Reconstructing limb function represents a shared goal between researchers and amputees.However,the development of human-machine interfaces for decoding multi-degree-of-freedom(multi-DoF)movements remains challenging d...Reconstructing limb function represents a shared goal between researchers and amputees.However,the development of human-machine interfaces for decoding multi-degree-of-freedom(multi-DoF)movements remains challenging due to muscle crosstalk,co-activation,and incomplete extraction of motor unit(MU)activities in surface electromyography(sEMG)signals.To address these issues,this study proposes an enhanced neural-driven musculoskeletal model(MM)by integrating MU classification into the decoding process.Six sequential two-DoF movement tasks were designed and a classification framework containing eight task-specific separation matrices was established based on the selective activation of the MUs.The interference between multi-DoF movements was significantly reduced by refining the separation matrices,which effectively removed the MUs co-activated by multiple DoFs.The refined separation matrices were used to derive neural drives,which were subsequently integrated into the proposed four-DoF MM,and the accuracy loss resulting from reduced MU counts was compensated through the iterative optimization of physiological parameters.The proposed method was evaluated by an offline experiment involving 13 participants,and then compared with both classical neural-driven and non-negative matrix factorization(NMF)-driven MMs.Results demonstrated significant improvements in both correlation coefficient and normalized root mean square error,especially in complex four-DoF movement tasks.This study offers a novel and biologically grounded decoding strategy that enhances multi-DoF movement prediction and provides a promising direction for advanced prosthetic control.展开更多
This paper introduces the high-speed electrical multiple unit （EMO） life cycle, including the design, manufacturing, testing, and maintenance stages. It also presents the train control and monitoring system （TCMS）...This paper introduces the high-speed electrical multiple unit （EMO） life cycle, including the design, manufacturing, testing, and maintenance stages. It also presents the train control and monitoring system （TCMS） software development platform, the TCMS testing and verification bench, the EMU driving simulation platform, and the EMU remote data transmittal and maintenance platform. All these platforms and benches combined together make up the EMU life cycle cost （LCC） system. Each platform facilitates EMU LCC management and is an important part of the system.展开更多
The wheel-rail adhesion control for regenerative braking systems of high speed electric multiple unit trains is crucial to maintaining the stability,improving the adhesion utilization,and achieving deep energy recover...The wheel-rail adhesion control for regenerative braking systems of high speed electric multiple unit trains is crucial to maintaining the stability,improving the adhesion utilization,and achieving deep energy recovery.There remain technical challenges mainly because of the nonlinear,uncertain,and varying features of wheel-rail contact conditions.This research analyzes the torque transmitting behavior during regenerative braking,and proposes a novel methodology to detect the wheel-rail adhesion stability.Then,applications to the wheel slip prevention during braking are investigated,and the optimal slip ratio control scheme is proposed,which is based on a novel optimal reference generation of the slip ratio and a robust sliding mode control.The proposed methodology achieves the optimal braking performancewithoutthewheel-railcontactinformation.Numerical simulation results for uncertain slippery rails verify the effectiveness of the proposed methodology.展开更多
基金supported by the National Key R&D Program of China(Grant No.2022YFB4700801)the National Natural Science Foundation of China(Grant No.52525504)the Emerging Frontiers Cultivation Program of Tianjin University Interdisciplinary Center。
文摘Reconstructing limb function represents a shared goal between researchers and amputees.However,the development of human-machine interfaces for decoding multi-degree-of-freedom(multi-DoF)movements remains challenging due to muscle crosstalk,co-activation,and incomplete extraction of motor unit(MU)activities in surface electromyography(sEMG)signals.To address these issues,this study proposes an enhanced neural-driven musculoskeletal model(MM)by integrating MU classification into the decoding process.Six sequential two-DoF movement tasks were designed and a classification framework containing eight task-specific separation matrices was established based on the selective activation of the MUs.The interference between multi-DoF movements was significantly reduced by refining the separation matrices,which effectively removed the MUs co-activated by multiple DoFs.The refined separation matrices were used to derive neural drives,which were subsequently integrated into the proposed four-DoF MM,and the accuracy loss resulting from reduced MU counts was compensated through the iterative optimization of physiological parameters.The proposed method was evaluated by an offline experiment involving 13 participants,and then compared with both classical neural-driven and non-negative matrix factorization(NMF)-driven MMs.Results demonstrated significant improvements in both correlation coefficient and normalized root mean square error,especially in complex four-DoF movement tasks.This study offers a novel and biologically grounded decoding strategy that enhances multi-DoF movement prediction and provides a promising direction for advanced prosthetic control.
文摘This paper introduces the high-speed electrical multiple unit （EMO） life cycle, including the design, manufacturing, testing, and maintenance stages. It also presents the train control and monitoring system （TCMS） software development platform, the TCMS testing and verification bench, the EMU driving simulation platform, and the EMU remote data transmittal and maintenance platform. All these platforms and benches combined together make up the EMU life cycle cost （LCC） system. Each platform facilitates EMU LCC management and is an important part of the system.
基金supported by the National Natural Science Foundation of China(Grant 51305437)Guangdong Innovative Research Team Program of China(Grant201001D0104648280)
文摘The wheel-rail adhesion control for regenerative braking systems of high speed electric multiple unit trains is crucial to maintaining the stability,improving the adhesion utilization,and achieving deep energy recovery.There remain technical challenges mainly because of the nonlinear,uncertain,and varying features of wheel-rail contact conditions.This research analyzes the torque transmitting behavior during regenerative braking,and proposes a novel methodology to detect the wheel-rail adhesion stability.Then,applications to the wheel slip prevention during braking are investigated,and the optimal slip ratio control scheme is proposed,which is based on a novel optimal reference generation of the slip ratio and a robust sliding mode control.The proposed methodology achieves the optimal braking performancewithoutthewheel-railcontactinformation.Numerical simulation results for uncertain slippery rails verify the effectiveness of the proposed methodology.
