A Study on Adaptive Scheduling Priority Control for Layered Cell Configuration

2017 ◽  
Vol E100.B (2) ◽  
pp. 372-379
Author(s):  
Atsushi NAGATE ◽  
Teruya FUJII ◽  
Masayuki MURATA
Keyword(s):  
Adaptive Scheduling ◽  
Cell Configuration

Integration of the aprotic CO2 reduction to oxalate at a Pb catalyst into a GDE flow cell configuration

2021 ◽  
Author(s):  
Maximilian König ◽  
Shih-Hsuan Lin ◽  
Jan Vaes ◽  
Deepak Pant ◽  
Elias Klemm
Keyword(s):  
Oxalic Acid ◽  
Co2 Reduction ◽  
Flow Cell ◽  
Aprotic Solvents ◽  
Cell Configuration ◽  
Carbon Neutral

The electrochemical CO2 reduction to oxalic acid in aprotic solvents could be a potential pathway to produce carbon-neutral oxalic acid. One of the challenges in the aprotic CO2 reduction are...

scholarly journals Towards workload-adaptive scheduling for HPC clusters

2020 ◽  
Author(s):  
Alexander V. Goponenko ◽  
Ramin Izadpanah ◽  
Jim M. Brandt ◽  
Damian Dechev
Keyword(s):  
Adaptive Scheduling

Adaptive scheduling in the cloud - SLA for Hadoop job scheduling

2015 ◽  
Author(s):  
Deveeshree Nayak ◽  
Venkata Swamy Martha ◽  
David Threm ◽  
Srini Ramaswamy ◽  
Summer Prince ◽  
...  
Keyword(s):  
Job Scheduling ◽  
Adaptive Scheduling

Adaptive scheduling method for dynamic robotic cell based on pattern classification algorithm

2018 ◽  
Vol 09 (05) ◽  
pp. 1850040 ◽  
Author(s):  
Chuyuan Wang ◽  
Linxuan Zhang ◽  
Chongdang Liu
Keyword(s):  
Real Time ◽  
Pattern Classification ◽  
Feature Selection Method ◽  
Classification Algorithm ◽  
Adaptive Scheduling ◽  
Gradient Boosting ◽  
Production Environment ◽  
Robotic Cell ◽  
Extreme Gradient Boosting ◽  
Scheduling Method

In order to deal with the dynamic production environment with frequent fluctuation of processing time, robotic cell needs an efficient scheduling strategy which meets the real-time requirements. This paper proposes an adaptive scheduling method based on pattern classification algorithm to guide the online scheduling process. The method obtains the scheduling knowledge of manufacturing system from the production data and establishes an adaptive scheduler, which can adjust the scheduling rules according to the current production status. In the process of establishing scheduler, how to choose essential attributes is the main difficulty. In order to solve the low performance and low efficiency problem of embedded feature selection method, based on the application of Extreme Gradient Boosting model (XGBoost) to obtain the adaptive scheduler, an improved hybrid optimization algorithm which integrates Gini impurity of XGBoost model into Particle Swarm Optimization (PSO) is employed to acquire the optimal subset of features. The results based on simulated robotic cell system show that the proposed PSO-XGBoost algorithm outperforms existing pattern classification algorithms and the newly learned adaptive model can improve the basic dispatching rules. At the same time, it can meet the demand of real-time scheduling.

Adaptive Scheduling with Postexamining User Selection Under Nonidentical Fading

2012 ◽  
Vol 61 (9) ◽  
pp. 4175-4183 ◽  
Author(s):  
F. Gaaloul ◽  
R. M. Radaydeh ◽  
Hong-Chuan Yang ◽  
M-S Aluoini
Keyword(s):  
Adaptive Scheduling ◽  
User Selection
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