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Agent-based modeling for data-driven enforcement: combining empirical data with behavioral theory for scenario-based analysis of inspections

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data & POLICY 2025年 7卷 e6-e6页

作者： Koid, Eunice van der Voort, Haiko Warnier, Martijn Delft Univ Technol Multiactor Syst Dept Delft Netherlands

Effective enforcement of laws and regulations hinges heavily on robust inspection policies. While data-driven approaches to testing the effectiveness of these policies are gaining popularity, they suffer significant drawbacks, particularly a lack of explainability and generalizability. This paper proposes an approach to crafting inspection policies that combines data-driven insights with behavioral theories to create an agent-based simulation model that we call a theory-infused phenomenological agent-based model (TIP-ABM). Moreover, this approach outlines a systematic process for combining theories and data to construct a phenomenological ABM, beginning with defining macro-level empirical phenomena. Illustrated through a case study of the Dutch inland shipping sector, the proposed methodology enhances explainability by illuminating inspectors' tacit knowledge while iterating between statistical data and underlying theories. The broader generalizability of the proposed approach beyond the inland shipping context requires further research. Policy Significance Statement Inspectorates often formulate inspection policies using models based on historical data. However, the specific behavioral interactions driving these models are often not well understood. This paper proposes a data-driven approach to constructing inspection models that shed light on the fundamental mechanisms contributing to the observed system-level behavior in real-world data. We illustrate the construction of a theory-infused phenomenological agent-based model by enriching historical data with behavioral theories. Adopting such models enables inspection agencies to glean valuable insights into the fundamental principles influencing inspection policies, thereby fostering the development of less biased models and more effective inspection policies.

关键词： agent-based modeling data-driven modeling inspections risk-based regulation

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A New Ridge Basis Function Neural Network for data-driven modeling and Prediction 10

A New Ridge Basis Function Neural Network for Data-Driven Mo...

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10th International Conference on Computer Science and Education (ICCSE)

作者： Wei, Hua-Liang Balikhin, Michael A. Walker, Simon N. Univ Sheffield Dept Automat Control & Syst Engn Sheffield S Yorkshire England

ISBN: (纸本)9781479966004

This paper presents a new type of neural network called ridge basis function neural network (RiBFNN), which in structure comprises two submodels, namely a linear submodel and a ridge basis function submodel. The proposed model has the following advantages. It is known that linear models are transparent and easily interpretable, the inclusion of a linear submodel can therefore determine how the overall evolution trend of the system behavior (output or response) depends on the system input (or dependent) variables. On the other hand, the inclusion of the ridge basis functions enables the identification of nonlinearities that cannot be revealed by linear models and thus can improve the overall prediction performance. The proposed network is applied to a real data modeling task in relation to the relativistic electron flux intensity prediction in space weather study, and relevant performance of the network is presented.

关键词： Ridge basis functions neural networks data-driven modeling system identification space weather

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data-driven modeling of Transportation Systems and Traffic data Analysis During a Major Power Outage in the Netherlands

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Procedia Computer Science 2015年 66卷 336-345页

作者： Valentin R. Melnikov Valeria V. Krzhizhanovskaya Alexander V. Boukhanovsky Peter M.A. Sloot ITMO University Saint-Petersburg Russia University of Amsterdam The Netherlands St. Petersburg State Polytechnic University Russia Netherlands Institute for Advanced Study The Netherlands Nanyang Technological University 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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Complex data-driven predictive modeling in personalized clinical decision support for Acute Coronary Syndrome episodes

Complex data-driven predictive modeling in personalized clin...

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16th Annual International Conference on Computational Science (ICCS)

作者： Krikunov, Alexey V. Bolgova, Ekaterina V. Krotov, Evgeniy Abuhay, Tesfamariam M. Yakovlev, Alexey N. Kovalchuk, Sergey V. ITMO Univ St Petersburg Russia Fed Almazov North West Med Res Ctr St Petersburg Russia

The objective of this paper is to demonstrate the development of complex model of clinical episode, based on data-driven approach, for decision support in treatment of ACS (Acute Coronary Syndrome). The idea is aimed at improving predictive capability of a data-driven model by combining different models within a composite data-driven model. It can be implemented either hierarchical or alternative combination of models. Three examples of data-driven models are described: simple classifier, outcome prediction based on reanimation time and states based prediction model, to be used as part of complex model of episodes. To implement the proposed approach, a generalized architecture of data-driven clinical decision support systems was developed. The solution is developed as a part of complex clinical decision support system for cardiac diseases for Federal Almazov North-West Medical Research Centre in Saint Petersburg, Russia.

关键词： data-driven modeling decision support system clinical DSS personalized medicine p4 medicine

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modeling of dynamic data-driven approach for the distributed steel rolling heating furnace temperature field

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NEURAL COMPUTING & APPLICATIONS 2022年第11期34卷 8959-8975页

作者： Bao, Qingfeng Zhang, Sen Guo, Jin Xu, Zhengguang Zhang, Zhenquan Univ Sci & Technol Beijing Sch Automat & Elect Engn Xueyuan Rd Beijing 100083 Peoples R China Univ Sci & Technol Beijing Key Lab Knowledge Automat Ind Proc Minist Educ Xueyuan Rd Beijing 100083 Peoples R China HBIS Grp Chengsteel Co HBIS Chengde 067001 Hebei Peoples R China

The dynamic modeling of the steel rolling heating furnace temperature field (SRHFTF) plays a very important role in the process control of the metallurgical industry. It can control and regulate the temperature of the SRHFTF accurately, save energy consumption and reduce industrial carbon emissions. The SRHFTF is a complex system with nonlinear, strong coupling, and multi-variable, it was difficult to build the mechanism model. Meanwhile, the existing data-driven modeling methodology has a gap which describes the dynamical model structure of SRHFTF. Therefore, this paper studies the neural network dynamic data-driven modeling methodology for the RHFTF. Firstly, the multi-input single-output of the SRHFTF neural network model matrices structure is constructed to improve the model fitting effect. Secondly, an improved neural network back-propagation algorithm is proposed, which has higher accuracy and lower computational cost than static data-driven models. Finally, the effectiveness of the artificial neural network dynamic data-driven model and the INNBP algorithm have been verified by the case of the SRHFTF. The simulation results show that the performance of the dynamic neural network model meets the design requirements.

关键词： data-driven modeling Model structure Model dynamic Distributed parameter Improved neural network back-propagation (INNBP) Steel rolling heating furnace temperature field (SRHFTF)

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data-driven Multi-Scale modeling and Optimization for Elastic Properties of Cubic Microstructures

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INTEGRATING MATERIALS AND MANUFACTURING INNOVATION 2022年第2期11卷 230-240页

作者： Hasan, M. Mao, Y. Choudhary, K. Tavazza, F. Choudhary, A. Agrawal, A. Acar, P. Virginia Tech Blacksburg VA 24061 USA Northwestern Univ Evanston IL USA NIST Gaithersburg MD 20899 USA Theiss Res La Jolla CA USA

The present work addresses gradient-based and machine learning (ML)-driven design optimization methods to enhance homogenized linear and nonlinear properties of cubic microstructures. The study computes the homogenized properties as a function of underlying microstructures by linking atomistic-scale and meso-scale models. Here, the microstructure is represented by the orientation distribution function that determines the volume densities of crystallographic orientations. The homogenized property matrix in meso-scale is computed using the single-crystal property values that are obtained by density functional theory calculations. The optimum microstructure designs are validated with the available data in the literature. The single-crystal designs, as expected, are found to provide the extreme values of the linear properties, while the optimum values of the nonlinear properties could be provided by single or polycrystalline microstructures. However, polycrystalline designs are advantageous over single crystals in terms of better manufacturability. With this in mind, an ML-based sampling algorithm is presented to identify top optimum polycrystal solutions for both linear and nonlinear properties without compromising the optimum property values. Moreover, an inverse optimization strategy is presented to design microstructures for prescribed values of homogenized properties, such as the stiffness constant (C-11) and in-plane Young's modulus (E-11). The applications are presented for aluminum (Al), nickel (Ni), and silicon (Si) microstructures.

关键词： data-driven modeling Multi-scale modeling Microstructure

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modeling on Steering Feedback Torque Based on data-driven Method

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2022年第5期27卷 2775-2785页

作者： Zhao, Rui Deng, Weiwen Ren, Bingtao Ding, Juan Beihang Univ Sch Transportat Sci & Engn Beijing 100191 Peoples R China Beihang Univ Beijing Adv Innovat Ctr Big Data & Brain Comp Beijing 100191 Peoples R China PanoSim Technol Co Ltd Jiaxing City 314000 Peoples R China

The steering feedback torque (SFT) is a key part of driving simulator and steer-by-wire system, which provides driver with desired road feel and vehicle motion dynamics. Therefore, accurately modeling of SFT is of great significance for driver to get better steering feel. Since SFT can be affected by many linear or nonlinear factors, it is appropriate to model SFT using data-driven method. In this article, we adopt artificial neural network (ANN) and Gaussian process regression (GPR) to build the SFT model, and analyze the performance. Considering the fact that the contributing factors for SFT may vary under different driving conditions, we employ K-Means to precluster the training dataset to improve the model accuracy. The model training and validation processes are mainly data-driven, and the results show that GPR and ANN can achieve similar prediction accuracy with the mean square error to be around 0.10. Since the GPR model can be trained much faster than ANN model, it is more suitable for real-time application. It is further demonstrated that using preclustered data based on K-Means for model training can significantly improve its accuracy without sacrificing its computational efficiency.

关键词： Artificial neural network (ANN) data-driven modeling Gaussian process regression (GPR) K-means cluster steering feedback torque (SFT)

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A data-driven Approach with Improved Generalization Performance to modeling Transient Behaviors of DC-DC Converters 5

A Data-driven Approach with Improved Generalization Performa...

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5th Asia Energy and Electrical Engineering Symposium (AEEES)

作者： Ge, Hanchen Ou, Zhongxi Huang, Zhicong South China Univ Technol Shien Ming Wu Sch Intelligent Engn Guangzhou Peoples R China Guangdong Power Grid Co Ltd Zhuhai Power Supply Bur Zhuhai Peoples R China

ISBN: (纸本)9781665490542

Nowadays, the data-driven approaches to modeling power electronics (PE) systems are mostly based on sequential neural networks (NNs). These approaches may require too much data since the NNs can not generalize across a wide range of inputs. To address this issue, this paper proposes a new data-driven approach to modeling the transient behaviors of DC-DC converters, which is based on fully-connected NNs. The proposed method introduced prior knowledge about linear systems and thus significantly improved the generalization performance. In this method, circuit parameters are first mapped into linear system characteristics by fully-connected NNs, and then the outputs are calculated by the inputs and the system characteristics. Experiment results show that the entire circuit topology with configurable parameter settings and initial conditions can be successfully modeled. Parameter change events are also supported by this approach.

关键词： DC-DC converter Machine Learning data-driven modeling

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A Training-Free data-driven Method for Input-Output modeling of Complex Processes 2nd

A Training-Free Data-Driven Method for Input-Output Modeling...

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2nd modeling, Estimation and Control Conference (MECC)

作者： Ruan, Jianqi Nooning, Bob Parkes, Ivan Blejde, Wal Chiu, George Jain, Neera Purdue Univ Sch Mech Engn W Lafayette IN 47907 USA Castrip LLC 1915 Rexford Rd Charlotte NC 28211 USA

In a variety of human-in-the-loop systems, variations among human operators can result in inconsistencies in process operation and product quality. While a variety of methods exist to mitigate this issue, they often require some model of the relationship between the human input and system output;unfortunately, obtaining such a model continues to be very difficult for highly complex processes such as industrial manufacturing processes. In this paper, we propose an innovative training-free data-driven (TFDD) modeling method that directly predicts the next state from the state transition information of all samples in a database. Because the prediction is directly derived from the database, the model does not require any training, nor does the model architecture change from one application to another. Through a case study on human operator supervisory control of twin-roll steel strip casting, we demonstrate the performance and advantages of the proposed TFDD method as compared to a baseline nonlinear autoregressive network with exogenous inputs (NARX) model trained using the same dataset. Copyright (c) 2022 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： data-driven modeling Human-in-the-loop Twin-roll casting

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Bayesian Gaussian Mixture Models for Enhanced Radar Sensor modeling: A data-driven Approach towards Sensor Simulation for ADAS/AD Development

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SENSORS 2024年第7期24卷 2177页

作者： Walenta, Kelvin Genser, Simon Solmaz, Selim Virtual Vehicle Res GmbH Inffeldgasse 21a A-8010 Graz Austria Inst Theoret & Computat Phys Petersgasse 16 A-8010 Graz Austria

In the realm of road safety and the evolution toward automated driving, Advanced Driver Assistance and Automated Driving (ADAS/AD) systems play a pivotal role. As the complexity of these systems grows, comprehensive testing becomes imperative, with virtual test environments becoming crucial, especially for handling diverse and challenging scenarios. Radar sensors are integral to ADAS/AD units and are known for their robust performance even in adverse conditions. However, accurately modeling the radar's perception, particularly the radar cross-section (RCS), proves challenging. This paper adopts a data-driven approach, using Gaussian mixture models (GMMs) to model the radar's perception for various vehicles and aspect angles. A Bayesian variational approach automatically infers model complexity. The model is expanded into a comprehensive radar sensor model based on object lists, incorporating occlusion effects and RCS-based detectability decisions. The model's effectiveness is demonstrated through accurate reproduction of the RCS behavior and scatter point distribution. The full capabilities of the sensor model are demonstrated in different scenarios. The flexible and modular framework has proven apt for modeling specific aspects and allows for an easy model extension. Simultaneously, alongside model extension, more extensive validation is proposed to refine accuracy and broaden the model's applicability.

关键词： virtual validation sensor simulation ADAS/AD development Gaussian mixture models radar modeling data-driven modeling

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