Structured design of reconfigurable logic control functions through sequential functional charts

2004 ◽  
Author(s):  
E. Carpanzano ◽  
A. Cataldo ◽  
D. Tilbury
Keyword(s):  
Reconfigurable Logic ◽  
Structured Design ◽  
Control Functions ◽  
Logic Control

Event-Condition-Action Systems for Reconfigurable Logic Control

2007 ◽  
Vol 4 (2) ◽  
pp. 167-181 ◽  
Author(s):  
E. Emanuel Almeida ◽  
Jonathan E. Luntz ◽  
Dawn M. Tilbury
Keyword(s):  
Reconfigurable Logic ◽  
Action Systems ◽  
Logic Control

scholarly journals Recon Figureable Logic Control for Manufacturing Systems

2014 ◽  
Vol 136 (12) ◽  
pp. S16-S23
Author(s):  
Dawn M. Tilbury
Keyword(s):  
Manufacturing Systems ◽  
Security And Privacy ◽  
The Internet ◽  
Manufacturing Plant ◽  
Reconfigurable Logic ◽  
Time Data ◽  
Physical Separation ◽  
Logic Control ◽  
Networking Technologies ◽  
The Internet Of Things

This article explores optimistic use of reconfigurable logic control for manufacturing systems. The rapid advancement of computing and networking technologies is enabling more data to be gathered, stored, and analyzed. It is possible for all of the machines in a manufacturing plant to be connected to the Internet of Things (IoT), with their production data stored either in a local database or in a cloud system. This opens up new avenues for online decision-making based on real-time data coming from the system. However, it also introduces significant cybersecurity challenges that will need to be addressed for successful deployment. Traditionally, security in a manufacturing plant was handled through physical separation and access gates with badge identification. Connecting the manufacturing plant to the Internet results in multiple opportunities for improving performance through better data analytics, as well as myriad challenges for safety, security, and privacy.

Modern TEM systems architecture

1986 ◽  
Vol 44 ◽  
pp. 602-605
Author(s):  
U. Gross ◽  
P. Hagemann
Keyword(s):  
Electron Microscope ◽  
Information Gathering ◽  
Systems Architecture ◽  
Control Functions ◽  
Transmission Electron ◽  
New Information ◽  
Comprehensive Information ◽  
Scanning Transmission ◽  
Diffraction Patterns ◽  
Analytical Equipment

By addition of analytical equipment, scanning transmission accessories and data processing equipment the basic transmission electron microscope (TEM) has evolved into a comprehensive information gathering system. This extension has led to increased complexity of the instrument as compared with the straightforward imaging microscope, since in general new information capacity has required the addition of new control hardware. The increased operational complexity is reflected in a proliferation of knobs and buttons.In the conventional electron microscope design the operating panel of the instrument has distinct control elements to alter optical conditions of the microscope column in different modes. As a consequence a multiplicity of control functions has been inevitable. Examples of this are the three pairs of focus and magnification controls needed for TEM imaging, diffraction patterns, and STEM images.

Conceptual framework for the design of logic control

1993 ◽  
Vol 2 (4) ◽  
pp. 246 ◽  
Author(s):  
L. Ferrarini ◽  
C. Maffezzoni
Keyword(s):  
Conceptual Framework ◽  
Logic Control

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

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