Electrochemical properties of ultra-long,aligned, carbon nanotube array electrode in organic electrolyte

The electrochemical properties of an electrochemical double-layer capacitor electrode based on an ultra-long (500 μm), aligned, carbon nanotube array (ACNTA) in Et₄NPF₆/propylene carbonate electrolyte are examined. The specific capacitance of the ACNTA electrode in an organic electrolyte is 24.5 F g⁻¹, which is larger than that obtained in an aqueous electrolyte. The results of ac impedance measurements show that the ACNTA electrode gives a high power density and an excellent rate capability in an organic electrolyte. It is shown that the ACNTA electrode has a lower equivalent series resistance and a better rate capability than activated carbon electrode. This is due to the fact that ACNTA possesses a larger pore size and a more a regular pore structure. Both these features are conformed by scanning electron microscopic and nitrogen gas adsorption studies.     

微波感应式电力线防盗在线监测系统

研制了一种微波感应式配电线路防盗在线监测系统，简述了其系统构成、技术特点和应用效果。整个系统由监测分机、监控中心、巡检人员组成，借助该系统进行了一系列实验。结果表明，当有大型移动物体靠近或攀登杆塔时，安装在杆塔上10 m左右的微波感应监测分机感应到移动物体信号，一方面进行语音警示，另一方面通过短信服务及时向监控中心发送短信，监控中心及时通知巡检人员，赶到事故现场，以最大限度地保证输电线路的安全。     

Modelling of induced aeration in turbine aerators by use of radial basis function neural networks

Gas induction in agitated vessels with turbine impellers can be modelled accurately by means of radial basis function neural nets. The results obtained with the radial basis neural net were significantly better than those obtained by multivariate regression models or standard back propagation neural nets. Moreover, by using the radial basis function neural net model, it was possible to conduct a sensitivity analysis of the variables affecting aeration. Increased medium density showed a strong adverse effect, while variation of the viscosity can cause an increase or a decrease in the rate of aeration, depending on the prevailing process conditions.     

A New Approach to Abstract Syntax with Variable Binding

The permutation model of set theory with atoms (FM-sets), devised by Fraenkel and Mostowski in the 1930s, supports notions of ‘name-abstraction’ and ‘fresh name’ that provide a new way to represent, compute with, and reason about the syntax of formal systems involving variable-binding operations. Inductively defined FM-sets involving the name-abstraction set former (together with Cartesian product and disjoint union) can correctly encode syntax modulo renaming of bound variables. In this way, the standard theory of algebraic data types can be extended to encompass signatures involving binding operators. In particular, there is an associated notion of structural recursion for defining syntax-manipulating functions (such as capture avoiding substitution, set of free variables, etc.) and a notion of proof by structural induction, both of which remain pleasingly close to informal practice in computer science. Received October 2000 / Accepted in revised form April 2001     

Simulating the Child's Acquisition of the Lexicon and Syntax—Experiences With Babel

This paper describes the theory and implementation ofBabel, a system which explores the hypothesis that much of the differences in the world's languages may be characterized by the inventory and properties of the lexical items and functional categories of those languages. The structure ofBabel assumes that functional categories are originally lacking in a child's syntax, and are acquired through a statistical induction process of lexical acquisition.Babel then uses information induced from the structure of the lexicon to create a model of syntax via a deductive, rule-based process. This model makes a number of predictions about the time course of language acquisition. These predictions are tested by runningBabel as a simulation of child language acquisition, using large samples of adult speech to children as input. The simulation results are shown to highly correlate to longitudinal studies of child language acquisition in English and Polish. Finally, the approach to handling noisy data withBabel is detailed.     

KNOWLEDGE-BASED FEATURE DISCOVERY FOR EVALUATION FUNCTIONS

Since Samuel's work on checkers over thirty years ago, much effort has been devoted to learning evaluation functions. However, all such methods are sensitive to the feature set chosen to represent the examples. If the features do not capture aspects of the examples significant for problem solving, the learned evaluation function may be inaccurate or inconsistent. Typically, good feature sets are carefully handcrafted and a great deal of time and effort goes into refining and tuning them. This paper presents an automatic knowledge-based method for generating features for evaluation functions. The feature set is developed iteratively: features are generated, then evaluated, and this information is used to develop new features in turn. Both the contribution of a feature and its computational expense are considered in determining whether and how to develop it further.
This method has been applied to two problem-solving domains: the Othello board game and the domain of telecommunications network management. Empirical results show that the method is able to generate many known features and several novel features and to improve concept accuracy in both domains.     

相变存储器存储单元瞬态电流测量

介绍了相变存储单元瞬态电流的测量方法。根据相变存储器的工作特性,利用微弱电流取样电阻测量法,合理选择测量参数测得了相变存储单元的瞬态电流,电阻与电流关系特性曲线和动态电阻。并根据测量电路等效电路模型分析影响测量的因素,估算出电路中的分布电容。     

基于滑模变结构的感应电动机直接转矩控制

针对感应电动机常规直接转矩控制中存在磁链、转矩脉动大,低速控制不精确等问题,在建立α-β坐标系感应电动机数学模型的基础上,提出了一种基于滑模变结构的新型直接转矩控制方法。该方法利用转速滑模变结构控制器代替常规直接转矩控制中的PI控制器,可有效减小常规直接转矩控制中的磁链和转矩脉动,增强了系统的稳定性。仿真结果证明了该方法的正确性。     

Intelligent control of doubly‐fed induction generator systems using PIDNNs

An intelligent control for a stand‐alone doubly‐fed induction generator (DFIG) system using a proportional‐integral‐derivative neural network (PIDNN) is proposed in this study. This system can be applied as a stand‐alone power supply system or as the emergency power system when the electricity grid fails for all sub‐synchronous, synchronous, and super‐synchronous conditions. The rotor side converter is controlled using field‐oriented control to produce 3‐phase stator voltages with constant magnitude and frequency at different rotor speeds. Moreover, the grid side converter, which is also controlled using field‐oriented control, is primarily implemented to maintain the magnitude of the DC‐link voltage. Furthermore, the intelligent PIDNN controller is proposed for both the rotor and grid side converters to improve the transient and steady‐state responses of the DFIG system for different operating conditions. Both the network structure and online learning algorithm are introduced in detail. Finally, the feasibility of the proposed control scheme is verified through experimentation. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

A Further Comparison of Splitting Rules for Decision-Tree Induction

One approach to learning classification rules from examples is to build decision trees. A review and comparison paper by Mingers (Mingers, 1989) looked at the first stage of tree building, which uses a splitting rule to grow trees with a greedy recursive partitioning algorithm. That paper considered a number of different measures and experimentally examined their behavior on four domains. The main conclusion was that a random splitting rule does not significantly decrease classificational accuracy. This note suggests an alternative experimental method and presents additional results on further domains. Our results indicate that random splitting leads to increased error. These results are at variance with those presented by Mingers.