深度学习识别光网络单元故障的设计与应用

为解决依赖装维上门鉴别光网络单元故障带来的不便,可以从机器视觉入手实现自动化故障识别。近年,ImageNet挑战赛的成功推动了物体识别技术的跨越式发展,特别是基于卷积的深度学习技术在视觉识别方面已经达到人类水平,为光网络单元故障的自动识别提供了技术基础。文章对识别光网络单元的工作状态进行了研究,将设备工作状态分为7个场景,提出了利用手机APP采集图片识别故障的解决方案并投入了实际生产;重点阐述了深度学习模块的设计与实现,提出一种通过算法整合的方式综合运用物体检测和图像分类算法,分3阶段逐步求精,解决了图片过滤,光网络单元型号和状态识别等问题,实现了基于计算机视觉自动识别光网络单元故障。从数据上看产品的端到端准确率超过84%,识别速度达到10 FPS,月均提供服务超过1万人次,在减少用户等待的同时节约了人力资源。     

基于深度学习的视频跟踪研究进展综述

近年来深度学习迅猛发展，颠覆了语音识别、图像分类、文本理解等领域的算法设计思路。深度学习因其具备强大的特征提取能力，在图像识别领域的成绩尤为突出。然而深度学习与视频监控领域的结合并不多，由于深度模型具有多层网络结构，算法复杂度大，训练和更新模型时比较耗时，很难满足实时性要求。回顾了深度学习的发展史，介绍了最近10年来国内外深度学习主要模型，论述了基于深度学习的目标跟踪算法，指出了各算法的优缺点，最后对当前该领域存在的问题和发展前景进行了总结和展望。     

Methodology for optimization of pouring variables using bottom pour for low masses of SS304 (<300 kg) in investment casting process (case study)

Bottom pour ladles with stopper rod systems are commonly used in the metal casting industry. However, stopper rod bottom-pouring systems have not yet been developed for the lower thermal masses of alloys typically used in the investment casting industry. Large thermal masses used with bottom pour systems are typically limited for ladles larger than 700 kg and to certain alloys with higher fluidity and longer solidification time like cast iron, aluminum alloys etc. In this study, bottom pour ladle designs and low thermal mass refractory systems have been developed and evaluated in production investment foundry trials with 300 kg pouring ladle. The ladles system and pouring practices used will be described along with the results from the pouring trials for SS304 that represents typical alloys used in Investment casting industries. Optimization of the variables used in an experimentation using Genetic algorithm is also explained.     

Effects of structural characteristics on microwave dielectric properties of low-loss (Zn1-xNix)ZrNbTaO8 ceramics

Low-loss (Zn_1-xNi_x)ZrNbTaO₈ (0.02?≤?x?≤?0.10) ceramics possessing single wolframite structure are initiatively synthesized by solid-state route. Based on the results of Rietveld refinement, complex chemical bond theory is used to establish the correlation between structural characteristics and microwave performance in this ceramic system. A small amount of Ni²⁺ (x?=?0.06) in A-site with the fixed substitution of Ta⁵⁺ in B-site can effectually raise the Q?×?f value of ZnZrNb₂O₈ ceramic, embodying a dense microstructure and high lattice energy. The dielectric constant and τ_f are mainly affected by bond ionicity and the average octahedral distortion. The (Zn_0.94Ni_0.06)ZrNbTaO₈ ceramic sample sintered at 1150?°C for 3?h exhibits an outstanding combination of microwave dielectric properties: ε_r =?27.88, Q?×?f?=?128,951?GHz, τ_f =?–39.9?ppm/°C. Thus, it is considered to be a candidate material for the communication device applications at high frequency.     

Dhh1 is a member of the SESA network

The correct separation of chromosomes during mitosis is necessary to prevent genetic instability and aneuploidy, which are responsible for cancer and other diseases, and it depends on proper centrosome duplication. In a recent study, we found that Smy2 can suppress the essential role of Mps2 in the insertion of yeast centrosome into the nuclear membrane by interacting with Eap1, Scp160, and Asc1 and designated this network as SESA (S my2, E ap1, S cp160, A sc1). Detailed analysis showed that the SESA network is part of a mechanism which regulates translation of POM34 mRNA. Thus, SESA is a system that suppresses spindle pole body duplication defects by repressing the translation of POM34 mRNA. In this study, we performed a genome-wide screening in order to identify new members of the SESA network and confirmed Dhh1 as a putative member. Dhh1 is a cytoplasmic DEAD-box helicase known to regulate translation. Therefore, we hypothesized that Dhh1 is responsible for the highly selective inhibition of POM34 mRNA by SESA.     

Cd-substituted Mg composites with dual-equivalent permeability and permittivity for high-frequency miniaturization antennas

Spinel Mg ferrites Mg_1-xCd_xCo_0.05Fe_1.95O₄ (x=0.0, 0.2, 0.3 and 0.4) as potential agents for the miniaturization of high frequency antennas are presented. All the synthesized compositions were experimentally revealed to possess pure spinel phase. Dual-equivalent permeability and permittivity (μ'≈26, ε'≈25, x=0.3 and μ'≈29, ε'≈28, x=0.4) from 5 to 100 MHz can be achieved by introducing Cd²⁺ ions, yielding large miniaturization factors of up to 25 and 28. To figure out the effects of Cd²⁺ ions substitution on magnetic and dielectric properties, the change in microstructure is mainly investigated. Meanwhile, enhanced magnetic properties including upward saturation magnetization (Ms) (approximately 47.60 emu/g) and reduced coercivity (approximately 54.22 Oe) are obtained due to increased grain size and denser microstructure arrangements reflected from scanning electron microscopy images. With low magnitude order of magnetic and dielectric losses factors (tanδ_ε reaches 10^-4, tanδ_μ reaches 10^-2), the composites can potentially exhibit high operating efficiencies at high frequencies.     

视频侦查中人像智能分析应用及算法优化

人像智能分析指的是对视频或录像中的人像进行结构化和可视化分析,对目标人物进行性别、年龄、发型等特征的智能识别,这项技术在视频侦查中有极高的应用价值。人像识别早期的算法是通过人工提取特征,通过学习低级视觉特征来针对不同属性进行分类学习,这种基于传统方法的模型表现常常不尽如人意。在计算机视觉领域,通过海量图像数据学习的神经网络比传统方法有更丰富的信息量和特征可以被提取。文章尝试通过深度学习技术训练神经网络模型对行人进行检测和识别,对于衣着不同的行人进行智能识别,具有更好的鲁棒性,提升了视频人像识别的准确率,拓展了人工智能技术在身份识别领域的应用。     

基于特征融合的中文简历解析方法研究

针对基于规则和统计的传统中文简历解析方法效率低、成本高、泛化能力差的缺点，提出一种基于特征融合的中文简历解析方法，即级联Word2Vec生成的词向量和用BLSTM（Bidirectional Long Short-Term Memory）建模字序列生成的词向量，然后再结合BLSTM和CRF（Conditional Random Fields）对中文简历进行解析（BLSTM-CRF）。为了提高中文简历解析的效率，级联包含字序列信息的词向量和用Word2Vec生成的词向量，融合成一个新的词向量表示；再由BLSTM强大的学习能力融合词的上下文信息，输出所有可能标签序列的分值给CRF层；再由CRF引入标签之间约束关系求解最优序列。利用梯度下降算法训练神经网络，使用预先训练的词向量和Dropout优化神经网络，最终完成对中文简历的解析工作。实验结果表明，所提的特征融合方法优于传统的简历解析方法。     

基于深度卷积神经网络的视觉里程计研究

视觉里程计利用视频信息来估计相机运动的位姿参数，实现对智能体的定位。传统视觉里程计方法需要特征提取、特征匹配/跟踪、外点剔除、运动估计、优化等流程，解算非常复杂，因此，提出了基于卷积神经网络的方法来实现端对端的单目视觉里程计。借助卷积神经网络对彩色图片自动学习提取图像帧间变化的全局特征，将用于分类的卷积神经网络转化为帧间时序特征网络，通过三层全连接层输出相机的帧间相对位姿参数。在KITTI数据集上的实验结果表明，提出的Deep-CNN-VO模型可以较准确地估计车辆的运动轨迹，证明了方法的可行性。在简化了复杂模型的基础上，与传统的视觉里程计系统相比，该模型的精度也有所提高。     

并联有源滤波器的改进重复控制策略研究

针对三相整流负载产生的6k±1次谐波,提出静止坐标系下的改进型6k±1重复控制策略。同时将比例控制与改进型重复控制相结合,设计改进重复控制并联比例的复合控制结构。为减小改进重复控制中延时环节不为整数的影响,采用基于Lagrange插值近似的FIR滤波器逼近分数延时特性。最后对系统进行稳定性分析和详细设计方法进行推导。通过Matlab仿真验证改进重复控制策略能有效跟踪6k±1次谐波且具有良好的补偿效果,动态响应较快。