Stainless steels are used in a wide range of complex environments due to their excellent corrosion resistance.Multiphase stainless steels can offer an excellent combination of strength,toughness and corrosion resistan...Stainless steels are used in a wide range of complex environments due to their excellent corrosion resistance.Multiphase stainless steels can offer an excellent combination of strength,toughness and corrosion resistance due to the coexistence of different microstructures.The microstructure and mechanical properties of a novel cast multiphase stainless steel,composed of martensite,ferrite,and austenite,were investigated following appropriate heat treatment processes:solution treatment at 1,050℃ for 0.5 h followed by water quenching to room temperature,and aging treatment at 500℃ for 4 h followed by water quenching to room temperature.Results show reversed austenite is formed by diffusion of Ni element during aging process,and the enrichment of Ni atoms directly determines the mechanical stability of austenite.The austenite with a lower Ni content undergoes a martensitic transformation during plastic deformation.The tensile strength of the specimen exceeds 1,100 MPa and the elongation exceeds 24%after solid solution,and further increases to 1,247 MPa and 25%after aging treatment.This enhancement is due to the TRIP effect of austenite and the precipitation of the nanoscale G-phase pinning dislocations in ferrite and martensite.展开更多
Foreign tourists'trips to China recently reached a new height,both online and offline.Travel agents around the world also made trips to China,generating more thoughts for the development of new routes and the desi...Foreign tourists'trips to China recently reached a new height,both online and offline.Travel agents around the world also made trips to China,generating more thoughts for the development of new routes and the design of innovative tourist products,and contributing to the development of China's inbound tourism.展开更多
With the advent of deep learning,self-driving schemes based on deep learning are becoming more and more popular.Robust perception-action models should learn from data with different scenarios and real behaviors,while ...With the advent of deep learning,self-driving schemes based on deep learning are becoming more and more popular.Robust perception-action models should learn from data with different scenarios and real behaviors,while current end-to-end model learning is generally limited to training of massive data,innovation of deep network architecture,and learning in-situ model in a simulation environment.Therefore,we introduce a new image style transfer method into data augmentation,and improve the diversity of limited data by changing the texture,contrast ratio and color of the image,and then it is extended to the scenarios that the model has been unobserved before.Inspired by rapid style transfer and artistic style neural algorithms,we propose an arbitrary style generation network architecture,including style transfer network,style learning network,style loss network and multivariate Gaussian distribution function.The style embedding vector is randomly sampled from the multivariate Gaussian distribution and linearly interpolated with the embedded vector predicted by the input image on the style learning network,which provides a set of normalization constants for the style transfer network,and finally realizes the diversity of the image style.In order to verify the effectiveness of the method,image classification and simulation experiments were performed separately.Finally,we built a small-sized smart car experiment platform,and apply the data augmentation technology based on image style transfer drive to the experiment of automatic driving for the first time.The experimental results show that:(1)The proposed scheme can improve the prediction accuracy of the end-to-end model and reduce the model’s error accumulation;(2)the method based on image style transfer provides a new scheme for data augmentation technology,and also provides a solution for the high cost that many deep models rely heavily on a large number of label data.展开更多
An integrated system has been provided with a-Si/H solar cells as energy conversion device,NiCo2O4 battery-supercapacitor hybrid(BSH)as energy storage device,and light emitting diodes(LEDs)as energy utilization device...An integrated system has been provided with a-Si/H solar cells as energy conversion device,NiCo2O4 battery-supercapacitor hybrid(BSH)as energy storage device,and light emitting diodes(LEDs)as energy utilization device.By designing three-dimensional hierarchical NiCo2O4 arrays as faradic electrode,with capacitive electrode of active carbon(AC),BSHs were assembled with energy density of 16.6 Wh kg-1,power density of 7285 W kg-1,long-term stability with 100% retention after 15,000 cycles,and rather low self-discharge.The NiCo2O4//AC BSH was charged to 1.6 V in 1 s by solar cells and acted as reliable sources for powering LEDs.The integrated system is rational for operation,having an overall efficiency of 8.1% with storage efficiency of 74.24%.The integrated system demonstrates a stable solar power conversion,outstanding energy storage behavior,and reliable light emitting.Our study offers a precious strategy to design a self-driven integrated system for highly efficient energy utilization.展开更多
Self-driving and semi-self-driving cars play an important role in our daily lives.The effectiveness of these cars is based heavily on the use of their surrounding areas to collect sensitive and vital information.Howev...Self-driving and semi-self-driving cars play an important role in our daily lives.The effectiveness of these cars is based heavily on the use of their surrounding areas to collect sensitive and vital information.However,external infrastructures also play significant roles in the transmission and reception of control data,cooperative awareness messages,and caution notifications.In this case,roadside units are considered one of themost important communication peripherals.Random distribution of these infrastructures will overburden the spread of self-driving vehicles in terms of cost,bandwidth,connectivity,and radio coverage area.In this paper,a new distributed roadside unit is proposed to enhance the performance and connectivity of these cars.Therefore,this approach is based primarily on k-means to find the optimal location of each roadside unit.In addition,this approach supports dynamicmobility with a long period of connectivity for each car.Further,this system can adapt to various locations(e.g.,highways,rural areas,urban environments).The simulation results of the proposed system are reflected in its efficiency and effectively.Thus,the system can achieve a high connectivity rate with a low error rate while reducing costs.展开更多
The randomness and complexity of urban traffic scenes make it a difficult task for self-driving cars to detect drivable areas, Inspired by human driving behaviors, we propose a novel method of drivable area detection ...The randomness and complexity of urban traffic scenes make it a difficult task for self-driving cars to detect drivable areas, Inspired by human driving behaviors, we propose a novel method of drivable area detection for self-driving cars based on fusing pixel information from a monocular camera with spatial information from a light detection and ranging （LIDAR） scanner, Similar to the bijection of collineation, a new concept called co-point mapping, which is a bijection that maps points from the LIDAR scanner to points on the edge of the image segmentation, is introduced in the proposed method, Our method posi- tions candidate drivable areas through self-learning models based on the initial drivable areas that are obtained by fusing obstacle information with superpixels, In addition, a fusion of four features is applied in order to achieve a more robust performance, In particular, a feature called drivable degree （DD） is pro- posed to characterize the drivable degree of the LIDAR points, After the initial drivable area is characterized by the features obtained through self-learning, a Bayesian framework is utilized to calculate the final probability map of the drivable area, Our approach introduces no common hypothesis and requires no training steps; yet it yields a state-of-art performance when tested on the ROAD-KITTI benchmark, Experimental results demonstrate that the proposed method is a general and efficient approach for detecting drivable area,展开更多
Based on the idea of infinitesimal analysis, we establish the basic model of relation between speed and flow. Since putting a certain amount of self-driving car will affect the average speed of mixed traffic flow, we ...Based on the idea of infinitesimal analysis, we establish the basic model of relation between speed and flow. Since putting a certain amount of self-driving car will affect the average speed of mixed traffic flow, we choose the proportion of self-driving car to be a variable, denoted by k. Based on the least square method, we find two critical values of k that are 38.63% and 68.26%. When k 38.63%, the self-driving cars have a negative influence to the traffic. When 38.63% < k < 68.26%, they have a positive influence to the traffic. When k > 68.26%, they have significant improvement to the traffic capacity of the road.展开更多
基金supported by the Inner Mongolia Autonomous Region Science and Technology Major Special Project(Grant No.2021SZD0082).
文摘Stainless steels are used in a wide range of complex environments due to their excellent corrosion resistance.Multiphase stainless steels can offer an excellent combination of strength,toughness and corrosion resistance due to the coexistence of different microstructures.The microstructure and mechanical properties of a novel cast multiphase stainless steel,composed of martensite,ferrite,and austenite,were investigated following appropriate heat treatment processes:solution treatment at 1,050℃ for 0.5 h followed by water quenching to room temperature,and aging treatment at 500℃ for 4 h followed by water quenching to room temperature.Results show reversed austenite is formed by diffusion of Ni element during aging process,and the enrichment of Ni atoms directly determines the mechanical stability of austenite.The austenite with a lower Ni content undergoes a martensitic transformation during plastic deformation.The tensile strength of the specimen exceeds 1,100 MPa and the elongation exceeds 24%after solid solution,and further increases to 1,247 MPa and 25%after aging treatment.This enhancement is due to the TRIP effect of austenite and the precipitation of the nanoscale G-phase pinning dislocations in ferrite and martensite.
文摘Foreign tourists'trips to China recently reached a new height,both online and offline.Travel agents around the world also made trips to China,generating more thoughts for the development of new routes and the design of innovative tourist products,and contributing to the development of China's inbound tourism.
基金the National Natural Science Foundation of China(51965008)Science and Technology projects of Guizhou[2018]2168Excellent Young Researcher Project of Guizhou[2017]5630.
文摘With the advent of deep learning,self-driving schemes based on deep learning are becoming more and more popular.Robust perception-action models should learn from data with different scenarios and real behaviors,while current end-to-end model learning is generally limited to training of massive data,innovation of deep network architecture,and learning in-situ model in a simulation environment.Therefore,we introduce a new image style transfer method into data augmentation,and improve the diversity of limited data by changing the texture,contrast ratio and color of the image,and then it is extended to the scenarios that the model has been unobserved before.Inspired by rapid style transfer and artistic style neural algorithms,we propose an arbitrary style generation network architecture,including style transfer network,style learning network,style loss network and multivariate Gaussian distribution function.The style embedding vector is randomly sampled from the multivariate Gaussian distribution and linearly interpolated with the embedded vector predicted by the input image on the style learning network,which provides a set of normalization constants for the style transfer network,and finally realizes the diversity of the image style.In order to verify the effectiveness of the method,image classification and simulation experiments were performed separately.Finally,we built a small-sized smart car experiment platform,and apply the data augmentation technology based on image style transfer drive to the experiment of automatic driving for the first time.The experimental results show that:(1)The proposed scheme can improve the prediction accuracy of the end-to-end model and reduce the model’s error accumulation;(2)the method based on image style transfer provides a new scheme for data augmentation technology,and also provides a solution for the high cost that many deep models rely heavily on a large number of label data.
基金the support of National Natural Science Foundation of China (Nos. 51702284 and 21878270)Zhejiang Provincial Natural Science Foundation of China (LR19B060002)+5 种基金the Startup Foundation for Hundred-Talent Program of Zhejiang University(112100-193820101/001/022)the support of Shenzhen Science and Technology Project of China (JCYJ20170412105400428)the support of Zhejiang Provincial Natural Science Foundation of China (LR16F040001)Open Project of Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang Universitythe support of Innovation Platform of Energy Storage Engineering and New Material in Zhejiang University (K19-534202-002)Provincial Innovation Team on Hydrogen Electric Hybrid Power Systems in Zhejiang Province
文摘An integrated system has been provided with a-Si/H solar cells as energy conversion device,NiCo2O4 battery-supercapacitor hybrid(BSH)as energy storage device,and light emitting diodes(LEDs)as energy utilization device.By designing three-dimensional hierarchical NiCo2O4 arrays as faradic electrode,with capacitive electrode of active carbon(AC),BSHs were assembled with energy density of 16.6 Wh kg-1,power density of 7285 W kg-1,long-term stability with 100% retention after 15,000 cycles,and rather low self-discharge.The NiCo2O4//AC BSH was charged to 1.6 V in 1 s by solar cells and acted as reliable sources for powering LEDs.The integrated system is rational for operation,having an overall efficiency of 8.1% with storage efficiency of 74.24%.The integrated system demonstrates a stable solar power conversion,outstanding energy storage behavior,and reliable light emitting.Our study offers a precious strategy to design a self-driven integrated system for highly efficient energy utilization.
文摘Self-driving and semi-self-driving cars play an important role in our daily lives.The effectiveness of these cars is based heavily on the use of their surrounding areas to collect sensitive and vital information.However,external infrastructures also play significant roles in the transmission and reception of control data,cooperative awareness messages,and caution notifications.In this case,roadside units are considered one of themost important communication peripherals.Random distribution of these infrastructures will overburden the spread of self-driving vehicles in terms of cost,bandwidth,connectivity,and radio coverage area.In this paper,a new distributed roadside unit is proposed to enhance the performance and connectivity of these cars.Therefore,this approach is based primarily on k-means to find the optimal location of each roadside unit.In addition,this approach supports dynamicmobility with a long period of connectivity for each car.Further,this system can adapt to various locations(e.g.,highways,rural areas,urban environments).The simulation results of the proposed system are reflected in its efficiency and effectively.Thus,the system can achieve a high connectivity rate with a low error rate while reducing costs.
基金This research was partially supported by the National Natural Science Foundation of China （61773312）, the National Key Research and Development Plan （2017YFC0803905）, and the Program of Introducing Talents of Discipline to University （B13043）.
文摘The randomness and complexity of urban traffic scenes make it a difficult task for self-driving cars to detect drivable areas, Inspired by human driving behaviors, we propose a novel method of drivable area detection for self-driving cars based on fusing pixel information from a monocular camera with spatial information from a light detection and ranging （LIDAR） scanner, Similar to the bijection of collineation, a new concept called co-point mapping, which is a bijection that maps points from the LIDAR scanner to points on the edge of the image segmentation, is introduced in the proposed method, Our method posi- tions candidate drivable areas through self-learning models based on the initial drivable areas that are obtained by fusing obstacle information with superpixels, In addition, a fusion of four features is applied in order to achieve a more robust performance, In particular, a feature called drivable degree （DD） is pro- posed to characterize the drivable degree of the LIDAR points, After the initial drivable area is characterized by the features obtained through self-learning, a Bayesian framework is utilized to calculate the final probability map of the drivable area, Our approach introduces no common hypothesis and requires no training steps; yet it yields a state-of-art performance when tested on the ROAD-KITTI benchmark, Experimental results demonstrate that the proposed method is a general and efficient approach for detecting drivable area,
文摘Based on the idea of infinitesimal analysis, we establish the basic model of relation between speed and flow. Since putting a certain amount of self-driving car will affect the average speed of mixed traffic flow, we choose the proportion of self-driving car to be a variable, denoted by k. Based on the least square method, we find two critical values of k that are 38.63% and 68.26%. When k 38.63%, the self-driving cars have a negative influence to the traffic. When 38.63% < k < 68.26%, they have a positive influence to the traffic. When k > 68.26%, they have significant improvement to the traffic capacity of the road.
