Sydney Metro and Melbourne Metro Rail Stochastic Comparison and Review

2021 ◽  
Vol 4 (2) ◽  
pp. 28-38
Author(s):  
Koorosh Gharehbaghi ◽  
Kathryn M. Robson ◽  
Neville Hurst ◽  
Matt Myers
Keyword(s):  
Performance Enhancement ◽  
Stochastic Comparison ◽  
Rail Transportation ◽  
The Novel ◽  
Key Factors ◽  
Smart System ◽  
Modeling And Control ◽  
Transportation Sector ◽  
And Control ◽  
Control Apparatus

This paper aims to review an innovative artificial intelligence (AI) apparatus to enhance the rail transportation performance. In this light, the Sydney Metro and Melbourne Metro rail will be compared, since both of these Australian rail networks employ complex AI as part of their overall performance enhancement schemes. These two case studies further highlight the novel critical aspects of AI in rail transportation sector such as recalibration through smart system design and automation, nonlinear controls and precise design, modeling and control apparatus, and so on. As a part of such a view, different aspects of AI systems such as increased reliability and safety were also investigated. This research found that with such enhancements of system performance, the overall transportation functioning would ultimately be significantly improved. Subsequently, AI in the Australian context can be further refined based on comprehensive integration of the key factors.

Optimum Pressure Control of Molten Metals for Casting Production Using a Novel Greensand Mold Press Casting Method

2010 ◽  
Vol 457 ◽  
pp. 453-458
Author(s):  
Kazuhiko Terashima ◽  
Ryosuke Tasaki ◽  
Yoshiyuki Noda ◽  
Kunihiro Hashimoto ◽  
Junichi Iwasaki ◽  
...  
Keyword(s):  
Pressure Control ◽  
Control Input ◽  
Molten Metals ◽  
Key Factors ◽  
Optimum Pressure ◽  
Modeling And Control ◽  
Casting Technique ◽  
Sequential Control ◽  
Novel Method ◽  
And Control

This paper presents a novel method of sand mold press casting. In this process, molten metal is poured into a drag mold, and then the cope mold is placed on top of the drag mold, and the two molds are matched. The mold design for casting, the pouring control and the velocity control of the press have been previously clarified as key factors in the manufacture of sound products. This paper presents methods for modeling and control of the molten metal’s pressure for novel sand press casting technique. Substituting detailed information for the complex mold shape, the poured volume and initial temperature into a developed control input generator, an optimum pressing velocity design and a robust design for defect-free production are proposed by the sequential control algorithm based on the construction of an inverse system comprised of a sequential switching from higher to lower speed. The effectiveness of adaptive press casting is demonstrated by using CFD model simulations.

Modeling and Control for Plant Dynamics Based on Reinforcement Learning

2009 ◽  
Vol 129 (4) ◽  
pp. 363-367
Author(s):  
Tomoyuki Maeda ◽  
Makishi Nakayama ◽  
Hiroshi Narazaki ◽  
Akira Kitamura
Keyword(s):  
Reinforcement Learning ◽  
Modeling And Control ◽  
Plant Dynamics ◽  
And Control
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