The electromagnetic environment of laneways in underground coal mines is an important area for the design of new electronic products,as well as a fundamental space for mine monitoring,surveillance,communications and c...The electromagnetic environment of laneways in underground coal mines is an important area for the design of new electronic products,as well as a fundamental space for mine monitoring,surveillance,communications and control systems.An investigation of electromagnetic interference in coal mines is essential for the enhancement of performances of these systems.In this study,a new field method is provided in which radiated emission tests in coal mine laneways have been carried out.We conclude that:1) the wiring motor vehicles can radiate interference with a bandwidth up to 1 GHz and with an amplitude 10 dBμV/m higher than the background noise;2) the PHS(Personal Handy phone System) mobile communication system can cause interference 40 dBμV/m higher than the background noise;3) an interference 25 dBμV/m higher than the background noise can be generated during the communication at a working bandwidth of 48.8 MHz;and 4) power cables,battery vehicles as well as mechanical and electrical dong rooms have little effect on the electromagnetic radiation environment in coal mine tunnels.展开更多
Though the lithium-ion battery is universally applied,the reliability of lithium-ion batteries remains a challenge due to various physicochemical reactions,electrode material degradation,and even thermal runaway.Accur...Though the lithium-ion battery is universally applied,the reliability of lithium-ion batteries remains a challenge due to various physicochemical reactions,electrode material degradation,and even thermal runaway.Accurate estimation and prediction of battery health conditions are crucial for battery safety management.In this paper,an end-cloud collaboration method is proposed to approach the track of battery degradation process,integrating end-side empirical model with cloud-side data-driven model.Based on ensemble learning methods,the data-driven model is constructed by three base models to obtain cloud-side highly accurate results.The double exponential decay model is utilized as an empirical model to output highly real-time prediction results.With Kalman filter,the prediction results of end-side empirical model can be periodically updated by highly accurate results of cloud-side data-driven model to obtain highly accurate and real-time results.Subsequently,the whole framework can give an accurate prediction and tracking of battery degradation,with the mean absolute error maintained below 2%.And the execution time on the end side can reach 261μs.The proposed end-cloud collaboration method has the potential to approach highly accurate and highly real-time estimation for battery health conditions during battery full life cycle in architecture of cyber hierarchy and interactional network.展开更多
A modified tracking differentiator is proposed. Firstly, a nonlinear odd exponent continuous function is adopted which is only stable at one equilibrium point and proved the global asymptotic stability of the modified...A modified tracking differentiator is proposed. Firstly, a nonlinear odd exponent continuous function is adopted which is only stable at one equilibrium point and proved the global asymptotic stability of the modified tracking differentiator by select a Lyapunov function. Through combining of the nonlinear and linear function properly, it can be sure that the state converges to the equilibrium point with high speed automatically no matter that the state was far away from the equilibrium point or near to it, and it can prevent the chattering.?Simulation results show that the modified tracking differentiator tracking results?are?superior to the classical nonlinear tracking differentiator, and the response?of state variables tracking differentiator estimated?is?almost coincide with the real state of the variables of the given system.展开更多
It is well known that groove texture with a careful design can be used to enhance the load‐carrying capacity of oil film under the conditions of hydrodynamic lubrication.In this study,a general parametric model was d...It is well known that groove texture with a careful design can be used to enhance the load‐carrying capacity of oil film under the conditions of hydrodynamic lubrication.In this study,a general parametric model was developed,and agenetic algorithm‐sequential quadratic programming hybrid method was adopted to obtain the global‐optimum profile of the groove texture.The optimized profiles at different rotating speeds are all chevrons.The numerical analysis results verified the effect of the optimization.In addition to the numerical optimization,experiments were conducted to validate the superiority of the optimized results.The experimental results show that the optimized groove texture can efficiently reduce the coefficient of friction(COF)and the temperature rise of the specimen.In particular,the optimized groove textures can achieve stable ultra‐low COF values(COF<0.01)under certain conditions.展开更多
Industrial Control Systems(ICSs)are the lifeline of a country.Therefore,the anomaly detection of ICS traffic is an important endeavor.This paper proposes a model based on a deep residual Convolution Neural Network(CNN...Industrial Control Systems(ICSs)are the lifeline of a country.Therefore,the anomaly detection of ICS traffic is an important endeavor.This paper proposes a model based on a deep residual Convolution Neural Network(CNN)to prevent gradient explosion or gradient disappearance and guarantee accuracy.The developed methodology addresses two limitations:most traditional machine learning methods can only detect known network attacks and deep learning algorithms require a long time to train.The utilization of transfer learning under the modification of the existing residual CNN structure guarantees the detection of unknown attacks.One-dimensional ICS flow data are converted into two-dimensional grayscale images to take full advantage of the features of CNN.Results show that the proposed method achieves a high score and solves the time problem associated with deep learning model training.The model can give reliable predictions for unknown or differently distributed abnormal data through short-term training.Thus,the proposed model ensures the safety of ICSs and verifies the feasibility of transfer learning for ICS anomaly detection.展开更多
基金supported by the National Natural Science Foundation of China (No.50674093)the National Scientific and Technological Support Projects (No.2006BAK03B00) and the Pingdingshan Coal Mine Group
文摘The electromagnetic environment of laneways in underground coal mines is an important area for the design of new electronic products,as well as a fundamental space for mine monitoring,surveillance,communications and control systems.An investigation of electromagnetic interference in coal mines is essential for the enhancement of performances of these systems.In this study,a new field method is provided in which radiated emission tests in coal mine laneways have been carried out.We conclude that:1) the wiring motor vehicles can radiate interference with a bandwidth up to 1 GHz and with an amplitude 10 dBμV/m higher than the background noise;2) the PHS(Personal Handy phone System) mobile communication system can cause interference 40 dBμV/m higher than the background noise;3) an interference 25 dBμV/m higher than the background noise can be generated during the communication at a working bandwidth of 48.8 MHz;and 4) power cables,battery vehicles as well as mechanical and electrical dong rooms have little effect on the electromagnetic radiation environment in coal mine tunnels.
基金financially supported by the National Natural Science Foundation of China(No.52102470)。
文摘Though the lithium-ion battery is universally applied,the reliability of lithium-ion batteries remains a challenge due to various physicochemical reactions,electrode material degradation,and even thermal runaway.Accurate estimation and prediction of battery health conditions are crucial for battery safety management.In this paper,an end-cloud collaboration method is proposed to approach the track of battery degradation process,integrating end-side empirical model with cloud-side data-driven model.Based on ensemble learning methods,the data-driven model is constructed by three base models to obtain cloud-side highly accurate results.The double exponential decay model is utilized as an empirical model to output highly real-time prediction results.With Kalman filter,the prediction results of end-side empirical model can be periodically updated by highly accurate results of cloud-side data-driven model to obtain highly accurate and real-time results.Subsequently,the whole framework can give an accurate prediction and tracking of battery degradation,with the mean absolute error maintained below 2%.And the execution time on the end side can reach 261μs.The proposed end-cloud collaboration method has the potential to approach highly accurate and highly real-time estimation for battery health conditions during battery full life cycle in architecture of cyber hierarchy and interactional network.
文摘A modified tracking differentiator is proposed. Firstly, a nonlinear odd exponent continuous function is adopted which is only stable at one equilibrium point and proved the global asymptotic stability of the modified tracking differentiator by select a Lyapunov function. Through combining of the nonlinear and linear function properly, it can be sure that the state converges to the equilibrium point with high speed automatically no matter that the state was far away from the equilibrium point or near to it, and it can prevent the chattering.?Simulation results show that the modified tracking differentiator tracking results?are?superior to the classical nonlinear tracking differentiator, and the response?of state variables tracking differentiator estimated?is?almost coincide with the real state of the variables of the given system.
文摘It is well known that groove texture with a careful design can be used to enhance the load‐carrying capacity of oil film under the conditions of hydrodynamic lubrication.In this study,a general parametric model was developed,and agenetic algorithm‐sequential quadratic programming hybrid method was adopted to obtain the global‐optimum profile of the groove texture.The optimized profiles at different rotating speeds are all chevrons.The numerical analysis results verified the effect of the optimization.In addition to the numerical optimization,experiments were conducted to validate the superiority of the optimized results.The experimental results show that the optimized groove texture can efficiently reduce the coefficient of friction(COF)and the temperature rise of the specimen.In particular,the optimized groove textures can achieve stable ultra‐low COF values(COF<0.01)under certain conditions.
基金supported in part by 2018 industrial Internet innovation and development project“Construction of Industrial Internet Security Standard System and Test and Verification Environment”in part by the National Industrial Internet Security Public Service Platform+2 种基金in part by the Fundamental Research Funds for the Central Universities(Nos.FRF-BD-19-012A and FRFTP-19-005A3)in part by the National Natural Science Foundation of China(Nos.81961138010,U1736117,and U1836106)in part by the Technological Innovation Foundation of Shunde Graduate School,University of Science and Technology Beijing(No.BK19BF006)。
文摘Industrial Control Systems(ICSs)are the lifeline of a country.Therefore,the anomaly detection of ICS traffic is an important endeavor.This paper proposes a model based on a deep residual Convolution Neural Network(CNN)to prevent gradient explosion or gradient disappearance and guarantee accuracy.The developed methodology addresses two limitations:most traditional machine learning methods can only detect known network attacks and deep learning algorithms require a long time to train.The utilization of transfer learning under the modification of the existing residual CNN structure guarantees the detection of unknown attacks.One-dimensional ICS flow data are converted into two-dimensional grayscale images to take full advantage of the features of CNN.Results show that the proposed method achieves a high score and solves the time problem associated with deep learning model training.The model can give reliable predictions for unknown or differently distributed abnormal data through short-term training.Thus,the proposed model ensures the safety of ICSs and verifies the feasibility of transfer learning for ICS anomaly detection.
