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: 0823960
• 负责人: Edwin Hou
• 金额: --
• 依托单位: New Jersey Institute of Technology
• 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 2012-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0823960&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.

该项目的目的是调查和开发一种基于基因库的资源分配方法，以实现在通信网络上的有效且有效的时间敏感的大规模多率系统集成。北卡罗来纳州汽车研究中心的驾驶员电子辅助测试受损是预防事故示范项目。该方法是使用基因库在各种交通环境中对车辆运动中的异常进行分类，并提供最佳的实时采样率适应和紧急干预。 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通过通信网络对分布式传感器，执行器和控制器代理进行自适应实时最佳资源分配。对于更广泛的影响，该项目是与工业应用的理论分析的协同整合。联合研究，双向教育计划和商业化是该项目的三层结构。宣传和传播工作包括针对学生的工业实习，夏季高中学生的经验，机构间访问和公开研讨会。积极追求代表性不足的群体的招募。这项研究有可能在美国将与车辆事故相关的成本降低到每年超过2500亿美元，同时使老年驾驶员能够保持流动性和生活质量。