基于视觉感知的智能扫地机器人的垃圾检测与分类

目的 为了提高扫地机器人的自主性和智能化程度，为扫地机器人配备视觉传感器，使其获得视觉感知能力，通过研究有效的垃圾检测分类模型与算法，实现对垃圾的定位与识别，引导扫地机器人对垃圾进行自动识别与按类处理，提高工作的目的性和效率，避免盲动和减少能耗。方法 选择检测速度较快的YOLOv2作为主网络模型，结合密集连接卷积网络，嵌入深层密集模块，对YOLOv2进行改进，提出一种YOLOv2-dense网络，该网络可以充分利用图像的高分辨率特征，实现图像浅层和深层特征的复用与融合。结果 测试结果表明，智能扫地机器人使用本文方法可以有效识别不同形态的常见垃圾类别，在真实场景中，测试识别准确率为84.98%，目标检测速度达到26帧/s。结论 实验结果表明，本文构建的YOLOv2-dense网络模型具有实时检测的速度，并且在处理具有不同背景、光照、视角与分辨率的图片时，表现出较强的适应和识别性能。在机器人移动过程中，可以保证以较高的准确率识别出垃圾的种类，整体性能优于原YOLOv2模型。     

Study on Flow Behavior and Heat Exchange Characteristics of a Capillary Tube-Suction Line Heat Exchanger

The present research is to develop a homogenous mathematical model to simulate capillary tube-suction line heat exchanger (CT-SL HX) based on the fundamentals of conservations of mass, momentum and energy with comprehensive experimental result validations. The computer model is fully validated by 72 experimental data with error bands of ±15%, ±2°C and ±35% on the mass flow rate prediction, the suction pipe outlet temperature, and the heat exchange estimation respectively. The results suggest that the internal diameter of the capillary tube, and the heat-transfer length of the CT-SL HX have demonstrated an apparent impact on the capillary tube outlet conditions and heat transfer across the segment.     

Super-Resolution Reconstruction of Images Based on Microarray Camera

In the field of images and imaging, super-resolution (SR) reconstruction of images is a technique that converts one or more low-resolution (LR) images into a highresolution (HR) image. The classical two types of SR methods are mainly based on applying a single image or multiple images captured by a single camera. Microarray camera has the characteristics of small size, multi views, and the possibility of applying to portable devices. It has become a research hotspot in image processing. In this paper, we propose a SR reconstruction of images based on a microarray camera for sharpening and registration processing of array images. The array images are interpolated to obtain a HR image initially followed by a convolution neural network (CNN) procedure for enhancement. The convolution layers of our convolution neural network are 3×3 or 1×1 layers, of which the 1×1 layers are used to improve the network performance particularly. A bottleneck structure is applied to reduce the parameter numbers of the nonlinear mapping and to improve the nonlinear capability of the whole network. Finally, we use a 3×3 deconvolution layer to significantly reduce the number of parameters compared to the deconvolution layer of FSRCNN-s. The experiments show that the proposed method can not only ameliorate effectively the texture quality of the target image based on the array images information, but also further enhance the quality of the initial high resolution image by the improved CNN.     

基于分支定界法的相控阵雷达事件调度算法

为解决相控阵雷达事件调度问题中调度成功率、时间偏移率与算法时效性三者之间的矛盾，本文提出一种基于分支定界法的调度算法；该算法首先在现有调度结果上调取所有可执行事件，即获得若干"节点"，多步递推后由多"层"节点形成若干条备选"分支"，扩大解空间范围，提高算法性能；然后通过"删减"操作删减掉收益较低分支，将解空间控制在一适当范围内，减小搜索盲目性，降低算法计算量.仿真实验表明，与基于综合优先级算法的调度结果相比，利用本文算法调度时调度成功率提升了52%；与基于时间指针算法的调度结果相比，本身算法时间偏移率降低了61%；与基于遗传算法的调度结果相比，本文算法调度耗时仅为前者1~2%.     

Cavern Boundary Determination with Pseudoplastic Fluid Based on the Apparent Viscosity Method

The cavern characteristics of xanthan gum solutions stirred by impellers of a perturbed six-bent-bladed turbine (6PBT) were investigated numerically using computational fluid dynamics (CFD) with laminar flow model. The apparent viscosity method was proposed to determine the boundary of the cavern. The cavern sizes predicted by apparent viscosity were in good agreement with the calculated results, and this method was not influenced by speed and impeller configuration. However, the predicted results of the cavern by the traditional speed method usually displayed a great deviation, especially at high speed. Therefore, it is feasible to determine the cavern boundary with the apparent viscosity, i.e., 0.25 times the yield viscosity of a pseudoplastic fluid, as the unified standard.     

P-IoT自组网模式研究

为降低专网物联网(P-IoT)的建网成本,实现灵活部署和增强抗毁性以满足行业用户的应用要求,需对P-IoT自组网模式进行研究。首先,阐述了P-IoT自组网模式的重要性;在此基础上,从系统架构、支持的拓扑类型、协议栈设计以及技术优势对P-IoT自组网模式加以分析;最后,分析了P-IoT自组网模式在智能抄表与智能林防领域的创新应用。P-IoT自组网模式在多种应用环境下都能很好地满足行业用户的需求,在未来专网市场P-IoT自组网模式必将具备极强的竞争力。     

Fabrication of poly(ε‐caprolactone) (PCL)/poly(propylene carbonate) (PPC)/ethylene‐α‐octene block copolymer (OBC) triple shape memory blends with cycling performance by constructing a co‐continuous phase morphology

In this paper, a triple shape memory material was prepared by ultra‐simple melt blending from poly(ε‐caprolactone) (PCL), poly(propylene carbonate) (PPC) and ethylene‐α‐octene block copolymer (OBC). The obtained material possessed a co‐continuous phase morphology and presented an excellent triple shape memory effect (triple‐SME). Theoretical prediction demonstrated that a special continuous phase morphology could be constructed by adjusting the proportions of the blend. Moreover, the results indicated that a close relationship existed between the phase morphology and the triple‐SME of PCL/PPC/OBC. The sample with 35 vol% PPC content contributed to the formation of a continuous phase morphology and exhibited the optimal triple‐SME. Additionally, the sample PCL/PPC/OBC (32.5/35/32.5) showed outstanding structure and performance stability during cycle loading–unloading tests, which evidenced the prominent cycling shape memory property (nearly 100% shape fixing and recovery of temporary shape). Overall, this work could provide an efficient, convenient and recyclable method to obtain high‐performance shape memory materials. © 2020 Society of Chemical Industry     

1226工作面切顶卸压沿空成巷技术应用研究

为解决掘进成本较高、接替紧张等问题,基于1226工作面地质条件,对运输巷进行切顶卸压沿空留巷设计,主要包括切顶预裂缝、恒阻大变形锚索和巷道支护参数的设计,取得较好效果。