TV正则化的双迭代RL湍流退化图像复原算法

RL算法是湍流退化图像复原的一类重要算法,但在迭代过程中存在噪声放大或过度平滑问题.围绕湍流退化图像复原问题对RL（Richardson-Lucy）算法及其改进方法进行研究：建立了一种短曝光成像探测湍流退化模型,并给出该模型中随机相位屏的数值模拟方法;推导了RL算法的基本原理和计算公式;为更好地从湍流退化图像中恢复目标,提出了TV正则化双迭代RL图像复原算法.改进算法引入总体变分约束,并对目标图像和点扩展函数采用内外循环双重迭代,充分利用了目标图像和点扩展函数的内在联系.仿真实验表明,算法能有效地保持了图像的细节信息,复原效果明显优于传统RL算法.     

多视图交叉一致性学习的半监督水面目标检测

为缓解基于半监督学习的水面目标检测对有限标注样本过拟合的问题,提高无标注样本中目标提取的有效性,提出了基于多视图交叉一致性学习的半监督水面目标检测算法。首先,该算法通过数据增强的方式为训练样本生成不同的视图以丰富数据集的多样性;然后,利用所提出的多视图目标判别器为无标注样本在线生成伪标签,有助于提取无标注样本的有效信息;最后,利用所提出的多视图交叉一致性学习使同一目标实例的不同视图的输出实现交叉一致性正则化,以促进检测模型学习判别性的特征从而降低过拟合的风险。在海上和内河数据集上的实验结果表明：文中所提算法能够提高特征提取的判别性,对多类别的水面目标检测精度达到91.0%,比全监督检测算法提高了18.7%,比其他半监督检测算法提高了3.8%以上;在检测速度上,该算法达到13.1帧/s,基本满足实时性要求。所提算法通过多视图交叉一致性学习提高特征的判别性和缓解检测模型的过拟合风险,有助于提高半监督水面目标检测的性能。     

On the accuracy of Landweber and Tikhonov reconstruction techniques in gas‐solid fluidized bed applications

As electrical capacitance tomography technique needs a sophisticated reconstruction, the accuracy of two of the most widely used reconstruction techniques (Landweber and Tikhonov) for gas‐fluidized bed applications were assessed. For this purpose, the results of two‐fluid model simulations were used as an input of reconstruction. After finding the optimum reconstruction parameters for the studied system, it is found that both techniques were able to obtain the radial profile and overall value of average volume fraction very well. Conversely, both methods were incapable to determine bubble sizes accurately especially small bubble sizes, unless the Landweber technique with inverted Maxwell concentration model is applied. The probability distribution of the reconstructed results was also smoother in transition between the emulsion and bubble phases compared to the reality. Finally, no significant differences in noise immunity of these two techniques were observed. © 2015 American Institute of Chemical Engineers AIChE J, 61: 4102–4113, 2015     

Gas emission source term estimation with 1-step nonlinear partial swarm optimization-Tikhonov regularization hybrid method

Source term identification is very important for the contaminant gas emission event.Thus,it is necessary to study the source parameter estimation method with high computation efficiency,high estimation accuracy and reasonable confidence interval.Tikhonov regularization method is a potential good tool to identify the source parameters.However,it is invalid for nonlinear inverse problem like gas emission process.2-step nonlinear and linear PSO (partial swarm optimization)-Tikhonov regularization method proposed previously have estimated the emission source parameters successfully.But there are still some problems in computation efficiency and confidence interval.Hence,a new 1-step nonlinear method combined Tikhonov regularization and PSO algorithm with nonlinear forward dispersion model was proposed.First,the method was tested with simulation and experiment cases.The test results showed that 1-step nonlinear hybrid method is able to estimate multiple source parameters with reasonable confidence interval.Then,the estimation performances of different methods were compared with different cases.The estimation values with 1-step nonlinear method were close to that with 2-step nonlinear and linear PSO-Tikhonov regularization method.1-step nonlinear method even performs better than other two methods in some cases,especially for source strength and downwind distance estimation.Compared with 2-step nonlinear method,1-step method has higher computation efficiency.On the other hand,the confidence intervals with the method proposed in this paper seem more reasonable than that with other two methods.Finally,single PSO algorithm was compared with 1-step nonlinear PSO-Tikhonov hybrid regularization method.The results showed that the skill scores of 1-step nonlinear hybrid method to estimate source parameters were close to that of single PSO method and even better in some cases.One more important property of 1-step nonlinear PSO-Tikhonov regularization method is its reasonable confidence interval,which is not obtained by single PSO algorithm.Therefore,1-step nonlinear hybrid regularization method proposed in this paper is a potential good method to estimate contaminant gas emission source term.     

基于带约束TSVD方法的粒径分布反演

利用光全散射法,在独立模式下通过测量可见光不同波段下的光谱消光值反演几种常见粒径分布,其中正问题利用反常衍射近似（ADA）计算得到估计值,测量值通过Mie理论计算得到,反问题利用截断奇异值分解（TSVD）的正则化方法,并结合粒径分布非负和粒径分布积分和为1两个约束条件优化反演结果,通过数值模拟证明了带约束的TSVD方法在粒径分布的反演中具有更高的反演精度、稳定性和抗噪性。     

Colour‐appearance modeling using feedforward networks with Bayesian regularization method— Part I: Forward model

In this article, a method of predicting colour appearance (from colorimetric attributes to colour‐appearance attributes, i.e., forward model) using an artificial neural network is presented. The neural network model developed is a multilayer feedforward neural network model for predicting colour appearance (FNNCAM for short). The model was trained by LUTCHI colour‐appearance datasets. The Levenberg–Marquardt algorithm is incorporated into the back‐propagation procedure to accelerate the training of FNNCAM and the Bayesian regularization method is applied to the training of neural networks to improve generalization. The results of FNNCAM obtained are quite promising. © 2000 John Wiley & Sons, Inc. Col Res Appl, 25, 424–434, 2000     

基于BP神经网络的旋风分离器分割粒径模化与预测

为精确建立分割粒径与旋风分离器结构参数和操作参数之间的复杂映射关系,发展了基于数据驱动的BP神经网络（BPNN）的分割粒径模型。使用全局量纲分析,提出环形空间雷诺数、表征旋风分离器本体尺寸影响的量纲为1数和排气芯管插入深度尺寸比作为网络输入参数,表征空气动力等效分割粒径大小的量纲为1尺寸作为网络输出参数,分别确定了训练算法和隐含层神经元个数对BPNN分割粒径模型预测精度的影响。结果表明：贝叶斯正则化算法优于L-M算法和拟牛顿算法,并在隐含层神经元个数为7时达到最优预测性能。与理论模型、半经验模型和多元回归模型进行比较,结果表明,贝叶斯正则化BPNN分割粒径模型展现出了较好的预测能力和泛化性能,模型预测的均方误差为0.136、决定系数为0.975。     

改进的BP网络模型及其在日径流预测中的应用

尝试了基于MATLAB6.5的人工神经网络工具箱在水文日径流预测中的应用,并采用贝叶斯正则化方法改进BP网络算法,从而提高了BP网络的推广能力。将该模型应用于大渡河流域日径流的预测,取得了较好的预测效果。     

基于Tikhonov正则化方法的移动荷载识别实验研究

根据正交异性板理论和模态叠加法,推导了移动荷载作用下简支板桥的荷载识别方程。为了解决识别方程病态问题,考虑Tikhonov正则化方法,选择最优正则参数值,从而得到方程的正则化解。为了验证Tikhonov正则化方法的有效性,进行了简支板桥的移动荷载识别模型实验,通过实验确定不同速度车辆经过板桥时的应变响应历程,并将所测得数据代入识别方程求得车辆荷载信息。实验和计算结果对比表明,通过引进Tikhonov正则化方法可以获得识别方程的稳定解,提高识别精度,识别结果与实际小车重量吻合较好。     

两项式弹性波阻抗反演方法在深层碳酸盐岩储层预测中的应用

川东北地区长兴组碳酸盐岩储层埋藏深度大,获取大角度地震信息困难,地震资料信噪比较低。为此,本文通过引入新的近似式,从中、小角度两个弹性阻抗数据体中提取岩石物性参数。文中首先以佐普里兹方程的Aki-Richards近似式为基础,详细推导了以纵横波阻抗表示的新的两项型弹性波阻抗公式,该式不仅考虑了密度与其他地球物理参数的联系,而且在舍弃密度项时,利用纵波速度与密度幂指数拟合关系式使密度近似为纵波阻抗更加合理;其次引入Tikhonov正则化方法以提高该方法反演的精度、稳定性和抗噪性。数值试算与实际应用结果都证明了该方法的可行性。