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2025 International conference on Pervasive Computational Technologies, ICPCT 2025

作者： Singh, Khushmeet Agarwal, Pratik Rana, Vikrant Kolli, Jayanth Cheruku, Saketh Reddy Musunuri, Aravindsundeep Dr. A.P.J. Abdul Kalam Technical University U.P. Lucknow India Symbiosis Institute of Technology Pune India Coer University Department of Artificial Intelligence and Machine Learning Roorkee India International Technological University Santa ClaraCA United States Wichita State University KS United States Manipal University India

ISBN: (纸本)9798331508685

choosing the right node to measure is the key to success in large-scale networks;network modeling is a critical tool for detecting performance. The growing size and complexity of modern networks make accurately predicting their performance a crucial requirement for efficient and reliable operation. Numerous network modeling approaches, however, each with unique strengths and limitations, have emerged in response to this. This paper provides a comparative critique of these network modeling approaches and their performance prediction capabilities in large-scale networks. The first category is the analytical modeling approach, where mathematical equations represent network performance. An approach similar to the described process gives very accurate and detailed predictions but must rely on a theoretical understanding of how networks behave and the assumption of ideal networks. The second approach, simulation modeling, designs a virtual model of the network and lets you test it to predict its performance. This allows for greater flexibility, and real-world factors can be included in the heuristics;however, it is potentially very computationally intensive. The third method is machine learning, which involves algorithms that learn patterns in historical network data and create predictions. While this method is able to process intricate data sets and adjust to evolving network states, its precision relies heavily on the quality of the training data. © 2025 IEEE.

关键词： Prediction models

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Study of electro-hydrogen cooperative optimization operation based on big data analysis 4

Study of electro-hydrogen cooperative optimization operation...

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4th International conference on Energy Utilization and Automation, ICEUA 2025

作者： Pan, Yingjie Fan, Yanfang Yin, Xin Liu, Guowei Zhang, Baojie Chang, Xiqinag School of Electrical Engineering Xinjiang University Urumqi830047 China State Grid Xinjiang Electric Power Company Limited Urumqi830011 China

With the deepening of the energy transition, the electro-hydrogen synergistic system, as an important energy solution, is facing challenges such as prominent light abandonment and low utilization of photovoltaic (PV), which requires the adoption of a more intelligent and refined management of the electro-hydrogen synergistic system. Traditional control and management methods make it difficult to cope with the uncertainty and complexity of the system. This study aims to promote the intelligence and efficiency of electro-hydrogen synergistic systems by using big data analytics to explore the potential of big data application in the field of electro-hydrogen synergistic systems by collecting, modeling, and analyzing the operating parameters of electro-hydrogen synergistic systems. The research steps include the collection, modeling, and analysis of contact line and PV output parameters. Combined with the local outlier factor (LOF) algorithm and real-time control of the number of electrolyzers based on the PV output, the theoretical basis and technical support for the efficient operation of the electro-hydrogen synergistic system are provided. It is of great significance for solving the key problems of current energy systems. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Advanced Analytics

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Distributed Photovoltaic Power Forecasting Considering Perceived data Quality

Distributed Photovoltaic Power Forecasting Considering Perce...

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2025 International conference on Electrical Automation and Artificial Intelligence, ICEAAI 2025

作者： Liu, Xiaodan Hou, Chunlei Du, Lishi Beijing FibrLINK Communications Co. Ltd. Beijing China

ISBN: (纸本)9798331506797

In distributed photovoltaic (PV) power generation systems, data quality plays a critical role in the accuracy of predictive models. However, the complexity of distributed PV sensor data, including issues such as missing values, noise and anomalous bias, often leads to data quality degradation, which seriously affects the reliability of the prediction. To address these challenges, this study proposes a novel forecasting method that integrates sensor data quality into the forecasting process to improve the reliability and robustness of forecasts. The method starts with a detailed analysis of distributed PV sensor data characteristics to identify patterns of data quality degradation and their impact on forecast accuracy. data recovery algorithm based on adaptive mean drift clustering with multi-paradigm optimisation is presented to effectively deal with data loss and reduce noise. In addition, an adversarial graphical neural network (GNN) is used for knowledge migration and spatio-temporal correlation modelling to make accurate predictions even in the presence of perceived data quality degradation. The GNN framework exploits spatial dependence and temporal dynamics to create robust prediction models. Simulation results show that the proposed method significantly improves the data quality and prediction accuracy of distributed PV power generation, providing a reliable basis for decision making in energy management systems. © 2025 IEEE.

关键词： adaptive mean drift clustering component distributed photovoltaic generation perceived data quality spatio-temporal correlation modeling style

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Quality-Relevant modeling and Monitoring of Industrial Cyber-Physical Systems: The Semi-Supervised Dynamic Latent Variable Models

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 39-47页

作者： Zhou, Le Wang, Yaoxin Wu, Yuanqing He, Shenghuang Song, Zhihuan Zhejiang University of Science and Technology Department of Automation and Electrical Engineering Hangzhou310023 China Guangdong University of Technology Department of Automation Guangzhou510006 China Dongguan University of Technology Department of Computer Science and Technology Dongguan523808 China Ningbo Industrial Internet Institute Ningbo315012 China Zhejiang University Department of Control Science and Engineering Hangzhou310027 China

In modern industrial cyber-physical systems, a mass of process variables has been obtained by the high-sampling online sensors. Meanwhile, the key quality indexes are usually obtained infrequently from the laboratory. Hence, these quality variables are with low sampling rate. To avail of the complete process and quality variables with various sampling rates in the dynamic processes, a set of semi-supervised dynamic latent variable models are proposed for dynamic modeling and quality-relevant monitoring. The proposed models have built a unified structure to consider both the auto-correlations and cross-correlations between the process and quality variables with unbalanced sampling sizes. Hence, the feature extraction of the time series data is dynamically adjusted under the guidance of the quality variables. Then, the quality-relevant monitoring schemes are proposed, which is validated by a numerical case and an actual wastewater treatment process. © 2024 IEEE. All rights reserved.

关键词： Indexes Probabilistic logic Kalman filters Noise Cyber-physical systems Analytical models Parameter estimation Time series analysis process monitoring Numerical models

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Development of a Python-based comprehensive employment data visualization platform 2

Development of a Python-based comprehensive employment data ...

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2nd International conference on Big data, Computational Intelligence, and Applications, BDCIA 2024

作者： Wu, Yuanyuan Li, Shenghao School of Big Data and Internet of Things Chongqing Vocational Institute of Engineering Chongqing404100 China

ISBN: (纸本)9781510689053

In order to achieve real-time monitoring of employment positions and evaluate industry development potential, a method for building a B/S based employment position data visualization monitoring platform was explored. Through core processes such as data collection, data cleaning, feature extraction, and visualization analysis, it provides support for relevant departments' supervision and decision-making. The platform calls the PyEcharts third-party library to achieve data visualization monitoring, uses Python to process and analyze employment data, draws data into analyzable charts, and stores them in the database. Then, relying on the Bootstrap framework, HTML, CSS, and JavaScript technologies are used to complete the front-end interactive pages of the platform. The data visualization monitoring platform is used to achieve data sharing and visualization chart analysis in the database, providing strong technical support for industry regulation. © 2025 SPIE.

关键词： data visualization Visualization data processing Industry data analysis data storage databases data modeling Image processing Analytical research

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Rapid optimization of laser powder bed fusion process: a high-throughput integrated multi-task robust modeling approach

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INTERNATIONAL JOURNAL OF EXTREME MANUFACTURING 2025年第4期7卷 045005-045005页

作者： Zhang, Han Song, Bingke Shi, Keyu Chen, Yusheng Yang, Biqi Chang, Miao Hu, Longhai Xing, Jinming Gu, Dongdong Nanjing Univ Aeronaut & Astronaut Coll Mat Sci & Technol Jiangsu Prov Engn Lab Laser Addit Mfg High Perform Nanjing 210016 Peoples R China Shanghai Inst Spacecraft Equipment Shanghai 200240 Peoples R China

Transpiration cooling is crucial for the performance of aerospace engine components, relying heavily on the processing quality and accuracy of microchannels. Laser powder bed fusion (LPBF) offers the potential for integrated manufacturing of complex parts and precise microchannel fabrication, essential for engine cooling applications. However, optimizing LPBF's extensive process parameters to control processing quality and microchannel accuracy effectively remains a significant challenge, especially given the time-consuming and labor-intensive nature of handling numerous variables and the need for thorough data analysis and correlation discovery. This study introduced a combined methodology of high-throughput experiments and Gaussian process algorithms to optimize the processing quality and accuracy of nickel-based high-temperature alloy with microchannel structures. 250 parameter combinations, including laser power, scanning speed, channel diameter, and spot compensation, were designed across ten high-throughput specimens. This setup allowed for rapid and efficient evaluation of processing quality and microchannel accuracy. Employing Bayesian optimization, the Gaussian process model accurately predicted processing outcomes over a broad parameter range. The correlation between various processing parameters, processing quality and accuracy was revealed, and various optimized process combinations were summarized. Verification through computed Tomography testing of the specimens confirmed the effectiveness and precision of this approach. The approach introduced in this research provides a way for quickly and efficiently optimizing the process parameters and establishing process-property relationships for LPBF, which has broad application value. Integrated high-throughput experimentation with 250 parameter settings into 10 specimens, enhancing LPBF data acquisition *** a multi-task Gaussian process model for accurate predictions of LPBF quality and accur

关键词： laser powder bed fusion process parameter high-throughput Gaussian process microchannel accuracy

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Quantitative analysis of cement raw materials composition by laser-induced breakdown spectroscopy based on iPLS feature band selection 24

Quantitative analysis of cement raw materials composition by...

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2nd International conference on Frontiers of Intelligent Manufacturing and Automation, CFIMA 2024

作者： Cai, Yongqi Ma, Xiaoyu Wang, Xiaohong Shandong Provincial Key Laboratory of Preparation and Measurement of Building Materials University of Jinan Shandong Jinan China School of Electrical Engineering University of Jinan Jinan Shandong China

ISBN: (纸本)9798400710681

The initial stage in the cement production process is the preparation of raw materials. This crucial step significantly influences the overall quality of the final cement product. In this paper, we investigate the effects of spectral preprocessing and sample set partitioning based on joint X-Y distance (SPXY), combined with interval PLS (iPLS) feature band selection, on the modeling of spectral data for four major oxides (CaO, SiO2, Al2O3 and Fe2O3) in cement raw materials using laser-induced breakdown spectroscopy (LIBS). The raw spectral data were preprocessed and normalized to minimize the impact of matrix effects on their stability. Then, the SPXY algorithm was used to partition the training and test sets for the 32 sample ***, the segmented spectral data were selected by iPLS feature bands and regression models were built based on partial least squares regression models to improve the accuracy of the models. The results show that the coefficient of determination (R2), root mean square error (RMSE), and mean relative error (MRE) of the modeling effects of the four oxides contents are significantly improved compared to those of the full-spectrum modeling. The results show that this method can significantly improve the accuracy of quantitative analysis. This approach offers a novel method for using LIBS technology in the compositional analysis of cement raw materials. © 2024 Copyright held by the owner/author(s).

关键词： Laser induced breakdown spectroscopy

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International conference of Electrical, Electronic and Networked Energy Systems, EENES 2024

International Conference of Electrical, Electronic and Netwo...

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International conference of Electrical, Electronic and Networked Energy Systems, EENES 2024

ISBN: (纸本)9789819620791

The proceedings contain 384 papers. The special focus in this conference is on Electrical, Electronic and Networked Energy Systems. The topics include: Novel control Strategy for Single-Phase Single-Stage Isolated AC/DC Converters;Dynamic Spatial-Temporal Graph Neural Network for Ultra Short-Term PV Power Generation Forecasting;selective Miniature Circuit Breaker Operation Mechanism Motion process Mechanical Properties Optimization analysis;a Compact All-Solid-State Bipolar Pulse Adder;a Novel Reluctance Machine with Skewed Magnetized Stator Permanent Magnet for Low-Speed High Torque Density Applications;numerical Simulation Study of the Film-Forming Characteristics of Water Mist in Battery Packs;research on Influence of Asynchronous Condenser on the Frequency Characteristics of New Energy Power System;Research on Electromagnetic Interference Suppression Method for Parallel-Connected Inverter Module Based on Si IGBT/SiC MOSFET;Research on the Operation Optimization Strategy of Electricity Sales Companies Based on GridLAB-D;loss analysis of Converter Transformer Under Non-Sinusoidal Operating Conditions;a Study of data Quality Optimisation Methods for Intelligent Power Marketing;a Dynamic Cluster-Aware modeling Approach for Distribution Networks Based on New Energy Intelligent Individuals;research on Distributed Wind and Solar Power modeling Method Based on Swarm Intelligence;synthesis Load Research and Load Model;design on Unipolar Symmetrical Trapezoidal Pulsed Current Transmission Circuit for Grounding Grid Fault Detection in Substations;A Modified VSG control Scheme with Transient Damping to Improve Transient and Frequency Stability;fault Detection Method of Substation Ground Grid Based on Transient Electromagnetic Method;planning of Renewable Energy Bases in Desert Areas Considering the Response Characteristics of Compressed Carbon Dioxide Energy Storage.

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International conference of Electrical, Electronic and Networked Energy Systems, EENES 2024

International Conference of Electrical, Electronic and Netwo...

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International conference of Electrical, Electronic and Networked Energy Systems, EENES 2024

ISBN: (纸本)9789819620418

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International conference of Electrical, Electronic and Networked Energy Systems, EENES 2024

International Conference of Electrical, Electronic and Netwo...

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International conference of Electrical, Electronic and Networked Energy Systems, EENES 2024

ISBN: (纸本)9789819618514

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