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Fuel cell health prognosis using Unscented Kalman Filter: Postal fuel cell electric vehicles case study

INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2019年第3期44卷 1930-1939页

作者： Chen, Kui Laghrouche, Salah Djerdir, Abdesslem Univ Bourgogne Franche Comt CNRS UMR 6174 FEMTO ST F-90000 Belfort Utbm France Univ Bourgogne Franche Comt CNRS FR 3539 FCLAB F-90000 Belfort Utbm France

The Proton Exchange Membrane Fuel Cell (PEMFC) health monitoring and management are of critical importance for the performance and cost efficiency of Fuel Cell Electric Vehicle (FCEV). Prognostics play an important role in improving the lifetime and reducing maintenance costs of PEMFC by predicting the degradation trend. In this paper, the degradation prediction of PEMFC is based on a novel model-driven method which combines the Unscented Kalman Filter (UKF) algorithm with the proposed voltage degradation model. The experimental data originated from the FCEVs which achieve postal delivery mission in the real road are used for construction and validation of the proposed model-driven prognostic method. At our best knowledge, this is the first application which uses field-based data for FC health prognosis. The influence of different lengths of measured voltage data on degradation prediction of PEMFC, and the degradation prediction performance of PEMFC in different FCEVs are also investigated by the proposed method. Test results show that the proposed model-driven method is able to accurately estimate the voltage degradation trend of PEMFC in the FCEV. When more data are applied to learning the degradation of PEMFC, the mean Relative Error (RE) in the prediction phase will decrease. Especially, when the learning data exceeds 45 h, the mean RE in prediction phase is reduced to 0.68%. Considering that the maximum mean RE in the prediction phase is 2.03% for 3 postal FCEVs, the proposed method can be applied in the degradation trend prediction of PEMFC in FCEV under real conditions. (C) 2018 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Prognostic Fuel cell electric vehicle Degradation prediction Proton exchange membrane fuel cell model-driven method Unscented kalman filter

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Learned Conjugate Gradient Descent Network for Massive MIMO Detection

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2020年 68卷 6336-6349页

作者： Wei, Yi Zhao, Ming-Min Hong, Mingyi Zhao, Min-Jian Lei, Ming Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Univ Minnesota Dept Elect & Comp Engn Minneapolis MN 55455 USA

In this work, we consider the use of model-driven deep learning techniques for massive multiple-input multiple-output (MIMO) detection. Compared with conventional MIMO systems, massive MIMO promises improved spectral efficiency, coverage and range. Unfortunately, these benefits are at the expense of significantly increased computational complexity. To reduce the complexity of signal detection and guarantee the performance, we present a learned conjugate gradient descent network (LcgNet), which is constructed by unfolding the iterative conjugate gradient descent (CG) detector. In the proposed network, instead of calculating the exact values of the scalar step-sizes, we explicitly learn their universal values. Also, we can enhance the proposed network by augmenting the dimensions of these step-sizes. Furthermore, in order to reduce the memory costs, a novel quantized LcgNet is proposed, where a low-resolution nonuniform quantizer is used to quantize the learned parameters. The quantizer is based on a specially designed soft staircase function with learnable parameters to adjust its shape. Meanwhile, due to fact that the number of learnable parameters is limited, the proposed networks are relatively easy to train. Numerical results demonstrate that the proposed network can achieve promising performance with much lower complexity.

关键词： Conjugate gradient descent deep learning massive MIMO detection model-driven method

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A new scheme of target detection for pulse Doppler radar in interrupted sampling repeater jamming environment

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IET RADAR SONAR AND NAVIGATION 2022年第11期16卷 1836-1850页

作者： Zhang, Yu Jiu, Bo Peng, Xiaojun Liu, Hongwei Jiang, Wenqi Xidian Univ Natiotud Lab Radar Signal Proc Xian 710071 Peoples R China Rasearch Acad Rocket Xian Peoples R China

Research on the anti-jamming target detection for pulsed Doppler (PD) radar is intensively important to the normal work of radar in complicated and volatile electromagnetic environments. Generally, traditional anti-jamming target detection methods involve several stages, each one of these stages is based on a model. Due to the issues of poor flexibility caused by multi-stage processing and model mismatch caused by model-based methods, the detection performance will be severely decreased. Therefore, a new target detection scheme for PD radar in an Interrupted-Sampling Repeater Jamming (ISRJ) environment is proposed, which is a dual-driven method of model and data. This innovative solution first achieves energy accumulation on the Doppler channel by a model-driven method, then proposes a series of preprocessing methods to process radar signals to adapt to a data-driven Convolutional Neural Network (CNN) detector. Finally, a CNN detector structure is proposed. The effectiveness of this novel scheme is demonstrated by a series of performance and visualisation experiments.

关键词： model-driven method model-based methods preprocessing methods dual-driven method complicated environments radar signals multistage processing pulsed Doppler radar traditional anti-jamming target detection methods jamming radar signal processing volatile electromagnetic environments convolutional neural nets object detection Doppler radar pulse Doppler radar Interrupted-Sampling Repeater Jamming environment target detection scheme interrupted sampling repeater Doppler channel detection performance data-driven Convolutional Neural Network detector PD radar

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A model-driven Infrastructure for Developing Product Line Architectures Using CVL

A Model-Driven Infrastructure for Developing Product Line Ar...

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7th Brazilian Symposium on Software Components, Architectures and Reuse (SBCARs)

作者： Nascimento, Amanda S. Rubira, Cecilia M. F. Burrows, Rachel Castor, Fernando Univ Estadual Campinas Inst Comp Campinas SP Brazil Univ Bath Dept Comp Sci Bath Avon England Univ Fed Pernambuco Informat Ctr Recife PE Brazil

ISBN: (纸本)9781479925315

Over recent years, software engineers have been evaluating the benefits of combining Software Product Line and model-driven Engineering, which is referred to as model-driven Product Line Engineering (MD-PLE), to achieve software architecture evolvability. In general terms, existing solutions for MD-PLE support high-level model refinement into lower level abstractions in order to reach code implementation of product line architectures. Nevertheless, the applicability of such approaches is limited due to either the unsystematic processes that do not clearly specify how to refine models to decrease the level of abstraction, or the lack of automation support. In this paper, we propose an infrastructure for MD-PLE. The infrastructure encompasses a model-driven, systematic and semiautomated engineering method that combines a set of existing process, tools, languages and models to implement product line architectures. Common Variability Language (CVL) is adopted to specify and resolve architectural variability explicitly and systematically. We employ our solution to develop a family of software fault tolerance techniques for service-oriented architectures. The results obtained suggest the proposed solution is useful and efficient to implement product line architectures. We report lessons learned from this case study and present directions for future work.

关键词： Software Product Lines model-driven method Common Variability Language

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Learned Conjugate Gradient Descent Network for Massive MIMO Detection

Learned Conjugate Gradient Descent Network for Massive MIMO ...

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IEEE International Conference on Communications (IEEE ICC) / Workshop on NOMA for 5G and Beyond

作者： Wei, Yi Zhao, Ming-Min Hong, Mingyi Zhao, Min-Jian Lei, Ming Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou Peoples R China Univ Minnesota Dept Elect & Comp Engn Minneapolis MN 55455 USA

ISBN: (纸本)9781728150895

In this work, we consider the use of model-driven deep learning (DL) techniques for signal detection in massive multiple-input multiple-output (MIMO) system. Massive MIMO promises improved spectral efficiency, coverage and reliability, compared to conventional MIMO systems. Unfortunately, these benefits usually come at the cost of significantly increased computational complexity. To address this difficulty, a learned conjugate gradient descent network, referred to as LcgNet, is presented by unfolding the iterative conjugate gradient descent (CG) detector. In the proposed network, instead of calculating the exact values of the scalar step-sizes for every problem instance, we explicitly learn their universal values. We show that the performance of the proposed network can be greatly improved by augmenting the dimensions of these step-sizes. Furthermore, due to the limited learnable parameters to be optimized, the proposed networks are easy and fast to train. Numerical results demonstrate that this approach can achieve superior performance over some state-of-the-art MIMO detectors such as the CG detector, the linear minimum mean squared error (LMMSE) detector etc., with much lower computational complexity.

关键词： Conjugate gradient descent deep learning massive MIMO detection model-driven method

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modeling and estimation of degradation for PEM fuel cells in real conditions of use for mobile applications

Modeling and estimation of degradation for PEM fuel cells in...

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作者： Chen, Kui

学位级别：博士

La PEMFC sont une source d'énergie propre en raison de ses avantages comme une efficacité énergétique élevée, un faible bruit, une température de fonctionnement faible et zé... 详细信息

La PEMFC sont une source d'énergie propre en raison de ses avantages comme une efficacité énergétique élevée, un faible bruit, une température de fonctionnement faible et zéro polluant. Cependant, la courte durée de vie causée par la dégradation a un grand impact sur l'intégration de la PEMFC dans les systèmes de transport. Le pronostic et la gestion de la santé sont un moyen important pour améliorer les performances et la durée de vie de la PEMFC. Cette thèse présente cinq méthodes de pronostic de dégradation pour la PEMFC. Elle examine l'influence des principales conditions de fonctionnement, incluant le courant de charge, la température, la pression d'hydrogène et l'humidité relative, sur la dégradation de la PEMFC. La dégradation globale et les phénomènes réversibles sont analysés en se basant sur les données numériques issues de trois expériences de PEMFC menées dans des conditions différentes d'usage (une flotte de véhicules à PEMFC et deux bancs de test de type laboratoire). Un premier modèle basé sur l'algorithme de filtre de Kalman UKF (Unscented Kalman Filter) et le modèle de dégradation de la tension est proposé pour prédire la dégradation de la PEMFC dans les véhicules électriques à pile à combustible. Puis, la méthode hybride basée sur l'analyse des ondelettes, la machine d'apprentissage extrême et l'algorithme génétique est proposée pour construire un deuxième modèle de dégradation de la PEMFC. Pour prévoir la dégradation du PEMFC avec des données de expérimentales limitées, la méthode améliorée basée sur sur la combinaison du modèle des réseaux de neurones gris, l'optimisation de l'essaim de particules et les méthodes de fenêtre mobile, est utilisée pour développer le troisième modèle. La quatrième contribution est un modèle de pronostic de vieillissement de la PEMFC fonctionnant dans différentes conditions, en utilisant le réseau neuronal de rétro-propagation et l'algorithme évolutif. Enfin, un pronostic de dégradation de la PEMFC basé sur le réseau

关键词： Les piles à combustible à membrane échangeuse de de protons (PEMFC) Véhicule électrique à pile à combustible Pronostic Méthode axée sur les modèles Méthode basée sur les données PEM fuel cell Fuel cell electric vehicle Prognostics model-driven method Data-driven method 620

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Variable step-size weighted zero-attracting sign algorithm

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SIGNAL PROCESSING 2020年 172卷 107542-107542页

作者： Chen, Xu Ni, Jingen Soochow Univ Sch Elect & Informat Engn Suzhou 215006 Peoples R China

The classical sign algorithm (SA) has been widely used in adaptive filters due to its low computational complexity and robustness against impulsive noise. In some applications the system to be estimated may be sparse. To improve the convergence rate of the SA for sparse system estimation, this paper incorporates a weighted l(1)-norm into the cost function built for the SA to develop a weighted zero-attracting SA (WZA-SA). Since the WZA-SA uses a constant step-size, it requires to take a tradeoff between fast convergence rate and small steady-state misalignment. To address this problem, we present a variable step-size (VSS) for the WZA-SA based on the mean-squared deviation (MSD) model-driven method. Simulation results are provided to verify its good performance for white or not highly correlated input signals. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Sign algorithm Zero-attraction Weighted l(1)-norm Variable step-size Mean-square deviation model-driven method

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Single image denoising via multi-scale weighted group sparse coding

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SIGNAL PROCESSING 2022年 200卷

作者： Ou, Yang Swamy, M. N. S. Luo, Jianqiao Li, Bailin Southwest Jiaotong Univ Sch Mech Engn Chengdu 610031 Peoples R China Concordia Univ Dept Elect & Comp Engn Montreal PQ H3G 1M8 Canada

Nonlocal self-similarity (NSS) property of natural images, which means that the structure of the image sub-patches will appear repeatedly within a certain area, has been widely exploited as an effective prior to establishing various models in image denoising task. However, most of the existing NSS-based de noising models exploit the NSS prior in single scale only, and for some of the image patches that do not appear repeatedly, undesirable ringing artifacts will occur in the restored image, and even the image content may be lost. Considering the fact that NSS exists both within the same scale and across different scales, in order to better restore the structure and the edges of images contaminated by noise, we propose, in this paper, a novel multi-scale weighted group sparse coding model (MS-WGSC) for image denoising, wherein the patch groups are constructed using multi-scale NSS priors. Furthermore, an alternating minimization method is proposed to obtain the solution for our model. Extensive experiments are conducted that demonstrate the competitiveness of the proposed model compared with that of stateof-the-art methods not only in terms of the quantitative metrics such as PSNR and SSIM, but also in perceptual quality.(c) 2022 Elsevier B.V. All rights reserved.

关键词： model-driven method multi-scale self-similarity weighted group sparse coding image denoising

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Comparison of model-driven soft measurement methods for compressor air flow in gas-steam combined cycle power units

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FLOW MEASUREMENT AND INSTRUMENTATION 2023年 94卷

作者： Le, Zengmeng Liang, Ying Xiong, Bo Huang, Yaowen Niu, Huoping Chen, Chaoming Zhang, Dongshuang Yan, Guoli Wen, Yanming Chen, Zhijun Wu, Feng Yang, Cheng Ma, Xiaoqian Zhongshan Jiaming Elect Power Co Ltd Torch Hightech Ind Dev Zone Zhongshan 528403 Peoples R China South China Univ Technol Sch Elect Power Guangzhou 510640 Peoples R China

The determination of the air flowrate at the compressor inlet is meaningful for the performance diagnosis of heavy-duty gas turbine combined cycle (GTCC) power units, whereas the compressor air flow generally cannot be directly obtained from the field data. In this paper, the procedures of three model-driven methods for soft measurement of compressor air flow were presented, including energy balance of the coupled HRSG (M1), static pressure at the compressor manifold (M2) and gas turbine joint operation line method (M3). In the case of a M701F4 GTCC power unit, the methods M1 and M3 were validated by the field and testing data;while the method M2 was calibrated and the effective area at the air manifold was decided as 12.606 m2 using M1 at the full load of gas turbine. The compressor air mass flow at overall loads was computed with the various methods. The comparative investigation to these methods shows that, the compressor inlet air flow is determined as 682.75 kg/s by M1 and 664.07 kg/s by M3, respectively, at the full load and at the ambient temperature 29.912 degrees C. The air flow obtained by M1 and M2 appears relatively higher than that by M3 at typical various loads of gas turbine;however, the method M3 shows a good accordance with the actual value of the air flow for turbine rotor cooling. Both M2 and M3 agree in the theoretical demonstration that compressor air flow is strongly related to the gas turbine load at certain conditions. The method M1 can be deemed as a reference for another two methods in the case of the steady operating state at full load;whereas it is not recommendable for the real-time measurement of compressor air flow, because of the large amounts of required input variables and the fluctuation in the flow meters.

关键词： Gas turbine combined cycle Air mass flowrate model-driven method Soft measurement Comparative investigation

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From model-driven to data-driven: A review of hysteresis modeling in structural and mechanical systems

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2023年 204卷

作者： Wang, Tianyu Noori, Mohammad Altabey, Wael A. Wu, Zhishen Ghiasi, Ramin Kuok, Sin-Chi Silik, Ahmed Farhan, Nabeel S. D. Sarhosis, Vasilis Farsangi, Ehsan Noroozinejad Shanghai Inst Technol Sch Urban Construct & Safety Engn Shanghai 201418 Peoples R China Calif Polytech State Univ San Luis Obispo Dept Mech Engn San Luis Obispo CA 93407 USA Univ Leeds Sch Civil Engn Leeds LS2 9JT England Southeast Univ Int Inst Urban Syst Engn IIUSE Nanjing 211189 Peoples R China Alexandria Univ Fac Engn Dept Mech Engn Alexandria 21544 Egypt Univ Coll Dublin Sch Civil Engn Struct Dynam & Assessment Lab Dublin D04 V1W8 Ireland Univ Macau Dept Civil & Environm Engn State Key Lab Internet Things Smart City Guangdong Hong Kong Macau Joint Lab Smart Cities Macau Peoples R China Nyala Univ Dept Civil Engn Nyala Sudan Western Sydney Univ Urban Transformat Res Ctr UTRC Penrith Australia

Hysteresis is a natural phenomenon that widely exists in structural and mechanical systems. The characteristics of structural hysteretic behaviors are complicated. Therefore, numerous methods have been developed to describe hysteresis. In this paper, a review of the available hysteretic modeling methods is carried out. Such methods are divided into: a) model-driven and b) datadriven methods. The model-driven method uses parameter identification to determine parameters. Three types of parametric models are introduced including polynomial models, differential based models, and operator based models. Four algorithms as least mean square error algorithm, Kalman filter algorithm, metaheuristic algorithms, and Bayesian estimation are presented to realize parameter identification. The data-driven method utilizes universal mathematical models to describe hysteretic behavior. Regression model, artificial neural network, least square support vector machine, and deep learning are introduced in turn as the classical data-driven methods. model-data driven hybrid methods are also discussed to make up for the shortcomings of the two methods. Based on a multi-dimensional evaluation, the existing problems and open challenges of different hysteresis modeling methods are discussed. Some possible research directions about hysteresis description are given in the final section.

关键词： Hysteresis modeling Structural and mechanical system model-driven method Data-driven method model-data hybrid driven method

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