多尺度特征融合深度学习建筑物的提取方法

提出一种基于多尺度特征融合的建筑物提取方法,结合新的网络DenseASPP-UNet,以实现影像多尺度特征的融合,进而高精度提取建筑物.通过Inria开源建筑物航空影像数据集进行验证,表明DenseASPP-UNet相比其他深度学习方法建筑物提取精度有很大的提升.     

软件模块故障倾向预测方法研究

研究了在区分故障严重程度下的软件模块故障倾向预测方法,将故障分为高严重程度和低严重程度两种类型,用统计分析和机器学习方法分析静态代码度量与故障倾向之间的关系。以公开和私有两种类型的失效数据集作为实验数据,分析发现,故障的严重程度影响预测性能,预测不同严重程度的故障需要选择不同的度量和分类模型,预测低严重程度故障的性能好于预测高严重程度故障的性能。     

发现学习在《GPS定位技术》教学中的应用研究

论述了发现式学习与教学的概念,提出了采用发现学习的方法进行《GPS定位技术》教学的可行性,分析了基于发现学习的教学设计和具体内容,指出了发现学习存在的不足。     

张量分类算法的遥感影像目标探测

提出了一种基于张量学习机的遥感影像目标探测方法。该方法基于张量数据模型和张量代数运算, 针对遥感影像数据多维或高维的特点, 将基于向量的监督法学习机扩展为基于张量的监督法学习机, 然后利用凸函数最优化理论和交互投影迭代法求得张量学习机的最优解。最后分别以高光谱遥感影像和高分辨率遥感影像为例, 使用张量学习机进行目标探测。实验表明, 与支持向量机等方法相比, 本文的方法在保持较高探测成功率的同时更好的抑制了虚警。     

“十三五”国家重点研发计划“蓝色粮仓科技创新”重点专项科技布局及评述

本文旨在介绍和评估国家重点研发计划“蓝色粮仓科技创新”重点专项在渔业领域科技创新方面的实施情况,并展望我国渔业发展的未来趋势。通过对重点专项的任务布局、项目经费概况、项目分布情况、承担与参与单位(人员)情况进行详细梳理和分析,概括和评述专项实施特点和项目成效。分析显示,该专项具有系统性布局、重点领域突破、产业链协同创新、产业环节差异性布局等特点。在种质创制、健康养殖、资源养护、友好捕捞、流通加工等五大产业需求上取得了系列重要成果,显著提高了我国渔业科技创新水平。基于“蓝色粮仓科技创新”重点专项的实施成效,我国渔业科技创新在“十四五”期间及以后将更加注重拓展水产养殖新空间,发展生态健康养殖模式和智慧渔业,提高养殖设施和装备水平,提升加工综合水平,加快冷链物流建设,高标准建设现代化海洋牧场等领域。这将有助于进一步优化优质蛋白食品产业结构,合理开发和综合利用淡水和海洋资源,推动我国渔业持续健康发展。     

"干中学"与产业集群核心能力的形成

本文构建了产业集群核心能力的分析框架,从理论上说明学习效应是产业集群发展的内生动力,产业集群的学习是一个“干中学”的过程.     

边境区位:一个基于企业集聚的理论框架和实证分析

本文整合了传统区位理论和新经济地理学理论有关边境区位的研究,构建了在开放经济条件下边境区企业集聚的分析框架,提出了一个包括要素成本、空间联系成本、交易成本、市场拓展、优惠政策等影响因素的集聚机理分析模型,运用该理论框架对美-墨边境区企业集聚进行了解释。     

中国古代牧业社会骨制品的初步考察:以新疆巴里坤石人子沟遗址为例

考古遗址出土骨制品的研究对于揭示古代社会的动物资源获取和利用、手工业生产、社会组织结构等问题具有重要意义.国内骨制品研究目前多集中在农业区域,这些研究为探讨新石器时代至青铜时代的动物使用及其与社会发展、早期国家形成的关系等问题作出了重要贡献.然而,针对牧业社会骨制品的考察十分缺乏.新疆哈密地区巴里坤草原分布有大量古代牧业文化遗址,石人子沟遗址(43°31′12.8″~43°34′28.9″N,93°13′44.8″~93°16′49.1″E)是其中一处青铜时代晚期至铁器时代早期的大型聚落.本文从动物考古学视角研究该遗址2006年至2011年发掘出土的426件骨制品.结果显示,石人子沟遗址骨制品的原料主要为以羊(Ovis aries/Capra hircus)、马(Equus caballus)为主的家养动物和以鹿(Cervidae)为主的野生动物,其中羊的比例最高(69.7%),鹿(13.8%)、马(2.3%)次之,这与中原地区青铜时代农业文化遗址的骨制品多以牛为原料的情况明显不同.羊在骨制品原料中占绝对多数且大量使用羊距骨制品的现象是对石人子沟遗址以牧业为主的生业经济方式的直接反映.石人子沟遗址不同类型骨制品的制作各具特点,但整体表现出"省时省力"的特点,即对使用部位细致打磨,对非使用部位仅做简单处理.与中原地区商周时期大型制骨作坊规范化、规模化、产业化的骨器生产不同,石人子沟遗址未见专门的制骨场所,骨制品生产的操作链条也并不完整.遗址的骨制品生产可能是以家庭为单元进行的,产品的专业化、精细化程度也相对较低.该研究填补了我国古代牧业文化遗址出土骨制品动物考古学专门研究的空白,为进一步探究我国古代不同区域、不同生业经济基础下的制骨手工业面貌提供了重要资料.     

Gully erosion spatial modelling: Role of machine learning algorithms in selection of the best controlling factors and modelling process

This investigation assessed the efficacy of 10 widely used machine learning algorithms (MLA) comprising the least absolute shrinkage and selection operator (LASSO), generalized linear model (GLM), stepwise generalized linear model (SGLM), elastic net (ENET), partial least square (PLS), ridge regression, support vector machine (SVM), classification and regression trees (CART), bagged CART, and random forest (RF) for gully erosion susceptibility mapping (GESM) in Iran. The location of 462 previously existing gully erosion sites were mapped through widespread field investigations, of which 70% (323) and 30% (139) of observations were arbitrarily divided for algorithm calibration and validation. Twelve controlling factors for gully erosion, namely, soil texture, annual mean rainfall, digital elevation model (DEM), drainage density, slope, lithology, topographic wetness index (TWI), distance from rivers, aspect, distance from roads, plan curvature, and profile curvature were ranked in terms of their importance using each MLA. The MLA were compared using a training dataset for gully erosion and statistical measures such as RMSE (root mean square error), MAE (mean absolute error), and R-squared. Based on the comparisons among MLA, the RF algorithm exhibited the minimum RMSE and MAE and the maximum value of R-squared, and was therefore selected as the best model. The variable importance evaluation using the RF model revealed that distance from rivers had the highest significance in influencing the occurrence of gully erosion whereas plan curvature had the least importance. According to the GESM generated using RF, most of the study area is predicted to have a low (53.72%) or moderate (29.65%) susceptibility to gully erosion, whereas only a small area is identified to have a high (12.56%) or very high (4.07%) susceptibility. The outcome generated by RF model is validated using the ROC (Receiver Operating Characteristics) curve approach, which returned an area under the curve (AUC) of 0.985, proving the excellent forecasting ability of the model. The GESM prepared using the RF algorithm can aid decision-makers in targeting remedial actions for minimizing the damage caused by gully erosion.     

Deep neural network application for 4D seismic inversion to changes in pressure and saturation: Optimizing the use of synthetic training datasets

In this work, we tackle the challenge of quantitative estimation of reservoir dynamic property variations during a period of production, directly from four-dimensional seismic data in the amplitude domain. We employ a deep neural network to invert four-dimensional seismic amplitude maps to the simultaneous changes in pressure, water and gas saturations. The method is applied to a real field data case, where, as is common in such applications, the data measured at the wells are insufficient for properly training deep neural networks, thus, the network is trained on synthetic data. Training on synthetic data offers much freedom in designing a training dataset, therefore, it is important to understand the impact of the data distribution on the inversion results. To define the best way to construct a synthetic training dataset, we perform a study on four different approaches to populating the training set making remarks on data sizes, network generality and the impact of physics-based constraints. Using the results of a reservoir simulation model to populate our training datasets, we demonstrate the benefits of restricting training samples to fluid flow consistent combinations in the dynamic reservoir property domain. With this the network learns the physical correlations present in the training set, incorporating this information into the inference process, which allows it to make inferences on properties to which the seismic data are most uncertain. Additionally, we demonstrate the importance of applying regularization techniques such as adding noise to the synthetic data for training and show a possibility of estimating uncertainties in the inversion results by training multiple networks.