Some fractal properties of the percolating backbone in two dimensions

A new algorithm is presented, based on elements of artificial intelligence theory, to determine the fractal properties of the backbone of the incipient infinite cluster. It is found that the fractal dimensionality of the backbone isd _f ^BB =1.61±0.01, the chemical dimensionality isd _t=1.40±0.01, and the fractal dimension of the minimum pathd _min=1.15 ± 0.02 for the two-dimensional triangular lattice.     

基于模型的制冷系统智能化仿真研究

为提高仿真方法对实际制冷空调装置多样性和复杂性的自适应性,将人工智能引入到制冷系统仿真研究中,构建基于数学模型与人工智能技术相结合方式的制冷系统智能仿真理论．本文介绍了上海交通大学在此方面所取得的研究成果,并提出了今后进一步发展的方向．本项研究有利于推进制冷装置设计方法现代化,研究思路对于一般热力系统仿真亦有参考价值．     

基于DIGNET网络的数据融合方法

针对数据融合和目标识别的特点,提出了基于DIGNET自组织聚类人工神经网络的数据融合方法。考虑到多传感器系统测量多个参量的特点,用并行的子网络结构代替中间隐层,实现了基于决策层的信息融合目标识别。利用仿真数据对基于DIGNET的数据融合方法进行了实验研究。实验结果表明,该方法具有数据正确分类率高和抗噪能力强等优点,有效地实现了融合识别。将该方法应用于前视红外和可见光双传感器目标跟踪系统的数据融合识别是可行的。     

Modern methods of improving the range accuracy of CTFM sonars

In a CTFM (continuous transmission frequency modulated) sonar, the error in range measurement is usually dominated by the low resolution of the range coding frequency of the system. Modern real time spectrum analysers using 1024 point DFTs (discrete Fourier transforms) can display the range codes of the sonar at a resolution approximately equal to 0.2%. To produce a range code accurate to half of the resolution, the linearity of the CTFM sweep must, however, be kept as low as 0.01%. This requirement creates several difficulties in the design of the frequency sweep when both non-linearity and stability of the voltage-controlled oscillator (VCO) and of the controlling voltage ramp are to be considered. A practical approach for achieving the above specifications is described. Properties of electronic components and of state of the art devices and the restrictions of their operating conditions are discussed. Illustrations are given for two measuring instruments: an underwater sonar system for quantitative assessment of fish population, and a diagnostic sonar.     

Transport through artificial single-molecule magnets: Spin-pair state sequential tunneling and Kondo effects

The transport properties of an artificial single-molecule magnet based on a CdTe quantum dot doped with a single Mn+2 ion(S=5/2) are investigated by the non-equilibrium Green function method.We consider a minimal model where the Mn-hole exchange coupling is strongly anisotropic so that spin-flip is suppressed and the impurity spin S and a hole spin s entering the quantum dot are coupled into spin pair states with(2S+1) sublevels.In the sequential tunneling regime,the differential conductance exhibits(2S+1) possible peaks,corresponding to resonance tunneling via(2S+1) sublevels.At low temperature,Kondo physics dominates transport and(2S+1) Kondo peaks occur in the local density of states and conductance.These peaks originate from the spin-singlet state formed by the holes in the leads and on the dot via higher-order processes and are related to the parallel and antiparallel spin pair states.     

微纳米加工技术在纳米物理与器件研究中的应用

物质在纳米尺度下可能呈现出与体材料不同的物理特件，这正是纳米科技发展的基础之一。要想探索在纳米尺度下材料物埋性质的变化规律及可能的应用领域，离不开相应的技术手段，微纳米加工技术作为当今高技术发展的重要技术领域之一，是实现功能人工纳米结构与器件微纳米化的基础。本文根据几个不同的应用领域，介绍了微纳米加工技术在纳米物理与器件研究领域的应用。     

模糊对向传播网络数据融合目标识别方法

重点研究了模糊对向传播网络 (FCPN)模型。针对数据融合和目标识别的特点 ,提出了基于模糊对向传播网络的融合目标识别方法和改进的模糊对向传播网络 (MFCPN)融合结构。利用仿真数据对网络的训练算法和融合结构进行了实验研究。结果表明 ,模糊对向传播网络较误差后向传播网络 (BPN)能够有效地实现融合识别 ;改进的模糊对向传播网络融合结构是可行的。同时还对FCPN和MFCPN应用于前视红外 (FLIR)和可见光摄像机目标跟踪系统进行了应用研究。     

基于BP算法神经网络的物理实验曲线拟合

介绍了人工神经元网络的基本原理和BP算法神经网络。将BP算法神经网络应用于物理实验中的幅频特性曲线拟合，在MATLAB下通过训练和仿真验证了应用BP算法神经网络拟合实验曲线的优越性。     

Modeling the Effect of Injection Molding Process Parameters on Warpage Using Neural Network Theory

A back propagation artificial neural network (BPANN) prediction model for warpage of injection-molded polypropylene was developed based on an orthogonal design method. The BPANN model was trained by the input and output data obtained from the moldflow software platform simulations. It is proved that the BPANN model can predict the warpage with reasonable accuracy. Utilizing the BPANN model, the effects of the process parameters, packing pressure (Pp), melt temperature (T_me), mold temperature (T_mo), packing time (t_p), cooling time (t_c), and fill pressure (p_f), on the warpage were investigated. The most important process parameter affecting the warpage was Pp, and the second most important was T_me. The rest of the process parameters, T_mo, t_p, t_c, and p_f, were found to be relatively less influential. Warpage increased with elevating T_mo. In contrast, an increase in Pp and T_me caused the warpage to decrease.     

基于光谱和神经网络模型的作物与杂草识别方法研究

利用光谱技术来识别作物与杂草是精细农业中一个非常重要的研究内容,但光谱数据中含有大量冗余数据,如何预处理以及建立识别模型,是决定识别准确率的关键。利用在325～1 075 nm波段的光谱识别了三种杂草（牛筋草、凹头苋、空心莲子草）与大豆幼苗。在幼苗生长的第三周与第六周分别采集杂草与作物的光谱,共378个样本。用其中的250个光谱样本,包括第一期和第二期采集的光谱样本,在采用db12小波经过三层分解后,将其小波系数作为输入数据建模,构造了一个径向基函数神经网络。然后,利用余下的光谱样本检验该模型的识别能力。结果表明,该模型对作物与杂草光谱具有极强的识别能力,只有3个第二期的牛筋草样本被判断为空心莲子草,其余的样本全部正确识别。这个结果表明,采用可见/近红外光谱识别大豆幼苗与三种伴随生长的杂草是可行的,同时也说明,随着作物的生长阶段的不同,其光谱的变化不会影响到种类识别。