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2024 International conference on Mechatronics and Intelligent control, ICMIC 2024

作者： Wang, Wenfu Huang, Lanlin College of Resources and Environment Yunnan Land and Resources Vocational College Kunming650217 China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

This study aims to explore the application of mechanical vibration control technology in improving environmental safety in mining. By introducing vibration machinery and screening machine technology, combined with the concept of intrinsic safety technology, a mechanical vibration control system suitable for mining is proposed. Based on advanced algorithm design, the system monitors and dynamically adjusts the vibration parameters in mining operations in real time through multi-dimensional system simulation. The simulation process covers the analysis of key indicators such as vibration screening efficiency, mechanical stability and environmental impact, and verifies the effectiveness of the system through precise data. The research results show that this technology has significant advantages in improving the screening efficiency and safety performance in the mining process, especially in reducing dust diffusion and noise pollution. By optimizing the parameter configuration of the vibration screening machine, the system can ensure efficient operation while greatly improving the intrinsic safety of the operation. © 2025 SPIE.

关键词： Machine vibrations

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Hybrid data-Driven and Mechanistic modeling Approaches for Gas Turbine CCPP control System Validation 1st

Hybrid Data-Driven and Mechanistic Modeling Approaches for G...

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1st Electrical Artificial Intelligence conference, EAIC 2024

作者： Wu, Ke Ji, Zheng Li, Jianliang Huang, Lei Chen, Zijing Guodian Nanjing Automation Co. Ltd. Nanjing210032 China Nanjing SAC Valmet Automation Co. Ltd. Nanjing210032 China China Huadian Corporation Ltd. Beijing100031 China

ISBN: (纸本)9789819640621

The utilization of heavy-duty gas turbines in power plant, coupled with a combined cycle system (CCPP), represents a significant alternative to conventional coal-fired boiler turbine units, primarily due to its swift load response capabilities and environmentally benign emissions profile. In China, the gas turbines control system (TCS) have been predominantly supplied and stringently regulated by international vendors. The domestically developed TCS needs extensive validation prior to its commercial deployment. The adequate fidelity model of the CCPP to represents both static and dynamic characteristics plays a critical role in this process. Hybrid modeling provides a promising option to tackle such complex problems. In hybrid modeling, the process-related principles are represented by mechanistic models, while the coefficients added to the first-principal models as adjustments to present unknown process characterizes, are described by data-driven models. This article highlights the significance of hybrid modeling in multiscale CCPP processes. The common design methodology is first introduced. The piece-by-piece devices modeling are then performed following the mass and energy flow. A novel control-oriented hardware-in-the-loop (HIL) simulator for TCS validation is introduced, which helps to reduce costs, mitigate the risk of equipment damage compared to comprehensive and dangerous on-site testing and expedites the commissioning process. Concluding remarks are provided, and future perspectives are pointed out at the end. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Combined cycle power plants

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data-Driven Digital Twin for Foundry Production process: Facilitating Best Practice Operations Investigation and Impact analysis 4th

Data-Driven Digital Twin for Foundry Production Process: Fac...

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4th Energy Informatics Academy conference

作者： Howard, Daniel Anthony Vaerbak, Magnus Ma, Zhipeng Jorgensen, Bo Norregaard Ma, Zheng Univ Southern Denmark Maersk McKinney Moller Inst SDU Ctr Energy Informat Fac Engn Odense Denmark

ISBN: (纸本)9783031747373;9783031747380

In the context of increasing environmental concerns, the iron and steel industry faces large pressure to reduce its energy consumption and carbon footprint while maintaining economic viability. This paper explores the implementation of best practice operations within foundry processes, specifically induction furnace melting, to enhance energy and cost efficiency and reduce CO2 emissions. A digital twin model is developed integrating discrete event simulation, system dynamics modeling, and symbolic regression to simulate the foundry production process and evaluate the impact of various operational practices. A large Danish foundry is used as a case study, providing data for induction furnace production incorporating various electricity market data sources. Symbolic regression models are deployed to accurately predict melt temperatures and energy requirements. Results indicate that adopting best practices can lead to significant savings - up to 21% in electricity costs and 14.2% in CO2 emissions - while improving productivity. The study also highlights a point of diminishing returns at 65% adherence to best practices due to existing production schedules. Furthermore, the study demonstrates the digital twin's potential as a decision-support tool in optimizing industrial process operations.

关键词： Digital twin best practice operations foundry production induction furnace melting process energy efficiency CO2 reduction cost efficiency

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

作者： Di Wu Anhui Vocational and Technical College Hefei China

ISBN: (数字)9798331504205

ISBN: (纸本)9798331504212

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.

关键词： Graphics Solid modeling Surface reconstruction Three-dimensional displays Accuracy Computational modeling Heuristic algorithms data models Stability analysis Surface treatment

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Connected Geotechnical Workflows Enable Dynamic Communication Mitigating Cost Pressures and Project Risk 5th

Connected Geotechnical Workflows Enable Dynamic Communicatio...

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5th International conference on Transportation Geotechnics

作者： Lowries, Richard Wu, Anton Hamidi, Farzin Seequent Christchurch New Zealand Seequent Melbourne Vic Australia Seequent Brisbane Qld Australia

ISBN: (纸本)9789819782239;9789819782215;9789819782208

Large transportation projects frequently encounter issues in management of Geotechnical data, including collection, analysis, transfer, accessibility, and version control. These difficulties can lead to inefficiencies, causing cost pressures and impact the ability to communicate project risk in a timely fashion. Historically, the absence of centralized databases and effective data management frameworks bottle neck the engineer's ability to process and interpret geotechnical data effectively. Commonly "dead end's" form in digital data transfer as paper or pdf files are exchanged, leading to time consuming processes for digitising when updating interpretations or utilising the data elsewhere. Consequently, decision-makers and stakeholders risk lacking access to crucial data or relying on outdated geotechnical information. We propose a dynamic, connected workflow that streamlines the data transfer between applications, enhancing communication of project risk from ground conditions. Connecting cloud-based site investigation with interpretation software allows the geo-professionals the confidence to know that the latest data is being used and new data can be easily utilised to update the interpretation from a single source of truth. Additionally utilising cloud connections between interpretation software and Geotechnical analysis software, ensures numerical analysis can be consistent and quickly rerun when the Geological interpretation changes. Having subsurface interpretation in a cloud environment provides capability for more efficient communication and collaboration with all key stakeholders. It provides designers access to the latest geological interpretation which can then be utilised within their designs. The paper will include examples where these benefits are being realised on existing projects of various scales.

关键词： Geotechnical workflow Connected workflows data management frameworks

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AI-Assisted data modeling and High-Resolution Image Synthesis: An Interactive Framework with Latent Diffusion Model Visualization

AI-Assisted Data Modeling and High-Resolution Image Synthesi...

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2025 International conference on Intelligent control, Computing and Communications, IC3 2025

作者： Borah, Asha Rani Archana Rani, K. Pushpa Ziara, Sajjad Lakhanpal, Sorabh Vidyadhari, Chalasanvi New Horizon College of Engineering Department of Computer Science Engineering Bangalore India Galgotias university Department of Electrical Electronics & Communication Engineering Uttar Pradesh Gerater Noida203201 India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India College of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq Lovely Professional University Punjab Phagwara India Griet Department of Information Technology Bachupally Telangana Hyderabad India

ISBN: (纸本)9798331527495

In this research paper, we present the design and implementation of an AI assisted interactive framework for data modeling and high resolution image synthesis that leverages both state of the art latent diffusion models and visualisation of the real-time algorithm. The system proposed enables user to be able to better understand complex data modeling processes and creates high resolution images in the latent space using diffusion. The framework is both interactive, allowing modifications in parameters and real time visualization of change, making it a useful tool both for educational and for advanced AI research. In this paper, we also discuss the underpinning AI-assisted data modeling algorithms, and why they enable efficient data manipulation as well as high quality image synthesis then provide simulation results and practical applications to illustrate the capabilities of the framework of visualizing the latent diffusion process and generating high resolution images. © 2025 IEEE.

关键词： Flow visualization

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A Knowledge Push Method to Support the Debugging process Design of Complex Electronic Products 24

A Knowledge Push Method to Support the Debugging Process Des...

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8th International conference on Electronic Information Technology and Computer Engineering, EITCE 2024

作者： Xue, Hao Chen, Xingyu Luo, Tiannan Li, Yang Liu, Yuhan Zhang, Kai School of Mechanical Engineering Sichuan University Sichuan Chengdu China Southwest China Research Institute of Electronic Equipment Sichuan Chengdu China

ISBN: (纸本)9798400710094

The core of design lies in the acquisition and application of knowledge. Knowledge push technology can effectively improve the utilization efficiency of knowledge by designers, thus enabling more efficient task completion. To address the current challenges in debugging process design, including insufficient knowledge management and the dynamic demand for knowledge, this paper proposes a knowledge push method tailored for the debugging process design of complex electronic products. By constructing a knowledge graph for debugging processes and utilizing a semantic similarity-based push algorithm, the method intelligently identifies knowledge needs during the design process, achieving precise knowledge delivery. On this basis, a prototype system supporting knowledge push for complex electronic product debugging design was developed. Taking the debugging process design of an elevator drive system as an example, the method has been proven to effectively improve design efficiency. © 2024 Copyright held by the owner/author(s).

关键词： Knowledge graph

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Damage Presence, Localization, and Quantification of Aircraft Structure Based on End-to-End Deep Learning Framework

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Chen, Gang Song, Wenrui Shao, Weihan Sun, Hu Qing, Xinlin Xiamen Univ Sch Aerosp Engn Xiamen 361005 Peoples R China Fuzhou Univ Maynooth Int Engn Coll Fuzhou 350108 Peoples R China

Structural health monitoring (SHM) plays a vital role in promptly identifying structural damage in aircraft, optimizing maintenance, and reducing costs;however, it faces significant challenges in practical applications, in that it mainly needs to process a large number of continuously collected sensor data, which are inevitably contaminated by random noise. This study, therefore, maps the relationship between Lamb wave signal data with noise and the health condition of aircraft structures using an end-to-end approach to construct a deep learning (DL) framework. The framework integrates deep residual convolutional networks (DRSNs) for feature extraction, efficient channel attention (ECA) for feature enhancement, and long short-term memory (LSTM) in analyzing time series data. Lamb wave signal datasets considering different damage locations and severity are obtained by lead zirconate titanate (PZT) sensors on the aircraft structure, and the datasets are destroyed by using multiple levels of Gaussian random noise to approximate the noise disturbances and unavoidable unpredictability of the industrial environment. The experimental data confirms that the performance metrics of the proposed framework for damage presence, localization, and quantification tasks are all above 97% in a noise-free environment. When dealing with high-noise scenes, the framework provides stronger antinoise robustness and higher accuracy compared to existing state-of-the-art (SOTA) methods. Quantitative analysis of ablation experiments and visualization of the t-distributed stochastic neighbor embedding (t-SNE) algorithm is applied to reveal the contribution of each component of the designed framework toward feature extraction and damage identification of Lamb wave signals in noisy environments.

关键词： Feature extraction Aircraft Location awareness Noise Training Monitoring Noise measurement data mining Atmospheric modeling Vibrations Attention mechanism end-to-end deep learning (DL) Lamb wave noise-robust structural health monitoring (SHM)

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HiGPP: A History-Informed Graph-Based process Predictor for Next Activity 22nd

HiGPP: A History-Informed Graph-Based Process Predictor for ...

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22nd International conference on Service Oriented Computing

作者： Wang, Jiaxing Lu, Chengliang Yu, Yifeng Cao, Bin Fang, Kai Fan, Jing Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou Peoples R China Univ Sci & Technol China Sch Informat Sci & Technol Hefei Peoples R China Zhejiang A&F Univ Coll Math & Comp Sci Hangzhou Peoples R China

ISBN: (纸本)9789819608041;9789819608058

Next activity prediction in business process monitoring is crucial for optimizing resource allocation and decision-making in service-oriented environments. Existing approaches often fail to integrate control flow with event attributes, resulting in incomplete modeling of process dynamics and inability to capture temporal dependencies between events. We propose HiGPP (History-informed Graph-based process Predictor), a novel method that constructs unified history-informed graphs from event logs, incorporating both control flow and multi-view event attributes. HiGPP innovatively encodes the temporal sequence and contextual data of event attributes using attribute-specific embedding layers and gated recurrent units (GRUs), effectively capturing historical dynamics within node embeddings. By leveraging GraphSAGE to aggregate neighborhood information, HiGPP refines embeddings to capture both local and global graph structures. HiGPP achieves superior performance in next activity prediction, with an average improvement of more than 2% in all evaluation metrics compared to the best baseline method. Our code is available at https://***/HiGPP/HiGPP.

关键词： process mining Predictive business monitoring Graph convolutional network Deep learning Next activity prediction

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10th International conference on Modelling and Simulation for Autonomous Systems, MESAS 2023

10th International Conference on Modelling and Simulation fo...

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10th International conference on Modelling and Simulation for Autonomous Systems, MESAS 2023

ISBN: (纸本)9783031713965

The proceedings contain 25 papers. The special focus in this conference is on Modelling and Simulation for Autonomous Systems. The topics include: Atlas Fusion 2.0 A ROS2 Based Real-Time Sensor Fusion Framework;UAS Flight Path Optimization Model for Effective Monitoring and Surveillance of the Buffer Zone in the UNFICYP Peacekeeping Mission;A Model-Based Design Approach for a System of Systems Based on an Integrated UAV Platform;practical Applicability of Tree Spacing Passability analysis on Vehicle Path Planning;where to Go and How to Get There: Tactical Terrain analysis for Military Unmanned Ground-Vehicle Mission Planning;a Survey of Trajectory Planning Algorithms for Off-Road Uncrewed Ground Vehicles;multi-physics and Multi-spectral Sensors Simulator for Autonomous Flight Functions Development;Conceptual Aspects of Counter-UAS Modelling and Simulation;challenges Associated with the Deployment of Autonomous Reconnaissance Systems on Future Battlefields;The Key Challenges of SBAD M development of Geoprocessing Tool for Wet Gap Crossing in Military Operations;digital Twin modeling for Machine Vision Testing in Autonomous Systems;a Situation analysis process in Computer-Generated Forces Team Behavior Within Air Combat Simulations Under Risk and Uncertainty: Concept and First Implementations;a Tactical Planning process in Computer-Generated Forces Team Behavior Within Air Combat Simulations: Concept and First Implementations;survey on Sensing, Modelling and Reasoning Aspects in Military Autonomous Systems;Camera Based AI Models Used with LiDAR data for Improvement of Detected Object Parameters;the analysis of Point Cloud Registration Methods for Natural Environment in Autonomous Driving;Hyperspectral data Dimensionality Reduction: A Comparative Study Between PCA and Autoencoder Methods.

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