Development of a radionuclide identification algorithm based on a convolutional neural network for radiation portal monitoring system

Koo, Bon Tak; Lee, Hyun Cheol; Bae, Kihun; Kim, Yongkwon; Jung, Jinhun; Park, Chang Su; Kim, Hong Suk; Min, Chul Hee

doi:10.1016/j.radphyschem.2020.109300

Cited by 23 publications

(8 citation statements)

References 16 publications

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“…Because of the professional characteristics, art courses in colleges and universities require a lot of picture resources and practical training as the basic requirements of teaching activities [10]. For teachers, the main means of classroom teaching activities is to bring visual effects to students in the form of pictures or videos [11]. However, the quality of online resource information is mostly mixed, the distribution of high-quality resources is not concentrated, and the blurring of picture pixels in network resources leads to deviations in the color level resolution and color tendency of paintings [12].…”

Section: Introductionmentioning

confidence: 99%

Application of Art Activities in Colleges and Universities Based on BP Neural Network Algorithm

Liang

Assembaiuly

Akbayeva

2022

Wireless Communications and Mobile Computing

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College education has also widely adopted new media technology, which has changed the previous single teaching method and enriched the teaching content. Art education plays an important role in higher education and is an indispensable part of our country’s quality education. The content, methods, and concepts of art education in colleges and universities are also changing with the advent of the new media era. New courses such as multimedia technology, network animation technology, and network advertising design have been added to the teaching curriculum system of colleges and universities for stimulating students’ interest in learning. The online network platform represented by WeChat has had a profound impact in the field of art education. Based on the technical characteristics of art works, this paper briefly classifies the characteristics of art works; the works perform the extraction of local data (picture elements); secondly, the texture feature parameters of each picture element are extracted by using the image histogram processing technology and the gray level co-occurrence matrix statistical analysis method; finally, a relatively mature machine learning model, BP, is introduced. The neural network algorithm learns the technical style characteristics of each part of the ink and wash work, so that on the whole, it can initially achieve the artistic style learning of the entire ink and wash painting.

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Section: Introductionmentioning

confidence: 99%

Application of Art Activities in Colleges and Universities Based on BP Neural Network Algorithm

Liang

Assembaiuly

Akbayeva

2022

Wireless Communications and Mobile Computing

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“…the French OPERA-AIR; IRSN, 2021a), gamma dose rate monitoring, (ex. French Teleray, Chojnacki, 2021) and radiation monitoring portal at harbour (Koo et al, 2021). ML is also intended to be used by military for the triage and management of victims in the case of radiological event (SSA, 2021).…”

Section: Radionuclides Identification and Detection Of Eventmentioning

confidence: 99%

Artificial intelligence and radiation protection. A game changer or an update?

et al. 2022

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Artificial intelligence (AI) is regarded as one of the most disruptive technology of the century and with countless applications. What does it mean for radiation protection? This article describes the fundamentals of machine learning (ML) based methods and presents the inaugural applications in different fields of radiation protection. It is foreseen that the usage of AI will increase in radiation protection. Consequently, this article explores some of the benefits and also the potential barriers and questions, including ethical ones, that can come out. The article proposes that collaboration between radiation protection professionals and data scientist experts can accelerate and guide the development of the algorithms for effective scientific and technological outcomes.

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“…In spite of that, several studies have been conducted on the spectroscopic applications of RPMs. These primarily involve radioisotope identifiers, signal processing [3][4][5][6], library matching [7,8], and machine learning [9][10][11][12][13][14] are utilized to identify the existence of radioisotopes based on the observed energy spectra.…”

Section: Introductionmentioning

confidence: 99%

Automatic energy calibration of radiation portal monitors using deep learning and spectral remapping

Jeon¹,

Park²,

Park³

et al. 2023

J. Inst.

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Radiation portal monitors comprising large-volume plastic scintillators are commonly used to monitor the smuggling of radioactive materials. Various applications have been proposed to perform radioisotope identification using these monitors. Such applications require calibration of the spectrum measured by the detector to obtain the physical energy spectrum. The relationship between the multichannel readout and energy bins depends on environmental conditions: it implies that energy calibration in radiation portal monitors should be performed periodically, even multiple times in a single day, thus demanding for a simple and fast energy calibration method. In this study, a deep learning model and a spectral remapping method were used to transform the raw detector output into an energy spectrum with constant energy bins. The deep learning model was designed to predict energy calibration parameters based on the channel spectrum of a single radioisotope. The dataset used to train the deep learning model was generated using the spectrum of the radiation portal monitor. A convolutional neural network model was utilized to evaluate the performance. The remapping method was designed to remap calibrated energy bins to fixed energy bins based on the linear interpolation of nearby bins. The performance of the neural network model and of the remapping method were then evaluated based on several measured spectra taken with different conditions, and found to be adequate to fulfill the requirements.

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Development of a radionuclide identification algorithm based on a convolutional neural network for radiation portal monitoring system

Cited by 23 publications

References 16 publications

Application of Art Activities in Colleges and Universities Based on BP Neural Network Algorithm

Application of Art Activities in Colleges and Universities Based on BP Neural Network Algorithm

Artificial intelligence and radiation protection. A game changer or an update?

Automatic energy calibration of radiation portal monitors using deep learning and spectral remapping

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