Collaborative Research: GOALI: AIS gene library based real-time resource allocation on time-sensitive large-scale multi-rate systems

合作研究：GOALI：时间敏感的大规模多速率系统上基于AIS基因库的实时资源分配

• 批准号: 0823952
• 负责人: Mo-Yuen Chow
• 金额: $ 19.8万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0823952&HistoricalAwards=false
• 关键词: Collaborative; Research; GOALI; AIS; gene

The objective of this project is to investigate and develop a gene library-based resource allocation methodology for effective and efficient time-sensitive large-scale multi-rate system integration over communication networks. The Impaired Driver Electronic Assistance Testbed at the North Carolina Center for Automotive Research serves as an accident prevention demonstration project. The approach is to use the gene library to classify and detect abnormalities in vehicle movements in various traffic environments and to provide optimal real-time sampling rate adaptation and emergency intervention. An artificial immune system is used to optimize the gene library, which can then be used in real time and can adapt to its environment to realized optimal solutions.With respect to intellectual merit, this research pursues a vertical integration of multiple layers of systems dynamics (from networks, to distributed agents, and to users) with the goal to provide efficient and reliable system operation by considering bandwidth constraints, hybrid structures, and different control topologies for adaptive real-time optimal resource allocation for distributed sensor, actuator, and controller agents over communication networks.With respect to broader impacts, this project is a synergistic integration of theoretical analyses with industrial applications. Joint research, bidirectional education programs, and commercialization serve as a three-tiered structure for the project. Outreach and dissemination efforts include industrial internships for students, summer high school student experiences, inter-institutional visits, and public seminars. Recruitment of underrepresented groups is actively pursued. The research has the potential to reduce vehicle accident-related costs from more than $250 billion per annum in the United States while enabling older drivers to retain mobility and quality of life.

本研究的目的是研究和开发一种基于基因库的资源分配方法，用于在通信网络上有效和高效的时间敏感的大规模多速率系统集成。 北卡罗来纳州汽车研究中心的受损驾驶员电子辅助试验台是一个事故预防示范项目。 该方法是使用基因库来分类和检测在各种交通环境中的车辆运动的异常，并提供最佳的实时采样率适应和紧急干预。 本研究利用人工免疫系统对基因库进行优化，使基因库能够真实的实时使用，并能适应环境，实现最优解（从网络，到分布式代理，以及到用户），其目标是通过考虑带宽约束来提供有效和可靠的系统操作，混合结构和不同的控制拓扑结构，用于分布式传感器，执行器和控制器代理在通信网络上的自适应实时最优资源分配。就更广泛的影响而言，该项目是理论分析与工业应用的协同集成。 联合研究、双向教育项目和商业化是该项目的三层结构。 外联和传播工作包括学生的工业实习、高中生暑期体验、机构间访问和公共研讨会。 积极征聘任职人数不足的群体。 这项研究有可能将美国每年超过2500亿美元的车辆事故相关成本降低，同时使老年驾驶员能够保持流动性和生活质量。