National Identity Predictive Models for the Real Time Prediction of European School’s Students: Preliminary Results

In this paper, authors extracted the data from the GPS equipment on the bus and established the real-time bus arrival time prediction model and bus running speed prediction model based on Kalman filtering technique. Analyse the error and build the error correction model. Firstly the bus running speed was predicted in the next section with the bus running speed prediction model, and then the bus arrival time was predicted with the real-time bus arrival time prediction model. Applying the newest information of bus running speed and bus arrival time, we were able to predict the real-time bus arrival time dynamically. The bus running speed prediction model and the real-time bus arrival time prediction model were assessed with the data of transit route NO.300 in Beijing. Lastly we assessed the real-time bus arrival time with the error between bus arrival time and real-time bus arrival time so that the prediction error was improved to 10 seconds which has higher prediction accuracy.

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The real-time prediction and inhibition of linguistic outcomes: Effects of language and literacy skill

Acta Psychologica ◽

10.1016/j.actpsy.2016.09.009 ◽

2016 ◽

Vol 171 ◽

pp. 72-84 ◽

Cited By ~ 8

Author(s):

Anuenue Kukona ◽

David Braze ◽

Clinton L. Johns ◽

W. Einar Mencl ◽

Julie A. Van Dyke ◽

...

Keyword(s):

Real Time ◽

Literacy Skill ◽

Language And Literacy ◽

The Real ◽

Time Prediction

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Ensemble Methods to Predict the Locality Scope of Indian and Hungarian Students for the Real Time: Preliminary Results

Advances in Intelligent Systems and Computing - Progress in Advanced Computing and Intelligent Engineering ◽

10.1007/978-981-15-6353-9_4 ◽

2020 ◽

pp. 37-48

Author(s):

Chaman Verma ◽

Zoltán Illés ◽

Veronika Stoffová

Keyword(s):

Real Time ◽

Ensemble Methods ◽

The Real ◽

Preliminary Results

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Data Center Cooling Prediction Using Artificial Neural Network

ASME 2007 InterPACK Conference, Volume 1 ◽

10.1115/ipack2007-33432 ◽

2007 ◽

Cited By ~ 8

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.

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