Estimation of petroleum contents in bituminous soil using compact submersible radio frequency sensor based on artificial neural network

2021 ◽  
Author(s):  
Aman Verma ◽  
Surabhi Jain ◽  
Nilesh Kumar Tiwari ◽  
M. Jaleel Akhtar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Radio Frequency ◽  
Frequency Sensor ◽  
Artificial Neural ◽  
Radio Frequency Sensor

Managing Uncertain Inventory in Supply Chain with Neural Network and Radio Frequency Identification (RFID)

2012 ◽  
pp. 155-170
Author(s):  
CKM Lee ◽  
Ng Wenwei Benjamin ◽  
Shaligram Pokharel
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Supply Chain ◽  
Radio Frequency ◽  
Real Time ◽  
Radio Frequency Identification ◽  
Bullwhip Effect ◽  
Distribution Centre ◽  
Frequency Identification ◽  
Artificial Neural

Demand uncertainty leads to fluctuations in inventory position at each echelon of a supply chain causing bullwhip effect, which can lead to significant cost and loss of efficiency and waste of resources. One of the aspects that can reduce potential bullwhip effect is the sharing of real time information for which the recently mass produced Radio Frequency Identification (RFID) can be of great value. The use of RFID technology can also help in increasing the visibility of the flow of goods and material, keeping track of the location and quantity at each distribution centre and warehouses. This will also help in the periodic and near real time optimization of inventory level of goods and material. The data collected with RFID can be analysed in artificial Neural Network (NN) to forecast the future demand. In this chapter, a framework is proposed by combining RFID with artificial neural network so that lean logistics can be realized in the supply chain.

scholarly journals Denoising of Radio Frequency Partial Discharge Signals Using Artificial Neural Network

Energies ◽  
2019 ◽  
Vol 12 (18) ◽  
pp. 3485 ◽  
Author(s):  
Amir Abbas Soltani ◽  
Ayman El-Hag
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Radio Frequency ◽  
Partial Discharge ◽  
Lifetime Estimation ◽  
Rf Signals ◽  
Artificial Neural ◽  
Rf Antenna ◽  
A New Technique ◽  
Artificial Neural Network Ann

One of the most promising techniques for condition monitoring of high voltage equipment insulation is partial discharge (PD) measurement using radio frequency (RF) antenna. Nevertheless, the accuracy of monitoring, classification, localization, or lifetime estimation could be negatively affected due to the interferences and noises measured simultaneously and contaminate the RF signals. Therefore, to achieve high accuracy of PD assessment, exploiting the denoising algorithms is inevitable. Hence, this paper seeks to introduce a new technique to suppress white noise, the most prevalent type of noise, especially for RF signals. In the proposed method, the ability of artificial neural network (ANN) in curve fitting is applied to denoising of different types of measured RF signals emitted from PD sources including ‘crack’, ‘internal void’, in the insulator discs and ‘sharp points’ from external hardware. The processes of denoising for named signals with the proposed method are carried out, and the obtained results are compared with the outputs of a wavelet transform-based method named energy conversation-based thresholding. In all tested signals, the proposed technique showed superior denoising capability.

Performance prediction of bended radio-frequency capacitors and inductors on plastic substrates using artificial neural network

2021 ◽  
pp. 2150288
Author(s):  
Kuibo Lan ◽  
Fei Wang ◽  
Qijun Zhang ◽  
Zhenqiang Ma ◽  
Guoxuan Qin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Radio Frequency ◽  
Flexible Electronics ◽  
High Performance ◽  
Original Method ◽  
Model Parameters ◽  
Ann Model ◽  
Plastic Substrates ◽  
Artificial Neural

Flexible radio-frequency (RF) capacitors and inductors on the plastic substrates have been fabricated and characterized under mechanical bending conditions. A novel method to predict the RF performance for them on different bending states is demonstrated. Artificial neural network (ANN) shows good modeling accuracy for the flexible RF passive components with bending strains from dc to resonant frequency ([Formula: see text] GHz for the capacitor/inductor). More importantly, the automatically generated ANN model, with no need of repeatedly tuning the model parameters, has demonstrated the ability to predict the RF responses for the flexible capacitors and inductors under arbitrary bending conditions with only a few sets of experimental data. Once established, this model can automatically learn the structure of the input date and predict the actual results on specific bending state which can provide an original method to measure the performance for flexible electronics on even extreme bent radius. The ANN model indicates good potential for accurate design, characterization and optimization of the high-performance flexible electronics.

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural

Forecasting travel demand: a comparison of logit and artificial neural network methods

1998 ◽  
Vol 49 (7) ◽  
pp. 717-722 ◽  
Author(s):  
M C M de Carvalho ◽  
M S Dougherty ◽  
A S Fowkes ◽  
M R Wardman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Travel Demand ◽  
Network Methods ◽  
Artificial Neural
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