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Data Driven control and Learning Systems (DDCLS)

作者： Yubin Cheng Zhihuan Song Xiaoyu Jiang Le Yao State Key Laboratory of Industrial Control Technology Institute of Industrial Process Control College of Control Science and Engineering Zhejiang University Hangzhou China School of Mathematics Hangzhou Normal University Hangzhou China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In recent years, pre-trained foundation models have developed rapidly and have been applied to various downstream tasks through fine-tuning methods. However, due to the existence of data silos, the lack of data for fine-tuning has become a vital issue preventing the application of the foundation model to industry. In addition, the high complexity of the network makes the foundation model prone to overfitting when used for data-constrained industrial tasks. Therefore, a federated tuning approach with adaptive weight shrinking, named FedAWS, is proposed for collaborative fine-tuning between factories. FedAWS adaptively selects the weight-shrinking factor to reduce overfitting and improve the generalization of the global model without relying on additional data sets or human experience. The case study on an industrial dataset demonstrates the effectiveness of FedAWS. This successful validation provides a new way for inter-factory collaboration to apply the foundation model to industry, thus facilitating the efficient utilization of industrial data.

关键词： Industries Learning systems Adaptation models Collaboration control systems Data models Production facilities

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Wind Speed Prediction Based on ARMA and SVR

Wind Speed Prediction Based on ARMA and SVR

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Data Driven control and Learning Systems (DDCLS)

作者： Xuguo Jiao Daoyuan Zhang Qinmin Yang Zhenyong Zhang Wenfeng Liu School of Information and Control Engineering Qingdao University of Technology Qingdao China State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China School of Civil Engineering Qingdao University of Technology Qingdao China

Wind power generation technology is one of the research hotspots of renewable energy nowadays. In order to ensure the stable and reliable operation of wind power generation equipment, wind speed prediction is very important. This paper provides a new idea for the wind speed prediction based on Autoregressive Moving Average (ARMA) and Support Vector Regression (SVR). First, to reasonably divide the original data into multiple historical data with strong correlation as features to predict the future wind speed, the ARMA model is employed and its partial autocorrelation coefficient is calculated. By this means, the input features can be optimally selected and the training set of the prediction model can be constructed. Further, SVR model is used to build the nonlinear relationship between the input features and future wind speed. Finally, through simulation, it proves that this method saves more time than try and error method in selecting input features, and through comparison with Backpropagation Neural Network (BPNN), it proves that this method can achieves higher wind speed prediction accuracy.

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Recent developments and perspectives of MXene-Based heterostructures in photocatalysis

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Chemosphere 2023年第1期338卷 139550页

作者： Meng, Xinyan Wang, Lufei Wang, Xiaoyu Zhen, Mengmeng Hu, Zhenzhong Guo, Sheng-Qi Shen, Boxiong Tianjin Key Laboratory of Clean Energy and Pollution Control School of Energy and Environmental Engineering Hebei University of Technology Tianjin300401 China

Energy crises and environmental degradation are serious in recent years. Inexhaustible solar energy can be used for photocatalytic hydrogen production or CO2 reduction to reduce CO2 emissions. At present, the development of efficient photocatalysts is imminent. MXene as new two-dimensional (2D) layered material, has been used in various fields in recent years. Based on its high conductivity, adjustable band gap structure and sizable specific surface area, the MXene is beneficial to hasten the separation and reduce the combination of photoelectron-hole pairs in photocatalysis. Nevertheless, the re-stacking of layers because of the strong van der Waals force and hydrogen bonding interactions seriously hinder the development of MXene material as photocatalysts. By contrast, the MXene-based heterostructures composed of MXene nanosheets and other materials not only effectively suppress the re-stacking of layers, but also show the superior synergistic effects in photocatalysis. Herein, the recent progress of the MXene-based heterostructures as photocatalysts in energy and environment fields is summarized in this review. Particularly, new synthetic strategies, morphologies, structures, and mechanisms of MXene-based heterostructures are highlighted in hydrogen production, CO2 reduction, and pollutant degradation. In addition, the structure-activity relationship between the synthesis strategy, components, morphology and structure of MXene-based heterostructures, and their photocatalytic properties are elaborated in detail. Finally, a summary and the perspectives on improving the application study of the heterostructures in photocatalysis are presented. © 2023

关键词： Photocatalytic activity

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Microbial of low-concentration antisite defects LiFePO4 for boosting lithium ion capture

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Nano Today 2025年 64卷

作者： Yuan, Jianhua Gu, Xiaosong Zhilin, Xing Zhang, Xiaochen Cao, Jianglin Chen, Hong Yu, Fei Ma, Jie Research Center for Environmental Functional Materials College of Environmental Science and Engineering Tongji University 1239 Siping Roa Shanghai200092 China Water Resources and Water Environment Engineering Technology Center Xinjiang Key Laboratory of Engineering Materials and Structural Safety School of Civil Engineering Kashi University Kashi844000 China Shenzhen Key Laboratory of Interfacial Science and Engineering of Materials State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control Guangdong Provincial Key Laboratory of Soil and Groundwater Pollution Control School of Environmental Science and Engineering Southern University of Science and Technology Shenzhen518055 China College of Oceanography and Ecological Science Shanghai Ocean University Shanghai201306 China

LiFePO4 (LFP) is recognized as a promising electrode material for capturing lithium ions (Li+);however, the presence of antisite defects in LFP crystals blocks the one-dimensional (1D) diffusion channel along the b-axis, thereby slowing down Li+ diffusion and compromising cycling stability. Herein, we propose a "microbial enzyme-catalyzed" green synthesis strategy, successfully preparing polymorphic LFPs characterized by a low concentration of antisite defects (+ diffusion and electron transport to effectively improve kinetics. Additionally, the carbon shell derived from polyphosphorus bacteria improved electrical conductivity and provided space to accommodate the volume expansion of LFP, ultimately enhancing cycling stability. Consequently, LFP@C-7 exhibited excellent Li+ capture capacity (3.05 mmol g−1) and rate (1.01 mmol g−1min−1), with a high separation factor of 212 (Mg/Li ratio of 60) and excellent cycling stability (capacity retention exceeding 83.3 % over 100 cycles). Electrochemical quartz crystal microbalance (EQCM-D) analysis confirmed the three-stage reaction of hydration, ion exchange, and desorption during Li+ capture by LFP@C-7. Furthermore, in-situ X-ray diffraction (XRD) demonstrated a reversible phase transition mechanism (FePO4→LiFePO4→FePO4) throughout the Li+ insertion and extraction cycles. This study provides a new feasible solution for the green synthesis of low-concentration antisite defects LFP. © 2025

关键词： Green Synthesis

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Design of an Intelligent High-Temperature Infrared Temperature Measurement System 3

Design of an Intelligent High-Temperature Infrared Temperatu...

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2023 3rd International Conference on Artificial Intelligence and Industrial technology Applications, AIITA 2023

作者： Jiang, Han Chen, Cai Wu, Conglin Ren, Zhi Yang, Jintang Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan University of Science and Technology Wuhan430081 China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan430081 China Precision Manufacturing Institute Wuhan University of Science and Technology Wuhan430081 China Wuhan Iron & Steel Group Echeng Iron & Steel Co. Ltd. Steelmaking Plant E'zhou43600 China

The system is designed to address the problem of untimely transmission of temperature measurement signals in the sampling process at the LF refinery location, as well as the subjective involvement of the operator in the traditional temperature measurement method, i.e. the technology and experience of the operation, which results in a certain systematic error in the temperature measurement results. In this paper, a steel plant is used as an experimental object to test the performance of the device and the practicality of the system in combination with the on-site operating environment;and the system differs from the traditional contact temperature measurement by visualizing the steel image and generating an intelligent temperature profile while measuring the temperature with the help of an infrared camera. The experimental results show that the system can be remotely controlled to achieve intelligent temperature measurement, and the percentage of temperature measurement error is controlled within 1.5%, which is of practical significance to the automation and intelligence of the LF refining furnace temperature measurement in the iron and steel metallurgical industry. © Published under licence by IOP Publishing Ltd.

关键词： Temperature measurement

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Lidar-Inertial and Downward Vision Based Mobile Robot SLAM in Digital Workshops 13

Lidar-Inertial and Downward Vision Based Mobile Robot SLAM i...

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13th IEEE Data Driven control and Learning Systems Conference, DDCLS 2024

作者： Zhang, Yanchao Geng, Hao Liu, Bin Zhang, Yinlong Yuan, Xudong Zhang, Sichao School of Information Science and Engineering Shenyang University of Technology Shenyang110020 China Guangzhou Institute of Industrial Intelligence Guangzhou511458 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China

ISBN: (纸本)9798350361674

In order to solve the problems of long-term cumulative error and the absence of absolute pose in the existing Lidar-Inertial Measurement Unit (IMU) fusion methods for Automated Guided Vehicle (AGV) Simultaneous Localization and Mapping (SLAM), we propose a new SLAM method for mobile robot, integrating Lidar, IMU, and a downward vision camera. Firstly, we tightly couple Lidar-IMU and downward vision camera using a multimodal fusion framework. Downward visual measurements are integrated as QR factors into the factor graph, providing absolute pose information to correct cumulative error in the Lidar-IMU system and enable the joint pose estimation of the AGV. Secondly, an enhanced Iterative Closest Point (ICP) point cloud matching algorithm has been developed to correct the incorrect pose. This approach achieves Scan-to-Map alignment by employing a coarse-to-fine matching strategy, thereby enhancing the accuracy of point cloud alignment. Finally, the extended Kalman filter is used to optimize the Lidar pose drift, as well as the Lidar mapping effectively by using the Lidar-IMU as the measurements and the downward visual measurements as the updates. The algorithm has been extensively evaluated on the developed AGV experimental platform. The experimental results show that the position error is less than 0.02 meters and the orientation error is less than 1°, which solves the problems of long-term error accumulation and the lack of absolute pose information for AGV navigation. © 2024 IEEE.

关键词： Extended Kalman filters

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Enhancing Scheduling Efficiency of Steel Logistics Parks by Optimized Storage Distribution

Enhancing Scheduling Efficiency of Steel Logistics Parks by ...

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2023 China Automation Congress, CAC 2023

作者： Xu, Zhibin Zheng, Song Wang, Jinlong Xu, Zhezhuang Chen, Boyu Zhang, Qingdong College of Electrical Engineering and Automation Fuzhou University Key Laboratory of Industrial Automation Control Technology and Information Processing Education Department of Fujian Province Fuzhou350000 China Sansteel Minguang Co. Ltd. Sanming353000 China

ISBN: (纸本)9798350303759

The efficient management of storage has become a critical determinant of operational efficiency due to the growing and diverse demand for steel logistics parks. However, the current storage methods consistently result in significant imbalances in the loading of steel products and operating times across multiple yards. To address this challenge, we propose optimizing the distribution of steel products based on demand levels and retrieval efficiency. Firstly, we present an optimization problem that employs demand-driven storage principles to formulate the scheduling of steel product storage. Then, we introduce a novel algorithm called the Adaptive Preferred Evolutionary Heuristic (APEH) to tackle this problem. The results from our experiments indicate that this model effectively enhances the storage structure, and the proposed algorithm consistently yields optimal solutions within a reasonable timeframe. © 2023 IEEE.

关键词： Optimization

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Predicting the State of Crane Spreader for Steel Bar Delivery Based on Machine Learning 13

Predicting the State of Crane Spreader for Steel Bar Deliver...

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13th IEEE International Conference on CYBER technology in Automation, control, and Intelligent Systems, CYBER 2023

作者： Liu, Zibin Xu, Zhezhuang Chen, Jian Cui, Chen Chen, Boyu Zhang, Qingdong College of Electrical Engineering and Automation Key Laboratory of Industrial Automation Control Technology and Information Processing Education Fuzhou University Department of Fujian Province Fuzhou350000 China Sansteel Minguang Co. Ltd Fujian Sanming353000 China

ISBN: (纸本)9798350315196

Deducing the twisting pattern of spreader is very important for crane operators in steel warehouse, since crane spreaders have to be rotated repeatedly at steel bar transport sites. To improve the efficiency of the steel bar delivery, the state prediction for crane spreaders is necessary. In order to fulfill this requirement, the HTSK-LN-ReLU model prediction method based on Optuna optimization is applied in this paper. We deploy the IMU on the spreader surface, and then obtain the state data during the operation of the crane spreader by wireless transmission. The data are first denoised through fourier filtering, and then the Optuna framework is used to optimize the HTSK-LN-ReLU model's hyperparameters. The evaluation index of mean square error is used for model evaluation, and finally the optimal hyperparameter combination of the model is selected from 200 comparison experiments. Experimental results demonstrate superior performance of the HTSK-LN-ReLU model in predicting the state of crane spreader for steel bar delivery. © 2023 IEEE.

关键词： Forecasting

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Fully Distributed Economic Dispatch for Cyber-physical Power System with Time Delays and Channel Noises

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Journal of Modern Power Systems and Clean Energy 2022年第6期10卷 1472-1481页

作者： Yuhang Zhang Ming Ni Yonghui Sun College of Energy and Electrical Engineering Hohai UniversityNanjing 210098China NARI Group Corporation(State Grid Electric Power Research Institute) Nanjing 211106China NARI Technology Co. Ltd.Nanjing 211106China State Key Laboratory of Smart Grid Protection and Control Nanjing 211106China

Economic dispatch problem(EDP)is a fundamental optimization problem in power system operation,which aims at minimizing the total generation *** fact,the power grid is becoming a cyber-physical power system(CPPS).Therefore,the quality of communication is a key *** this paper,considering two important factors,i.e.,time delays and channel noises,a fully distributed consensus based algorithm is proposed for solving *** critical maximum allowable upper bounds of heterogeneous communication delays and self-delays are *** should be pointed out that the proposed algorithm can be robust against the time-varying delays and channel noises considering generator *** addition,even with time-varying delays and channel noises,the power balance of supply and demand is not broken during the *** simulation studies are presented to validate the correctness and superiority of the developed results.

关键词： Distributed economic dispatch consensus-based algorithm cyber-physical power system time delay channel noises

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Research on Multi-dimensional Bilingual Teaching Model of Computer Courses Supported by Artificial Intelligence 17th

Research on Multi-dimensional Bilingual Teaching Model of Co...

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17th International Conference on Computer Science and Education, ICCSE 2022

作者： Zhao, Changhua Mi, Chunqiao Tangbo School of Computer Science and Engineering Huaihua University Hunan Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Hunan Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Hunan Huaihua418000 China

ISBN: (纸本)9789819924486

Aiming at the problems of the integration of computer course teaching with international teaching and engineering certification, this study designs a multi-dimensional teaching mode of computer courses through the support of Artificial Intelligence. It analyzes the teaching resources, learning services, teaching process and teaching means involved in computer course teaching. Taking the course of "Computer Introduction" as an example, this study explores the multi-dimensional bilingual teaching mode supported by Artificial Intelligence, trains computer talents who meet the ability requirements of the era of Artificial Intelligence, and provides ideas for the teaching innovation of other courses. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Artificial intelligence

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