Broader Societal Issues of Nanotechnology

Nanoscale science and engineering are providing unprecedented understanding and control over the basic building blocks of matter, leading to increased coherence in knowledge, technology, and education. The main reason for developing nanotechnology is to advance broad societal goals such as improved comprehension of nature, increased productivity, better healthcare, and extending the limits of sustainable development and of human potential. This paper outlines societal implication activities in nanotechnology R&D programs. The US National Nanotechnology Initiative annual investment in research with educational and societal implications is estimated at about $30 million (of which National Science Foundation (NSF) awards about $23 million including contributions to student fellowships), and in nanoscale research with relevance to environment at about $50 million (of which NSF awards about $30 million and EPA about $6 million). An appeal is made to researchers and funding organizations worldwide to take timely and responsible advantage of the new technology for economic and sustainable development, to initiate societal implications studies from the beginning of the nanotechnology programs, and to communicate effectively the goals and potential risks with research users and the public.     

Decentralized iterative learning control for a class of large scale interconnected dynamical systems

The problem of decentralized iterative learning control for a class of large scale interconnected dynamical systems is considered. In this paper, it is assumed that the considered large scale dynamical systems are linear time-varying, and the interconnections between each subsystem are unknown. For such a class of uncertain large scale interconnected dynamical systems, a method is presented whereby a class of decentralized local iterative learning control schemes is constructed. It is also shown that under some given conditions, the constructed decentralized local iterative learning controllers can guarantee the asymptotic convergence of the local output error between the given desired local output and the actual local output of each subsystem through the iterative learning process. Finally, as a numerical example, the system coupled by two inverted pendulums is given to illustrate the application of the proposed decentralized iterative learning control schemes.     

数字全息技术的原理和应用

数字全息是随着现代计算机和CCD技术发展而产生的一种新的全息成像技术．文章主要介绍数字全息技术的基本原理，数字全息重建中的主要方法以及数字全息技术以其独特的优点在各个领域中的应用．     

数码相机原理与系统设计研究

介绍了数码相机的工作原理 ,分析了数码相机的系统结构 ,提出了设计数码相机的处理流程。典型的数码相机系统有镜头、闪光灯、光学取景器、LCD显示屏、图像数据存储扩展设备接口、图像数据传输接口、供电系统以及核心处理器等八个主要模块。数码相机的数据流向从图像传感器开始 ,止于图像数据的存储和传输。数据流的处理主要包括模数转换、光学黑电平钳位、针对镜头的边缘畸变的运算修正、坏像素处理、白平衡处理、伽马校正、色彩合成处理、边缘检测 (锐度检测 )和伪彩色检测 (伪彩色抑制 )、JPEG压缩和图像存储器等模块     

Quantitative evaluation of two-dimensional dynamic deformations using digital holography

An optical system for the parallel evaluation of in- and out-of-plane dynamical deformations will be described. A double pulse laser with pulse separation in the microsecond range is used for the investigations. Two separate interferograms of an object under test, in its undeformed and deformed state, are recorded in a few microseconds. The object is illuminated from two different directions and imaged onto a CCD sensor. This produces two sensitivity vectors. The reference beams have different directions in order to produce two directional spatial carriers. The Fourier method is used for quantitative evaluation, and the measurements along different sensitivity vectors are separated in the Fourier domain. The phases of the two interferograms are obtained from the complex amplitudes and the two dimensional deformation is calculated from the phases. Experimental results are presented.     

Application of a Near-Optimal Reinforcement Learning Controller to a Robotics Problem in Manufacturing: A Hybrid Approach

Optimization theory provides a framework for determining the best decisions or actions with respect to some mathematical model of a process. This paper focuses on learning to act in a near-optimal manner through reinforcement learning for problems that either have no model or the model is too complex. One approach to solving this class of problems is via approximate dynamic programming. The application of these methods are established primarily for the case of discrete state and action spaces. In this paper we develop efficient methods of learning which act in complex systems with continuous state and action spaces. Monte-Carlo approaches are employed to estimate function values in an iterative, incremental procedure. Derivative-free line search methods are used to obtain a near-optimal action in the continuous action space for a discrete subset of the state space. This near-optimal control policy is then extended to the entire continuous state space via a fuzzy additive model. To compensate for approximation errors, a modified procedure for perturbing the generated control policy is developed. Convergence results under moderate assumptions and stopping criteria are established.     

基于DEM的山地可照时间的空间分布

以山区高空间分辨率的DEM数据为主要数据源，在提取纬度、坡度、坡向等相应的地形要素栅格数据的基础上，利用GIS技术对山区可照时间的空间分布进行了模拟，实现了山区可照时间空间分布规律的可视化表达，分析了山区可照时间的空间分布特征，对于山区的农业、林业和生态环境等方面的研究具有重要意义．     

随机环境中分枝过程的等价定理

给定了随机环境中分枝过程(BPRE)的精确定义,讨论了有关的可测性问题和BPRE的基本性质．在此基础上,证明了BPRE的一个等价定理．     

随机环境下的对称零程过程的大偏差原理

利用相对熵方法及鞅方法给出了随机环境下的对称零程过程的经验测度的收敛速度函数.     

圆感应同步器数显表检测方法研究

介绍了圆感应同步器数显表测角系统的工作原理,以及在静态和动态测角方法。提出了两种提高动态测量精度的方法。实验数据验证了方法的合理性。