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 attent...
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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.
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. ...
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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 a...
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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 dynamic...
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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.
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 ...
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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. ...
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ISBN:
(数字)9798331533991
ISBN:
(纸本)9798331534004
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.
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 a...
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ISBN:
(数字)9798331533991
ISBN:
(纸本)9798331534004
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.
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 est...
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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.
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 vi...
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The hazardous and complex nature of underground mining imposes stringent safety requirements on autonomous driving. T-shaped roadways, common in such environments, present significant challenges for non-line-of-sight ...
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