基于PIC16F877A和TL16C754的异步通信串口扩展及实现

文章介绍了基于PIC16F877A单片机和TL16C754异步通信芯片实现异步通信串口扩展的整体方案。该系统主要由PIC16F877A单片机、TL16C754及SN54LS245驱动芯片构成,通过开展相应的试验,证明了该处理方案的有效性,并在某工程项目中应用。     

基于人工智能的光电搜跟技术

本文在分析光电搜跟技术工作模式的基础上,介绍人工智能在图像识别、搜跟技术中的应用,研究基于人工智能的多目标轨迹关联和跟踪策略决策工作原理及处理流程,展望基于人工智能的光电搜跟技术在新作战体系下的应用。     

Research on flame classification and recognition based on object detection and similarity fusion

The color, shape, and other appearance characteristics of the flame emitted by different flame engines are different. In order to make a preliminary judgment on the category of the device to which it belongs through studying exterior characteristics of the flame, this paper uses the flame of matches, lighters, and candles to simulate different types of flames. It is hoped that the flames can be located and classified by detecting the characteristics of flames using the object detection algorithm. First, different types of fire are collected for the dataset of experiments. The mmDetection toolbox is then used to build several different object detection frameworks, in which the dataset can be trained and tested. The object detection model suitable for this kind of problem is obtained through the evaluation index analysis. The model is ResNet50-based faster-region-convolutional neural network ( Faster R- CNN), whose mean average-precision ( mAP) is 93.6% . Besides, after clipping the detected flames through object detection, a similarity fusion algorithm is used to aggregate and classify the three types of flames. Finally, the color components are analyzed to obtain the red, green, blue ( RGB) color histograms of the three flames.     

Semantic segmentation of track image based on deep neural network

In this paper, deep learning technology was utilited to solve the railway track recognition in intrusion detectionproblem. The railway track recognition can be viewed as semantic segmentation task which extends imageprocessing to pixel level prediction. An encoder-decoder architecture DeepLabv3 + model was applied in this workdue to its good performance in semantic segmentation task. Since images of the railway track collected from thevideo surveillance of the train cab were used as experiment dataset in this work, the following improvements weremade to the model. The first aspect deals with over-fitting problem due to the limited amount of training data. Dataaugmentation and transfer learning are applied consequently to rich the diversity of data and enhance modelrobustness during the training process. Besides, different gradient descent methods are compared to obtain theoptimal optimizer for training model parameters. The third problem relates to data sample imbalance, cross entropy(CE) loss is replaced by focal loss (FL) to address the issue of serious imbalance between positive and negativesample. Effectiveness of the improved DeepLabv3 + model with above solutions is demonstrated by experimentresults with different system parameters.     

数字伺服驱动技术在卷烟机改造中的应用

介绍了数字伺服系统的发展情况及应用现状,并结合当前国内的主流卷烟机passim8000机组紧头控制系统的不足,阐述了利用数字伺服电机如何加以改进。