Multi-view collaborative modeling method for complex system

2016 ◽  
Vol 07 (03) ◽  
pp. 1650030
Author(s):  
Xiaodong Huang ◽  
Li Zhang ◽  
Jing Zhou ◽  
Yaofei Ma
Keyword(s):  
Complex System ◽  
Complete System ◽  
System Modeling ◽  
Modeling Method ◽  
System Model ◽  
Separation Of Concerns ◽  
Unified Framework ◽  
Collaborative Modeling ◽  
Semantic Models ◽  
And Storage

It is hard to model a complex system by simply combining its partial characteristics. This paper presents a multi-view collaborative modeling method for complex system. Multi users, multi subjects, multi granularities and multi models in complex system modeling are unified to multi views. The principles and schemes for the decomposition of complex systems are introduced. According to the principle of separation of concerns, a complex system can be decomposed to a variety of model views. Collaborative modeling means that the views can be associated and integrated to form complete system model through their semantic. According to the form of representation, the models are generalizes to three kinds, i.e., view models, semantic models and storage models, which provides a unified framework for conversion and mapping between the models. Under the guidance of the method, a tool for the modeling of warship C2 system is developed. Applications show that the tool can support collaborative modeling and design for C2 system outstandingly.

scholarly journals МОДЕЛЬ ПРОЦЕСУ ВІДНОВЛЕННЯ ІНФОРМАЦІЇ З УШКОДЖЕНИХ НОСІЇВ

2018 ◽  
Vol 120 (2) ◽  
pp. 26-37
Author(s):  
В. Б. Швайченко ◽  
О. П. Гребінь ◽  
Н. Ф. Левенець
Keyword(s):  
Conceptual Model ◽  
System Modeling ◽  
Recovery Process ◽  
Sound Quality ◽  
Computer Processing ◽  
System Model ◽  
Information Recovery ◽  
Restoration Process ◽  
Processing Modes

Improving the quality of the restored information in the process of restoration and restoration of phonograms.Synthesis of the system model on the basis of analysis of the processes of restoration and restoration of phonograms from media of various types and computer processing. The characteristics of the conceptual model of the restoration and restoration of the phonogram are determined. The structure of the system model of the information recovery process is developed. A lot of concepts and connections between concepts are defined. The structure of the system modeling restoration and restoration of phonograms is defined. A conceptual model of the restoration and restoration process is proposed. The distribution of artifacts over the playback and processing modes of a phonogram is justified. Details of the type of content with features of the effect on the state of the phonogram.The solutions obtained are the basis of the methodology for carrying out the process of restoration and restoration of phonograms by the criterion of sound quality.

Modeling the Propagation of Failures in Software Driven Hardware Systems to Enable Risk-Informed Design

2008 ◽  
Author(s):  
David C. Jensen ◽  
Irem Y. Tumer ◽  
Tolga Kurtoglu
Keyword(s):  
Complex System ◽  
Product Life Cycle ◽  
System Modeling ◽  
Software Component ◽  
Temperature Data ◽  
System Level ◽  
Design Stage ◽  
Redundancy Management ◽  
Hardware System ◽  
Failure Propagation

Software-driven hardware configurations account for the majority of modern complex systems. The often costly failures of such systems can be attributed to software specific, hardware specific, or software/hardware interaction failures. The understanding of the propagation of failures in a complex system is critical because, while a software component may not fail in terms of loss of function, a software operational state can cause an associated hardware failure. The least expensive phase of the product life cycle to address failures is during the design stage. This results in a need to evaluate how a combined software/hardware system behaves and how failures propagate from a design stage analysis framework. Historical approaches to modeling the reliability of these systems have analyzed the software and hardware components separately. As a result significant work has been done to model and analyze the reliability of either component individually. Research into interfacing failures between hardware and software has been largely on the software side in modeling the behavior of software operating on failed hardware. This paper proposes the use of high-level system modeling approaches to model failure propagation in combined software/hardware system. Specifically, this paper presents the use of the Function-Failure Identification and Propagation (FFIP) framework for system level analysis. This framework is applied to evaluate nonlinear failure propagation within the Reaction Control System Jet Selection of the NASA space shuttle, specifically, for the redundancy management system. The redundancy management software is a subset of the larger data processing software and is involved in jet selection, warning systems, and pilot control. The software component that monitors for leaks does so by evaluating temperature data from the fuel and oxidizer injectors and flags a jet as having a failure by leak if the temperature data is out of bounds for three or more cycles. The end goal is to identify the most likely and highest cost paths for fault propagation in a complex system as an effective way to enhance the reliability of a system. Through the defining of functional failure propagation modes and path evaluation, a complex system designer can evaluate the effectiveness of system monitors and comparing design configurations.

Information-Applied Technology with Nonlinear System Modeling Method of Elman Network Based on Particle Swarm Optimization

2014 ◽  
Vol 952 ◽  
pp. 307-310
Author(s):  
Yan Ming Wei ◽  
Hua Ping Li ◽  
Hai Long Gao
Keyword(s):  
Particle Swarm Optimization ◽  
Nonlinear System ◽  
System Modeling ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Modeling Method ◽  
Elman Network ◽  
Swarm Optimization ◽  
Nonlinear System Modeling ◽  
Fast Training

In order to improve the modeling ability for nonlinear system, an Elman modeling method based on Particle Swarm Optimization (PSO) algorithm is proposed. It uses PSO algorithm to optimize the parameters of Elman network. The simulation result shows that the proposed hybrid method combined Elman with PSO algorithm has a good modeling performance with fast training rate for complex nonlinear system.

WISDOM Antenna Pattern in the presence of Rover and Soil

2021 ◽  
Author(s):  
Wolf-Stefan Benedix ◽  
Dirk Plettemeier ◽  
Christoph Statz ◽  
Yun Lu ◽  
Ronny Hahnel ◽  
...  
Keyword(s):  
Pattern Matching ◽  
Ground Penetrating Radar ◽  
Electromagnetic Waves ◽  
Complete System ◽  
Near Field ◽  
System Model ◽  
Soil Types ◽  
Broadband Antenna ◽  
Shallow Subsurface ◽  
Ground Penetrating

<p>The WISDOM ground-penetrating radar aboard the 2022 ESA-Roscosmos Rosalind-Franklin ExoMars Rover will probe the shallow subsurface of Oxia Planum using electromagnetic waves. A dual-polarized broadband antenna assembly transmits the WISDOM signal into the Martian subsurface and receives the return signal. This antenna assembly has been extensively tested and characterized w.r.t. the most significant antenna parameters (gain, pattern, matching). However, during the design phase, these parameters were simulated or measured without the environment, i.e., in the absence of other objects like brackets, rover vehicle, or soil. Some measurements of the rover's influence on the WISDOM data were performed during the instrument's integration.</p><p>It was shown that the rover structure and close surroundings in the near-field region of the WISDOM antenna assembly have a significant impact on the WISDOM signal and sounding performance. Hence, it is essential to include the simulations' environment, especially with varying surface and underground.</p><p>With this contribution, we outline the influences of rover and ground on the antenna's pattern and particularly on the footprint. We employ a 3D field solver with a complete system model above different soil types, i.e., subsurface materials with various combinations of permittivity and conductivity.</p>

A New Multilayer Belief Rule Base Model for Complex System Modeling

2021 ◽  
pp. 1-12
Author(s):  
You Cao ◽  
Zhijie Zhou ◽  
Guanyu Hu ◽  
Changhua Hu ◽  
Shuaiwen Tang ◽  
...  
Keyword(s):  
Complex System ◽  
System Modeling ◽  
Rule Base ◽  
Belief Rule Base ◽  
Complex System Modeling
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