Various strategies,including controls of morphology,oxidation state,defect,and doping,have been developed to improve the performance of Cu-based catalysts for CO_(2) reduction reaction(CO_(2)RR),generating a large amo...Various strategies,including controls of morphology,oxidation state,defect,and doping,have been developed to improve the performance of Cu-based catalysts for CO_(2) reduction reaction(CO_(2)RR),generating a large amount of data.However,a unified understanding of underlying mechanism for further optimization is still lacking.In this work,combining first-principles calculations and machine learning(ML)techniques,we elucidate critical factors influencing the catalytic properties,taking Cu-based single atom alloys(SAAs)as examples.Our method relies on high-throughput calculations of 2669 CO adsorption configurations on 43 types of Cu-based SAAs with various surfaces.Extensive ML analyses reveal that low generalized coordination numbers and valence electron number are key features to determine catalytic performance.Applying our ML model with cross-group learning scheme,we demonstrate the model generalizes well between Cu-based SAAs with different alloying elements.Further,electronic structure calculations suggest surface negative center could enhance CO adsorption by back donating electrons to antibonding orbitals of CO.Finally,several SAAs,including PCu,AgCu,GaCu,ZnCu,SnCu,GeCu,InCu,and SiCu,are identified as promising CO_(2)RR catalysts.Our work provides a paradigm for the rational design and fast screening of SAAs for various electrocatalytic reactions.展开更多
The elastic parameters of soft tissues are important for medical diagnosis and virtual surgery simulation. In this study, we propose a novel nonlinear parameter estimation method for soft tissues. Firstly, an in-house...The elastic parameters of soft tissues are important for medical diagnosis and virtual surgery simulation. In this study, we propose a novel nonlinear parameter estimation method for soft tissues. Firstly, an in-house data acquisition platform was used to obtain external forces and their corresponding deformation values, To provide highly precise data for estimating nonlinear param- eters, the measured forces were corrected using the constructed weighted combination forecasting model based on a support vector machine (WCFM_SVM). Secondly, a tetrahedral finite element parameter estimation model was established to describe the physical characteristics of soft tissues, using the substitution parameters of Young's modulus and Poisson's ratio to avoid solving compli- cated nonlinear problems. To improve the robustness of our model and avoid poor local minima, the initial parameters solved by a linear finite element model were introduced into the parameter estimation model. Finally, a self-adapting Levenberg-Marquardt (LM) algorithm was presented, which is capable of adaptively adjusting iterative parameters to solve the established parameter estimation model. The maximum absolute error of our WCFM SVM model was less than 0.03 Newton, resulting in more accurate forces in comparison with other correction models tested. The maximum absolute error between the calculated and measured nodal displacements was less than 1.5 mm, demonstrating that our nonlinear parameters are precise.展开更多
This paper presents a novel mixed reality based navigation system for accurate respiratory liver tumor punctures in radiofrequency ablation(RFA).Our system contains an optical see-through head-mounted display device(O...This paper presents a novel mixed reality based navigation system for accurate respiratory liver tumor punctures in radiofrequency ablation(RFA).Our system contains an optical see-through head-mounted display device(OST-HMD),Microsoft Holo Lens for perfectly overlaying the virtual information on the patient,and a optical tracking system NDI Polaris for calibrating the surgical utilities in the surgical scene.Compared with traditional navigation method with CT,our system aligns the virtual guidance information and real patient and real-timely updates the view of virtual guidance via a position tracking system.In addition,to alleviate the difficulty during needle placement induced by respiratory motion,we reconstruct the patientspecific respiratory liver motion through statistical motion model to assist doctors precisely puncture liver tumors.The proposed system has been experimentally validated on vivo pigs with an accurate real-time registration approximately 5-mm mean FRE and TRE,which has the potential to be applied in clinical RFA guidance.展开更多
A popular and challenging task in video research,frame interpolation aims to increase the frame rate of video.Most existing methods employ a fixed motion model,e.g.,linear,quadratic,or cubic,to estimate the intermedia...A popular and challenging task in video research,frame interpolation aims to increase the frame rate of video.Most existing methods employ a fixed motion model,e.g.,linear,quadratic,or cubic,to estimate the intermediate warping field.However,such fixed motion models cannot well represent the complicated non-linear motions in the real world or rendered animations.Instead,we present an adaptive flow prediction module to better approximate the complex motions in video.Furthermore,interpolating just one intermediate frame between consecutive input frames may be insufficient for complicated non-linear motions.To enable multi-frame interpolation,we introduce the time as a control variable when interpolating frames between original ones in our generic adaptive flow prediction module.Qualitative and quantitative experimental results show that our method can produce high-quality results and outperforms the existing stateof-the-art methods on popular public datasets.展开更多
基金supported by the National Natural Science Foundation of China (Grant Nos.62006219 and 62001266)Guangdong Innovative and Entrepre-neurial Research Team Program (grant No.2017ZT07C341)+2 种基金the Bureau of Industry and Information Technology of Shenzhen for the 2017 Graphene Manufacturing Innovation Center Project (No.201901171523)the China Postdoctoral Science Foundation (No.2020M680506)Guangdong Basic and Applied Basic Research Foundation (No.2020A1515110338).
文摘Various strategies,including controls of morphology,oxidation state,defect,and doping,have been developed to improve the performance of Cu-based catalysts for CO_(2) reduction reaction(CO_(2)RR),generating a large amount of data.However,a unified understanding of underlying mechanism for further optimization is still lacking.In this work,combining first-principles calculations and machine learning(ML)techniques,we elucidate critical factors influencing the catalytic properties,taking Cu-based single atom alloys(SAAs)as examples.Our method relies on high-throughput calculations of 2669 CO adsorption configurations on 43 types of Cu-based SAAs with various surfaces.Extensive ML analyses reveal that low generalized coordination numbers and valence electron number are key features to determine catalytic performance.Applying our ML model with cross-group learning scheme,we demonstrate the model generalizes well between Cu-based SAAs with different alloying elements.Further,electronic structure calculations suggest surface negative center could enhance CO adsorption by back donating electrons to antibonding orbitals of CO.Finally,several SAAs,including PCu,AgCu,GaCu,ZnCu,SnCu,GeCu,InCu,and SiCu,are identified as promising CO_(2)RR catalysts.Our work provides a paradigm for the rational design and fast screening of SAAs for various electrocatalytic reactions.
基金supported by the National Natural Science Foundation of China (Grant No.61373107)Wuhan Science and Technology Program, China (Grant No.2016010101010022)
文摘The elastic parameters of soft tissues are important for medical diagnosis and virtual surgery simulation. In this study, we propose a novel nonlinear parameter estimation method for soft tissues. Firstly, an in-house data acquisition platform was used to obtain external forces and their corresponding deformation values, To provide highly precise data for estimating nonlinear param- eters, the measured forces were corrected using the constructed weighted combination forecasting model based on a support vector machine (WCFM_SVM). Secondly, a tetrahedral finite element parameter estimation model was established to describe the physical characteristics of soft tissues, using the substitution parameters of Young's modulus and Poisson's ratio to avoid solving compli- cated nonlinear problems. To improve the robustness of our model and avoid poor local minima, the initial parameters solved by a linear finite element model were introduced into the parameter estimation model. Finally, a self-adapting Levenberg-Marquardt (LM) algorithm was presented, which is capable of adaptively adjusting iterative parameters to solve the established parameter estimation model. The maximum absolute error of our WCFM SVM model was less than 0.03 Newton, resulting in more accurate forces in comparison with other correction models tested. The maximum absolute error between the calculated and measured nodal displacements was less than 1.5 mm, demonstrating that our nonlinear parameters are precise.
基金supported in part by the National Natural Science Foundation of China(Nos.U1813204 and 61802385)in part by HK RGC TRS project T42-409/18-R+2 种基金in part by HK RGC project CUHK14225616in part by CUHK T Stone Robotics Institute,CUHKin part by the Science and Technology Plan Project of Guangzhou(No.201704020141).
文摘This paper presents a novel mixed reality based navigation system for accurate respiratory liver tumor punctures in radiofrequency ablation(RFA).Our system contains an optical see-through head-mounted display device(OST-HMD),Microsoft Holo Lens for perfectly overlaying the virtual information on the patient,and a optical tracking system NDI Polaris for calibrating the surgical utilities in the surgical scene.Compared with traditional navigation method with CT,our system aligns the virtual guidance information and real patient and real-timely updates the view of virtual guidance via a position tracking system.In addition,to alleviate the difficulty during needle placement induced by respiratory motion,we reconstruct the patientspecific respiratory liver motion through statistical motion model to assist doctors precisely puncture liver tumors.The proposed system has been experimentally validated on vivo pigs with an accurate real-time registration approximately 5-mm mean FRE and TRE,which has the potential to be applied in clinical RFA guidance.
基金supported by the Research Grants Council of the Hong Kong Special Administrative Region,under RGC General Research Fund(Project No.CUHK 14201017)Shenzhen Science and Technology Program(No.JCYJ20180507182410327)the Science and Technology Plan Project of Guangzhou(No.201704020141)。
文摘A popular and challenging task in video research,frame interpolation aims to increase the frame rate of video.Most existing methods employ a fixed motion model,e.g.,linear,quadratic,or cubic,to estimate the intermediate warping field.However,such fixed motion models cannot well represent the complicated non-linear motions in the real world or rendered animations.Instead,we present an adaptive flow prediction module to better approximate the complex motions in video.Furthermore,interpolating just one intermediate frame between consecutive input frames may be insufficient for complicated non-linear motions.To enable multi-frame interpolation,we introduce the time as a control variable when interpolating frames between original ones in our generic adaptive flow prediction module.Qualitative and quantitative experimental results show that our method can produce high-quality results and outperforms the existing stateof-the-art methods on popular public datasets.