基于条件信息卷积生成对抗网络的图像识别

传统的图像识别方法需要大量有标签样本进行训练，且模型训练难以达到稳定。针对这些问题，结合条件生成网络和信息最大化生成网络的结构优势建立了条件信息卷积生成网络（C-Info-DCGAN）。模型增加图像的类别信息和潜在信息作为输入数据，然后利用Q网络去更好地发挥类别信息和潜在信息对训练的引导作用，并且利用深度卷积网络来加强对图像特征的提取能力。实验结果表明，该方法能够加快模型训练收敛速度，并有效提高图像识别的准确率。     

Transfer learning between crop types for semantic segmentation of crops versus weeds in precision agriculture

Agricultural robots rely on semantic segmentation for distinguishing between crops and weeds to perform selective treatments and increase yield and crop health while reducing the amount of chemicals used. Deep‐learning approaches have recently achieved both excellent classification performance and real‐time execution. However, these techniques also rely on a large amount of training data, requiring a substantial labeling effort, both of which are scarce in precision agriculture. Additional design efforts are required to achieve commercially viable performance levels under varying environmental conditions and crop growth stages. In this paper, we explore the role of knowledge transfer between deep‐learning‐based classifiers for different crop types, with the goal of reducing the retraining time and labeling efforts required for a new crop. We examine the classification performance on three datasets with different crop types and containing a variety of weeds and compare the performance and retraining efforts required when using data labeled at pixel level with partially labeled data obtained through a less time‐consuming procedure of annotating the segmentation output. We show that transfer learning between different crop types is possible and reduces training times for up to 80%. Furthermore, we show that even when the data used for retraining are imperfectly annotated, the classification performance is within 2% of that of networks trained with laboriously annotated pixel‐precision data.     

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。     

一种基于深度森林的恶意代码分类方法

针对当前恶意代码静态分析方法精度不足的问题，将恶意代码映射为无压缩的灰度图像，然后根据图像变换方法将图像变换为恒定大小的图像，使用方向梯度直方图提取图像的特征，最后提出一种基于深度森林的恶意代码分类方法.实验中选择不同家族的多个恶意代码样本进行分类，验证了该方法的有效性，并且实验结果优于近期提出的SPAM-GIST方法.     

大涡模拟Smagorinsky模型用于磨粒流精密加工喷嘴的质量控制研究

为研究固液两相磨粒流加工喷嘴小孔过程中的流场分布、涡旋形成规律及涡旋的存在对磨粒流加工的影响机制，采用Smagorinsky亚格子模型对磨粒流加工喷嘴小孔的流道进行大涡数值模拟，并使用磨粒流对变直径喷嘴工件进行加工试验。数值模拟发现磨粒流流体中磨粒与壁面的碰撞与剪切作用随流体的速度增大而增大，同一截面的速度存在速度差，其中还伴随涡旋的存在；通过试验研究发现：经固液两相磨粒流加工后的喷嘴小孔表面质量得到明显提高，喷嘴经过四次不同入口速度的磨粒流加工后大孔处表面粗糙度Ra由1.24 μm降至0.542 μm，小孔处表面粗糙度Ra由1.21 μm降至0.437 μm。结论显示固液两相磨粒流加工技术可有效提高被加工喷嘴工件的内表面质量，加工时同一截面的速度存在速度差，速度差的存在利于涡旋的形成，涡旋的存在利于提高磨粒流加工过程的剪切作用，有助于获得高质量的喷嘴小孔内通道表面。     

Large scale cathodic exfoliation of graphite using deep eutectic solvent and water mixture

A short-time and low-cost synthesis route was used to produce large lateral size (from 2 to 15 μm) from monolayers to few layers of graphene by a two-step process of electrochemical exfoliation with a deep eutectic solvent in a mixture with water that can be reused, and ultrasonic bath. The graphene was characterized by SEM, TEM, AFM, Raman and electrochemical activity. During the electrochemical exfoliation, high expanded graphene particles were obtained and these were dispersed in a mixture of water with 5%wt ethylene glycol by an ultrasonic bath in order to complete the exfoliation process. An enhancement of the electrical conductivity of these dispersions was obtained with the increase of graphene concentration, 0.38 mg/mL, which best result was achieved with 30 wt% water and a DC voltage of 10 V. It was possible to add a conductive layer to a glass substrate with the graphene obtained and Tyndall effect was observed.     

采煤工作面煤层注水工艺优化与应用

针对目前煤层注水存在的施工钻孔效率低、钻孔深度达不到要求、注水钻孔封孔不严、封孔器回收困难等问题，研究注水工艺的改进优化，对钻机、钻具及封孔器进行重新设计选型，形成了一整套的注水装备。该装备的应用提高了施工效率，为降低工作面粉尘危害、抑制工作面瓦斯涌出、防治煤炭自然发火等危害探索出了更加高效的途径和方法。现场应用表明，煤层注水有效地改善了工作面作业环境，为工作面实现高产高效奠定了基础。     

基于深度学习的传感器优化布置方法

针对传感器优化布置(optimal sensor placement,简称OSP)问题,提出了一种新的使用深度神经网络的解决方案,并以简化的桥梁形状的桁架结构中的振动测试传感器优化为例进行了验证。首先,选择一种传统的传感器优化布置方法,对自动化生成的大量不同的桁架结构分别进行传感器优化布置计算,将所得优化布置结果在进行数据预处理后构建出深度学习方法所需要的训练集与验证集;其次,使用Python语言和深度学习框架TensorFlow设计实现与本研究问题适配的深度神经网络模型并训练;然后,随机生成了新的桁架结构参数;最后,将深度神经网络输出的传感器布置结果和传统方法的计算结果进行了比较,验证了本研究方法的有效性以及在速度上、可移植性与可扩展性方面的性能优势。     

多层圆片级堆叠THz硅微波导结构的制作

基于微电子机械系统(MEMS)工艺,提出一种多层圆片堆叠的THz硅微波导结构及其制作方法。为了验证该结构在制作THz无源器件中的优势,基于6层圆片堆叠的硅微波导结构,设计了一种中心频率365 GHz、带宽80 GHz的功率分配/合成结构,并对其进行了仿真。研究了制作该结构的工艺流程,攻克了工艺过程中的关键技术,包括硅深槽刻蚀技术和多层热压键合技术,并给出了工艺结果。最终实现了多层圆片堆叠功率分配/合成结构的工艺制作和测试。测试结果表明,尽管样品的插入损耗较仿真值增加3 dB左右,考虑到加工误差和夹具损耗等情况,样品主要技术指标与设计值较为一致。