Deep learning in nuclear industry: A survey

Tang, Chenwei; Yu, Caiyang; Gao, Yi; Jian-ming, Chen; Yang, Jiaming; Lang, Jiuling; Liu, Chuan; Ling, Zhong; He, Zhenan; Lv, Jiancheng

doi:10.26599/bdma.2021.9020027

Cited by 18 publications

(6 citation statements)

References 94 publications

(138 reference statements)

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“…The computing platform needs graphical and tensor processing units to reduce the processing time. DL has been applied to big data [ 58 ], medical [ 59 , 60 ], computer vision [ 61 , 62 ], power systems [ 63 ], nuclear power [ 64 ], etc. ( Figure 6 ).…”

Section: Tbi Detection Using Artificial Intelligencementioning

confidence: 99%

Traumatic Brain Injury (TBI) Detection: Past, Present, and Future

Alouani

Elfouly

2022

Biomedicines

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Traumatic brain injury (TBI) can produce temporary biochemical imbalance due to leaks through cell membranes or disruption of the axoplasmic flow due to the misalignment of intracellular neurofilaments. If untreated, TBI can lead to Alzheimer’s, Parkinson’s, or total disability. Mild TBI (mTBI) accounts for about about 90 percent of all TBI cases. The detection of TBI as soon as it happens is crucial for successful treatment management. Neuroimaging-based tests provide only a structural and functional mapping of the brain with poor temporal resolution. Such tests may not detect mTBI. On the other hand, the electroencephalogram (EEG) provides good spatial resolution and excellent temporal resolution of the brain activities beside its portability and low cost. The objective of this paper is to provide clinicians and scientists with a one-stop source of information to quickly learn about the different technologies used for TBI detection, their advantages and limitations. Our research led us to conclude that even though EEG-based TBI detection is potentially a powerful technology, it is currently not able to detect the presence of a mTBI with high confidence. The focus of the paper is to review existing approaches and provide the reason for the unsuccessful state of EEG-based detection of mTBI.

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Section: Tbi Detection Using Artificial Intelligencementioning

confidence: 99%

Traumatic Brain Injury (TBI) Detection: Past, Present, and Future

Alouani

Elfouly

2022

Biomedicines

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“…Similarly, unforeseen circumstances development will also be considered for the new development and integration in the Micro Reactor technology. The Third Phase will be the experimental phase which demonstrates the suggested AI and ML-based Robotic systems for the Micro Reactor development [12,13].…”

Section: Future Workmentioning

confidence: 99%

Application of Artificial Intelligence and Machine Learning in Robotic Systems for Nuclear Micro Reactor Development and Manufacturing

Rathod,

Patel

2024

J Arti Inte & Cloud Comp

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In the dynamic FinTech industry, proactively managing risks has become vital in securing long-term viability and resilience for lending and investment activities. Nevertheless, the industry looks promising, concurrently embracing sophisticated technologies with all accompanying financial innovations. However, the high risks due to introducing innovation make the process even more problematic [1]. The emergence of predictive analytics, an outstanding data-driven technique, as a vital tool in detecting, analyzing, and organizing plans to avoid danger rather than to deal with the aftermath has had an immense impact. This article seeks to illustrate the relevance and utility of predictive analytics within the FinTech banking and investment sectors to the extent of transforming institutional approaches to risk management.

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“…With the development of AI and the proposal of "Industry 4.0", more and more AI technologies are introduced into the modern industry chain to improve production efficiency, reduce operation cost, improve operation safety, and realize risk avoidance. Meanwhile, Deep Learning (DL), as an important technology of AI, has made amazing progress in theoretical and applied research in the industry, which vigorously promotes the development of informatization, digitization, and intelligence of the modern industry [26] . By fitting the functional relationship between operating variables and product, these methods can analyze the reaction process and its influence mechanism from multiple angles in an all-round way, avoiding the over simplification of a large number of factors in the mechanical modeling and finding the relationship between the input and output of system process when the process mechanism is unknown or too complex.…”

Section: Fcc Product Analysis and Optimizationmentioning

confidence: 99%

Artificial Intelligence Methods Applied to Catalytic Cracking Processes

Yang

Lei

et al. 2023

Big Data Min. Anal.

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Fluidic Catalytic Cracking (FCC) is a complex petrochemical process affected by many highly non-linear and interrelated factors. Product yield analysis, flue gas desulfurization prediction, and abnormal condition warning are several key research directions in FCC. This paper will sort out the relevant research results of the existing Artificial Intelligence (AI) algorithms applied to the analysis and optimization of catalytic cracking processes, with a view to providing help for the follow-up research. Compared with the traditional mathematical mechanism method, the AI method can effectively solve the difficulties in FCC process modeling, such as high-dimensional, nonlinear, strong correlation, and large delay. AI methods applied in product yield analysis build models based on massive data. By fitting the functional relationship between operating variables and products, the excessive simplification of mechanism model can be avoided, resulting in high model accuracy. AI methods applied in flue gas desulfurization can be usually divided into two stages: modeling and optimization. In the modeling stage, data-driven methods are often used to build the system model or rule base; In the optimization stage, heuristic search or reinforcement learning methods can be applied to find the optimal operating parameters based on the constructed model or rule base. AI methods, including data-driven and knowledge-driven algorithms, are widely used in the abnormal condition warning. Knowledge-driven methods have advantages in interpretability and generalization, but disadvantages in construction difficulty and prediction recall. While the data-driven methods are just the opposite. Thus, some studies combine these two methods to obtain better results.

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Deep learning in nuclear industry: A survey

Cited by 18 publications

References 94 publications

Traumatic Brain Injury (TBI) Detection: Past, Present, and Future

Traumatic Brain Injury (TBI) Detection: Past, Present, and Future

Application of Artificial Intelligence and Machine Learning in Robotic Systems for Nuclear Micro Reactor Development and Manufacturing

Artificial Intelligence Methods Applied to Catalytic Cracking Processes

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