A simple method to prepare two-dimensional hexagonal boron nitride(h-BN) scalably is essential for practical applications. Despite intense research in this area, high-yield production of two-dimensional h-BN with larg...A simple method to prepare two-dimensional hexagonal boron nitride(h-BN) scalably is essential for practical applications. Despite intense research in this area, high-yield production of two-dimensional h-BN with large size and high crystallinity is still a key challenge. In the present work, we propose a simple exfoliation process for boron nitride nanosheets(BNNSs) with high crystallinity by sonication-assisted hydrothermal method, via the synergistic effect of the high pressure, and cavitation of the sonication. Compared with the method only by sonication, the sonication-assisted hydrothermal method can get the fewer-layer BNNSs with high crystallinity.Meanwhile, it can reach higher yield of nearly 1.68%, as the hydrothermal method with the yield of only 0.12%. The simple sonication-assisted hydrothermal method has potential applications in exfoliating other layered materials, thus opening new ways to produce other layered materials in high yield and high crystallinity.展开更多
Integrating Tiny Machine Learning(TinyML)with edge computing in remotely sensed images enhances the capabilities of road anomaly detection on a broader level.Constrained devices efficiently implement a Binary Neural N...Integrating Tiny Machine Learning(TinyML)with edge computing in remotely sensed images enhances the capabilities of road anomaly detection on a broader level.Constrained devices efficiently implement a Binary Neural Network(BNN)for road feature extraction,utilizing quantization and compression through a pruning strategy.The modifications resulted in a 28-fold decrease in memory usage and a 25%enhancement in inference speed while only experiencing a 2.5%decrease in accuracy.It showcases its superiority over conventional detection algorithms in different road image scenarios.Although constrained by computer resources and training datasets,our results indicate opportunities for future research,demonstrating that quantization and focused optimization can significantly improve machine learning models’accuracy and operational efficiency.ARM Cortex-M0 gives practical feasibility and substantial benefits while deploying our optimized BNN model on this low-power device:Advanced machine learning in edge computing.The analysis work delves into the educational significance of TinyML and its essential function in analyzing road networks using remote sensing,suggesting ways to improve smart city frameworks in road network assessment,traffic management,and autonomous vehicle navigation systems by emphasizing the importance of new technologies for maintaining and safeguarding road networks.展开更多
A higher value of the dielectric constant of h-BN makes it quite favourable material in energy storing device. The variation in dielectric constant was observed as a function of thickness. In this research work multil...A higher value of the dielectric constant of h-BN makes it quite favourable material in energy storing device. The variation in dielectric constant was observed as a function of thickness. In this research work multilayers of Hexagonal Boron Nitride (h-BN) was fabricated by using the Chemical exfoliation method. Two solvents Dimethylformamide (DMF) and Isopropyl Alcohol (IPA) were used for the exfoliation of h-BN. Successful sonication of hexagonal boron nitride led to the formation of Boron Nitride nanosheets (BNNs). The stable dispersibility of h-BN in Dimethylformamide and Isopropyl Alcohol was confirmed by UV Visible Spectroscopy, X-ray diffraction (XRD) and Scanning electron microscopy (SEM) confirm the mono crystallite structure (002) and nanoflakes like morphology of h-BN respectively. This appropriate strategy offered a feasible route to produce multilayer of hexagonal boron nitride. After the successful fabrication of h-BN multilayers its dielectric properties were calculated by using LCR meter. Profilometer revealed the variation in thickness and value of Dielectric constant was calculated by using its formula.展开更多
Binary neural networks(BNNs)show promising utilization in cost and power-restricted domains such as edge devices and mobile systems.This is due to its significantly less computation and storage demand,but at the cost ...Binary neural networks(BNNs)show promising utilization in cost and power-restricted domains such as edge devices and mobile systems.This is due to its significantly less computation and storage demand,but at the cost of degraded performance.To close the accuracy gap,in this paper we propose to add a complementary activation function(AF)ahead of the sign based binarization,and rely on the genetic algorithm(GA)to automatically search for the ideal AFs.These AFs can help extract extra information from the input data in the forward pass,while allowing improved gradient approximation in the backward pass.Fifteen novel AFs are identified through our GA-based search,while most of them show improved performance(up to 2.54%on ImageNet)when testing on different datasets and network models.Interestingly,periodic functions are identified as a key component for most of the discovered AFs,which rarely exist in human designed AFs.Our method offers a novel approach for designing general and application-specific BNN architecture.GAAF will be released on GitHub.展开更多
文摘A simple method to prepare two-dimensional hexagonal boron nitride(h-BN) scalably is essential for practical applications. Despite intense research in this area, high-yield production of two-dimensional h-BN with large size and high crystallinity is still a key challenge. In the present work, we propose a simple exfoliation process for boron nitride nanosheets(BNNSs) with high crystallinity by sonication-assisted hydrothermal method, via the synergistic effect of the high pressure, and cavitation of the sonication. Compared with the method only by sonication, the sonication-assisted hydrothermal method can get the fewer-layer BNNSs with high crystallinity.Meanwhile, it can reach higher yield of nearly 1.68%, as the hydrothermal method with the yield of only 0.12%. The simple sonication-assisted hydrothermal method has potential applications in exfoliating other layered materials, thus opening new ways to produce other layered materials in high yield and high crystallinity.
基金supported by the National Natural Science Foundation of China(61170147)Scientific Research Project of Zhejiang Provincial Department of Education in China(Y202146796)+2 种基金Natural Science Foundation of Zhejiang Province in China(LTY22F020003)Wenzhou Major Scientific and Technological Innovation Project of China(ZG2021029)Scientific and Technological Projects of Henan Province in China(202102210172).
文摘Integrating Tiny Machine Learning(TinyML)with edge computing in remotely sensed images enhances the capabilities of road anomaly detection on a broader level.Constrained devices efficiently implement a Binary Neural Network(BNN)for road feature extraction,utilizing quantization and compression through a pruning strategy.The modifications resulted in a 28-fold decrease in memory usage and a 25%enhancement in inference speed while only experiencing a 2.5%decrease in accuracy.It showcases its superiority over conventional detection algorithms in different road image scenarios.Although constrained by computer resources and training datasets,our results indicate opportunities for future research,demonstrating that quantization and focused optimization can significantly improve machine learning models’accuracy and operational efficiency.ARM Cortex-M0 gives practical feasibility and substantial benefits while deploying our optimized BNN model on this low-power device:Advanced machine learning in edge computing.The analysis work delves into the educational significance of TinyML and its essential function in analyzing road networks using remote sensing,suggesting ways to improve smart city frameworks in road network assessment,traffic management,and autonomous vehicle navigation systems by emphasizing the importance of new technologies for maintaining and safeguarding road networks.
文摘A higher value of the dielectric constant of h-BN makes it quite favourable material in energy storing device. The variation in dielectric constant was observed as a function of thickness. In this research work multilayers of Hexagonal Boron Nitride (h-BN) was fabricated by using the Chemical exfoliation method. Two solvents Dimethylformamide (DMF) and Isopropyl Alcohol (IPA) were used for the exfoliation of h-BN. Successful sonication of hexagonal boron nitride led to the formation of Boron Nitride nanosheets (BNNs). The stable dispersibility of h-BN in Dimethylformamide and Isopropyl Alcohol was confirmed by UV Visible Spectroscopy, X-ray diffraction (XRD) and Scanning electron microscopy (SEM) confirm the mono crystallite structure (002) and nanoflakes like morphology of h-BN respectively. This appropriate strategy offered a feasible route to produce multilayer of hexagonal boron nitride. After the successful fabrication of h-BN multilayers its dielectric properties were calculated by using LCR meter. Profilometer revealed the variation in thickness and value of Dielectric constant was calculated by using its formula.
文摘Binary neural networks(BNNs)show promising utilization in cost and power-restricted domains such as edge devices and mobile systems.This is due to its significantly less computation and storage demand,but at the cost of degraded performance.To close the accuracy gap,in this paper we propose to add a complementary activation function(AF)ahead of the sign based binarization,and rely on the genetic algorithm(GA)to automatically search for the ideal AFs.These AFs can help extract extra information from the input data in the forward pass,while allowing improved gradient approximation in the backward pass.Fifteen novel AFs are identified through our GA-based search,while most of them show improved performance(up to 2.54%on ImageNet)when testing on different datasets and network models.Interestingly,periodic functions are identified as a key component for most of the discovered AFs,which rarely exist in human designed AFs.Our method offers a novel approach for designing general and application-specific BNN architecture.GAAF will be released on GitHub.
