Operational transfer path analysis(OTPA)is an advanced vibration and noise transfer path identification and contribution evaluation method.However,the application of OTPA to rail transit vehicles considers only the ex...Operational transfer path analysis(OTPA)is an advanced vibration and noise transfer path identification and contribution evaluation method.However,the application of OTPA to rail transit vehicles considers only the excitation amplitude and ignores the influence of the excitation phase.This study considers the influence of the excitation amplitude and phase,and analyzes the contribution of the secondary suspension path to the floor vibration when the metro vehicle runs at 60 km/h,using an analysis based on the OTPA method.The results show that the vertical direction of the anti-rolling torsion bar area provides the maximum contribution to the floor vibration,with a contribution of 22.1%,followed by the longitudinal vibration of the air spring area,with a contribution of 17.1%.Based on the contribution analysis,a transfer path optimization scheme is proposed,which may provide a reference for the optimization of the transfer path of metro vehicles in the future.展开更多
Recently revealed beam stealing attacks could greatly threaten the security and privacy of IEEE 802.11ad communications.The premise to restore normal network service is detecting and locating beam stealing attackers w...Recently revealed beam stealing attacks could greatly threaten the security and privacy of IEEE 802.11ad communications.The premise to restore normal network service is detecting and locating beam stealing attackers without their cooperation.Current consistency-based methods are only valid for one single attacker and are parametersensitive.From the viewpoint of image processing,this paper proposes an algorithm to jointly detect and locate multiple beam stealing attackers based on RSSI(Received Signal Strength Indicator)map without the training process involved in deep learning-based solutions.Firstly,an RSSI map is constructed based on interpolating the raw RSSI data for enabling high-resolution localization while reducing monitoring cost.Secondly,three image processing steps,including edge detection and segmentation,are conducted on the constructed RSSI map to detect and locate multiple attackers without any prior knowledge about the attackers.To evaluate our proposal’s performance,a series of experiments are conducted based on the collected data.Experimental results have shown that in typical parameter settings,our algorithm’s positioning error does not exceed 0.41 m with a detection rate no less than 91%.展开更多
基金Supported by National Natural Science Foundation of China(Grant Nos.U1934203,U1734201)Sichuan Science and Technology Program(Grant No.2020YJ0254)Fundamental Research Funds for the State Key Laboratory of Traction Power(Grant No.2019-Q02).
文摘Operational transfer path analysis(OTPA)is an advanced vibration and noise transfer path identification and contribution evaluation method.However,the application of OTPA to rail transit vehicles considers only the excitation amplitude and ignores the influence of the excitation phase.This study considers the influence of the excitation amplitude and phase,and analyzes the contribution of the secondary suspension path to the floor vibration when the metro vehicle runs at 60 km/h,using an analysis based on the OTPA method.The results show that the vertical direction of the anti-rolling torsion bar area provides the maximum contribution to the floor vibration,with a contribution of 22.1%,followed by the longitudinal vibration of the air spring area,with a contribution of 17.1%.Based on the contribution analysis,a transfer path optimization scheme is proposed,which may provide a reference for the optimization of the transfer path of metro vehicles in the future.
基金This work was supported in part by the National Natural Science Foundation of China(Grant No.61671471)。
文摘Recently revealed beam stealing attacks could greatly threaten the security and privacy of IEEE 802.11ad communications.The premise to restore normal network service is detecting and locating beam stealing attackers without their cooperation.Current consistency-based methods are only valid for one single attacker and are parametersensitive.From the viewpoint of image processing,this paper proposes an algorithm to jointly detect and locate multiple beam stealing attackers based on RSSI(Received Signal Strength Indicator)map without the training process involved in deep learning-based solutions.Firstly,an RSSI map is constructed based on interpolating the raw RSSI data for enabling high-resolution localization while reducing monitoring cost.Secondly,three image processing steps,including edge detection and segmentation,are conducted on the constructed RSSI map to detect and locate multiple attackers without any prior knowledge about the attackers.To evaluate our proposal’s performance,a series of experiments are conducted based on the collected data.Experimental results have shown that in typical parameter settings,our algorithm’s positioning error does not exceed 0.41 m with a detection rate no less than 91%.
