New mist-edge-fog-cloud system architecture for thermal error prediction and control enabled by deep-learning

2022 ◽  
Vol 109 ◽  
pp. 104626
Author(s):  
Hongquan Gui ◽  
Jialan Liu ◽  
Chi Ma ◽  
Mengyuan Li ◽  
Shilong Wang
2020 ◽  
Vol 116 ◽  
pp. 102624
Author(s):  
Sudatta Mohanty ◽  
Alexey Pozdnukhov ◽  
Michael Cassidy

2010 ◽  
Vol 139-141 ◽  
pp. 1864-1868 ◽  
Author(s):  
Bi Wen Li ◽  
Jin Feng Xiao ◽  
Chun Liang Zhang ◽  
Liang Bin Hu

Relative to gear shaping and gear hob, using gear slotting produces herringbone gear which is used in nuclear power turbine speed redactor has more obvious technical and economic benefits, but profile angle error of slotting cutter causes profile error and base pitch deviation of herringbone gear. It will result in high-frequency noise which damages the tactical and technical performance of warship. By analyzed the wire cutting system of rack cutter used in MAAG type gear machining for herringbone gear, the mathematical model of error prediction for rack cutter profile angle was founded. On the base of model of error prediction, the principle of error control and the method of revision control are presented. Finally, the example of prediction and control is provided.


Author(s):  
Tamara Green

Much of the literature, policies, programs, and investment has been made on mental health, case management, and suicide prevention of veterans. The Australian “veteran community is facing a suicide epidemic for the reasons that are extremely complex and beyond the scope of those currently dealing with them.” (Menz, D: 2019). Only limited work has considered the digital transformation of loosely and manual-based historical records and no enablement of Artificial Intelligence (A.I) and machine learning to suicide risk prediction and control for serving military members and veterans to date. This paper presents issues and challenges in suicide prevention and management of veterans, from the standing of policymakers to stakeholders, campaigners of veteran suicide prevention, science and big data, and an opportunity for the digital transformation of case management.


2009 ◽  
Vol 325 (1-2) ◽  
pp. 85-105 ◽  
Author(s):  
P.A. Meehan ◽  
P.A. Bellette ◽  
R.D. Batten ◽  
W.J.T. Daniel ◽  
R.J. Horwood

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