Global weak solutions to a spatio-temporal fractional Landau–Lifshitz–Bloch equation

The present paper deals with global existence of weak solutions of a time-space fractional Landau–Lifshitz–Bloch equation involving the weak Caputo derivative and a fractional Laplacian. We use Faedo–Galerkin method with some commutator estimates in order to prove global existence of weak solutions for the model. The uniqueness is also discussed in a special one dimensional case.     

改进ILoG算子的故障检测方法

针对强背景噪声干扰下轮对轴承故障特征微弱、难以准确检测的问题,提出了一种自适应改进高斯拉普拉斯(improved Laplacian of Gaussian,简称ILoG)算子的微弱故障检测方法。ILoG算子滤波器具有优良的信号突变特征检测能力,将其用于轮对轴承故障信号的冲击特征检测,同时利用水循环算法(water cycle algorithm,简称WCA)的寻优特性,并行搜寻筛选最佳的ILoG算子影响参数,通过对参数优化后ILoG算子滤波后信号做进一步包络解调分析,提取出轮对轴承微弱的故障特征信息。对实际轮对轴承外圈和内圈故障信号分析的结果表明,该方法可以有效检测出轴承微弱故障特征频率,故障检测效果优于小波阈值和多尺度形态学差值滤波方法。     

Laplacian regularized robust principal component analysis for process monitoring

Principal component analysis (PCA) is one of the most widely used techniques for process monitoring. However, it is highly sensitive to sparse errors because of the assumption that data only contains an underlying low-rank structure. To improve classical PCA in this regard, a novel Laplacian regularized robust principal component analysis (LRPCA) framework is proposed, where the “robust” comes from the introduction of a sparse term. By taking advantage of the hypergraph Laplacian, LRPCA not only can represent the global low-dimensional structures, but also capture the intrinsic non-linear geometric information. An efficient alternating direction method of multipliers is designed with convergence guarantee. The resulting subproblems either have closed-form solutions or can be solved by fast solvers. Numerical experiments, including a simulation example and the Tennessee Eastman process, are conducted to illustrate the improved process monitoring performance of the proposed LRPCA.     

Decomposition and replacement: Spatial knowledge distillation for monocular depth estimation

Knowledge distillation has become a key technique for making smart and light-weight networks through model compression and transfer learning. Unlike previous methods that applied knowledge distillation to the classification task, we propose to exploit the decomposition-and-replacement based distillation scheme for depth estimation from a single RGB color image. To do this, Laplacian pyramid-based knowledge distillation is firstly presented in this paper. The key idea of the proposed method is to transfer the rich knowledge of the scene depth, which is well encoded through the teacher network, to the student network in a structured way by decomposing it into the global context and local details. This is fairly desirable for the student network to restore the depth layout more accurately with limited resources. Moreover, we also propose a new guidance concept for knowledge distillation, so-called ReplaceBlock, which replaces blocks randomly selected in the decoded feature of the student network with those of the teacher network. Our ReplaceBlock gives a smoothing effect in learning the feature distribution of the teacher network by considering the spatial contiguity in the feature space. This process is also helpful to clearly restore the depth layout without the significant computational cost. Based on various experimental results on benchmark datasets, the effectiveness of our distillation scheme for monocular depth estimation is demonstrated in details. The code and model are publicly available at : https://github.com/tjqansthd/Lap_Rep_KD_Depth.     

一种改进的基于KNN颜色线性模型抠图算法*

介绍了一种减少用户标记和改进的基于KNN(K Nearest Neighbors)颜色线性模型的图像软抠取算法.通过ESCG(Efficient Spectral Clustering on Graphs)算法对输入图像进行谱聚类,用户只需选择某些类中确定的前景、背景像素,便能生成只包含少数未知像素的三分图.基于KNN颜色线性模型的抠图算法将局部平滑假设与非局部原理相结合,但在毛发及前景背景像素近似区域抠取效果并不理想,提出的改进算法将焦点特征添加到特征向量中,最小化基于图拉普拉斯矩阵的二次目标函数并确定泰知像素的透明度值.实验表明,改进算法在毛发、孔洞或者图像前景背景近似的区域都能有好的抠取效果.     

拉普拉斯特征映射算法在滚动轴承故障识别中的应用

由于滚动轴承故障的非线性和非平稳性特征,传统线性方法不能准确发现和识别出故障类型及其受损情况,该文提出使用流形学习拉普拉斯特征映射(LE)算法对滚动轴承故障进行识别.在由幅值、时域统计指标和由小波包函数分解得到的能量比作为特征向量构建的高维特征空间中,使用LE算法和两种传统的降维方法PCA、MDS进行对比,提取出最敏感、最能表征滚动轴承运行状态的低维特征量,再使用模式识别进行分类,聚类结果用三维图形表示.以样本识别率和模式识别中的类内距和类间距作为评价指标,模拟实验结果表明:LE算法不仅能有效地识别出滚动轴承故障类型而且能区分和识别出轴承外圈在不同受损情况下的运行样本.     

Image denoising via sparse coding using eigenvectors of graph Laplacian

Image denoising plays an important role in image processing, which aims to separate clean images from the noisy images. A number of methods have been presented to deal with this practical problem in the past decades. In this paper, a sparse coding algorithm using eigenvectors of the graph Laplacian (EGL-SC) is proposed for image denoising by considering the global structures of images. To exploit the geometry attributes of images, the eigenvectors of the graph Laplacian, which are derived from the graph of noised patches, are incorporated in the sparse model as a set of basis functions. Sequently, the corresponding sparse coding problem is presented and efficiently solved with a relaxed iterative method in the framework of the double sparsity model. Meanwhile, as the denoising performance of the EGL-SC significantly depends on the number of the used eigenvectors, an optimal strategy for the number selection is employed. A parameter called as out-of-control rate is set to record the percentage of the denoised patches that suffer from serious residual errors in the sparse coding procedure. Thus, with the eigenvector number increasing, the appropriate number can be heuristically selected when the out-of-control rate falls below an empirical threshold. Experiments illustrate that the EGL-SC can achieve a better performance than some other well-developed denoising methods, especially in the structural similarity index for the noise of large deviations.     

Optimization of multiple region quantizer for Laplacian source

This paper proposes a multiple region quantizer composed of quantizers defined on different disjunctive regions of an input signal. In particular, for the two region and the three region cases, the paper provides a complete optimization of a multiple region companded quantizer for the Laplacian source of unit variance. The analysis of the multiple region quantizer is limited to a three region case due to the complexity of the optimization problem and due to the fact that much more complex multiple region quantizer models obtained for a higher number of regions could slightly improve the performances. Two-stage optimization is performed with respect to the number of reconstruction levels of each quantizer composing the considered multiple region companded quantizer and with respect to the region bounds. It is shown that optimal parameters depend only on the fractional part of the required average bit rate. In order to design the three region optimal quantizer, Lloyd–Max's algorithm and Newton–Kantorovich iterative method are used with the three region optimal companded quantizer as the initial solution. The gradient Newton–Kantorovich iterative method is used to provide better convergence speed than Lloyd–Max's algorithm, which is essential in cases where effective initialization solution of Lloyd–Max's algorithm is missing. It is shown that the three region optimal companded quantizer have signal to quantization noise ratio value close to the one of the three region optimal quantizer, where a simpler design procedure is the benefit of the three region optimal companded quantizer over the three region optimal one.     

Gaussian-Hermite moment-based depth estimation from single still image for stereo vision

Depth information of objects plays a significant role in image-based rendering. Traditional depth estimation techniques use different visual cues including the disparity, motion, geometry, and defocus of objects. This paper presents a novel approach of focus cue-based depth estimation for still images using the Gaussian-Hermite moments (GHMs) of local neighboring pixels. The GHMs are chosen due to their superior reconstruction ability and invariance properties to intensity and geometric distortions of objects as compared to other moments. Since depths of local neighboring pixels are significantly correlated, the Laplacian matting is employed to obtain final depth map from the moment-based focus map. Experiments are conducted on images of indoor and outdoor scenes having objects with varying natures of resolution, edge, occlusion, and blur contents. Experimental results reveal that the depth estimated from GHMs can provide anaglyph images with stereo quality better than that provided by existing methods using traditional visual cues.