Co-management and the co-production of knowledge: Learning to adapt in Canada's Arctic

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.     

循环神经网络在雷达临近预报中的应用

该文将循环神经网络（recurrent neural network,RNN）应用于雷达临近预报。使用预测循环神经网络（predictive RNN）架构,利用雷达历史组合反射率因子建模,给出雷达组合反射率因子未来1 h的预报结果。预测循环神经网络的核心是在长短时记忆单元（long short-term memory,LSTM）中增加时空记忆模块,能够提取雷达回波不同尺度的空间特征,配合循环神经网络架构,可以有效解决反射率因子预测问题。北京大兴雷达和广州雷达长时间序列的独立检验结果和2个强对流天气个例检验结果表明:该方法和传统的基于交叉相关法的1 h雷达外推临近预报相比,在20 dBZ和30 dBZ检验项目内,临界成功指数（CSI）可以提升0.15~0.30,命中率（POD）提高0.15~0.25,虚警率（FAR）降低0.15~0.20,该方法对反射率因子强度变化有一定预报能力。     

Carbon pricing in practice: a review of existing emissions trading systems

This article analyses the implementation of emissions trading systems (ETSs) in eight jurisdictions: the EU, Switzerland, the Regional Greenhouse Gas Initiative (RGGI) and California in the US, Québec in Canada, New Zealand, the Republic of Korea and pilot schemes in China. The article clarifies what is working, what isn’t and why, when it comes to the practice of implementing an ETS. The eight ETSs are evaluated against five main criteria: environmental effectiveness, economic efficiency, market management, revenue management and stakeholder engagement. Within each of these categories, ETS attributes ? including abatement cost, stringency of the cap, improved allocation practices over time and the trajectory of price stability ? are assessed for each system. Institutional learning, administrative prudence, appropriate carbon revenue management and stakeholder engagement are identified as key ingredients for successful ETS regimes. Recent implementation of ETSs in regions including California, Québec and South Korea indicates significant institutional learning from prior systems, especially the EU ETS, with these regions implementing more robust administrative and regulatory structures suitable for handling unique national and sub-national opportunities and constraints. The analysis also shows that there is potential for a ‘double dividend’ in emissions reductions even with a modest carbon price, provided the cap tightens over time and a portion of the auctioned revenues are reinvested in other emissions-reduction activities. Knowledge gaps exist in understanding the interaction of pricing instruments with other climate policy instruments and how governments manage these policies to achieve optimum emissions reductions with lower administrative costs.

Key policy insights

• Countries are learning from each other on ETS implementation.

• Administrative and regulatory structures of ETS jurisdictions appear to evolve and become more robust in every ETS analysed.

• A ‘double dividend’ for emissions reductions may also exist in cases where mitigation occurs as a result of the ETS policy and when auction revenues are reinvested in other emissions-reduction activities.

     

中等传输距离下的无线功率传输链路第1部分:实现工作状态最优化

中程无线功率传输（WPT）可以采用几种不同的方式实现,如通过电感或电容耦合、谐振或非谐振网络实现.本文主要研究了通过感应耦合谐振器实现的WPT链路,而且只着重研究了利用2个谐振器的链路（直接链路）并工作在主谐振频率下的情况.研究结果表明,当工作在主谐振频率下,可以根据网络参数来对传输效率或负载功率进行优化.     

基于Faster R-CNN的食品图像检索和分类

面向食品领域的图像检索和分类等方面的研究成为多媒体分析和应用领域越来越受关注的研究课题之一.当前的主要研究方法基于全图提取视觉特征,但由于食品图像背景噪音的存在使得提取的视觉特征不够鲁棒,进而影响食品图像检索和分类的性能.为此,本文提出了一种基于Faster R-CNN网络的食品图像检索和分类方法.首先通过Faster R-CNN检测图像中的候选食品区域,然后通过卷积神经网络（CNN）方法提取候选区域的视觉特征,避免了噪音的干扰使得提取的视觉特征更具有判别力.此外,选取来自视觉基因库中标注好的食品图像集微调Faster R-CNN网络,以保证Faster R-CNN食品区域检测的准确度.在包括233类菜品和49 168张食品图像的Dish-233数据集上进行实验.全面的实验评估表明：基于Faster R-CNN食品区域检测的视觉特征提取方法可以有效地提高食品图像检索和分类的性能.     

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

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.     

高分辨率遥感影像的深度学习变化检测方法

为提升高分辨率遥感影像的变化检测精度,提出一种利用深度学习的变化检测方法。在预处理的基础上,利用顾及邻域信息的改进变化矢量分析算法和灰度共生矩阵算法获取影像间光谱和纹理变化,并通过设置自适应采样区间提取最可能的变化和未变化区域样本。构建并训练包含标签层的高斯伯努利深度限制玻尔兹曼机模型,以提取变化和未变化区域深层特征,从而有效辨别变化区域。通过WorldView-3与Pléiades-1影像的试验表明本文方法在变化检测精度方面优于对比方法。     

面向高光谱图像分类的半监督空谱判别分析

为充分利用高光谱图像蕴藏的空间信息提升分类精度,提出了面向高光谱图像分类的半监督空谱判别分析(S3 DA)算法。考虑高光谱图像数据集的空间一致性,首先利用少量标记样本定义类内散度矩阵,保存数据集同类像元的光谱近邻结构;再利用无标记样本定义空间近邻像元散度矩阵,揭示像元间的空间近邻结构和地物的空间分布结构信息。S3 DA既保持数据集在光谱域的可分性,又保存了无标记样本蕴藏的空间域近邻结构,增强了同类像元和空间近邻像元在投影子空间的聚集性,从而提升分类性能。在PaviaU和Indian Pines数据集的试验表明,总体分类精度分别达到81.50%和71.77%。与传统的光谱方法比较,该算法能有效提升高光谱图像数据集的地物分类精度。     

案例推理的制图综合应用背景与方法

从挖掘和利用经验数据的角度出发,提出了基于案例推理的智能化制图综合方法。首先探讨了基于算法和模型的自动综合实现方式所面临的瓶颈,并提出了采用制图综合案例推理辅助制图综合的思想;然后设计了制图综合专家案例的三元表示方法,以及获取、使用和管理流程;最后对实现该基于案例推理的智能化制图综合方法的五个关键性步骤进行了分析,并深入阐述该方法的原理和可行性,提出了基于案例推理的智能综合辅助系统架构。     

浅谈如何提高测绘工程专业学生对遥感原理与方法课程学习的兴趣

结合笔者在中南大学为期9年的遥感原理与方法课程教学实践,分析了中南大学测绘工程专业学生在该课程教学方面的学习态度和课程认识方面的现状,提出在教学过程初期加强学生对遥感历史文化及名人传记的学习,在课程教学期间自己查询专业应用前景及专业最新动态新闻,在上课期间主动向老师提问3种方法,以考核方式计入学生平时成绩,以期增强学生对该课程的学习兴趣和主动学习能力及独立思考能力,为后续遥感专业相关课程的学习奠定一定的基础。在近几年的教学实践过程中,发现该方法有效地提高了学生对该专业课的学习兴趣,学习效果超越往届学生,实践结果充分证明了该方法的有效性。