A New Result on Regular Designs under Baseline Parameterization

The study on designs for the baseline parameterization has aroused attention in recent years. This paper focuses on two-level regular designs for the baseline parameterization. A general result on the relationship between K-aberration and word length pattern is developed.

Numerical estimation of choice of the regularization parameter for NMR T2 inversion

Nuclear Magnetic inversion is the basis of NMR Resonance （NMR） T2 logging interpretation. The regularization parameter selection of the penalty term directly influences the NMR T2 inversion result. We implemented both norm smoothing and curvature smoothing methods for NMR T2 inversion, and compared the inversion results with respect to the optimal regular- ization parameters （（Xopt） which were selected by the dis- crepancy principle （DP）, generalized cross-validation （GCV）, S-curve, L-curve, and the slope of L-curve methods, respectively. The numerical results indicate that the DP method can lead to an oscillating or oversmoothed solution which is caused by an inaccurately estimated noise level. The （Xopt selected by the L-curve method is occa- sionally small or large which causes an undersmoothed or oversmoothed T2 distribution. The inversion results from GCV, S-curve and the slope of L-curve methods show satisfying inversion results. The slope of the L-curve method with less computation is more suitable for NMR T2 inversion. The inverted T2 distribution from norm smoothing is better than that from curvature smoothing when the noise level is high.

Regularization and Choice of the Parameter for the Third Kind Nonlinear Volterra-Stieltjes Integral Equation Solutions

The article is considering the third kind of nonlinear Volterra-Stieltjes integral equations with the solution by Lavrentyev regularizing operator. A uniqueness theorem was proved, and a regularization parameter was chosen. This can be used in further development of the theory of the integral equations in non-standard problems, classes in the numerical solution of third kind Volterra-Stieltjes integral equations, and when solving specific problems that lead to equations of the third kind.

Effect of regularization parameters on geophysical reconstruction

In this paper we discuss the edge-preserving regularization method in the reconstruction of physical parameters from geophysical data such as seismic and ground-penetrating radar data. In the regularization method a potential function of model parameters and its corresponding functions are introduced. This method is stable and able to preserve boundaries, and protect resolution. The effect of regularization depends to a great extent on the suitable choice of regularization parameters. The influence of the edge-preserving parameters on the reconstruction results is investigated and the relationship between the regularization parameters and the error of data is described.

Evolvement regularity of signal control parameters of urban road in cold region

In snow-icy road environment, the survey data indicate that the largest decrease in traffic flow running characters occurs when snow and ice begin to accumulate on the road surface. Saturation flow is decreased by 16% , speed is decreased by 30% , and start-up lost time is increased by 27%. Based on the signal control theory of HCM and Webster, the character values of traffic flow in different urban road environments were investigated, and the evolvement regularity of signal control parameters such as cycle, split, green time, offset, yellow time and red time in snow-icy road environment was analyzed. The impact factors and the changes in the scope of signal control parameters were achieved. Simulation results and practical application show that the signal control plan of road enviromnent without snow and ice will increase the vehicle delay, stop length and traffic congestion in snow-icy road environment. Thus, the traffic signal control system should address a suitable signal control plan based on different road environments.

面向鲁棒图结构防御的过参数化图神经网络

图数据在现实应用中普遍存在,图神经网络(GNN)被广泛应用于分析图数据,然而GNN的性能会被图结构上的对抗攻击剧烈影响.应对图结构上的对抗攻击,现有的防御方法一般基于图内聚先验进行低秩图结构重构.但是现有的图结构对抗防御方法无法自适应秩真值进行低秩图结构重构,同时低秩图结构与下游任务语义存在错配.为了解决以上问题,基于过参数化的隐式正则效应提出过参数化图神经网络(OPGNN)方法,并形式化证明所提方法可以自适应求解低秩图结构,同时证明节点深层表征上的过参数化残差链接可以有效解决语义错配.在真实数据集上的实验结果表明,OPGNN方法相对于现有基线方法具有更好的鲁棒性,同时,OPGNN方法框架在不同的图神经网络骨干上如GCN、APPNP和GPRGNN上显著有效.

基于RepVGG-A0改进的公路车型识别网络

针对当前车型识别过程中检测精度与实时性难以平衡的问题,提出了一种基于RepVGG-A0改进的公路车型识别网络,利用结构重参数化思想融合多分枝网络以提升网络推理速度。使用混合空洞卷积替换传统卷积,强化了模型对大目标的识别能力。在网络主干中插入融合残差结构的坐标注意力(RES-CA)模块,提升了网络对有效特征信息的提取能力,同时避免了梯度消失与梯度退化造成的影响。此外采用了标签平滑正则化方法对损失函数进行改进,降低了模型过拟合对检测结果的影响,提升了模型的泛化性。经验证,本方法在公路车辆数据集BIT-Vehicle上的识别准确率达到了97.17%,较原模型提升了2.67%,优于现有的ResNet-18,VGG等网络模型,同时保证了模型的检测速度。

Adaptive Parameter Selection for Total Variation Image Deconvolution

In this paper,we propose a discrepancy rule-based method to automatically choose the regularization parameters for total variation image restoration problems. The regularization parameters are adjusted dynamically in each iteration.Numerical results are shown to illustrate the performance of the proposed method.

Downward continuation of airborne geomagnetic data based on two iterative regularization methods in the frequency domain

Downward continuation is a key step in processing airborne geomagnetic data. However,downward continuation is a typically ill-posed problem because its computation is unstable; thus, regularization methods are needed to realize effective continuation. According to the Poisson integral plane approximate relationship between observation and continuation data, the computation formulae combined with the fast Fourier transform（FFT）algorithm are transformed to a frequency domain for accelerating the computational speed. The iterative Tikhonov regularization method and the iterative Landweber regularization method are used in this paper to overcome instability and improve the precision of the results. The availability of these two iterative regularization methods in the frequency domain is validated by simulated geomagnetic data, and the continuation results show good precision.

A Preconditioned Fractional Tikhonov Regularization Method for Large Discrete Ill-posed Problems

The generalized Tikhonov regularization method is one of the most classical methods for the solution of linear systems of equations that arise from the discretization of linear ill-posed problems.However,the approximate solution obtained by the Tikhonov regularization method in general form may lack many details of the exact solution.Combining the fractional Tikhonov method with the preconditioned technique,and using the discrepancy principle for determining the regularization parameter,we present a preconditioned projected fractional Tikhonov regularization method for solving discrete ill-posed problems.Numerical experiments illustrate that the proposed algorithm has higher accuracy compared with the existing classical regularization methods.

A Regularized Super Resolution Algorithm for Generalized Gaussian Noise

In this paper,an iterative regularized super resolution (SR) algorithm considering non-Gaussian noise is proposed.Based on the assumption of a generalized Gaussian distribution for the contaminating noise,an lp norm is adopted to measure the data fidelity term in the cost function.In the meantime,a regularization functional defined in terms of the desired high resolution (HR) image is employed,which allows for the simultaneous determination of its value and the partly reconstructed image at each iteration step.The convergence is thoroughly studied.Simulation results show the effectiveness of the proposed algorithm as well as its superiority to conventional SR methods.

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 regularizafion 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.

A New Extended BIC and Sequential Lasso Regression Analysis and Their Application in Classification

In this paper, firstly, we propose a new method for choosing regularization parameter λ for lasso regression, which differs from traditional method such as multifold cross-validation, our new method gives the maximum value of parameter λ directly. Secondly, by considering another prior form over model space in the Bayes approach, we propose a new extended Bayes information criterion family, and under some mild condition, our new EBIC (NEBIC) is shown to be consistent. Then we apply our new method to choose parameter for sequential lasso regression which selects features by sequentially solving partially penalized least squares problems where the features selected in earlier steps are not penalized in the subsequent steps. Then sequential lasso uses NEBIC as the stopping rule. Finally, we apply our algorithm to identify the nonzero entries of precision matrix for high-dimensional linear discrimination analysis. Simulation results demonstrate that our algorithm has a lower misclassification rate and less computation time than its competing methods under considerations.

电站锅炉蛇形管CAD系统

针对锅炉行业产品设计需要,依据蛇形管设计规则,采用Visual C++5.0为开发工具,设计了蛇形管CAD系统.系统采用DXF文件接口技术实现与AutoCAD的数据交换.实践应用表明,该系统功能完备,可靠性、稳定性好,适用,方便.

求解二维Fredholm积分方程的参数化信赖域方法

给出了求解二维第一类Fredholm积分方程信赖域方法。通过引入正则化参数将离散后的Fredholm积分方程转化带参数的最优化问题,借助于KKT条件将二次信赖域子问题参数化,并进行分析求解,最后给出了数值模拟。

基于边缘保护的正则化超分辨重构技术

本文研究单帧图像超分辨重构的正则化方法。利用图像分片连续、降质算子的有限支撑性和空不变性等先验信息,借鉴一元分段函数拟合的思想,构造图像超分辨重构的目标函数,设计快速有效的迭代算法,保护边缘,降低块状效应,获取高分辨图像。仿真结果表明,本文方法得到的超分辨重构图像的效果较最近邻插值法、双线性插值法的效果好。

线性算子与右端项都近似给定的有限维正则化方法

本文利用有限维正则化方法来求解线性算子与右端项皆有噪声时的问题，并给出了该方法的误差估计及正则参数选取的标准．

虚实相生--参数化风景园林规划设计教学研究

数字技术的发展带来了风景园林规划设计方法与方式的变革,参数化风景园林规划设计离不开人机交互环节,在理性的基础上融入了感性,符合风景园林作为"科学与艺术"的学科特征。东南大学在风景园林规划设计教学中引入了参数化方法,自2012年起在本科四年级"景园规划设计III"课程中进行多年教学实践,逐步形成了一套参数化风景园林规划设计教学方法。着重阐述了参数化风景园林规划设计教学特点与优势,从设计教学程序与要点两方面介绍参数化风景园林规划设计教学组织。

Optimization of regularization parameter of inversion in particle sizing using light extinction method

In particle sizing by light extinction method, the regularization parameter plays an important role in applying regularization to find the solution to ill-posed inverse problems. We combine the generalized cross-validation （GCV） and L-curve criteria with the Twomey-NNLS algorithm in parameter optimization. Numerical simulation and experimental validation show that the resistance of the newly developed algorithms to measurement errors can be improved leading to stable inversion results for unimodal particle size distribution.

Retrieval of Atmospheric Temperature and Moisture Vertical Profiles from Satellite Advanced Infrared Sounder Radiances with a New Regularization Parameter Selecting Method

Considering the characteristics of nonlinear problems,a new method based on the L-curve method and including the concept of entropy was designed to select the regularization parameter in the one-dimensional variational analysis-based sounding retrieval method.In the first iteration,this method uses an empirical regularization parameter derived by minimizing the entropy of variables.During subsequent iterations,it uses the L-curve method to select the regularization parameter in the vicinity of the regularization parameter selected in the last iteration.The new method was employed to select the regularization parameter in retrieving atmospheric temperature and moisture profiles from Atmospheric Infrared Sounder radiance measurements selected from the first day of each month in 2008.The results show that compared with the original L-curve method,the new method yields 5.5%and 2.5%improvements on temperature and relative humidity profiles,respectively.Compared with the discrepancy principle method,the improvements on temperature and relative humidity profiles are 1.6%and 2.0%,respectively.