Aiming at the innovative design requirements of rehabilitation robots with multiple kinematically coupled components and the current absence of systematic processes in the design of such mechanisms,this paper presents...Aiming at the innovative design requirements of rehabilitation robots with multiple kinematically coupled components and the current absence of systematic processes in the design of such mechanisms,this paper presents the concept of a multi-output component mechanism(MOCM).A classification methodology for the MOCM is proposed based on the operational coupling between the actuators and the output components within closedloop mechanisms.Building on the classification results,a design methodology for a kinematically coupled MOCM(KCMOCM)is proposed based on the actuation distribution within the closed-loop sub-mechanisms.First,the number and relative kinematic characteristics of the output components are determined based on the application environment of the mechanism.These components are then grouped and classified according to motion similarity principles,followed by the design of closed-loop sub-mechanisms with actuators for each group,ultimately forming a complete KCMOCM.Taking the sit-stand-lie-bed mechanism in a spinal cord injury lower-limb rehabilitation robot as an example,this study comprehensively considers the multi-posture transition task requirements and spatial constraint characteristics of lower-limb rehabilitation training to design the mechanism.By applying the mechanism design methodology,six practical novel configurations are developed with established evaluation criteria,and kinematic analysis and experimental validation are performed on the optimized configuration.The results demonstrate that the optimized configuration satisfies the multi-posture rehabilitation training requirements for lower limbs.This validates the efficacy of the design methodology.Furthermore,the scalability of the design methodology is validated through the development of a robotic finger rehabilitation mechanism.展开更多
To cater the need for real-time crack monitoring of infrastructural facilities,a CNN-regression model is proposed to directly estimate the crack properties from patches.RGB crack images and their corresponding masks o...To cater the need for real-time crack monitoring of infrastructural facilities,a CNN-regression model is proposed to directly estimate the crack properties from patches.RGB crack images and their corresponding masks obtained from a public dataset are cropped into patches of 256 square pixels that are classified with a pre-trained deep convolution neural network,the true positives are segmented,and crack properties are extracted using two different methods.The first method is primarily based on active contour models and level-set segmentation and the second method consists of the domain adaptation of a mathematical morphology-based method known as FIL-FINDER.A statistical test has been performed for the comparison of the stated methods and a database prepared with the more suitable method.An advanced convolution neural network-based multi-output regression model has been proposed which was trained with the prepared database and validated with the held-out dataset for the prediction of crack-length,crack-width,and width-uncertainty directly from input image patches.The pro-posed model has been tested on crack patches collected from different locations.Huber loss has been used to ensure the robustness of the proposed model selected from a set of 288 different variations of it.Additionally,an ablation study has been conducted on the top 3 models that demonstrated the influence of each network component on the pre-diction results.Finally,the best performing model HHc-X among the top 3 has been proposed that predicted crack properties which are in close agreement to the ground truths in the test data.展开更多
Biological data in fishery ecology have complex structures and are highly heterogeneous.Catch per unit effort(CPUE)estimated from fishery-dependent data are often used to characterize abundance indices(AI)of fish spec...Biological data in fishery ecology have complex structures and are highly heterogeneous.Catch per unit effort(CPUE)estimated from fishery-dependent data are often used to characterize abundance indices(AI)of fish species,which is critical in fish stock assessment.However,additional considerations need to be undertaken to ensure robust estimation because of the latently complicated structures in fishery-dependent data.Here,we elaborated the process of constructing multi-output artificial neural network models to standardize CPUE for heterogeneous fishing operations and applied it to the skipjack tuna(Katsuwonus pelamis)in the western and central Pacific Ocean(WCPO).Seasonal,spatial,and environmental factors were input variables,and the CPUE of four types of skipjack tuna fisheries were set as output variables.The optimal structure for multi-output neural network was evaluated by systematic comparison in 100 runs hold-out cross-validation.The results showed that the final multi-output neural network model with high accuracy can predict the spatial and temporal trends of skipjack tuna abundance.展开更多
基金Supported by National Key Research and Development Program of China(Grant No.2019YFB1312500)。
文摘Aiming at the innovative design requirements of rehabilitation robots with multiple kinematically coupled components and the current absence of systematic processes in the design of such mechanisms,this paper presents the concept of a multi-output component mechanism(MOCM).A classification methodology for the MOCM is proposed based on the operational coupling between the actuators and the output components within closedloop mechanisms.Building on the classification results,a design methodology for a kinematically coupled MOCM(KCMOCM)is proposed based on the actuation distribution within the closed-loop sub-mechanisms.First,the number and relative kinematic characteristics of the output components are determined based on the application environment of the mechanism.These components are then grouped and classified according to motion similarity principles,followed by the design of closed-loop sub-mechanisms with actuators for each group,ultimately forming a complete KCMOCM.Taking the sit-stand-lie-bed mechanism in a spinal cord injury lower-limb rehabilitation robot as an example,this study comprehensively considers the multi-posture transition task requirements and spatial constraint characteristics of lower-limb rehabilitation training to design the mechanism.By applying the mechanism design methodology,six practical novel configurations are developed with established evaluation criteria,and kinematic analysis and experimental validation are performed on the optimized configuration.The results demonstrate that the optimized configuration satisfies the multi-posture rehabilitation training requirements for lower limbs.This validates the efficacy of the design methodology.Furthermore,the scalability of the design methodology is validated through the development of a robotic finger rehabilitation mechanism.
文摘To cater the need for real-time crack monitoring of infrastructural facilities,a CNN-regression model is proposed to directly estimate the crack properties from patches.RGB crack images and their corresponding masks obtained from a public dataset are cropped into patches of 256 square pixels that are classified with a pre-trained deep convolution neural network,the true positives are segmented,and crack properties are extracted using two different methods.The first method is primarily based on active contour models and level-set segmentation and the second method consists of the domain adaptation of a mathematical morphology-based method known as FIL-FINDER.A statistical test has been performed for the comparison of the stated methods and a database prepared with the more suitable method.An advanced convolution neural network-based multi-output regression model has been proposed which was trained with the prepared database and validated with the held-out dataset for the prediction of crack-length,crack-width,and width-uncertainty directly from input image patches.The pro-posed model has been tested on crack patches collected from different locations.Huber loss has been used to ensure the robustness of the proposed model selected from a set of 288 different variations of it.Additionally,an ablation study has been conducted on the top 3 models that demonstrated the influence of each network component on the pre-diction results.Finally,the best performing model HHc-X among the top 3 has been proposed that predicted crack properties which are in close agreement to the ground truths in the test data.
基金supported by the National Key R&D Program of China(No.2023YFD2401303).
文摘Biological data in fishery ecology have complex structures and are highly heterogeneous.Catch per unit effort(CPUE)estimated from fishery-dependent data are often used to characterize abundance indices(AI)of fish species,which is critical in fish stock assessment.However,additional considerations need to be undertaken to ensure robust estimation because of the latently complicated structures in fishery-dependent data.Here,we elaborated the process of constructing multi-output artificial neural network models to standardize CPUE for heterogeneous fishing operations and applied it to the skipjack tuna(Katsuwonus pelamis)in the western and central Pacific Ocean(WCPO).Seasonal,spatial,and environmental factors were input variables,and the CPUE of four types of skipjack tuna fisheries were set as output variables.The optimal structure for multi-output neural network was evaluated by systematic comparison in 100 runs hold-out cross-validation.The results showed that the final multi-output neural network model with high accuracy can predict the spatial and temporal trends of skipjack tuna abundance.
基金国家自然科学基金重大项目(the Grand National Natural Science Foundation of China under Grant No.90407008)国家自然科学基金重点项目(the Key National Natural Science Foundation of China Under Grant No.60633060)安徽省自然科学基金(the Natural Science Foundation of Anhui Province of China under Grant No.050420103)
