Dynamic Behavior Of Complex Systems Research Articles

Tool support to implement LOTOS formal specifications

LOTOS is a specification language that aims to describe the dynamic behavior of complex systems. To a large extent, LOTOS semantics is operational, which gives an opportunity to execute the specifications. There may be several targets in specification execution, three of which are considered in the paper: rapid prototyping, system testing, and real product derivation. The outstanding problem is to map abstract entities onto real entities, both to effectively act on the environment, and to allow the environment to influence specification behavior. For a final product, performance must be assessed too, as well as other non-functional requirements. The paper describes the opportunities provided by TOPO, a compiler from LOTOS into either C or Ada code. Supported features are described and wrapped into a method. Performance figures are provided too, in order to make estimates on realization performance after the specification style.

A New Real-Time Simulator for Power System Studies

A new real-time simulator for power system studies is presented, including static VAR compensators, machines, HVDC stations, filters, lines, torsional shaft oscillation model. It is suitable for the study of the dynamic behaviour of complex systems and investigation of control problems also with real control apparatus. Mathematical models, the simulation principle, the hardware realization and experience with the simulator are described.

A New Real Time Simulatro for Power System Studies

A new real-time simulator for power system studies is presented, including static VAR compensators, machines, HVDC stations, filters, lines, torsional shaft oscillation model. It is suitable for the study of the dynamic behaviour of complex systems and investigation of control problems also with real control apparatus. Mathematical models, the simulation principle, the hardware realization and experience with the simulator are described.

Preliminary Design of Mooring Systems

The preliminary design of mooring systems is formulated by separating the quasi-steady solution from the dynamic solution. A multiple time-scale expansion provides the appropriate equations, which are nonlinear for the quasi-steady part and linear space varying for the dynamic part. The fast dynamic solution consists of a fast varying and a slowly varying part with respect to space. An asymptotic solution is obtained by using the WKB method for the fast part, while an approximate expression is derived for the slow part. The resulting solution is simple and can be used to determine the dynamic behavior of complex systems, while permitting an extensive parametric search and the use of spectral techniques. This formulation leads to rational measures of the dynamic performance which, combined with cost considerations obtained from the static solution, permit an optimal selection of the system parameters. An example demonstrates the features of this methodology.

Techniques in the consolidation, characterization, and expression of physiologic signals in the time domain

The analysis of the dynamic behavior of complex systems requires the simultaneous and continuous monitoring of many variables, often over extended periods of time. In older to prepare the large amounts of data so obtained for analysis, efficient methods for data reduction and the characterization of condensed time series using appropriate screening and statistical procedures are essential. In this paper a solution to two of these problems is dealt with, namely the characterization of multivariate data, recored from biological systems under stress, in the time domain and the screening of study data for redundancy. The processing of such data is subject to two major constraints: the variables are frequently nonnormally distributed and the order of the behavior of the system to be characterized is frequently not known. The approach described comprises an initial data reduction for intermediate storage by a factor of one thousand to seventy through an averaging procedure. The resulting second time series is then characterized in terms of a) steady-state values during both the control and the response state; b) the variability of these steady state values and c) the shape and magnitude of the transient part of the response. The number of parameters resulting from this procedure is further reduced by screening the data for redundancy without investigator bias using a functional proposed by Andrews. Further processing of the data includes cluster and principal component analysis in an attempt to identify biologically meaningful basic system dimensions.