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.

该项目的目标是研究和开发一种基于基因库的资源分配方法，用于通过通信网络进行有效且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测试台是一个事故预防示范项目。该方法是利用基因库对各种交通环境下的车辆运动异常进行分类和检测，并提供最佳的实时采样率适应和紧急干预。利用人工免疫系统优化基因库，使其可以实时使用并适应环境，实现最优解决方案。在智力价值方面，本研究追求多层系统动力学的垂直整合（从网络到分布式代理，再到用户），其目标是通过考虑带宽约束、混合结构和不同的控制拓扑，为分布式传感器、执行器和控制器代理提供自适应实时最优资源分配，从而提供高效可靠的系统运行通过通信网络。就更广泛的影响而言，该项目是一个理论分析与工业应用的协同整合。联合研究、双向教育计划和商业化构成了该项目的三层结构。外展和传播工作包括学生的工业实习、暑期高中生体验、机构间访问和公共研讨会。正在积极招募代表性不足的群体。这项研究有可能将美国每年与车辆事故相关的成本减少超过 2500 亿美元，同时使老年驾驶员能够保持行动能力和生活质量。