驱动X光二极管的级联Blumlein型脉冲网络过电压脉冲分析

为了提高装置容许的运行电压以提高辐射剂量产额,开展了放电过程中影响脉冲形成网络过电压幅值因素的研究。在引入开关导通不同步性和导通电阻条件下,建立了适用于任意电阻性负载的级联Blumlein型脉冲形成网络电压波过程理论模型,基于波过程模型进一步分析了影响脉冲形成网络过电压幅值的因素。研究表明开关不同步是产生过电压的主要因素,过电压峰值出现在开关闭合后的3倍形成网络电长度时刻。随着网络级联级数的增加,二极管阻抗与源阻抗匹配情况下最大过电压可达到-2倍充电电压,而二极管阻抗下降使得过电压幅值得以加强,阻抗过早崩溃可使过电压幅值接近充电电压的-3倍。     

基于BP神经网络的复杂彩色图像文本定位*

针对复杂彩色图像中文本的特征,提出了基于小波变换和BP神经网络甄别文本区域的算法.该算法首先利用文本块的边缘特征遴选出备选图像块,而后采用小波变换提取备选图像块的纹理特征,把这些纹理特征参量连同图像块的颜色特征和笔画特征参量输入训练好的BP神经网络,判断备选图像块是否包含文本.该方法运算简单,定位时间短.采用专用的文本定位比赛用图进行实验的结果表明,定位准确率可达到92％,召回率为87.4%.     

一种基于激光直写的水浊度传感结构

提出并制作了一种基于激光照射改变折射率方法的光度比浊度传感单元,综合了导波光学及检测相关知识原理.以UV光敏树脂为基底材料,采用“气体干法”除胶及分步固化的方法获得了该芯片化结构.传感器整个制作过程仅仅需要7~8 min因而使本方法具有很高的效率.由直写成型的单元组建传感系统对标准悬浊液样品进行了实验测试,所得的相关数据在SISO算法神经元网络系统中进行处理,获得了传感器浊度与对应散射系数的关系曲线.通过试剂检验的方法得知实际相对误差在5%左右.     

利用可调谐的混沌Fabry-Perot激光器实现波分复用无源光网络的断点检测

波分复用无源光网络具有支路多、节点密的特征,为精确定位各支路的断点,提出了一种基于可调谐混沌Fabry-Perot激光器的检测方法.将光反馈多纵模Fabry-Perot半导体激光器作为混沌光源,在改变反馈光光波模式的条件下输出波长可调谐的混沌激光.以探测光的波长标记各被测支路,将探测信号和携带延时信息的回波信号进行互相关运算,根据相关曲线峰值的位置即可完成定位.分析了可调谐混沌源的特性,并以1×4的波分复用无源光网络为例,进行了初步的实验验证,结果表明该方法可以精确定位光网络支路中连接点及断点的位置,空间分辨率达4 cm,且与探测距离无关.     

Neural Estimator of Information for Time-Series Data with Dependency

Novel approaches to estimate information measures using neural networks are well-celebrated in recent years both in the information theory and machine learning communities. These neural-based estimators are shown to converge to the true values when estimating mutual information and conditional mutual information using independent samples. However, if the samples in the dataset are not independent, the consistency of these estimators requires further investigation. This is of particular interest for a more complex measure such as the directed information, which is pivotal in characterizing causality and is meaningful over time-dependent variables. The extension of the convergence proof for such cases is not trivial and demands further assumptions on the data. In this paper, we show that our neural estimator for conditional mutual information is consistent when the dataset is generated with samples of a stationary and ergodic source. In other words, we show that our information estimator using neural networks converges asymptotically to the true value with probability one. Besides universal functional approximation of neural networks, a core lemma to show the convergence is Birkhoff’s ergodic theorem. Additionally, we use the technique to estimate directed information and demonstrate the effectiveness of our approach in simulations.     

On network form and function

Moving from the observation that drainage network configurations minimizing total energy dissipation are stationary solutions of the general equation describing landscape evolution, we review theoretical and observational evidence on river patterns and their scale-invariant structure. Exact results complemented by numerical annealing of the basic equation in the presence of additive noise suggest that configurations at (or very close to) the global minimum of energy dissipation differ from dynamically accessible states, which have rather different scaling properties and conform much better to natural forms. Thus we argue that, at least in the fluvial landscape, Nature works through imperfect searches for dynamically accessible optimal configurations. We also show that optimal networks are spanning loopless configurations only under precise physical requirements. This is stated in a form applicable to generic networks, suggesting that other branching structures occurring in Nature (e.g. scale-free and looping) may possibly arise through optimality to selective pressures. Indeed, we show that this is the case.     

Parameter estimation of continuous variable quantum key distribution system via artificial neural networks

Continuous-variable quantum key distribution(CVQKD)allows legitimate parties to extract and exchange secret keys.However,the tradeoff between the secret key rate and the accuracy of parameter estimation still around the present CVQKD system.In this paper,we suggest an approach for parameter estimation of the CVQKD system via artificial neural networks(ANN),which can be merged in post-processing with less additional devices.The ANN-based training scheme,enables key prediction without exposing any raw key.Experimental results show that the error between the predicted values and the true ones is in a reasonable range.The CVQKD system can be improved in terms of the secret key rate and the parameter estimation,which involves less additional devices than the traditional CVQKD system.