|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | An evaluation framework for energy aware buildings using statistical model checking显示文摘Cyber-physical systems are to be found in numerous applications throughout society.The principal barrier to develop trustworthy cyber-physical systems is the lack of expressive modelling and specification formalisms supported by efficient tools and methodologies.To overcome this barrier,we extend in this paper the modelling formalism of the tool UPPAAL-SMC to stochastic hybrid automata,thus providing the expressive power required for modelling complex cyber-physical systems.The application of Statistical Model Checking provides a highly scalable technique for analyzing performance properties of this formalisms.A particular kind of cyber-physical systems are Smart Grids which together with Intelligent,Energy Aware Buildings will play a major role in achieving an energy efficient society of the future.In this paper we present a framework in UPPAAL-SMC for energy aware buildings allowing to evaluate the performance of proposed control strategies in terms of their induced comfort and energy profiles under varying environmental settings(e.g.weather,user behavior etc.).To demonstrate the intended use and usefulness of our framework,we present an application to the Hybrid Systems Verification Benchmark. | DAVID Alexandre DU DeHui LARSEN Kim G. MIKUCIONIS Marius SKOU Arne | 2012 | Science China(Information Sciences)2012，55，12： | 3 |
| 2 | Anomalies of total column CO and 03 associated with great earthquakes in recent years显示文摘 | Cui Yueju Du Jianguo Zhang Dehui | 2013 | Natural Hazards and Earth System Sci- ences2013，13，10： | 1 |
| 3 | A novel spatio-temporal trajectory data-driven development approach for autonomous vehicles显示文摘Nowadays,autonomous driving has been attracted widespread attention from academia and industry.As we all know,deep learning is effective and essential for the development of AI components of Autonomous Vehicles(AVs).However,it is challenging to adopt multi-source heterogenous data in deep learning.Therefore,we propose a novel data-driven approach for the delivery of high-quality Spatio-Temporal Trajectory Data(STTD)to AVs,which can be deployed to assist the development of AI components with deep learning.The novelty of our work is that the meta-model of STTD is constructed based on the domain knowledge of autonomous driving.Our approach,including collection,preprocessing,storage and modeling of STTD as well as the training of AI components,helps to process and utilize huge amount of STTD efficiently.To further demonstrate the usability of our approach,a case study of vehicle behavior prediction using Long Short-Term Memory(LSTM)networks is discussed.Experimental results show that our approach facilitates the training process of AI components with the STTD. | Menghan ZHANG Mingjun MA Jingying ZHANG Mingzhuo ZHANG Bo LIW Dehui DU | 2021 | Frontiers of Earth Science2021，15，3： | 0 |
| 4 | A proof-based method of hybrid systems development using differential invariants显示文摘 | Jie LIU Jing LIU Miaomiao ZHANG Haiying SUN Xiaohong CHEN Dehui DU Mingsong CHEN | 2018 | Frontiers of Computer Science2018，12，5： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员