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A Dual-head Prompt Gamma Imaging System for Online Monitoring in Proton Therapy

A Dual-head Prompt Gamma Imaging System for Online Monitorin...

2022 IEEE Nuclear science Symposium, Medical Imaging Conference, and Room Temperature Semiconductor Detector Conference, IEEE NSS MIC RTSD 2022

作者： Zhang, Hongyang Zhao, Bo Fan, Peng Wang, Shi Lu, Wenzhuo Wu, Zhaoxia Ma, Tianyu Liu, Yaqiang Ministry of Education Department of Engineering Physics Beijing100084 China Tsinghua University Department of Engineering Physics Beijing100084 China Beijing Institute of Spacecraft Environment Engineering China Academy of Space Technology and Science Technology on Reliability and Environmental Engineering Laboratory Beijing100094 China

ISBN: (纸本)9781665488723

Proton therapy is one of the most advanced radiotherapy technologies. However, due to range uncertainty, real-time monitoring of proton dose deposition is of great importance in proton therapy.A dual-head prompt gamma imaging system has been proposed in previous work. The system design provides high spatial resolution and ensures enough detection efficiency for high signal-to-noise imaging during pencil beam scanning. In this work, the dual-head imaging system is assembled and calibrated. Detectability of range shift and range deviation accuracy are evaluated under 60 and 100 MeV proton beam. Detection efficiency (at the center of the FOV) and spatial resolution of the dual-head system are given by point source *** shift of 1 mm is able to be distinguished by the vertical detector by locating the most distal edge position (MDEP) of prompt gamma profile. Proton range deviation and range deviation accuracy of the vertical detector are both less than 1 mm at typical count level of a single spot in pencil beam scanning. Detection efficiency of the dual-head imaging system is 0.62% (FOV center) and =3 mm spatial resolution is achieved in the whole *** dual-head prompt gamma imaging system shows excellent performance in detection efficiency and spatial resolution. Proton range shift detectability of the system is preliminarily verified. The system should be tested under clinical proton beam setup in future work. © 2022 IEEE.

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Preparation and Properties of High Temperature Transient Thin Film Thermopile Heat Flux Sensor

SSRN

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SSRN 2023年

作者： Xie, Shanghang Zhu, Jin Jiang, Hongchuan Zhao, Xiaohui Liu, Baorui Jia, Zhouxia Chengdu610054 China Science and Technology on Reliability and Environment Engineering Laboratory Beijing Institute of Structure and Environment Engineering Beijing100076 China

The thermopile of the sensor consists of 192 pairs Pt-PtRh10 thermocouples with length 1 mm and width 100 μm in serpentine series were deposited on Al2O3 ceramic substrates by photolithography-coating process. And a thermal resistance layer with mesh cavity structure with lower thermal conductivity was proposed, four thermal resistance layers were spaced over the thermopile contacts to increase the sensitivity of the sensor. Then a wind tunnel was used to calibrate the sensitivity of the sensor by comparison method and simulated its performance in the actual application environment, and the response time was measured by pulsed laser. The results shows that the output potential of the thin film heat flux sensor has a linear characteristic with temperature from 200 to 500 °C, the sensitivity is about 0.627 m2·μV/W and the average response time is about 12.7 μs. The sample is stable in wind tunnel tests and responds quickly to the change of heat flux. © 2023, The Authors. All rights reserved.

关键词： Binary alloys

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Ultra-Stable Ferrimagnetic Second-Order Topological Insulator in 2D Metal-Organic Framework

arXiv

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arXiv 2025年

作者： Wang, Meijun Zhong, Yong-An Jin, Lei Liu, Ying Dai, Xuefang Liu, Guodong Zhang, Xiaoming State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin300130 China School of Materials Science and Engineering Hebei University of Technology Tianjin300130 China

Two-dimensional (2D) magnetic second-order topological insulators (SOTIs) exhibit distinct topological phases characterized by spin-polarized zero-dimensional (0D) corner states, which have garnered significant interest. However, 2D ferrimagnetic (FiM) SOTIs, particularly those that simultaneously exhibit ultra-stable corner states, are still lacking. Here, based on first-principles calculations and theoretical analysis, we reveal such SOTI state in a 2D metal-organic framework (MOF) material, Cr(pyz)2 (pyz = pyrazine). This material exhibits FiM ground state with an easy axis aligned along [001] direction. It hosts a nontrivial real Chern number in the spin-up channel, enabled by PT symmetry, with 0D corner states observable in disk. In contrast, the spin-down channel exhibits a trivial gapped bulk state. Notably, the topological corner states in monolayer Cr(pyz)2 show high robustness, even if the symmetries are broken by introducing defects, the corner states persist. We also considered other external perturbations, including uniaxial/biaxial strain, ligand rotation, and electric fields, the corner states still remain stable. Even more, the energy positions of the corner states are also nearly unchanged. This work is the first to identify ultra-stable FiM SOTI state in the MOF system, and provide an ideal platform for future experimental investigations and applications in spintronic devices. Copyright © 2025, The Authors. All rights reserved.

关键词： Topological insulators

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A Fault Diagnosis Method for Power Electronic Circuits Based on GADF Coding and Channel Split Residual Network

A Fault Diagnosis Method for Power Electronic Circuits Based...

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International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)

作者： Linghui Meng Jinyang Xie Zhenwei Zhou Yiqiang Chen Science and Technology on Reliability Physics and Application of Electronic Component Laboratory China Electronic Product Reliability and Environmental Testing Research Institute Guangzhou China School of Electrical Engineering Beijing Jiaotong University Beijing China

ISBN: (数字)9798350388855

ISBN: (纸本)9798350388862

With the objective to achieve an accurate diagnosis of commonly encountered fault problems in power electronic circuits and to prevent the occurrence of catastrophic failures, a soft fault diagnosis model for dc-dc converter based on the gram angle difference filed (GADF) and the channel split residual network (CSRN) is proposed. Firstly, the original fault data is coded by GADF to generate the fault data set. Secondly, channel split preprocessing (CSP) and Darwinian selection mechanism (DSM) are introduced to enhance the residual network and further construct the CSRN. CSP emphasizes the important channel features in the fault feature images by achieving a brute-force enhancement of the channel information and highlighting subtle features. DSM retains the key features and removes the secondary features during network training. The GADF-CSRN diagnosis model proposed in this paper is verified by the fault data collected from a dc-dc converter. The result showed that a diagnosis accuracy of 97.92% is achieved, surpassing the accuracy of conventional method by at least 1.85%, while exhibiting better noise resistance.

关键词： Fault diagnosis Training Resistance Accuracy DC-DC power converters Power electronics Sensors Circuit faults Integrated circuit modeling Residual neural networks

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Injury Prediction of Nonstandard Posture Occupants in Typical Intelligent Vehicle Frontal Impact

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IEEE Transactions on Consumer Electronics 2024年

作者： Wang, Fang Liu, Zishuo Wang, Mingliang Hu, Lin Liu, Zhangchi Zhang, Ying Liu, Yu Changsha University of Science and Technology School of Automotive and Mechanical Engineering Changsha410114 China Changsha University of Science and Technology Hunan Province Key Laboratory of Safety Design and Reliability Technology for Engineering Vehicle Changsha410114 China China Automotive Engineering Research Institute Co. Ltd Chongqing401122 China

As future intelligent vehicles are gradually brought into use, the vehicle driving scenario may show a predictable and highly diversified trend, which introduces a new challenge to the design of occupant injury protection in vehicle collisions. In this regard, quantitatively analyzing the sensitivity of occupant injury responses to highly diverse occupant postures and accurately predicting the risk of occupant injury related to specific OOP (out-of-position) postures are crucial for designing the crash safety of intelligent vehicles. In this study, a variety of typical occupant OOP postures are considered. Numerical simulations are conducted with existing occupant restraint systems and human body numerical models to investigate the correlation between OOP posture factors (such as AEB (automatic emergency braking) level, torso left/right tilt angle, body size, and seatback tilt angle) and the predicted injury responses. An OOP occupant injury prediction model for frontal collisions is established using BP (backpropagation) neural networks based on the simulation data. Our study shows that the OOP posture factors are correlated with various injury criteria to different extents. The seatback tilt angle is significantly and positively correlated with neck injury and is also clearly related to upper leg injury. The occupant torso left/right tilt angle is remarkably negatively correlated with chest injury, especially chest compression. The occupant size mainly affects lower leg injury, particularly tibia force, while the effect of AEB is less notable than these factors. The BP neural network-based model for the prediction of OOP occupant injury showed Adequate prediction performance in terms of consistency index (higher than 0.9 for all injury criteria) and prediction accuracy (higher than 84 for all injury criteria, except for the viscous criterion VC and neck extension bending moment). This study may serve as an important reference for the design of intelligent occupant

关键词： Automobile bodies

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Weakness Ranking Method for Subsystems of Heavy-Duty Machine Tools Based on FMECA Information

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Chinese Journal of Mechanical engineering 2021年第1期34卷 156-167页

作者： Zhaojun Yang Jinyan Guo Hailong Tian Chuanhai Chen Yongfu Zhu Jia Liu China Key Laboratory of CNC Equipment Reliability Ministry of EducationChangchun 130025China School of Mechanical and Aerospace Engineering Jilin UniversityChangchun 130025China College of Materials Science and Engineering Jilin UniversityChangchun 130025China School of Electrical and Mechanical Engineering Changchun University of Science and TechnologyChangchun 130025China

Heavy-duty machine tools are composed of many subsystems with different functions,and their reliability is governed by the reliabilities of these *** is important to rank the weaknesses of subsystems and identify the weakest subsystem to optimize products and improve their ***,traditional ranking methods based on failure mode effect and critical analysis(FMECA)does not consider the complex maintenance of ***,a weakness ranking method for the subsystems of heavy-duty machine tools is proposed based on generalized FMECA *** this method,eight reliability indexes,including maintainability and maintenance cost,are considered in the generalized FMECA ***,the cognition best worst method is used to calculate the weight of each screened index,and the weaknesses of the subsystems are ranked using a technique for order preference by similarity to an ideal ***,based on the failure data collected from certain domestic heavy-duty horizontal lathes,the weakness ranking result of the subsystems is obtained to verify the effectiveness of the proposed *** improved weakness ranking method that can comprehensively analyze and identify weak subsystems is proposed herein for designing and improving the reliability of complex electromechanical products.

关键词： Complex electromechanical products Weakness ranking method Failure mode effect and critical analysis(FMECA) Cognition best worst method(CBWM) Technique for order preference by similarity to an ideal solution(TOPSIS)

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reliability Modelling Method Based on The Stepwise Refined Performance Surrogate Model

Reliability Modelling Method Based on The Stepwise Refined P...

引用

Prognostics and System Health Management Conference (PHM-Qingdao)

作者： Haoran Liu Qingyuan Zhang Xiaoyang Li Li Yu Jing Yuan Rui Kang School of Reliability and System Engineering Beihang University Beijing China Science and Technology on Reliability and Environmental Engineering Laboratory Beijing China Zhongfa Aviation Institute of Beihang University Hangzhou China Shenzhen Weimin Institute of Reliability Systems Engineering Shenzhen China

reliability modelling based on product performance is a hot issue in recent years. Since the relationships between performance and numerous influencing factors are greatly terribly complex, surrogate model with experimental designs has received wide attention. However, due to the limitations of experimental capabilities and conditions, it is difficult to control and measure all the variables in a set of experiments. Hence, it is often difficult to balance the generality, significance and accuracy in one single model. To overcome these issues, in this paper, a stepwise refined performance surrogate model (SRPSM) is proposed. Further, by introducing the concept of performance margin in belief reliability theory, a novel reliability modeling method based on SRPSM is developed. To illustrate the proposed method, the reliability model building process of a new electrical connector product is shown as an example.

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Effect of Mg/Si Ratio on Synergistic Improvement of Formability and Yield Strength in Al-Mg-Si-Zn Alloys

SSRN

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SSRN 2024年

作者： Liu, Xu Zhang, Shao-You Liu, Ze-Tian Wang, Hui-Yuan State Key Laboratory of Reliability and Intelligence of Electrical Equipment School of Material Science and Engineering Hebei University of Technology Tianjin300130 China School of Materials Science and Engineering Inner Mongolia University of Technology Hohhot010051 China

The design of Al-Mg-Si-Zn series alloys combines the advantages of 6xxx and 7xxx series alloys and break through the limitations of strength and formability matching of traditional alloys. In this study, we achieved an exceptional strength-formability combination in the Al-0.84Mg-0.79Si-3.38Zn (wt.%) alloy. Following a solution treatment at 550 ℃ for 3 min, the alloy exhibited an elongation of ~36.8%, with a yield strength of ~356 MPa after artificial aging at 175 ℃ for 8 h. The effects of different Mg/Si ratios (~0.6, ~1.2, and ~2.5, at.%) on the strength-formability interplay in Al-Mg-Si-Zn alloys were investigated. The alloy with a low Mg/Si ratio (~0.6) facilitated the precipitation of fine β″ phases characterized by a high number density (~8.4 nm-3 × 10-5). A Mg/Si ratio of ~1.2 proved advantageous for precipitating larger β″ phases with a higher volume fraction (~0.69%), resulting in an elevated precipitation strengthening effect (~290 MPa). However, with an increase in the Mg/Si ratio to ~2.5, the reduced availability of Si atoms for forming Mg-Si clusters led to a decrease in the number density of precipitates. Additionally, the interaction among Fe, Mn, and Si elements resulted in the presence of blocky α-Al(FeMn)Si phases, as opposed to the needle-like β-AlFeSi phases, in the alloy with a Mg/Si ratio of ~1.2. This effectively prevented the degradation of elongation. Our findings present a comprehensive strategy for the compositional design of high-performance aluminum alloys. © 2024, The Authors. All rights reserved.

关键词： Silicon alloys

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Pre-Trained Large Language Model Based Remaining Useful Life Transfer Prediction of Bearing

arXiv

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arXiv 2025年

作者： Tao, Laifa Zhao, Zhengduo Wang, Xuesong Li, Bin Zhan, Wenchao Su, Xuanyuan Li, Shangyu Huang, Qixuan Liu, Haifei Lu, Chen Lian, Zhixuan Hangzhou International Innovation Institute Beihang University China Institute of Reliability Engineering Beihang University China National Key Laboratory of Science and Technology on Reliability and Environmental Engineering China School of Reliability and Systems Engineering Beihang University China China Electronics Information Industry Group Co. Ltd. Sixth Research Institute Beijing China

Accurately predicting the remaining useful life (RUL) of rotating machinery, such as bearings, is crucial for equipment reliability and minimizing unexpected failures in industrial systems. Despite recent advancements, data-driven deep learning methods face challenges in practical industrial settings due to inconsistent data distributions between training and testing phases, and limited generalization capabilities for long-term RUL predictions. To address these issues, we propose LM4RUL, a framework for RUL prediction based on pre-trained Large language Model (LLM). LM4RUL leverages the generalization and reasoning capabilities of LLM to transfer predictive knowledge from pre-training, effectively overcoming data inconsistencies and enhancing prediction accuracy. This represents a meaningful advancement in the artificial intelligence field, being among the first efforts to successfully apply LLM to RUL prediction tasks without the need for additional manual instruction, thereby extending the boundaries of AI applications beyond natural language processing and into complex industrial scenarios. The framework includes the local scale perception representation component, which captures fine-grained bearing degradation trends by tokenizing vibration data, and hybrid embedding learning, which selectively freezes and fine-tunes parameters to model complex nonlinear degradation. A two-stage fine-tuning approach further integrates pre-trained knowledge, enhancing adaptability without complex architectures. Experiments on authoritative datasets demonstrate that LM4RUL significantly improves long-term RUL prediction and adaptability, achieving high precision and robustness. This work bridges a critical gap in RUL prediction with advanced AI techniques and highlights its practical impact on equipment reliability and operational safety, showcasing its value for predictive maintenance in industrial AI applications. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep learning

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Multipole Transfer Matrix Model-Based Sparse Bayesian Learning Approach for Sound Source Identification

SSRN

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SSRN 2024年

作者： Pan, Wei Wei, Long Feng, Daofang Shi, Youtai Chen, Yan Li, Min Collaborative Innovation Center of Steel Technology University of Science and Technology Beijing Beijing100083 China Key Laboratory of Fluid Interaction with Material Ministry of Education University of Science and Technology Beijing Beijing100083 China Science and Technology on Reliability and Environment Engineering Laboratory Beijing Institute of Structure and Environment Engineering Beijing100076 China

Accurate source identification is key to solving complex flow-induced noise problems, and sources often have non-uniform radiation patterns. At the same time, traditional methods have many application limitations, including the need for prior knowledge of the sound field, as well as inability to apply to coherent sources or coexisting multipoles. A multipole transfer matrix model-based sparse Bayesian learning approach for sound source identification (MT-SBL) is proposed. In this algorithm, the transfer relationship for the multipole radiation patterns of sound sources is established. Then, the signal sparse reconstruction is transformed into an iterative updating problem of the feature parameters under a sparse Bayesian learning (SBL) framework, which leads to the accurate identification of multipole sources. Numerical simulations and experiments on electric speakers in an anechoic environment validate the proposed algorithm. Compared with the existing methods, the algorithm is more advantageous in terms of accuracy, resolution, and computational efficiency. Further experiments were conducted on the identification of supersonic jet noise sources, verifying the effectiveness of the method for identifying strongly directional sound sources. © 2024, The Authors. All rights reserved.

关键词： Computational efficiency

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