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2022 29th International Conference on nuclear engineering, ICONE 2022

作者： Cao, Xinlei Lu, Zhenming Feng, Yuxiao Bai, Juan Liu, Bing Shen, Ke College of Materials Science and Engineering Hunan University Hunan Changsha China Hunan Province Key Laboratory for Advanced Carbon Materials and Applied Technology Hunan University Hunan Changsha China Hubei Province Key Laboratory of Coal Conversion and New Carbon Materials Wuhan University of Science and Technology Hubei Wuhan China Inner Mongolia Jincai Mining Co. Ltd. Inner Mongolia Hohhot China Institute of Nuclear and New Energy Technology Tsinghua University Beijing China

ISBN: (纸本)9780791886502

Matrix graphite (MG) constitutes a major component of the fuel element in high-temperature gas-cooled reactors (HTRs), and its performance directly affects the economy and safety of HTRs. A3-3 MG, developed in Germany in the last century, is still used today without significant changes. However, with the successful commercialization of HTR, there is an urgent need to develop MG that is suitable for the characteristics of each country. In this paper, comparative studies on the powder processing of natural flake graphite were carried out based on the raw materials from Inner Mongolia. First, the influence of the sequence of purification and pulverization on the basic properties of graphite powder was studied. Second, the influence of the degree of spheroidization of the natural flake graphite powder on the properties of the MG was further investigated, and the controlling mechanism of the spheroidization process on the microstructure and physical properties of the MG was revealed. The results of this study provide important insights for optimizing the preparation process of HTR fuel element MG. © 2022 by ASME.

关键词： Gas cooled reactors

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Hierarchical Porous Cellulose Monolith Prepared by Thermally Induced Phase Separation for High-Efficiency Uranium Adsorption

SSRN

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SSRN 2023年

作者： Sun, Peng Ma, Lin Liao, Jian Wang, Jiejun Huang, Chen Xu, Lu Wang, Jie Fu, Mengtao Zhou, Qingzhi Ma, Hongjuan Hunan Provincial Key Laboratory of Radon Institute of Nuclear Science and Technology University of South China Hengyang421001 China Shanghai Applied Radiation Institute School of Environmental and Chemical Engineering Shanghai University Shanghai200444 China Shanghai Institute of Applied Physics Chinese Academy of Sciences Shanghai201800 China College of Ocean Engineering Guangxi Key Laboratory of Optoelectronic Information Processing Guilin University of Electronic Technology Beihai536000 China

The development and application of new and efficient adsorption materials are key issues in the treatment of nuclear wastewater. This paper introduces a novel hierarchical porous amidoxime cellulose monolith synthesized through thermally induced phase separation (TIPS) using low-cost, biodegradable, and renewable natural cellulose. This monolith demonstrates remarkable efficacy in adsorbing uranyl ion. The inherent hydrophilicity of natural cellulose, coupled with the hierarchical porous structure achieved through TIPS, imparts strong mass transfer capabilities to the material. The adsorption properties of this amidoxime cellulose monolith for uranyl ions were evaluated in both batch and flow-through column adsorption scenarios. Remarkably, the adsorption saturation was achieved within a concise period of 13 h in batch adsorption. The hierarchical porous structure of the amidoxime cellulose monolith exhibited exceptional adsorption performance for uranyl ion, achieving a removal rate of 99.99%. Moreover, this removal rate was sustained for 200 min, and the monolith proved recyclable for at least 5 cycles in flow-through column adsorption tests involving nuclear wastewater. When used for adsorption in real wastewater environments, the material achieved the removal of over 99.96% of total α and β radioactivity. These findings underscore the high efficiency of the newly developed hierarchical porous amidoxime cellulose monolith in uranyl ion adsorption. They emphasize its substantial potential for the efficient adsorption and removal of uranyl ion from nuclear wastewater. © 2023, The Authors. All rights reserved.

关键词： Cellulose

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An improved SNIP denoising algorithm applied in EDXRF

An improved SNIP denoising algorithm applied in EDXRF

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2019 International Conference on applied Machine Learning and Data Science, ICAMLDS 2019

作者： Ge, Liangquan Li, Hui Tang, Chuanfeng Li, Fei Zhu, Kai Yang, Jin Applied Nuclear Technology in Geosciences Key Laboratory Sichuan Province China College of Nuclear Technology and Automation Engineering Chengdu University of Technology China

Based on the phenomenon of peak height reduction in existing denoising algorithms, this paper modifies the traditional SNIP algorithm, proposes an improved SNIP algorithm and applies it to EDXRF denoising. Two evaluation parameters RMSE and PCC are used to compare the improved SNIP algorithm with the traditional SNIP algorithm. The experimental results show that the improved SNIP algorithm has great advantages both in similarity and relevance, and the results of the improved SNIP algorithm are more consistent with the characteristics of the original data peak than the original SNIP algorithm, which is beneficial to the qualitative and quantitative analysis of the energy dispersive X-ray fluorescence spectrum. © Published under licence by IOP Publishing Ltd.

关键词： Energy dispersive spectroscopy

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Application of small sample BP neural network in quantitative analysis of EDXRF

Application of small sample BP neural network in quantitativ...

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2019 International Conference on applied Machine Learning and Data Science, ICAMLDS 2019

作者： Li, Fei Tang, Zhuoyao Ge, Liangquan Wu, Nanxing Cheng, Feng Sun, Kun Applied Nuclear Technology in Geosciences Key Laboratory Sichuan Province China College of Nuclear Technology and Automation Engineering Chengdu University of Technology China

Quantitative analysis of EDXRF is affected by matrix effect, randomness of nuclear radiation, interaction of elements, statistical fluctuation of radiation detection process, etc. Its algorithm needs to consider many factors, and the established functional relationship is often a complex non-linear function. Therefore, effective quantitative analysis method has always been the key research direction of spectral resolution technology. In this paper, we use MATLAB software to study the effect of BP neural network in quantitative analysis of EDXRF by establishing the non-linear relationship between the counting rate of each element and the content of a single element in a small sample. The experimental results show that the small sample neural network can establish a stable structure and be applied to quantitative analysis of EDXRF, but the number of samples restricts the accuracy of prediction results, most of which can only guarantee 20% to 30% relative error. © Published under licence by IOP Publishing Ltd.

关键词： MATLAB

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Research on gamma spectrum semi-quantitative analysis based on convolutional neural network

Research on gamma spectrum semi-quantitative analysis based ...

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2019 International Conference on applied Machine Learning and Data Science, ICAMLDS 2019

作者： Li, Fei Wang, Jing Ge, Liangquan Hu, Fangyan Cheng, Feng Sun, Kun Applied Nuclear Technology in Geosciences Key Laboratory Sichuan Province China College of Nuclear Technology and Automation Engineering Chengdu University of Technology China

In the field of practical application of nuclear technology, one of the vital steps is the interpretation of gamma energy spectrum, which can obtain the type and content of radionuclides and achieve further analyzation. Convolutional neural network is one the most basic and effective algorithm structures in deep learning because it has local receptive field, weight sharing and down sampling structure, it solves the problem of parameter expansion caused by full connection and reduces the number of weights. That can be used to study the resolution of gamma-ray spectra of convolutional neural networks. This paper introduces the spectral forming principle of gamma spectra and the convolutional neural network, and USES the convolutional neural network to study the spectral decomposition of gamma spectra. In this paper, a multi-layer convolution neural network model is built based on C# software. The convolution neural network is applied to gamma-ray spectrum decomposition, and the U, Th, K nuclides are identified and semi-quantitatively calculated. By identifying and analysing different energy spectrum data, it is shown that the modified model structure can be applied to the spectral decomposition of gamma spectrum. © Published under licence by IOP Publishing Ltd.

关键词： Convolution

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Mechanical-Electric-Hydraulic Coupling Dynamics of Hydraulic Drilling Rig

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Journal of the Chinese Society of Mechanical Engineers, Transactions of the Chinese Institute of Engineers, Series C/Chung-Kuo Chi Hsueh Kung Ch'eng Hsuebo Pao 2020年第5期41卷 595-604页

作者： Chen, Guangzhu Jiang, Chenglin Miao, Changwei Fu, Lin Qi, Liangzhao College of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu610065 China

The coupling relationship of the hydraulic drilling rig, as a complex mechanical system with mechanical-electric-hydraulic, is very important to its working performance. The internal leakage of the hydraulic motor (a direct drive component of the drilling rig) is easy to appear and difficult to be directly observed, so it is especially important to analyze the working performance of the drilling rig under the internal leakage condition of the hydraulic motor. This paper focuses on the analysis of mechanical-electric-hydraulic dynamics of hydraulic drilling rig under no loading and loading. Firstly, a coupling dynamics model of the drilling rig is established theoretically and then the dynamic response of the drilling rig is analyzed by simulation. Secondly, the leakage sensitivity simulation and analysis of hydraulic motor is performed to explore the impact of the internal leakage of the hydraulic motor on the dynamic response of the drilling rig. Finally, a dynamic response test platform of the drilling rig is built, the experiment result agrees better with the simulation result under different leakage ratios of the hydraulic motor, and the leakage ratio of the hydraulic motor is sensitive to the rotational speed and load torque of the drilling rig. © 2020, Chinese Mechanical engineering Society. All right reserved.

关键词： Sensitivity analysis

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An interpretable deep transfer learning method for fault diagnosis of nuclear power plants under multiple power level conditions

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Annals of nuclear Energy 2025年 222卷

作者： Zhanguo Ma Wenhao Jia Long Tian Jing Cui Dihao Zheng Ziyang Cui School of Automation Harbin University of Science and Technology 150080 Harbin Heilongjiang China Fundamental Science on Nuclear Safety and Simulation Technology Laboratory College of Nuclear Science and Technology Harbin Engineering University 150001 Harbin Heilongjiang China

nuclear power plants (NPPs) operations under different power level conditions (i.e., different operating modes) often exhibit non-independent and identically distributed (non-IID) characteristics in their fault-related parameters, posing significant challenges to traditional data-driven fault diagnosis methods. To address this issue, the study proposes a fault diagnosis approach that combines deep transfer learning with an interpretable multi-variable gated recurrent unit (IMV-GRU) model. The proposed approach incorporates a hybrid loss strategy integrating adaptive focal loss (AFL) and maximum mean discrepancy (MMD) to improve cross-power-level feature transfer capability. The interpretability of IMV-GRU is demonstrated through its autonomous quantification of multi-variable contribution degrees, enabling feature selection to optimize computational efficiency and mitigate interference from non-key variables. Experimental results demonstrate that the proposed method is effective in cross-power-level fault diagnosis, with particularly significant accuracy improvements under sparse data conditions. Furthermore, the effectiveness of extracting multi-variable contribution degrees is validated, highlighting its value in fault diagnosis.

关键词： Fault diagnosis Multiple power level conditions Neural network interpretability Transfer learning

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Systematic Advancement of Yolo Object Detector For Real-Time Detection of Objects

Systematic Advancement of Yolo Object Detector For Real-Time...

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International Conference on Wavelet Active Media technology and Information Processing (ICWAMTIP)

作者： Ejiyi Chukwuebuka Joseph Olusola Bamisile Nneji Ugochi Qin Zhen Ndalahwa Ilakoze Chikwendu Ijeoma School of Information and Software Engineering University of Electronic Science and Technology of China College of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu China School of Information and Communication Engineering University of Electronic Science and Technology of China

ISBN: (纸本)9781665413657

This paper explicates the systematic advancements that were observed from the inception of the YOLO (You Only Look Once) object detector to the most recent version 4. Since its introduction in late 2015, YOLO has recorded tremendous implementation as well as improvements and applications. In this work, a brief survey of the YOLO network is presented considering the introduction that was made to each version that succeeded each preceding version and the advancement on how the model performed with detection. We used the latest version of the network (YOLOv4) to train 50 classes of objects that we considered popular objects for real-time detection. The model trained obtained an mAP of 64.80% @IoU of 0.5 and when deployed for real-time detection, it achieved a 43FPS speed of detection.

关键词： Systematics Computational modeling Detectors Object detection Information processing Media Real-time systems

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Fast Reaction Kinetics and Commendable Low-Temperature Adaptability of Zinc Batteries Enabled by Aprotic Water-Acetamide Symbiotic Solvation Sheath (vol 63, e202316841, 2024)

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ANGEWANDTE CHEMIE-INTERNATIONAL EDITION 2024年第25期63卷 e202316841-e202316841页

作者： Wang, Shuyun Chen, Shengmei Ying, Yiran Li, Gang Wang, Haipeng Cheung, Ka Kiu Keith Meng, Qingjun Huang, Haitao Ma, Longtao Zapien, Juan Antonio School of Materials Science and Engineering Guangdong Provincial Key Laboratory of Advanced Energy Storage Materials South China University of Technology Guangzhou 510641 P. R. China Department of Materials and Science Engineering City University of Hong Kong Hong Kong P. R. China Department of Applied Physics and Research Institute for Smart Energy The Hong Kong Polytechnic University Hung Hom Kowloon P. R. China Frontiers Science center for Flexible Electronics Institute of Flexible Electronics Northwestern Polytechnical University Xi'an 710072 P. R. China College of Nuclear Equipment and Nuclear Engineering Yantai University No. 30 Qingquan Road Shandong 264005 China Shaanxi University of Science and Technology Weiyang University Campus Xi'an 710021 China

Although rechargeable aqueous zinc batteries are cost effectiveness, intrinsicly safe, and high activity, they are also known for bringing rampant hydrogen evolution reaction and corrosion. While eutectic electrolytes can effectively eliminate these issues, its high viscosity severely reduces the mobility of Zn2+ ions and exhibits poor temperature adaptability. Here, we infuse acetamide molecules with Lewis base and hydrogen bond donors into a solvated shell of Zn[(H2O)6]2+ to create Zn(H2O)3(ace)(BF4)2. The viscosity of 1ace-1H2O is 0.032 Pa s, significantly lower than that of 1ace-0H2O (995.6 Pa s), which improves ionic conductivity (9.56 mS cm−1) and shows lower freezing point of −45 °C, as opposed to 1ace-0H2O of 4.04 mS cm−1 and 12 °C, respectively. The acidity of 1ace-1H2O is ≈2.8, higher than 0ace-1H2O at ≈0.76, making side reactions less likely. Furthermore, benefiting from the ZnCO3/ZnF2-rich organic/inorganic solid electrolyte interface, the Zn || Zn cells cycle more than 1300 hours at 1 mA cm−2, and the Zn || Cu operated over 1800 cycles with an average Coulomb efficiency of ≈99.8 %. The Zn || PANI cell cycled over 8500 cycles, with a specific capacity of 99.8 mAh g−1 at 5 A g−1 at room temperature, and operated at −40 °C with a capacity of 66.8 mAh g−1.

关键词： Electrochemistry Solid Electrolyte Interface Solvation Shell Zinc Battery

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Automated Leukemia Screening and Sub-types Classification Using Deep Learning

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Computer Systems Science & engineering 2023年第9期46卷 3541-3558页

作者： Chaudhary Hassan Abbas Gondal Muhammad Irfan Sarmad Shafique Muhammad Salman Bashir Mansoor Ahmed Osama M.Alshehri Hassan H.Almasoudi Samar M.Alqhtani Mohammed M.Jalal Malik A.Altayar Khalaf F.Alsharif Department of Computer Science COMSATS University Islamabad44000Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Control Automotive and Robotics LabNational Center of Robotics and Automation(NCRA HEC)MirpurPakistan Mirpur University of Science and Technology(MUST)MirpurPakistan Department of Computer Science Virtual University of PakistanLahore54000Pakistan Department of Clinical Laboratory Sciences College of Applied Medical SciencesNajran UniversityNajran61441Saudi Arabia Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Medical Laboratory Technology Faculty of Applied Medical SciencesUniversity of TabukTabukSaudi Arabia Department of Clinical Laboratory Sciences College of Applied Medical SciencesTaif UniversityP.O.Box 11099Taif21944Saudi Arabia

Leukemia is a kind of blood cancer that damages the cells in the blood and bone marrow of the human *** produces cancerous blood cells that disturb the human’s immune system and significantly affect bone marrow’s production ability to effectively create different types of blood cells like red blood cells(RBCs)and white blood cells(WBC),and *** can be diagnosed manually by taking a complete blood count test of the patient’s blood,from which medical professionals can investigate the signs of leukemia ***,two other methods,microscopic inspection of blood smears and bone marrow aspiration,are also utilized while examining the patient for ***,all these methods are labor-intensive,slow,inaccurate,and require a lot of human experience and *** authors have proposed automated detection systems for leukemia diagnosis to overcome these *** have deployed digital image processing and machine learning algorithms to classify the cells into normal and blast ***,these systems are more efficient,reliable,and fast than previous manual diagnosing ***,more work is required to classify leukemia-affected cells due to the complex characteristics of blood images and leukemia cells having much intra-class variability and inter-class *** this paper,we have proposed a robust automated system to diagnose leukemia and its *** have classified ALL into its sub-types based on FAB classification,i.e.,L1,L2,and L3 types with better *** have achieved 96.06%accuracy for subtypes classification,which is better when compared with the state-of-the-art methodologies.

关键词： Healthcare cancer detection deep learning convolutional neural network

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