A neural-network enhanced modeling method for real-time evaluation of the temperature distribution in a data center

2019 ◽  
Vol 31 (12) ◽  
pp. 8379-8391 ◽  
Author(s):  
Qiu Fang ◽  
Zhe Li ◽  
Yaonan Wang ◽  
Mengxuan Song ◽  
Jun Wang
Keyword(s):  
Neural Network ◽  
Temperature Distribution ◽  
Real Time ◽  
Data Center ◽  
Modeling Method ◽  
Time Evaluation

The use of neural network and discrete Fourier transform for real-time evaluation of friction stir welding

2011 ◽  
Vol 11 (8) ◽  
pp. 4839-4846 ◽  
Author(s):  
Enkhsaikhan Boldsaikhan ◽  
Edward M. Corwin ◽  
Antonette M. Logar ◽  
William J. Arbegast
Keyword(s):  
Neural Network ◽  
Fourier Transform ◽  
Friction Stir Welding ◽  
Real Time ◽  
Discrete Fourier Transform ◽  
Friction Stir ◽  
Time Evaluation

scholarly journals Improving ENIGMA-style Clause Selection while Learning From History

2021 ◽  
pp. 543-561
Author(s):  
Martin Suda
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Theorem Prover ◽  
Central Idea ◽  
Theorem Provers ◽  
Recursive Neural Network ◽  
Time Evaluation ◽  
Automatic Theorem ◽  
Learning From History

AbstractWe re-examine the topic of machine-learned clause selection guidance in saturation-based theorem provers. The central idea, recently popularized by the ENIGMA system, is to learn a classifier for recognizing clauses that appeared in previously discovered proofs. In subsequent runs, clauses classified positively are prioritized for selection. We propose several improvements to this approach and experimentally confirm their viability. For the demonstration, we use a recursive neural network to classify clauses based on their derivation history and the presence or absence of automatically supplied theory axioms therein. The automatic theorem prover Vampire guided by the network achieves a 41 % improvement on a relevant subset of SMT-LIB in a real time evaluation.

Data Center Cooling Prediction Using Artificial Neural Network

2007 ◽  
Author(s):  
Saurabh K. Shrivastava ◽  
James W. VanGilder ◽  
Bahgat G. Sammakia
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Data Center ◽  
Cooling Performance ◽  
The Real ◽  
Time Prediction ◽  
Linear Relationships ◽  
The Neural Network ◽  
Data Center Cooling ◽  
Computational Fluid Dynamics Cfd

An analytical approach using artificial intelligence has been developed for assessing the cooling performance of data centers. This paper discusses the use of a Neural Network (NN) model in the real-time prediction of the cooling performance of a cluster of equipment in a data center environment. The NN model is used to predict the Capture Index (CI) [1] as a function of rack power, cooler airflow and physical/geometric arrangement for a cluster located in a simple room environment. The Neural Network is “trained” on thousands of hypothetical but realistic cluster variations for which CI values have been computed using either PDA [2] or full Computational Fluid Dynamics (CFD). The great value of the NN approach lies in its ability to capture the non-linear relationships between input parameters and corresponding capture indices. The accuracy of the NN approach is 3.8% (Root Mean Square Error) for a set of example scenarios discussed here. Because of the real-time nature of the calculations, the NN approach readily facilitates optimization studies. Example cases are discussed which show the integration of the NN approach and a genetic algorithm used for optimization.

Neural Network-Based Prediction and Control of Air Flow in a Data Center

2012 ◽  
Vol 4 (2) ◽  
Author(s):  
Flavio de Lorenzi ◽  
Christof Vömel
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Real Time ◽  
Data Center ◽  
Cooling System ◽  
Air Flow ◽  
Data Center Cooling ◽  
Prediction And Control ◽  
Reduce Energy Consumption ◽  
And Control

As modern data centers continue to grow in power, size, and numbers, there is an urgent need to reduce energy consumption by optimized cooling strategies. In this paper, we present a neural network-based prediction of air flow in a data center that is cooled through perforated floor tiles. With a significantly smaller execution time than computational fluid dynamics, it predicts in real-time server inlet temperatures and can detect whether prevalent air flow cools the servers sufficiently to guarantee safe operation. Combined with a cooling system model, we obtain a temperature and air flow control algorithm that is fast and accurate enough to find an optimal operating point of the data center cooling system in real-time. We also demonstrate the performance of our algorithm on a reference data center and show that energy consumption can be reduced by up to 30%.

scholarly journals Real-Time Evaluation of 26-GBaud PAM-4 Intensity Modulation and Direct Detection Systems for Data-Center Interconnects

2016 ◽  
Author(s):  
Nicklas Eiselt ◽  
Helmut Griesser ◽  
Jinlong Wei ◽  
Annika Dochhan ◽  
Michael Eiselt ◽  
...  
Keyword(s):  
Real Time ◽  
Data Center ◽  
Direct Detection ◽  
Intensity Modulation ◽  
Detection Systems ◽  
Time Evaluation
Sign in / Sign up

Export Citation Format

Share Document