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16th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)

作者： Romain, Blandine Letort, Veronique Lucidarme, Olivier Rouet, Laurence d'Alche-Buc, Florence Ecole Cent Paris MAS Chatenay Malabry France Philips Res Suresnes France Hosp La Pitie Salpetriere AP HP Paris France CNRS LRI 8623 INRIA Saclay Orsay France Univ Evry IBISC Evry France

ISBN: (纸本)9783642407635;9783642407628

Today's follow-up of patients presenting abdominal tumors is generally performed through acquisition of dynamic sequences of contrast-enhanced CT. Estimating parameters of appropriate models of contrast intake diffusion through tissues should help characterizing the tumor physiology, but is impeded by the high level of noise inherent to the acquisition conditions. To improve the quality of estimation, we consider parameter estimation in voxels as a multi-task learning problem (one task per voxel) that takes advantage from the similarity between two tasks. We introduce a temporal similarity between tasks based on a robust distance between observed contrast-intake profiles of intensity. Using synthetic images, we compare multi-task learning using this temporal similarity, a spatial similarity and a single-task learning. The similarities based on temporal profiles are shown to bring significant improvements compared to the spatial one. Results on real CT sequences also confirm the relevance of the approach.

关键词： Multi-task learning CT perfusion model parameter estimation

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DIFFERENCE BETWEEN model parameter-estimation AND CURVE FITTING

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JOURNAL OF THE INTERNATIONAL ASSOCIATION FOR MATHEMATICAL GEOLOGY 1985年第4期17卷 483-483页

作者： CUROE, JE NASBURG, RE MOBIL EXPLORAT & PROD SERV INC DALLASTX 75221 TEXAS INSTRUMENTS INC DALLASTX 75265

Within a single well, a least-squares model such as Ø = a +bΔT sonic may be appropriate, insofar as random errors in core measurement and in sonic tool response are concerned. However, if several wells are in the reservoir, and if structural variation across the field exists, no single least-squares solution will adequately describe all wells with the same precision. Interpolation, via a cubic spline, is offered as an alternative to model this type of variation. However, to control both inter- and intra-well variability, a combination of least squares and interpolation is the model of choice.

关键词： bicubic spline least squares model parameter estimation porosity

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parameter estimation OF MULTI-CONSTITUENT WATER QUALITY model FOR THE LIANGXI RIVER BY MARQUARDT METHOD

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Journal of Geographical Sciences 1994年第Z1期4卷 110-118页

作者： Liu Shuxia(Institute of Geography, CAS, Beijing 100101People’s Republic of China)

A multi-constituent water quality model is presented,Which relates carbonaceous biochemical oxygen demand (CBOD),amonia (NH3-N), nitrite(NO2-N), nitrate(NO3-N) and dissolvedoxygen(DO). The parameters are solved by Marquardt Method (i. e.,Dampled Least Square Method) while initial values inoptimization are produced by Monte-Carlo Method. The Potential ofthe method as a parameter estimation aid is demonstrated for theapplication to the Liangyi Rver, JiangSu Province of China and by aspecial comparison with Gauss Method.

关键词： nitrogen pollution water quality model parameter estimation Marquardt Method Monte-Carlo Method

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Fast global flexible power point tracking of photovoltaic systems under partial shading condition with tracking-affiliated environmental parameter estimation

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ENERGY 2025年 318卷

作者： Yang, Jun Peng, Qiao Liu, Tianqi Liu, Youbo Gu, Tingyun Meng, Jinhao Sichuan Univ Coll Elect Engn Chengdu 610065 Peoples R China Guizhou Power Grid Co Ltd Elect Power Res Inst Guiyang 550000 Peoples R China Xi An Jiao Tong Univ Sch Elect Engn Xian 710049 Peoples R China

In the modern power system with increasing uncertainties and risks, grid-supporting services are required for photovoltaic (PV) systems, thus requiring flexible power point tracking (FPPT) to achieve high-quality gridsupporting performance. The PV systems usually suffer partial shading condition (PSC), stimulating the demand of global FPPT (GFPPT) method. However, keeping the speed and accuracy of power point tracking accuracy at the same time are challenging due to the uncertainty of environmental condition. This paper proposes a fast GFPPT (F-GFPPT) method based on a modified search-skip-judge global maximum power point tracking (SSJGMPPT) algorithm, where a tracking-affiliated environmental parameter estimation (EPE) is developed for the multi-peak power-voltage (P-V) curve fitting. Firstly, the modified SSJ-GMPPT is applied to collect the key information points of the P-V curve of PV system under PSC, including the section-dividing points and the maximum power points. Then, the key information points collected in the tracking process are utilized to sequentially identify the voltage of each PV panel and estimate the environmental parameters, which is then utilized in the model-based multi-peak P-V curve fitting. With the identified P-V curve, the flexible power point (FPP) of PV system can be accurately and rapidly located, and the F-GFPPT is realized. Simulations and hardware-in-the-loop experimental case studies validate the performance of the proposed method, which facilitates fast and accurate flexible active power control of PV system under PSC. Compared with previous methods, the proposed method eliminates the redundant searching process and steady-state oscillation, while achieving higher power regulation accuracy.

关键词： Photovoltaic system Partial shading condition Fast global flexible power point tracking Flexible active power control model parameter estimation

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Probabilistic Assessment of Constitutive model parameters:Insight from a Statistical Damage Constitutive model and a Simple Critical State Hypoplastic model

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Journal of Earth Science 2025年第2期36卷 685-699页

作者： Yang Xue Fasheng Miao Yiping Wu Linwei Li Daniel Dias Yang Tang Key Laboratory of Smart Earth Beijing 100029China Key Laboratory of Geological Survey and Evaluation of Ministry of Education China University of Geosciences(Wuhan)Wuhan 430074China School of Geosciences Yangtze UniversityWuhan 430100China College of Resources and Environmental Engineering Guizhou UniversityGuiyang 550025China Laboratory 3SR Grenoble Alpes UniversityGrenoble 38000France Engineering Digital Technology Center Central-South Architectural Design Institute Co.LtdWuhan 430071China

The constitutive model is essential for predicting the deformation and stability of rocksoil *** estimation of constitutive model parameters is a necessary and important task for the reliable characterization of mechanical ***,constitutive model parameters cannot be evaluated accurately with a limited amount of test data,resulting in uncertainty in the prediction of stress-strain *** paper proposes a Bayesian analysis framework to address this *** combines the Bayesian updating with the structural reliability and adaptive conditional sampling methods to assess the equation parameter of constitutive *** on the triaxial and ring shear tests on shear zone soils from the Huangtupo landslide,a statistical damage constitutive model and a critical state hypoplastic constitutive model were used to demonstrate the effectiveness of the proposed ***,the parameter uncertainty effects of the damage constitutive model on landslide stability were *** show that reasonable assessments of the constitutive model parameter can be well *** variability of stress-strain curves is strongly related to the model prediction *** estimation uncertainty of constitutive model parameters should not be ignored for the landslide stability *** study provides a reference for uncertainty analysis and parameter assessment of the constitutive model.

关键词： probabilistic back analysis Bayesian approach model parameter estimation constitutive model landslide stability engineering geology

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model parameter estimations of the multi-channel turbulence response from flutter flight tests based on autoregressive spectra

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JOURNAL OF LOW FREQUENCY NOISE VIBRATION AND ACTIVE CONTROL 2023年第1期42卷 173-191页

作者： Duan, Shiqiang Zheng, Hua Northwestern Polytech Univ Sch Power & Energy Youyi Rd 127 Xian 710072 Peoples R China

Signal processing of flutter flight test data enables verification of aircraft flutter design, and the signal from a flutter flight test excited by atmospheric turbulence is a particularly important form of the flutter test. Owing to the randomness of atmospheric turbulence excitation, multi-channel analysis of turbulence responses at various positions in the same component can improve the analytical accuracy of flutter signal processing. The relationship between the maximum singular value of the multi-channel turbulence response power spectral density matrix and the system self-power spectral density function is elucidated herein using a frequency domain decomposition method. However, there is a contradiction in the power spectral density function between the spectral line density and the spectral line smoothing calculated based on the periodogram of the frequency domain decomposition. By applying an autoregressive spectral model, the power spectral density function of the turbulence response is calculated to achieve spectral line smoothing and sufficient spectral line density. Additionally, the power spectral density function is then used to construct the power spectral density function matrix of the multi-channel turbulence response, and the maximum singular value curve is calculated based on the singular value decomposition of each spectral pin. Finally, the modal parameters of the turbulence response signal are estimated via multi-modal frequency domain fitting. The developed approach is validated based on simulations and flutter flight test turbulence response signals.

关键词： autoregressive spectrum multi-channel turbulence response model parameter estimation singular value decomposition frequency polynomial fitting

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On-Line model parameter estimations for Time-Delay Systems

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2013年第8期E96D卷 1867-1870页

作者： Park, Jung Hun Han, Soohee Kwon, Bokyu POSCO Tech Res Labs Phang Gyeongsangbuk D South Korea Konkuk Univ Dept Elect Engn Seoul South Korea Kangwon Natl Univ Dept Control & Instrumentat Engn Samcheok Gangwon Do South Korea

This paper concerns a problem of on-line model parameter estimations for multiple time-delay systems. In order to estimate unknown model parameters from measured state variables, we propose two schemes using Lyapunov's direct method, called parallel and series-parallel model estimators. It is shown through a numerical example that the proposed parallel and series-parallel model estimators can be effective when sufficiently rich inputs are applied.

关键词： model parameter estimation time-delay system Lyapunov's direct method

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A model-based approach to quality monitoring of a polymerization process without online measurement of product specifications

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COMPUTERS & INDUSTRIAL ENGINEERING 2017年 106卷 123-136页

作者： Nogueira, Idelfonso Fontes, Cristiano Sartori, Isabel Pontes, Karen Embirucu, Marcelo Univ Fed Bahia Escola Politecn Polytech Inst Ind Engn Program Rua Prof Aristides Novis 2 BR-40210630 Salvador BA Brazil

This paper presents a model-based approach for on-line monitoring of difficult-to-measure quality variables with the description of their dynamic behavior. The strategy comprises the use of a Virtual On-line Analyzer (VOA), based on an empirical model, whose development is supported on the one hand by experimental data, and on the other hand by a complex phenomenological model. A systematic approach to the selection and estimation of parameters of the phenomenological model from experimental data is also presented in order to adjust this model to the behavior of the industrial process. Regarding the experimental data, this complete model has additional information (such as the dynamic behavior of the process, embedded in the knowledge used in its formulation) and it is able to provide synthetic data for VOA training which comprises a NARX (Non-linear AutoRegressive with Exogeneous inputs) neural network model. A real case study comprising quality monitoring in a polymerization system is investigated. The available measurements of the main polymer properties are performed at a low frequency and are not able to represent the dynamic behavior and support decision-making at the operational level, especially during online grade transitions (changes in product specifications). The results show the ability of the neural model to predict the quality variables with a high frequency (small sampling period), enabling and supporting decision-making for quality monitoring of the polymer in real time. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Product quality control Industrial process Neural model Decision-making model parameter estimation

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Accurate Probabilistic Characterization of Battery Estimates by Using Large Deviation Principles for Real-Time Battery Diagnosis

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IEEE TRANSACTIONS ON ENERGY CONVERSION 2013年第4期28卷 860-870页

作者： Chen, Ziqiang Wang, Le Yi Yin, George Lin, Feng Wang, Caisheng Shanghai Jiao Tong Univ Sch Mech & Power Engn Shanghai 200030 Peoples R China Wayne State Univ Dept Elect & Comp Engn Detroit MI 48202 USA Wayne State Univ Dept Math Detroit MI 48202 USA

Reliability of battery diagnosis depends on accurate estimation of the state of charge (SOC) and battery characterizing parameters including maximum capacity, internal impedance, polarization coefficients, and their probabilistic characterizations. This paper develops a framework that employs real-time operating data to estimate jointly the SOC and parameters, performs statistical analysis to derive quantitative diagnostic procedures with error analysis. Convergence of the algorithms, asymptotic distributions, and diagnosis reliability analysis are performed rigorously by using stochastic differential equations, central limit theorems, and large deviations principles. Simulated case studies and experimental data are used to illustrate the diagnosis algorithms and their capabilities. Experimental studies are conducted to verify the results.

关键词： Battery diagnosis battery management systems large deviations principles (LDP) model parameter estimation state of charge (SOC) estimation statistical analysis

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Application of current switching method to estimate the model parameters of proton exchange membrane fuel cell

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SIMULATION modelLING PRACTICE AND THEORY 2010年第1期18卷 35-50页

作者： Chang, Wen-Yeau St Johns Univ Dept Elect Engn Taipei 25135 Taiwan

This paper presents a model parameter estimation method for the proton exchange membrane fuel cell (PEMFC). The parameter estimation method is based on the current switching technique, intentionally switching the output current of PEMFC to obtain the waveform of the transient terminal voltage. The parameter values are found by analyzing transient waveform measurement results from the current switching test. To demonstrate the effectiveness of the current switching method, the method is tested on a practical PEMFC stack. Good agreement between the experimental and simulated results is obtained. The test results show the parameter estimation method is efficient and reliable. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Proton exchange membrane fuel cell model parameter estimation Current switching method

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