真空冷冻干燥技术在三七加工中的应用

介绍了真空冷冻干燥的基本原理，并据三七的特点，探讨三七真空冷冻干燥的工艺流程及特点，简要分析了三七冷冻干燥的前景。     

一种基于改进的动态能量管理的无线传感器节点节能技术研究

针对无线传感器节点的结构特点,以动态能量管理技术为出发点,结合实际应用场合,提出了一种改进的动态能量管理节能模式,建立了不同工作模式下的无线传感器节点能耗模型,并对不同工作模式下的能耗进行了量化分析。分析结果表明,改进后的动态能量管理技术能够有效地降低无线传感器的能量损耗。分析结果为下一步无线传感器节点的节能改进设计打下了良好的基础。     

自硬型芯工艺在异型铸件生产中的应用

针对异型铸件(指有狭窄缝隙、结构形状特殊、难于用常规水玻璃涂料工艺进行结壳),介绍了灌浆式自硬型芯的制作过程,提示了模具开设的要点,着重分析了浇注、补缩系统的设置等精密铸造工艺的要素,使自硬型芯工艺成功地在较大异型精铸件生产上得到应用。     

A marker-less assembly stage recognition method based on corner feature

Fast and robust product assembly stage recognition is a key step in human–machine cooperative assembly. To solve the recognition problem of similar adjacent assembly stages, a marker-less assembly stage recognition method is proposed based on corner feature between an assembling product and a digital model. Considering the geometric features of mechanical products, a corner identification method is proposed based on the circumferential angle difference (CADF). Then a corner matching method based on distance constraint is studied for ICP registration to realize the point cloud registration between the product and digital model. Based on the registration relationship, a similarity algorithm based on proximity point proportion is used to calculate the similarity between models and the input assembling product. The model with the greatest similarity is taken as the stage recognition result. In experiments on four group assembling products, the average stage recognition accuracy is 96.15%, which indicates that the proposed method can solve the stage recognition problem. The corner identification method based on the CADF outperforms the Harris-3D corner detection method in the efficiency of assembly stage recognition.     

YOLOv5-SFE: An algorithm fusing spatio-temporal features for detecting and recognizing workers' operating behaviors

The occurrence of production accidents can be effectively reduced by monitoring workers' operating behaviors in real time. However, most of the monitoring tasks are currently performed by the monitoring personnel, which takes up a lot of manpower and material resources. To solve this problem, a YOLOv5-SFE algorithm is proposed in this paper for real-time detection and recognition of workers' operating behaviors. The YOLOv5-SFE algorithm makes the following contributions: (1) During data preprocessing, a hash sampling algorithm is used to extract frames with low similarity. (2) A feature enhancement module is designed and integrated into YOLOv5 to distinguish between valid and invalid information. (3) A convolution-based spatio-temporal feature fusion module is designed and is inserted after the extraction of spatial features to extract the temporal features between multiple frames. The videos of workers' operating behaviors are from factories’ industrial scene. The improved algorithm in this paper was trained and tested on the dataset. Compared with the original algorithm, the accuracy of the algorithm improves from 89.3% to 94.7%, the recall improves from 81.5% to 90.8%, and the mean average precision(mAP) improves from 88.2% to 92%. The results show that the improved algorithm is able to accurately detect and recognize workers' operating behaviors in real time, thereby improving the safety of the production process.     

Intelligent analysis method of dam material gradation for asphalt-core rock-fill dam based on enhanced Cascade Mask R-CNN and GCNet

Reasonable dam materials’ gradation design for asphalt-core rock-fill dams is one of the main ways to control permeability. It is a challenge to test whether it can meet the requirements of dam construction. The computer vision method provides a new idea for asphalt-core rock-fill dam material gradation testing. However, due to the characteristics of densely overlapping and multi-scale sizes of dam material particles, the traditional image segmentation methods and algorithms cannot achieve accurate segmentation of dam materials’ images, and it is hard to apply the segmentation result to quantify the gradation curve. In this research, the enhanced Cascade Mask R-CNN with ResNet and PAFPN (Path Aggregation Feature Pyramid Networks) is proposed. Multi-scale features extracted by ResNet and feature ensemble can be realized using PAFPN. Data augmentation (DA) and online hard example mining (OHEM) are also applied in segmentation model training. Moreover, the GCNet is proposed to calibrate the gradation curve. The nonlinear relationship between the real gradation and the one based on the segmentation results can be revealed and the model of dam materials’ gradation analysis can be established. In the research, the enhanced Cascade Mask R-CNN can achieve 84.2 mAP, which is higher than that of Cascade Mask R-CNN with 74.9 mAP. The effectiveness of the proposed module and training strategies is proved using ablation experiments. The average error of each level for the gradation calibration using GCNet is 0.55%, 1.87%, 2.22%, 1.18%, and 2.42% respectively. The accuracy can meet the requirements of hydraulic engineering construction, which verifies the effectiveness of the GCNet network for gradation calibration, and the research provides a new method and technology for intelligent gradation testing of the asphalt-core rock-fill dam.     

An energy-efficient multi-objective integrated process planning and scheduling for a flexible job-shop-type remanufacturing system

This study considers an energy-efficient multi-objective integrated process planning and scheduling (IPPS) problem for the remanufacturing system (RMS) integrating parallel disassembly, flexible job-shop-type reprocessing, and parallel reassembly shops with the goal of realizing the minimization of both energy cost and completion time. The multi-objective mixed-integer programming model is first constructed with consideration of operation, sequence, and process flexibilities in the RMS for identifying this scheduling issue mathematically. An improved spider monkey optimization algorithm (ISMO) with a global criterion multi-objective method is developed to address the proposed problem. By embedding dynamic adaptive inertia weight and various local neighborhood searching strategies in ISMO, its global and local search capabilities are improved significantly. A set of simulation experiments are systematically designed and conducted for evaluating ISMO’s performance. Finally, a case study from the real-world remanufacturing scenario is adopted to assess ISMO’s ability to handle the realistic remanufacturing IPPS problem. Simulation results demonstrate ISMO’s superiority compared to other baseline algorithms when tackling the energy-aware IPPS problem regarding solution accuracy, computing speed, solution stability, and convergence behavior. Meanwhile, the case study results validate ISMO’s supremacy in solving the real-world remanufacturing IPPS problem with relatively lower energy usage and time cost.     

基于错误模式和模型检验的静态代码分析方法

为提高程序编写的正确率,减少软件开发和维护开销,提出一种基于错误模式和模型检验的静态代码分析方法。该方法将C语言程序常见的错误模式以CTL公式表示,形成可扩展的CTL公式库,生成待检测程序的控制流图(CFG)后,将CFG抽象并转化为等价的 Kripke结构,利用标号算法实现模型检验,由此验证程序的正确性。基于CoSy编译平台的实验结果表明,该方法能正确查找出程序中存在的错误模式,且具有良好的可扩展性。     

基于JC-5600ATE的单/双电源运算放大器测试方法

集成运算放大器具有成本低、功能多等优点,可广泛用于自动控制系统、测量仪表等其他电子设备中。近年来集成运放的指标在不断提高,性能也在不断提升,伴随着运算放大器的发展,对用来考核运算放大器指标的测试技术也提出了相应的要求,对测试系统(Automatic Testing Equipment,以下简称ATE)的性能要求也相应变高。阐述了单、双电源集成运算放大器在JC-5600ATE上的测试方法,并分析了两者的测试差异。     

背景典型斑点特征的聚类分析

迷彩是一项重要的目标防护手段,斑点形状设计是进行迷彩设计的基础。首先自动提取背景图像中的明显斑点,然后分析背景斑点周长、面积、伸长度、矩形度、细长度、分散度等特征的统计特性,最后将长度、矩形度、细长度、分散度作为斑点特征向量对提取出的背景斑点进行HCM聚类分析,进而得到背景典型斑点的特征参数。该方法能得到大样本背景图像斑点特征的统计特征,对自动、定量化的迷彩设计具有一定意义。