基于“动态学习数据分析”的智慧课堂 评价考核体系构建

This paper firstly describes the current situation and limitations of learning evaluation and assessment in colleges and uni- versities; then, it analyzes the possibility of implementing the learning evaluation and assessment based on "dynamic learning data analysis"; then, it describes the design process of the learning evaluation and assessment system in detail; finally, it determines the various assessment items and carries out quantitative processing to better apply them to actual teaching.     

基于大数据的五年制高职护生在线学习行为要素研究

在线学习已逐渐成为高职院校一种重要学习形式,该领域也成为当前研究热点之一。对在线学习进行介绍,同时对大数据技术在在线学习平台中的应用进行分析,重点对五年制高职护生在线学习行为要素进行研究分析,为更好地开展在线学习评价提供参考,促进高职院校更好地开展在线学习,提高在线学习质量。     

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。     

Steganography using a 3-player game

Image steganography aims to securely embed secret information into cover images. Until now, adaptive embedding algorithms such as S-UNIWARD or Mi-POD, were among the most secure and most often used methods for image steganography. With the arrival of deep learning and more specifically, Generative Adversarial Networks (GAN), new steganography techniques have appeared. Among them is the 3-player game approach, where three networks compete against each other. In this paper, we propose three different architectures based on the 3-player game. The first architecture is proposed as a rigorous alternative to two recent publications. The second takes into account stego noise power. Finally, our third architecture enriches the second one with a better interaction between embedding and extracting networks. Our method achieves better results compared to existing works Hayes and Danezis (2017), Zhu et al. (2018), and paves the way for future research on this topic.     

大采高综放工作面强动压端头顶板控制技术

为了解决大采高综放工作面回风侧端头顶板控制问题,针对马道头矿8211工作面的地质情况,通过对回风顺槽超前注浆加固,提高超前支护范围和支护强度,并对回风侧端头三角区煤壁和顶板进行化学浆加固的技术方案,有效控制了顺槽超前区围岩变形和工作面回风侧端头三角区煤壁片帮,保证了工作面端头区顶板安全。     

Energy efficiency of fibre reinforced soil formation at small element scale: Laboratory and numerical investigation

This paper explores the aspects related to the energy consumption for the compaction of unreinforced and fibre reinforced samples fabricated in the laboratory. It is well known that, for a fixed soil density, the addition of fibres invariably results in an increased resistance to compaction. However, similar peak strength properties of a dense unreinforced sample can be obtained using looser granular soil matrices mixed with small quantities of fibres. Based on both experimental and discrete element modelling (DEM) procedures, this paper demonstrates that less compaction energy is required for building loose fibre reinforced sand samples than for denser unreinforced sand samples while both samples show similar peak strength properties. Beyond corroborating the macro-scale experimental observations, the result of the DEM analyses provides an insight into the local micro-scale mechanisms governing the fibre-grain interaction. These assessments focus on the evolution of the void ratio distribution, re-arrangement of soil particles, mobilisation of stresses in the fibres, and the evolution of the fibre orientation distribution during the stages of compaction.     

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。     

智能变电站设备管控大数据分析系统研究

结合数据挖掘、大数据技术在电力系统中的应用现状，分析和设计了一种智能变电站设备管控大数据分析系统。对变电站设备运行状态的大数据特征及基本框架进行了分析，并重点阐述了变电站设备运行状态大数据分析系统的数据集成与预处理、数据存储与处理、设备状态评估模型以及数据可视化展示。实验结果表明，所设计的大数据分析系统能够大幅度提升变电站设备数据分析智能化水平，为变电站管控稳定运行提供更加有效和实用性的技术支撑。     

人工监督神经网络在煤层裂缝发育区识别中的应用

针对在地震属性应用中难以精细识别煤层裂缝发育区边界的难题,开展了基于人工监督神经网络技术的煤层裂缝发育区应用研究。从地震资料中获取倾角导向体以提取高质量的地震属性,以多属性为指导进行人工拾取样点,并基于多层感知器进行神经网络机器训练学习,建立裂缝的最优属性集,拓展整体数据后获得裂缝概率体,从而识别划分出煤层裂缝发育区。该技术在山西阳泉新元矿区进行了应用,煤层裂缝发育区的识别结果明显优于属性直接识别,但勘探区内暂无钻井资料,预测效果还有待进一步验证。