主客观信息融合的港口综合能源系统评价指标体系     

何大春  谈健  苏宜强  解翔  宋天立  蔡超  陈曦 《上海海事大学学报》2020,41(3):110-116

为建立绿色港口，实现港口的节能环保，对港口综合能源系统(port integrated energy system，PIES)的评价方法进行研究，提出一种主客观信息融合的PIES评价指标体系，将主观评价与数理统计模型进行结合，兼顾评价过程中的主客观因素，并采用基于马氏距离的统计检验方法对评分矩阵进行修正，实现对PIES的全面评价。通过青岛港董家口港区的调研数据，验证该方法的有效性。  相似文献   

基于隐马尔科夫模型的VLCC目的港预测     

杨春  王一丹  徐晖  胡勤友  潘亚兰 《上海海事大学学报》2020,41(4):42-49

超大型油船(very large crude carrier，VLCC)目的港预测对海运原油流向预测以及货源地未来运力估计具有重要作用。针对VLCC的AIS目的港信息存在缺失、更新不及时、不准确等现象，提出一种基于隐马尔科夫模型的VLCC目的港预测方法。分析船舶AIS轨迹数据，得到油船历史停靠港口序列；根据VLCC轨迹提取习惯航路，以航路中的交叉点为依据设置观测线；利用船舶航行轨迹数据判断船舶是否经过观测线以及经过观测线的方向，对不同方向分别计算船舶在挂靠港间的转移概率矩阵和船舶挂靠港与观测线间的输出概率矩阵，建立VLCC目的港预测模型并进行预测。研究结果表明：在大多数情况下VLCC目的港预测的准确率可以达到70%以上；航线越固定、运行越规律的船舶，预测准确率越高；船舶越靠近目的港，预测越准确；重载状态下的船舶目的港预测更准确。  相似文献   

基于深度学习的SSD模型尾矿库自动提取     

闫凯  沈汀  陈正超  闫弘轩 《中国科学院大学学报》2020,37(3):360-367

针对华北地区尾矿库自动提取问题，将基于深度学习的SSD目标检测模型应用于遥感图像尾矿库提取。首先标记华北地区2 000个样本，随机挑选1 500个作为训练样本，剩余样本作为测试样本，验证模型的检测精度。分析卷积层对应感受野与图像中尾矿库尺寸关系，发现原始SSD模型漏检误检大型尾矿库。改进SSD模型结构，提出增加额外卷积层的策略，提高对大型尾矿库目标的检测精度。实验表明，在置信度阈值为0.3时，改进的SSD模型相比原始模型，检测精确率提高10.0%，召回率提高14.4%，提高了大型尾矿库检测精度。验证了基于深度学习的SSD目标检测模型自动提取尾矿库的可行性以及改进算法的有效性。  相似文献   

基于距离方程组的机载SAR图像立体定位方法     

潘志刚  王超  曹舸 《中国科学院大学学报》2020,37(6):798-804

提出一种基于距离方程组的机载SAR图像立体定位方法，该方法使用同一成像区域的3幅SAR图像进行无控制点定位，首先确定目标在3幅图像中对应的同名像点，解析该像点对应的雷达平台瞬时经纬度坐标、海拔高度以及目标到雷达平台的斜距值，在地心坐标系下建立3幅图像对应同名像点的距离方程组，对非线性方程组进行求解得到同名像点基于地心坐标系的坐标值，并通过坐标转换得到目标点的经纬度坐标及海拔高度，实现三维定位。对影响定位精度的系统误差因素进行仿真分析，利用实飞的机载SAR图像进行目标定位实验，结果验证了该方法的有效性。  相似文献   

基于正态云可拓理论的锚泊安全评价     

鲁峰  李伟  宁君  于仁海 《上海海事大学学报》2020,41(4):30-35

为提高锚泊安全评价精度，保障船舶锚泊安全，针对传统锚泊安全评价方法未考虑评价等级界限的不确定性和模糊性等问题，将正态云可拓理论应用于锚泊安全评价中。采用层次分析法和基于可拓学的简单关联函数确定权重的方法分别确定锚泊安全评价指标的主观和客观权重，通过经验因子将主观与客观权重进行结合。构建基于正态云可拓理论的锚泊安全评价模型，计算待评物元与标准可拓物元的关联度，通过信息熵理论确定锚泊安全等级。根据信息熵理论定义可信因子，使得评价结果的可信度易于定量计算。实例计算验证了本模型的有效性和优越性。  相似文献   

Ancient Mural Classification Method Based on Improved AlexNet Network     

Jianfang Cao  Hongyan Cui  Qi Zhang  Zibang Zhang 《文物保护研究》2020,65(7):411-423

ABSTRACT

As an important part of art and culture, ancient murals depict a variety of different artistic images, and these individual images have important research value. For research purposes, it is often important to first determine the type of objects represented in a painting. However, the mural painting environment makes datasets difficult to collect, and long-term exposure leads to underlying features that are not distinct, which makes this task challenging. This study proposes a convolutional neural network model based on the classic AlexNet network model and combines it with feature fusion to automatically classify ancient mural images. Due to the lack of large-scale mural datasets, the model first expands the dataset by applying image enhancement algorithms such as scaling, brightness conversion, noise addition, and flipping; then, it extracts the underlying features (such as fresco edges) shared by the first stage of a dual channel structure. Subsequently, a second-stage deep abstraction is conducted on the features extracted by the first stage using a two-channel network, each of which has a different structure. The obtained characteristics from both channels are merged, and a loss function is constructed to obtain the classification result. This approach improves the model's robustness and feature expression ability. The model achieves an accuracy of 84.24%, a recall rate of 84.15%, and an F1-measure of 84.13% when applied to a constructed mural image dataset. Compared with the AlexNet model and other improved convolutional neural network models, the proposed model improves each evaluation index by approximately 5%, verifying the rationality and effectiveness of the model for automatic mural image classification. The mural classification model proposed in this paper comprehensively considers the influences of network width and depth and can extract rich details from mural images from multiple local channels. An effective classification method could help researchers manage and protect mural images in an orderly fashion and quickly and effectively search for target images in a digital mural library based on a specified image category, aiding mural condition monitoring and restoration efforts as well as archaeological and art historical research.  相似文献   

Influence of Process Parameters on Chlorinated Biocide Decontamination by li-CO2 on Artificially Contaminated Model Materials     

Tiziana Lombardo  Marie Wörle  Vera Hubert  Erwin Hildbrand  Christina Hinterleitner  Ingo Mayer 《文物保护研究》2020,65(2):65-76

ABSTRACT

Previously, heavy use of biocides for the treatment of objects made of organic materials in museum collections (e.g. ethnographic, historical collections) was very common. Now suitable decontamination methods/technologies are being sought. A decontamination treatment by li-CO₂ was optimized that considered the specific requirements of museum objects. The treatment was tested on model materials artificially contaminated with biocide solutions containing dichlorodiphenyltrichloroethane (DDT), pentachlorophenol (PCP), lindane, and permethrin or cypermethrin. High decontamination was achieved for DDT, lindane, PCP, and permethrin on artificially biocide loaded wool and wood model materials. Optimal process parameter settings for li-CO₂ decontamination include a single 30?min cycle for woolen materials and three sequential 30?min cycles each for wood. These methods allow a reduction of at least 90% of all biocides for wool and between 70% and 85% for wood. Decontamination of the latter was more effective for less polar biocides as DDT and permethrin. Despite a significant improvement in decontamination for wood using co-solvents such as acetone, ethanol, and methyl tertiary-butyl ether (MTBE), their use is not recommended due to the increased risk of damage to the objects, in particular if a surface coating is present.  相似文献   

Digital Module 05: Diagnostic Measurement—The G‐DINA Framework https://ncme.elevate.commpartners.com     

Wenchao Ma  Jimmy de la Torre 《Educational Measurement》2019,38(2):114-115

In this ITEMS module, we introduce the generalized deterministic inputs, noisy “and” gate (G‐DINA) model, which is a general framework for specifying, estimating, and evaluating a wide variety of cognitive diagnosis models. The module contains a nontechnical introduction to diagnostic measurement, an introductory overview of the G‐DINA model, as well as common special cases, and a review of model‐data fit evaluation practices within this framework. We use the flexible GDINA R package, which is available for free within the R environment and provides a user‐friendly graphical interface in addition to the code‐driven layer. The digital module also contains videos of worked examples, solutions to data activity questions, curated resources, a glossary, and quizzes with diagnostic feedback.  相似文献   

Limitations on the human capital transferability of adult migrants in Spain: Incentive or barrier for a new investment in education?     

Jacobo Muñoz-Comet  María Miyar-Busto 《European Journal of Education》2018,53(4):586-599

Many empirical studies on immigrant integration document the benefits of an education acquired in the country of destination. In this article; we study how the degree of human capital transferability affects an immigrant's chances of studying in Spain. We used data from the Spanish Labour Force Survey (2008–2015) for a sample of adults aged 18–55 who had left the educational system. The main findings show that natives are the ethnic group with the highest likelihood of re-enrolling in education. Amongst immigrants, those with fewer limitations on human capital transferability also invest more. These results question the predictions of the Immigrant Human Capital Investment model which expect that immigrants whose human capital depreciates upon arrival and those who can obtain a higher return on a new investment in education are therefore more likely to study. Moreover, the effect of skill transferability differs depending on the type of obstacle and the kind of education. Immigrants with less linguistic and cultural familiarity with the host country are less likely to undertake non-formal education than formal education. However, when the transferability obstacles are related to administrative requirements and legal restrictions, the probability of investing in non-formal education is higher. We conclude that immigrants with low skill transferability are not only less well integrated in the labour market upon arrival, but are also less able to redress that disadvantage through a new investment in education.  相似文献   

地球椭球模型中太阳位置计算的改进     

李文  赵永超 《中国科学院大学学报》2019,36(3):363-375

准确高效的太阳位置计算在遥感辐射定标、太阳能获取等多个领域具有重要应用价值。针对传统太阳赤纬角算法对不同年份数据差异考虑不足的问题，采用数值拟合法提出适用于不同年份的改进公式。根据误差曲线所呈现的周期性，采用傅里叶展开法提出以4年为周期的赤纬角改进算法，并推导出地球椭球模型下的太阳高度角公式。蒙气差影响太阳位置观测与计算。针对传统蒙气差算法在低仰角下误差较大的问题，提出0°~30°仰角下蒙气差的改进公式。把改进算法与相应的传统算法进行误差对比，结果表明，改进算法在太阳赤纬角、太阳高度角、低仰角下蒙气差计算的误差均明显降低，计算过程简单高效，符合相关工程项目应用需求。  相似文献