The Experts below are selected from a list of 84 Experts worldwide ranked by ideXlab platform

Jung Woo Ryu - One of the best experts on this subject based on the ideXlab platform.

  • Adaptively Trained Artificial Neural Network Identification of Left Ventricular Assist Device
    1996
    Co-Authors: Sang-hyun Kim, Hun Mo Kim, Jung Woo Ryu
    Abstract:

    This paper presents a Neural Network Identification(NNI) method for modeling of highly complicated nonLinear and time varing human system with a Pneumatically driven mock circulatory system of Left Ventricular Assist Device(LVAD). This system consists of electronic circuits and Pneumatic driving circuits. The initiation of systole and the pumping duration can be determined by the computer program. The Line pressure from a pressure transducer inserted in the Pneumatic Line was recorded System modeling is completed using the adaptively trained backpropagation learning algorithms with input variables, heart rate(HR), systole-diastole rate(SDR), which can vary state of system. Output parameters are preload, afterload which indicate the systemic dynamic characteristics. Consequently, the neural network shows good approximation of nonLinearity, and characteristics of left Ventricular Assist Device. Our results show that the neural network leads to a significant improvement in the modeling of highly nonLinear Left Ventricular Assist Device.

  • Adaptively trained artificial neural network identification of left ventricular assist device
    Proceedings of 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 1
    Co-Authors: Hun Mo Kim, Sang-hyun Kim, Jung Woo Ryu
    Abstract:

    Presents a Neural Network Identification (NNI) method for modeling of the highly complicated nonLinear and time varying human system with a Pneumatically driven mock circulation system and Left Ventricular Assist Device (LVAD). This system consists of electronic circuits and Pneumatic driving circuits. The initiation of systole and the pumping duration can be determined by a computer program. The Line pressure from a pressure transducer inserted in the Pneumatic Line was recorded. System modeling is completed using the adaptively trained backpropagation learning algorithms with input variables, Heart Rate (HR), Systole-Diastole Rate (SDR), which can vary the state of the system, and preload and afterload, which indicate the systemic dynamic characteristics, and output parameters are preload and afterload.

Hun Mo Kim - One of the best experts on this subject based on the ideXlab platform.

  • Adaptively Trained Artificial Neural Network Identification of Left Ventricular Assist Device
    1996
    Co-Authors: Sang-hyun Kim, Hun Mo Kim, Jung Woo Ryu
    Abstract:

    This paper presents a Neural Network Identification(NNI) method for modeling of highly complicated nonLinear and time varing human system with a Pneumatically driven mock circulatory system of Left Ventricular Assist Device(LVAD). This system consists of electronic circuits and Pneumatic driving circuits. The initiation of systole and the pumping duration can be determined by the computer program. The Line pressure from a pressure transducer inserted in the Pneumatic Line was recorded System modeling is completed using the adaptively trained backpropagation learning algorithms with input variables, heart rate(HR), systole-diastole rate(SDR), which can vary state of system. Output parameters are preload, afterload which indicate the systemic dynamic characteristics. Consequently, the neural network shows good approximation of nonLinearity, and characteristics of left Ventricular Assist Device. Our results show that the neural network leads to a significant improvement in the modeling of highly nonLinear Left Ventricular Assist Device.

  • Adaptively trained artificial neural network identification of left ventricular assist device
    Proceedings of 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 1
    Co-Authors: Hun Mo Kim, Sang-hyun Kim, Jung Woo Ryu
    Abstract:

    Presents a Neural Network Identification (NNI) method for modeling of the highly complicated nonLinear and time varying human system with a Pneumatically driven mock circulation system and Left Ventricular Assist Device (LVAD). This system consists of electronic circuits and Pneumatic driving circuits. The initiation of systole and the pumping duration can be determined by a computer program. The Line pressure from a pressure transducer inserted in the Pneumatic Line was recorded. System modeling is completed using the adaptively trained backpropagation learning algorithms with input variables, Heart Rate (HR), Systole-Diastole Rate (SDR), which can vary the state of the system, and preload and afterload, which indicate the systemic dynamic characteristics, and output parameters are preload and afterload.

Sang-hyun Kim - One of the best experts on this subject based on the ideXlab platform.

  • Adaptively Trained Artificial Neural Network Identification of Left Ventricular Assist Device
    1996
    Co-Authors: Sang-hyun Kim, Hun Mo Kim, Jung Woo Ryu
    Abstract:

    This paper presents a Neural Network Identification(NNI) method for modeling of highly complicated nonLinear and time varing human system with a Pneumatically driven mock circulatory system of Left Ventricular Assist Device(LVAD). This system consists of electronic circuits and Pneumatic driving circuits. The initiation of systole and the pumping duration can be determined by the computer program. The Line pressure from a pressure transducer inserted in the Pneumatic Line was recorded System modeling is completed using the adaptively trained backpropagation learning algorithms with input variables, heart rate(HR), systole-diastole rate(SDR), which can vary state of system. Output parameters are preload, afterload which indicate the systemic dynamic characteristics. Consequently, the neural network shows good approximation of nonLinearity, and characteristics of left Ventricular Assist Device. Our results show that the neural network leads to a significant improvement in the modeling of highly nonLinear Left Ventricular Assist Device.

  • Adaptively trained artificial neural network identification of left ventricular assist device
    Proceedings of 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 1
    Co-Authors: Hun Mo Kim, Sang-hyun Kim, Jung Woo Ryu
    Abstract:

    Presents a Neural Network Identification (NNI) method for modeling of the highly complicated nonLinear and time varying human system with a Pneumatically driven mock circulation system and Left Ventricular Assist Device (LVAD). This system consists of electronic circuits and Pneumatic driving circuits. The initiation of systole and the pumping duration can be determined by a computer program. The Line pressure from a pressure transducer inserted in the Pneumatic Line was recorded. System modeling is completed using the adaptively trained backpropagation learning algorithms with input variables, Heart Rate (HR), Systole-Diastole Rate (SDR), which can vary the state of the system, and preload and afterload, which indicate the systemic dynamic characteristics, and output parameters are preload and afterload.

Antonio Bicchi - One of the best experts on this subject based on the ideXlab platform.

  • ICRA - Dynamic morphological computation through damping design of soft material robots: application to under-actuated grippers
    2019 International Conference on Robotics and Automation (ICRA), 2019
    Co-Authors: Antonio Di Lallo, Manuel G. Catalano, Manolo Garabini, Giorgio Grioli, Marco Gabiccini, Antonio Bicchi
    Abstract:

    This article presents the design of soft material robots with tunable damping properties. This study derives from the investigation of an under-actuated dynamic approach involving multi-chamber Pneumatic systems. The co-design of the mechanical parameters (stiffness and damping) of the system along with the time profile of the input allows to obtain different behaviors using a reduced number of feeding Line. In this work we analyze via simulations and experiments several approaches to tune the damping of soft robots. The most effective solution employs a layer of granular material immersed in viscous oil within the chamber wall. This method has been employed to realize bending actuators with a continuous deformation pattern. Finally, we show an application involving a two-fingered gripper fed by a single Pneumatic Line, which is able to perform pinch and power grasp.

Antonio Di Lallo - One of the best experts on this subject based on the ideXlab platform.

  • ICRA - Dynamic morphological computation through damping design of soft material robots: application to under-actuated grippers
    2019 International Conference on Robotics and Automation (ICRA), 2019
    Co-Authors: Antonio Di Lallo, Manuel G. Catalano, Manolo Garabini, Giorgio Grioli, Marco Gabiccini, Antonio Bicchi
    Abstract:

    This article presents the design of soft material robots with tunable damping properties. This study derives from the investigation of an under-actuated dynamic approach involving multi-chamber Pneumatic systems. The co-design of the mechanical parameters (stiffness and damping) of the system along with the time profile of the input allows to obtain different behaviors using a reduced number of feeding Line. In this work we analyze via simulations and experiments several approaches to tune the damping of soft robots. The most effective solution employs a layer of granular material immersed in viscous oil within the chamber wall. This method has been employed to realize bending actuators with a continuous deformation pattern. Finally, we show an application involving a two-fingered gripper fed by a single Pneumatic Line, which is able to perform pinch and power grasp.