Group testing involves discovering a small subset of distinguished subjects from a large population while efficiently reducing the total number of tests.It has been widely used for industrial testing,information techn...Group testing involves discovering a small subset of distinguished subjects from a large population while efficiently reducing the total number of tests.It has been widely used for industrial testing,information technology,and biology,especially epidemic screening.Tests,in reality,are noisy for the presence of false outcomes.Some tests are accurate but time-consuming,while others are cheaper but less accurate.Exactly which test to use is constrained by various considerations,such as availability,cost,accuracy,and efficiency.In this paper,we propose flexible,efficient,and accurate tests(FEATs).FEATs are based on group testing with simple but careful designs by incorporating ideas such as close contact cliques and repeated tests.FEATs could dramatically improve the efficiency or accuracy of existing tests.For example,for accurate but slow tests,the FEAT can improve efficiency multiple times without compromising accuracy.On the other hand,for fast but inaccurate tests,the FEAT can sharply reduce the false-negative rate(FNR)and significantly increase efficiency.Theoretical justifications are provided.We point out some scenarios where the FEAT can be effectively employed.展开更多
Surface quality monitoring of manufacturing products is critical for manufacturing industries to ensure product quality and production efficiency.With the rapid development of 3D scanning technology,high-density 3D po...Surface quality monitoring of manufacturing products is critical for manufacturing industries to ensure product quality and production efficiency.With the rapid development of 3D scanning technology,high-density 3D point cloud data can be generated by 3D scanners in complex manufacturing systems.However,due to the challenges of complex surface modeling and various types,it lacks effective surface anomaly detection methods that can meet the practical requirements regarding detection accuracy and speed.This survey aims to review the surface anomaly detection methodology of manufacturing products based on 3D machine vision.Specifically,the machine learning methodologies will be systematically reviewed for 3D point cloud data modeling and anomaly detection.Related public data sets for this research are also summarized.Finally,the future research directions are pointed out.展开更多
The development of high-efficiency perovskite solar cells(PSCs)demands a comprehensive control of multi-scale factors that influence device performance.In recent years,artificial intelligence(AI),represented by machin...The development of high-efficiency perovskite solar cells(PSCs)demands a comprehensive control of multi-scale factors that influence device performance.In recent years,artificial intelligence(AI),represented by machine learning(ML),has rapidly become a key tool for the design and optimization of PSCs.However,current ML models often oversimplify the design of PSCs at the device level,making it difficult to capture the complexity of their multi-scale features.Moreover,they are constrained by relatively small and specialized datasets,which limits their generalizability across diverse device architectures and fabrication methods.In this work,we developed a full-process AI framework based on over 20,000 experimentally measured PSC samples and approximately 260 multi-scale features.This framework offers significant advantages in both sample diversity and feature richness.It combines material selection,fabrication processes,and environmental factors to provide a more accurate and comprehensive optimization solution for PSCs.We addressed challenges from data diversity and heterogeneity through feature engineering and model training,which results in a highly generalizable PSC performance prediction model with comparable prediction error to small-scale models.The framework enables precise optimization of specific features for any PSCs,and provides valuable insights for designing high-performance photovoltaic devices.展开更多
Nested simulation encompasses the estimation of functionals linked to conditional expectations through simulation techniques.In this paper,we treat conditional expectation as a function of the multidimensional conditi...Nested simulation encompasses the estimation of functionals linked to conditional expectations through simulation techniques.In this paper,we treat conditional expectation as a function of the multidimensional conditioning variable and provide asymptotic analyses of general nonparametric least squared estimators on sieve,without imposing specific assumptions on the function’s form.Our study explores scenarios in which the convergence rate surpasses that of the standard Monte Carlo method and the one recently proposed based on kernel ridge regression.We use kernel ridge regression with inducing points and neural networks as examples to illustrate our theorems.Numerical experiments are conducted to support our statements.展开更多
文摘Group testing involves discovering a small subset of distinguished subjects from a large population while efficiently reducing the total number of tests.It has been widely used for industrial testing,information technology,and biology,especially epidemic screening.Tests,in reality,are noisy for the presence of false outcomes.Some tests are accurate but time-consuming,while others are cheaper but less accurate.Exactly which test to use is constrained by various considerations,such as availability,cost,accuracy,and efficiency.In this paper,we propose flexible,efficient,and accurate tests(FEATs).FEATs are based on group testing with simple but careful designs by incorporating ideas such as close contact cliques and repeated tests.FEATs could dramatically improve the efficiency or accuracy of existing tests.For example,for accurate but slow tests,the FEAT can improve efficiency multiple times without compromising accuracy.On the other hand,for fast but inaccurate tests,the FEAT can sharply reduce the false-negative rate(FNR)and significantly increase efficiency.Theoretical justifications are provided.We point out some scenarios where the FEAT can be effectively employed.
基金upported by the National Natural Science Foundation of China under(Grant Nos.72371219,72001139,52372308 and 72371217)Guangdong Basic and Applied Basic Research Foundation(Grant No.2023A1515011656)+3 种基金Guangzhou Funding Program(Grant No.2025A04J5288)Guangzhou-HKUST(GZ)Joint Funding Program(Grant Nos.2023A03J0651 and 2024A03J0680)Guangzhou Industrial Informatic and Intelligence Key Laboratory(No.2024A03J0628)Nansha Key Area Science and Technology(Project Nos.2023ZD003,and Project No.2021JC02X191).
文摘Surface quality monitoring of manufacturing products is critical for manufacturing industries to ensure product quality and production efficiency.With the rapid development of 3D scanning technology,high-density 3D point cloud data can be generated by 3D scanners in complex manufacturing systems.However,due to the challenges of complex surface modeling and various types,it lacks effective surface anomaly detection methods that can meet the practical requirements regarding detection accuracy and speed.This survey aims to review the surface anomaly detection methodology of manufacturing products based on 3D machine vision.Specifically,the machine learning methodologies will be systematically reviewed for 3D point cloud data modeling and anomaly detection.Related public data sets for this research are also summarized.Finally,the future research directions are pointed out.
基金supported by the National Natural Science Foundation of China(52302333 to Bai Y,52373233 to Sun Y)the SIAT International Joint Lab Project(E3G113 to Sun Y)+1 种基金the Shenzhen Science and Technology Program(KQTD20221101093647058 to Bai Y and Sun Y,Shenzhen KJZD20231025152759001 to Bai Y)the Guangdong Basic and Applied Basic Research Foundation(2023A1515012788 to Bai Y,2024A1515010679 to Sun Y).
文摘The development of high-efficiency perovskite solar cells(PSCs)demands a comprehensive control of multi-scale factors that influence device performance.In recent years,artificial intelligence(AI),represented by machine learning(ML),has rapidly become a key tool for the design and optimization of PSCs.However,current ML models often oversimplify the design of PSCs at the device level,making it difficult to capture the complexity of their multi-scale features.Moreover,they are constrained by relatively small and specialized datasets,which limits their generalizability across diverse device architectures and fabrication methods.In this work,we developed a full-process AI framework based on over 20,000 experimentally measured PSC samples and approximately 260 multi-scale features.This framework offers significant advantages in both sample diversity and feature richness.It combines material selection,fabrication processes,and environmental factors to provide a more accurate and comprehensive optimization solution for PSCs.We addressed challenges from data diversity and heterogeneity through feature engineering and model training,which results in a highly generalizable PSC performance prediction model with comparable prediction error to small-scale models.The framework enables precise optimization of specific features for any PSCs,and provides valuable insights for designing high-performance photovoltaic devices.
文摘Nested simulation encompasses the estimation of functionals linked to conditional expectations through simulation techniques.In this paper,we treat conditional expectation as a function of the multidimensional conditioning variable and provide asymptotic analyses of general nonparametric least squared estimators on sieve,without imposing specific assumptions on the function’s form.Our study explores scenarios in which the convergence rate surpasses that of the standard Monte Carlo method and the one recently proposed based on kernel ridge regression.We use kernel ridge regression with inducing points and neural networks as examples to illustrate our theorems.Numerical experiments are conducted to support our statements.
