等强度婴儿脑MR图像分割的深度学习方法综述

磁共振（magnetic resonance,MR）成像作为一种安全非侵入式的成像技术,可以提供高分辨率且具有不同对比度的大脑图像,被越来越多地应用于婴儿大脑研究中。将婴儿脑MR图像准确地分割为灰质、白质和脑脊液,是研究早期大脑发育模式不可或缺的基础处理环节。由于在等强度阶段（6~9月龄）婴儿脑MR图像中,灰质和白质信号强度基本一致,组织对比度极低,导致此阶段的脑组织分割非常具有挑战性。基于深度学习的等强度婴儿脑MR图像分割方法,由于其卓越的性能受到研究人员的广泛关注,但目前尚未有文献对该领域的方法进行系统总结和分析。因此本文对目前基于深度学习的等强度婴儿脑MR图像分割方法进行了系统总结,从基本思想、网络架构、性能及优缺点4个方面进行了介绍。并针对其中的典型算法在iSeg-2017数据集上的分割结果进行了对比分析,最后对等强度婴儿脑MR图像分割中存在的问题及未来研究方向进行展望。本文通过对目前基于深度学习的等强度婴儿脑MR图像分割方法进行总结,可以看出深度学习方法已经在等强度期婴儿脑分割中展现出巨大优势,相比传统方法在分割精度和效率上均有较大提升,将进一步促进人类人脑早期发育研究。     

Hatching egg classification based on CNN with channel weighting and joint supervision

Multimedia Tools and Applications - Convolutional neural networks (CNNs) show state-of-the-art performance in tackling a variety of visual tasks. It is expected that a CNN can be applied to the...     

Hatching eggs classification based on deep learning

In order to realize the fertility detection and classification of hatching eggs, a method based on deep learning is proposed in this paper. The 5-days hatching eggs are divided into fertile eggs, dead eggs and infertile eggs. Firstly, we combine the transfer learning strategy with convolutional neural network (CNN). Then, we use a network of two branches. In the first branch, the dataset is pre-trained with the model trained by AlexNet network on large-scale ImageNet dataset. In the second branch, the dataset is directly trained on a multi-layer network which contains six convolutional layers and four pooling layers. The features of these two branches are combined as input to the following fully connected layer. Finally, a new model is trained on a small-scale dataset by this network and the final accuracy of our method is 99.5%. The experimental results show that the proposed method successfully solves the multi-classification problem in small-scale dataset of hatching eggs and obtains high accuracy. Also, our model has better generalization ability and can be adapted to eggs of diversity.     

云环境下面向隐私保护的用户行为分析

针对云环境下基于隐私Petri网（PPN）的用户行为,主要是存在隐私泄露的行为,通过软件监测应用程序获取调用的相关接口,分析存在隐私泄露用户行为的特点。主要通过研究正常QQ软件与植入了木马病毒的QQ软件在运行时所调用的系统序列的异同,根据调用的序列在建模软件中分别建立全局PPN模型,进行分析对比。最后对所分析的用户行为进行测试,研究PPN模型的相关算法,对PPN模型进行定性分析和定量分析,分别计算其4项指标值：可能性、严重性、操纵性、隐秘性。实验结果表明,与正常QQ相比,恶意的QQ不仅会越权访问其他数据,未经授权篡改权限获取相应数据;其次,包含隐私泄露行为的软件访问重要资源的能力更强,甚至会通过网络发送隐私数据。     

Layered Metal-Halide Perovskite Single-Crystalline Microwire Arrays for Anisotropic Nonlinear Optics

Hybrid organic–inorganic metal-halide perovskites with diverse structure tunability are promising for nonlinear-optical (NLO) applications, such as frequency conversion and electro-optic modulation. For integrated NLO devices, single-crystalline perovskite micro- and nanostructures with high quality and multifunctionality are in high demand. However, the fabrication of single-crystalline perovskites arrays is still challenging in regulating liquid dynamics and crystal growth simultaneously. Herein, a capillary-bridge-manipulated strategy is established to steer the dewetting process of microdroplets and provide spatial confinement for crystal growth. These 1D perovskite microwire arrays show regulated geometry, pure orientation, and single crystallinity. Chiral ammonium molecules are introduced into the metal-halide octahedral quantum wells to break the centrosymmetry of the perovskite, allowing the perovskite to exhibit excellent second-order NLO properties. The as-prepared microwire arrays also demonstrate linearly polarized second harmonic generation and two-photon fluorescence. Microwire arrays exhibit higher second harmonic conversion efficiency compared with their polycrystalline thin-film counterparts. It is believed that this strategy for the fabrication of chiral perovskite microstructure arrays holds great promise for NLO integrated applications and opens up an avenue to explore multifunctional chiral perovskites.     

Fault diagnosis of rolling bearing based on back propagation neural network optimized by cuckoo search algorithm

In order to improve the accuracy of rolling bearing fault diagnosis in mechanical equipment, a new fault diagnosis method based on back propagation neural network optimized by cuckoo search algorithm is proposed. This method use the global search ability of the cuckoo search algorithm to constantly search for the best weights and thresholds, and then give it to the back propagation neural network. In this paper, wavelet packet decomposition is used for feature extraction of vibration signals. The energy values of different frequency bands are obtained through wavelet packet decomposition, and they are input as feature vectors into optimized back propagation neural network to identify different fault types of rolling bearings. Through the three sets of simulation comparison experiments of Matlab, the experimental results show that, Under the same conditions, compared with the other five models, the proposed back propagation neural network optimized by cuckoo search algorithm has the least number of training iterations and the highest diagnostic accuracy rate. And in the complex classification experiment with the same fault location but different bearing diameters, the fault recognition correct rate of the back propagation neural network optimized by cuckoo search algorithm is 96.25%.

     

密文数据库保序编码方法的研究与改进

不可信云计算环境下的数据隐私保护问题逐渐成为研究重点,而保护隐私的主要方法之一就是对数据库中的记录加密,但对密文进行排序、范围查询等操作较为困难。保序加密能使密文的大小顺序与明文保持一致,支持上述对密文的操作。2013年提出的mOPE(mutable Order-Preserving Encoding)可变保序编码是一种基于二叉搜索树编码的保序加密方法,支持任意的数据类型,且除了明文顺序外不泄露其他任何信息。由于保序编码可能随着插入或删除记录而变更,服务器额外开销较大。本文对此作出改进并提出cmOPE(custom and mutable Order-Preserving Encoding)方法,基于构造完全二叉搜索树来调整保序编码,降低了编码变更带来的额外开销。实验结果显示,修改了编码调整策略的mOPE方法有效地降低了服务器的计算开销,提高了对保序密文增删改的效率。     

云加端的嵌套滑动窗口故障信号在线检测方法研究*

针对当前制造业生产线设备的故障检测效率以及检测方法通用性不高的问题,本文提出了一种云端融合的动态嵌套滑动窗口故障信号在线检测算法。该算法采用云服务跟智能终端在线检测相结合的架构,利用云服务大存储量和高计算速度、精度的优势,解决了终端设备对故障信号处理能力不足以及仅能对线上数据进行单次扫描的问题。云计算中心根据数据流的波动情况初步确定滑动窗口大小,再根据对异常信号的判断,向智能终端反馈故障信号的大小和相对位置,通过动态嵌套滑动窗口对其进行定位。理论分析和实验结果表明,该方法对周期信号有较好的通用性,而且有效提高了故障检测的效率。     

Improved ADI Scheme for Linear Hyperbolic Equations: Extension to Nonlinear Cases and Compact ADI Schemes

This paper studies the Galerkin finite element approximation of time-fractional Navier–Stokes equations. The discretization in space is done by the mixed finite element method. The time Caputo-fractional derivative is discretized by a finite difference method. The stability and convergence properties related to the time discretization are discussed and theoretically proven. Under some certain conditions that the solution and initial value satisfy, we give the error estimates for both semidiscrete and fully discrete schemes. Finally, a numerical example is presented to demonstrate the effectiveness of our numerical methods.