@inproceedings {2172,
	title = {CAD-based scatter compensation for polychromatic reconstruction of additive manufactured parts},
	booktitle = {IEEE ICIP},
	year = {2021},
	pages = {2948-2952},
	doi = {10.1109/ICIP42928.2021.9506536},
	author = {Domenico Iuso and Ehsan Nazemi and Nathana{\"e}l Six and Bjorn De Samber and Jan De Beenhouwer and Jan Sijbers}
}
@article {2135,
	title = {Combination of X-ray tube and GMDH neural network as a nondestructive and potential technique for measuring characteristics of gas-oil{\textendash}water three phase flows},
	journal = {Measurement},
	volume = {168},
	year = {2021},
	chapter = {108427},
	abstract = {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{\textendash}water volume fractions of a three phase flow. One GMDH neural network was considered for recognizing flow patterns and two GMDH networks were implemented to predict the volume fractions. The recorded photon energy spectra from the two sodium iodide detectors were defined as the inputs of the three GMDH neural networks. The type of flow pattern and volume fractions were the output obtained from the first and the other two GMDH neural networks, respectively. Through the application of the proposed methodology, all of the flow patterns were recognized correctly except one single case. The volume fraction was also predicted with RMS error of less than 3.1.},
	doi = {10.1016/j.measurement.2020.108427},
	author = {Mohammadmehdi Roshani and Giang Phan and Gholam Hossein Roshani and Robert Hanus and Enrico Corniani and Ehsan Nazemi}
}
@article {2137,
	title = {Evaluation of flow pattern recognition and void fraction measurement in two phase flow independent of oil pipeline{\textquoteright}s scale layer thickness},
	journal = {Alexandria Engineering Journal},
	volume = {60},
	year = {2021},
	abstract = {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 flow pattern and gas volume percentage in two phase flow independent of petroleum pipeline{\textquoteright}s scale layer thickness. The proposed system includes a dual energy gamma source, composed of Barium-133 and Cesium-137 radioisotopes, and two sodium iodide detectors for recording the transmitted and scattered photons. Support Vector Machine was implemented for regime identification and Multi-Layer Perceptron with Levenberg Marquardt algorithm was utilized for void fraction prediction. Total count in the scattering detector and counts under photo peaks of Barium-133 and Cesium-137 were assigned as the inputs of networks. The results show the ability of presented system to identify the annular regime and measure the void fraction independent of petroleum pipeline{\textquoteright}s scale layer thickness.},
	author = {Mohammadmehdi Roshani and Giang T.T. Phan and Gholam Hossein Roshani and Robert Hanus and Trung Duong and Enrico Corniani and Ehsan Nazemi and ElMostafa Kalmouni}
}
@article {2163,
	title = {Monte-Carlo-Based Estimation of the X-ray Energy Spectrum for CT Artifact Reduction},
	journal = {Applied Sciences},
	volume = {11},
	year = {2021},
	chapter = {3145},
	doi = {https://doi.org/10.3390/app11073145},
	author = {Ehsan Nazemi and Nathana{\"e}l Six and Domenico Iuso and Bjorn De Samber and Jan Sijbers and Jan De Beenhouwer}
}
@article {2136,
	title = {Application of GMDH neural network technique to improve measuring precision of a simplified photon attenuation based two-phase flowmeter},
	journal = {Flow Measurement and Instrumentation},
	volume = {75},
	year = {2020},
	abstract = {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 to estimate the gas volumetric percentage in a two-phase flow without any dependency to flow regime pattern. The simple photon attenuation based system is comprised of a cobalt-60 radioisotope and only one 25.4 mm {\texttimes} 25.4 mm sodium iodide crystal detector. Four extracted features from recorded photon spectrum in sodium iodide crystal detector were used as the inputs of GMDH neural network. Equations related to the combination of the features and the error rate of each approximation is also reported in this paper. Applying the mentioned technique, the gas volumetric percentage in an oil-gas two phase flow was determined with the root mean square error of less than 2.71 without any dependency to the flow pattern. The obtained measuring precision in this study is at least 2.1 times better than reported in previous studies.},
	doi = {10.1016/j.flowmeasinst.2020.101804},
	author = {Mohammadmehdi Roshani and Mohammad Amir Sattari and Peshawa Jammal Muhammad Ali and Gholam Hossein Roshani and Enrico Corniani and Ehsan Nazemi}
}
@article {2134,
	title = {Proposing a gamma radiation based intelligent system for simultaneous analyzing and detecting type and amount of petroleum by-products},
	journal = {Nuclear Engineering and Technology},
	year = {2020},
	type = {Research regular paper},
	abstract = {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 four different petroleum by-products. The detection system is composed of a dual energy gamma source, including americium-241 and barium-133 radioisotopes, and one 2.54 cm {\texttimes} 2.54 cm sodium iodide detector for recording the transmitted photons. Two signals recorded in transmission detector, namely the counts under photo peak of Americium-241 with energy of 59.5 keV and the counts under photo peak of Barium-133 with energy of 356 keV, were applied to the ANN as the two inputs and volume percentages of petroleum by-products were assigned as the outputs.},
	author = {Mohammadmehdi Roshani and Giang Phan and Rezhna Hassan Faraj and Nhut-Huan Phan and Gholam Hossein Roshani and Enrico Corniani and Ehsan Nazemi}
}
