检索结果-内蒙古大学图书馆

A model order reduction approach to create patient-specific mechanical models of human liver in computational medicine applications

引用

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019年 170卷 95-106页

作者： Lauzeral, Nathan Borzacchiello, Domenico Kugler, Michael George, Daniel Remond, Yves Hostettler, Alexandre Chinesta, Francisco Ecole Cent Nantes High Performance Comp Inst ICI Nantes France Univ Strasbourg CNRS ICube Strasbourg France IRCAD Strasbourg France CNRS ENSAM ParisTech PIMM Paris France

Background and objective: This paper focuses on computer simulation aspects of Digital Twin models in the medical framework. In particular, it addresses the need of fast and accurate simulators for the mechanical response at tissue and organ scale and the capability of integrating patient-specific anatomy from medical images to pinpoint the individual variations from standard anatomical models. Methods: We propose an automated procedure to create mechanical models of the human liver with patient-specific geometry and real time capabilities. The method hinges on the use of Statistical Shape Analysis to extract the relevant anatomical features from a database of medical images and Model Order Reduction to compute an explicit parametric solution for the mechanical response as a function of such features. The Sparse Subspace Learning, coupled with a Finite Element solver, was chosen to create low-rank solutions using a non-intrusive sparse sampling of the feature space. Results: In the application presented in the paper, the statistical shape model was trained on a database of 385 three dimensional liver shapes, extracted from medical images, in order to create a parametrized representation of the liver anatomy. This parametrization and an additional parameter describing the breathing motion in linear elasticity were then used as input in the reduced order model. Results show a consistent agreement with the high fidelity Finite Element models built from liver images that were excluded from the training dataset. However, we evidence in the discussion the difficulty of having compact shape parametrizations arising from the extreme variability of the shapes found in the dataset and we propose potential strategies to tackle this issue. Conclusions: A method to represent patient-specific real-time liver deformations during breathing is proposed in linear elasticity. Since the proposed method does not require any adaptation to the direct Finite Element solver used in the trai

关键词： Real-time simulation Patient-specific modeling data-based modeling Statistical shape analysis Finite element modeling Human liver

来源：评论

学校读者我要写书评

暂无评论

data-based prediction under uncertainty: CDF-quantile distributions and info-gap robustness

引用

JOURNAL OF MATHEMATICAL PSYCHOLOGY 2018年 87卷 11-30页

作者： Ben-Haim, Yakov Smithson, Michael Technion Israel Inst Technol Technol & Econ IL-32000 Haifa Israel Australian Natl Univ Res Sch Psychol Canberra ACT Australia

data underlie understanding of processes and prediction of the future. However, things change;data from one population at one time may have uncertain relevance for modeling or prediction in another population or at another time. data-based prediction in a changing world requires two complementary capabilities: versatile modeling, integrated with management of uncertainty. We develop a response to this challenge. We focus on statistical models of bounded random variables, associated with additional non-probabilistic uncertainties. We employ CDF-quantile distributions to model the probabilistic aspects of these phenomena. Non-probabilistic uncertainties in parameter values and in the shapes of probability distributions are modeled and managed with the method of robust satisficing from info-gap theory. The robustness to uncertainty is evaluated for alternative estimators. We show that putatively optimal estimators may be less robust than sub-optimal estimators, suggesting preference for a sub-optimal estimator in some circumstances. These two attributes-statistical accuracy and info-gap robustness-trade off against one another. The info-gap robustness function quantifies this trade off. Generic propositions specify the robustness functions and their trade offs, and characterize a class of situations in which preference for sub-optimal estimators can occur. Three examples are discussed. (C) 2018 Elsevier Inc. All rights reserved.

关键词： Probabilistic prediction Non-probabilistic uncertainty data-based modeling CDF-quantile distributions Info-gap theory Robustness

来源：评论

学校读者我要写书评

暂无评论

Real-time furnace balancing of steam methane reforming furnaces

引用

CHEMICAL ENGINEERING RESEARCH & DESIGN 2018年 134卷 238-256页

作者： Anh Tran Pont, Madeleine Crose, Marquis Christofides, Panagiotis D. Univ Calif Los Angeles Dept Chem & Biomol Engn Los Angeles CA 90095 USA Univ Calif Los Angeles Dept Elect & Comp Engn Los Angeles CA 90095 USA

This paper focuses on the development of a real-time furnace-balancing scheme for a reformer at a centralized hydrogen facility using steam methane reforming (SMR) technology so that the reformer fuel input can be optimized in real-time to increase the plant throughput and to reject operational disturbances associated with flow control valves. Initially, the framework for the furnace-balancing scheme, the statistical-based model identification and the valve-to-flow-rate converter developed in Tran et al. (2017a, 2018) are integrated with a heuristic search algorithm to create a real-time balancing procedure, which recursively calculates different total fuel flow rates of which the respective spatial distribution to burners is optimized until key operational specifications, e.g., the reformer throughput is maximized, and the outer tube wall temperature (OTWT) along the reforming tube length of all reforming tubes must not exceed the design temperature of the reforming tube wall, are satisfied. Subsequently, a com;1;putational fluid dynamic (CFD) model of the reformer developed in Tran et al. (2017b) is used to represent the on-line unit at the SMR-based hydrogen facility and is used to characterize the previously unstudied dynamic behavior of the reformer, based on which we develop an optimal strategy to implement the optimized total fuel flow rate to maximize the reformer throughput. Finally, a case study in which the balancing procedure is implemented on the reformer initially operated under the nominal reformer input is proposed, and the results are used to demonstrate that the furnace-balancing scheme successfully determines the optimized reformer fuel input to increase the reformer throughput while meeting the OTWT limits. (C) 2018 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

关键词： Steam methane reforming CFD modeling data-based modeling Process optimization Furnace balancing

来源：评论

学校读者我要写书评

暂无评论

modeling CROWD DYNAMICS THROUGH COARSE-GRAINED data ANALYSIS

引用

MATHEMATICAL BIOSCIENCES AND ENGINEERING 2018年第6期15卷 1271-1290页

作者： Motsch, Sebastien Moussaid, Mehdi Guillot, Elsa G. Moreau, Mathieu Pettre, Julien Theraulaz, Guy Appert-Rolland, Cecile Degond, Pierre Arizona State Univ Sch Math & Stat Sci Tempe AZ 85287 USA Max Planck Inst Human Dev Adapt Behav & Cognit Grp Berlin Germany CNRS Ctr Rech Sur Cognit Anim CBI F-31062 Toulouse France Univ Toulouse 3 Paul Sabatier F-31062 Toulouse France INRIA Rennes Bretagne Atlant Campus Beaulieu Rennes France CNRS Lab Phys Theor Orsay France Imperial Coll London Dept Math London SW7 2AZ England

Understanding and predicting the collective behaviour of crowds is essential to improve the efficiency of pedestrian flows in urban areas and minimize the risks of accidents at mass events. We advocate for the development of crowd traffic management systems, whereby observations of crowds can be coupled to fast and reliable models to produce rapid predictions of the crowd movement and eventually help crowd managers choose between tailored optimization strategies. Here, we propose a Bi-directional Macroscopic (BM) model as the core of such a system. Its key input is the fundamental diagram for bi-directional flows, i.e. the relation between the pedestrian fluxes and densities. We design and run a laboratory experiments involving a total of 119 participants walking in opposite directions in a circular corridor and show that the model is able to accurately capture the experimental data in a typical crowd forecasting situation. Finally, we propose a simple segregation strategy for enhancing the traffic efficiency, and use the BM model to determine the conditions under which this strategy would be beneficial. The BM model, therefore, could serve as a building block to develop on the fly prediction of crowd movements and help deploying real-time crowd optimization strategies.

关键词： Pedestrian traffic bi-directional flux collective behaviour data-based modeling macroscopic model

来源：评论

学校读者我要写书评

暂无评论

data-based Predictive Optimization for Byproduct Gas System in Steel Industry

引用

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2017年第4期14卷 1761-1770页

作者： Zhao, Jun Sheng, Chunyang Wang, Wei Pedrycz, Witold Liu, Quanli Dalian Univ Technol Sch Control Sci & Engn Dalian 116024 Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2R3 Canada

In light of significant complexity of the byproduct gas system in steel industry (which limits an ability to establish its physics-based model), this paper proposes a data-based predictive optimization (DPO) method to carry out real-time adjusting for the gas system. Two stages of the method, namely, the prediction modeling and real-time optimization, are involved. At the prediction stage, the states of the optimized objectives, the consumption of the outsourcing natural gas and oil, the power generation, and the tank levels, are forecasted based on a proposed mixed Gaussian kernel-based prediction intervals (PIs) construction model. The Jacobian matrix of this model is represented by a kernel matrix through derivation, which greatly facilitates the subsequent calculation. At the second stage, a rolling optimization based on a mathematical programming technique involving continuous and integer decision-making variables is developed via the PIs. To demonstrate the performance of the DPO method, the practical data coming from the energy center of a steel plant are employed. The results show that the proposed DPO method can supply the human operators with effective solution for secure and economically justified optimization of the gas system. Note to Practitioners-Given that the byproduct gas system in steel industry can hardly be described by a physics or mechanism-based model, its operation is widely realized by the experience-based manual measure at present, which exhibits a very low automation level. Since a large number of real-time energy data have been accumulated by the existing SCADA system implemented in most of steel plants, a novel data-driven real-time predictive optimization method is proposed in this study. The proposed method aims at the short term energy optimization, thus the sample interval of the real-time data acquired from the SCADA system is set as 1 minute. The application system can provide the rolling optimized solution via real-time predictin

关键词： Byproduct gas system data-based modeling energy optimization prediction interval (PI)

来源：评论

学校读者我要写书评

暂无评论

Temperature balancing in steam methane reforming furnace via an integrated CFD/data-based optimization approach

引用

COMPUTERS & CHEMICAL ENGINEERING 2017年 104卷 185-200页

作者： Tran, Anh Aguirre, Andres Crose, Marquis Durand, Helen Christofides, Panagiotis D. Univ Calif Los Angeles Dept Chem & Biomol Engn Los Angeles CA 90095 USA Univ Calif Los Angeles Dept Elect Engn Los Angeles CA 90095 USA

In this work, we introduce a furnace-balancing scheme that generates an optimal furnace-side feed distribution that has the potential to improve the thermal efficiency of a reformer. The furnace-balancing scheme is composed of four major components: data generation, model identification, a model-based furnace-balancing optimizer and a termination checker. Initially, a computational fluid dynamics (CFD) model of an industrial-scale reformer, developed in our previous work, is used for the data generation as the model has been confirmed to simulate the typical transport and chemical reaction phenomena observed during reformer operation, and the CFD simulation data is in good agreement with various sources in literature. Then, we propose a model identification process in which the algorithm is formulated based on the least squares regression method, basic knowledge of radiative heat transfer and the existing furnace-side flow pattern. Subsequently, we propose a model -based furnace-balancing optimizer that is formulated as an optimization problem within which the valve position distribution is the decision variable, and minimizing the sum of the weighted squared deviations of the outer reforming tube wall temperatures from a set-point value for all reforming tubes with a penalty term on the deviation of the valve positions from their fully open positions is the objective function. CFD simulation results provide evidence that the optimized furnace-side feed distribution created by the furnace-balancing scheme can reduce the severity of nonuniformity in the spatial distribution of furnace-side temperature in the combustion chamber even when the reformer is under the influence of common valve-related disturbances. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Methane reforming Furnace-balancing Process operation Process optimization data-based modeling Valve defects

来源：评论

学校读者我要写书评

暂无评论

data-based Predictive optimization for Byproduct Gas System in Steel Industry 13

Data-based Predictive optimization for Byproduct Gas System ...

引用

13th IEEE Conference on Automation Science and Engineering (IEEE CASE)

ISBN: (纸本)9781509067817

In light of significant complexity of the byproduct gas system in steel industry (which limits an ability to establish its physics-based model), this study proposes a data-based predictive optimization (DPO) method to carry out real-time adjusting for the gas system. Two stages of the method, namely the prediction modeling and real-time optimization, are involved. At the prediction stage, the states of the optimized objectives, the consumption of the outsourcing natural gas and oil, the power generation and the tank levels, are forecasted based on a proposed mixed Gaussian kernel-based prediction intervals (PIs) construction model. The Jacobian matrix of this model is represented by a kernel matrix through derivation, which greatly facilitates the subsequent calculation. At the second stage, a rolling optimization based on a mathematical programming technique involving continuous and integer decision-making variables is developed via the prediction intervals.

关键词： byproduct gas system energy optimization data-based modeling prediction interval

来源：评论

学校读者我要写书评

暂无评论

data-driven soft sensor approach for online quality prediction using state dependent parameter models

引用

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2017年 162卷 130-141页

作者： Bidar, Bahareh Sadeghi, Jafar Shahraki, Farhad Khalilipour, Mir Mohammad Univ Sistan & Baluchestan Ctr Proc Integrat & Control CPIC Dept Chem Engn Zahedan 98164 Iran

The goal of this paper is to design and implementation of a new data-driven soft sensor that uses state dependent parameter (SDP) models to improve product quality monitoring. The SDP model parameters assumed to be function of the system states, which are estimated by data-based modeling philosophy and SDP method. Soft sensing performance of the proposed method is validated on a simulated continuous stirred tank reactor and an industrial debutanizer column. A comparative study of different soft sensing methods for online monitoring of debutanizer column is also carried out. The results show that the process non-linearity can also be addressed under this modeling method and the change of the process is also well tracked when missing data exist in the observed data. The results indicate that the new model is much more robust and reliable with less model parameters, which make it useful for industrial applications. In addition, the performance indexes show the superiority of the proposed model over other conventional soft sensing methods.

关键词： Soft sensor data-based modeling State dependent parameter Debutanizer column Quality prediction Missing value

来源：评论

学校读者我要写书评

暂无评论

Online Monitoring for Industrial Processes Quality Control Using Time Varying Parameter Model

引用

INTERNATIONAL JOURNAL OF ENGINEERING 2018年第4期31卷 524-532页

作者： Moghadam, R. Parvizi Shahraki, F. Sadeghi, J. Univ Sistan & Baluchestan Dept Chem Engn Ctr Proc Integrat & Control Zahedan Iran

A novel data-driven soft sensor is designed for online product quality prediction and control performance modification in industrial units. A combined approach of time variable parameter (TVP) model, dynamic auto regressive exogenous variable (DARX) algorithm, nonlinear correlation analysis and criterion-based elimination method is introduced in this work. The soft sensor performance validation is tested by data set of an industrial SRU. The comparative study indicated the result associated with more robust soft sensor and more appropriate performance index values compared to other methods for SRU soft sensor design in diverse achievements. Due to high prediction accuracy, the low complication of the model and also saving of time, this technique can be very noticeable in industrial processes control.

关键词： Soft Sensor Time Varying Parameter SRU Quality Estimation Identification data-based modeling

来源：评论

学校读者我要写书评

暂无评论

Active learning strategy for smart soft sensor development under a small number of labeled data samples

引用

JOURNAL OF PROCESS CONTROL 2014年第9期24卷 1454-1461页

作者： Ge, Zhiqiang Zhejiang Univ Dept Control Sci & Engn Inst Ind Proc Control State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China

This contribution proposes a new active learning strategy for smart soft sensor development. The main objective of the smart soft sensor is to opportunely collect labeled data samples in such a way as to minimize the error of the regression process while minimizing the number of labeled samples used, and thus to reduce the costs related to labeling training samples. Instead of randomly labeling data samples, the smart soft sensor only labels those data samples which can provide the most significant information for construction of the soft sensor. In this paper, without loss of generality, the smart soft sensor is built based on the widely used principal component regression model. For performance evaluation, an industrial case study is provided. Compared to the random sample labeling strategy, both accuracy and stability have been improved by the active learning strategy based smart soft sensor. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Soft sensor Smart Active learning Unlabeled data samples data-based modeling

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：