一种基于FCOS神经网络的小建筑物目标检测方法北大核心

提出了一种基于FCOS神经网络的小建筑物目标检测算法,针对FCOS模型在特征提取阶段提取到的小建筑物目标特征较少问题,引入多尺度检测和可变形卷积方式,加强网络对小建筑物目标的特征提取能力,并通过改进后的SGE注意力机制降低特征图中的干扰噪声权重。改进后的网络可以提取到更多的小建筑物目标特征,对环境干扰噪声的鲁棒性更强。在自己搭建的数据集上进行了实验测试,结果表明,在相同环境下网络改进后建筑物的整体检测准确率提升了1.7%,其中对小建筑物目标提升了3.6%,减少了小建筑物目标漏检、误检的问题。     

控制爆破法处理卡钻事故

介绍了在包头市某工程实施管道穿越黄河施工中,采用爆破法处理卡钻的经验。针对深水环境条件及钻杆内径小不宜采用集团装药的条件,确定采用"小直径爆破筒,钻杆内部装药"的爆破方案,阐述了爆破设计及施工注意事项。可供类似工程参考。     

Neural Style Transfer for image within images and conditional GANs for destylization

In this paper, the feature representation of an image by CNN is used to hide the secret image into the cover image. The style of the cover image hides the content of the secret image and produce a stego image using Neural Style Transfer (NST) algorithm, which resembles the cover image and also contains the semantic content of secret image. The main technical contributions are to hide the content of the secret image in the in-between hidden layered style features of the cover image, which is the first of its kind in the present state-of-art-technique. Also, to recover the secret image from the stego image, destylization is done with the help of conditional generative adversarial networks (GANs) using Residual in Residual Dense Blocks (RRDBs). Further, stego images from different layer combinations of content and style features are obtained and evaluated. Evaluation is based on the visual similarity and quality loss between the cover-stego pair and the secret-reconstructed secret pair of images. From the experiments, it has been observed that the proposed algorithm has 43.95 dB Peak Signal-to-Noise Ratio (PSNR)), .995 Structural Similarity Index (SSIM), and .993 Visual Information Fidelity (VIF) for the ImageNet dataset. The proposed algorithm is found to be more robust against StegExpose than the traditional methods.     

我国水-能源-粮食耦合关系及影响因素

为缓解我国水、能源和粮食资源紧张问题，促进资源可持续利用，构建水-能源-粮食系统，利用耦合协调度模型对我国的30个省（自治区、直辖市）进行测算,并利用空间杜宾模型分析主要影响因素。结果表明：2003—2017年，我国能源、粮食评价[JP]指数高于水资源评价指数，系统综合评价指数逐年递增；大部分省份耦合协调度处于初级协调水平且呈现逐年上升的态势，个别省份耦合协调度濒临失调；耦合协调度空间自相关性较强，虽有明显波动，但是呈现逐年加强的态势；影响耦合协调度的主要因素有从业人口数、固定资产投资额、人均生产总值、人口总数、[JP]文盲人口占比、工业污染排放、城镇化。     

High-roubustness keystroke recognition method based on acoustic spatial gradient

For the fluctuation of CFCC caused by environmental noise is the main reason for the low accuracy of keystroke detection,the spatial characteristics of adjacent between CFCC were studied,and the spatial gradient structure of CFCC based on points was established.On this basis,the effect of CFCC spatial gradient on keystroke content recognition and the selection of precise neighborhood points were studied on training and testing.Finally,a high-robustness keystroke recognition algorithm based on acoustic signals was constructed.Extensive experiments in different environments demonstrate that the proposed CFCC spatial gradient sound feature achieves great performance and the recognition accuracy is 96.15%.     

基于信息熵抑制的大数据特征隐匿性加密仿真

针对传统大数据特征加密方法中大数据精度较差、加密耗时较长的问题,提出基于信息熵抑制的大数据特征隐匿性加密方法。分析信息熵抑制相关理论,采用信息熵映射函数对大数据分类,将信息熵抑制过程转化为计算2个文件相似性的过程,并根据余弦相似度公式以及Hamming距离值计算数据相似度,完成数据消冗。在完成数据消冗的基础上,分析分组密码方法和ECC加密方法,结合两种方法得到大数据特征隐匿性加密处理计算公式,实现基于信息熵抑制的大数据特征隐匿性加密。实验研究结果表明,所提方法能够有效去除冗余数据,提升大数据精度,加快数据加密的处理速度,高效完成大数据加密过程。     

异质性环境规制对绿色全要素生产率的影响机制——基于能源消费结构的调节作用

为探讨能源消费结构调节下异质性环境规制对绿色全要素生产率的影响机制，论文利用SBM方向距离函数测得全国各省市“经济-资源-环境-社会”四位一体的绿色全要素生产率，并将环境规制工具分为命令控制型、经济激励型、治理投入型和公众参与型4类，构建了空间杜宾模型以及门槛效应模型，进而得出在能源消费结构调节下，异质性环境规制对绿色全要素生产率的直接作用、间接作用以及空间溢出效应。研究结果表明：1）经济激励型和公众参与型环境规制通过调节能源消费结构对当地和邻地的绿色全要素生产率产生影响；2）在门槛检验方面，能源消费结构对命令控制型、经济激励型和公众参与型环境规制影响绿色全要素生产率存在门槛效应，同时在能源消费结构调节下，命令控制型和经济激励型环境规制对其本身对绿色全要素生产率的间接作用存在门槛效应。建议政府在制定环境规制时要考虑到其与能源消费结构的交互作用，并且在不同能源消费水平地区采用适宜的规制方法和强度，以更好地发挥环境规制作用。     

中国省际绿色技术创新效率测度及影响因素研究

文章基于2011—2017年中国30个省份面板数据，运用super-SBM模型对绿色技术创新效率进行测算，并利用ML指数对绿色技术创新效率进行动态分析，最后通过空间滞后模型分析影响效率的关键因素。研究结果表明：中国绿色技术创新效率呈逐渐增长的趋势，但整体效率偏低，有较大进步空间；中国绿色技术创新效率存在明显区域差异，呈现由东部向西部递减的格局；绿色技术创新水平的提高是技术效率和技术进步共同贡献的结果；产业结构、区域开放程度和教育水平对绿色技术创新效率产生积极的正向影响，环境规制和研发投入强度对绿色技术创新效率影响不显著。     

Uncovering sentiment and retweet patterns of disaster-related tweets from a spatiotemporal perspective – A case study of Hurricane Harvey

Social media has been widely used for emergency communication both in disaster-affected areas and unaffected areas. Comparing emotional reaction and information propagation between on-site users and off-site users from a spatiotemporal perspective can help better comprehend collective human behavior during natural disasters. In this study, we investigate sentiment and retweet patterns of disaster-affected areas and disaster-unaffected areas at different stages of Hurricane Harvey. The results show that off-site tweets were more negative than on-site tweets, especially during the disaster. As for retweet patterns, indifferent-neutral and positive tweets spread broader than mixed-neutral and negative tweets. However, negative tweets spread faster than positive tweets, which reveals that social media users were more sensitive to negative information in disaster situations. With the development of the disaster, social media users were more sensitive to on-site positive messages than off-site negative posts. This data-driven study reveals the significant effect of sentiment expression on the publication and re-distribution of disaster-related messages. It generates implications for emergency communication and disaster management.