Pattern recognition based adaptive real-time scheduling

Xiao An Shi; Xing She Zhou; Jian Hua Gu; Yi Lin

Pattern recognition based adaptive real-time scheduling

Xiao An Shi, Xing She Zhou, Jian Hua Gu, Yi Lin

School of Computer Science

Northwestern Polytechnical University Xian

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Unmanned and autonomous real-time system generally run in uncertain, highly dynamic environments. Currently, there is no easy way to model such kind of systems. This paper presents a Pattern Recognition based Adaptive Real-time Scheduling (PRARS) framework for adaptive real-time systems. The usage of Pattern Recognition Theory provides a scientific underpinning on PID control. Through processing feature information, establishing character mode collection, pattern recognizing, and building control rule collection, we implement the PRARS. This enables us to fulfill more precise and efficient QoS and admission control, and guarantees the dynamical requirements of resources. Thus complex modeling methods could be avoided. The algorithm ensures robust performance of real-time tasks.

Original language	English
Title of host publication	International Conference on Machine Learning and Cybernetics
Pages	3160-3166
Number of pages	7
State	Published - 2003
Event	2003 International Conference on Machine Learning and Cybernetics - Xi'an, China Duration: 2 Nov 2003 → 5 Nov 2003

Publication series

Name	International Conference on Machine Learning and Cybernetics
Volume	5

Conference

Conference	2003 International Conference on Machine Learning and Cybernetics
Country/Territory	China
City	Xi'an
Period	2/11/03 → 5/11/03

Keywords

Adaptive
Control
Pattern Recognition
Real-time

Cite this

@inproceedings{21881a035ae142e68fcac2e93cb4f804,

title = "Pattern recognition based adaptive real-time scheduling",

abstract = "Unmanned and autonomous real-time system generally run in uncertain, highly dynamic environments. Currently, there is no easy way to model such kind of systems. This paper presents a Pattern Recognition based Adaptive Real-time Scheduling (PRARS) framework for adaptive real-time systems. The usage of Pattern Recognition Theory provides a scientific underpinning on PID control. Through processing feature information, establishing character mode collection, pattern recognizing, and building control rule collection, we implement the PRARS. This enables us to fulfill more precise and efficient QoS and admission control, and guarantees the dynamical requirements of resources. Thus complex modeling methods could be avoided. The algorithm ensures robust performance of real-time tasks.",

keywords = "Adaptive, Control, Pattern Recognition, Real-time",

author = "Shi, {Xiao An} and Zhou, {Xing She} and Gu, {Jian Hua} and Yi Lin",

year = "2003",

language = "英语",

isbn = "0780378652",

series = "International Conference on Machine Learning and Cybernetics",

pages = "3160--3166",

booktitle = "International Conference on Machine Learning and Cybernetics",

note = "2003 International Conference on Machine Learning and Cybernetics ; Conference date: 02-11-2003 Through 05-11-2003",

}

TY - GEN

T1 - Pattern recognition based adaptive real-time scheduling

AU - Shi, Xiao An

AU - Zhou, Xing She

AU - Gu, Jian Hua

AU - Lin, Yi

PY - 2003

Y1 - 2003

N2 - Unmanned and autonomous real-time system generally run in uncertain, highly dynamic environments. Currently, there is no easy way to model such kind of systems. This paper presents a Pattern Recognition based Adaptive Real-time Scheduling (PRARS) framework for adaptive real-time systems. The usage of Pattern Recognition Theory provides a scientific underpinning on PID control. Through processing feature information, establishing character mode collection, pattern recognizing, and building control rule collection, we implement the PRARS. This enables us to fulfill more precise and efficient QoS and admission control, and guarantees the dynamical requirements of resources. Thus complex modeling methods could be avoided. The algorithm ensures robust performance of real-time tasks.

AB - Unmanned and autonomous real-time system generally run in uncertain, highly dynamic environments. Currently, there is no easy way to model such kind of systems. This paper presents a Pattern Recognition based Adaptive Real-time Scheduling (PRARS) framework for adaptive real-time systems. The usage of Pattern Recognition Theory provides a scientific underpinning on PID control. Through processing feature information, establishing character mode collection, pattern recognizing, and building control rule collection, we implement the PRARS. This enables us to fulfill more precise and efficient QoS and admission control, and guarantees the dynamical requirements of resources. Thus complex modeling methods could be avoided. The algorithm ensures robust performance of real-time tasks.

KW - Adaptive

KW - Control

KW - Pattern Recognition

KW - Real-time

UR - http://www.scopus.com/inward/record.url?scp=1542276797&partnerID=8YFLogxK

M3 - 会议稿件

AN - SCOPUS:1542276797

SN - 0780378652

T3 - International Conference on Machine Learning and Cybernetics

SP - 3160

EP - 3166

BT - International Conference on Machine Learning and Cybernetics

T2 - 2003 International Conference on Machine Learning and Cybernetics

Y2 - 2 November 2003 through 5 November 2003

ER -

Pattern recognition based adaptive real-time scheduling

Abstract

Publication series

Conference

Keywords

Other files and links

Fingerprint

Cite this