检索结果-内蒙古大学图书馆

A Multimodal Deep Learning Approach for Very Short-Term Solar Forecasts using Sky Images and Historical Numerical Data

引用

Renewable Energy 2025年

作者： Anto Leoba Jonathan Olusola Bamisile Dongsheng Cai Chukwuebuka Joseph Ejiyi Joseph Junior Nkou Nkou Kombou Victor Chiagoziem C. Ukwuoma Liu Wei Qi Huang College of Nuclear Technology and Automation Engineering Chengdu University of Technology Sichuan P.R. 610059 China Sichuan Engineering Technology Research Center for Industrial Internet Intelligent Monitoring and Application Chengdu University of Technology Sichuan P.R. 610059 China Energy Environment and Society Division University of Dundee United Kingdom School of Computer Science and Engineering (School of Cyber Security) Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China (UESTC) Chengdu 611731 China

The increased solar energy integration into the power system introduces various issues due to its intermittent nature. As solar power penetration increases, grid management becomes increasingly complex, highlighting the importance of precise solar power forecasts. This paper addresses a critical challenge in solar power integration, highlighting the significance of very short-term solar forecasting (VSTSF) for grid operators. We propose a novel MULSKIN hybrid framework that combines a custom ResNet and a novel targeted feature attention mechanism (TFAM), which dynamically focuses on the most relevant regions in sky images, improving the model’s ability to capture essential cloud dynamics and spatiotemporal patterns. Unlike existing methods in literature that either use image (sky images) or numerical (meteorological) data, the proposed MULSKIN approach integrates both data types, allowing for a more holistic understanding of atmospheric conditions. This paper contributes to advancing the field of solar forecasting by presenting a new, effective, and scalable solution suitable for operational deployment in modern energy systems. With six years of high-resolution data, the model outperforms traditional machine learning (ML), deep learning (DL) models, and baseline reference models, achieving a forecast skill score of 32.84% and a root mean square error (RMSE) of 54.36W/m 2 . Our Results indicate that incorporating sky images alongside numerical meteorological data significantly enhances forecasting accuracy, with the MULSKIN model demonstrating its superiority in capturing dynamic cloud movements and weather patterns over short time horizons. The findings underscore the potential of combining multimodal data sources to address the challenges of VSTSF, offering promising improvements in solar irradiance prediction for real-time grid operations.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Design of high-energy X-ray conversion target based on linear electron accelerator

引用

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine 2025年 224卷 111951页

作者： Hongchao Yang Xingfan Yang Yinghong Tang Heng Dai Pengcheng Li Guohua Liang Yadong Wu Xianguo Tuo Qibiao Wang College of Nuclear Technology and Automation Engineering Chengdu University of Technology East Third Road 1 Erxian Bridge 610059 Chengdu China Institute of Applied Electronics China Academy of Engineering Physics Mian Shan Road 64 621900 Mianyang China. College of Nuclear Technology and Automation Engineering Chengdu University of Technology East Third Road 1 Erxian Bridge 610059 Chengdu China. School of Computer Science and Engineering Sichuan University of Science and Engineering Huixing Road 519 Ziliujing District 643000 Zigong China. School of Physics and Electronic Engineering Sichuan University of Science and Engineering Huixing Road 519 Ziliujing District 643000 Zigong China. School of Physics and Electronic Engineering Sichuan University of Science and Engineering Huixing Road 519 Ziliujing District 643000 Zigong China. Electronic address: wangqibiao@***.

High-energy X-rays can be employed to inspect fuel elements, even weld seams, cracks, and internal structural deformations of the components due to their density-sensitive characteristics and strong penetration capabilities. The quality of these X-rays is determined by various factors such as the X-ray conversion target, the cooling system for the conversion target, and the filter type, thereby influencing the detection accuracy. This study was conducted using an electron linear accelerator with an energy of 10 MeV and an average current of 150 μA. Firstly, we designed the conversion target material and geometry structure by Monte Carlo method. The results indicate that the optimal target material is tungsten and the optimum shape of the conversion target is a cylinder with dimensions of Φ 40 × 2.2 mm. Second, finite element analysis was applied to design the conversion target cooling system. After 60 min of electron beam irradiation, the local maximum temperature of the conversion target is approximately 1330 °C. Lastly, theoretical analysis and Monte Carlo simulations were used to design the X-ray filters. The result demonstrates that a double-layer filter consisting of 8 mm bismuth and 4 mm iron can increase the proportion of high-energy X-rays by 13.89 ± 0.05 %. Finally, the results of using the attenuation method to measure the X-ray energy spectrum produced by an electron linear accelerator indicate that without a filter 78.81 % of the X-rays are above 1 MeV and with a filter this proportion increases to 80.55 %.

关键词： Attenuation curves COMSOL Conversion target Geant4 High energy X-ray nuclear fuel

来源：评论

学校读者我要写书评

暂无评论

Search for lepton portal dark matter in the PandaX-4T experiment

引用

Physics Letters, Section B: nuclear, Elementary Particle and High-Energy Physics 2025年 864卷

作者： Ning, Xuyang Bo, Zihao Chen, Wei Chen, Xun Chen, Yunhua Cheng, Zhaokan Cui, Xiangyi Fan, Yingjie Fang, Deqing Gao, Zhixing Geng, Lisheng Giboni, Karl Guo, Xunan Guo, Xuyuan Guo, Zichao Han, Chencheng Han, Ke He, Changda He, Jinrong Huang, Di Huang, Houqi Huang, Junting Hou, Ruquan Hou, Yu Ji, Xiangdong Ji, Xiangpan Ju, Yonglin Li, Chenxiang Li, Jiafu Li, Mingchuan Li, Shuaijie Li, Tao Li, Zhiyuan Lin, Qing Liu, Jianglai Lu, Congcong Lu, Xiaoying Luo, Lingyin Luo, Yunyang Ma, Wenbo Ma, Yugang Mao, Yajun Meng, Yue Pang, Binyu Qi, Ningchun Qian, Zhicheng Ren, Xiangxiang Shan, Dong Shang, Xiaofeng Shao, Xiyuan Shen, Guofang Shen, Manbin Sun, Wenliang Tao, Yi Wang, Anqing Wang, Guanbo Wang, Hao Wang, Jiamin Wang, Lei Wang, Meng Wang, Qiuhong Wang, Shaobo Wang, Siguang Wang, Wei Wang, Xiuli Wang, Xu Wang, Zhou Wei, Yuehuan Wu, Weihao Wu, Yuan Xiao, Mengjiao Xiao, Xiang Xiong, Kaizhi Xu, Yifan Yao, Shunyu Yan, Binbin Yan, Xiyu Yang, Yong Ye, Peihua Yu, Chunxu Yuan, Ying Yuan, Zhe Yun, Youhui Zeng, Xinning Zhang, Minzhen Zhang, Peng Zhang, Shibo Zhang, Shu Zhang, Tao Zhang, Wei Zhang, Yang Zhang, Yingxin Zhang, Yuanyuan Zhao, Li Zhou, Jifang Zhou, Jiaxu Zhou, Jiayi Zhou, Ning Zhou, Xiaopeng Zhou, Yubo Zhou, Zhizhen Bai, Yang Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai 201210 China School of Physics and Astronomy Shanghai Jiao Tong University Shanghai 200240 China State Key Laboratory of Dark Matter Physics Key Laboratory for Particle Astrophysics and Cosmology (MoE) Shanghai Key Laboratory for Particle Physics and Cosmology New Cornerstone Science Laboratory Shanghai Jiao Tong University Shanghai 200240 China Shanghai Jiao Tong University Sichuan Research Institute Chengdu 610213 China Jinping Deep Underground Frontier Science and Dark Matter Key Laboratory of Sichuan Province Liangshan 615000 China Yalong River Hydropower Development Company Ltd. 288 Shuanglin Road Chengdu 610051 China Sino-French Institute of Nuclear Engineering and Technology Sun Yat-Sen University Zhuhai 519082 China Department of Physics Yantai University Yantai 264005 China Key Laboratory of Nuclear Physics Ion-beam Application (MOE) Institute of Modern Physics Fudan University Shanghai 200433 China School of Physics Beihang University Beijing 102206 China Peng Huanwu Collaborative Center for Research and Education Beihang University Beijing 100191 China International Research Center for Nuclei and Particles in the Cosmos Beijing Key Laboratory of Advanced Nuclear Materials and Physics Beihang University Beijing 100191 China Southern Center for Nuclear-Science Theory (SCNT) Institute of Modern Physics Chinese Academy of Sciences Huizhou 516000 China SJTU Paris Elite Institute of Technology Shanghai Jiao Tong University Shanghai 200240 China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai 200240 China Department of Physics University of Maryland College Park 20742 MD United States School of Physics Nankai University Tianjin 300071 China School of Physics Sun Yat-Sen University Guangzhou 510275 China State Key Laboratory of Particle Detection and Electronics University of Science and Technology of China Hefei 230026 China Department of Mod

We report a search for a lepton-portal dark matter model, where dark matter couples to a charged lepton in the standard model. This simplified model naturally leads to photon-mediated dark matter interactions with nuclei, making it suitable for direct dark matter detection experiments. Matching to the framework of non-relativistic effective field theory for dark matter, we report the first sensitive search for this model using data from the PandaX-4T commissioning run. Our results yield strong constraints on Dirac fermion dark matter but relatively weaker constraints on Majorana dark matter due to the suppression of effective photon interactions. These constraints complement those obtained from the collider and indirect detection experiments. © 2025 The Author(s)

关键词：

来源：评论

学校读者我要写书评

暂无评论

Search for Solar Boosted Dark Matter Particles at the PandaX-4T Experiment

arXiv

引用

arXiv 2024年

作者： Shen, Guofang Bo, Zihao Chen, Wei Chen, Xun Chen, Yunhua Cheng, Zhaokan Cui, Xiangyi Fan, Yingjie Fang, Deqing Gao, Zhixing Geng, Lisheng Giboni, Karl Guo, Xunan Guo, Xuyuan Guo, Zichao Han, Chencheng Han, Ke He, Changda He, Jinrong Huang, Di Huang, Houqi Huang, Junting Hou, Ruquan Hou, Yu Ji, Xiangdong Ji, Xiangpan Ju, Yonglin Li, Chenxiang Li, Jiafu Li, Mingchuan Li, Shuaijie Li, Tao Li, Zhiyuan Lin, Qing Liu, Jianglai Lu, Congcong Lu, Xiaoying Luo, Lingyin Luo, Yunyang Ma, Wenbo Ma, Yugang Mao, Yajun Meng, Yue Ning, Xuyang Pang, Binyu Qi, Ningchun Qian, Zhicheng Ren, Xiangxiang Shan, Dong Shang, Xiaofeng Shao, Xiyuan Shen, Manbin Sun, Wenliang Tao, Yi Wang, Anqing Wang, Guanbo Wang, Hao Wang, Jiamin Wang, Lei Wang, Meng Wang, Qiuhong Wang, Shaobo Wang, Siguang Wang, Wei Wang, Xiuli Wang, Xu Wang, Zhou Wei, Yuehuan Wu, Weihao Wu, Yuan Xiao, Mengjiao Xiao, Xiang Xiong, Kaizhi Xu, Yifan Yao, Shunyu Yan, Binbin Yan, Xiyu Yang, Yong Ye, Peihua Yu, Chunxu Yuan, Ying Yuan, Zhe Yun, Youhui Zeng, Xinning Zhang, Minzhen Zhang, Peng Zhang, Shibo Zhang, Shu Zhang, Tao Zhang, Wei Zhang, Yang Zhang, Yingxin Zhang, Yuanyuan Zhao, Li Zhou, Jifang Zhou, Jiaxu Zhou, Jiayi Zhou, Ning Zhou, Xiaopeng Zhou, Yubo Zhou, Zhizhen An, Haipeng Nie, Haoming New Cornerstone Science Laboratory Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai201210 China School of Physics Beihang University Beijing102206 China Shanghai Key Laboratory for Particle Physics and Cosmology Shanghai200240 China Shanghai Jiao Tong University Sichuan Research Institute Chengdu610213 China Jinping Deep Underground Frontier Science and Dark Matter Key Laboratory of Sichuan Province China Yalong River Hydropower Development Company Ltd. 288 Shuanglin Road Chengdu610051 China Sino-French Institute of Nuclear Engineering and Technology Sun Yat-Sen University Zhuhai519082 China Department of Physics Yantai University Yantai264005 China Institute of Modern Physics Fudan University Shanghai200433 China Peng Huanwu Collaborative Center for Research and Education Beihang University Beijing100191 China International Research Center for Nuclei and Particles in the Cosmos Beijing Key Laboratory of Advanced Nuclear Materials and Physics Beihang University Beijing100191 China Institute of Modern Physics Chinese Academy of Sciences Huizhou516000 China SJTU Paris Elite Institute of Technology Shanghai Jiao Tong University Shanghai200240 China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Physics University of Maryland College ParkMD20742 United States School of Physics Nankai University Tianjin300071 China School of Physics Sun Yat-Sen University Guangzhou510275 China State Key Laboratory of Particle Detection and Electronics University of Science and Technology of China Hefei230026 China Department of Modern Physics University of Science and Technology of China Hefei230026 China Research Center for Particle Science and Technology Institute of Frontier and Interdisciplinary Science Shandong University Shandong Qingdao266237 China Key Laboratory of Particle Physics and Particle Irradiation of Ministry of Education Shandong University Shandong Qingdao266237

We present a novel constraint on light dark matter utilizing 1.54 tonne·year of data acquired from the PandaX-4T dual-phase xenon time projection chamber. This constraint is derived through detecting electronic recoil signals resulting from the interaction with solar-enhanced dark matter flux. Low-mass dark matter particles, lighter than a few MeV/c2, can scatter with the thermal electrons in the Sun. Consequently, with higher kinetic energy, the boosted dark matter component becomes detectable via contact scattering with xenon electrons, resulting in a few keV energy deposition that exceeds the threshold of PandaX-4T. We calculate the expected recoil energy in PandaX-4T considering the Sun’s acceleration and the detection capabilities of the xenon detector. The first experimental search results using the xenon detector yield the most stringent cross-section of 3.51 × 10−39 cm2 at 0.08 MeV/c2 for a solar boosted dark matter mass ranging from 0.02 to 10 MeV/c2, achieving a 23 fold improvement compared with earlier experimental studies. © 2024, CC BY.

关键词： Kinetic energy

来源：评论

学校读者我要写书评

暂无评论

Search for lepton portal dark matter in the PandaX-4T experiment

arXiv

引用

arXiv 2024年

作者： Ning, Xuyang Bo, Zihao Chen, Wei Chen, Xun Chen, Yunhua Cheng, Zhaokan Cui, Xiangyi Fan, Yingjie Fang, Deqing Gao, Zhixing Geng, Lisheng Giboni, Karl Guo, Xunan Guo, Xuyuan Guo, Zichao Han, Chencheng Han, Ke He, Changda He, Jinrong Huang, Di Huang, Houqi Huang, Junting Hou, Ruquan Hou, Yu Ji, Xiangdong Ji, Xiangpan Ju, Yonglin Li, Chenxiang Li, Jiafu Li, Mingchuan Li, Shuaijie Li, Tao Li, Zhiyuan Lin, Qing Liu, Jianglai Lu, Congcong Lu, Xiaoying Luo, Lingyin Luo, Yunyang Ma, Wenbo Ma, Yugang Mao, Yajun Meng, Yue Pang, Binyu Qi, Ningchun Qian, Zhicheng Ren, Xiangxiang Shan, Dong Shang, Xiaofeng Shao, Xiyuan Shen, Guofang Shen, Manbin Sun, Wenliang Tao, Yi Wang, Anqing Wang, Guanbo Wang, Hao Wang, Jiamin Wang, Lei Wang, Meng Wang, Qiuhong Wang, Shaobo Wang, Siguang Wang, Wei Wang, Xiuli Wang, Xu Wang, Zhou Wei, Yuehuan Wu, Weihao Wu, Yuan Xiao, Mengjiao Xiao, Xiang Xiong, Kaizhi Xu, Yifan Yao, Shunyu Yan, Binbin Yan, Xiyu Yang, Yong Ye, Peihua Yu, Chunxu Yuan, Ying Yuan, Zhe Yun, Youhui Zeng, Xinning Zhang, Minzhen Zhang, Peng Zhang, Shibo Zhang, Shu Zhang, Tao Zhang, Wei Zhang, Yang Zhang, Yingxin Zhang, Yuanyuan Zhao, Li Zhou, Jifang Zhou, Jiaxu Zhou, Jiayi Zhou, Ning Zhou, Xiaopeng Zhou, Yubo Zhou, Zhizhen Bai, Yang New Cornerstone Science Laboratory Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai201210 China Shanghai Key Laboratory for Particle Physics and Cosmology Shanghai200240 China Shanghai Jiao Tong University Sichuan Research Institute Chengdu610213 China Jinping Deep Underground Frontier Science and Dark Matter Key Laboratory of Sichuan Province Liangshan615000 China Yalong River Hydropower Development Company Ltd. 288 Shuanglin Road Chengdu610051 China Sino-French Institute of Nuclear Engineering and Technology Sun Yat-Sen University Zhuhai519082 China Department of Physics Yantai University Yantai264005 China Institute of Modern Physics Fudan University Shanghai200433 China School of Physics Beihang University Beijing102206 China Peng Huanwu Collaborative Center for Research and Education Beihang University Beijing100191 China International Research Center for Nuclei and Particles in the Cosmos Beijing Key Laboratory of Advanced Nuclear Materials and Physics Beihang University Beijing100191 China Institute of Modern Physics Chinese Academy of Sciences Huizhou516000 China SJTU Paris Elite Institute of Technology Shanghai Jiao Tong University Shanghai200240 China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Physics University of Maryland College ParkMD20742 United States School of Physics Nankai University Tianjin300071 China School of Physics Sun Yat-Sen University Guangzhou510275 China State Key Laboratory of Particle Detection and Electronics University of Science and Technology of China Hefei230026 China Department of Modern Physics University of Science and Technology of China Hefei230026 China Research Center for Particle Science and Technology Institute of Frontier and Interdisciplinary Science Shandong University Qingdao266237 China Key Laboratory of Particle Physics and Particle Irradiation of Ministry of Education Shandong University Qingdao266237 C

关键词： Photons

来源：评论

学校读者我要写书评

暂无评论

A method of fiducial-value normalizing to leveling the global average atomic number of lunar surface calculated by orbital Gamma spectroscopy data

A method of fiducial-value normalizing to leveling the globa...

引用

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

作者： Zhao, Jiankun Xiong, Chao Yan, Wenxun Ge, Liangquan School of Nuclear Science and Engineering East China University of Technology Nanchang330013 China College of Applied Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu610059 China Fundamental Science on Radioactive Geology and Exploration Technology Laboratory East China Institute of Technology Nanchang Jiangxi330013 China

As "banded structure" is a vexed question in orbital Gamma spectroscopy data inversion. In this paper, a new method to level the lunar global data of average atomic number by the fiducial-value in the stable composition circle is proposed. In order to verify the accuracy, the existing moon-sampling rock data as the "standard sample" and fast neutron data are also presented. The slope of the positive fit curve is 1.875 and the correlation coefficient is 0.72. And some geological characteristics, such as the structural boundaries and internal features of the Antarctic Aiken Basin, can identify obviously. © Published under licence by IOP Publishing Ltd.

关键词： Moon

来源：评论

学校读者我要写书评

暂无评论

Corrosion Assessment of Carbon Steel Using Texture and Color Features

Corrosion Assessment of Carbon Steel Using Texture and Color...

引用

Advanced Electronic Materials, Computers and Software engineering (AEMCSE), International Conference on

作者： Jiacheng Li Haoting Liu Weijie Wu Shaohua Yang Liang Sheng Beijing Engineering Research Center of Industrial Spectrum Imaging School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China State Key Laboratory of Intense Pulsed Radiation Simulation and Effect Northwest Institute of Nuclear Technology Xi'an China

ISBN: (数字)9781728181431

ISBN: (纸本)9781728181448

This paper presents an assessment method for the corrosion evaluation of carbon steel. The digital image processing technology is used to extract the texture and color characteristics of rusted carbon steel images. First, Q235 carbon steel images with different degrees of corrosion were collected by digital camera and microscope. Second, the texture features of rusted carbon steel surface are extracted based on Gray Level Co-occurrence Matrix, the color features are extracted using color moments. Third, the extracted features are combined and normalized as the inputs of Support Vector Machine (SVM) for training and classification. We use Particle Swarm Optimization (PSO) algorithm to optimize the SVM classifier. The accuracy of the classification is up to 97.5%.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Progressive contrastive representation learning for defect diagnosis in aluminum disk substrates with a bio-inspired vision sensor

引用

Expert Systems with Applications 2025年 289卷

作者： Peng Chen Ruijin Zhang Changbo He Yaqiang Jin Shuai Fan Junyu Qi Chengning Zhou Chun Zhang College of Engineering Shantou University Shantou 515063 Guangdong China Key Laboratory of Intelligent Manufacturing Technology Ministry of Education Shantou 515063 Guangdong China College of Electrical Engineering and Automation Anhui University Hefei 230601 China School of Qilu Transportation Shandong University Jinan 250061 Shandong China School of Mechanical and Electrical Engineering Chengdu University of Technology Chengdu 610059 Sichuan China Electronics & Drives Reutlingen University Reutlingen 72762 Germany Nuclear Power Institute of China Chengdu 610213 Sichuan China

Traditional industrial surface defect detection using CCD/CMOS cameras faces limitations in detecting minute defects on aluminum substrates in dynamic industrial scenes. While event cameras can capture small defects through event information flow (EIF), they struggle with noise-related challenges. Additionally, current contrastive learning frameworks employ simplified loss computation mechanisms that limit their capability to discover and characterize novel categories. This study addresses these challenges through two main innovations: (1) a novel event stream imaging technique that combines equal time window segmentation, overlapping windows, and Gaussian filtering to enhance data quality, and (2) Progressive Contrastive Representation Learning (PCRL), a sophisticated framework for handling both known and unknown fault classes. The PCRL framework overcomes traditional limitations through a four-stage loss function strategy and structured analysis of intra-cluster and inter-cluster relationships, enabling better feature extraction and model convergence for previously unseen patterns. Through systematic validation, our approach demonstrates superior performance in noise reduction, unknown fault detection, and precise surface defect classification, offering a robust solution for industrial applications. Through systematic validation, our methodology exhibits exceptional efficacy in mitigating signal noise, identifying novel fault patterns, and executing high-precision surface anomaly categorization, yielding a comprehensive accuracy rate of 93.51 % across all test cases and demonstrating remarkable performance with 95.36 % accuracy in previously unencountered fault categories, thereby presenting a sophisticated solution for industrial implementations while establishing a foundation for subsequent scholarly investigations in this field.

关键词： Aluminum disk substrates Bio-inspired vision sensor Contrastive learning Event stream imaging Fault diagnosis Multi-stage loss function Semi-supervised learning

来源：评论

学校读者我要写书评

暂无评论

Localizing the Recurrent Laryngeal Nerve via Ultrasound with a Bayesian Shape Framework

arXiv

引用

arXiv 2022年

作者： Dou, Haoran Han, Luyi He, Yushuang Xu, Jun Ravikumar, Nishant Mann, Ritse Frangi, Alejandro F. Yap, Pew-Thian Huang, Yunzhi Institute for AI in Medicine School of Automation Nanjing University of Information Science and Technology Nanjing China Institute for AI in Medicine School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing China School of Computing University of Leeds Leeds United Kingdom School of Medicine University of Leeds Leeds United Kingdom Department of Radiology and Nuclear Medicine Radboud University Medical Centre Nijmegen Netherlands Netherlands West China Hospital of Sichuan University Chengdu China Department of Cardiovascular Sciences KU Leuven Leuven Belgium Department of Electrical Engineering KU Leuven Leuven Belgium Alan Turing Institute London United Kingdom University of North Carolina Chapel Hill United States

Tumor infiltration of the recurrent laryngeal nerve (RLN) is a contraindication for robotic thyroidectomy and can be difficult to detect via standard laryngoscopy. Ultrasound (US) is a viable alternative for RLN detection due to its safety and ability to provide real-time feedback. However, the tininess of the RLN, with a diameter typically less than 3 mm, poses significant challenges to the accurate localization of the RLN. In this work, we propose a knowledge-driven framework for RLN localization, mimicking the standard approach surgeons take to identify the RLN according to its surrounding organs. We construct a prior anatomical model based on the inherent relative spatial relationships between organs. Through Bayesian shape alignment (BSA), we obtain the candidate coordinates of the center of a region of interest (ROI) that encloses the RLN. The ROI allows a decreased field of view for determining the refined centroid of the RLN using a dual-path identification network, based on multi-scale semantic information. Experimental results indicate that the proposed method achieves superior hit rates and substantially smaller distance errors compared with state-of-the-art methods. © 2022, CC BY.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

A method of fiducial-value normalizing to leveling the global average atomic number of lunar surface calculated by orbital Gamma spectroscopy data

A method of fiducial-value normalizing to leveling the globa...

引用

作者： Jiankun Zhao Chao Xiong Wenxun Yan Liangquan Ge School of Nuclear Science and Engineering East China University of Technology College of Applied Nuclear Technology and Automation Engineering Chengdu University of Technology Fundamental Science on Radioactive Geology and Exploration Technology Laboratory East China Institute of Technology

As "banded structure" is a vexed question in orbital Gamma spectroscopy data *** this paper,a new method to level the lunar global data of average atomic number by the fiducial-value in the stable composition circle is *** order to verify the accuracy,the existing moon-sampling rock data as the "standard sample" and fast neutron data are also *** slope of the positive fit curve is 1.875 and the correlation coefficient is *** some geological characteristics,such as the structural boundaries and internal features of the Antarctic Aiken Basin,can identify obviously.

关键词： A method of fiducial-value normalizing to leveling the global average atomic number of lunar surface calculated by orbital Gamma spectroscopy data Gamma

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：