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JL
Jie Liu
77
Documents
Identifiants chercheurs
- jie-liu
- 0000-0003-0895-7598
- IdRef : 18572390X
Présentation
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Big Data Analytics for Reputational Reliability Assessment Using Customer Review Data31st European Safety and Reliability Conference, ESREL 2021, Sep 2021, Angers, France. pp.2336-2343, ⟨10.3850/978-981-18-2016-8_434-cd⟩
Communication dans un congrès
hal-04317735v1
|
|
|
KNN-FSVM for Fault Detection in High-Speed Trains2018 IEEE International Conference on Prognostics and Health Management (ICPHM), 2018, Chongqing, China
Communication dans un congrès
hal-01989032v1
|
|
Model ensemble-based prognostic framework for fatigue crack growth prediction2017 2nd International Conference on System Reliability and Safety (ICSRS), Dec 2017, Milan, Italy. ⟨10.1109/ICSRS.2017.8272843⟩
Communication dans un congrès
hal-01784274v1
|
Efficient Sparsity-Based Algorithm for Parameter Estimation of the Tri-Exponential Intra Voxel Incoherent Motion (IVIM) Model Application to Diffusion-Weighted MR Imaging in the Liver7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Dec 2017, Curacao, Netherlands Antilles. ⟨10.1109/CAMSAP.2017.8313196⟩
Communication dans un congrès
hal-01783432v1
|
|
|
Multisensor fault detection and isolation using Kullback Leibler Divergence : Application to data vibration signalsInternational Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC), Aug 2017, Shanghai, China. ⟨10.1109/sdpc.2017.65⟩
Communication dans un congrès
hal-01577713v1
|
Dynamic reliability assessment and prognostics with monitored data for multiple dependent degradation components26th European Safety and Reliability Conference, ESREL 2016, Sep 2016, unknown, France. pp.117
Communication dans un congrès
hal-04365301v1
|
|
A framework for asset prognostics from fleet dataIEEE Prognostics and System Health Management Conference, Oct 2016, Chengdu, France. ⟨10.1109/PHM.2016.7819824⟩
Communication dans un congrès
hal-01787966v1
|
|
|
Adaptive Support Vector Regression for Long-Term Prediction under Nonstationay Environments: Prognostics of Components in Nuclear Power Plants2015 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE 2015), Jul 2015, Beijing, China
Communication dans un congrès
hal-01176337v1
|
Adaptive Support Vector Regression for Long-Term Prediction under Non stationary Environments: Prognostics of Components in Nuclear Power Plants2015 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, Aug 2015, Beijing, China
Communication dans un congrès
hal-01787957v1
|
|
|
Dynamic Weighted PSVR-Based Ensembles for Prognostics of Nuclear ComponentsFLINS 2014, Aug 2014, João Pessoa, Brazil
Communication dans un congrès
hal-01108176v1
|
|
AN EFFICIENT ONLINE LEARNING APPROACH FOR SUPPORT VECTOR REGRESSIONsecond european conference of the prognostics and health management society 2014, Jul 2014, Nantes, France. ⟨10.1142/9789814619998_0032⟩
Communication dans un congrès
hal-01090273v1
|
|
Probabilistic Support Vector Regression for Short-Term Prediction of Power Plants EquipmentPrognostics and System Health Management Conference - PHM-2013, Sep 2013, Milano, Italy. pp.1-6
Communication dans un congrès
hal-00838776v1
|
|
Short-Term Prediction for Nuclear Power Plant Failure Scenarios Using an Ensemble-based ApproachESREL 2013, Sep 2013, Amsterdam, Netherlands. pp.1-5
Communication dans un congrès
hal-00838785v1
|
|
galsC: A Language for Event-Driven Embedded SystemsDATE'05, Mar 2005, Munich, Germany. pp.1050-1055
Communication dans un congrès
hal-00181268v1
|
CEPC Conceptual Design Report: Volume 2 - Physics & Detector2021
Pré-publication, Document de travail
hal-03191220v1
|
|
Examples of Fano manifolds with non-pseudoeffective tangent bundle2020
Pré-publication, Document de travail
hal-02998483v1
|
|
Stability of tangent bundles of complete intersections and effective restriction2019
Pré-publication, Document de travail
hal-02373473v1
|
|
Seshadri constants of the anticanonical divisors of Fano manifolds with large index2019
Pré-publication, Document de travail
hal-02373471v1
|
|
Multi-exponential IVIM MRI model identification : application to the quantification of tissue diffusion and perfusionSignal and Image processing. Université de Rennes, 2020. English. ⟨NNT : 2020REN1S115⟩
Thèse
tel-03285401v1
|
|
Geometry of Fano varieties : subsheaves of the tangent bundle and fundamental divisorAlgebraic Geometry [math.AG]. Université Côte d'Azur, 2018. English. ⟨NNT : 2018AZUR4038⟩
Thèse
tel-02000801v1
|
|
Failure prognostics by support vector regression of time series data under stationary/nonstationary environmental and operational conditionsOther. Ecole Centrale Paris, 2015. English. ⟨NNT : 2015ECAP0019⟩
Thèse
tel-01249593v1
|
Fonctionnalisation des nanotubes de carbone : de la chimie à la caractérisationAutre. Université Henri Poincaré - Nancy 1, 2006. Français. ⟨NNT : 2006NAN10152⟩
Thèse
tel-01746571v1
|