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International conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Xue, Hongjiao Jia, Limin Peng, Fei Sun, Xuan Wang, Hongyan Li, Ying School of Automotive Engineering Beijing Polytechnic Beijing100176 China Beijing Automation Engineering School Beijing100101 China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9789819639564

Passenger flow control is the most direct and effective way to solve the problem of metro line congestion. To solve the problem that the random arrival characteristics of passenger flow affect the reliability of the passenger flow control strategy, this paper provides a robust passenger flow control strategy for a metro line under a short-turning operation pattern in peak hours according to the dynamic, time-varying and random characteristics of passenger flow. Firstly, a short-turning operation pattern environment is constructed, and the arrival rate of historical inbound passenger flow is obtained by statistical analysis. Poisson process is used as a parameter to generate multi-scene random arrival passenger flow data. Secondly, a robust passenger flow control and ride strategy is proposed considering the passenger flow OD of the stations inside and outside the short-turning sections. Combining with the strategy, a robust passenger flow collaborative control model is constructed with multi-scene random arrival passenger flow data as input. Finally, an empirical study was carried out on the Beijing Fangshan metro line. The results show that the robust passenger flow control strategy is applicable to the three passenger flow scenarios, and can ensure the passengers’ safety in the three passenger flow scenarios. Moreover, compared with the separate passenger flow control in a single passenger flow scenario, the passenger waiting time under the robust passenger flow control strategy only increases by 1.19%, 4.47% and 4.42% respectively, which basically ensures the travel efficiency of passengers. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Urban transportation

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Measurement and analysis of Extrusion Forces in Filament-Based Additive Manufacturing Technologies Using ABS Filament 24th

Measurement and Analysis of Extrusion Forces in Filament-Bas...

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24th International Symposium for Production Research, ISPR 2024

作者： Christodoulou, Ioannis Trikkas, Antonios Markopoulos, Angelos School of Mechanical Engineering National Technical University of Athens Zografou Greece

ISBN: (纸本)9783031835827

This paper explores the measurement and analysis of extrusion forces in the Fused Deposition modeling (FDM) process, utilizing a custom-built 3D printer designed for high-performance additive manufacturing. A load cell, strategically positioned between the extruder and the hotend, was employed to capture force data during extrusion. The experiments were conducted across various extrusion temperatures (200, 225, 250 °C) and filament feed rates (ranging from 1 to 12 mm/s) to examine the impact of these parameters on the extrusion force. Acrylonitrile Butadiene Styrene (ABS) filament was selected as the material for all experiments due to its widespread use and availability in the industry, making it a standard choice for many FDM applications. The study leverages Klipper firmware for precise control of the printing conditions, ensuring accuracy in the data collected. The findings contribute to a deeper understanding of the extrusion dynamics in FDM, offering valuable insights for optimizing the process and validating numerical models designed to predict extrusion behavior. This research highlights the critical role of force measurement in advancing the capabilities and reliability of additive manufacturing. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Extrusion

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8th International conference on Construction, Architecture and Technosphere Safety, ICCATS 2024

8th International Conference on Construction, Architecture a...

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8th International conference on Construction, Architecture and Technosphere Safety, ICCATS 2024

ISBN: (纸本)9783031804816

The proceedings contain 67 papers. The special focus in this conference is on Construction, Architecture and Technosphere Safety. The topics include: Lime Compositions with the Additive of Polysilicate Solution for Restoration of Building Walls;durability of Lime Coatings with Polysaccharides Additives;study of the Structure and Properties of the Heat-Affected Area of Welded Joints;quickly Constructed Connection of Precast Concrete Elements with Dismantling Possibility;use of Dispersion analysis to Assess Significance of Effect of Production process Factors on Construction Plywood Parameters;strength of a Bending Reinforced Concrete Element in the Zone of Shear Forces;the Influence of the Properties of Permafrost Soils on the Seismic Resistance of Civil Buildings;study of the Influence of Carbon Nanotubes on the Characteristics of Fine-Grained Concrete;selection of the Climatic Information processing Period for Calculating Design Climate data;on the Shear Resistance of Mortars for Precast Piles in Permafrost;features of Solving Problems of Calculating Systems with Ideal Elastoplastic Material Behavior Using the Finite Element Method in the Form of a Classical Mixed Method;activated Cement-Sand Composition as a Nanomodifying Additive in Concrete;constructive Solutions of Energy-Active External Fences with an Estimated Justification of Heat Gain from Solar Radiation into the Premises;specifics of the Influence of Quartz and Glauconite Sand on the Concrete Strength;modeling of Nodal Elements of Spatial Rod Structures;development and Improvement of Energy Efficiency of a Heat Utilization System from the Ice Arena Refrigeration Machine;influence of Preliminary Electric Curing Parameters on Physical and Mechanical Properties of Concrete.

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Unleashing WSN Potential: Bowerbird Optimized Energy-Conscious Multipath Clustering with Causal Dilated Cosine for Enhanced Load Balancing 4

Unleashing WSN Potential: Bowerbird Optimized Energy-Conscio...

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4th International conference on Sentiment analysis and Deep Learning, ICSADL 2025

作者： Joon, Rakesh Kumar Anitha, R. Selvan, Saravana Latha Mageshwari, P.S. Gayathri Priya, S. Malathi, K. Ganga Institute of Technology and Management Department of Electronics and Communication Engineering Haryana Kablana124104 India S.A. Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Chennai600077 India School of Professional Engineering Manukau Institute of Technology Auckland2104 New Zealand Tamil Nadu Kavaraipettai601206 India R.M.D. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Kavaraipettai601206 India Saveetha Engineering College Department of Artificial Intelligence and Machine Learning Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331523923

Wireless Sensor Networks (WSNs) are essential in the collection of real time data across different fields, including environmental monitoring and process control. However, due to the bounded amount of energy in sensor nodes, network lifetime becomes an issue, which hinders performance and reliability. Thus, optimization of WSN performance in an attempt to minimize energy consumption is essential for reliable data transmission. In conventional routing and clustering models, network load distribution is not achieved adequately, resulting in a more significant loading of nodes and faster exhaustion of energy. To solve these problems, it is necessary to carefully design load distribution and path finding algorithms to support long-term WSN operation. The objective of this research is to design a framework-Ski-CDiCo-BO-that integrates Skill Optimization for clustering, and ensuring an energy-efficient paths while reducing redundant transmissions on nodes and conserving node energies, and uses the Causal Dilated Cosine Architecture for sophisticated load balancing, and Bowerbird Optimization for improving the parameterization of neural networks. analysis reveals that the presented Ski-CDiCo-BO framework provides improved performance of various parameters including data delivery success rate of above 99.8% and network lifetime gain by up to 99.5%. Also, load distribution accuracy remains above 99.3%, which indicates that the framework is capable of avoiding node overload and end-to-end delay improved upon by 99.6%, indicating that the solution maintains high parameters even in dynamically changing conditions. In conclusion, Ski-CDiCo-BO maximizes WSN efficiency by implementing cutting-edge clustering, load balancing, and optimization techniques, setting new standards for energy-conscious and reliable sensor network performance across diverse applications. © 2025 IEEE.

关键词： Sensor nodes

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Open-loop linear modeling method for unstable flow utilizing built-in data-driven feedback controllers

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Physical Review Fluids 2025年第3期10卷 033902-033902页

作者： Chuanqiang Gao (高传强) Xinyu Yang (杨新宇) Kai Ren (任凯) Weiwei Zhang (张伟伟) School of Aeronautics Northwestern Polytechnical University Xi'an 710072 China International Joint Institute of Artificial Intelligence on Fluid Mechanics Northwestern Polytechnical University Xi'an 710072 China and National Key Laboratory of Aircraft Configuration Design Xi'an 710072 China

The linear models of complex unstable flow systems serve as essential foundations for conducting model-based flow characteristic analysis and control design. The primary obstacle faced by linear modeling these systems lies in balancing the inherent flow disturbances with the necessary excitation signals. This article develops a linear modeling approach based on the idea of closed-loop identification. To extend the applicability of traditional linear modeling techniques, this method employs time-variant or strongly nonlinear data-driven controllers, such as model-free adaptive controllers and deep reinforcement learning controllers, to automatically adjust the amplitude of the predesigned training signal in real time during the training process. Consequently, the strict requirements for obtaining the unstable steady base flow of unstable systems and for designing training signals are weakened. These data-driven controllers have been validated to fulfill the identifiability conditions of closed-loop systems derived from the uniqueness and convergence consistency of identification parameters. Then, the feasibility of the proposed linear modeling method is validated through two typical examples: low Reynolds number laminar flow around a cylinder and transonic buffet flow around a NACA0012 airfoil. The results confirm that this method successfully identifies the intrinsic linear characteristics of different flow systems, thus facilitating the advancement and application of model-based control design and simplification methods.

关键词： Reynolds number

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Maximizing Efficiency and controlling Congestion in Interlinked Queueing Models for Integrated Voice and data Communication Networks

Maximizing Efficiency and Controlling Congestion in Interlin...

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Device Intelligence, Computing and Communication Technologies, (DICCT), International conference on

作者： Amit Kumar Prabhdeep Singh Virk Neeraj Kumar Department of Mathematics Chandigarh University Mohali Punjab India Department of CSE (AI & ML) Raj Kumar Goel Institute of Technology Ghaziabad U.P India

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

This paper introduces an interdependent tandem queueing model for communication networks. The model effectively transports voice and data traffic by adopting strong packet switching and multiplexing capabilities. Application of the bit-dropping scheme as the best congestion control mechanism in the network model encapsulates one of the primary thrusts of this study. Performance measures of statistical multiplexing are built in the research, e.g., approximating the correlated arrival and service processes to the Poisson process, an empirical model employed in the modeling of random events. Importantly, the study illustrates the practical advantages of interdependent communication networks. In particular, it shows their ability to decrease the average transmission delay, or the time for data to pass through the network, as well as buffer level fluctuation. These results show the potential of interdependent networks to optimize network efficiency, a significant issue in modern communication systems.

关键词： Multiplexing Power transmission lines Sensitivity analysis Computational modeling Transmission line measurements Throughput data models Delays Communication networks data communication

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Modelling System for Decision Support of Epidemic Diseases in Ukraine 19th

Modelling System for Decision Support of Epidemic Diseases i...

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19th International conference on Mathematical modeling and Simulation of Systems, MODS 2024

作者： Pylypenko, Oleksandr Kivva, Sergii Brovchenko, Igor Institute of Mathematical Machines and System Problems NAS of Ukraine Glushkov Prosp. 42 Kyiv03187 Ukraine

ISBN: (纸本)9783031907340

The aim of the article is to develop and apply a modelling system to support decision-making regarding the spread of infectious diseases. The recent course of the COVID-19 pandemic worldwide has emphasised the need for software tools to prevent, analyse, and manage the epidemic process. Although by the time the pandemic began, there were already quite a few mathematical tools for modelling infectious diseases, practical experience with their application revealed their weaknesses and limitations. One of the main shortcomings of mathematical models is the uncertainty of parameters in the case of an epidemic caused by a new, unknown infection. When there is not enough time to conduct medical experiments to determine the properties of the virus, the challenge arises to develop algorithms for calibrating models based on available observational data. This work develops a modelling system for managing the epidemic process, which allows for the prediction of the spread of the COVID-19 viral infection, taking into account the age groups of the population and their spatial heterogeneity. It also includes a parameter optimisation block that allows for flexible adjustment of the model as new information about the virus and new statistical data become available. Model parameter calibration is based on comparing the predicted results with observed data by solving a nonlinear constrained least squares problem. The modelling system was applied to simulate four waves of COVID-19 in Ukraine. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Uncertainty analysis

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Preconditioning Full Wave Inversion Using Geological Structure-Oriented Gradient Filtering

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2025年 22卷

作者： Gebre, Mesay Geletu Pinheiro, Daniel N. Duarte, Edwin F. da Costa, Carlos A. N. Araujo, Ramon C. F. da Silva, Danyelle de Araujo, Joao M. Univ Fed Grande Norte Dept Fis BR-59078900 Natal RN Brazil Wolkite Univ Dept Phys Wolkite 07 Ethiopia Univ Fed Rio Grande do Norte Dept Comp Engn & Automat Lab Parallel Architectures Signal Proc BR-59078900 Natal RN Brazil

Full waveform inversion (FWI) is inherently an ill-posed inverse problem. Gradient preconditioning in FWI is crucial for improving the convergence and stability of the inversion process, as it helps refine the gradient and mitigate the ill-posed nature of FWI. Conventional strategies involve applying smoothing operators, such as Gaussian or Laplacian filters, directly at each FWI iteration. While these operators are easy to implement, they can compromise the structural information embedded in the gradient. To address this drawback, we propose a structure-oriented filter that enhances the gradient's structure while smoothing the gradient along known geological slopes. This preconditioning method effectively preserves the geological structures. We applied the proposed approach to 3-D field data from the Brazilian presalt region and compared the inversion results with those obtained using conventional strategies. The enhancement in resolution and structural coherence is striking, with most of the velocity model's details being preserved. In addition, the reverse time migrated (RTM) image computed using the FWI velocity model obtained with the proposed preconditioning strategy revealed more detailed geological features associated with complex salt layers, with more focused and sharper edges compared with the RTM image derived from the velocity model using conventional preconditioning methods. To further validate the FWI velocity model obtained with our method, we performed quality control tests, including observed and predicted data comparison and phase residual analysis, which confirmed the reliability of the results. We believe this technique has the potential to be applied to a wide range of field data in the future.

关键词： Geology Computational modeling data models Filters Three-dimensional displays Smoothing methods Salt Mathematical models Solid modeling Numerical models Full waveform inversion (FWI) gradient preconditioning ocean bottom node (OBN) structure-oriented

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A Novel Software Tool for Automated and Integrated Building Energy Model Calibration 11th

A Novel Software Tool for Automated and Integrated Building ...

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11th International conference of Ar.Tec.

作者： Maracchini, Gianluca D'Orazio, Marco Di Giuseppe, Elisa Revel, Gian Marco Univ Trento Dept Civil Environm & Mech Engn I-38123 Trento Italy Marche Polytech Univ Dept Construct Civil Engn & Architecture I-60131 Ancona Italy Marche Polytech Univ Dept Ind Engn & Math Sci I-60131 Ancona Italy

ISBN: (纸本)9783031718656;9783031718632;9783031718625

In the EU, the building sector significantly impacts energy consumption and greenhouse gas emissions, accounting for 40% of the total energy use and 35% of emissions, mainly due to the energy inefficiency of the building stock. With energy demand expected to increase over the next decade, improving building energy efficiency is essential for meeting EU sustainability goals. Building Energy Models (BEMs) are crucial for evaluating and enhancing building performance throughout their lifecycle. However, a notable "energy performance gap" usually exists between predicted and actual energy use, exacerbated by challenges in accurately inputting numerous variables and the simplifications inherent in modeling. BEM calibration (BC) approaches are often adopted to reduce these discrepancies, aimed at adjusting model inputs to match output with the observed data. Yet, there is not a universal consensus on which is the best calibration method, with manual and automated approaches offering different benefits. Automated methods, especially those using optimization algorithms, have gained prominence for their efficiency and ability to handle uncertainties. However, BC still significantly depends on the energy modelers' expertise. This paper introduces a novel software tool for automated BC, aiming to simplify the process by integrating expert knowledge, sensitivity analysis, and optimization algorithms techniques in a unique workflow. This tool reduces the dependence of BC success on modeler expertise, representing a significant step towards more accessible automated BC in the research field and engineering practice, thence allowing a more effective design of energy conservation measures.

关键词： Building Energy Model Model calibration Optimization algorithm Sensitivity analysis Software

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2nd International conference on Smart Electrical Grid and Renewable Energy, SEGRE 2024

2nd International Conference on Smart Electrical Grid and Re...

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2nd International conference on Smart Electrical Grid and Renewable Energy, SEGRE 2024

ISBN: (纸本)9789819619641

The proceedings contain 76 papers. The special focus in this conference is on Smart Electrical Grid and Renewable Energy. The topics include: Point Cloud Denoising Method Based on Improved PointCleanNet;dynamic Dual-Strategy Update of Offline-to-Online Reinforcement Learning for Optimal Energy System Scheduling;knowledge Extraction Method of Electric Power Equipment Defects Based on Transfer Learning;a Secondary Bidding Strategy for the Charging Station Day-Ahead Market Based on the Evaluation of the Dispatchable Capacity of Electric Vehicles;primary Frequency Modulation control Strategy of Energy Storage System Based on State of Charge;building Integrated Energy System Expansion Planning Considering Reward-Punishment Ladder Carbon Mechanism and Load Uncertainty;Simulation analysis and Verification of Four-Air-Gap Current Transducer Based on TMR;optimal control of Large Number of Air Conditioners Driven by Dynamic Electric Carbon Emission Factor;application of a Bi-level Optimization Model for Energy Storage Capacity Allocation in Distribution Network with Renewable Energy Integration;power Load Disaggregation Method Based on Sparse Constraint;optimal Primary Frequency Support Demand Dispatch for Multiple Wind Turbines Considering Loss of Captured Wind Energy;promoting Eco-Friendly Power and Traffic Network Operations Using Machine Learning Techniques;Advanced-data Analytics for Household Power Consumption Forecasting Using CNN-LSTM Hybrid Network;research on the Interruption Performance of Environmentally Friendly 126 kV Circuit Breaker;research on Identification of Electricity Theft in Regional Power Grid Under New Energy Scenario;nonlinear modeling for One Pipe with Multiple Units Under Gird Connected of Pumped Storage Stations;a Methodology for the Evaluation of the Power Supply Capability of Subway Traction Power Supply System.

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