東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

A guided simulation methodology for ...

Hu, Yunwei.

FindBook

Google Book

Amazon

博客來

A guided simulation methodology for dynamic probabilistic risk assessment of complex systems.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	A guided simulation methodology for dynamic probabilistic risk assessment of complex systems./
作者:	Hu, Yunwei.
面頁冊數:	239 p.
附註:	Source: Dissertation Abstracts International, Volume: 66-06, Section: B, page: 3250.
Contained By:	Dissertation Abstracts International66-06B.
標題:	Engineering, General. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3178734
ISBN:	0542185598

A guided simulation methodology for dynamic probabilistic risk assessment of complex systems.
Hu, Yunwei.

A guided simulation methodology for dynamic probabilistic risk assessment of complex systems. - 239 p.

Source: Dissertation Abstracts International, Volume: 66-06, Section: B, page: 3250.

Thesis (Ph.D.)--University of Maryland, College Park, 2005.

Probabilistic risk assessment (PRA) is a systematic process of examining how engineered systems work to ensure safety. With the growth of the size of the dynamic systems and the complexity of the interactions between hardware, software, and humans, it is extremely difficult to enumerate the risky scenarios by the traditional PRA methods. Over the past 15 years, a host of DPRA methods have been proposed to serve as supplemental tools to traditional PRA to deal with complex dynamic systems. A new dynamic probabilistic risk assessment framework is proposed in this dissertation. In this framework a new exploration strategy is employed. The engineering knowledge of the system is explicitly used to guide the simulation to achieve higher efficiency and accuracy. The engineering knowledge is reflected in the "Planner" which is responsible for generating plans as a high level map to guide the simulation. A scheduler is responsible for guiding the simulation by controlling the timing and occurrence of the random events. During the simulation the possible random events are proposed to the scheduler at branch points. The scheduler decides which events are to be simulated. Scheduler would favor the events with higher values. The value of a proposed event depends on the information gain from exploring that scenario, and the importance factor of the scenario. The information gain is measured by the information entropy, and the importance factor is based on the engineering judgment. The simulation results are recorded and grouped for later studies. The planner may "learn" from the simulation results, and update the plan to guide further simulation.

ISBN: 0542185598Subjects--Topical Terms:

1020744
Engineering, General.

A guided simulation methodology for dynamic probabilistic risk assessment of complex systems.
LDR:03017nmm 2200277 4500 001 1813526
005 20060503131731.5
008 130610s2005 eng d
020 $a 0542185598
035 $a (UnM)AAI3178734
035 $a AAI3178734
040 $a UnM $c UnM
100 1 $a Hu, Yunwei. $3 1903025
245 1 2 $a A guided simulation methodology for dynamic probabilistic risk assessment of complex systems.
300 $a 239 p.
500 $a Source: Dissertation Abstracts International, Volume: 66-06, Section: B, page: 3250.
500 $a Chair: Ali Mosleh.
502 $a Thesis (Ph.D.)--University of Maryland, College Park, 2005.
520 $a Probabilistic risk assessment (PRA) is a systematic process of examining how engineered systems work to ensure safety. With the growth of the size of the dynamic systems and the complexity of the interactions between hardware, software, and humans, it is extremely difficult to enumerate the risky scenarios by the traditional PRA methods. Over the past 15 years, a host of DPRA methods have been proposed to serve as supplemental tools to traditional PRA to deal with complex dynamic systems. A new dynamic probabilistic risk assessment framework is proposed in this dissertation. In this framework a new exploration strategy is employed. The engineering knowledge of the system is explicitly used to guide the simulation to achieve higher efficiency and accuracy. The engineering knowledge is reflected in the "Planner" which is responsible for generating plans as a high level map to guide the simulation. A scheduler is responsible for guiding the simulation by controlling the timing and occurrence of the random events. During the simulation the possible random events are proposed to the scheduler at branch points. The scheduler decides which events are to be simulated. Scheduler would favor the events with higher values. The value of a proposed event depends on the information gain from exploring that scenario, and the importance factor of the scenario. The information gain is measured by the information entropy, and the importance factor is based on the engineering judgment. The simulation results are recorded and grouped for later studies. The planner may "learn" from the simulation results, and update the plan to guide further simulation.
520 $a SIMPRA is the software package which implements the new methodology. It provides the users with a friendly interface and a rich DPRA library to aid in the construction of the simulation model. The engineering knowledge can be input into the Planner, which would generate a plan automatically. The scheduler would guide the simulation according to the plan. The simulation generates many accident event sequences and estimates of the end state probabilities.
590 $a School code: 0117.
650 4 $a Engineering, General. $3 1020744
690 $a 0537
710 2 0 $a University of Maryland, College Park. $3 657686
773 0 $t Dissertation Abstracts International $g 66-06B.
790 1 0 $a Mosleh, Ali, $e advisor
790 $a 0117
791 $a Ph.D.
792 $a 2005
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3178734