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CAV-generated mobility data modeling mechanism for adaptive signal control

Journal of Traffic and Transportation Engineering (English Edition) 2025年第2期12卷 361-377页

作者： Lin, Wei Wei, Heng Yang, Lan Zhao, Xiangmo College of Civil Engineering Fuzhou University Fuzhou350116 China ART-EngineS Transportation Research Laboratory Department of Civil and Architectural Engineering and Construction Management University of Cincinnati CincinnatiOH45221 United States School of Information Engineering Chang'an University Xi'an710064 China

The effectiveness of adaptive traffic signal control highly relies on accurate and accountable identification of dynamic arrival turning movement demand on approaches and other traffic flow parameters measuring traffic states. Emerging connected vehicle (CV) and/or autonomous vehicle (AV)-generated mobility data can be potentially used as a new data source in support of the adaptive signal control. In the long-run, the CV/AV-generated data source could gradually substitute traditional inductive loop data as the maturity levels of the relevant data process techniques are progressively increasing. However, use of the CV/AV-generated data is still yet mature due to lack of the data process mechanism and models to integrate the data into the adaptive traffic signal control system. It is hence an imperative need to develop the mechanism for processing the CV/AV-generated data source in order to facilitate improving the efficiency of the adaptive traffic signal control schemes. This paper presents a developed methodological framework along with associated data models which can be used to configure an intelligent CV/AV data fusion in support of adaptive signal control operations. A proof-of-concept study has been conducted to test the developed models via comparison of the CV/AV-data-driven scenario and the traditional-detection-data-supported scenarios. The paper presents the modeling framework along with performance analysis of the testing study, which demonstrates positive outcomes in terms of reduced queue length and throughput, as well as benefit-cost ratios. © 2025 Periodical Offices of Changâ€™an University

关键词： Traffic signals

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Models, modeling and model-based systems in the era of computers, machine learning and AI

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COMPUTERS & CHEMICAL ENGINEERING 2025年 194卷

作者： Mansouri, Seyed Soheil Sivaram, Abhishek Savoie, Christopher J. Gani, Rafiqul Tech Univ Denmark Dept Chem & Biochem Engn DK-2800 Lyngby Denmark Sqale Ole Maaloes Vej 3 DK-2200 Copenhagen Denmark PSE SPEED Co Ordrup Jagtvej 42D 3TV DK-2920 Charlottenlund Denmark East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai Peoples R China Szecheny Istvan Univ Dept Appl Sustainabil Gyor Hungary

Models, representing a system under study with respect to problems such as process design, process control, product synthesis and many more, are at the core of most computer-aided solution techniques. The representation of a system through a model is done in different ways, such as, symbols, data, mathematical equations, and/or some combination of these. The workflow or process of creating a proxy mathematical representation (model) of a given target system is referred to as modeling. Model-based software tools incorporate the developed models within the steps of their systematic workflow through simultaneous or decomposed solution strategies related to synthesis, design, analysis, etc., of specific systems. In this perspective paper we highlight the various ways systems can be represented by models, the different ways the required models are developed through modeling techniques, and examples of model-based software tools developed to solve different process and product engineering problems. Two types of systems - process systems and chemical systems, are considered. Important issues and challenges are highlighted and perspectives on how they can be addressed are presented.

关键词： Models modeling: model-based tools Chemical systems process systems

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Research on the Model of Optimizing the Intelligent Construction process by Using Three-Dimensional Graphics modeling

Research on the Model of Optimizing the Intelligent Construc...

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2025 Asia-Europe conference on Cybersecurity, Internet of Things and Soft Computing, CITSC 2025

作者： Wu, Di Anhui Vocational and Technical College Hefei China

ISBN: (纸本)9798331504205

This study proposes a modeling optimization method based on three-dimensional graphics for the modeling problem of complex geometric structures in the intelligent construction process. This method uses the Marching Cubes algorithm to efficiently generate meshes for the target structure. The complex geometric structure is decomposed into multiple cubic units, and then the model is reconstructed based on boundary judgment and interpolation operations. The system design includes an algorithm optimization module, a three-dimensional graphics rendering module, and a model simulation module to ensure the accuracy and efficiency of modeling. In the simulation process, the advantages of this method in terms of accuracy and real-time performance were verified by comparing indicators such as surface reconstruction error, mesh generation speed, and data processing efficiency of different models. The experimental results show that the proposed algorithm can effectively reduce the error rate in the modeling process, while improving the stability and reliability of data processing, laying a solid foundation for the digitization and automation of intelligent construction. data analysis shows that this method can improve the computational efficiency by about 30% and control the error within 0.5%, significantly enhancing the application potential of three-dimensional graphics in intelligent construction. © 2025 IEEE.

关键词： Smart manufacturing

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data-Driven Closed-Loop process Fault Diagnosis With Enhanced Fault Dynamics

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2025年

作者： Liu, Qiang Wen, Li Wu, Gaochang Chai, Tianyou Qin, S. Joe Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Lingnan Univ Sch Data Sci Hong Kong 999077 Peoples R China

In a closed-loop system, feedback control may transfer process faults from source variables to other variables that obscure the diagnosis of the root cause of the fault. Taking the fused magnesium furnace (FMF) as an example, the effects of process faults, e.g., the semimolten condition,may propagate through the controlled system. To address this issue, this article proposes a new data-driven process fault diagnosis method for dynamic processes in the presence of feedback control. A new residual analysis (RA) method is first proposed to extract features of process faults in the feedback-invariant subspace. Moreover, a new fault diagnosis algorithm, which incorporates support vector machines (SVM) with leaky integrate-and-fire neurons (LIF), named LIF-SVM, is proposed. Unlike traditional diagnosis methods which classify each element independently, LIF-SVM efficiently takes into account the fault dynamics. The features of process faults extracted by RA are used by LIF-SVM for diagnosis to constitute the new RALIF-SVM method. Results from experimental studies on a simulated 4x4 dynamic process and a real FMF show that the diagnosis accuracies using the proposed method increase by 10.9% and 9.15%, respectively, compared to the traditional subspace reconstruction method.

关键词： Fault diagnosis Feature extraction Support vector machines process control Training Testing Magnesium data models Closed loop systems Accuracy Closed-loop systems data-driven modeling dynamic processes fault diagnosis process faults

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control design and analysis for autonomous underwater vehicles using integral quadratic constraints

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control ENGINEERING PRACTICE 2025年 154卷

作者： Sinha, Sourav Farhood, Mazen Stilwell, Daniel J. Virginia Tech Kevin T Crofton Dept Aerosp & Ocean Engn Blacksburg VA 24061 USA Virginia Tech Bradley Dept Elect & Comp Engn Blacksburg VA 24061 USA

This paper addresses the design and analysis of path-following controllers for an autonomous underwater vehicle (AUV) using a robustness analysis framework based on integral quadratic constraints (IQCs). The AUV is modeled as a linear fractional transformation (LFT) on uncertainties and is affected by exogenous inputs such as measurement noise and ocean currents. The proposed approach leverages a learning-based method to approximate the nonlinear hydrodynamic model with a linear parameter-varying one. Additionally, modeling uncertainties are incorporated into the other subsystem models of the AUV to capture the discrepancies between the outputs of the postulated mathematical abstractions and the experimental data. The resulting uncertain LFT system adequately captures the AUV behavior within a desired envelope. Ocean current disturbances are treated as uncertainties within the LFT system and properly characterized to reduce conservatism. The robust performance level, obtained from IQC analysis, serves as a qualitative measure of a controller's performance, and is utilized in guiding the controller design process. The proposed approach is employed to design H infinity and H2 controllers for the AUV. A comprehensive IQC-based analysis is subsequently conducted to demonstrate the robustness of the designed controllers to modeling uncertainties and disturbances. To validate the analysis results, extensive nonlinear simulations and underwater experiments are performed. The outcomes showcase the efficacy and reliability of the proposed approach in achieving robust control for the AUV.

关键词： Autonomous underwater vehicle (AUV) Robust control Integral quadratic constraint (IQC) Uncertainty analysis Path following H-2 control H-infinity control

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Hybrid self-learning model for the prediction and control of sintering furnace temperature

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control ENGINEERING PRACTICE 2025年 154卷

作者： Dai, Yuanshen Chen, Ning Shao, Zhijiang Zhejiang Univ Coll Control Sci & Engn Hangzhou 310027 Zhejiang Peoples R China BASF China Co Ltd Shanghai 200137 Peoples R China Cent South Univ Sch Automat Changsha 410083 Hunan Peoples R China

Ternary cathode materials are important components of lithium-ion batteries. However, the sintering process during manufacturing is challenging to control due to the inaccessibility of key dynamic variables and the frequent fluctuations in operating conditions. These lead to high energy consumption and inconsistent product quality. In this paper, we propose a hybrid self-learning prediction model and control method for sintering furnace temperature based on both first-principle and process data. Firstly, a mechanism model with temperature time delay is established based on energy flow analysis in the furnace. To capture the tail gas temperature dynamic in the mechanism model, a Ventingformer-based prediction data-driven model is proposed. In this model, a memory updating technique and an autoregressive module based on the Transformer framework are developed to identify long-time dependencies and respond to variations in input sequences. Then, a hybrid self-learning modeling framework is designed. Based on the established hybrid model, a multiscale objective function-based nonlinear model predictive control (MSCF-NMPC) method is proposed to achieve precise tracking control of the internal temperature in the furnace. A multiscale objective function with short-term cost in terms of energy consumption and tracking accuracy as well as long-term cost in terms of energy loss is constructing in the control optimization problem. Finally, the proposed hybrid self-learning model and MSCF-NMPC method are verified using the actual process data from a sintering furnace, demonstrating the effectiveness of the proposed method. The results offer practical guidance for industrial applications.

关键词： Ternary cathode materials Sintering furnace Self-learning hybrid model Multi-scale objective function Nonlinear model predictive control

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Performance analysis and Verification of Elastic Circuits under process Variations

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INTERNATIONAL JOURNAL OF ENGINEERING 2025年第10期38卷 2273-2287页

作者： Zaeemi, M. Mohammadi, S. Univ Tehran Coll Engn Sch Elect & Comp Engn Tehran Iran Inst Res Fundamental Sci IPM Sch Comp Sci Tehran Iran

The diversity of designs and optimization methods for elastic systems makes evaluating their performance challenging. This paper proposes a method based on xMAS and high-level modeling to analyze the performance, functional and timing verification of regular and early evaluation synchronous elastic circuits while considering process variations. The xMAS framework provides modularity, precise semantics, and executable models, enhancing formal verification and high-level analysis capabilities over existing approaches. The proposed platform calculates the throughput value, which is the most critical performance factor in elastic circuits. The power, delay, and PDP of all early evaluation elastic components are evaluated under process variations and compared to those of regular elastic circuits. The results indicate that early evaluation properties increase the sensitivity of circuit components to process variations, making their performance less predictable. modeling results of the Elastic DLX microprocessor highlight these findings by demonstrating that process variations can cause a 26% reduction in throughput and lead to a 0.2% chance of synchronization errors between data and control signals. These findings underscore the critical need to account for process variations when designing and verifying early evaluation elastic circuits to maintain performance reliability.

关键词： Elastic Circuits Early Evaluation High-level modeling Performance analysis process Variation

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Improving data-Driven Inferential Sensor modeling by Industrial Knowledge: A Bayesian Perspective

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2025年第2期55卷 1043-1055页

作者： Chen, Zhichao Wang, Hao Song, Zhihuan Ge, Zhiqiang Zhejiang Univ Coll Control Sci & Engn State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China Guangdong Univ Petrochem Technol Guangdong Prov Key Lab Petrochem Equipment Fault D Maoming 525000 Peoples R China Southeast Univ Sch Math Nanjing 210096 Peoples R China

Accurate quality variable inference by process variables is the core of industrial inferential sensor modeling, where recent advancements have seen deep learning (DL) models achieving remarkable success. However, integrating knowledge of unit operations is critical for improving inferential sensor performance, yet it has received little attention. The main challenge lies in the incompleteness and correctness of industrial knowledge due to its semi-empirical nature and inevitable engineering errors. Addressing this, this article introduces the gradient knowledge network based on the graph neural network's message-passing mechanism within the variational Bayesian inference framework, which naturally copes with the abovementioned issues by fusing observational data. Initially, the prior knowledge about the process variables, which mirrors the graph in graph neural network, is parameterized as Dirichlet distribution based on the analysis of message-passing mechanism. However, the divergence computation and normalization constraints are challenging for model implementation. To navigate these challenges, the Bayesian inference problem is transformed into an optimization problem, subsequently recast as a simulation problem induced by the gradient field, ensuring compatibility with DL backends. Furthermore, a theoretical iteration equation is derived to maintain the normalization constraint. The architecture of the proposed model and its learning algorithm are then detailed. Finally, various experiments are conducted on two real industrial processes to demonstrate the model's efficacy from the perspective of prediction accuracy, sensitivity analysis, and ablation study.

关键词： Mathematical models Feature extraction Predictive models Knowledge engineering Bayes methods data models Computational modeling Accuracy Analytical models Mirrors Deep learning (DL) graph neural network inferential sensor knowledge automation variational inference

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modeling the working pressure of a cement vertical roller mill using SHAP-XGBoost: A "conscious lab of grinding principle" approach

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POWDER TECHNOLOGY 2025年 457卷

作者： Fatahi, Rasoul Abdollahi, Hadi Noaparast, Mohammad Hadizadeh, Mehdi Univ Tehran Coll Engn Sch Min Engn Tehran Iran

The optimization and control of Vertical Roller Mill (VRM) circuits are critical for industrial processes, yet limited modeling has slowed progress in operator training, error minimization, and laboratory cost savings. To overcome limitations, the innovative "Conscious Lab" (CL) was introduced, utilizing industrial datasets and Explainable Artificial Intelligence (XAI) techniques. For the first time, CL combines Shapley Additive Explanations (SHAP) with machine learning models, such as XGBoost and Random Forest, to optimize VRM operations. Differential pressure and feed rate were identified as the most influential parameters of working pressure, essential for maintaining stable operations. Robust linear correlations (coefficients: 0.94 for feed rate, 0.84 for main drive power, and 0.83 for differential pressure) and nonlinear marginal plots (0.95, 0.81, and 0.81) highlighted how increases in these parameters significantly raise working pressure. The XGBoost model achieved remarkable prediction accuracy (0.99 for training and 0.98 for testing/validation) with a low RMSE (0.01), confirmed by 5-fold cross-validation. SHAP analysis further verified the relationship between working pressure and key parameters, aligning with VRM grinding principles. The CL approach introduces a data-driven control system enabling real-time decision-making, process optimization, and improved production efficiency, showcasing the transformative potential of advanced data analytics for industrial applications.

关键词： control and optimization VRM Explainable artificial intelligence Working pressure

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Physics-based model as a versatile tool towards advanced process control of the naphtha distillation unit

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CHEMICAL ENGINEERING RESEARCH & DESIGN 2025年 218卷 53-65页

作者： Stabrov, Sergey Shevlyagina, Svetlana Russian Acad Sci Inst Automat & Control Proc Far Eastern Branch Vladivostok 690041 Russia Far Eastern Fed Univ Vladivostok 690922 Russia

Following the recent successes with low input data variability and soft sensor design under feed composition changes, this study proposes, among other things, the use of a physics-based approach to improve multivariable model predictive control (MPC) of naphtha distillation. Unlike the industrial settings, where the influence of other manipulated variables is difficult to exclude due to the actions of the human operator, a physically based modeling provides close to an ideal step-by-step and one-by-one testing of the chemical process, resulting in improved accuracy of the transfer function matrix used for MPC design. The proposed approach has been tested on canonical and alternative control schemes used in stabilized naphtha production. Importantly, the physicsbased model resolved all the issues associated with unavailability to reach the set points in controlling the quality of end products when compared with MPC built on the industrial data only irrespective of the control scheme considered. As a result, the steady-state controllability analysis and the closed-loop process behavior highlight that an alternative control structure with transfer function matrix obtained on a physics-based model is a better choice for the industrial case study. Thus, the developed strategy for MPC design was approved as relevant for the cases when a preliminary control scheme requires an update or optimized control scheme without affecting production is of great demand.

关键词： Model predictive control Physics-based model Naphtha distillation Dynamic simulation controllability analysis

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