An integrated system has been provided with a-Si/H solar cells as energy conversion device,NiCo_(2)O_(4)battery-supercapacitor hybrid(BSH)as energy storage device,and light emitting diodes(LEDs)as energy utilization d...An integrated system has been provided with a-Si/H solar cells as energy conversion device,NiCo_(2)O_(4)battery-supercapacitor hybrid(BSH)as energy storage device,and light emitting diodes(LEDs)as energy utilization device.By designing three-dimensional hierarchical NiCo_(2)O_(4)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 NiCo_(2)O_(4)//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
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
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
In this article, we used the self-excitation and self-inductance characteristics of polyvinylidene fluoride(PVDF) piezoelectric materials, combined with the powerful signal processing and calculation analysis capabili...In this article, we used the self-excitation and self-inductance characteristics of polyvinylidene fluoride(PVDF) piezoelectric materials, combined with the powerful signal processing and calculation analysis capabilities of integrated circuits, for the first time to explore a set of microcantilever sensor "readout system" without additional driver(self-driving) and can realize self-sensing external signal(self-sensing).It was successfully applied to the unlabeled detection of avian influenza virus(AIV) H9N_(2). The specific force of the antigen-antibody complexes on the surface of the microcantilever leads to the change of the stress of the cantilever, which drives the constructed detection device, and does not require an additional excitation source to drive it, that is, the self-driving part. At the same time, due to the movement of piezoelectric charges in the film caused by the positive piezoelectric effect of the PVDF film, self-inductive charges are generated on the surface of the sensor dielectric. The charge signal is converted into a voltage signal, and the sensing part is completed, that is, self-sensing. The immunosensor has a linear range of100-1000 ng/m L with a detection limit of 2.9 ng/m L. The method will also open up a new avenue for the detection of other analytes based on antigen-antibody responses.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
The self-driving cars are highly developed and about to meet the market, but the driving strategies and corresponding behaviors with others still need to be tested. In this paper, based on its characteristics and beha...The self-driving cars are highly developed and about to meet the market, but the driving strategies and corresponding behaviors with others still need to be tested. In this paper, based on its characteristics and behaviors of manual-driving vehicles, we propose the driving strategies of manual-driving cars as well as self-driving cars. And we use the cellular automaton to simulate the traffic reality under different conditions, and to evaluate the efficiency of a road when self-driving cars are put into use. This research can be a reference by traffic planning and vehicles performance test, and further research can be designed in a model which can calculate the efficiency of a road when the percentage of self-driving cars are different.展开更多
Self-diiviiig tour is one of the most important wajrs for people to travel, and network travel notes actually reflect the traveling information of self-driving tourists. In this paper, witii the network travel notes o...Self-diiviiig tour is one of the most important wajrs for people to travel, and network travel notes actually reflect the traveling information of self-driving tourists. In this paper, witii the network travel notes of self-driving tourists as the tesearch object^ methods such as text analysis and visualization were adopted to study behavior patterns of self-driving tourists, tourism experience, time-space migration, and distribution of tourism resources in Inner Mongolia, fi：om the multiple dimensions of mobile drivers, perceived, dimensions, and spatial migration. The results showed tiiat ①self-cidviiig tourists had a variety of motivations for traveling, in which love for nature dominated; ②self-driving tour destinations were mainly Hulunbuir, Ordos, and Alxa League; ③spatial migration was characterized by obvious seasonal fluctuations. The fesearch on the behavior of self-driving tourists in Inner Mongolia is an important part of the study of the connection between tourism resources and market connection in Inner Mongolia, and is of significance for guiding the theory, practice and poliqr foimuktion of self-doving tours in Inner Mongolia.展开更多
基金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,NiCo_(2)O_(4)battery-supercapacitor hybrid(BSH)as energy storage device,and light emitting diodes(LEDs)as energy utilization device.By designing three-dimensional hierarchical NiCo_(2)O_(4)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 NiCo_(2)O_(4)//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.
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.
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.
基金the financial support from National Natural Science Foundation of China (No. 22102141)the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)+2 种基金Nature Science Foundation of Jiangsu Province No.BK20190905Project for Science and Technology of Yangzhou(No. YZ2020067)the open funds of the Ministry of Education Key Lab for Avian Preventive Medicine (No. YF202020)。
文摘In this article, we used the self-excitation and self-inductance characteristics of polyvinylidene fluoride(PVDF) piezoelectric materials, combined with the powerful signal processing and calculation analysis capabilities of integrated circuits, for the first time to explore a set of microcantilever sensor "readout system" without additional driver(self-driving) and can realize self-sensing external signal(self-sensing).It was successfully applied to the unlabeled detection of avian influenza virus(AIV) H9N_(2). The specific force of the antigen-antibody complexes on the surface of the microcantilever leads to the change of the stress of the cantilever, which drives the constructed detection device, and does not require an additional excitation source to drive it, that is, the self-driving part. At the same time, due to the movement of piezoelectric charges in the film caused by the positive piezoelectric effect of the PVDF film, self-inductive charges are generated on the surface of the sensor dielectric. The charge signal is converted into a voltage signal, and the sensing part is completed, that is, self-sensing. The immunosensor has a linear range of100-1000 ng/m L with a detection limit of 2.9 ng/m L. The method will also open up a new avenue for the detection of other analytes based on antigen-antibody responses.
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.
文摘The self-driving cars are highly developed and about to meet the market, but the driving strategies and corresponding behaviors with others still need to be tested. In this paper, based on its characteristics and behaviors of manual-driving vehicles, we propose the driving strategies of manual-driving cars as well as self-driving cars. And we use the cellular automaton to simulate the traffic reality under different conditions, and to evaluate the efficiency of a road when self-driving cars are put into use. This research can be a reference by traffic planning and vehicles performance test, and further research can be designed in a model which can calculate the efficiency of a road when the percentage of self-driving cars are different.
基金Sponsored by Scientific Research Projects of Colleges and Universities in the Inner Mongolia Autonomous Region(NJSY018)
文摘Self-diiviiig tour is one of the most important wajrs for people to travel, and network travel notes actually reflect the traveling information of self-driving tourists. In this paper, witii the network travel notes of self-driving tourists as the tesearch object^ methods such as text analysis and visualization were adopted to study behavior patterns of self-driving tourists, tourism experience, time-space migration, and distribution of tourism resources in Inner Mongolia, fi：om the multiple dimensions of mobile drivers, perceived, dimensions, and spatial migration. The results showed tiiat ①self-cidviiig tourists had a variety of motivations for traveling, in which love for nature dominated; ②self-driving tour destinations were mainly Hulunbuir, Ordos, and Alxa League; ③spatial migration was characterized by obvious seasonal fluctuations. The fesearch on the behavior of self-driving tourists in Inner Mongolia is an important part of the study of the connection between tourism resources and market connection in Inner Mongolia, and is of significance for guiding the theory, practice and poliqr foimuktion of self-doving tours in Inner Mongolia.
