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ECCS-EPSRC - ShiRAS: Towards Safe and Reliable Autonomy in Sensor Driven Systems

ECCS-EPSRC - ShiRAS：在传感器驱动系统中实现安全可靠的自治

基本信息

批准号：
1903466
负责人：
Nidhal Bouaynaya
金额：
$ 29.96万
依托单位：
Rowan University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-06-01 至 2023-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1903466&HistoricalAwards=false
关键词：
ECCS EPSRC ShiRAS Towards Safe

项目摘要

Modern sensors generate massive amounts of data. Algorithms that are data-driven, able to train themselves or "self-tune", have revolutionized the area of autonomous systems. However, capturing confidence from the integration of heterogeneous large-scale data remains a challenging task for these algorithms. Our work will develop pioneering approaches that will introduce safe and reliable autonomy at different levels in sensor-driven systems. The main focus is on machine learning methods with quantified uncertainty or confidence bounds for the provided solutions. This research will entail significant theoretical knowledge through formal development, analysis and evaluation of the proposed approaches, resulting in safe and reliable machine intelligence. Scalable, effective and robust algorithms will be available for one of the most critical challenges in computational intelligence. Understanding and assessing the uncertainty of modern machine learning models has critical consequences, especially when the output of such models is fed into higher-level decision making processes. These include autonomous drones and vehicles, diagnosis in the medical domain and surveillance. Case studies and applications of this research include industry and government partners in the areas of detection of weapon and contraband (in collaboration with the Transportation Security Laboratory at the US Department of Homeland Security), rotorcraft safety (US Federal Aviation Administration), intelligent transportation systems (UK Highways England, Valerann Ltd UK, and TransDAC, US), and smart cities and surveillance (QinetiQ Ltd UK, Thales Ltd). Open-source software libraries, based on state-of-the-art deep learning frameworks, e.g., TensorFlow and PyTorch, will be built for the rapid dissemination of the developed core computing techniques. The proposed effort also includes integrating the research into the undergraduate and graduate curriculums, developing outreach packages which deliver hands-on experiences covering the fields of autonomous monitoring and the issues facing future intelligent transportation systems and the cities as a whole.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

现代传感器产生大量的数据。数据驱动的、能够自我训练或“自我调整”的算法，已经彻底改变了自主系统领域。然而，从异构大规模数据的集成中获取信心对于这些算法来说仍然是一项具有挑战性的任务。我们的工作将开发出开创性的方法，在传感器驱动的系统中引入不同级别的安全可靠的自主性。主要关注的是机器学习方法，为提供的解决方案提供量化的不确定性或置信范围。这项研究将需要通过正式的开发、分析和评估所提出的方法来获得重要的理论知识，从而产生安全可靠的机器智能。可扩展、有效和健壮的算法将可用于计算智能中最关键的挑战之一。理解和评估现代机器学习模型的不确定性具有至关重要的意义，特别是当这些模型的输出被输入到更高级别的决策过程中时。其中包括自主无人机和车辆、医疗领域的诊断和监控。该研究的案例研究和应用包括武器和违禁品探测领域的行业和政府合作伙伴（与美国国土安全部运输安全实验室合作），旋旋机安全（美国联邦航空管理局），智能交通系统（英国英国高速公路公司，英国Valerann有限公司和美国TransDAC），以及智能城市和监控（QinetiQ有限公司英国，泰雷兹有限公司）。开源软件库，基于最先进的深度学习框架，如TensorFlow和PyTorch，将被建立为快速传播开发的核心计算技术。拟议的努力还包括将研究整合到本科和研究生课程中，开发外展包，提供涵盖自主监控领域的实践经验，以及未来智能交通系统和整个城市面临的问题。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（16）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Bayes-SAR Net: Robust SAR Image Classification with Uncertainty Estimation Using Bayesian Convolutional Neural Network

DOI：
10.1109/radar42522.2020.9114737
发表时间：
2020-04
期刊：
2020 IEEE International Radar Conference (RADAR)
影响因子：
0
作者：
Dimah Dera;G. Rasool;N. Bouaynaya;Adam Eichen;Stephen Shanko;J. Cammerata;S. Arnold
通讯作者：
Dimah Dera;G. Rasool;N. Bouaynaya;Adam Eichen;Stephen Shanko;J. Cammerata;S. Arnold

Self-Compression in Bayesian Neural Networks

DOI：
10.1109/mlsp49062.2020.9231550
发表时间：
2020-09
期刊：
2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP)
影响因子：
0
作者：
Giuseppina Carannante;Dimah Dera;G. Rasool;N. Bouaynaya
通讯作者：
Giuseppina Carannante;Dimah Dera;G. Rasool;N. Bouaynaya

A Weighted Variance Approach for Uncertainty Quantification in High Quality Steel Rolling