This study investigated the problems of non-cooperative target recognition and relative motion estimation during spacecraft rendezvous maneuvers.A structure integrating an Inertial Measurement Unit(IMU)and a visual ca...This study investigated the problems of non-cooperative target recognition and relative motion estimation during spacecraft rendezvous maneuvers.A structure integrating an Inertial Measurement Unit(IMU)and a visual camera was presented.The angular velocity output of the IMU was used to calculate the motion trajectories of star points in multiple image frames,which can highlight the motion of non-cooperative targets with respect to the image background to improve the probability of target recognition.To solve the problem of target misidentification caused by new star points entering the field of view,a target-tracking link based on IMU prediction was introduced to track the position of the target in the image.Furthermore,a measurement model was constructed using the line-of-sight vector generated from target recognition,and the relative motion state was estimated using a Huber-based non-linear filter.Semi-physical and numerical simulations were performed to evaluate the effectiveness and efficiency of the proposed method.展开更多
Reward-modulated spike-timing-dependent plasticity(R-STDP)is a promising biomimetic learning rule in neuromorphic intelligent systems for implementing tasks in variable environments.Nevertheless,realizing R-STDP in a ...Reward-modulated spike-timing-dependent plasticity(R-STDP)is a promising biomimetic learning rule in neuromorphic intelligent systems for implementing tasks in variable environments.Nevertheless,realizing R-STDP in a single synaptic device for building compact and energy-efficient neuromorphic systems remains challenging.Here,we report a two-dimensional ferroelectric memtransistor to emulate the RSTDP learning rule by effectively reconfiguring the STDP and anti-STDP.The thermionic emission and tunneling behavior of charges at the ferroelectric interface can be regulated via vertical electric field in a multi-terminal manner,allowing for controllable polarization reversal of synaptic plasticity and transition between STDP and anti-STDP.This enables faithful realization of the R-STDP feature in a single device with energy consumption of~1.3 nJ(the lowest known to date),approximately 10^(6) times lower than that of its complementary metal-oxide-semiconductor(CMos)counterpart.By leveraging the synaptic characteristics in the hardware device,we construct spiking neural networks(SNNs)trained with R-STDP to perform robotic recognition and tracking tasks.The SNN achieves 95.1% accuracy on the MNIST dataset using only 8000 parameters,and faster convergence speed requiring only one data batch with 100% inference in the few-shot learning task.Moreover,a robotic arm motion control system configured with R-STDP exhibits 85.5% success rate in tracking both the static and moving targets,illustrating its outstanding adaptability to the dynamic environments.This work provides a potential hardware building block to support compact neuromorphic systems for the application of interactive artificialintelligenceagents.展开更多
The semicylindrical time projection chamber(scTPC)is designed to measure the angular distribution of the cross section for intermediate-energy(3He,t)charge-exchange reactions in inverse kinematics.The scTPC prototype ...The semicylindrical time projection chamber(scTPC)is designed to measure the angular distribution of the cross section for intermediate-energy(3He,t)charge-exchange reactions in inverse kinematics.The scTPC prototype comprises a cathode,field cage,drift region,amplification structure based on a multilayer thick gas electron multiplier(THGEM),and a readout plane with 886 zigzag-shaped pads.The gain uniformity of the THGEM and the drift velocity of electrons were calibrated.Track recognition based on the Hough transform was then developed to reconstruct cosmic ray tracks and determine their position resolution.The position resolution of secondary particle tracks resulting from collisions between the heavy-ion beam and the 3He target was measured,yielding an x-resolution of 0.71 mm and a z-resolution of 0.73 mm.The scTPC demonstrates sufficient energy and spatial resolution to support charge-exchange reaction experiments in inverse kinematics.展开更多
Apple fruits on trees tend to swing because of wind or other natural causes,therefore reducing the accuracy of apple picking by robots.To increase the accuracy and to speed up the apple tracking and identifying proces...Apple fruits on trees tend to swing because of wind or other natural causes,therefore reducing the accuracy of apple picking by robots.To increase the accuracy and to speed up the apple tracking and identifying process,tracking and recognition method combined with an affine transformation was proposed.The method can be divided into three steps.First,the initial image was segmented by Otsu’s thresholding method based on the two times Red minus Green minus Blue(2R-G-B)color feature;after improving the binary image,the apples were recognized with a local parameter adaptive Hough circle transformation method,thus improving the accuracy of recognition and avoiding the long,time-consuming process and excessive fitted circles in traditional Hough circle transformation.The process and results were verified experimentally.Second,the Shi-Tomasi corners detected and extracted from the first frame image were tracked,and the corners with large positive and negative optical flow errors were removed.The affine transformation matrix between the two frames was calculated based on the Random Sampling Consistency algorithm(RANSAC)to correct the scale of the template image and predict the apple positions.Third,the best positions of the target apples within 1.2 times of the prediction area were searched with a de-mean normalized cross-correlation template matching algorithm.The test results showed that the running time of each frame was 25 ms and 130 ms and the tracking error was more than 8%and 20%in the absence of template correction and apple position prediction,respectively.In comparison,the running time of our algorithm was 25 ms,and the tracking error was less than 4%.Therefore,test results indicate that speed and efficiency can be greatly improved by using our method,and this strategy can also provide a reference for tracking and recognizing other oscillatory fruits.展开更多
Purpose-In response to these shortcomings,this paper proposes a dynamic obstacle detection and tracking method based on multi-feature fusion and a dynamic obstacle recognition method based on spatio-temporal feature v...Purpose-In response to these shortcomings,this paper proposes a dynamic obstacle detection and tracking method based on multi-feature fusion and a dynamic obstacle recognition method based on spatio-temporal feature vectors.Design/methodology/approach-The existing dynamic obstacle detection and tracking methods based on geometric features have a high false detection rate.The recognition methods based on the geometric features and motion status of dynamic obstacles are greatly affected by distance and scanning angle,and cannot meet the requirements of real traffic scene applications.Findings-First,based on the geometric features of dynamic obstacles,the obstacles are considered The echo pulse width feature is used to improve the accuracy of obstacle detection and tracking;second,the space-time feature vector is constructed based on the time dimension and space dimension information of the obstacle,and then the support vector machine method is used to realize the recognition of dynamic obstacles to improve the obstacle The accuracy of object recognition.Finally,the accuracy and effectiveness of the proposed method are verified by real vehicle tests.Originality/value-The paper proposes a dynamic obstacle detection and tracking method based on multi-feature fusion and a dynamic obstacle recognition method based on spatio-temporal feature vectors.The accuracy and effectiveness of the proposed method are verified by real vehicle tests.展开更多
In this paper,deep learning technology was utilited to solve the railway track recognition in intrusion detection problem.The railway track recognition can be viewed as semantic segmentation task which extends image p...In this paper,deep learning technology was utilited to solve the railway track recognition in intrusion detection problem.The railway track recognition can be viewed as semantic segmentation task which extends image processing to pixel level prediction.An encoder-decoder architecture DeepLabv3+model was applied in this work due to its good performance in semantic segmentation task.Since images of the railway track collected from the video surveillance of the train cab were used as experiment dataset in this work,the following improvements were made to the model.The first aspect deals with over-fitting problem due to the limited amount of training data.Data augmentation and transfer learning are applied consequently to rich the diversity of data and enhance model robustness during the training process.Besides,different gradient descent methods are compared to obtain the optimal optimizer for training model parameters.The third problem relates to data sample imbalance,cross entropy(CE)loss is replaced by focal loss(FL)to address the issue of serious imbalance between positive and negative sample.Effectiveness of the improved DeepLabv3+model with above solutions is demonstrated by experiment results with different system parameters.展开更多
基金funded by the China Postdoctoral Science Foundation(No.2023M730337)。
文摘This study investigated the problems of non-cooperative target recognition and relative motion estimation during spacecraft rendezvous maneuvers.A structure integrating an Inertial Measurement Unit(IMU)and a visual camera was presented.The angular velocity output of the IMU was used to calculate the motion trajectories of star points in multiple image frames,which can highlight the motion of non-cooperative targets with respect to the image background to improve the probability of target recognition.To solve the problem of target misidentification caused by new star points entering the field of view,a target-tracking link based on IMU prediction was introduced to track the position of the target in the image.Furthermore,a measurement model was constructed using the line-of-sight vector generated from target recognition,and the relative motion state was estimated using a Huber-based non-linear filter.Semi-physical and numerical simulations were performed to evaluate the effectiveness and efficiency of the proposed method.
基金supported by the National Key Research and Development Program of China(2023YFA1407800 and 2024YFA1410700)the National Natural Science Foundation of China(62374038,62104041,62204051,and 62374040)+1 种基金the Natural Science Foundation of Shanghai(22ZR1405700)the Shanghai Rising-Star Program(22QA1401000 and 24QA2700400).
文摘Reward-modulated spike-timing-dependent plasticity(R-STDP)is a promising biomimetic learning rule in neuromorphic intelligent systems for implementing tasks in variable environments.Nevertheless,realizing R-STDP in a single synaptic device for building compact and energy-efficient neuromorphic systems remains challenging.Here,we report a two-dimensional ferroelectric memtransistor to emulate the RSTDP learning rule by effectively reconfiguring the STDP and anti-STDP.The thermionic emission and tunneling behavior of charges at the ferroelectric interface can be regulated via vertical electric field in a multi-terminal manner,allowing for controllable polarization reversal of synaptic plasticity and transition between STDP and anti-STDP.This enables faithful realization of the R-STDP feature in a single device with energy consumption of~1.3 nJ(the lowest known to date),approximately 10^(6) times lower than that of its complementary metal-oxide-semiconductor(CMos)counterpart.By leveraging the synaptic characteristics in the hardware device,we construct spiking neural networks(SNNs)trained with R-STDP to perform robotic recognition and tracking tasks.The SNN achieves 95.1% accuracy on the MNIST dataset using only 8000 parameters,and faster convergence speed requiring only one data batch with 100% inference in the few-shot learning task.Moreover,a robotic arm motion control system configured with R-STDP exhibits 85.5% success rate in tracking both the static and moving targets,illustrating its outstanding adaptability to the dynamic environments.This work provides a potential hardware building block to support compact neuromorphic systems for the application of interactive artificialintelligenceagents.
基金was supported by National Key R&D Program of China(No.2022YFE0103900)the National Natural Science Foundation of China(Nos.11875301,11875302,U1867214,U1832105,U2032166 and U1832167)+2 种基金the Fundamental Research Funds for the Central Universities(No.lzujbky-2022-sp06)the CAS“Light of West China”Programthe Heavy Ion Research Facility in Lanzhou。
文摘The semicylindrical time projection chamber(scTPC)is designed to measure the angular distribution of the cross section for intermediate-energy(3He,t)charge-exchange reactions in inverse kinematics.The scTPC prototype comprises a cathode,field cage,drift region,amplification structure based on a multilayer thick gas electron multiplier(THGEM),and a readout plane with 886 zigzag-shaped pads.The gain uniformity of the THGEM and the drift velocity of electrons were calibrated.Track recognition based on the Hough transform was then developed to reconstruct cosmic ray tracks and determine their position resolution.The position resolution of secondary particle tracks resulting from collisions between the heavy-ion beam and the 3He target was measured,yielding an x-resolution of 0.71 mm and a z-resolution of 0.73 mm.The scTPC demonstrates sufficient energy and spatial resolution to support charge-exchange reaction experiments in inverse kinematics.
基金This work was financially supported by Basic Public Welfare Research Project of Zhejiang Province(Grant No.LGN20E050007).
文摘Apple fruits on trees tend to swing because of wind or other natural causes,therefore reducing the accuracy of apple picking by robots.To increase the accuracy and to speed up the apple tracking and identifying process,tracking and recognition method combined with an affine transformation was proposed.The method can be divided into three steps.First,the initial image was segmented by Otsu’s thresholding method based on the two times Red minus Green minus Blue(2R-G-B)color feature;after improving the binary image,the apples were recognized with a local parameter adaptive Hough circle transformation method,thus improving the accuracy of recognition and avoiding the long,time-consuming process and excessive fitted circles in traditional Hough circle transformation.The process and results were verified experimentally.Second,the Shi-Tomasi corners detected and extracted from the first frame image were tracked,and the corners with large positive and negative optical flow errors were removed.The affine transformation matrix between the two frames was calculated based on the Random Sampling Consistency algorithm(RANSAC)to correct the scale of the template image and predict the apple positions.Third,the best positions of the target apples within 1.2 times of the prediction area were searched with a de-mean normalized cross-correlation template matching algorithm.The test results showed that the running time of each frame was 25 ms and 130 ms and the tracking error was more than 8%and 20%in the absence of template correction and apple position prediction,respectively.In comparison,the running time of our algorithm was 25 ms,and the tracking error was less than 4%.Therefore,test results indicate that speed and efficiency can be greatly improved by using our method,and this strategy can also provide a reference for tracking and recognizing other oscillatory fruits.
文摘Purpose-In response to these shortcomings,this paper proposes a dynamic obstacle detection and tracking method based on multi-feature fusion and a dynamic obstacle recognition method based on spatio-temporal feature vectors.Design/methodology/approach-The existing dynamic obstacle detection and tracking methods based on geometric features have a high false detection rate.The recognition methods based on the geometric features and motion status of dynamic obstacles are greatly affected by distance and scanning angle,and cannot meet the requirements of real traffic scene applications.Findings-First,based on the geometric features of dynamic obstacles,the obstacles are considered The echo pulse width feature is used to improve the accuracy of obstacle detection and tracking;second,the space-time feature vector is constructed based on the time dimension and space dimension information of the obstacle,and then the support vector machine method is used to realize the recognition of dynamic obstacles to improve the obstacle The accuracy of object recognition.Finally,the accuracy and effectiveness of the proposed method are verified by real vehicle tests.Originality/value-The paper proposes a dynamic obstacle detection and tracking method based on multi-feature fusion and a dynamic obstacle recognition method based on spatio-temporal feature vectors.The accuracy and effectiveness of the proposed method are verified by real vehicle tests.
基金the Key Special Project in Intergovernmental International Scientific and Technological Innovation Cooperation of the National Key Research and Development Program of China(2017YFE0118600)。
文摘In this paper,deep learning technology was utilited to solve the railway track recognition in intrusion detection problem.The railway track recognition can be viewed as semantic segmentation task which extends image processing to pixel level prediction.An encoder-decoder architecture DeepLabv3+model was applied in this work due to its good performance in semantic segmentation task.Since images of the railway track collected from the video surveillance of the train cab were used as experiment dataset in this work,the following improvements were made to the model.The first aspect deals with over-fitting problem due to the limited amount of training data.Data augmentation and transfer learning are applied consequently to rich the diversity of data and enhance model robustness during the training process.Besides,different gradient descent methods are compared to obtain the optimal optimizer for training model parameters.The third problem relates to data sample imbalance,cross entropy(CE)loss is replaced by focal loss(FL)to address the issue of serious imbalance between positive and negative sample.Effectiveness of the improved DeepLabv3+model with above solutions is demonstrated by experiment results with different system parameters.
