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2022 7th International Seminar on Computer technology, Mechanical and Electrical engineering, ISCME 2022

作者： Yang, Peijie Yang, Jian Yang, Jiaxin Liu, Li Peng, Yujie Chen, Jianwei Zhang, Bei Nie, Xuan Tian, Yingyu Sang, Ziyang School of Mechanical and Electrical Engineering Chengdu University of Technology China College of Nuclear Technology and Automation Engineering Chengdu University of Technology China

Aiming at the problems of lack of stability, poor adaptability, and weak load capacity of existing robots in complex environments. The corresponding adaptation methods are proposed through the analysis of various harsh environments in nature, and statistics are made. On this basis, a new type of metamorphic structure based on metamorphic theory and bionics principle is proposed, and the relevant mechanical structure of the chassis of the metamorphic robot is designed;The mechanical system and motion are designed, optimized, and analyzed by using Solidworks and MATLAB joint simulation platform;The motion planning and kinematics simulation of the bionic metamorphic robot chassis are carried out through ADAMS software;Stress simulation of loaded parts is carried out based on ANSYS. The results show that the design size and motion planning are reasonable and meet the bionic metamorphic robot chassis requirements for working environment and grasping operation. © Published under licence by IOP Publishing Ltd.

关键词： Bionics

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Searching for neutrinoless double-beta decay of 136Xe with PandaX-4T

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Science Bulletin 2025年

作者： Zhang, Shu 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, 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, 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 New Cornerstone Science Laboratory Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai201210 China School of Physics Sun Yat-sen University Guangzhou510275 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 Xichang615000 China Yalong River Hydropower Development Company 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 Beijing Key Laboratory of Advanced Nuclear Materials and Physics Beihang University Beijing102206 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 Park Maryland20742 United States School of Physics Nankai University Tianjin300071 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 China School of Physics Peking University Beijing100871 China College of Nuclear Technol

We report the search for neutrinoless double-beta decay of 136Xe from the PandaX-4T experiment with a 3.7-tonne natural xenon target. The data reconstruction and the background modeling are optimized in the MeV energy region. A blind analysis is performed with data from the commissioning run and the first science run. No significant excess of signal over the background is observed. A lower limit on the half-life of 136Xe neutrinoless double-beta decay is established to be 2.1×1024 yr at the 90% confidence level, with a 136Xe exposure of 44.6 kg⋅yr. Our result represents the most stringent constraint from a natural xenon detector to date. © 2025 Science China Press

关键词： Majorana neutrino Natural xenon detector Neutrinoless double-beta decay

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Statistical and Machine Learning Methods for Magnitude Scale Conversion and Gutenberg-Richter Analysis: Insights from Regional Seismicity of Morrocco

SSRN

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SSRN 2025年

作者： Adetokunbo, Peter Eluyemi, Ayodeji Adekunle Jegede, Eniolayimika Aguda, Segun Omoseyin, Tunji Mohanty, Debasis D. Mbire, Manzunzu Brassnavy Amponsah, Paulina Baruah, Saurabh AI and Automation IBM Innovation Studio CoppellTX United States Ile-Ife Nigeria Areleos Global Enterprise Huntsville Houston Texas United States Department of Project Management Missouri State University SpringfieldMO United States Department of Safety Sciences and Environmental Engineering Indiana University of Pennsylvania IndianaPA United States Assam Jorhat785006 India National University of Science and Technology Zimbabwe Zimbabwe School of Nuclear and Allied Sciences Ghana Atomic Energy Commission Ghana Missouri State University United States

This investigation presents a thorough examination of earthquake magnitude scales and seismicity parameters for Morocco using statistical and machine learning methodologies. This study developed robust magnitude conversion models using Multiple Linear Regression, Huber, and Elastic Net regression techniques, attaining R2 values of 0.691, 0.689, and 0.670, respectively, with residuals conforming to Gaussian distributions. The Gutenberg-Richter parameters were estimated using two complementary approaches: the Shi-Bolt method, yielding Mc = 4.2, b-value = 0.75, and a-value = 5.34, and an alternative approach similar to Mc95-Mc90-maximum curvature, resulting in Mc = 4.25, b-value = 0.80, and a-value = 5.58. The observed b-values (0.75-0.80), which fall below the global average of 1.0, indicate an elevated likelihood of higher-magnitude seismic events in the region. This aligns with Morocco's intricate tectonic setting at the Africa-Eurasia Plate boundary. The magnitude completeness threshold of approximately 4.2 underscores the necessity for enhanced seismic monitoring, given the region’s seismic hazard potential. These findings establish a robust framework for seismic hazard assessment in Morocco and may inform analogous analyses in regions with similar tectonic configurations. The integration of multiple analytical approaches offer novel insights into the reliability of magnitude conversion. © 2025, The Authors. All rights reserved.

关键词： Multiple linear regression

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Design of Frequency-domain Controlled Source for Electromagnetic Prospecting Based on Multi-frequency Resonance 2

Design of Frequency-domain Controlled Source for Electromagn...

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2017 IEEE 2nd Information technology, Networking, Electronic and automation Control Conference (ITNEC 2017)

作者： Wang Zhu Yan Liu Xuegui Zhu School of Electronic Information Engineering China West Normal University College of Nuclear Technology and Automation Engineering Chengdu University of Technology College of Electrical Engineering Chongqing University

ISBN: (纸本)9781509064151;9781509064144

In order to overcome the low efficiency and poor resolution of the one-frequency-one-time frequency-domain electromagnetic(FDEM) method, a well-organized multifrequency resonant network is presented to transmit a synthetized current with two or more frequencies. A lowfrequency square pulse is used as the test signal to find the resonant frequencies, based on which the barcode driving signals are generated by the multi-frequency sinusoidal pulse width modulation(SPWM) method. The simulation results prove that the frequency leakage is reduced a lot and most of the energy is concentrated at the resonant frequencies. The resonant network and control technology is useful for obvious improvement of the sounding efficiency and resolution of FDEM method.

关键词： FDEM multi-frequency resonance SPWM inverter high resolution

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Anti-Explosion Impulsive Behaviors of the Coal Mine Refuge Chamber's Cabin

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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 2017年第2期38卷 155-163页

作者： Chen, Guang-Zhu Wang, Jin-Jun Bao, Jiu-Sheng College of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu610059 China School of Mechanical and Electrical Engineering Jiangsu Collaborative Innovation Center of Intelligent Mining Equipment China University of Mining and Technology Xuzhou221116 China

The refuge chamber has been widely accepted as a necessary part in the emergency rescue system of the coal mine because they can be conveniently installed and moved in the underground workplace. The refuge chamber's cabin should have an excellent anti-explosion performance to the impulsive load resulted from an explosion, which is necessary for the normal operation of its internal systems. In this paper, by the combination of theoretical analysis, numerical simulation and experiments, the anti-explosion performance of KJYF-96/8 type coal mine refuge chamber's cabin was studied. Firstly, according to the working condition, a behavior model characterizing the gas and coal dust explosion was established. Secondly, the anti-explosion performance of the cabin was numerically simulated and then analyzed. Furthermore, the gas and coal dust explosion experiments were carried out in an underground laneway. Based on these works, the variation curve of the equivalent triangular impulsive load wave with the change of time, the maximal stress and deformation of the cabin in explosion, and the damage of the cabin in the actual explosion test were obtained. It is found that the newly-designed KJYF-96/8 type coal mine refuge chamber can meet the anti-explosion requirements. © 2017, Chinese Mechanical engineering Society. All right reserved.

关键词： Coal mines

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A novel approach for feature extraction from a gamma‑ray energy spectrum based on image descriptor transferring for radionuclide identification

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nuclear Science and Techniques 2022年第12期33卷 88-104页

作者： Hao‑Lin Liu Hai‑Bo Ji Jiang‑Mei Zhang Cao‑Lin Zhang Jing Lu Xing‑Hua Feng Department of Automation University of Science and Technology of ChinaHefei 230026China School of Information Engineering Southwest University of Science and TechnologyMianyang 621010China Fundamental Science on Nuclear Wastes and Environment Safety Laboratory Southwest University of Science and TechnologyMianyang 621010China

This study proposes a novel feature extraction approach for radionuclide identification to increase the precision of identification of the gamma-ray energy spectrum *** easier utilization of the information contained in the spectra,the vectors of the gamma-ray energy spectra from Euclidean space,which are fingerprints of the different types of radionuclides,were mapped to matrices in the Banach ***,to make the spectra in matrix form easier to apply to image-based deep learning frameworks,the matrices of the gamma-ray energy spectra were mapped to images in the RGB color space.A deep convolutional neural network(DCNN)model was constructed and trained on the ImageNet *** mapped gamma-ray energy spectrum images were applied as inputs to the DCNN model,and the corresponding outputs of the convolution layers and fully connected layers were transferred as descriptors of the images to construct a new classification model for radionuclide *** transferred image descriptors consist of global and local features,where the activation vectors of fully connected layers are global features,and activations from convolution layers are local features.A series of comparative experiments between the transferred image descriptors,peak information,features extracted by the histogram of the oriented gradients(HOG),and scale-invariant feature transform(SIFT)using both synthetic and measured data were applied to 11 classical *** results demonstrate that although the gamma-ray energy spectrum images are completely unfamiliar to the DCNN model and have not been used in the pre-training process,the transferred image descriptors achieved good classification *** global features have strong semantic information,which achieves an average accuracy of 92.76%and 94.86%on the synthetic dataset and measured dataset,*** results of the statistical comparison of features demonstrate that the proposed approach outperforms the peak-se

关键词： Radionuclide identification Feature extraction Transfer learning Gamma energy spectrum analysis Image descriptor

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Position reconstruction and surface background model for the PandaX-4T detector

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nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 2025年 1077卷

作者： Qian, Zhicheng Gu, Linhui Cheng, Chen 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 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 Shanghai Key Laboratory for Particle Physics and Cosmology Shanghai200240 China School of Physics Beihang University Beijing102206 China New Cornerstone Science Laboratory Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai201210 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 Park MD20742 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 Qingdao 266237 Shandong China Key Laboratory of Particle Physics and Particle Irradiation of Ministry of Education Shandong University Qingdao 266237 Shandon

We report the position reconstruction methods and surface background model for the PandaX-4T dark matter direct search experiment. In this work, two position reconstruction algorithms are developed: template matching (TM) method and photon acceptance function (PAF) method. Both methods determine the horizontal position of events based on the light pattern of secondary scintillation collected by the light sensors. After a comprehensive evaluation of resolution, uniformity, and robustness, the PAF method was selected for position reconstruction, while the TM method was employed for verification. The PAF method achieves a bulk event resolution of 1.0 mm and a surface event resolution of 4.4 mm for a typical S2 signal with a bottom charge of 1500 PE (about 14 keV). The uniformity is around 20%. Robustness studies reveal average deviations of 5.1 mm and 8.8 mm for the commissioning run (Run0) and the first science run (Run1), respectively, due to the deactivation of certain PMTs. A data-driven surface background model is developed based on the PAF method. The surface background is estimated to be 0.09±0.06 events for Run0 (0.54 tonne⋅year) and 0.17±0.11 events for Run1 (1.00 tonne⋅year). © 2025 Elsevier B.V.

关键词： Template matching

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Technique for Anomaly Information Reconstruction in Airborne Radiation Data Based on Dictionary Learning Algorithm

Technique for Anomaly Information Reconstruction in Airborne...

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2023 International Conference on Computer Applications technology, CCAT 2023

作者： Xiong, Chao Wang, Xin Qiao, Xin Zhou, Zhi Wu, Hexi Ministry of Education Engineering Research Center of Nuclear Technology Application Nanchang China East China University of Technology School of Nuclear Science and Engineering Nanchang China Chengdu University of Technology The College of Nuclear Technology and Automation Engineering Chengdu China

ISBN: (纸本)9798350301540

This paper introduced the K-SVD algorithm of machine learning to Decompose and Reconstruct the airborne radiation data. According to the sparse representation method, the inaccuracy of gridding interpolation due to the mismatch of data dimension was reduced and then a self-adaptive dictionary was constructed via training the original data. After reconstruction by dictionary learning, the increase in terms of two indicators peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) as well as the noise signal extracted which obeyed a stripe-like distribution along the sideline testified that the KSVD encoding learning processing technique had effective results in reconstructing and de-noising airborne radiation data. © 2023 IEEE.

关键词： Signal to noise ratio

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Voidage correction method for DEM study of 3D pebble flows in a bed of cycloidal base

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Particuology 2024年第10期93卷 30-40页

作者： Zirui Xia Minglei Shi Quan Zou Nan Gui Xingtuan Yang Jiyuan Tu Shengyao Jiang Institute of Nuclear and New Energy Technology Collaborative Innovation Center of Advanced Nuclear Energy TechnologyKey Laboratory of Advanced Reactor Engineering and SafetyMinistry of EducationTsinghua UniversityBeijing100084China Department of Engineering Physics Tsinghua UniversityBeijing100084China Department of Automation Tsinghua UniversityBeijing100084China School of Engineering RMIT UniversityMelbourneVIC3083Australia

Voidage(porosity or void fraction)in packed particles(or pebbles)is of fundamental importance in calculating the pressure drop,obtaining the drag,predicting the bed permeability,estimating the neutron streaming,*** the case when particles are deformed,a method of voidage correction during the packing state is proposed using a Discrete Element Method(DEM)simulation of 3D pebble flow inside a bed of cycloidal base.A function to evaluate the remaining volume of a pebble intercepted by horizontal and vertical planes is proposed for voidage *** that,the process of solving voidage distribution is provided in *** this method,the voidage inside the cycloidal-base pebble bed is obtained to refer to reported similar data for *** method can be potentially used for dynamical voidage calculation in CFD-DEM simulation which can get suitable voidage distribution after the correction.

关键词： Voidage Pebbleflow Packed bed Discreteelementmethod Cycloidal base KD-Tree Volume correction

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Multi-robot coordinated exploration of indoor environments using semantic information

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Science China(Information Sciences) 2018年第7期61卷 247-257页

作者： Guangsheng LI Wusheng CHOU Fang YIN School of Mechanical Engineering and Automation Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing Key Laboratory of Advanced Nuclear Materials and Physics Beihang University

Dear editor,Multi-robot exploration of indoor environments is a fundamental problem in mobile robotics[1].In the context of multi-robot exploration,the main challenge is in achieving coordination among the robots such that they can be better distributed over the environment for simultaneously exploring different regions and avoiding interference with other robots[2,3].To date,many approaches

关键词： In Multi-robot coordinated exploration of indoor environments using semantic information DBSCAN Figure HMM

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