Time Dependent Data Mining in RAVEN

2016 ◽  
Author(s):  
Joshua Joseph Cogliati ◽  
Jun Chen ◽  
Japan Ketan Patel ◽  
Diego Mandelli ◽  
Daniel Patrick Maljovec ◽  
...  
Keyword(s):  
Data Mining ◽  
Time Dependent ◽  
Dependent Data

Complementing Visual Data Mining with the Sound Dimension: Sonification of Time Dependent Data

2008 ◽  
pp. 236-247 ◽  
Author(s):  
Monique Noirhomme-Fraiture ◽  
Olivier Schöller ◽  
Christophe Demoulin ◽  
Simeon J. Simoff
Keyword(s):  
Data Mining ◽  
Time Dependent ◽  
Dependent Data ◽  
Visual Data ◽  
Visual Data Mining

Prediction of Wax Deposits for Crude Pipelines Using Time-Dependent Data Mining

SPE Journal ◽  
2021 ◽  
pp. 1-22
Author(s):  
Bo Yao ◽  
Jiaqi Chen ◽  
Chuanxian Li ◽  
Fei Yang ◽  
Guangyu Sun ◽  
...  
Keyword(s):  
Data Mining ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Time Dependent ◽  
Dependent Data ◽  
Inlet Temperature ◽  
Long Distance ◽  
Temperature Prediction ◽  
Swarm Optimization ◽  
Wax Deposits

Summary Accurately predicting wax deposits in a crude pipeline through empirical formulas or numerical modeling is unreliable because of the incomplete mechanism and the time-dependent unsteady actual operating conditions. With the help of the data collected by the supervisory control and data acquisition system of pipelines, wax deposit prediction is made possible by developing the time-dependent data mining method. In this article, the data from a typical long-distance crude pipeline in China operating over a 4-year time period was investigated. The inlet temperature prediction was first conducted by developing the long short-term memory (LSTM)-recurrent neural networks (RNNs) model, during which the feature sequencing, overfitting problems, and optimal hyperparameters were fully considered. Because of the time sequence cell, the accuracy of the LSTM-RNN model, as well as the time consumption, is much better than the RNN model when dealing with a great deal of data over a long period of time. Taking the inlet temperature prediction results as input features, the prediction model of average wax deposit thickness was established based on the backpropagation (BP) neural network and optimized by the particle swarm optimization (PSO), chaos particle swarm optimization (CPSO), and adaptive chaos particle swarm optimization (ACPSO) algorithms. The conclusions and associated algorithm from this article help to determine the reasonable pigging circle of long-distance pipelines practically. It could also be applied to guide the wax deposit prediction in the wellbore or oil-gatheringpipes.

scholarly journals Learning Bounds of ERM Principle for Sequences of Time-Dependent Samples

2015 ◽  
Vol 2015 ◽  
pp. 1-8
Author(s):  
Mingchen Yao ◽  
Chao Zhang ◽  
Wei Wu
Keyword(s):  
Time Dependent ◽  
Dependent Data ◽  
Empirical Risk Minimization ◽  
Risk Minimization ◽  
Practical Applications ◽  
Empirical Risk ◽  
Learning Tasks ◽  
Generalization Bound ◽  
Erm Principle ◽  
Dependent Samples

Many generalization results in learning theory are established under the assumption that samples are independent and identically distributed (i.i.d.). However, numerous learning tasks in practical applications involve the time-dependent data. In this paper, we propose a theoretical framework to analyze the generalization performance of the empirical risk minimization (ERM) principle for sequences of time-dependent samples (TDS). In particular, we first present the generalization bound of ERM principle for TDS. By introducing some auxiliary quantities, we also give a further analysis of the generalization properties and the asymptotical behaviors of ERM principle for TDS.

scholarly journals Progress and Regress of Time Dependent Data and Application in Bank Branch

2014 ◽  
Vol 2014 ◽  
pp. 1-9
Author(s):  
F. Hosseinzadeh Lotfi ◽  
Z. Taeb ◽  
S. Abbasbandy
Keyword(s):  
Decision Making ◽  
Commercial Bank ◽  
Spline Function ◽  
Time Dependent ◽  
Malmquist Productivity Index ◽  
Productivity Index ◽  
Dependent Data ◽  
Bank Branch ◽  
Decision Making Unit ◽  
Cubic Spline Function

To evaluate each decision making unit having time dependent inputs and outputs data, a new method has been developed and reported here. This method uses the Malmquist productivity index, and is a very simple function based on Cubic Spline function to determine the progress and regress of that unit. To show the capability of this developed method, the data of 9 branches of a commercial bank has been used, evaluated, and reported.

scholarly journals Applying Time-Dependent Data for Fluorescence Tomography

2007 ◽  
Author(s):  
Ralf B. Schulz ◽  
Martin Schweiger ◽  
Cosimo D'Andrea ◽  
Gianluca Valentini ◽  
Jörg Peter ◽  
...  
Keyword(s):  
Time Dependent ◽  
Dependent Data ◽  
Fluorescence Tomography
Sign in / Sign up

Export Citation Format

Share Document