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International Technology and Innovation Conference 2009

作者： CHEN Huawei*, WANG Aimin,NING Ruxin *Beijing Institute of Technology, Beijing, P.R.China

Aimed to the problem of model bound with data and structure rigidity in simulation, hierarchical modelling and simulation technology for dynamical production logistic system is presented with the view of data-driven and dynamical hierarchy relating. Representative model of four control levels including factory(FM), workshop(SM), cell(CM) and equipment(EM) and its dynamic modeller (DM) was put forward. data-driven model generation and running mechanism was explained to solve the automatic modeling problem of fast application and system integration. Flexible simulation control mechanism based on dynamic layer relating was constructed by using BUFFER and AGV. Realization rules and conditions of dynamic layer relating was analyzed in detail, and its procedure parsed by examples. Development and implementation indicates the practical values of the hierarchical modeling and simulation technology of the dynamic production logistic system.

关键词： production logistic system hierarchical model dynamic modeller data-driven modeling dynamic layer relating

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The validity domain of hybrid models and its application in process optimization

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CHEMICAL ENGINEERING AND PROCESSING-PROCESS INTENSIFICATION 2007年第11期46卷 1054-1066页

作者： Kahrs, O. Marquardt, W. Rhein Westfal TH Aachen Lehrstuhl Prozesstech D-52056 Aachen Germany

Hybrid modeling is an attractive approach for processes, whose underlying physical phenomena, such as chemical reactions or heat and mass transfer, are not fully understood. A hybrid model combines a rigorous model part that represents available process knowledge with empirical model parts describing unknown phenomena. One of the key advantages of hybrid models over empirical models is its ability to extrapolate beyond the identification data domain, which is valuable in many applications such as process optimization and control. However, the validity domain of hybrid models is not universal and should be checked during model application to prevent misleading conclusions. This paper, therefore, presents two complementary validity criteria for hybrid models: the convex hull criterion checks, whether each empirical model part only interpolates the data encountered during model identification;the confidence interval criterion calculates confidence intervals for the hybrid model prediction. These criteria can be added as validity constraints to an optimization problem, as is demonstrated in a case study on a steady-state optimization of an ethylene glycol production process. (c) 2007 Elsevier B.V. All rights reserved.

关键词： hybrid modeling validity domain extrapolation process optimization data-driven modeling semi-empirical modeling neural networks

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A behavior-based model of the hydra, phylum cnidaria

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9th European Conference on Artificial Life

作者： Aktius, Malin Nordahl, Mats Ziemke, Tom Univ Skovde Sch Human & Informat SE-54128 Skovde Sweden Gothenburg Univ Dept Appl Inform Technol SE-41756 Gothenburg Sweden Chalmers Dept Appl Inform Technol SE-41756 Gothenburg Sweden

ISBN: (纸本)9783540749127

Behavior-based artificial systems, e.g. mobile robots, are frequently designed using (various degrees and levels of) biology as inspiration, but rarely modeled based on actual quantitative empirical data. This paper presents a data-driven behavior-based model of a simple biological organism, the hydra. Four constituent behaviors were implemented in a simulated animal, and the overall behavior organization was accomplished using a colony-style architecture (CSA). The results indicate that the CSA, using a priority-based behavioral hierarchy suggested in the literature, can be used to model behavioral properties like latency, activation threshold, habituation, and duration of the individual behaviors of the hydra. Limitations of this behavior-based approach are also discussed.

关键词： behavior-based modeling data-driven modeling hydra colony-style architecture

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Spatial and Temporal Resolution in datadriven Process modeling of an Integrated Newsprint Mill

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CHEMICAL PRODUCT AND PROCESS modeling 2007年第1期2卷

作者： Harrison, Robert P. Stuart, Paul Ecole Polytech Montreal Montreal PQ Canada

Like many globalized industries, the pulp and paper sector finds itself with an increasingly demanding clientele, who continually expect a better and cheaper product. An important design strategy being employed to address this objective is through an analysis of the vast quantity of process and product data accumulated in plant-wide data historians, in order to improve operations. Mill processes are multivariate, meaning that the interactions between the variables are as important as the variables themselves. Process relationships must therefore be modeled as a group, using an appropriate simulation technique like Multivariate Analysis (MVA), with suitable data pre-processing to account for process upsets and other disturbances. In a previous paper, using an Eastern Canadian newsprint mill as an industrial case study, we showed that it was possible to find statistically significant correlations between wood chip refiner operation, intermediate pulp quality, and final paper quality using data-driven models. This was true even though some important process parameters went unmeasured, process lags changed with time, and the operation of key equipment items changed gradually with use. The present study compares the use of different timescales and combinations of unit operations to determine which ones yield the best MVA simulations. Because plant operating data were used, and experimental design was not practical, it is possible that some of the correlations found could be attributable to coincidence. We therefore added and removed variables and time periods to explore the validity of the models. The best MVA models were obtained by using a shorter (1-hour) data timescale, although use of a weighted-average filter helped to bridge the gap between these faster readings and the slower paper quality trends.

关键词： data-driven modeling process modeling newsprint mill multivariate analysis

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data-driven modeling of multiple longshore bars and their interactions

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COASTAL ENGINEERING 2003年第3期48卷 151-170页

作者： Rózynski, G Polish Acad Sci Inst Hydroengn PL-80953 Gdansk Poland

Long-term bathymetric surveys at a coastal segment of the southern Baltic coast were investigated with empirical orthogonal functions (EOF) to determine the characteristic evolution patterns of multiple longshore bars and with canonical correlation analysis (CCA) to evaluate the importance of interactions among them. The investigated beach is located at Lubiatowo on the Polish Coast and is mildly sloping with multiple, usually four bars, and a median grain size of 0.22 mm, being typical for the coast in the southern Baltic Sea. data on bed topography have been collected at Lubiatowo since 1987, predominantly twice a year, to record changes in the seabed along 27 lines covering a longshore x cross-shore area of 2600 in x 1000 in. The EOF analysis revealed three persistent long-term cross-shore patterns accounting for about 2/3 of the overall signal variance and describing key elements of the evolution of multiple bars. Moreover, it detected subsystems of two inner and outer bars. The CCA study demonstrated that more that 60% of the evolution of inner bars can be explained by the variability of outer bars. The two subsystems thus display significant interactions;they can be largely attributed to vertical variations of the whole nearshore zone, expressed through changes in the equilibrium, Dean-type profiles. Such changes can only be produced by large scale phenomena, e.g. sequences of extreme events, affecting the whole nearshore topography. Hence, part of the variability of inner bars not related to the evolution of outer bars should stem from changes occurring during calm periods, when small waves pass over outer bars unaffected. The paper demonstrates usefulness of the CCA method in coastal engineering and a more general strategy of data-driven investigations with more than one method in order to make use of synergy generated with such a complex approach. The benefit of such a synergy is that results from both investigations can be critically intercompared, so more

关键词： nearshore morphodynamics data-driven modeling multiple longshore bars multivariate signal analysis

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Long-term shoreline response of a nontidal, barred coast

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COASTAL ENGINEERING 2005年第1期52卷 79-91页

作者： Rozynski, G Polish Acad Sci Inst Hydroengn PL-80953 Gdansk Poland

Long-term variations of shoreline positions along the southern Baltic coast were investigated using multichannel singular spectrum analysis (MSSA) to determine the most dominant long-term response patterns. The investigated beach is located at Lubiatowo on the Polish Coast and is mildly sloping with multiple bars. data on coastal morphology have been collected at Lubiatowo including (1) bathymetric surveys since 1987 twice a year, and (2) beach topography surveys since 1983 every 4 weeks on the average, extending from the shoreline to the dune foot. Furthermore, several dedicated field campaigns have been carried out at Lubiatowo, as well as measurements of deep-water wave properties since 1998. MSSA was employed to the whole data set of shoreline position from all survey lines. In summary, three patterns emerged reproducing alongshore standing waves with different periods 7 to 8, 20+ and several decades. They represent long-term shoreline response, such that at some locations the longest wave is most predominant, at other locations the medium cycle predominates, whereas the shortest is the most prominent at yet other locations. However, all three can be detected at every location monitored, eliminating the confusion resulting from ordinary singular spectrum analysis (SSA) analysis, done previously for the same data set. (C) 2004 Elsevier B.V. All rights reserved.

关键词： data-driven modeling shoreline evolution multichannel singular spectrum analysis lagged covariances reconstructed components

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data-driven approaches to digital human modeling

Data-driven approaches to digital human modeling

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2nd International Symposium on 3D data Processing, Visualization, and Transmission (3DPVI 2004)

作者： Magnenat-Thalmann, N Seo, H Univ Geneva MIRALab CH-1211 Geneva Switzerland

ISBN: (纸本)0769522238

data-driven approach is an appealing way to depict people in a virtual world. The captured shape and movement data from real people are structured and combined to reproduce or create new samples in an intuitive and controllable way. We focus on the body shape modeling and elucidate the issues related to data-driven methods. The difficulty of adopting data-driven approach for human body shape modeling is due in part to the intrinsic articulated structure of the body. Since such internal structure is not measured with most of existing capture devices available today, it has to be calculated through estimation. We develop a framework for collecting and managing range scan data that automatically estimates this structure from user-tagged landmarks. By framing the captured and structurally annotated data so that statistic implicit is exploited for synthesizing new body shapes, our technique support time-saving generation of animatable body models with high realism.

关键词： range scan data human body data-driven modeling articulated deformation animation semantic annotation

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A function-decomposition method for development of hierarchical multi-attribute decision models

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DECISION SUPPORT SYSTEMS 2004年第3期36卷 215-233页

作者： Bohanec, M Zupan, B Jozef Stefan Inst SI-1000 Ljubljana Slovenia Univ Ljubljana Sch Publ Adm Ljubljana Slovenia Univ Ljubljana Fac Comp & Informat Sci Ljubljana Slovenia Baylor Coll Med Dept Mol & Human Genet Houston TX 77030 USA

Function decomposition is a recent machine learning method that develops a hierarchical structure from class-labeled data by discovering new aggregate attributes and their descriptions. Each new aggregate attribute is described by an example set whose complexity is lower than the complexity of the initial set. We show that function decomposition can be used to develop a hierarchical multi-attribute decision model from a given unstructured set of decision examples. The method implemented in a system called HINT is experimentally evaluated on a real-world housing loans allocation problem and on the rediscovery of three hierarchical decision models. The experimentation demonstrates that the decomposition can discover meaningful and transparent decision models of high classification accuracy. We specifically study the effects of human interaction through either assistance or provision of background knowledge for function decomposition, and show that this has a positive effect on both the comprehensibility and classification accuracy. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： multi-attribute decision making hierarchical models function decomposition data-driven modeling data mining

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Negative correlation learning approach for T-S fuzzy models

Negative correlation learning approach for T-S fuzzy models

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IEEE International Conference on Systems, Man and Cybernetics

作者： Cai, YP Sun, XM Jia, PF Tsinghua Univ State Key Lab Intelligent Technol & Syst Beijing 100084 Peoples R China

ISBN: (纸本)0780385667

In this paper an adaptive approach of achieving a proper model structure in data-driven T-S fuzzy models is proposed. By introducing negative correlation learning in the creation of the fuzzy model, the training error of the entire model is decomposed to individual rule errors with correlation penalty term. Fuzzy rules can be trained and evaluated separately. On the other hand, negative correlation learning minimizes the mutual information between rules, so that a set of cooperative and complementary fuzzy rules can be obtained. The correlation penalty term also provides a way of measuring the validity of each rule. Algorithms of generating and eliminating rules can be developed based on it, thus the appropriate structure of the model can be obtained independent to the initial number of rules.

关键词： fuzzy modeling negative correlation learning data-driven modeling

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modeling Industrial Fermentation data Using Grid of Linear Models (GOLM)

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IFAC Proceedings Volumes 2004年第3期37卷 327-332页

作者： Mads Thaysen Dennis Bonné Sten Bay Jørgensen BioProcess Laboratories Novo Nordisk A/S Smørmosevej 8 8A1.067 2880 Bagsvœrd Denmark CAPEC Department of Chemical Engineering Technical University of Denmark

This paper presents and illustrates the application of a novel modeling method entitled Grid of Linear Models for the identification of local, yet interdependent models for time-varying and non-stationary fermentation operations from operational data. It is described how the estimation of model parameters applies regularisation to introduce interdependence of local models in the 1-dimensional grid spanning the operation time span. The methodology is applied to data from an industrial fermentation process and is shown to provide a good description of the process behavior evaluated on validation data in one step ahead prediction and pure simulation scenarios

关键词： Time-varying systems parameter estimation industrial fermentation data-driven modeling

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