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Altering the Residual Stress in High-Carbon Steel through Promoted Dislocation Movement and Accelerated Carbon Diffusion by Pulsed Electric Current

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Acta Metallurgica Sinica(English Letters) 2023年第9期36卷 1511-1522页

作者： Kun Yi Siqi Xiang Mengcheng Zhou Xinfang Zhang Furui Du School of Metallurgical and Ecological Engineering University of Science and Technology BeijingBeijing100083China State Key Laboratory of Automatic Control Technology for Mining and Metallurgy Process Beijing102628China

Residual stress in high-carbon steel affects the dimensional accuracy, structural stability, and integrity of components. Although the evolution of residual stress under an electric field has received extensive attention, its elimination mechanism has not been fully clarified. In this study, it was found that the residual stress of high-carbon steel could be effectively relieved within a few minutes through the application of a low density pulse current. The difference between the current pulse treatment and traditional heat treatment in reducing residual stress is that the electric pulse provides additional Gibbs free energy for the system, which promotes dislocation annihilation and carbon atom diffusion to form carbides, thus reducing the free energy of the system. The electroplastic and thermal effects of the pulse current promoted the movement of dislocations under the electric field, thus eliminating the internal stress caused by dislocation entanglement. The precipitation of carbides reduced the carbon content of the steel matrix and lattice shrinkage, thereby reducing the residual tensile stress. Considering that a pulsed current has the advantages of small size, small power requirement, continuous output, and continuously controllable parameters, it has broad application prospects for eliminating residual stress.

关键词： Residual stress Pulsed electric current Carbide precipitation

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RIS-assisted Channel Prediction Model for Mine V2X Systems 5

RIS-assisted Channel Prediction Model for Mine V2X Systems

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5th International Conference on Computer, Big Data and Artificial Intelligence, ICCBD+AI 2024

作者： Cheng, Yuanxun Hu, Qingsong Luo, Yujia Zhang, Yuansheng China University of Mining and Technology School of Information and Control Engineering Xuzhou China Beijing Key Laboratory of Process Automation in Mining & Metallurgy State Key Laboratory of Intelligent Optimized Manufacturing in Mining & Metallurgy Process Beijing China

ISBN: (纸本)9798331533991

The complex and dynamic environment of mines leads to time-variability and uncertainty in channel propagation, which affects the stability and reliability of Vehicle-to-Everything (V2X)communication in mine networks. Channel prediction can help mitigate these issues. Introducing reconfigurable intelligence surface (RIS) into the mine V2X system can enhance signal coverage and quality in the complex mine environment. To address the issue of long-term error accumulation in existing channel prediction models, this paper analyzes the prediction error of channel state information (CSI) in mine V2X systems. It was observed that the error distribution exhibits characteristics of an approximate normal distribution, revealing its predictability. Utilizing this characteristic, this paper proposes an error correction strategy to suppress long-term error accumulation during the prediction process. Furthermore, an adaptive method for optimizing RIS phases is designed based on the Snow Ablation Optimizer. Specifically, this paper utilizes a metaheuristic optimization algorithm and deep learning techniques to propose a mine V2X channel prediction model that incorporates the error correction strategy. Experimental results show that optimizing the RIS phase can effectively enhance channel gain, and the mine V2X channel prediction model incorporating the error correction strategy can effectively suppress cumulative errors during the prediction process, thereby improving prediction accuracy. © 2024 IEEE.

关键词： Normal distribution

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RIS-Aided Localization Method Based on DNN-SSA Fusion in NLOS Scenarios 5

RIS-Aided Localization Method Based on DNN-SSA Fusion in NLO...

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5th International Conference on Computer, Big Data and Artificial Intelligence, ICCBD+AI 2024

作者： Luo, Yujia Hu, Qingsong Cheng, Yuanxun Yuan, Shuya Zhang, Yuansheng China University of Mining and Technology School of Information and Control Engineering Xuzhou China State Key Laboratory of Intelligent Optimized Manufacturing in Mining & Metallurgy Process Beijing China Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing China

ISBN: (纸本)9798331533991

With the advent of the 6G era, precise positioning and reliable, energy-efficient data transmission have become critical for the Internet of Things. However, high-frequency signals like millimeter-wave and terahertz are easily obstructed, making traditional LOS-based methods ineffective in complex environments. To overcome this, NLOS localization methods and Reconfigurable Intelligent Surfaces offer promising solutions by dynamically modifying the wireless environment. This paper introduces a Direct Position Determination method that integrates Deep Neural Networks (DNN) with the Sparrow Search Algorithm (SSA) to tackle the challenges of high computational complexity and low localization accuracy in RIS-aided systems. An NLOS scenario with RIS is established, and a far-field channel model is derived. The method uses DNN to model the nonlinear relationship between the likelihood function and user position, and then employs SSA to optimize the DNN-based fitness function, efficiently solving the objective function. Experimental results show that the DNN-SSA method consistently achieves high localization accuracy across different scenarios, even at low SNR levels, demonstrating its effectiveness and potential for 6G high-precision localization. © 2024 IEEE.

关键词： Deep neural networks

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Rapid prediction of flow and concentration fields in solid-liquid suspensions of slurry electrolysis tanks

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International Journal of Minerals,metallurgy and Materials 2024年第9期31卷 2006-2016页

作者： Tingting Lu Kang Li Hongliang Zhao Wei Wang Zhenhao Zhou Xiaoyi Cai Fengqin Liu State Key Laboratory of Advanced Metallurgy University of Science and Technology BeijingBeijing 100083China State Key Laboratory of Intelligent Optimized Manufacturing in Mining&Metallurgy Process Beijing 102628China School of Materials Science and Engineering Lanzhou University of TechnologyLanzhou 730050China School of Metallurgical and Ecological Engineering University of Science and Technology BeijingBeijing 100083China

Slurry electrolysis(SE),as a hydrometallurgical process,has the characteristic of a multitank series connection,which leads to various stirring conditions and a complex solid suspension *** computational fluid dynamics(CFD),which requires high computing resources,and a combination with machine learning was proposed to construct a rapid prediction model for the liquid flow and solid concentration fields in a SE *** scientific selection of calculation samples via orthogonal experiments,a comprehensive dataset covering a wide range of conditions was established while effectively reducing the number of simulations and providing reasonable weights for each ***,a prediction model of the SE tank was constructed using the K-nearest neighbor *** results show that with the increase in levels of orthogonal experiments,the prediction accuracy of the model improved *** model established with four factors and nine levels can accurately predict the flow and concentration fields,and the regression coefficients of average velocity and solid concentration were 0.926 and 0.937,*** with traditional CFD,the response time of field information prediction in this model was reduced from 75 h to 20 s,which solves the problem of serious lag in CFD applied alone to actual production and meets real-time production control requirements.

关键词： slurry electrolysis solid-liquid suspension computational fluid dynamics K-nearest neighbor algorithm rapid prediction

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Semantic Segmentation Research of Motion Blurred Images by Event Camera 7

Semantic Segmentation Research of Motion Blurred Images by E...

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7th CAA International Conference on Vehicular control and Intelligence, CVCI 2023

作者： Liu, Xiangyong Yin, Fengfeng Jiang, Wei Fan, Siwen College of Automotive Studies Tongji University State Key Laboratory of Process Automation in Mining & Metallurgy Beijing Key Laboratory of Process Automation in Mining & Metallurgy Bgrimm Technology Group Department of Electromechanical Engineering Shanghai China State Key Laboratory of Process Automation in Mining & Metallurgy Beijing Key Laboratory of Process Automation in Mining & Metallurgy Bgrimm Technology Group Beijing China Linyi Engineering Branch China Railway Jinan Group Co Ltd Linyi China School of Mechatronical Engineering Shanghai China

ISBN: (纸本)9798350340488

The semantic segmentation of road objects is a prerequisite for autonomous driving. Rapid or bumpy movement of vehicles can lead to the images to be blurred, which reduces the safety of autonomous driving. This study combines the dynamic visual sensor (DVS) and RGB camera to improve the detection accuracy of road semantic segmentation. Firstly, based on DVS and RGB images, we develop two datasets of blurred images, including the stone and run datasets. Then, a double channels' detection network is designed with several Swin-transformer modules, and the two channels are designed to extract features from DVS and RGB images, respectively. In addition, a feature fusion module is designed to extract attention from DVS images. Finally, experiments demonstrate that our proposed double-channels' fusion method achieves the state-of-the-art segmentation performance for blurred images. © 2023 IEEE.

关键词： blurred images double channels' network dynamic visual sensors feature fusion semantic segmentation

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Stochastic configuration networks with robust supervised least squares regression

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Neurocomputing 2025年 647卷

作者： Chu, Fei Sun, Zihang Wang, Yu Zhang, Yong School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China The State Key Laboratory of Process Automation in Mining and Metallurgy/Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing100160 China Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou221116 China

Stochastic Configuration Networks (SCNs) are widely used in regression modeling to fit data distributions due to their fast convergence and powerful learning capabilities. However, in practical industrial applications, labeled outliers in the dataset inevitably appear, which can destroy the internal structure and connection of the data, and not only seriously reduce the prediction accuracy of SCNs, but may also increase the risk of overfitting. To cope with this problem, this paper proposes a new algorithm - Robust Supervised Stochastic Configuration Networks (RSSCNs). In this paper, firstly, the loss function is constructed based on the concave function of the error, which extends the applicability of the robust supervised least squares regression. Second, robust supervised least squares regression is introduced into the SCN algorithm for the first time, which enhances the robustness of the model and reduces the risk of model overfitting. Finally, the inequality constraint of the SCN algorithm is reconstructed according to the loss function, which optimizes the parameter selection and accelerates the convergence of the model. In the experimental section, RSSCNs are applied to a function fitting experiment, several benchmark datasets, and an industrial dataset from a dense medium coal preparation process, and the performance is compared with other algorithms. The experimental results show that for these datasets, RSSCNs perform favorably in terms of accuracy and robustness, validating the effectiveness of the algorithm. © 2025

关键词： Linear regression

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RIS-assisted Channel Prediction Model for Mine V2X Systems

RIS-assisted Channel Prediction Model for Mine V2X Systems

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Computer, Big Data and Artificial Intelligence (ICCBD+AI), International Conference on

作者： Yuanxun Cheng Qingsong Hu Yujia Luo Yuansheng Zhang School of Information and Control Engineering China University of Mining and Technology Xuzhou China State Key Laboratory of Intelligent Optimized Manufacturing in Mining & Metallurgy Process Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing China

ISBN: (数字)9798331533991

ISBN: (纸本)9798331534004

关键词： Deep learning Adaptation models Accuracy Predictive models Reconfigurable intelligent surfaces Gaussian distribution Prediction algorithms Approximation algorithms Error correction Vehicle-to-everything

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RIS-Aided Localization Method Based on DNN-SSA Fusion in NLOS Scenarios

RIS-Aided Localization Method Based on DNN-SSA Fusion in NLO...

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Computer, Big Data and Artificial Intelligence (ICCBD+AI), International Conference on

作者： Yujia Luo Qingsong Hu Yuanxun Cheng Shuya Yuan Yuansheng Zhang School of Information and Control Engineering China University of Mining and Technology Xuzhou China State Key Laboratory of Intelligent Optimized Manufacturing in Mining & Metallurgy Process Beijing China Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing China

ISBN: (数字)9798331533991

ISBN: (纸本)9798331534004

关键词： Location awareness 6G mobile communication Wireless communication Accuracy Simulation Search methods Artificial neural networks Reconfigurable intelligent surfaces Nonlinear optics Signal to noise ratio

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The Prediction Model of SAG Mill Load Based on Industrial Big-data mining

The Prediction Model of SAG Mill Load Based on Industrial Bi...

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第43届中国控制会议

作者： Guicheng Wang Changyu Wang Zhicheng Bai Changliang Guan Min Zhang Xiaobin Li School of Electrical and Electronics Engineering Shanghai Institute of Technology State Key Laboratory of Intelligent Optimized Manufacturing in Mining & Metallurgy Process and Beijing Key Laboratory of Process Automation in Mining & Metallurgy

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Regarding the modeling problem of grinding process in mining,taking the operating load of semi-automatic grinding mill as the research object,starting from industrial big data analysis,using data mining methods to establish decision tree models and random forest models,and evaluating and predicting the constructed *** testing,decision tree models with different effects can be obtained,indicating that the decision tree model has a certain degree of flexibility,and selecting parameters can lead to a model with good *** addition,under the same conditions,a random forest model was constructed,and simulation results showed that the combination model constructed using the random forest algorithm was superior to a single decision tree model,with better prediction performance.

关键词： SAG mill data mining big-data analysis decision tree random forest

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Trend-Aware Patch Feature Fusion Transformer for Fault Diagnosis of Vibration Signals

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IEEE Sensors Journal 2025年

作者： Zhang, Ruicong Chu, Fei Qiu, Yazhi He, Dakuo Wang, Fuli China University of Mining and Technology School of Information and Control Engineering Xuzhou221000 China State Key Laboratory of Intelligent Optimized Manufacturing in Mining & Metallurgy Process Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing100160 China Northeastern University College of Information Science and Engineering State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Northeastern University College of Information Science and Engineering Shenyang110000 China

The Vision Transformer (ViT) has demonstrated reliable performance in industrial equipment fault diagnosis by capturing global features and long-range dependencies in vibration signals. Nonetheless, when processing vibration signals, ViT primarily focuses on the correlations between patches, while neglecting the trend variation information hidden between signal units. This limitation hinders the model’s ability to extract latent fault information. To address this issue, we propose a trend-aware patch feature fusion Transformer (TAPformer) for fault diagnosis of vibration signals. Specifically, we design both patch-level and signal-level trend-aware self-attention blocks and adopt a weighted approach to integrate coarse-grained patch-level trend features with fine-grained signal-level trend features at the output of each self-attention head. This approach enables the model to capture fault information hidden within trend variations, effectively enhancing its fault detection capabilities. TAPformer establishes optimal weighted fusion hyperparameters through ablation experiments and is tested on four public datasets. Compared to eight advanced models, TAPformer achieves the highest accuracy across all four datasets, demonstrating the effectiveness and generalization capability of the proposed method. © 2001-2012 IEEE.

关键词： Fault detection

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