The aim of this study is to improve the efficiency of external corrosion inspection of pipes in chemical plants.Currently,the preferred method involves manual inspection of images of corroded pipes;however,this places...The aim of this study is to improve the efficiency of external corrosion inspection of pipes in chemical plants.Currently,the preferred method involves manual inspection of images of corroded pipes;however,this places significant workload on human experts owing to the large number of required images.Furthermore,visual assessment of corrosion levels is prone to subjective errors.To address these issues,we developed an AI(artificial intelligence)-based corrosion-diagnosis system(AI corrosion-diagnosis system)and implemented it in a factory.The proposed system architecture was based on HITL(human-in-the-loop)ML(machine learning)[1].To overcome the difficulty of developing a highly accurate ML model during the PoC(proof-of-concept)stage,the system relies on cooperation between humans and the ML model,utilizing human expertise during operation.For instance,if the accuracy of the ML model was initially 60%during the development stage,a cooperative approach would be adopted during the operational stage,with humans supplementing the remaining 40%accuracy.The implemented system’s ML model achieved a recall rate of approximately 70%.The system’s implementation not only contributed to the efficiency of operations by supporting diagnosis through the ML model but also facilitated the transition to systematic data management,resulting in an overall workload reduction of approximately 50%.The operation based on HITL was demonstrated to be a crucial element for achieving efficient system operation through the collaboration of humans and ML models,even when the initial accuracy of the ML model was low.Future efforts will focus on improving the detection of corrosion at elevated locations by considering using video cameras to capture pipe images.The goal is to reduce the workload for inspectors and enhance the quality of inspections by identifying corrosion locations using ML models.展开更多
The exoskeleton robot is a typical man–machine integration system in the human loop.The ideal man–machine state is to achieve motion coordination,stable output,strong personalization,and reduce man–machine confront...The exoskeleton robot is a typical man–machine integration system in the human loop.The ideal man–machine state is to achieve motion coordination,stable output,strong personalization,and reduce man–machine confrontation during motion.In order to achieve an ideal man–machine state,a Time-varying Adaptive Gait Trajectory Generator(TAGT)is designed to estimate the motion intention of the wearer and generate a personalized gait trajectory.TAGT can enhance the hybrid intelligent decision-making ability under human–machine collaboration,promote good motion coordination between the exoskeleton and the wearer,and reduce metabolic consumption.An important feature of this controller is that it utilizes a multi-layer control strategy to provide locomotion assistance to the wearer,while allowing the user to control the gait trajectory based on human–robot Interaction(HRI)force and locomotion information.In this article,a Temporal Convolutional Gait Prediction(TCGP)model is designed to learn the personalized gait trajectory of the wearer,and the control performance of the model is further improved by fusing the predefined gait trajectory method with an adaptive interactive force control model.A human-in-the-loop control strategy is formed with the feedback information to stabilize the motion trajectory of the output joints and update the system state in real time based on the feedback from the inertial and interactive force signal.The experimental study employs able-bodied subjects wearing the exoskeleton for motion trajectory control to evaluate the performance of the proposed TAGT model in online adjustments.Data from these evaluations demonstrate that the controller TAGT has good motor coordination and can satisfy the subject to control the motor within a certain range according to the walking habit,guaranteeing the stability of the closed-loop system.展开更多
文摘The aim of this study is to improve the efficiency of external corrosion inspection of pipes in chemical plants.Currently,the preferred method involves manual inspection of images of corroded pipes;however,this places significant workload on human experts owing to the large number of required images.Furthermore,visual assessment of corrosion levels is prone to subjective errors.To address these issues,we developed an AI(artificial intelligence)-based corrosion-diagnosis system(AI corrosion-diagnosis system)and implemented it in a factory.The proposed system architecture was based on HITL(human-in-the-loop)ML(machine learning)[1].To overcome the difficulty of developing a highly accurate ML model during the PoC(proof-of-concept)stage,the system relies on cooperation between humans and the ML model,utilizing human expertise during operation.For instance,if the accuracy of the ML model was initially 60%during the development stage,a cooperative approach would be adopted during the operational stage,with humans supplementing the remaining 40%accuracy.The implemented system’s ML model achieved a recall rate of approximately 70%.The system’s implementation not only contributed to the efficiency of operations by supporting diagnosis through the ML model but also facilitated the transition to systematic data management,resulting in an overall workload reduction of approximately 50%.The operation based on HITL was demonstrated to be a crucial element for achieving efficient system operation through the collaboration of humans and ML models,even when the initial accuracy of the ML model was low.Future efforts will focus on improving the detection of corrosion at elevated locations by considering using video cameras to capture pipe images.The goal is to reduce the workload for inspectors and enhance the quality of inspections by identifying corrosion locations using ML models.
文摘The exoskeleton robot is a typical man–machine integration system in the human loop.The ideal man–machine state is to achieve motion coordination,stable output,strong personalization,and reduce man–machine confrontation during motion.In order to achieve an ideal man–machine state,a Time-varying Adaptive Gait Trajectory Generator(TAGT)is designed to estimate the motion intention of the wearer and generate a personalized gait trajectory.TAGT can enhance the hybrid intelligent decision-making ability under human–machine collaboration,promote good motion coordination between the exoskeleton and the wearer,and reduce metabolic consumption.An important feature of this controller is that it utilizes a multi-layer control strategy to provide locomotion assistance to the wearer,while allowing the user to control the gait trajectory based on human–robot Interaction(HRI)force and locomotion information.In this article,a Temporal Convolutional Gait Prediction(TCGP)model is designed to learn the personalized gait trajectory of the wearer,and the control performance of the model is further improved by fusing the predefined gait trajectory method with an adaptive interactive force control model.A human-in-the-loop control strategy is formed with the feedback information to stabilize the motion trajectory of the output joints and update the system state in real time based on the feedback from the inertial and interactive force signal.The experimental study employs able-bodied subjects wearing the exoskeleton for motion trajectory control to evaluate the performance of the proposed TAGT model in online adjustments.Data from these evaluations demonstrate that the controller TAGT has good motor coordination and can satisfy the subject to control the motor within a certain range according to the walking habit,guaranteeing the stability of the closed-loop system.
