Imaging hyperspectral technology has distinctive advantages of non-destructive and non-contact measurement,and the integration of spectral and spatial data.These characteristics present new methodologies for intellige...Imaging hyperspectral technology has distinctive advantages of non-destructive and non-contact measurement,and the integration of spectral and spatial data.These characteristics present new methodologies for intelligent geological sensing in tunnels and other underground engineering projects.However,the in situ acquisition and rapid classification of hyperspectral images in underground still faces great challenges,including the difficulty in obtaining uniform hyperspectral images and the complexity of deploying sophisticated models on mobile platforms.This study proposes an intelligent lithology identification method based on partition feature extraction of hyperspectral images.Firstly,pixel-level hyperspectral information from representative lithological regions is extracted and fused to obtain rock hyperspectral image partition features.Subsequently,an SG-SNV-PCA-DNN(SSPD)model specifically designed for optimizing rock hyperspectral data,performing spectral dimensionality reduction,and identifying lithology is integrated.In an experimental study involving 3420 hyperspectral images,the SSPD identification model achieved the highest accuracy in the testing set,reaching 98.77%.Moreover,the speed of the SSPD model was found to be 18.5%faster than that of the unprocessed model,with an accuracy improvement of 5.22%.In contrast,the ResNet-101 model,used for point-by-point identification based on non-partitioned features,achieved a maximum accuracy of 97.86%in the testing set.In addition,the partition feature extraction methods significantly reduce computational complexity.An objective evaluation of various models demonstrated that the SSPD model exhibited superior performance,achieving a precision(P)of 99.46%,a recall(R)of 99.44%,and F1 score(F1)of 99.45%.Additionally,a pioneering in situ detection work was carried out in a tunnel using underground hyperspectral imaging technology.展开更多
The modular design technology is of importance increasingly,as product structure is more and more complex.Modular design systems face challenging problems as the design information tends to be dynamic,redundant,and ve...The modular design technology is of importance increasingly,as product structure is more and more complex.Modular design systems face challenging problems as the design information tends to be dynamic,redundant,and very large.This paper describes a novel approach for handling them.In this approach,a partition is firstly performed for the complex structural components by mapping functions to the structures layer by layer.Based on this partition,a comprehensive design matrix is then developed to identify the key design mode which is driven by a special function.The design process is also programmed by analyzing the coupled information on both the functional and structural hierarchies.Then,the integrated knowledge model based on object-oriented method and hybrid inference method is constructed.In this model,knowledge can be organized at hierarchical classification and expressed with different forms.Finally,the methodology developed has been applied to a real application in automobile cylinder block design and the results are presented.展开更多
Data partitioning techniques are pivotal for optimal data placement across storage devices,thereby enhancing resource utilization and overall system throughput.However,the design of effective partition schemes faces m...Data partitioning techniques are pivotal for optimal data placement across storage devices,thereby enhancing resource utilization and overall system throughput.However,the design of effective partition schemes faces multiple challenges,including considerations of the cluster environment,storage device characteristics,optimization objectives,and the balance between partition quality and computational efficiency.Furthermore,dynamic environments necessitate robust partition detection mechanisms.This paper presents a comprehensive survey structured around partition deployment environments,outlining the distinguishing features and applicability of various partitioning strategies while delving into how these challenges are addressed.We discuss partitioning features pertaining to database schema,table data,workload,and runtime metrics.We then delve into the partition generation process,segmenting it into initialization and optimization stages.A comparative analysis of partition generation and update algorithms is provided,emphasizing their suitability for different scenarios and optimization objectives.Additionally,we illustrate the applications of partitioning in prevalent database products and suggest potential future research directions and solutions.This survey aims to foster the implementation,deployment,and updating of high-quality partitions for specific system scenarios.展开更多
基金support from the National Natural Science Foundation of China(Grant Nos.52379103,52279103)the Natural Science Foundation of Shandong Province(Grant No.ZR2023YQ049).
文摘Imaging hyperspectral technology has distinctive advantages of non-destructive and non-contact measurement,and the integration of spectral and spatial data.These characteristics present new methodologies for intelligent geological sensing in tunnels and other underground engineering projects.However,the in situ acquisition and rapid classification of hyperspectral images in underground still faces great challenges,including the difficulty in obtaining uniform hyperspectral images and the complexity of deploying sophisticated models on mobile platforms.This study proposes an intelligent lithology identification method based on partition feature extraction of hyperspectral images.Firstly,pixel-level hyperspectral information from representative lithological regions is extracted and fused to obtain rock hyperspectral image partition features.Subsequently,an SG-SNV-PCA-DNN(SSPD)model specifically designed for optimizing rock hyperspectral data,performing spectral dimensionality reduction,and identifying lithology is integrated.In an experimental study involving 3420 hyperspectral images,the SSPD identification model achieved the highest accuracy in the testing set,reaching 98.77%.Moreover,the speed of the SSPD model was found to be 18.5%faster than that of the unprocessed model,with an accuracy improvement of 5.22%.In contrast,the ResNet-101 model,used for point-by-point identification based on non-partitioned features,achieved a maximum accuracy of 97.86%in the testing set.In addition,the partition feature extraction methods significantly reduce computational complexity.An objective evaluation of various models demonstrated that the SSPD model exhibited superior performance,achieving a precision(P)of 99.46%,a recall(R)of 99.44%,and F1 score(F1)of 99.45%.Additionally,a pioneering in situ detection work was carried out in a tunnel using underground hyperspectral imaging technology.
基金the National Natural Science Foundation of China （No. 50935006）； the National High Technology Research and Development Program （863） of China （No. 2009AA04Z147）；the Science- Technology Research and Development Program of Shaanxi Province （No. 2008KW-07）
文摘The modular design technology is of importance increasingly,as product structure is more and more complex.Modular design systems face challenging problems as the design information tends to be dynamic,redundant,and very large.This paper describes a novel approach for handling them.In this approach,a partition is firstly performed for the complex structural components by mapping functions to the structures layer by layer.Based on this partition,a comprehensive design matrix is then developed to identify the key design mode which is driven by a special function.The design process is also programmed by analyzing the coupled information on both the functional and structural hierarchies.Then,the integrated knowledge model based on object-oriented method and hybrid inference method is constructed.In this model,knowledge can be organized at hierarchical classification and expressed with different forms.Finally,the methodology developed has been applied to a real application in automobile cylinder block design and the results are presented.
基金supported by the National Key Research and Development Program of China under Grant No.2023YFB4503603the National Natural Science Foundation of China under Grant Nos.62072460,62076245,and 62172424the Beijing Natural Science Foundation under Grant No.4212022.
文摘Data partitioning techniques are pivotal for optimal data placement across storage devices,thereby enhancing resource utilization and overall system throughput.However,the design of effective partition schemes faces multiple challenges,including considerations of the cluster environment,storage device characteristics,optimization objectives,and the balance between partition quality and computational efficiency.Furthermore,dynamic environments necessitate robust partition detection mechanisms.This paper presents a comprehensive survey structured around partition deployment environments,outlining the distinguishing features and applicability of various partitioning strategies while delving into how these challenges are addressed.We discuss partitioning features pertaining to database schema,table data,workload,and runtime metrics.We then delve into the partition generation process,segmenting it into initialization and optimization stages.A comparative analysis of partition generation and update algorithms is provided,emphasizing their suitability for different scenarios and optimization objectives.Additionally,we illustrate the applications of partitioning in prevalent database products and suggest potential future research directions and solutions.This survey aims to foster the implementation,deployment,and updating of high-quality partitions for specific system scenarios.
