Ehsan Nazemi's research while affiliated with University of Antwerp and other places
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Publications (32)
Neuromorphic engineering is an essential science field which incorporates the basic aspects of issues together such as: physics, mathematics, electronics, etc. The primary block in the Central Nervous System (CNS) is neurons that have functional roles such as: receiving, processing, and transmitting data in the brain. This paper presents Wilson Mul...
The main required organ of the biological system is the Central Nervous System (CNS), which can influence the other basic organs in the human body. The basic elements of this important organ are neurons, synapses, and glias (such as astrocytes, which are the highest percentage of glias in the human brain). Investigating, modeling, simulation, and h...
Scale formation inside oil and gas pipelines is always one of the main threats to the efficiency of equipment and their depreciation. In this study, an artificial intelligence method method is presented to provide the flow regime and volume percentage of a two-phase flow while considering the presence of scale inside the test pipe. In this non-inva...
One of the factors that significantly affects the efficiency of oil and gas industry equipment is the scales formed in the pipelines. In this innovative, non-invasive system, the inclusion of a dual-energy gamma source and two sodium iodide detectors was investigated with the help of artificial intelligence to determine the flow pattern and volume...
This paper presents a methodology to monitor the liquid petroleum products which pass through transmission pipes. A simulation setup consisting of an X-ray tube, a detector, and a pipe was established using a Monte Carlo n-particle X-version transport code to investigate a two-by-two mixture of four different petroleum products, namely, ethylene gl...
To the best knowledge of the authors, in all the former studies, a fixed value of X-ray tube voltage has been used for investigating gas–liquid two-phase flow characteristics, while the energy of emitted X-ray radiations that depends on the tube voltage can significantly affect the measurement precision of the system. The purpose of present study i...
Astrocyte cells form the largest cell population in the brain and can influence neuron behavior. These cells provide appropriate feedback control in regulating neuronal activities in the Central Nervous System (CNS). This paper presents a set of equations as a model to describe the interactions between neurons and astrocyte. A VHDL–AMS-based tripar...
Measuring fluid characteristics is of high importance in various industries such as the polymer, petroleum, and petrochemical industries, etc. Flow regime classification and void fraction measurement are essential for predicting the performance of many systems. The efficiency of multiphase flow meters strongly depends on the flow parameters. In thi...
Deposition of scale layers inside pipelines leads to many problems, e.g., reducing the internal diameter of pipelines, damage to drilling equipment because of corrosion, increasing energy consumption because of decreased efficiency of equipment, and shortened life, etc., in the petroleum industry. Gamma attenuation could be implemented as a non-inv...
In this research, a methodology consisting of an X-ray tube, one Pyrex-glass pipe, and two NaI detectors was investigated to determine the type of flow regimes and volume fractions of gas-oil-water three-phase flows. Three prevalent flow patterns—namely annular, stratified, and homogenous—in various volume percentages—10% to 80% with the step of 10...
Scale deposits can reduce equipment efficiency in the oil and petrochemical industry. The gamma attenuation technique can be used as a non-invasive effective tool for detecting scale deposits in petroleum pipelines. The goal of this study is to propose a dual-energy gamma attenuation method with radial basis function neural network (RBFNN) to deter...
To the best knowledge of the authors, in former studies in the field of measuring volume fraction of gas, oil, and water components in a three-phase flow using gamma radiation technique, the existence of a scale layer has not been considered. The formed scale layer usually has a higher density in comparison to the fluid flow inside the oil pipeline...
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-...
Radiation-based instruments have been widely used in petrochemical and oil industries to monitor liquid products transported through the same pipeline. Different radioactive gamma-ray emitter sources are typically used as radiation generators in the instruments mentioned above. The idea at the basis of this research is to investigate the use of an...
In this paper, the feasibility of using an X-ray tube instead of radioisotope sources for measuring volume fractions of gas, oil, and water in two typical flow regimes of three-phase flows, namely, annular and stratified, is evaluated. This study’s proposed detection system is composed of an X-ray tube, a 1 inch × 1 inch NaI detector, and one Pyrex...
In this paper, the implementation of artificial neural networks (multilayer perceptron [MLP] and radial base functions [RBF]) and the upgraded Markov chain model have been studied and performed to identify the human behavior patterns during rock, paper, and scissors game. The main motivation of this research is the design and construction of an int...
The main objective of the present research is to combine the effect of scale thickness on the flow pattern and characteristics of two-phase flow that is used in oil industry. In this regard, an intelligent nondestructive technique based on combination of gamma radiation attenuation and artificial intelligence is proposed to determine the type of fl...
Precise metering of the void fraction is one of the important problems in the oil, chemical and petrochemical industries. For void fraction measurement, there are different kinds of sensors with different configurations. In this regard, the capacitance-based sensor and gamma-ray attenuation-based sensor are very well known as two most accurate and...
In this investigation, a fan-beam photon attenuation based system, including one X-ray tube and two sodium iodide crystal detectors, combined with group method of data handling (GMDH) neural network is proposed to recognize type of flow regime and predict gas-oil-water volume fractions of a three phase flow. One GMDH neural network was considered f...
Determining the type of flow pattern and gas volumetric percentage with high precision is one of the vital topics for researchers in this field. For this, in this paper, three different types of liquid-gas two-phase flow regimes, namely annular, stratified, and homogenous were simulated in various gas volumetric percentages ranging from 5% to 90%....
It is important for operators of poly-pipelines in petroleum industry to continuously monitor characteristics of transferred fluid such as its type and amount. To achieve this aim, in this study a dual energy gamma attenuation technique in combination with artificial neural network (ANN) is proposed to simultaneously determine type and amount of fo...
Multiphase flowmeters have an important role to play in the industry and any attempts that lead to improvements in this field are of great interest. In the current study, group method of data handling (GMDH) technique was applied in order to increase measuring precision of a simple photon attenuation based two-phase flowmeter that has the ability t...
In this paper, X-ray tube is introduced as a potential alternative for radioisotope sources used in radiation based liquid-gas two-phase flowmeters. X-ray tubes have lots of advantages over the radioisotope sources such as having an adjustable emitting photon's energy, being safer from point of view of radiation health physics during the transporta...
This study presents a novel method for determining the density of liquid phase in annular regime of two phase flows by utilizing dual modality densitometry technique and artificial neural network (ANN) model of radial basis function (RBF). The detection system is comprised of one 137Cs radioactive source, one 1 inch sodium iodide (NaI) scintillatio...
The use of adaptive neuro-fuzzy inference system (ANFIS) has been reported for predicting the volume fractions in a gas–oil–water multiphase system. In fact, the volume fractions in the annular three-phase flow are measured based on a dual energy metering system consisting of \(^{152}\hbox {Eu}\) and \(^{137}\hbox {Cs}\) and one NaI detector using...
The void fraction is one of the most important parameters characterizing a multiphase flow. The prediction of the performance of any system operating with more than single phase relies on our knowledge and ability to measure the void fraction. In this work, a validated simulation study was performed in order to predict the void fraction independent...
The problem of how to precisely measure the volume fractions of oil-gas-water mixtures in a pipeline remains as one of the main challenges in the petroleum industry. This paper reports the capability of Radial Basis Function (RBF) in forecasting the volume fractions in a gas-oil-water multiphase system. Indeed, in the present research, the volume f...
Changes of fluid properties (especially density) strongly affect the performance of radiation-based multiphase flow meter and could cause error in recognizing the flow pattern and determining void fraction. In this work, we proposed a methodology based on combination of multi-beam gamma ray attenuation and dual modality densitometry techniques usin...
Strontium (Sr) and Cesium (Cs) are two important nuclear fission products which are present in the radioactive wastewater resulting from nuclear power plants. They should be treated by considering environmental and economic aspects. In this study, artificial neural network (ANN) was implemented to evaluate the optimal experimental conditions in con...
Citations
... Reference [43] concerns the Izhikevich model implementation. The authors presented the detailed state-of-the-art and a new approach to realizing Izhikevich neurons. ...
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... There are many analytical methods for approximating complex and non-linear functions, among which the artificial neural network is known as a reliable, intelligent, and accurate tool. In various fields of science, including engineering, medicine, and experimental sciences, neural networks are used for modeling, prediction, classification, pattern recognition, etc. [33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51]. One of the most famous and widely used neural networks is the radial basis function (RBF) neural network. ...
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... Recently, optimization algorithms [8,9] and neural network techniques have been used to improve performance of electronic circuits, such as in [10][11][12][13][14], which also have been used in the designing of the BPF [15] and coupler [16]. In [15], a narrow band BPF at 2.2 GHz is designed, with a hairpin structure. ...
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... Many researchers have shifted in recent years to using an X-ray source instead of gamma in their structures to avoid issues such as requiring personnel to wear protective clothing when working with this device (due to its inability to be turned off). X-ray tubes were used to distinguish multiphase flow characteristics [10,13]. In [10], for instance, the aforementioned complementary source was used. ...
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... If the created neural network can show accuracy on these data sets, it is guaranteed to provide acceptable performance under operational conditions. Numerous aspects of chemical and petrochemical engineering [27][28][29][30][31][32], electrical engineering [33][34][35][36][37][38], civil engineering [39,40], instrumentation and control engineering [41][42][43], and nanoelectronic [44][45][46][47] problems have recently been addressed by computational and numerical calculations, as well as Digital Signal Processing (DSP) and particularly Artificial Neural Networks (ANN), a very potent mathematical tool. ...
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... This approach demonstrates the neural information encoding mechanism through calcium oscillations. Moreover, a pre-synaptic neuron, the synaptic terminal, a post-synaptic neuron, and an astrocyte cell that acts as a controller module to the neurons' spiking frequency are all included in the VHDL-AMS-based tripartite synapse model, and are demonstrated in [12]. In one of our own previous studies [13], we presented a bit-efficient digital astrocyte-neuron circuit with low computational cost that implemented a linear approximation of the astrocytes' calcium dynamics along with a modified version of the Izhikevich neuron model provided by [14] and [15]. ...
... As it can be used to optimize products and reduce costs. A large and growing body of literature has investigated the use of AI in several industries [28][29][30][31]. Therefore, the use of AI can be a good alternative to be used for gaining more information from capacitance-based sensors [32]. ...
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... As it can be used to optimize products and reduce costs. A large and growing body of literature has investigated the use of AI in several industries [28][29][30][31]. Therefore, the use of AI can be a good alternative to be used for gaining more information from capacitance-based sensors [32]. ...
![[object Object]](https://i1.rgstatic.net/ii/profile.image/1168094173102080-1655506761043_Q64/Saba-Amiri-6.jpg)
... Temporal features extracted from the signals received by the detector were also used to train two MLP neural networks in their proposed structure. The study [11] looked at three-phase flows and simulated them in three different regimes (annular, stratified, and homogeneous) at varying volumes. Three RBF neural networks were developed in this study and trained using the frequency characteristics of the received signals, yielding satisfactory results. ...
... There are many analytical methods for approximating complex and non-linear functions, among which the artificial neural network is known as a reliable, intelligent, and accurate tool. In various fields of science, including engineering, medicine, and experimental sciences, neural networks are used for modeling, prediction, classification, pattern recognition, etc. [33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51]. One of the most famous and widely used neural networks is the radial basis function (RBF) neural network. ...
