Advanced prediction of tunnel boring machine performance based on big data

Predicting the performance of a tunneling boring machine is vitally important to avoid any possible accidents during tunneling boring.The prediction is not straightforward due to the uncertain geological conditions and the complex rock-machine interactions.Based on the big data obtained from the 72.1 km long tunnel in the Yin-Song Diversion Project in China,this study developed a machine learning model to predict the TBM performance in a real-time manner.The total thrust and the cutterhead torque during a stable period in a boring cycle was predicted in advance by using the machine-returned parameters in the rising period.A long short-term memory model was developed and its accuracy was evaluated.The results show that the variation in the total thrust and cutterhead torque with various geological conditions can be well reflected by the proposed model.This real-time predication shows superior performance than the classical theoretical model in which only a single value can be obtained based on the single measurement of the rock properties.To improve the accuracy of the model a filtering process was proposed.Results indicate that filtering the unnecessary parameters can enhance both the accuracy and the computational efficiency.Finally,the data deficiency was discussed by assuming a parameter was missing.It is found that the missing of a key parameter can significantly reduce the accuracy of the model,while the supplement of a parameter that highly-correlated with the missing one can improve the prediction.     

Landslide identification using machine learning

Landslide identification is critical for risk assessment and mitigation.This paper proposes a novel machinelearning and deep-learning method to identify natural-terrain landslides using integrated geodatabases.First,landslide-related data are compiled,including topographic data,geological data and rainfall-related data.Then,three integrated geodatabases are established;namely,Recent Landslide Database(Rec LD),Relict Landslide Database(Rel LD)and Joint Landslide Database(JLD).After that,five machine learning and deep learning algorithms,including logistic regression(LR),support vector machine(SVM),random forest(RF),boosting methods and convolutional neural network(CNN),are utilized and evaluated on each database.A case study in Lantau,Hong Kong,is conducted to demonstrate the application of the proposed method.From the results of the case study,CNN achieves an identification accuracy of 92.5%on Rec LD,and outperforms other algorithms due to its strengths in feature extraction and multi dimensional data processing.Boosting methods come second in terms of accuracy,followed by RF,LR and SVM.By using machine learning and deep learning techniques,the proposed landslide identification method shows outstanding robustness and great potential in tackling the landslide identification problem.     

基于人工智能的川东北三叠系杂卤石地震识别

作为钾肥的主要来源之一,杂卤石矿藏具有旺盛的需求。我国川东北地区三叠系海相地层中,杂卤石矿藏发育。但该地区杂卤石矿体埋藏较深、分布散乱,勘探难度极大。为降低开发成本,需要充分借助地震资料的优势,为钾盐储层的描述提供钻前支持。针对该地区杂卤石层地震反射特征复杂、常规波阻抗反演方法难以直接预测的问题,本文引入人工智能算法对地震资料进行数据挖掘。本方法首先通过模糊粗糙集属性约简,提取适合富钾地层表征的敏感地震属性;然后以极限学习机为数据挖掘工具,自主学习敏感地震属性和井中实测钾含量曲线的非线性映射关系,形成了基于极限学习机的杂卤石预测方法。应用结果证明:利用极限学习机人工智能方法成功地对川东北地区的杂卤石进行了预测,取得了预期的地质效果,为该地区利用地震数据探寻杂卤石提供了一条新的勘探思路。     

Climate-governance entrepreneurship,higher-order learning,and sustainable consumption: the case of the state of Oregon,United States

The ongoing devolution of climate policy-making to sub-national levels has prompted growing interest in policy entrepreneurship by individuals who are politically and technically creative and institutionally resourceful. This paper investigates the case of the materials-management programme in the Oregon Department of Environmental Quality which has emerged as a national and international leader by focusing on the role of household consumption in greenhouse gas (GHG) emissions. Two noteworthy innovations involve the development of a consumption-based GHG emissions inventory and introduction of policies aimed at facilitating construction of small homes (so-called Accessory Dwelling Units, ADU). The case traces over several decades the higher order learning processes within the group and their entrepreneurship toward affecting broader changes in emission accounting and climate policies in Oregon. The paper identifies the enabling factors for these innovations, and considers: how to create the conditions for learning, experimentation, and policy entrepreneurship; how to reproduce these conditions in different locales; and how to recognize and foster innovations that arise outside the established mainstream ‘climate community’. It also stresses the benefits of breaking down the barriers between science-based analysis and policy. The two questions frequently raised in the climate policy debate – how to bring researchers and practitioners together to develop efficacious policies; and how to replicate successful programmes and policies across different communities, jurisdictions, and locations – should be re-examined. It may be more appropriate to ask instead: How to create conditions for learning, experimentation, and policy entrepreneurship; and how to reproduce these conditions in different locales.

Key policy insights

• Using a consumption-based greenhouse gas emission inventory instead of a sector-based inventory radically changes climate policy priorities, shifting the emphasis from technological fixes to curbing household consumption.

• Policy innovations thrive in teams that combine technical and scientific competencies with: a commitment to addressing societal problems; interest in inquiry, experimentation, and learning; entrepreneurship; and strategic and political savvy.

• These qualities require breaking down artificial barriers between science and policy.

• Transformative policy ideas can originate within institutional nodes that operate outside of an established community of expertise and authority; and these should be identified and fostered.

     

基于深度学习的FY3D/MERSI和EOS/MODIS云检测模型研究

针对FY3D/MERSI和EOS/MODIS的云检测问题,提出了一种基于深度学习技术的全自动云检测算法,首次将深度学习引入到卫星影像云检测领域。本算法使用深度全卷积神经网络(Deep Convolutional Neural Networks)作为核心结构,基于EOS/MODIS基本云检测原理选择合适的通道作为特性向量参数,针对不同的场景进行分类和网络模型的训练,最终得到基于深度学习的云检测模型。经过EOS/MODIS数据和FY3D/MERSI数据的测试,云检测的精度达到98%以上,可以看出基于深度学习的云检测算法能够用于云检测,该算法具有效率高、精度高等特点,云检测效果理想。     

Transformational adaptation on the farm: Processes of change and persistence in transitions to ‘climate-smart’ regenerative agriculture

Regenerative agriculture, an alternative form of food and fiber production, concerns itself with enhancing and restoring resilient systems supported by functional ecosystem processes and healthy, organic soils capable of producing a full suite of ecosystem services, among them soil carbon sequestration and improved soil water retention. As such, climate change mitigation and adaptation are incidental to a larger enterprise that employs a systems approach to managing landscapes and communities. The transformative potential of regenerative agriculture has seen growing attention in the popular press, but few empirical studies have explored the processes by which farmers enter into, navigate, and, importantly, sustain the required paradigm shift in their approach to managing their properties, farm businesses, and personal lives. We draw on theories and insights associated with relational thinking to analyze the experiences of farmers in Australia who have undertaken and sustained transitions from conventional to regenerative agriculture. We present a conceptual framework of “zones of friction and traction” occurring in personal, practical, and political spheres of transformation that both challenge and facilitate the transition process. Our findings illustrate the ways in which deeply held values and emotions influence and interact with mental models, worldviews, and cultural norms as a result of regular monitoring; and how behavioral change is sustained through the establishment of self-amplifying positive feedbacks involving biophilic emotions, a sense of well-being, and an ever-expanding worldview. We conclude that transitioning to regenerative agriculture involves more than a suite of ‘climate-smart’ mitigation and adaptation practices supported by technical innovation, policy, education, and outreach. Rather, it involves subjective, nonmaterial factors associated with culture, values, ethics, identity, and emotion that operate at individual, household, and community scales and interact with regional, national and global processes. Findings have implications for strategies aimed at facilitating a large-scale transition to climate-smart regenerative agriculture.     

遥感影像超分辨率重建的字典学习类算法

近年来基于字典学习的超分辨率重建技术已成为图像处理领域的研究热点,相比基于重建的超分辨率方法,基于学习的方法充分利用了先验知识,在放大倍数较高时,仍可取得较好的效果,因此被公认为一种非常有前途的方法。本文对国内外已有的基于字典学习的超分辨率重建方法进行了系统研究,梳理了3种基于字典学习超分重建算法的基本原理及优缺点。此外,本文根据遥感影像的特点,使用同一数据源进行字典学习,利用不同字典学习算法分别生成高、低联合字典对,采用不同尺寸大小及缩放倍数的测试图像,进行超分辨率重建,对各种算法的重建性能、鲁棒性和复杂度进行综合分析,进一步研究了各种算法对遥感影像不同应用需求的适用性。     

学习分析视角下测绘地理信息专业教学资源库开发

针对专业教学资源库建设及应用现状,分析了项目建设在用户服务面向、信息资源组织及学习过程指导中存在的不足;尝试基于学习分析的数据采集、模型分析、可视化反馈等技术优势开发学习平台;实现了专业教学资源库的个性化施教功能,实现了解决自我差异分析、自我评估及导向分析及自我预测及监控分析等问题;以测绘地理信息专业中《地理信息系统基础》课程资源库开发为例,就学习分析在专业教学资源库中的平台框架设计、平台运行流程设计中的具体应用进行了实证说明。     

Guidelines for organizing a fisheries learning exchange

This short communication piece presents guidelines and challenges for organizing fisheries learning exchanges (FLEs). Non-governmental organizations, government agencies, and resource users use FLEs to share best practices and bridge knowledge gaps between small-scale fishing communities and stakeholder groups. Even though FLEs are widely used and have numerous cited benefits, there are challenges associated with planning and implementing FLEs. To overcome these challenges and maximize FLEs’ effectiveness, the authors describe guidelines for FLE organizers. The guidelines are based on the perspectives of over 20 FLE experts collected during 2013 through questionnaires, interviews, discussions, and surveys. The guidelines include steps that organizers should take before, during, and after a FLE. For instance, there was broad consensus that before a FLE, it is important to select a diverse group of participants, including both conservation advocates and critics, and to create an exchange agreement outlining the roles and responsibilities of participants. During a FLE, cultural activities and daily reflections by participants are valuable to the exchange process. After a FLE, activities that formalize the participants’ involvement in the FLE are important, such as welcome-home ceremonies and participation certificates. Follow-up support for FLE participants is perceived as an essential step in the FLE and should be included in the FLE's budget. Finally, challenges in organizing FLEs are explicitly described and potential solutions to overcome those challenges are provided. The authors researched and compiled these guidelines and challenges to inform and improve the increasingly widespread use of FLEs.     

The exemplar‐aided constructor of hyper‐rectangles learning algorithm for stream flow estimation

The exemplar‐aided constructor of hyper‐rectangles (EACH) model which simulates human intelligence by learning from experience and adjusting in time, proposed by Salzberb (1991), is presented and modified to strengthen its performance in variable stream flow extension. The modification is intended to resolve the contradiction between building hyper‐rectangles and predictive accuracy in which the number of hyper‐rectangles becomes too large if higher accuracy is required. To explore the feasibility of the modified EACH, a mathematical function is simulated by the model. It is then applied to extend the 10‐day stream flow records according to the nearby rainfall and/or stream flow gauges. The results show that the modified EACH achieves the goal of saving memory space and promoting predictive accuracy, and its performance is better than those of the original EACH and traditional methods. This research suggests that the modified EACH shows considerable promise in stream flow estimation. Copyright © 2000 John Wiley & Sons, Ltd.