Widrow-cellular neural network and optoelectronic implementation

A new type of optoelectronic cellular neural network has been developed by providing the capability of coefficients adjusment of cellular neural network (CNN) using Widrow based perceptron learning algorithm. The new supervised cellular neural network is called Widrow-CNN. Despite the unsupervised CNN, the proposed learning algorithm allows to use the Widrow-CNN for various image processing applications easily. Also, the capability of CNN for image processing and feature extraction has been improved using basic joint transform correlation architecture. This hardware application presents high speed processing capability compared to digital applications. The optoelectronic Widrow-CNN has been tested for classic CNN feature extraction problems. It yields the best results even in case of hard feature extraction problems such as diagonal line detection and vertical line determination.     

地源热泵的运行特性模拟研究

依据圆柱源理论,建立起了耦合地面热泵机组和地下埋管换热器特性的模拟模型,该模型可用于长期运行的地源热泵系统的短时间步长运行特性模拟。探讨了模拟过程中有关参数的确定方法,并运用所建模型对地源热泵的冬季和夏季运行特性进行了模拟。运行特性模拟与实验数据的验证结果表明,所建模型可以对地源热泵的运行特性做出符合实际的预测。     

某轴流压气机叶尖间隙效应实验研究

本文对某低速轴流压气机叶尖间隙尺度对性能的影响进行了实验研究,并通过不同的机匣结构情况研究一种叶尖漏流不敏感机匣。实验表明,不同的间隙尺度对叶轮机总体性能存在十分明显的影响。对绝大部分运行状态,小间隙下的压升和效率均明显高于大间隙的情况。另外,所采用的毛刷式和篦齿式的机匣实验结果表明,不论毛刷式还是篦齿式机匣,在大流量负攻角情况下对性能的影响不大,但毛刷式机匣并不能达到提高失速裕度的目的,而篦齿式机匣在小流量情况下对压升有所提高,但对效率影响不大。     

衍射光学透镜色散特性的仿真研究

在白光照射下,衍射光学透镜的焦距不仅同材料色散和波长色散有关,而且还同孔径因子有关。基于折射率随波长变化的经验公式,利用自编的仿真计算程序包,得到了透镜焦距、材料色散、波长色散、孔径因子之间的关系曲线。所获得的理论分析和仿真计算结果为进一步设计消色差的纯衍射目视光学系统提供了理论依据,具有一定的指导意义。     

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.     

How to assess and report the performance of a stochastic algorithm on a benchmark problem: mean or best result on a number of runs?

Some authors claim that reporting the best result obtained by a stochastic algorithm in a number of runs is more meaningful than reporting some central statistic. In this short note, we analyze and refute the main argument brought in favor of this statement.     

Approximation bounds for quadratic maximization and max-cut problems with semidefinite programming relaxation

In this paper,we consider a class of quadratic maximization problems.For a subclass of the problems,we show that the SDP relaxation approach yields an approximation solution with the ratio is dependent on the data of the problem with α being a uniform lower bound.In light of this new bound,we show that the actual worst-case performance ratio of the SDP relaxation approach (with the triangle inequalities added) is at least α δd if every weight is strictly positive,where δd ＞ 0 is a constant depending on the problem dimension and data.     

Characterization of reduced iso‐α‐acids derived from hops (Humulus lupulus) by NMR

Reduced forms of iso‐α‐acids (isohumulones), used in modern beer brewing were separated and characterized by ¹H and ¹³C NMR spectroscopy. Components from mixtures of rho‐iso‐α‐acids, tetrahydro‐iso‐α‐acids, and hexahydro‐iso‐α‐acids were isolated using high‐performance liquid chromatography (HPLC) and analyzed by use of one‐ and two‐dimensional NMR experiments. The data presented assign the identities of the main peaks in the HPLC traces for the reduced iso‐α‐acids. Previous tentative assignments regarding the cis and trans configurations and the structures of the acyl residues of the reduced iso‐α‐acids were confirmed and extensive NMR assignments were made. Furthermore, the previously unknown stereochemistry in the C‐4 side‐chain of the rho‐ and hexahydro‐iso‐α‐acids was assigned. Copyright © 2003 John Wiley & Sons, Ltd.     

闭排队网络中性能势与无穷小实现因子之间的关系

In this paper,the concept of the infinitesimal realization factor is extended to the parameter-dependent performance functions in closed queueing networks. Then the concepts of realization matrix (its elements are called realization factors) and performance potential are introduced,and the relations between infinitesimal realization factors and these two quantities are discussed. This provides a united framework for both IPA and non IPA approaches. Finally,another physical meaning of the service rate is given.     

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.