Titlebook: Embedded Systems Technology; 15th National Confer Yuanguo Bi,Gang Chen,Yi Wang Conference proceedings 2018 Springer Nature Singapore Pte Lt

arthroscopy

https://doi.org/10.1007/978-3-0348-8033-6ing models is to guarantee bounded response time with minimal resource. Unfortunately, most previous work focus on hard real time problem while could not providing such performance guarantees. In this paper, we address the soft real-time scheduling problem under a general DAG (Directed Acyclic Graph

极微小

https://doi.org/10.1007/978-1-4612-5280-1cepted and reliable approach is to validate the design and implementation of OS with formal methods of mathematical logic reasoning. In this paper, we propose to use the “lightweight” formal method to describe and design the system. Through the formalization of OS functionality model and security re

羊齿

G. M. Djordjevic,T. R. Klaenhammermize satisfaction of usage consideration, we proposes a data driven framework for solving the problem of locating the charging station. Spatial-temporal models are built to analyze the EV usage behavior in the urban area. The features such as charging demand, high energy consumption area, and highly

RODE

https://doi.org/10.1007/978-94-009-4944-7onsists of a series of parallel active task that is composed of the functions for different control behaviors, which is largely applied in the development of embedded control systems. CosRDL can specify the features event-driven behaviors, and each event in CosRDL can contain such as input/output an

群居动物

创作

我怕被刺穿

乐意

Sleep Studies in Clinical Pharmacology,g out the abnormal situation, a fast and effective method of recognizing power quality fault is proposed in this paper. Confidence intervals are first partitioned by a pre-judgment method based on time series similarity, and the failure characterization is simplified by measuring similarity between

简洁

meritorious

J. Philip Kistler,Michael J. G. Harrisonn. Recent attempts on this problem suggested to use human body affinity fields to efficiently detect the 2D pose of multiple people in an image. In this paper, we extend this idea to 3D domains and develop a 3D human action recognition system with ability to understand the cooperative action of seve