Due to the advancements in remote sensing technologies,the generation of hyperspectral imagery(HSI)gets significantly increased.Accurate classification of HSI becomes a critical process in the domain of hyperspectral ...Due to the advancements in remote sensing technologies,the generation of hyperspectral imagery(HSI)gets significantly increased.Accurate classification of HSI becomes a critical process in the domain of hyperspectral data analysis.The massive availability of spectral and spatial details of HSI has offered a great opportunity to efficiently illustrate and recognize ground materials.Presently,deep learning(DL)models particularly,convolutional neural networks(CNNs)become useful for HSI classification owing to the effective feature representation and high performance.In this view,this paper introduces a new DL based Xception model for HSI analysis and classification,called Xcep-HSIC model.Initially,the presented model utilizes a feature relation map learning(FRML)to identify the relationship among the hyperspectral features and explore many features for improved classifier results.Next,the DL based Xception model is applied as a feature extractor to derive a useful set of features from the FRML map.In addition,kernel extreme learning machine(KELM)optimized by quantum-behaved particle swarm optimization(QPSO)is employed as a classification model,to identify the different set of class labels.An extensive set of simulations takes place on two benchmarks HSI dataset,namely Indian Pines and Pavia University dataset.The obtained results ensured the effective performance of the XcepHSIC technique over the existing methods by attaining a maximum accuracy of 94.32%and 92.67%on the applied India Pines and Pavia University dataset respectively.展开更多
With the intensifying competition in the integrated circuit(IC)industry,the high turnover rate of integrated circuit engineers has become a prominent issue affecting the technological continuity of high-precision,spec...With the intensifying competition in the integrated circuit(IC)industry,the high turnover rate of integrated circuit engineers has become a prominent issue affecting the technological continuity of high-precision,specialized,and innovative enterprises.As a representative of such enterprises,JL Technology has faced challenges to its R&D efficiency due to talent loss in recent years.This study takes this enterprise as a case to explore feasible paths to reduce turnover rates through optimizing training and career development systems.The research designs a method combining learning maps and talent maps,utilizes a competency model to clarify the direction for engineers’skill improvement,implements talent classification management using a nine-grid model,and achieves personalized training through Individual Development Plans(IDPs).Analysis of the enterprise’s historical data reveals that the main reasons for turnover are unclear career development paths and insufficient resources for skill improvement.After pilot implementation,the turnover rate in core departments decreased by 12%,and employee satisfaction with training increased by 24%.The results indicate that matching systematic talent reviews with dynamic learning resources can effectively enhance engineers’sense of belonging.This study provides a set of highly operational management tools for small and medium-sized high-precision,specialized,and innovative technology enterprises,verifies their applicability in such enterprises,and offers replicable experiences for similar enterprises to optimize their talent strategies[1].展开更多
Dear Editor,This letter proposes an innovative open-vocabulary 3D scene understanding model based on visual-language model.By efficiently integrating 3D point cloud data,image data,and text data,our model effectively ...Dear Editor,This letter proposes an innovative open-vocabulary 3D scene understanding model based on visual-language model.By efficiently integrating 3D point cloud data,image data,and text data,our model effectively overcomes the segmentation problem[1],[2]of traditional models dealing with unknown categories[3].By deeply learning the deep semantic mapping between vision and language,the network significantly improves its ability to recognize unlabeled categories and exceeds current state-of-the-art methods in the task of scene understanding in open-vocabulary.展开更多
Fast prediction of microstructural responses based on realistic material topology is vital for linking process,structure,and properties.This work presents a digital framework for metallic materials using microscale fe...Fast prediction of microstructural responses based on realistic material topology is vital for linking process,structure,and properties.This work presents a digital framework for metallic materials using microscale features.We explore deep learning for two primary goals:(1)segmenting experimental images to extract microstructural topology,translated into spatial property distributions;and(2)learning mappings from digital microstructures to mechanical fields using physics-informed operator learning.Loss functions are formulated using discretized weak or strong forms,and boundary conditions-Dirichlet and periodic-are embedded in the network.Input space is reduced to focus on key features of 2D and 3D materials,and generalization to varying loads and input topologies are demonstrated.Compared to FEM and FFT solvers,our models yield errors under 1–5%for averaged quantities and are over 1000×faster during 3D inference.展开更多
Machine learning(ML)has demonstrated its potential in atomistic simulations to bridge the gap between accurate first-principles methods and computationally efficient empirical potentials.This is achieved by learning m...Machine learning(ML)has demonstrated its potential in atomistic simulations to bridge the gap between accurate first-principles methods and computationally efficient empirical potentials.This is achieved by learning mappings between a system’s structure and its physical properties.State-ofthe-art models for potential energy surfaces typically represent chemical structures through(semi-)local atomic environments.However,this approach neglects long-range interactions(most notably electrostatics)and non-local phenomena such as charge transfer,leading to significant errors in the description of molecules or materials in polar anisotropic environments.To address these challenges,ML frameworks that predict self-consistent charge distributions in atomistic systemsusing the Charge Equilibration(QEq)method are currently popular.In this approach,atomic charges are derived from an electrostatic energy expression that incorporates environment-dependent atomic electronegativities.Herein,we explore the limits of this concept at the example of the previously reported Kernel Charge Equilibration(kQEq)approach,combined with local short-ranged potentials.To this end we consider prototypical systems with varying total charge states and applied electric fields.We find that charge equilibration-based models perform well in most situations.However,we also find that some pathologies of conventional QEq carry over to the ML variants in the form of spurious charge transfer and overpolarization in the presence of static electric fields.This indicates a need for new methodological developments.展开更多
文摘Due to the advancements in remote sensing technologies,the generation of hyperspectral imagery(HSI)gets significantly increased.Accurate classification of HSI becomes a critical process in the domain of hyperspectral data analysis.The massive availability of spectral and spatial details of HSI has offered a great opportunity to efficiently illustrate and recognize ground materials.Presently,deep learning(DL)models particularly,convolutional neural networks(CNNs)become useful for HSI classification owing to the effective feature representation and high performance.In this view,this paper introduces a new DL based Xception model for HSI analysis and classification,called Xcep-HSIC model.Initially,the presented model utilizes a feature relation map learning(FRML)to identify the relationship among the hyperspectral features and explore many features for improved classifier results.Next,the DL based Xception model is applied as a feature extractor to derive a useful set of features from the FRML map.In addition,kernel extreme learning machine(KELM)optimized by quantum-behaved particle swarm optimization(QPSO)is employed as a classification model,to identify the different set of class labels.An extensive set of simulations takes place on two benchmarks HSI dataset,namely Indian Pines and Pavia University dataset.The obtained results ensured the effective performance of the XcepHSIC technique over the existing methods by attaining a maximum accuracy of 94.32%and 92.67%on the applied India Pines and Pavia University dataset respectively.
文摘With the intensifying competition in the integrated circuit(IC)industry,the high turnover rate of integrated circuit engineers has become a prominent issue affecting the technological continuity of high-precision,specialized,and innovative enterprises.As a representative of such enterprises,JL Technology has faced challenges to its R&D efficiency due to talent loss in recent years.This study takes this enterprise as a case to explore feasible paths to reduce turnover rates through optimizing training and career development systems.The research designs a method combining learning maps and talent maps,utilizes a competency model to clarify the direction for engineers’skill improvement,implements talent classification management using a nine-grid model,and achieves personalized training through Individual Development Plans(IDPs).Analysis of the enterprise’s historical data reveals that the main reasons for turnover are unclear career development paths and insufficient resources for skill improvement.After pilot implementation,the turnover rate in core departments decreased by 12%,and employee satisfaction with training increased by 24%.The results indicate that matching systematic talent reviews with dynamic learning resources can effectively enhance engineers’sense of belonging.This study provides a set of highly operational management tools for small and medium-sized high-precision,specialized,and innovative technology enterprises,verifies their applicability in such enterprises,and offers replicable experiences for similar enterprises to optimize their talent strategies[1].
基金supported by CAFUC(ZHMH 2022-005)Key Laboratory of Flight Techniques and Flight Safety(FZ2022ZZ06)Flight Technology and Flight Safety of Civil Aviation Administration of China(FZ2022KF10).
文摘Dear Editor,This letter proposes an innovative open-vocabulary 3D scene understanding model based on visual-language model.By efficiently integrating 3D point cloud data,image data,and text data,our model effectively overcomes the segmentation problem[1],[2]of traditional models dealing with unknown categories[3].By deeply learning the deep semantic mapping between vision and language,the network significantly improves its ability to recognize unlabeled categories and exceeds current state-of-the-art methods in the task of scene understanding in open-vocabulary.
基金the funding support provided to develop the present work in the project Cluster of Excellence“Internet of Production”(project:390621612).
文摘Fast prediction of microstructural responses based on realistic material topology is vital for linking process,structure,and properties.This work presents a digital framework for metallic materials using microscale features.We explore deep learning for two primary goals:(1)segmenting experimental images to extract microstructural topology,translated into spatial property distributions;and(2)learning mappings from digital microstructures to mechanical fields using physics-informed operator learning.Loss functions are formulated using discretized weak or strong forms,and boundary conditions-Dirichlet and periodic-are embedded in the network.Input space is reduced to focus on key features of 2D and 3D materials,and generalization to varying loads and input topologies are demonstrated.Compared to FEM and FFT solvers,our models yield errors under 1–5%for averaged quantities and are over 1000×faster during 3D inference.
文摘Machine learning(ML)has demonstrated its potential in atomistic simulations to bridge the gap between accurate first-principles methods and computationally efficient empirical potentials.This is achieved by learning mappings between a system’s structure and its physical properties.State-ofthe-art models for potential energy surfaces typically represent chemical structures through(semi-)local atomic environments.However,this approach neglects long-range interactions(most notably electrostatics)and non-local phenomena such as charge transfer,leading to significant errors in the description of molecules or materials in polar anisotropic environments.To address these challenges,ML frameworks that predict self-consistent charge distributions in atomistic systemsusing the Charge Equilibration(QEq)method are currently popular.In this approach,atomic charges are derived from an electrostatic energy expression that incorporates environment-dependent atomic electronegativities.Herein,we explore the limits of this concept at the example of the previously reported Kernel Charge Equilibration(kQEq)approach,combined with local short-ranged potentials.To this end we consider prototypical systems with varying total charge states and applied electric fields.We find that charge equilibration-based models perform well in most situations.However,we also find that some pathologies of conventional QEq carry over to the ML variants in the form of spurious charge transfer and overpolarization in the presence of static electric fields.This indicates a need for new methodological developments.
