Design of adaptive spatiotemporal information and control systems

自适应时空信息与控制系统设计

• 批准号: 9227-2012
• 负责人: Zaremba, Marek
• 金额: $ 1.31万
• 依托单位: Québec en Outaouais
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2012
• 资助国家: 加拿大
• 起止时间: 2012-01-01 至 2013-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=509323
• 关键词: Design; adaptive; spatiotemporal; information; control

Steep increases in the data gathering, processing and networking capabilities of today's remote sensing systems have put remote sensing firmly on the path to become a pervasive technology, affecting society in a variety of ways. What is now critically needed are powerful object detection and scene interpretation methods amenable to automated processing and interpretation of this wealth of information. This project builds on the synergy of learning control and pattern recognition/image processing to propose innovative techniques for modeling, monitoring, and control of complex, dynamic spatiotemporal systems as encountered mainly in environmental applications. The overall objective is to develop an integrated design methodology that addresses issues relating to the adaptive processing of large quantities of multi-dimensional, heterogeneous sensor data. More specific objectives include research on theoretical and methodological foundations for the design and optimization of biologically-inspired attentive vision algorithms for spatiotemporal saliency in heterogeneous, sparse remote sensing imagery, and the development of novel iterative learning methods to solve ill-posed object detection and classification problems. The methodological approach has been defined in the context of the scarcity of ground truth reference information, which can be difficult and costly to acquire. It involves the development of hybrid technologies integrating kernel-based methods and pertinent machine learning paradigms (deterministic learning and iterative learning) for time-variable, uncertain system monitoring and control applications. This interdisciplinary project will help develop research personnel at all levels, with its training program significantly enhanced by the opportunities arising from the collaboration with industrial partners. Apart from the contribution to new scientific knowledge, the results of this research will directly benefit Canadian society, helping to meet the need for sophisticated information technologies to efficiently monitor and manage natural resources in Canada's vast territory.

当今遥感系统的数据收集，处理和网络功能的大幅度增加，使遥感牢固地传感成为一种普遍的技术，以多种方式影响社会。现在，至关重要的是强大的对象检测和场景解释方法，可以自动处理和解释这些信息。该项目建立在学习控制和模式识别/图像处理的协同基础上，以提出用于建模，监视和控制复杂，动态时空系统的创新技术，主要是在环境应用中遇到的。总体目标是开发一种集成的设计方法，该方法解决了与大量多维，异质传感器数据的适应性处理有关的问题。更具体的目标包括研究理论和方法论基础，用于设计和优化生物学启发的专注视力算法，用于异质，稀疏的遥感图像中时空显着性，以及开发新颖的迭代学习方法，以使秘密对象检测和分类问题进行不良的迭代学习方法。方法论方法是在缺乏地面真理参考信息的背景下定义的，这可能是困难且昂贵的。它涉及整合基于内核方法的混合技术和相关的机器学习范例（确定性学习和迭代学习），以进行时间变化，不确定的系统监控和控制应用程序。这个跨学科的项目将有助于在各个层面上发展研究人员，其培训计划大大提高了与工业伙伴的合作机会。除了对新科学知识的贡献外，这项研究的结果还将直接使加拿大社会受益，有助于满足对复杂信息技术的需求，以有效地监视和管理加拿大广阔领土的自然资源。