Inverse identification of constitutive parameters from heat source fields: A local approach applied to hyperelasticity

In this paper, a new inverse identification method of constitutive parameters is developed from full kinematic and thermal field measurements. It consists in reconstructing the heat source field from two different approaches by using the heat diffusion equation. The first one requires the temperature field measurement and the value of the thermophysical parameters. The second one is based on the kinematic field measurement and the choice of a thermo-hyperelastic model that contains the parameters to be identified. The identification is carried out at the local scale, ie, at any point of the heat source field, without using the boundary conditions. In the present work, the method is applied to the challenging case of hyperelasticity from a heterogeneous test. Due to large deformations undergone by the rubber specimen tested, a motion compensation technique is developed to plot the kinematic and the thermal fields at the same points before reconstructing the heterogeneous heat source field. In the present case, the constitutive parameter of the Neo-Hookean model has been identified, and its distribution has been characterized with respect to the strain state at the surface of a cross-shaped specimen.     

Underwater image enhancement with global–local networks and compressed-histogram equalization

Due to the light absorption and scattering, captured underwater images usually contain severe color distortion and contrast reduction. To address the above problems, we combine the merits of deep learning and conventional image enhancement technology to improve the underwater image quality. We first propose a two-branch network to compensate the global distorted color and local reduced contrast, respectively. Adopting this global–local network can greatly ease the learning problem, so that it can be handled by using a lightweight network architecture. To cope with the complex and changeable underwater environment, we then design a compressed-histogram equalization to complement the data-driven deep learning, in which the parameters are fixed after training. The proposed compression strategy is able to generate vivid results without introducing over-enhancement and extra computing burden. Experiments demonstrate that our method significantly outperforms several state-of-the-arts in both qualitative and quantitative qualities.     

在线超低温预浓缩-全二维气相色谱-飞行时间质谱联用系统在大气挥发性有机物监测中的应用

采用自主研制的在线超低温预浓缩设备，连续在线采集大气样品或者连续采集苏玛罐离线样品，对样品进行超低温捕集浓缩和二次超低温冷冻聚焦，采用全二维气相色谱-飞行时间质谱联用技术分离检测浓缩后的挥发性有机物，实现对大气中C2~C12共100多种挥发性有机物的浓缩富集和定性定量分析。本研究对该方法的线性、重复性、加标回收率、检出限等性能进行表征，并与常规一维气相色谱-四极杆质谱联用系统的定性定量结果进行比较。结果表明，该方法不仅满足大气中挥发性有机物检测的性能指标要求，且组分定性定量的准确性、灵敏度等指标均优于一维气相色谱-四极杆质谱联用技术，在挥发性有机物成分较复杂的大气样品检测中具有明显优势。     

Study of traffic characteristics based on internet web real-time traffic conditions by image identification technology

With the rapid development of Internet, it is increasingly convenient to obtain real-time traffic condition information, which has greatly stimulated the improvement of urban traffic guidance. Traffic conditions are generally divided into four grades in the existing network platform, which are expressed in different colours. The understanding of traffic condition is still at the level of abstract senses. Therefore, it is difficult to grasp the characteristics of urban traffic. To this end, a new idea is proposed in this paper, and the new idea is to study the urban traffic characteristics based on real-time traffic condition information extraction with image identification technology. With this method, we can not only quantify the abstract traffic condition information, but also solve the loss of traffic condition information. In addition, an instance is analysed in this paper, it shows that it can provide references for urban traffic organization management very well.     

Neural network based non-invasive method to detect anemia from images of eye conjunctiva

Detection of anemia can be done by examining the hemoglobin concentration level in the blood using complete blood count, which is an invasive, time-consuming, and costly technique. Preliminary methods for detecting anemia include examining the color of the palpebral conjunctiva, which is a non-invasive method, but color perception may vary from person to person. This study aims to develop a computerized non-invasive technique for anemia detection. We propose a novel machine learning model using the artificial neural network to detect anemic patients from the images of eye conjunctiva. Since limited and small dataset has been used in the earlier approaches, this may cause over fitting of the model. We have improved the number of available training images using image augmentation techniques. To standardize a non-invasive method, we have used computer vision algorithms for preprocessing and feature extraction. This article derives the backpropagation rules mathematically for adjusting the weights for the proposed neural network model. After hyper parameter tuning and using the mathematically derived backpropagation rules, the model was able to achieve the best accuracy of 97.00% with sensitivity 99.21% and specificity 95.42% on the created dataset.     

基于自监督学习的病理图像层次分割

在苏木精-伊红(HE)染色病理图像中，细胞染色分布的不均匀和各类组织形态的多样性给病理图像的自动分割带来极大挑战。为解决该问题，提出了一种基于自监督学习的病理图像三步层次分割方法，对病理图像中各类组织进行由粗略到精细的全自动逐层分割。首先，根据互信息的计算结果在RGB色彩空间中进行特征选择；其次，采用K-means聚类将图像初步分割为各类组织结构的色彩稳定区域与模糊区域；然后，以色彩稳定区域为训练集采用朴素贝叶斯分类对模糊区域进行进一步分割，得到完整的细胞核、细胞质和胞外间隙这三类组织结构；最后，对细胞核部分进行结合形状和色彩强度的混合分水岭分割得到细胞核间的精确边界，进而量化计算细胞核个数、核占比、核质比等指标。对脑膜瘤HE染色病理图像的分割实验结果表明，所提方法对于染色和细胞形态差异保持较高的鲁棒性，各类组织区域分割误差在5%以内，在细胞核分割精度的对比实验中平均正确率在96%以上，满足临床自动图像分析的要求，其量化结果可以为定量病理分析提供依据。     

Damage evolution around white etching layer during uniaxial loading

Rolling contact fatigue cracks and thermally induced defects are common problems in the railway industry especially as demands for increasing loads, speeds, and safety continue to rise. Often, the two types of defects are found together in the field, however, whether one causes the other to occur is not completely agreed upon. The effect of thermal damage, in the form of a martensite spot on pearlitic steel test bars, on the fatigue life in uniaxial low cycle fatigue experiments was investigated by the authors. However, the focus of the current work was to characterize the damage evolution from the low cycle fatigue (LCF) tests and correlate the crack initiation and propagation with the initial thermal damage. Residual stress measurements, digital image correlation, and X‐ray tomography were used to characterize the effects of the thermal damage before, during, and after fatigue testing, respectively. It was found that the thermal damage causes strain accumulation and crack initiation at the interface between the two materials. The strain evolution was visualized using digital image correlation (DIC), clearly showing the strain concentrations at the top and bottom of the white etching layers (WEL), where the residual stresses are also most tensile. X‐ray tomography confirmed the planar crack growth from the martensite spot.     

基于彩色图像处理的浮选尾煤灰分软测量研究

浮选尾煤灰分是浮选产品的一个重要指标。针对选煤厂浮选尾煤灰分多采用离线检测而无法实现在线准确测量，以及当前浮选软测量多采用单一的灰度图像从而导致软测量模型精度及适应性较差的问题，提出了一种基于彩色图像处理的浮选尾煤软测量方法，建立了基于最小二乘支持向量机(LSSVM)的浮选尾煤灰分软测量模型。模型以不同颜色空间的彩色特征、灰度均值以及浓度特征为输入变量，以尾煤灰分作为输出变量，采用粒子群优化算法对LSSVM模型参数进行优化。结果表明：所建立的尾煤灰分软测量模型可以较好地实现浮选尾矿灰分的在线预测，引入浮选尾矿图像的彩色特征可以提高尾煤图像分析的精度，预测精度达96.89%。研究成果在柳湾选煤厂现场应用，并取得了较好的尾矿灰分测量效果。     

A Student's t-based density peaks clustering with superpixel segmentation (tDPCSS) method for image color clustering

Image color clustering is a basic technique in image processing and computer vision, which is often applied in image segmentation, color transfer, contrast enhancement, object detection, skin color capture, and so forth. Various clustering algorithms have been employed for image color clustering in recent years. However, most of the algorithms require a large amount of memory or a predetermined number of clusters. In addition, some of the existing algorithms are sensitive to the parameter configurations. In order to tackle the above problems, we propose an image color clustering method named Student's t-based density peaks clustering with superpixel segmentation (tDPCSS), which can automatically obtain clustering results, without requiring a large amount of memory, and is not dependent on the parameters of the algorithm or the number of clusters. In tDPCSS, superpixels are obtained based on automatic and constrained simple non-iterative clustering, to automatically decrease the image data volume. A Student's t kernel function and a cluster center selection method are adopted to eliminate the dependence of the density peak clustering on parameters and the number of clusters, respectively. The experiments undertaken in this study confirmed that the proposed approach outperforms k-means, fuzzy c-means, mean-shift clustering, and density peak clustering with superpixel segmentation in the accuracy of the cluster centers and the validity of the clustering results.     

二维反三角超混沌系统及其在图像加密上的应用

为了进一步提高混沌系统的混沌特性，为图像加密算法提供更可靠的混沌系统，增强图像加密算法的安全性，提出了一种基于二维反三角超混沌系统的新型图像加密算法。首先，在一维三角混沌函数的基础上构建了一个二维反三角超混沌系统，通过分岔图和Lyapunov指数等仿真实验，验证了该系统具有更广的混沌区间和更强随机性的迭代序列，遍历性更加优秀；然后，基于此混沌系统，采用"置乱-扩散"策略，根据不同密钥生成的不同超混沌序列，对图像矩阵进行无重复置乱和循环移位扩散，循环三次得到密文，完成加密过程；最后，对图像加密方案进行了直方图分析、密钥空间分析、相邻像素相关性分析、明文敏感性分析和信息熵分析等性能测试。其中密文图像的相关指标参数像素变化率（NPCR）和统一平均变化强度（UACI）的测试值非常接近于它们的理想期望值，信息熵的测试结果约为7.997，也非常接近于理想期望值8。实验结果表明，此图像加密系统具有更可靠的安全性，抵抗攻击能力强，在图像安全领域具有较好的应用前景。