Precise gamma based two-phase flow meter using frequency feature extraction and only one detector

Hosseini, Siavash; Roshani, Gholam Hossein; Setayeshi, Saeed

doi:10.1016/j.flowmeasinst.2020.101693

Cited by 37 publications

(23 citation statements)

References 22 publications

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“…The transformed dataset is returned using IFFT. In addition, other studies used the FFT algorithm to perform feature extraction [28,29,37]. For instance, Ansari used Fast Fourier Transform (FFT) to extract features in the EEG dataset [28], with better accuracy than other classifiers.…”

Section: Fast Fourier Transform and Inverse Fast Fourier Transformmentioning

confidence: 99%

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Determining threshold value on information gain feature selection to increase speed and prediction accuracy of random forest

2021

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Feature selection is a pre-processing technique used to remove unnecessary characteristics, and speed up the algorithm's work process. A part of the technique is carried out by calculating the information gain value of each dataset characteristic. Also, the determined threshold rate from the information gain value is used in feature selection. However, the threshold value is used freely or through a rate of 0.05. Therefore this study proposed the threshold rate determination using the information gain value’s standard deviation generated by each feature in the dataset. The threshold value determination was tested on 10 original datasets transformed by FFT and IFFT and classified using Random Forest. On processing the transformed dataset with the proposed threshold this study resulted in lower accuracy and longer execution time compared to the same process with Correlation-Base Feature Selection (CBF) and a standard 0.05 threshold method. Similarly, the required accuracy value is lower when using transformed features. The study showed that by processing the original dataset with a standard deviation threshold resulted in better feature selection accuracy of Random Forest classification. Furthermore, by using the transformed feature with the proposed threshold excluding the imaginary numbers leads to a faster average time than the three methods compared.

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Section: Fast Fourier Transform and Inverse Fast Fourier Transformmentioning

confidence: 99%

“…Therefore, based on the results of these studies, FFT is applied in this study. Besides being applied to the dataset, FFT is also applied to feature extraction [28,29], and selection [30]. Gowid et al [30] used used this process to develop robust, fast, and automated feature selection algorithms for mechanical systems.…”

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confidence: 99%

Determining threshold value on information gain feature selection to increase speed and prediction accuracy of random forest

2021

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“…The type of flow pattern impacts the efficiency of the separation process in such a way that the percentage of each component indicates whether the drilling needs to be stopped or not. The mixture of gamma radiation and ANNs have contributed in a lot of researches as a practical tool [1][2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

“…In the research, the input ports of the GMDH neural network were time-domain features, which were extracted from the recorded spectrum. In similar studies, to establish the volume percentages and type of flow patterns with high accuracy, researchers evaluated many time-and frequency-domain characteristics, and they presented the best separator characteristics using an innovative method [5,6]. Some structures of MLP neural networks were investigated for the volume fraction calculation in 3-phase flows [7].…”

Section: Introductionmentioning

confidence: 99%

Application of Feature Extraction and Artificial Intelligence Techniques for Increasing the Accuracy of X-ray Radiation Based Two Phase Flow Meter

et al. 2021

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The increasing consumption of fossil fuel resources in the world has placed emphasis on flow measurements in the oil industry. This has generated a growing niche in the flowmeter industry. In this regard, in this study, an artificial neural network (ANN) and various feature extractions have been utilized to enhance the precision of X-ray radiation-based two-phase flowmeters. The detection system proposed in this article comprises an X-ray tube, a NaI detector to record the photons, and a Pyrex-glass pipe, which is placed between detector and source. To model the mentioned geometry, the Monte Carlo MCNP-X code was utilized. Five features in the time domain were derived from the collected data to be used as the neural network input. Multi-Layer Perceptron (MLP) was applied to approximate the function related to the input-output relationship. Finally, the introduced approach was able to correctly recognize the flow pattern and predict the volume fraction of two-phase flow’s components with root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of less than 0.51, 0.4 and 1.16%, respectively. The obtained precision of the proposed system in this study is better than those reported in previous works.

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“…This data can be presented in a continuous wave form, so that it can be presented briefly by using the FFT algorithm [19]. Other researchers also used the FFT algorithm to perform feature extraction [20], [21]. Pratama, in his research stated that real and imaginary numbers resulted from the FFT calculation can be used for the feature selection algorithm [22].…”

Section: Introductionmentioning

confidence: 99%

Determining Threshold Value on Information Gain Feature Selection to Increase Speed and Prediction Accuracy of Random Forest

Prasetiyowati

Maulidevi

Surendro

2020

Preprint

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Feature selection is a preprocessing technique aims to remove the unnecessary features and speed up the algorithm's work process. One of the feature selection techniques is by calculating the information gain value of each feature in a dataset. From the information gain value obtained, then the determined threshold value will be used to make feature selection. Generally, the threshold value is used freely, or using a value of 0.05. This study proposed the determination of the threshold value using the standard deviation of the information gain value generated by each feature in the dataset. The determination of this threshold value was tested on ten original datasets and datasets that had been transformed by FFT and IFFT, then classified using Random Forest. The results of the average value of accuracy and the average time required from the Random Forest classification using the proposed threshold value are better compared to the results of feature selection with a threshold value of 0.05 and the Correlation-Base Feature Selection algorithm. Likewise, the result of the average accuracy value of the proposed threshold using a transformed dataset in terms are better than the threshold value of 0.05 and the Correlation-Base Feature Selection algorithm. However, the calculation results for the average time required are higher (slower).

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Precise gamma based two-phase flow meter using frequency feature extraction and only one detector

Cited by 37 publications

References 22 publications

Determining threshold value on information gain feature selection to increase speed and prediction accuracy of random forest

Determining threshold value on information gain feature selection to increase speed and prediction accuracy of random forest

Application of Feature Extraction and Artificial Intelligence Techniques for Increasing the Accuracy of X-ray Radiation Based Two Phase Flow Meter

Determining Threshold Value on Information Gain Feature Selection to Increase Speed and Prediction Accuracy of Random Forest

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