《国际汉语能力标准》中值得商榷的地方——兼与《欧洲语言共同参考框架:学习、教学、评估》比较     

徐海冰  程燕 《广东海洋大学学报》2011,31(5):91-95

通过对《国际汉语能力标准》的考察,同时与《欧洲语言共同参考框架:学习、教学、评估》比较,发现《国际汉语能力标准》存在三个方面的问题:结构不合理、内容有缺失以及评级等级标准不科学,以此研究结果为《国际汉语能力标准》的修订提供参考。  相似文献   

A Bayesian network approach for predicting seismic liquefaction based on interpretive structural modeling     

Ji-Lei Hu  Xiao-Wei Tang 《Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards》2015,9(3):200-217

The Bayesian network (BN) is a type of graphical network based on probabilistic inference that has been gradually applied to assessment of seismic liquefaction potential. However, how to construct a robust BN remains underexplored in this field. This paper aims to present an efficient hybrid approach combining domain knowledge and data to construct a BN that facilitates the integration of multiple factors and the quantification of uncertainties within a network model to assess seismic liquefaction. Initially, only using given domain knowledge, a naive network model can be constructed using interpretive structural modeling. Thereafter, some effective information about the naive model is provided to construct a robust model using structural learning of BN from historic data. Finally, the returning predictive results and the predictive results are compared to other methods including non-probabilistic and probabilistic models for seismic liquefaction using the metrics of the overall accuracy, the area under the curve of receiver operating characteristic, prediction, recall and F₁ score. The methodology proposed in this paper achieved better performance, and we discussed the power and value of the proposed approach at the end of this paper, which suggest that BN is a good alternative tool for seismic liquefaction prediction.  相似文献   

基于半监督核模糊c-均值算法的北京一号小卫星多光谱图像分类   总被引：1，自引：0，他引：1  

刘小芳  何彬彬  李小文 《测绘学报》2011,40(3):301-306

针对遥感图像数据大多不服从高斯分布以及遥感图像分类存在非线性、模糊性和标记数据少等问题,提出基于半监督核模糊c-均值算法的多光谱遥感图像分类方法.首先,把半监督学习理论和核理论同时引入模糊c-均值算法,形成半监督核模糊c-均值算法.然后,用该算法与k-均值算法、最大似然算法、多类支持向量、半监督核支持向量、模糊c-均值...  相似文献   

The social and political complexities of learning in carbon capture and storage demonstration projects     

Nils Markusson  Atsushi Ishii 《Global Environmental Change》2011,21(2):293-302

Demonstration of a fully integrated power plant with carbon capture and storage (CCS) at scale has not yet been achieved, despite growing international political interest in the potential of the technology to contribute to climate change mitigation and calls from multiple constituents for more demonstration projects. Acknowledging the scale of learning that still must occur for the technology to advance towards deployment, multiple CCS demonstration projects of various scales are emerging globally. Current plans for learning and knowledge sharing associated with demonstration projects, however, seem to be limited and narrowly conceived, raising questions about whether the projects will deliver on the expectations raised. Through a comparison of the structure, framing and socio-political context of three very different CCS demonstration projects in different places and contexts, this paper explores the complexity of social learning associated with demonstration projects. Variety in expectations of the demonstration projects’ objectives, learning processes, information sharing mechanisms, public engagement initiatives, financing and collaborative partnerships are highlighted. The comparison shows that multiple factors including the process of building support for the project, the governance context and the framing of the project matter for the learning in demonstration projects. This analysis supports a broader conceptualization of learning than that currently found in CCS demonstration plans - a result with implications for both future research and practice.  相似文献   

Co-management and the co-production of knowledge: Learning to adapt in Canada's Arctic   总被引：3，自引：0，他引：3  

Derek Armitage  Fikret BerkesAaron Dale  Erik Kocho-SchellenbergEva Patton 《Global Environmental Change》2011,21(3):995-1004

Co-management institutional arrangements have an important role in creating conditions for social learning and adaptation in a rapidly changing Arctic environment, although how that works in practice has not been clearly articulated. This paper draws on three co-management cases from the Canadian Arctic to examine the role of knowledge co-production as an institutional trigger or mechanism to enable learning and adapting. Experience with knowledge co-production across the three cases is variable but outcomes illustrate how co-management actors are learning to learn through uncertainty and environmental change, or learning to be adaptive. Policy implications of this analysis are highlighted and include the importance of a long-term commitment to institution building, an enabling policy environment to sustain difficult social processes associated with knowledge co-production, and the value of diverse modes of communication, deliberation and social interaction.  相似文献   

基于深度学习的钨钼找矿靶区预测方法研究     

蔡惠慧  朱伟  李孜轩  刘园园  李龙斌  刘畅 《地球信息科学学报》2019,21(6):928-936

随着矿产勘查工作由浅部矿向深部隐伏矿、由易识别矿向难识别矿发展,找矿难度日益增大,地质专家越来越重视新理论、新方法、新技术的应用。深度学习作为人工智能的前沿领域/技术,对于实现矿产资源预测“智能化预测评价”具有得天独厚的优势。本文以陕西省镇安县西部钨钼矿集区单元素化探异常原始数据为基础,提出了基于深度学习的钨钼矿产评价方法。该方法以归一化地球化学数据作为模型训练数据,通过深度学习中深度自编码网络方法实现异常值提取进而识别重点成矿有利地段,实现矿产资源找矿远景区定性预测。研究结果表明,在对957条单元素化探异常原始数据分类且做好模型标签后,整个过程在计算机的“黑盒子”中自动完成学习和预测,相较于传统预测研究方法,本文方法具有自动化程度高和客观性强的特征。此外,本文利用已知矿点构建训练数据集,采用随机森林方法对预测区进行矿产资源找矿靶区预测圈定,为进一步缩小找矿靶区范围提供科学依据。  相似文献   

一种多层特征融合的道路场景实时分割方法     

周继苗  李必军  陈世增 《测绘通报》2020,(1):10-15

道路场景理解是自动驾驶领域中重要模块之一，它可以提供关于道路更丰富的信息，对于建立高精度地图和实时规划都具有重要作用。其中，语义分割可以为图像每个像素赋予类别信息，是自动驾驶场景理解中最常用的方法。但是，目前常用的语义分割算法在速度和精度上大都不能达到很好的平衡。本文在MobileNetV2的基础上，提出了一种多层次特征融合的方法，使得网络可以在实时运行的同时保证精度满足实际应用的需求，并在Cityscapes数据集上进行了试验验证和分析。  相似文献   

复杂卫星图像中的小目标船舶识别   总被引：1，自引：1，他引：0  

姚红革  王诚  喻钧  白小军  李蔚 《遥感学报》2020,24(2):116-125

船舶作为海上的重要目标,实现对船舶自动识别有重要的意义。针对卫星图像中云雾、海岸背景等复杂海情对船舶识别带来的干扰,以及小目标船舶高漏检率问题,本文提出一种多尺度深度学习模型训练策略,在此基础上构建了一种船舶识别的深度学习网络,该网络可分为多尺度训练、特征提取、生成目标建议区域、船舶分类这4个部分。首先,采用多尺度的训练策略,将多尺度的船舶样本送入网络中进行训练,这样在训练样本中加入了大量小目标船舶的样本,使网络充分提取到小目标船舶的特征;其次,通过卷积神经网络对目标船舶进行特征自适应提取;然后,目标区域建议网络可依据卷积神经网络提取到的特征,在图像中找到感兴趣目标区域,即框定船舶的位置;最后,通过多个全连接层的组合,将高维特征映射到一个4元组中,再运用分类函数输出每一类船舶的概率值,概率值最大的则为该船舶的类别。同时为解决云雾遮挡和海岸背景的干扰,采用了一种负样本增强学习的方法,在样本数据集中加入了大量只含有云雾和海岸背景的图片,进行负样本扩充,增强网络模型对云雾及海岸背景的特征学习能力,以此解决复杂海情的影响。实验结果表明,所提方法有效解决了复杂海情条件下的船舶识别难,以及小目标船舶识别难的问题,实现了复杂海情条件下的船舶识别。同时,与现有成熟的深度学习目标识别算法相比,本文算法的精确度和召回率分别提升了6.98%和18.17%,所训练的模型具有良好的泛化能力和鲁棒性。  相似文献   

基于EEMD和ARIMA的海温预测模型研究          下载免费PDF全文

张莹  谭艳春  彭发定  廖杏杰  余昱昕 《海洋学研究》2019,37(1):9-14

类型丰富、时空分辨率高的海洋探测数据,为信号分解和机器学习算法的应用提供了可能。本文针对如何建立有效的海温预测模型这一问题,使用高时空分辨率的海表温度(SST)融合产品,引入信号处理领域的集合经验模态分解(EEMD)和机器学习领域的自回归积分滑动平均模型(ARIMA)。首先利用最适于分解自然信号的EEMD方法,将海温数据分解成多个确定频率的序列;再利用ARIMA分别对各个频率的序列进行预测,最后将各个序列的预测结果进行组合。该方法在丰富数据的支撑下,比以往直接使用海温数据所建立的预测模型精度更高,为更好地进行海温预测提供了新方法。  相似文献   

基于机器学习的高精度高分辨率气象因子时空估计     

方颖  李连发 《地球信息科学学报》2019,21(6):799-813

气象变量常作为重要的影响因子出现在环境污染、疾病健康和农业等领域,而高分辨率的气象资料可作为众多研究的基础数据,对推进相关研究的发展意义重大。本文以中国大陆为研究区域,利用2015年824个气象站点的气温、相对湿度和风速3套数据,结合不同的解释变量组合,分别构建了各自的GAM和残差自编码器神经网络（简称残差网络）模型,以10倍交叉验证判断模型是否过拟合。研究结果表明：① GAM和残差网络方法都不存在过拟合问题,同GAM相比,残差网络显著提高了模型预测的精度（3个气象因素的交叉验证CV R²平均提高了0.21,CV RMSE平均降低了37%）,其中相对湿度模型的提升幅度最大（CV R²:0.85 vs. 0.52,CV RMSE:7.53% vs. 13.59%）;② 残差模型的结果较普通克里格插值结果和再分析资料更接近站点观测数据,表明残差网络可作为高分辨率气象数据研制的可靠方法。此外,研究还发现在相对湿度模型中加入臭氧浓度和气温、在风速模型中加入GLDAS风速再分析资料,可提升模型的性能。  相似文献