报告题目：AI Enabled Drone Detection and Negation
报 告 人：Houbing Song(宋厚冰)，Embry-Riddle Aeronautical University (安柏瑞德航空航天大学)
报告摘要：Driven by mega trends including growth in global transportation demand, climate change, sustainability and energy use, and technology convergence, assured autonomy for aviation transformation is emerging. Ever-increasing levels of automation and autonomy are transforming aviation, and this trend will continue to accelerate. However, reports of unmanned aircraft systems (UAS) sightings from pilots, citizens and law enforcement have increased dramatically over the past several years. There is an urgent need for safe integration of UAS into the National Air Space (NAS), which requires research in several areas, including communications, human-machine interfaces, sense-and-avoid, and separation assurance. Artificial intelligence (AI) can provide new ways of approaching problems. In this talk, I will present our recent research findings on the use of AI to help address the challenge of UAS Detection and Negation.
报告人简介：Houbing Song received the Ph.D. degree in electrical engineering from the University of Virginia, Charlottesville, VA, in August 2012, and the M.S. degree in civil engineering from the University of Texas, El Paso, TX, in December 2006.
In August 2017, he joined the Department of Electrical, Computer, Software, and Systems Engineering, Embry-Riddle Aeronautical University (called "the Harvard of the Sky", the world's oldest, largest, and most prestigious university specializing in aviation and aerospace, and the only fully accredited, aviation-oriented university in the world), Daytona Beach, FL, where he is currently an Assistant Professor and the Director of the Security and Optimization for Networked Globe Laboratory (SONG Lab, www.SONGLab.us). He served on the faculty of West Virginia University from August 2012 to August 2017. In 2007 he was an Engineering Research Associate with the Texas A&M Transportation Institute. He serves as an Associate Technical Editor for IEEE Communications Magazine. He is the editor of four books, including Smart Cities: Foundations, Principles and Applications, Hoboken, NJ: Wiley, 2017, Security and Privacy in Cyber-Physical Systems: Foundations, Principles and Applications, Chichester, UK: Wiley-IEEE Press, 2017, Cyber-Physical Systems: Foundations, Principles and Applications, Boston, MA: Academic Press, 2016, and Industrial Internet of Things: Cybermanufacturing Systems, Cham, Switzerland: Springer, 2016. He is the author of more than 200 articles. His research interests include cyber-physical systems, cybersecurity and privacy, internet of things, edge computing, big data analytics, unmanned aircraft systems, connected vehicle, smart and connected health, and wireless communications and networking. Dr. Song is a senior member of both IEEE and ACM.
报告题目：基于博弈论的系统资源分配策略
报 告 人：范训礼 教授，西北大学
报告摘要：Existing static grid resource scheduling algorithms, which are limited to minimizing the makespan, cannot meet the needs of resource scheduling required by cloud computing. Current cloud infrastructure solutions provide operational support at the level of resource infrastructure only. When hardware resources form the virtual resource pool, virtual machines are deployed for use transparently. Considering the competing characteristics of multi-tenant environments in cloud computing, this paper proposes a cloud resource allocation model based on an imperfect information Stackelberg game (CSAM-IISG) using a hidden Markov model (HMM) in a cloud computing environment. CSAM-IISG was shown to increase the profit of both the resource supplier and the applicant. Firstly, we used the HMM to predict the service provider’s current bid using the historical resources based on demand. Through predicting the bid dynamically, an imperfect information Stackelberg game (IISG) was established. The IISG motivates service providers to choose the optimal bidding strategy according to the overall utility, achieving maximum profits. Based on the unit prices of different types of resources, a resource allocation model is proposed to guarantee optimal gains for the infrastructure supplier. The proposed resource allocation model can support synchronous allocation for both multi-service providers and various resources. The simulation results demonstrated that the predicted price was close to the actual transaction price, which was lower than the actual value in the game model. The proposed model was shown to increase the profits of service providers and infrastructure suppliers simultaneously.
报告人简介：范训礼，教授/博士，主要从事网络信息安全、网络行为控制、无线传感网、和知识图谱应用研究。主持国家重点研发计划现代服务业重点专项--民族民间文化资源传承与开发利用技术集成与应用示范、国家863计划信息安全项目、863计划信息安全应急计划项目、国家信息安全应用示范工程、国家自然科学基金、国家科技重大专项（“核高基”）、教育部留学回国人员项目、中国博士后研究基金项目一等资助项目、江苏省自然基金重点项目、江苏省科技攻关项目、陕西省重点产业创新链项目、陕西省自然科学基金以及西安市科技攻关项目等20余项；获江苏省科技进步一等奖1项；在国内外学术期刊和国际会议发表论文140余篇，其中SCI一区、二区、三区等索引30篇，EI索引30余篇。主持参与教育部和陕西省双语课程《操作系统》、陕西省MOOC课程《数据结构》；指导学生参加全国大学生电子竞赛获得国家二等奖1次和陕西省一等奖2次；指导学生参加全国高校无线网大赛获西北赛区一等奖和二等奖。
时间：2018年12月20日（周四）上午8:30
地点：成栋楼1014
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