Study on Recognition Method of Similar Weather Scenes in Terminal Area

Weather is a key factor affecting the control of air traffic. Accurate recognition and classification of similar weather scenes in the terminal area is helpful for rapid decision-making in air traffic flow management. Current researches mostly use traditional machine learning methods to extract features of weather scenes, and clustering algorithms to divide similar scenes. Inspired by the excellent performance of deep learning in image recognition, this paper proposes a terminal area similar weather scene classification method based on improved deep convolution embedded clustering (IDCEC), which uses the combination of the encoding layer and the decoding layer to reduce the dimensionality of the weather image, retaining useful information to the greatest extent, and then uses the combination of the pre-trained encoding layer and the clustering layer to train the clustering model of the similar scenes in the terminal area. Finally, terminal area of Guangzhou Airport is selected as the research object, the method proposed in this article is used to classify historical weather data in similar scenes, and the performance is compared with other state-of-the-art methods. The experimental results show that the proposed IDCEC method can identify similar scenes more accurately based on the spatial distribution characteristics and severity of weather; at the same time, compared with the actual flight volume in the Guangzhou terminal area, IDCEC's recognition results of similar weather scenes are consistent with the recognition of experts in the field.     

Transfer Learning on Deep Neural Networks to Detect Pornography

While the internet has a lot of positive impact on society, there are negative components. Accessible to everyone through online platforms, pornography is, inducing psychological and health related issues among people of all ages. While a difficult task, detecting pornography can be the important step in determining the porn and adult content in a video. In this paper, an architecture is proposed which yielded high scores for both training and testing. This dataset was produced from 190 videos, yielding more than 19 h of videos. The main sources for the content were from YouTube, movies, torrent, and websites that hosts both pornographic and non-pornographic contents. The videos were from different ethnicities and skin color which ensures the models can detect any kind of video. A VGG16, Inception V3 and Resnet 50 models were initially trained to detect these pornographic images but failed to achieve a high testing accuracy with accuracies of 0.49, 0.49 and 0.78 respectively. Finally, utilizing transfer learning, a convolutional neural network was designed and yielded an accuracy of 0.98.     

基于多流网络一致性的视频显著性检测

现有的视频显著性检测算法通常采用双流结构提取视频的时空线索,其中运动信息作为双流结构的一个分支,在显著物体发生剧烈或慢速移动时存在运动估计准确率低的问题,并且不合理的训练数据或方案使得权重偏向单个分支结构。提出一种基于多流网络一致性的视频显著性检测算法MSNC。设计并使用一种新的三重网络结构提取预选目标区域的颜色信息、时序信息和先验特征,通过先验特征补偿运动流的缺陷,并提高运动线索的利用率。采用多流一致性融合模型优化三流分支,得到不同特征的最佳融合方案。同时通过循环训练策略平衡三重网络的权重,以避免网络过度拟合单流分支,从而有效地提高运动估计和定位的准确率。在Davis数据集上的实验结果表明,相比PCSA、SSAV、MGA等算法,该算法的鲁棒性更优,其maxF和S-Measure值分别达到0.893和0.912,MAE仅为0.021。     

改革实践教学模式、培养学生综合能力──增设给水排水专业质量检测实习的体会

对给排水专业在优化专业教学体系,深化专科教学模式及教学内容改革中,实施的四次大型综合性实践教学,进行了认真总结．经过改变随课跟进的实验教学旧模式,在教学计划中增设了“水质指标综合测试与水质评价”实习内容使分散的教学实验得以“水质监测与评价”为骨架,有机穿插在实践教学中,为专科学校培养复合应用型人才,进行了有益的探索与实践,并获得较好效果．     

基于组态王6.5的离心泵微机在线监控系统

离心输油泵是石油化工产品输送的主要设备,微机在线监控系统主要用于炼油厂、乙烯厂、油库、输油管道等油气输送领域离心输油泵运行状态的监测、控制以及检修的预报等。本设计采用操作指导控制系统以及工厂的分布式层次化结构思想,以离心输油泵输入输出轴的振动参数为主要测试参数,并辅以离心输油泵油温、轴瓦温度监测,同时采用ADAM4000智能模块和RS—485网络,通过监控控制仪转换成RS—232信号给计算机,实现所需参数的显示与控制和计算机对离心输油泵的快速启停控制。系统软件基于Windows XP平台,运用北京亚控组态王6.5版开发而成,具有实用、实时、可靠与友好等特点。     

智能车竞赛中道路的视频识别方案

视频信号的的采集和处理是一个复杂的过程,这需要程序设计者了解视频信号的构成,充分利用复合视频信号中的各种同步信息作为视频信号采集的控制信号,从而完成信号的采集。     

煤矿综合监控系统在灾害防治方面的作用与发展

在简要介绍我国煤矿灾害的预防、救援现状的基础上,阐述了综合监控系统在提供准确信息、正确避险指示和减少灾害损失等方面的重要作用;结合实际案例就综合监控系统如何配合六大系统的要求,采用无线通信、信息融合、视频监控、物联网等智能预警新技术,健全系统配置、完善系统功能、提高避险能力等进行了分析与论述。最后,提出了借鉴采矿业发达国家的成熟技术与经验,充分发挥煤矿综合监控系统的作用,积极探索井下新型安全防护体系的建设,达到零死亡率的现代化矿井的发展方向。     

Video analysis based on volumetric event detection

During the past decade, feature extraction and knowledge acquisition based on video analysis have been extensively researched and tested on many applications such as closed-circuit television(CCTV)data analysis, large-scale public event control, and other daily security monitoring and surveillance operations with various degrees of success. However, since the actual video process is a multi-phased one and encompasses extensive theories and techniques ranging from fundamental image processing, computational geometry and graphics, and machine vision, to advanced artificial intelligence, pattern analysis, and even cognitive science, there are still many important problems to resolve before it can be widely applied. Among them, video event identification and detection are two prominent ones. Comparing with the most popular frame-to-frame processing mode of most of today's approaches and systems, this project reorganizes video data as a 3D volume structure that provides the hybrid spatial and temporal information in a unified space. This paper reports an innovative technique to transform original video frames to 3D volume structures denoted by spatial and temporal features. It then highlights the volume array structure in a so-called "pre-suspicion" mechanism for a later process. The focus of this report is the development of an effective and efficient voxel-based segmentation technique suitable to the volumetric nature of video events and ready for deployment in 3D clustering operations. The paper is concluded with a performance evaluation of the devised technique and discussion on the future work for accelerating the pre-processing of the original video data.     

矿井主通风机自动监控系统的设计

为了保障主通风机安全、可靠、经济地运行,提出了一种基于S7-300 PLC的矿井主通风机自动监控系统的设计方案;给出了该系统的结构,详细介绍了该系统中PLC的硬件及软件结构、模拟量的检测实现及采用智能型PID调节器XMA5000对风机负压和流量进行自动调节的方法。实际应用表明,该系统能够实现通风机的就地、集中、远方3种控制方式,提高了主通风机的自动化程度和运行可靠性。