螺旋槽管强化传热原理及在石化装置上的应用前景

详细介绍了螺旋槽管换热器的强化传热原理 ,阐明了在无相变和有相变两种工况下 ,螺旋槽管换热器的强化作用 ,并介绍了螺旋槽管换热器的设计计算方法。说明了在石油化工装置上应用的必要性     

A Symbolic Pole/Zero Extraction Methodology Based on Analysis of Circuit Time-Constants

This paper introduces a methodology for symbolic pole/zero extraction based on the formulation of the time-constant matrix of the circuits. This methodology incorporates approximation techniques specifically devoted to achieve an optimum trade-off between accuracy and complexity of the symbolic root expressions. The capability to efficiently handle even large circuits will be demonstrated through several practical circuits.     

0. 6 􀀁m CMOS 雷达天线专用控制芯片设计

雷达系统中天线控制电路完成上位机的初始化和扫描角度控制,要求具有高可靠性和低静态电流,用专用集成电路进行设计具有明显优势.采用Verilog HDL语言描述了系统的逻辑功能,超前进位结构的加/减法器提高了电路的工作速度.利用0.6 μm CMOS工艺完成了天线控制电路的物理实现,芯片面积为1.695 mm×1.631 mm.     

Primary hydration of poly(acrylic acid) sodium salts

Summary The equilibrium water contents of linear poly(acrylic acid) sodium salts with different degrees of neutralisation were found to be dependent on temperature and relative humidity. An octahedral model for the primary hydration of poly(acrylic acid) sodium salts (HIRAOKA and YOKOYAMA 1980) was critically evaluated in the light of these findings and an anomaly in the water uptake versus neutralisation curve at approximately 33% neutralisation was explained by the counterion condensation theory. (MANNING 1979).     

Bounds on the number of samples needed for neural learning.

The relationship between the number of hidden nodes in a neural network, the complexity of a multiclass discrimination problem, and the number of samples needed for effect learning are discussed. Bounds for the number of samples needed for effect learning are given. It is shown that Omega(min (d,n) M) boundary samples are required for successful classification of M clusters of samples using a two-hidden-layer neural network with d-dimensional inputs and n nodes in the first hidden layer.     

K-winner networks.

A special class of mutually inhibitory networks is analyzed, and parameters for reliable K-winner performance are presented. The network dynamics are modeled using interactive activation, and results are compared with the sigmoid model. For equal external inputs, network parameters that select the units with the larger initial activations (the network converges to the nearest stable state) are derived. Conversely, for equal initial activations, networks that select the units with larger external inputs (the network converges to the lowest energy stable state) are derived. When initial activations are mixed with external inputs, anomalous behavior results. These discrepancies are analyzed with several examples. Restrictions on initial states are derived which ensure accurate K-winner performance when unequal external inputs are used.     

Intelligent identification and control for autonomous guided vehicles using adaptive fuzzy-based algorithms

Fuzzy logic was first suggested as the mechanism by which humans drive cars. This paper addresses the use of fuzzy logic and algorithms towards the intelligent autonomous motion control of land vehicles. To cope with vehicle complexities, internal parametric changes, and with unpredictable environmental effects, the controllers that are presented, whilst heuristic in nature, are self-organizing or self-learning in that they generate automatically by observation an experiential rule base that models the vehicle, and via an appropriate performance index an optimal control rule base that is robust to large parametric changes. The methodology presented is applicable to any complex process which is too difficult to model or control using conventional methods, or which has relied on the experience of a human operator. An overview of fuzzy logic and static fuzzy logic control (akin to expert systems) is provided, together with illustrative examples.