Wilkinson Power Divider with Band-pass Filtering Response and Harmonics Suppression Using Open and Short Stubs

Lotfi, Saeedeh; Roshani, Saeed; Roshani, Sobhan; Gilan, Maryam Shirzadian

doi:10.1515/freq-2019-0200

Cited by 45 publications

(10 citation statements)

References 13 publications

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“…It is worth mentioning that in recent years, various mathematical approaches have been used in different research fields, such as electrical and computer engineering [32][33][34][35][36][37][38][39][40][41][42], mechanical engineering [43][44][45][46][47][48][49], civil and urban engineering [50][51][52][53], biomedical engineering [54], industrial engineering [55][56][57][58] and physics [59], but among them, ANN is the most well-known and powerful numerical tool for prediction and classification [60][61][62][63][64][65][66][67].A multilayer perceptron (MLP) is one of the most powerful and commonly used classes of feed-forward artificial neural network (ANN). A multilayer perceptron, in its simplest form, consists of three essential layers: 1.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Enhanced Gamma-Ray Attenuation-Based Detection System Using an Artificial Neural Network

et al. 2022

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Scale deposition is the accumulation of various materials in the walls of transmission lines and unwanted parts in the oil and gas production system. It is a leading moot point in all transmission lines, tanks, and petroleum equipment. Scale deposition leads to drastic detrimental problems, reduced permeability, pressure and production losses, and direct financial losses due to the failure of some equipment. The accumulation of oil and gas leads to clogged pores and obstruction of fluid flow. Considering the passage of a two-phase flow, our study determines the thickness of the scale, and the flow regime is detected with the help of two Multilayer Perceptron (MLP) networks. First, the diagnostic system consisting of a dual-energy source, a steel pipe, and a NaI detector was implemented, using the Monte Carlo N Particle Code (MCNP). Subsequently, the received signals were processed, and properties were extracted using the wavelet transform technique. These features were considered as inputs of an Artificial Neural Network (ANN) model used to determine the type of flow regimes and predict the scale thickness. By accurately classifying the flow regimes and determining the scale inside the pipe, our proposed method provides a platform that could enhance many areas of the oil industry.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Enhanced Gamma-Ray Attenuation-Based Detection System Using an Artificial Neural Network

et al. 2022

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“…In recent years, different mathematical approaches have been used for analyzing data in many engineering fields [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39], but it has been proven that the Artificial Neural network (ANN) is the most well-known and powerful tool for prediction and classification. ANNs can be expressed as a mathematical system which consists of several neurons performing in a parallel way, produced in either one or several layers [40,41].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Accurate Flow Regime Classification and Void Fraction Measurement in Two-Phase Flowmeters Using Frequency-Domain Feature Extraction and Neural Networks

et al. 2022

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Two-phase flow is very important in many areas of science, engineering, and industry. Two-phase flow comprising gas and liquid phases is a common occurrence in oil and gas related industries. This study considers three flow regimes, including homogeneous, annular, and stratified regimes ranging from 5–90% of void fractions simulated via the Mont Carlo N-Particle (MCNP) Code. In the proposed model, two NaI detectors were used for recording the emitted photons of a cesium 137 source that pass through the pipe. Following that, fast Fourier transform (FFT), which aims to transfer recorded signals to frequency domain, was adopted. By analyzing signals in the frequency domain, it is possible to extract some hidden features that are not visible in the time domain analysis. Four distinctive features of registered signals, including average value, the amplitude of dominant frequency, standard deviation (STD), and skewness were extracted. These features were compared to each other to determine the best feature that can offer the best separation. Furthermore, artificial neural network (ANN) was utilized to increase the efficiency of two-phase flowmeters. Additionally, two multi-layer perceptron (MLP) neural networks were adopted for classifying the considered regimes and estimating the volumetric percentages. Applying the proposed model, the outlined flow regimes were accurately classified, resulting in volumetric percentages with a low root mean square error (RMSE) of 1.1%.

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“…In recent years, different mathematical approaches have been used for analyzing data in plenty of engineering fields [ 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 ], but it has been proven that an artificial neural network (ANN) is the most well-known and powerful tool for prediction and classification. RBF neural networks, abbreviated as “Radial Basis Function”, are special types of artificial neural networks that are distance-based and measure the similarity between data based on distance.…”

Section: Radial Basis Function Neural Networkmentioning

confidence: 99%

Increasing the Efficiency of a Control System for Detecting the Type and Amount of Oil Product Passing through Pipelines Based on Gamma-Ray Attenuation, Time Domain Feature Extraction, and Artificial Neural Networks

et al. 2022

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Instantaneously determining the type and amount of oil product passing through pipelines is one of the most critical operations in the oil, polymer and petrochemical industries. In this research, a detection system is proposed in order to monitor oil pipelines. The system uses a dual-energy gamma source of americium-241 and barium-133, a test pipe, and a NaI detector. This structure is implemented in the Monte Carlo N Particle (MCNP) code. It should be noted that the results of this simulation have been validated with a laboratory structure. In the test pipe, four oil products—ethylene glycol, crude oil, gasoil, and gasoline—were simulated two by two at various volume percentages. After receiving the signal from the detector, the feature extraction operation was started in order to provide suitable inputs for training the neural network. Four time characteristics—variance, fourth order moment, skewness, and kurtosis—were extracted from the received signal and used as the inputs of four Radial Basis Function (RBF) neural networks. The implemented neural networks were able to predict the volume ratio of each product with great accuracy. High accuracy, low cost in implementing the proposed system, and lower computational cost than previous detection methods are among the advantages of this research that increases its applicability in the oil industry. It is worth mentioning that although the presented system in this study is for monitoring of petroleum fluids, it can be easily used for other types of fluids such as polymeric fluids.

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Wilkinson Power Divider with Band-pass Filtering Response and Harmonics Suppression Using Open and Short Stubs

Cited by 45 publications

References 13 publications

Enhanced Gamma-Ray Attenuation-Based Detection System Using an Artificial Neural Network

Enhanced Gamma-Ray Attenuation-Based Detection System Using an Artificial Neural Network

Accurate Flow Regime Classification and Void Fraction Measurement in Two-Phase Flowmeters Using Frequency-Domain Feature Extraction and Neural Networks

Increasing the Efficiency of a Control System for Detecting the Type and Amount of Oil Product Passing through Pipelines Based on Gamma-Ray Attenuation, Time Domain Feature Extraction, and Artificial Neural Networks

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