AN EXPERT SYSTEM FOR TUNING PARTICLE BEAM ACCELERATORS

1990 ◽  
Vol 04 (03) ◽  
pp. 357-369
Author(s):  
DARREL L. LAGER ◽  
HAL R. BRAND ◽  
WILLIAM J. MAURER
Keyword(s):  
Expert System ◽  
Real Time ◽  
Experimental Test ◽  
Particle Beam ◽  
Particle Beams ◽  
Software Program ◽  
Intelligent Assistant ◽  
And Control

An expert system that acts as an intelligent assistant to operators tuning a particle beam accelerator was developed. The system incorporates three approaches to tuning: (1) Duplicating within a software program the reasoning and the procedures used by an operator to tune an accelerator. This approach has been used to steer particle beams through the transport section of Lawrence Livermore National Laboratory's Advanced Test Accelerator and through the injector section of the Experimental Test Accelerator. (2) Using a model to simulate the position of a beam in an accelerator. The simulation is based on data taken directly from the accelerator while it is running. This approach will ultimately be used by operators of the Experimental Test Accelerator to first compare actual and simulated beam performance in real time, then to determine which set of parameters is optimum in terms of centering the beam, and finally to feed those parameters to the accelerator. Operators can also use the model to determine if a component has failed. (3) Using a mouse to manually select and control the magnets that steer the beam. Operators on the Experimental Test Accelerator can also use the mouse to call up windows that display the horizontal and vertical positions of the beam as well as its current.

FMS Real time scheduling and control with expert system techniques

1991 ◽  
Vol 24 (14) ◽  
pp. 246-251
Author(s):  
Yan Hongsen ◽  
Zhang Jinge ◽  
Wang Yan ◽  
Li Zhenwei
Keyword(s):  
Expert System ◽  
Real Time ◽  
Real Time Scheduling ◽  
Time Scheduling ◽  
And Control

Integration of distributed multi-analyzer monitoring and control in bioprocessing based on a real-time expert system

2003 ◽  
Vol 103 (3) ◽  
pp. 237-248 ◽  
Author(s):  
Christian Cimander ◽  
Thomas Bachinger ◽  
Carl-Fredrik Mandenius
Keyword(s):  
Expert System ◽  
Real Time ◽  
Monitoring And Control ◽  
And Control

Real-time greenhouse monitoring and control with an expert system

1989 ◽  
Vol 3 (4) ◽  
pp. 273-285 ◽  
Author(s):  
B.K. Jacobson ◽  
Pierce H. Jones ◽  
J.W. Jones ◽  
J.A. Paramore
Keyword(s):  
Expert System ◽  
Real Time ◽  
Monitoring And Control ◽  
And Control

Microstructural investigation of surface roughness in MBE-grown AlAs

1995 ◽  
Vol 53 ◽  
pp. 454-455
Author(s):  
R. Rajesh ◽  
R. Droopad ◽  
C. H. Kuo ◽  
R. W. Carpenter ◽  
G. N. Maracas
Keyword(s):  
Thin Film ◽  
Real Time ◽  
Growth Control ◽  
Native Oxide ◽  
Effusion Cell ◽  
Surface Oxide Layer ◽  
Microstructural Investigation ◽  
Mbe Growth ◽  
Film Substrate ◽  
And Control

Knowledge of material pseudodielectric functions at MBE growth temperatures is essential for achieving in-situ, real time growth control. This allows us to accurately monitor and control thicknesses of the layers during growth. Undesired effusion cell temperature fluctuations during growth can thus be compensated for in real-time by spectroscopic ellipsometry. The accuracy in determining pseudodielectric functions is increased if one does not require applying a structure model to correct for the presence of an unknown surface layer such as a native oxide. Performing these measurements in an MBE reactor on as-grown material gives us this advantage. Thus, a simple three phase model (vacuum/thin film/substrate) can be used to obtain thin film data without uncertainties arising from a surface oxide layer of unknown composition and temperature dependence.In this study, we obtain the pseudodielectric functions of MBE-grown AlAs from growth temperature (650°C) to room temperature (30°C). The profile of the wavelength-dependent function from the ellipsometry data indicated a rough surface after growth of 0.5 μm of AlAs at a substrate temperature of 600°C, which is typical for MBE-growth of GaAs.

Adaptive Signal Analysis and Interpretation for Real-time Intelligent Patient Monitoring

1994 ◽  
Vol 33 (01) ◽  
pp. 60-63 ◽  
Author(s):  
E. J. Manders ◽  
D. P. Lindstrom ◽  
B. M. Dawant
Keyword(s):  
Computer Architecture ◽  
Real Time ◽  
Data Abstraction ◽  
Monitoring Strategy ◽  
Intelligent Monitoring ◽  
Patient Status ◽  
On Line ◽  
Main Components ◽  
And Control ◽  
Adaptive Signal

Abstract:On-line intelligent monitoring, diagnosis, and control of dynamic systems such as patients in intensive care units necessitates the context-dependent acquisition, processing, analysis, and interpretation of large amounts of possibly noisy and incomplete data. The dynamic nature of the process also requires a continuous evaluation and adaptation of the monitoring strategy to respond to changes both in the monitored patient and in the monitoring equipment. Moreover, real-time constraints may imply data losses, the importance of which has to be minimized. This paper presents a computer architecture designed to accomplish these tasks. Its main components are a model and a data abstraction module. The model provides the system with a monitoring context related to the patient status. The data abstraction module relies on that information to adapt the monitoring strategy and provide the model with the necessary information. This paper focuses on the data abstraction module and its interaction with the model.

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