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Advanced Computing and Endogenous Security, International Conference on

作者： Siyi Hong Yihan He Jianpeng Zhang Chao Jiang Yingjun Deng College of Intelligence and Computing Tianjin University China School of Mathematics Tianjin University China National Digital Switching Systems E&T Research Center Zhengzhou China Science and Technology on Particle Transport and Seperation Laboratory Tianjin China Center for Applied Mathematics Tianjin University China

ISBN: (纸本)9781665424509

In modern industry, the accurate prediction of remaining useful life(RUL) contributes to the equipment safety and economic effectiveness. Aiming at the RUL prediction, this paper proposed a Bayesian temporal convolutional network (BayesianTCN) under the Bayesian deep learning framework. BayesianTCN outputs not only the RUL prediction, but also the associated confidence interval by Monte-Carlo simulation. This quantifies the RUL prediction uncertainty. Experimental results on CMAPSS datasets show that our model has higher fitting degree and lower uncertainty than BayesianLSTM, and performs well whether in simple or complex conditions.

关键词： Uncertainty Monte Carlo methods Estimation Production Bayes methods Safety Security

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Corrigendum to“Numerical studies of atomic three-step photoionization processes with non-monochromatic”

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Chinese Physics B 2024年第6期33卷 628-628页

作者：卢肖勇王立德李云飞 Science and Technology on Particle Transport and Separation Laboratory Tianjin 300180China

The Eq.(6)about the time correlation function of the chaotic field model is corrected,along with the corresponding *** enlargement factors of ρ_(33,B) and ρ_(ion,B) in the legends of Figs.2(b),2(c)and 2(d)are *** co... 详细信息

关键词： corrigendum non-monochromatic laserﬁeld chaoticfield model

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Effects of Multiple Contaminant Components on Spectral Performance of Reflective Mirrors in Resonance Ionization Spectroscopy

Effects of Multiple Contaminant Components on Spectral Perfo...

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Pacific-Rim Laser Damage 2023: Optical Materials for High-Power Lasers

作者： Zhang, Yulin Wang, Yanzhi Jiang, Yugang Shao, Jianda Zhang, Zhizhong Laboratory of Thin Film Optics Shanghai Institute of Optics and Fine Mechanics No. 390 Qinghe Road Jiading District Shanghai201800 China National Key Laboratory of Particle Transport and Separation Technology No. 168 Jintang Road Hedong District Tianjin300171 China Tianjin Key Laboratory of Optical Thin Films Tianjin Jinhang Institute of Technical Physics No. 357 Baoshui Road Dongli District Tianjin300300 China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Materials for High Power Laser Shanghai Institute of Optics and Fine Mechanics No. 390 Qinghe Road Jiading District Shanghai201800 China

ISBN: (纸本)9781510672826

With the increasing scope of applications of laser, it is crucial to study the pollution of optical components for regulating beams in intricate environments. In resonance ionization spectroscopy instruments, the optics may suffer from contamination caused by both the analyzed sample and environmental impurities, which can cause detrimental effects on their optical properties, potentially leading to their degradation or destruction. The paper analyses the spectral performance of two categories of reflective mirrors that have been exposed to four kinds of contamination (Nd, Nd-oxide, Fe, and C) for comparison of reflectance degradation process variations. It is found that the impact of multiple contaminant components on the spectral performance of reflective mirrors is directly linked to their light transmission characteristics, with metal contamination in particular causing the most severe degradation. © 2023 SPIE.

关键词： Contamination

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High Temperature Oxidation Behavior of Plasma Surface Cr-Nb-Si Alloying System on Γ -Tial

SSRN

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SSRN 2022年

作者： Wei, Dongbo Hu, Yujin Lv, Xuming Li, Fengkun Yang, Kai Dang, Bo Pang, Xueqing Zhang, Pingze College of Materials Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China Materials Preparation and Protection for Harsh Environment Key Laboratory Ministry of Industry and Information Technology Nanjing211106 China Science and Technology on Particle Transport and Separation Key National Defense Laboratory Research Institute of Physical and Chemical Engineering of Nuclear Industry Tianjin300180 China

The Cr-Nb-Si alloy system has attracted extensive attention of researchers due to its excellent oxidation resistance at high temperature. It is worth noting that the higher Cr content accelerates the transformation of the non-protective oxide Nb2O5 in the oxide scale, avoiding the occurrence of "pesting". And the increased mixing of CrNbO4 in the oxide is beneficial to improve the compactness of the oxide scale and inhibit the internal diffusion of O. The oxidation growth rate of the CrNbSi alloy system is only 1/16 of that of γ-TiAl at 800℃,100h. © 2022, The Authors. All rights reserved.

关键词： Niobium alloys

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Uncertainty Quantification for Remaining Useful Lifetime Prediction with Multi-Channel Sensory Data

Uncertainty Quantification for Remaining Useful Lifetime Pre...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Yingjun Deng Huaming Wu Chao Jiang Center for Applied Mathematics Tianjin University Tianjin P. R. China Science and Technology on Particle Transportand Seperation Laboratory Tianjin P. R. China

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066322

For remaining useful lifetime (RUL) prediction with multi-channel sensory data, long-term prediction has more uncertainty than short-term prediction. In this paper, the ratio of mean to variance was considered to measure the uncertainty propagation rate (UPR) of RUL prediction over time. Furthermore, we use a recurrent neural network (RNN) as the linking function for the mean of inverse Gaussian distributed RUL to construct a two-stage hybrid model. Later the RNN and the UPR are jointly trained with sensory data and failure records via alternating minimization. Proposed algorithms are validated in a simulation test.

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Research on No-Failure Data Evaluation Method Based on Imported Failure Information

Research on No-Failure Data Evaluation Method Based on Impor...

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2019 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, QR2MSE 2019

作者： Qian, Yi Jiang, Chao Hebei University of Technology School of Mechanical Engineering Tianjin300401 China Science and Technology on Particle Transport and Separation Laboratory Tianjin300180 China

ISBN: (纸本)9781728114279

For the case of no-failure data under the weibull distribution, when the prior distribution is Beta distribution, the ranges of super-parameters a and b are determined by the characteristics of probability density function. After introducing failure information, the method of the multi-level bayes estimation is modified. The robustness and feasibility of the method are verified by comparing the error of failure probability and the reliability calculated. © 2019 IEEE.

关键词： Weibull distribution

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Data-driven predictive model of coal permeability based on microscopic fracture structure characterization

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Journal of Rock Mechanics and Geotechnical Engineering 2025年

作者： Tianhao Yan Xiaomeng Xu Jiafeng Liu Yihuai Zhang Muhammad Arif Xiaowei Xu Qiang Wang College of Energy Environment and Safety Engineering China Jiliang University Hangzhou 310018 China School of Electrical and Information Engineering Tianjin University Tianjin 300072 China James Watt School of Engineering University of Glasgow Glasgow G12 8QQ United Kingdom Department of Petroleum Engineering Khalifa University Abu Dhabi 2533 United Arab Emirates Science and Technology on Particle Transport and Separation Laboratory Tianjin 300180 China

Accurate prediction of coal reservoir permeability is crucial for engineering applications, including coal mining, coalbed methane (CBM) extraction, and carbon storage in deep unmineable coal seams. Owing to the inherent heterogeneity and complex internal structure of coal, a well-established method for predicting permeability based on microscopic fracture structures remains elusive. This paper presents a novel integrated approach that leverages the intrinsic relationship between microscopic fracture structure and permeability to construct a predictive model for coal permeability. The proposed framework encompasses data generation through the integration of three-dimensional (3D) digital core analysis and numerical simulations, followed by data-driven modeling via machine learning (ML) techniques. Key data-driven strategies, including feature selection and hyperparameter tuning, are employed to improve model performance. We propose and evaluate twelve data-driven models, including multilayer perceptron (MLP), random forest (RF), and hybrid methods. The results demonstrate that the ML model based on the RF algorithm achieves the highest accuracy and best generalization capability in predicting permeability. This method enables rapid estimation of coal permeability by inputting two-dimensional (2D) computed tomography images or parameters of the microscopic fracture structure, thereby providing an accurate and efficient means of permeability prediction.

关键词： Microscopic fracture structure Lattice Boltzmann method Machine learning Coal permeability Predictive model

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Research of Reliability Allocation Based on Gray Theory

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IOP Conference Series: Materials science and Engineering 2021年第2期1043卷

作者： Y Qian C Jiang L Su PhD Student Hebei University of Technology Tianjin P.R. China Senior Engineer Key Laboratory of National Defense Science and Technology about Particle Transport and Enrichment Technology Tianjin P.R. China

A comprehensive reliability allocation method, which is applicable to the cases of lack of data in design stage, as well as the complexity of the influence factors and uncertainty of expert evaluation, is presented based on the gray system theory. The key of this method is to modify the weights of influence factors which are associated with expert scoring data by gray correlation analysis before reliability indices are assigned linearly to each subsystem. Based on gray evaluation with 4 levels, the application of this method is illustrated with the reliability allocation of a rotary machine under the consideration that complexity, technical level, importance and environment condition are chosen as influence factors. Compared with the actual faults statistics results, the validity and feasibility of the method is verified.

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Research on No-Failure Data Evaluation Method Based on Imported Failure Information

Research on No-Failure Data Evaluation Method Based on Impor...

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International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (ICQR2MSE)

作者： Yi Qian Chao Jiang School of Mechanical Engineering Hebei University of Technology Tianjin P.R. China Science and Technology on Particle Transport and Separation Laboratory Tianjin P.R. China

ISBN: (数字)9781728114279

ISBN: (纸本)9781728114286

关键词： Estimation Weibull distribution Bayes methods Probability density function Robustness Engines

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Portable Acceleration of CMS Computing Workflows with Coprocessors as a Service

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Computing and Software for Big science 2024年第1期8卷 1-36页

作者： Hayrapetyan, A. Tumasyan, A. Adam, W. Andrejkovic, J.W. Bergauer, T. Chatterjee, S. Damanakis, K. Dragicevic, M. Hussain, P.S. Jeitler, M. Krammer, N. Li, A. Liko, D. Mikulec, I. Schieck, J. Schöfbeck, R. Schwarz, D. Sonawane, M. Templ, S. Waltenberger, W. Wulz, C.-E. Darwish, M.R. Janssen, T. Mechelen, P. Van Bols, E.S. D’Hondt, J. Dansana, S. De Moor, A. Delcourt, M. Faham, H. El Lowette, S. Makarenko, I. Müller, D. Sahasransu, A.R. Tavernier, S. Tytgat, M. Onsem, G. P. Van Putte, S. Van Vannerom, D. Clerbaux, B. Das, A.K. De Lentdecker, G. Favart, L. Gianneios, P. Hohov, D. Jaramillo, J. Khalilzadeh, A. Khan, F.A. Lee, K. Mahdavikhorrami, M. Malara, A. Paredes, S. Thomas, L. Bemden, M. Vanden Velde, C. Vander Vanlaer, P. De Coen, M. Dobur, D. Hong, Y. Knolle, J. Lambrecht, L. Mestdach, G. Amarilo, K. Mota Rendón, C. Samalan, A. Skovpen, K. Bossche, N. Van Den Linden, J. van der Wezenbeek, L. Benecke, A. Bethani, A. Bruno, G. Caputo, C. Delaere, C. Donertas, I.S. Giammanco, A. Jaffel, K. Jain, Sh. Lemaitre, V. Lidrych, J. Mastrapasqua, P. Mondal, K. Tran, T.T. Wertz, S. Alves, G.A. Coelho, E. Hensel, C. De Oliveira, T. Menezes Moraes, A. Teles, P. Rebello Soeiro, M. Júnior, W. L. Aldá Pereira, M. Alves Gallo Filho, M. Barroso Ferreira Malbouisson, H. Brandao Carvalho, W. Chinellato, J. Da Costa, E.M. Da Silveira, G.G. De Jesus Damiao, D. De Souza, S. Fonseca De Souza, R. Gomes Martins, J. Herrera, C. Mora Mundim, L. Nogima, H. Pinheiro, J.P. Santoro, A. Sznajder, A. Thiel, M. Pereira, A. Vilela Bernardes, C.A. Calligaris, L. Tomei, T. R. Fernandez Perez Gregores, E.M. Mercadante, P.G. Novaes, S.F. Orzari, B. Padula, Sandra S. Aleksandrov, A. Antchev, G. Hadjiiska, R. Iaydjiev, P. Misheva, M. Shopova, M. Sultanov, G. Dimitrov, A. Litov, L. Pavlov, B. Petkov, P. Petrov, A. Shumka, E. Keshri, S. Thakur, S. Cheng, T. Javaid, T. Yuan, L. Hu, Z. Liu, J. Yi, K. Chen, G.M. Chen, H.S. Chen, M. Iemmi, F. Jiang, C.H. Kapoor, A. Liao, H. Liu, Z.-A. Sharma, R. Song, J.N. Tao, J. Wang, C. Wang, J. Wang, Z. Zhang, H. Agapitos Yerevan Physics Institute Yerevan Armenia Institut für Hochenergiephysik Vienna Austria Universiteit Antwerpen Antwerpen Belgium Vrije Universiteit Brussel Brussel Belgium Université Libre de Bruxelles Bruxelles Belgium Ghent University Ghent Belgium Université Catholique de Louvain Louvain-la-Neuve Belgium Centro Brasileiro de Pesquisas Fisicas Rio de Janeiro Brazil Universidade do Estado do Rio de Janeiro Rio de Janeiro Brazil Universidade Estadual Paulista Universidade Federal do ABC São Paulo Brazil Institute for Nuclear Research and Nuclear Energy Bulgarian Academy of Sciences Sofia Bulgaria University of Sofia Sofia Bulgaria Instituto De Alta Investigación Universidad de Tarapacá Casilla 7 D Arica Chile Beihang University Beijing China Department of Physics Tsinghua University Beijing China Institute of High Energy Physics Beijing China State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China Sun Yat-sen University Guangzhou China University of Science and Technology of China Hefei China Nanjing Normal University Nanjing China Institute of Modern Physics and Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) - Fudan University Shanghai China Zhejiang University Hangzhou Zhejiang China Universidad de Los Andes Bogota Colombia Universidad de Antioquia Medellin Colombia University of Split Faculty of Electrical Engineering Mechanical Engineering and Naval Architecture Split Croatia Faculty of Science University of Split Split Croatia Institute Rudjer Boskovic Zagreb Croatia University of Cyprus Nicosia Cyprus Charles University Prague Czech Republic Escuela Politecnica Nacional Quito Ecuador Universidad San Francisco de Quito Quito Ecuador Academy of Scientific Research and Technology of the Arab Republic of Egypt Egyptian Network of High Energy Physics Cairo Egypt Center for High Energy Physics (CHEP-FU) Fayoum University El-Fayoum Egypt National Institute of Chemical Physics and Biophysic

Computing demands for large scientific experiments, such as the CMS experiment at the CERN LHC, will increase dramatically in the next decades. To complement the future performance increases of software running on central processing units (CPUs), explorations of coprocessor usage in data processing hold great potential and interest. Coprocessors are a class of computer processors that supplement CPUs, often improving the execution of certain functions due to architectural design choices. We explore the approach of Services for Optimized Network Inference on Coprocessors (SONIC) and study the deployment of this as-a-service approach in large-scale data processing. In the studies, we take a data processing workflow of the CMS experiment and run the main workflow on CPUs, while offloading several machine learning (ML) inference tasks onto either remote or local coprocessors, specifically graphics processing units (GPUs). With experiments performed at Google Cloud, the Purdue Tier-2 computing center, and combinations of the two, we demonstrate the acceleration of these ML algorithms individually on coprocessors and the corresponding throughput improvement for the entire workflow. This approach can be easily generalized to different types of coprocessors and deployed on local CPUs without decreasing the throughput performance. We emphasize that the SONIC approach enables high coprocessor usage and enables the portability to run workflows on different types of coprocessors. © The Author(s) 2024.

关键词： CMS Machine learning Offline and computing

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