FLAM) of spectral-domain Green’s Functions in a planar layered medium is developed in this paper. SDP-FLAM, a combination of FLAM with the steepest descent path algorithm (SDP), is employed to accurately evaluate the spatial-domain Green’s functions in a layered medium. According to the theory of complex analysis, the relationship among the poles, branch points and Riemann sheets is also analyzed rigorously. To inverse the Green’s functions from spectral to spatial domain, SDP-FLAM method and discrete complex image method (DCIM) are applied to the non-near field region and the near filed region, respectively. The significant advantage of SDP-FLAM lies in its capability of calculating Green’s functions in a layered medium of moderate thickness with loss or without loss. Some numerical examples are presented to validate SDP-FLAM method. Supported by the National Natural Science Foundation of China (Grant No. 60621002), and the State Key Development Program for Basic Research of China (Grant No. 2009CB320200)     

入侵检测中贝叶斯分类器的改进和设计

针对入侵检测系统在实时检测能力和自适应能力方面的不足,提出了一个改进的贝叶斯分类器,通过引入滑动窗口技术改善入侵检测的实时性.同时通过所设计的性能调节器对贝叶斯分类器中参数的动态设置,实现了入侵检测系统的自适应性.改进后的贝叶斯分类器有效地实现了入侵检测的实时性、主动性和自适应性.