The Bond Graph as a Unified Data Base for Engineering System Design

1975 ◽  
Vol 97 (4) ◽  
pp. 1333-1337 ◽  
Author(s):  
R. C. Rosenberg
Keyword(s):  
Data Base ◽  
Large Scale ◽  
Dynamic Models ◽  
Systems Design ◽  
Bond Graph ◽  
Graph Representation ◽  
Model Description ◽  
Engineering System ◽  
State Equations ◽  
Engineering System Design

In developing a unified data base for support of engineering systems design there are several important factors to consider, such as efficiency of model description, ease of modifying models, and characteristics of assembling device models into systems. The multipart model and its associated bond graph representation can serve very effectively as a unified data base, especially when devices and systems involve several energy domains simultaneously (e.g., electromechanical or hydromechanical transduction). In addition to providing a succinct, flexible data base for linear and nonlinear, static and dynamic models, bond graphs can be processed causally to reveal important information about alternative input-output choices and device-level coupling factors when submodels are assembled into systems. Particularly for large-scale nonlinear systems this is an important feature in aiding the formulation of state equations. Illustrations of the bond graph data base approach are given.

Zero Dynamics of Physical Systems From Bond Graph Models—Part I: SISO Systems

1999 ◽  
Vol 121 (1) ◽  
pp. 10-17 ◽  
Author(s):  
S. Y. Huang ◽  
K. Youcef-Toumi
Keyword(s):  
System Analysis ◽  
Mimo Systems ◽  
Geometric Approach ◽  
Bond Graph ◽  
Graph Representation ◽  
Zero Dynamics ◽  
Feedback Systems ◽  
State Equations ◽  
Graph Models ◽  
Physical Systems

Zero dynamics is an important feature in system analysis and controller design. Its behavior plays a major role in determining the performance limits of certain feedback systems. Since the intrinsic zero dynamics can not be influenced by feedback compensation, it is important to design physical systems so that they possess desired zero dynamics. However, the calculation of the zero dynamics is usually complicated, especially if a form which is closely related to the physical system and suitable for design is required. In this paper, a method is proposed to derive the zero dynamics of physical systems from bond graph models. This method incorporates the definition of zero dynamics in the differential geometric approach and the causality manipulation in the bond graph representation. By doing so, the state equations of the zero dynamics can be easily obtained. The system elements which are responsible for the zero dynamics can be identified. In addition, if isolated subsystems which exhibit the zero dynamics exist, they can be found. Thus, the design of physical systems including the consideration of the zero dynamics become straightforward. This approach is generalized for MIMO systems in the Part II paper.

Reflections on Engineering Systems and Bond Graphs

1993 ◽  
Vol 115 (2B) ◽  
pp. 242-251 ◽  
Author(s):  
R. C. Rosenberg
Keyword(s):  
Mechanical Engineer ◽  
Problem Solving ◽  
Large Scale ◽  
Bond Graph ◽  
Engineering System ◽  
Engineering Systems ◽  
Bond Graphs ◽  
Modern Engineering ◽  
Versatile Tool ◽  
Systems Simulation

An important aspect of modern engineering systems is their great diversity. Often they include interactions among different physical domains, contain control subsystems, and are large-scale and complex. The bond graph is a powerful and versatile tool that can help the engineer to design modern engineering systems. Three issues are explored from a bond graph perspective: modeling of engineering systems, simulation of their behavior, and teaching about engineering systems. It is the author’s observation that bond graph methodology is one of the most useful engineering system techniques available and belongs in the problem-solving tool kit of every mechanical engineer. This paper develops a rationale for this viewpoint both for readers familiar with bond graph methods and for readers to whom they are new.

Multiport Models for the Kinematic and Dynamic Analysis of Gear Power Transmissions

1979 ◽  
Vol 101 (2) ◽  
pp. 258-267 ◽  
Author(s):  
R. R. Allen
Keyword(s):  
Large Scale ◽  
Dynamic Models ◽  
Power Transmission ◽  
Bond Graph ◽  
Angular Velocities ◽  
Machine Elements ◽  
Gear Trains ◽  
Machine Systems ◽  
Kinematic Mechanisms ◽  
Kinematic Transformation

The kinematics of a gear power transmission may be characterized by a power-conserving kinematic transformation between independent and dependent angular velocities. The conjugate of this transform provides a relation between input and output torques. A bond graph multiport representing these kinematic relations provides a power-conserving core to which dissipative, inertial, and compliance effects may be added. This dynamic model of a power transmission may be connected with other machine elements (such as other kinematic mechanisms, motors, driveshafts, and loads) to form large-scale, computable dynamic models. Bond graph techniques are shown to facilitate the process of developing and assembling computable dynamic models for the study of gear trains as elements of machine systems. A numerical example is presented.

The Karolinska Hospital Information System

1974 ◽  
Vol 13 (03) ◽  
pp. 125-140 ◽  
Author(s):  
Ch. Mellner ◽  
H. Selajstder ◽  
J. Wolodakski
Keyword(s):  
Information System ◽  
Data Processing ◽  
Data Base ◽  
Hospital Information System ◽  
Care Delivery ◽  
Traditional Approach ◽  
Processing System ◽  
Systems Design ◽  
Data Processing System ◽  
Karolinska Hospital

The paper gives a report on the Karolinska Hospital Information System in three parts.In part I, the information problems in health care delivery are discussed and the approach to systems design at the Karolinska Hospital is reported, contrasted, with the traditional approach.In part II, the data base and the data processing system, named T1—J 5, are described.In part III, the applications of the data base and the data processing system are illustrated by a broad description of the contents and rise of the patient data base at the Karolinska Hospital.

scholarly journals A Novel Method to Predict Drug-Target Interactions Based on Large-Scale Graph Representation Learning

Cancers ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 2111
Author(s):  
Bo-Wei Zhao ◽  
Zhu-Hong You ◽  
Lun Hu ◽  
Zhen-Hao Guo ◽  
Lei Wang ◽  
...  
Keyword(s):  
Drug Target ◽  
Large Scale ◽  
Computational Models ◽  
Structural Information ◽  
Characteristic Curve ◽  
Representation Learning ◽  
Graph Representation ◽  
Convolutional Network ◽  
Novel Method

Identification of drug-target interactions (DTIs) is a significant step in the drug discovery or repositioning process. Compared with the time-consuming and labor-intensive in vivo experimental methods, the computational models can provide high-quality DTI candidates in an instant. In this study, we propose a novel method called LGDTI to predict DTIs based on large-scale graph representation learning. LGDTI can capture the local and global structural information of the graph. Specifically, the first-order neighbor information of nodes can be aggregated by the graph convolutional network (GCN); on the other hand, the high-order neighbor information of nodes can be learned by the graph embedding method called DeepWalk. Finally, the two kinds of feature are fed into the random forest classifier to train and predict potential DTIs. The results show that our method obtained area under the receiver operating characteristic curve (AUROC) of 0.9455 and area under the precision-recall curve (AUPR) of 0.9491 under 5-fold cross-validation. Moreover, we compare the presented method with some existing state-of-the-art methods. These results imply that LGDTI can efficiently and robustly capture undiscovered DTIs. Moreover, the proposed model is expected to bring new inspiration and provide novel perspectives to relevant researchers.

LARGE-SCALE PREDICTOR VALIDATION IN PROJECT A: DATA COLLECTION PROCEDURES AND DATA BASE PREPARATION

1990 ◽  
Vol 43 (2) ◽  
pp. 301-311 ◽  
Author(s):  
WINNIE Y. YOUNG ◽  
JANIS S. HOUSTON ◽  
JAMES H. HARRIS ◽  
R. GENE HOFFMAN ◽  
LAURESS L. WISE
Keyword(s):  
Data Collection ◽  
Data Base ◽  
Large Scale
Sign in / Sign up

Export Citation Format

Share Document