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20th IEEE International Conference on Computer Supported Cooperative Work in Design (CSCWD)

作者： Trudel, Eric Liu, Yan Yang, Chunsheng Natl Res Council Canada Ottawa ON Canada

ISBN: (纸本)9781509019151

A grade crossing is defined as an intersection between a roadway and a railway at the same elevation or grade. Multiple new prevention measures have been implemented to reduce the number of train-vehicle collisions;however, crossing safety is still a major issue as accidents still frequently occur. The push for data-driven models to evaluate risks at grade crossings has also increased to keep up with the changing technologies. There are many different protection types (gates with bells, cross-buck, stop-sign, mirrors and etc.) that serve to warn or stop oncoming traffic. Many attributes have an inherent impact on accident frequency;including the protection type, train speed, traffic volume and e.t.c. To address which factors are most important, we propose a data-driven modeling method to effectively analyze the impact of multiple factors that affect crossing safety and subsequently provide scientific insight for key factors for enhancing crossing safety. In this work, the Canadian crossing accident database for the years of 2004 - 2013 was used with additional generated features to enhance the scope of the study. These include features that were computed using GIS and sightline measurements. data-driven modeling using RandomForests were used to rank and analyze 21 attributes for each protection type. From the analysis results it is possible to identify which key factors have the highest influence on improving safety and collision prediction at grade crossings.

关键词： Grade crossing data-driven modeling feature generation machine learning algorithm RandomForest regression crossing safety GIS

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A new data-driven modeling method for fermentation processes

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2016年 152卷 88-96页

作者： Yang, Qiangda Gao, Hongbo Zhang, Weijun Chi, Zhongyuan Yi, Zhi Northeastern Univ Sch Met Shenyang 110819 Peoples R China Northeastern Univ Sch Mech Engn & Automat Shenyang 110819 Peoples R China

An accurate model is the premise for successfully implementing fermentation process optimization. Most data-driven models that are widely applied to fermentation processes are unfit for optimization or provide low precision. This paper presents a new data-driven modeling method for directly developing an ANN-based differential model that is fit for optimization. Moreover, this model can provide high precision because it can be discretized using the sampling period of the control variables as the step length. The lack of data pairs is addressed by transforming the model-training problem into a dynamic system parameter identification problem. Further, a particle swarm optimization algorithm with a time-varying escape mechanism (PSOE) is constructed to determine the model parameters. Finally, the uniform design method is used to select the model structure. The results of experiments conducted using practical data for a lab-scale nosiheptide batch fermentation process confirm the effectiveness of the proposed modeling method and PSOE algorithm. (C) 2016 Elsevier B.V. All rights reserved.

关键词： data-driven modeling Artificial neural networks Particle swarm optimization Fermentation process Nosiheptide

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Hybrid Analytical and data-driven modeling for Feed-Forward Robot Control

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SENSORS 2017年第2期17卷 311页

作者： Reinhart, Rene Felix Shareef, Zeeshan Steil, Jochen Jakob Fraunhofer Res Inst Mechatron Syst Design IEM Zukunftsmeile 1 D-33102 Paderborn Germany Tech Univ Carolo Wilhelmina Braunschweig Inst Robot & Proc Control IRP Muhlenpfordstr 23 D-38106 Braunschweig Germany

Feed-forward model-based control relies on models of the controlled plant, e.g., in robotics on accurate knowledge of manipulator kinematics or dynamics. However, mechanical and analytical models do not capture all aspects of a plant's intrinsic properties and there remain unmodeled dynamics due to varying parameters, unmodeled friction or soft materials. In this context, machine learning is an alternative suitable technique to extract non-linear plant models from data. However, fully data-based models suffer from inaccuracies as well and are inefficient if they include learning of well known analytical models. This paper thus argues that feed-forward control based on hybrid models comprising an analytical model and a learned error model can significantly improve modeling accuracy. Hybrid modeling here serves the purpose to combine the best of the two modeling worlds. The hybrid modeling methodology is described and the approach is demonstrated for two typical problems in robotics, i.e., inverse kinematics control and computed torque control. The former is performed for a redundant soft robot and the latter for a rigid industrial robot with redundant degrees of freedom, where a complete analytical model is not available for any of the platforms.

关键词： machine learning data-driven modeling mechanical modeling error models inverse kinematics inverse dynamics learning feed-forward control soft robot

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An automated toolchain for the data-driven and dynamical modeling of combined sewer systems

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WATER RESEARCH 2017年 126卷 88-100页

作者： Troutman, Sara C. Schambach, Nathaniel Love, Nancy G. Kerkez, Branko Univ Michigan Civil & Environm Engn 2350 Hayward StGG Brown Bldg Ann Arbor MI 48109 USA Univ Michigan Mech Engn GG Brown Lab 2350 Hayward St Ann Arbor MI 48109 USA

The recent availability and affordability of sensors and wireless communications is poised to transform our understanding and management of water systems. This will enable a new generation of adaptive water models that can ingest large quantities of sensor feeds and provide the best possible estimates of current and future conditions. To that end, this paper presents a novel data-driven identification/learning toolchain for combined sewer and stormwater systems. The toolchain uses Gaussian Processes to model dry-weather flows (domestic wastewater) and dynamical System Identification to represent wet weather flows (rainfall runoff). By using a large and high-resolution sensor dataset across a real-world combined sewer system, we illustrate that relatively simple models can achieve good forecasting performance, subject to a finely-tuned and continuous re-calibration procedure. The data requirements of the proposed toolchain are evaluated, showing sensitivity to spatial heterogeneity and unique time scales across which models of individual sites remain representative. We identify a near-optimal time record, or data "age," for which historical measurements must be available to ensure good forecasting performance. We also show that more data do not always lead to a better model due to system uncertainty, such as shifts in climate or seasonal wastewater patterns. Furthermore, the individual components of the model (wet- and dry-weather) often require different volumes of historical observations for optimal forecasting performance, thus highlighting the need for a flexible re-calibration toolchain rather than a one-size-fits-all approach. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Sensor networks data-driven modeling Smart water systems Combined sewer

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Selecting the Best VM across Multiple Public Clouds: A data-driven Performance modeling Approach

Selecting the <i>Best</i> VM across Multiple Public Clouds: ...

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Symposium on Cloud Computing (SoCC)

作者： Yadwadkar, Neeraja. J. Hariharan, Bharath Gonzalez, Joseph E. Smith, Burton Katz, Randy H. Univ Calif Berkeley Berkeley CA 94720 USA Facebook AI Res Menlo Pk CA USA Microsoft Res Redmond WA USA

ISBN: (纸本)9781450350280

Users of cloud services are presented with a bewildering choice of VM types and the choice of VM can have significant implications on performance and cost. In this paper we address the fundamental problem of accurately and economically choosing the best VM for a given workload and user goals. To address the problem of optimal VM selection, we present PARIS, a data-driven system that uses a novel hybrid offline and online data collection and modeling framework to provide accurate performance estimates with minimal data collection. PARIS is able to predict workload performance for different user-specified metrics, and resulting costs for a wide range of VM types and workloads across multiple cloud providers. When compared to sophisticated baselines, including collaborative filtering and a linear interpolation model using measured workload performance on two VM types, PARIS produces significantly better estimates of performance. For instance, it reduces runtime prediction error by a factor of 4 for some workloads on both AWS and Azure. The increased accuracy translates into a 45% reduction in user cost while maintaining performance.

关键词： Cloud Computing Resource Allocation Performance Prediction data-driven modeling

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data-driven modeling and characterization of anti-angiogenic molecule effects on tumoral vascular density

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2015年 20卷 52-60页

作者： Tylcz, J. -B. El Alaoui-Lasmaili, K. Djermoune, E. -H. Thomas, N. Faivre, B. Bastogne, T. Univ Lorraine CRAN UMR 7039 Vandoeuvre Les Nancy France CNRS CRAN UMR 7039 Vandoeuvre Les Nancy France INRIA BIGS Saclay France

Angiogenesis is the phenomenon by which new blood vessels are created from preexisting ones. But this natural process is also involved, in a chaotic way, in tumor development. Many molecules have shown particular efficiency in inhibiting this phenomenon, hopefully leading to either: (i) a reorganization of the neovessels allowing a better tumor uptake of cytotoxic molecules (as chemotherapy) or (ii) a deprivation of the tumor vascular network with the view to starve it. However, characterizing the anti-angiogenic effects of a molecule remains difficult, mainly because the proposed physical modeling approaches have barely been confronted to in vivo data, which are not directly available. This paper presents an original approach to characterize and analyze the anti-angiogenic responses in cancerology that allows biologists to account for spatial and dynamical dimensions of the problem. The proposed solution relies on the association of a specific biological in vivo protocol using skinfold chambers, image processing and dynamic system identification. An empirical model structure of the anti-angiogenic effect of a tested molecule is selected according to experimental data. Finally the model is identified and its parameters are used to characterize and compare responses of the tested molecule. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： data-driven modeling System identification Image processing Cancer Angiogenesis

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data-driven modeling of pharmacological systems using endpoint information fusion

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COMPUTERS IN BIOLOGY AND MEDICINE 2015年 61卷 36-47页

作者： Kim, Chang-Sei Fazeli, Nima Hahn, Jin-Oh Univ Maryland Dept Mech Engn College Pk MD 20742 USA

This study investigated the feasibility of deriving data-driven model of a class of pharmacological systems using the information fusion of endpoint responses. For a class of pharmacological systems subsuming conventional steady-state dose-response models, compartmental pharmacokinetic-pharmacodynamic models and indirect response models, a relation between multiple endpoint responses was formalized and analyzed to elucidate if this class of systems is identifiable, i.e., if the data-driven model of this class of systems can be derived from the endpoint responses alone. It was shown that this class of systems is fully identifiable in case all the responses involve effect compartments. However, it was also observed that persistently exciting dose profiles may be required in accurately deriving reliable data-driven model with low variance. The findings from the identifiability analysis were demonstrated using benchmark pharmacological system examples. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： data-driven modeling Pharmacological system Endpoint information fusion Identifiability Parametric variance

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data-driven modeling and Simulation of PV Array 2

Data-Driven Modeling and Simulation of PV Array

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2nd International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Thomas, Mini S. Nisar, Amira JamiaMilliaIslamia New Delhi India

ISBN: (纸本)9789380544168

This paper presents a technique to build a simulation model of a Photovoltaic (PV) array using Solar PV data. The Solar PV data is fetched from a physical solar panel operating at a solar power generating station. PV arrays are generally modeled using mathematical equations that describe the behavior of PV system and simulated in MATLAB with mathematical operator library and functions. The proposed modeling and simulation technique does not require formulation and implementation of mathematical equations. To carry out the data-driven (DD) modeling of PV array, information regarding the actual voltage and current generated by the same array with reference to the actual irradiance and temperature is to be known. This can be obtained from experimental results, PV simulator, excel/data sheets of PV plant, etc. Authors have carried out data-driven modeling of PV array by first gathering PV test data from a solar power plant and then directly importing it into Simulink curve fitting tool to obtain a surface-fit object. Using this object a 2-dimensional lookup table is modeled to represent a PV array. The model can be integrated in any power system or power electronics application network for simulation studies regarding performance analysis of the solar PV system. The modeling and simulation with the proposed technique is simple and easy and can be done with any set of PV data, acquired using any data acquisition method or tool.

关键词： data acquisition data-driven modeling photovoltaic array surface-fit 2-dimensional lookup table

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data-driven modeling of transportation systems and traffic data analysis during a major power outage in the Netherlands 4th

Data-driven modeling of transportation systems and traffic d...

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4th International Young Scientists Conference on Computational Science (YSC)

作者： Melnikov, Valentin R. Krzhizhanovskaya, Valeria V. Boukhanovsky, Alexander V. Sloot, Peter M. A. ITMO Univ St Petersburg Russia Univ Amsterdam NL-1012 WX Amsterdam Netherlands St Petersburg State Polytech Univ St Petersburg Russia Netherlands Inst Adv Study Humanities & Social Sci Wageningen Netherlands Nanyang Technol Univ Singapore 639798 Singapore

Efficient methods and tools for road network planning and traffic management are critically important in the ever more urbanized world. The goal of our research is the development of a data-driven multiscale modeling approach for accurate simulation of road traffic in real-life transportation networks, with applications in real-time decision support systems and urban planning. This paper reviews the multiscale traffic models, describes the traffic sensor data collected from 25000 sensors along the arterial roads in the Netherlands, and discusses the applicability of sensor data to model calibration and validation on each modeling scale. We also present a road network graph model and the reconstructed Dutch road network. Finally, we show the results of traffic data analysis during the major power outage in North Holland on 27 March 2015, paying special attention to one of the most affected locations around the A9/E19 interchange near Amsterdam airport Schiphol.

关键词： transportation systems data-driven modeling complex networks traffic flow multiscale modeling traffic sensor data power outage

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data-driven mechanical behavior modeling of granular biomass materials

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COMPUTERS AND GEOTECHNICS 2025年 177卷

作者： Li, Xuyang Jin, Wencheng Klinger, Jordan Saha, Nepu Lajnef, Nizar Idaho Natl Lab Energy & Environm Sci & Technol Directorate 1955 N Fremont Ave Idaho Falls ID 83415 USA Michigan State Univ Dept Civil & Environm Engn 428 S Shaw Ln E Lansing MI 48824 USA Texas A&M Univ Harold Vance Dept Petr Engn 245 Spence St College Stn TX 77843 USA

Significant equipment downtime, mainly caused by the variable attributes and complex mechanical behavior of granular biomass materials, has challenged the great potential of biofuel generation. Expensive and timeconsuming lab experiments are not affordable in fully characterizing material behavior for samples with all possible attributes. In this paper, we present data-driven modeling of the cyclic axial compression and ring-shear stress-strain behavior for granular biomass materials with different moisture contents and mean particle sizes. Laboratory characterization data of milled pine samples, with a mean particle size of 2, 4, and 6 mm and a moisture content of 0%, 20%, and 40%, were employed to train two machine learning (ML) models independently. The sequential model takes a sequence of loading history into Gated Recurrent Units (GRU) and predicts the next step of strain/stress with a feed-forward neural network. In contrast, the incremental model takes only the current state to predict the stiffness/compliance coefficient and compute the stress/strain increment. Both models account for material variations in particle sizes and moisture content. After training, both models demonstrate their high efficiency and accuracy in predicting the mechanical behavior of samples with a diverse set of unseen material properties, augmenting the existing laboratory data set. They also effectively capture the underlying physics, which involves increased compressibility as moisture content rises and a linear relationship between applied compression and shear strength. This effort established the foundation for comprehensive data-driven constitutive modeling tailored for unconventional granular materials.

关键词： Biomass granular materials Mechanical behavior data-driven modeling Gated recurrent unit Feed-forward neural network

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