Aiming at the problem of low accuracy of traditional target detection methods for target detection in endoscopes in substation environments, a CNN-based real-time detection method for masked targets is proposed. The m...Aiming at the problem of low accuracy of traditional target detection methods for target detection in endoscopes in substation environments, a CNN-based real-time detection method for masked targets is proposed. The method adopts the overall design of backbone network, detection network and algorithmic parameter optimisation method, completes the model training on the self-constructed occlusion target dataset, and adopts the multi-scale perception method for target detection. The HNM algorithm is used to screen positive and negative samples during the training process, and the NMS algorithm is used to post-process the prediction results during the detection process to improve the detection efficiency. After experimental validation, the obtained model has the multi-class average predicted value (mAP) of the dataset. It has general advantages over traditional target detection methods. The detection time of a single target on FDDB dataset is 39 ms, which can meet the need of real-time target detection. In addition, the project team has successfully deployed the method into substations and put it into use in many places in Beijing, which is important for achieving the anomaly of occlusion target detection.展开更多
A super redundant serpentine manipulator has slender structure and multiple degrees of freedom.It can travel through narrow spaces and move in complex spaces.This manipulator is composed of many modules that can form ...A super redundant serpentine manipulator has slender structure and multiple degrees of freedom.It can travel through narrow spaces and move in complex spaces.This manipulator is composed of many modules that can form different lengths of robot arms for different application sites.The increase in degrees of freedom causes the inverse kinematics of redundant manipulator to be typical and immensely increases the calculation load in the joint space.This paper presents an integrated optimization method to solve the path planning for obstacle avoidance and discrete trajectory tracking of a super redundant manipulator.In this integrated optimization,path planning is established on a Bezier curve,and particle swarm optimization is adopted to adjust the control points of the Bezier curve with the kinematic constraints of manipulator.A feasible obstacle avoidance path is obtained along with a discrete trajectory tracking by using a follow-the-leader strategy.The relative distance between each two discrete path points is limited to reduce the fitting error of the connecting rigid links to the smooth curve.Simulation results show that this integrated optimization method can rapidly search for the appropriate trajectory to guide the manipulator in obtaining the target while achieving obstacle avoidance and meeting joint constraints.The proposed algorithm is suitable for 3D space obstacle avoidance and multitarget path tracking.展开更多
Joint probabilistic data association is an effective method for tracking multiple targets in clutter, but only the target kinematic information is used in measure-to-track association. If the kinematic likelihoods are...Joint probabilistic data association is an effective method for tracking multiple targets in clutter, but only the target kinematic information is used in measure-to-track association. If the kinematic likelihoods are similar for different closely spaced targets, there is ambiguity in using the kinematic information alone; the cor-rect association probability will decrease in conventional joint probabilistic data association algorithm and track coalescence will occur easily. A modified algorithm of joint probabilistic data association with classification-aided is presented, which avoids track coalescence when tracking multiple neighboring targets. Firstly, an identification matrix is defined, which is used to simplify validation matrix to decrease computational complexity. Then, target class information is integrated into the data association process. Performance comparisons with and without the use of class information in JPDA are presented on multiple closely spaced maneuvering targets tracking problem. Simulation results quantify the benefits of classification-aided JPDA for improved multiple targets tracking, espe-cially in the presence of association uncertainty in the kinematic measurement and target maneuvering. Simulation results indicate that the algorithm is valid.展开更多
This study firstly defines a set of arrangement rule for perforated holes of multi-hole orifice(MO), and then presents three critical geometrical parameters including total number of performated hole, equivalent diame...This study firstly defines a set of arrangement rule for perforated holes of multi-hole orifice(MO), and then presents three critical geometrical parameters including total number of performated hole, equivalent diameter ratio and distribution density of perforated holes, which are to quantify MO structure. This paper built the throttling test apparatus for nice test MO plates, which were designed according to orthogonal theory. The experiments were conducted to investigate the effect of three critical geometerical parameters on the pressure loss coefficient of test MOs. Results show that equivalent diameter ratio is the dominant prameter affecting MO throttling characterstic.展开更多
The problem of two order statistics detection schemes for the detection of a spatially distributed target in white Gaussian noise are studied.When the number of strong scattering cells is known,we first show an optima...The problem of two order statistics detection schemes for the detection of a spatially distributed target in white Gaussian noise are studied.When the number of strong scattering cells is known,we first show an optimal detector,which requires many processing channels.The structure of such optimal detector is complex.Therefore,a simpler quasi-optimal detector is then introduced.The quasi-optimal detector,called the strong scattering cells' number dependent order statistics(SND-OS) detector,takes the form of an average of maximum strong scattering cells with a known number.If the number of strong scattering cells is unknown in real situation,the multi-channel order statistics(MC-OS) detector is used.In each channel,a various number of maximums scattered from target are averaged.Then,the false alarm probability analysis and thresholds sets for each channel are given,following the detection results presented by means of Monte Carlo simulation strategy based on simulated target model and three measured targets.In particular,the theoretical analysis and simulation results highlight that the MC-OS detector can efficiently detect range-spread targets in white Gaussian noise.展开更多
This paper considers the problems of target tracking and obstacle avoidance for multi-agent systems.To solve the problem that multiple agents cannot effectively track the target while avoiding obstacle in dynamic envi...This paper considers the problems of target tracking and obstacle avoidance for multi-agent systems.To solve the problem that multiple agents cannot effectively track the target while avoiding obstacle in dynamic environment,a novel control algorithm based on potential function and behavior rules is proposed.Meanwhile,the interactions among agents are also considered.According to the state whether an agent is within the area of its neighbors influence,two kinds of potential functions are presented.Meanwhile,the distributed control input of each agent is determined by relative velocities as well as relative positions among agents,target and obstacle.The maximum linear speed of the agents is also discussed.Finally,simulation studies are given to demonstrate the performance of the proposed algorithm.展开更多
A decision-making problem of missile-target assignment with a novel particle swarm optimization algorithm is proposed when it comes to a multiple target collaborative combat situation.The threat function is establishe...A decision-making problem of missile-target assignment with a novel particle swarm optimization algorithm is proposed when it comes to a multiple target collaborative combat situation.The threat function is established to describe air combat situation.Optimization function is used to find an optimal missile-target assignment.An improved particle swarm optimization algorithm is utilized to figure out the optimization function with less parameters,which is based on the adaptive random learning approach.According to the coordinated attack tactics,there are some adjustments to the assignment.Simulation example results show that it is an effective algorithm to handle with the decision-making problem of the missile-target assignment(MTA)in air combat.展开更多
Multi-target tracking(MTT) is a research hotspot of wireless sensor networks at present.A self-organized dynamic cluster task allocation scheme is used to implement collaborative task allocation for MTT in WSN and a s...Multi-target tracking(MTT) is a research hotspot of wireless sensor networks at present.A self-organized dynamic cluster task allocation scheme is used to implement collaborative task allocation for MTT in WSN and a special cluster member(CM) node selection method is put forward in the scheme.An energy efficiency model was proposed under consideration of both energy consumption and remaining energy balance in the network.A tracking accuracy model based on area-sum principle was also presented through analyzing the localization accuracy of triangulation.Then,the two models mentioned above were combined to establish dynamic cluster member selection model for MTT where a comprehensive performance index function was designed to guide the CM node selection.This selection was fulfilled using genetic algorithm.Simulation results show that this method keeps both energy efficiency and tracking quality in optimal state,and also indicate the validity of genetic algorithm in implementing CM node selection.展开更多
A throttling experiment for the multi-hole orifice (MO) using water was conducted based on the conclusion of key parameters affecting the MO throttling performance. Testing MOs and standard orifice plates (SOs) were d...A throttling experiment for the multi-hole orifice (MO) using water was conducted based on the conclusion of key parameters affecting the MO throttling performance. Testing MOs and standard orifice plates (SOs) were designed for the throttling experiment to compare the throttling effect using the equivalent diameter ratio (RED) and diameter ratio (RD) as key parameters, respectively. Meanwhile, effective metrical conditions were provided for experimental accuracy. The throttling model form was determined according to the theoretical throttling model of SO. Then the unknown parameters involved were identified by experimental data. A good concordance between the modeling computation and experimental results shows a validation of the MO throtting model.展开更多
In this paper, the problems of target tracking and obstacle avoidance for multi-agent networks with input constraints are investigated. When there is a moving obstacle, the control objectives are to make the agents tr...In this paper, the problems of target tracking and obstacle avoidance for multi-agent networks with input constraints are investigated. When there is a moving obstacle, the control objectives are to make the agents track a moving target and to avoid collisions among agents. First, without considering the input constraints, a novel distributed controller can be obtained based on the potential function. Second, at each sampling time, the control algorithm is optimized. Furthermore, to solve the problem that agents cannot effectively avoid the obstacles in dynamic environment where the obstacles are moving, a new velocity repulsive potential is designed. One advantage of the designed control algorithm is that each agent only requires local knowledge of its neighboring agents. Finally, simulation results are provided to verify the effectiveness of the proposed approach.展开更多
Much research mainly focuses on the batch processing method (e.g. maximum likelihood method) when bearings-only multiple targets tracking of bistatic sonar system is considered. In this paper, the idea of recursive pr...Much research mainly focuses on the batch processing method (e.g. maximum likelihood method) when bearings-only multiple targets tracking of bistatic sonar system is considered. In this paper, the idea of recursive processing method is presented and employed, and corresponding data association algorithms, i.e. a multi-objective ant-colony-based optimization algorithm and an easy fast assignment algorithm are developed to solve the measurements-to-measurements and measurements-to-tracks data association problems of bistatic sonar system, respectively. Monte-Carlo simulations are induced to evaluate the effectiveness of the proposed methods.展开更多
Aiming at the effective realization of particle filter for maneuvering target tracking in multi-sensor measurements,a novel multi-sensor multiple model particle filtering algorithm with correlated noises is proposed.C...Aiming at the effective realization of particle filter for maneuvering target tracking in multi-sensor measurements,a novel multi-sensor multiple model particle filtering algorithm with correlated noises is proposed.Combined with the kinetic evolution equation of target state,a multi-sensor multiple model particle filter is firstly constructed,which is also used as the basic framework of a new algorithm.In the new algorithm,in order to weaken the adverse influence from random measurement noises in the measuring process of particle weight,a weight optimization strategy is introduced to improve the reliability and stability of particle weight.In addition,considering the correlated noise existing in the practical engineering,a decoupling method of correlated noise is given by the rearrangement and transformation of the state transition equation and measurement equation.Since the weight optimization strategy and noise decoupling method adopt respectively the center fusion structure and the off-hne way,it improves the adverse effect effectively on computational complexity for increasing state dimension and sensor number.Finally,the theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm.展开更多
An algorithm of highly maneuvering target tracking is proposed to solve the problem of large tracking error caused by strong maneuver. In this algorithm, a new estimator, named as multi-parameter fusion Singer(MF-Sing...An algorithm of highly maneuvering target tracking is proposed to solve the problem of large tracking error caused by strong maneuver. In this algorithm, a new estimator, named as multi-parameter fusion Singer(MF-Singer) model is derived based on the Singer model and the fuzzy reasoning method by using radial acceleration and velocity of the target, and applied to the problem of maneuvering target tracking in strong maneuvering environment and operating environment. The tracking performance of the MF-Singer model is evaluated and compared with other manuevering tracking models. It is shown that the MF-Singer model outperforms these algorithms in several examples.展开更多
This paper proposed a robust method based on the definition of Mahalanobis distance to track ground moving target. The feature and the geometry of airborne ground moving target tracking systems are studied at first. B...This paper proposed a robust method based on the definition of Mahalanobis distance to track ground moving target. The feature and the geometry of airborne ground moving target tracking systems are studied at first. Based on this feature, the assignment relation of time-nearby target is calculated via Mahalanobis distance, and then the corresponding transformation formula is deduced. The simulation results show the correctness and effectiveness of the proposed method.展开更多
The acute effect of acupuncture on Alzheimer’s disease,i.e.,on brain activation during treatment,has been reported.However,the effect of long-term acupuncture on brain activation in Alzheimer’s disease is unclear.Th...The acute effect of acupuncture on Alzheimer’s disease,i.e.,on brain activation during treatment,has been reported.However,the effect of long-term acupuncture on brain activation in Alzheimer’s disease is unclear.Therefore,in this study,we performed long-term needling at Zusanli(ST36)or a sham point(1.5 mm lateral to ST36)in a rat Alzheimer’s disease model,for 30 minutes,once per day,for 30 days.The rats underwent 18F-fluorodeoxyglucose positron emission tomography scanning.Positron emission tomography images were processed with SPM2.The brain areas activated after needling at ST36 included the left hippocampus,the left orbital cortex,the left infralimbic cortex,the left olfactory cortex,the left cerebellum and the left pons.In the sham-point group,the activated regions were similar to those in the ST36 group.However,the ST36 group showed greater activation in the cerebellum and pons than the sham-point group.These findings suggest that long-term acupuncture treatment has targeted regulatory effects on multiple brain regions in rats with Alzheimer’s disease.展开更多
A multi-hole vertical-cavity surface-emitting laser(VCSEL) operating in stable single mode with a low threshold current was produced by introducing multi-leaf scallop holes on the top distributed Bragg-reflector of an...A multi-hole vertical-cavity surface-emitting laser(VCSEL) operating in stable single mode with a low threshold current was produced by introducing multi-leaf scallop holes on the top distributed Bragg-reflector of an oxidationconfined 850 nm VCSEL.The single-mode output power of 2.6 mW,threshold current of 0.6 mA,full width of half maximum lasing spectrum of less than 0.1 nm,side mode suppression ratio of 28.4 dB,and far-field divergence angle of about 10 are obtained.The effects of different hole depths on the optical characteristics are simulated and analysed,including far-field divergence,spectrum and lateral cavity mode.The single-mode performance of this multi-hole device is attributed to the large radiation loss from the inter-hole spacing and the scattering loss at the bottom of the holes,particularly for higher order modes.展开更多
In order to improve the performance of the probability hypothesis density(PHD) algorithm based particle filter(PF) in terms of number estimation and states extraction of multiple targets, a new probability hypothesis ...In order to improve the performance of the probability hypothesis density(PHD) algorithm based particle filter(PF) in terms of number estimation and states extraction of multiple targets, a new probability hypothesis density filter algorithm based on marginalized particle and kernel density estimation is proposed, which utilizes the idea of marginalized particle filter to enhance the estimating performance of the PHD. The state variables are decomposed into linear and non-linear parts. The particle filter is adopted to predict and estimate the nonlinear states of multi-target after dimensionality reduction, while the Kalman filter is applied to estimate the linear parts under linear Gaussian condition. Embedding the information of the linear states into the estimated nonlinear states helps to reduce the estimating variance and improve the accuracy of target number estimation. The meanshift kernel density estimation, being of the inherent nature of searching peak value via an adaptive gradient ascent iteration, is introduced to cluster particles and extract target states, which is independent of the target number and can converge to the local peak position of the PHD distribution while avoiding the errors due to the inaccuracy in modeling and parameters estimation. Experiments show that the proposed algorithm can obtain higher tracking accuracy when using fewer sampling particles and is of lower computational complexity compared with the PF-PHD.展开更多
Detection and tracking of multi-target with unknown and varying number is a challenging issue, especially under the condition of low signal-to-noise ratio(SNR). A modified multi-target track-before-detect(TBD) method ...Detection and tracking of multi-target with unknown and varying number is a challenging issue, especially under the condition of low signal-to-noise ratio(SNR). A modified multi-target track-before-detect(TBD) method was proposed to tackle this issue using a nonstandard point observation model. The method was developed from sequential Monte Carlo(SMC)-based probability hypothesis density(PHD) filter, and it was implemented by modifying the original calculation in update weights of the particles and by adopting an adaptive particle sampling strategy. To efficiently execute the SMC-PHD based TBD method, a fast implementation approach was also presented by partitioning the particles into multiple subsets according to their position coordinates in 2D resolution cells of the sensor. Simulation results show the effectiveness of the proposed method for time-varying multi-target tracking using raw observation data.展开更多
We propose a high-performance path planning algorithm for automatic target tracking in the applications of real-time simulation and visualization of large-scale terrain datasets, with a large number of moving objects ...We propose a high-performance path planning algorithm for automatic target tracking in the applications of real-time simulation and visualization of large-scale terrain datasets, with a large number of moving objects (such as vehicles) tracking multiple moving targets. By using a modified Dijkstra’s algorithm, an optimal path between each vehicle-target pair over a weighted grid-presented terrain is computed and updated to eliminate the problem of local minima and losing of tracking. Then, a dynamic path re-planning strategy using multi-resolution representation of a dynamic updating region is proposed to achieve high-performance by trading-off precision for efficiency, while guaranteeing accuracy. Primary experimental results showed that our algorithm successfully achieved 10 to 96 frames per second interactive path-replanning rates during a terrain simulation scenario with 10 to 100 vehicles and multiple moving targets.展开更多
This paper introduces an approach for visual tracking of multi-target with occlusion occurrence. Based on the author's previous work in which the Overlap Coefficient (OC) is used to detect the occlusion, in this p...This paper introduces an approach for visual tracking of multi-target with occlusion occurrence. Based on the author's previous work in which the Overlap Coefficient (OC) is used to detect the occlusion, in this paper a method of combining Bhattacharyya Coefficient (BC) and Kalman filter innovation term is proposed as the criteria for jointly detecting the occlusion occurrence. Fragmentation of target is introduced in order to closely monitor the occlusion development. In the course of occlusion, the Kalman predictor is applied to determine the location of the occluded target, and the criterion for checking the re-appearance of the occluded target is also presented. The proposed approach is put to test on a standard video sequence, suggesting the satisfactory performance in multi-target tracking.展开更多
文摘Aiming at the problem of low accuracy of traditional target detection methods for target detection in endoscopes in substation environments, a CNN-based real-time detection method for masked targets is proposed. The method adopts the overall design of backbone network, detection network and algorithmic parameter optimisation method, completes the model training on the self-constructed occlusion target dataset, and adopts the multi-scale perception method for target detection. The HNM algorithm is used to screen positive and negative samples during the training process, and the NMS algorithm is used to post-process the prediction results during the detection process to improve the detection efficiency. After experimental validation, the obtained model has the multi-class average predicted value (mAP) of the dataset. It has general advantages over traditional target detection methods. The detection time of a single target on FDDB dataset is 39 ms, which can meet the need of real-time target detection. In addition, the project team has successfully deployed the method into substations and put it into use in many places in Beijing, which is important for achieving the anomaly of occlusion target detection.
基金Supported by National Natural Science Foundation of China(Grant No.61733017)Foundation of State Key Laboratory of Robotics of China(Grant No.2018O13)Shanghai Pujiang Program of China(Grant No.18PJD018).
文摘A super redundant serpentine manipulator has slender structure and multiple degrees of freedom.It can travel through narrow spaces and move in complex spaces.This manipulator is composed of many modules that can form different lengths of robot arms for different application sites.The increase in degrees of freedom causes the inverse kinematics of redundant manipulator to be typical and immensely increases the calculation load in the joint space.This paper presents an integrated optimization method to solve the path planning for obstacle avoidance and discrete trajectory tracking of a super redundant manipulator.In this integrated optimization,path planning is established on a Bezier curve,and particle swarm optimization is adopted to adjust the control points of the Bezier curve with the kinematic constraints of manipulator.A feasible obstacle avoidance path is obtained along with a discrete trajectory tracking by using a follow-the-leader strategy.The relative distance between each two discrete path points is limited to reduce the fitting error of the connecting rigid links to the smooth curve.Simulation results show that this integrated optimization method can rapidly search for the appropriate trajectory to guide the manipulator in obtaining the target while achieving obstacle avoidance and meeting joint constraints.The proposed algorithm is suitable for 3D space obstacle avoidance and multitarget path tracking.
基金Defense Advanced Research Project "the Techniques of Information Integrated Processing and Fusion" in the Eleventh Five-Year Plan (513060302).
文摘Joint probabilistic data association is an effective method for tracking multiple targets in clutter, but only the target kinematic information is used in measure-to-track association. If the kinematic likelihoods are similar for different closely spaced targets, there is ambiguity in using the kinematic information alone; the cor-rect association probability will decrease in conventional joint probabilistic data association algorithm and track coalescence will occur easily. A modified algorithm of joint probabilistic data association with classification-aided is presented, which avoids track coalescence when tracking multiple neighboring targets. Firstly, an identification matrix is defined, which is used to simplify validation matrix to decrease computational complexity. Then, target class information is integrated into the data association process. Performance comparisons with and without the use of class information in JPDA are presented on multiple closely spaced maneuvering targets tracking problem. Simulation results quantify the benefits of classification-aided JPDA for improved multiple targets tracking, espe-cially in the presence of association uncertainty in the kinematic measurement and target maneuvering. Simulation results indicate that the algorithm is valid.
基金Sponsored by National Natural Science Foundation of China (Grant No50578049)
文摘This study firstly defines a set of arrangement rule for perforated holes of multi-hole orifice(MO), and then presents three critical geometrical parameters including total number of performated hole, equivalent diameter ratio and distribution density of perforated holes, which are to quantify MO structure. This paper built the throttling test apparatus for nice test MO plates, which were designed according to orthogonal theory. The experiments were conducted to investigate the effect of three critical geometerical parameters on the pressure loss coefficient of test MOs. Results show that equivalent diameter ratio is the dominant prameter affecting MO throttling characterstic.
基金supported by the Major Program of National Natural Science Foundation of China (10990012)the National Natural Science Foundation of China (61201296,61271024)+1 种基金the Fundamental Research Funds for the Central Universities (K5051202037)Guangxi Key Lab of Wireless Wideband Communication & Signal Processing (12205)
文摘The problem of two order statistics detection schemes for the detection of a spatially distributed target in white Gaussian noise are studied.When the number of strong scattering cells is known,we first show an optimal detector,which requires many processing channels.The structure of such optimal detector is complex.Therefore,a simpler quasi-optimal detector is then introduced.The quasi-optimal detector,called the strong scattering cells' number dependent order statistics(SND-OS) detector,takes the form of an average of maximum strong scattering cells with a known number.If the number of strong scattering cells is unknown in real situation,the multi-channel order statistics(MC-OS) detector is used.In each channel,a various number of maximums scattered from target are averaged.Then,the false alarm probability analysis and thresholds sets for each channel are given,following the detection results presented by means of Monte Carlo simulation strategy based on simulated target model and three measured targets.In particular,the theoretical analysis and simulation results highlight that the MC-OS detector can efficiently detect range-spread targets in white Gaussian noise.
基金supported by National Basic Research Program of China (973 Program) (No. 2010CB731800)Key Program of National Natural Science Foundation of China (No. 60934003)Key Project for Natural Science Research of Hebei Education Department(No. ZD200908)
文摘This paper considers the problems of target tracking and obstacle avoidance for multi-agent systems.To solve the problem that multiple agents cannot effectively track the target while avoiding obstacle in dynamic environment,a novel control algorithm based on potential function and behavior rules is proposed.Meanwhile,the interactions among agents are also considered.According to the state whether an agent is within the area of its neighbors influence,two kinds of potential functions are presented.Meanwhile,the distributed control input of each agent is determined by relative velocities as well as relative positions among agents,target and obstacle.The maximum linear speed of the agents is also discussed.Finally,simulation studies are given to demonstrate the performance of the proposed algorithm.
基金jointly granted by the Science and Technology on Avionics Integration Laboratory and the Aeronautical Science Foundation of China (No. 2016ZC15008)
文摘A decision-making problem of missile-target assignment with a novel particle swarm optimization algorithm is proposed when it comes to a multiple target collaborative combat situation.The threat function is established to describe air combat situation.Optimization function is used to find an optimal missile-target assignment.An improved particle swarm optimization algorithm is utilized to figure out the optimization function with less parameters,which is based on the adaptive random learning approach.According to the coordinated attack tactics,there are some adjustments to the assignment.Simulation example results show that it is an effective algorithm to handle with the decision-making problem of the missile-target assignment(MTA)in air combat.
基金Projects(90820302,60805027)supported by the National Natural Science Foundation of ChinaProject(200805330005)supported by the Research Fund for the Doctoral Program of Higher Education,ChinaProject(2009FJ4030)supported by Academician Foundation of Hunan Province,China
文摘Multi-target tracking(MTT) is a research hotspot of wireless sensor networks at present.A self-organized dynamic cluster task allocation scheme is used to implement collaborative task allocation for MTT in WSN and a special cluster member(CM) node selection method is put forward in the scheme.An energy efficiency model was proposed under consideration of both energy consumption and remaining energy balance in the network.A tracking accuracy model based on area-sum principle was also presented through analyzing the localization accuracy of triangulation.Then,the two models mentioned above were combined to establish dynamic cluster member selection model for MTT where a comprehensive performance index function was designed to guide the CM node selection.This selection was fulfilled using genetic algorithm.Simulation results show that this method keeps both energy efficiency and tracking quality in optimal state,and also indicate the validity of genetic algorithm in implementing CM node selection.
基金the National Natural Science Foundation of China(Grant No.50578049)
文摘A throttling experiment for the multi-hole orifice (MO) using water was conducted based on the conclusion of key parameters affecting the MO throttling performance. Testing MOs and standard orifice plates (SOs) were designed for the throttling experiment to compare the throttling effect using the equivalent diameter ratio (RED) and diameter ratio (RD) as key parameters, respectively. Meanwhile, effective metrical conditions were provided for experimental accuracy. The throttling model form was determined according to the theoretical throttling model of SO. Then the unknown parameters involved were identified by experimental data. A good concordance between the modeling computation and experimental results shows a validation of the MO throtting model.
基金supported by National Basic Research Program of China (973 Program) (No. 2010CB731800)Key Project of National Science Foundation of China (No. 60934003)+2 种基金National Nature Science Foundation of China (No. 61074065)Key Project for Natural Science Research of Hebei Education Department, PRC(No. ZD200908)Key Project for Shanghai Committee of Science and Technology (No. 08511501600)
文摘In this paper, the problems of target tracking and obstacle avoidance for multi-agent networks with input constraints are investigated. When there is a moving obstacle, the control objectives are to make the agents track a moving target and to avoid collisions among agents. First, without considering the input constraints, a novel distributed controller can be obtained based on the potential function. Second, at each sampling time, the control algorithm is optimized. Furthermore, to solve the problem that agents cannot effectively avoid the obstacles in dynamic environment where the obstacles are moving, a new velocity repulsive potential is designed. One advantage of the designed control algorithm is that each agent only requires local knowledge of its neighboring agents. Finally, simulation results are provided to verify the effectiveness of the proposed approach.
基金This paper was supported by the Natural Science Foundation of Jiangsu Province, China (No. BK2004132).
文摘Much research mainly focuses on the batch processing method (e.g. maximum likelihood method) when bearings-only multiple targets tracking of bistatic sonar system is considered. In this paper, the idea of recursive processing method is presented and employed, and corresponding data association algorithms, i.e. a multi-objective ant-colony-based optimization algorithm and an easy fast assignment algorithm are developed to solve the measurements-to-measurements and measurements-to-tracks data association problems of bistatic sonar system, respectively. Monte-Carlo simulations are induced to evaluate the effectiveness of the proposed methods.
基金Supported by the National Natural Science Foundation of China(No.61300214)the National Natural Science Foundation of Henan Province(No.132300410148)+1 种基金the Post-doctoral Science Foundation of China(No.2014M551999)the Funding Scheme of Young Key Teacher ofHenan Province Universities(No.2013GGJS-026)
文摘Aiming at the effective realization of particle filter for maneuvering target tracking in multi-sensor measurements,a novel multi-sensor multiple model particle filtering algorithm with correlated noises is proposed.Combined with the kinetic evolution equation of target state,a multi-sensor multiple model particle filter is firstly constructed,which is also used as the basic framework of a new algorithm.In the new algorithm,in order to weaken the adverse influence from random measurement noises in the measuring process of particle weight,a weight optimization strategy is introduced to improve the reliability and stability of particle weight.In addition,considering the correlated noise existing in the practical engineering,a decoupling method of correlated noise is given by the rearrangement and transformation of the state transition equation and measurement equation.Since the weight optimization strategy and noise decoupling method adopt respectively the center fusion structure and the off-hne way,it improves the adverse effect effectively on computational complexity for increasing state dimension and sensor number.Finally,the theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm.
基金supported by the National Natural Science Foundation of China(6153102061471383)
文摘An algorithm of highly maneuvering target tracking is proposed to solve the problem of large tracking error caused by strong maneuver. In this algorithm, a new estimator, named as multi-parameter fusion Singer(MF-Singer) model is derived based on the Singer model and the fuzzy reasoning method by using radial acceleration and velocity of the target, and applied to the problem of maneuvering target tracking in strong maneuvering environment and operating environment. The tracking performance of the MF-Singer model is evaluated and compared with other manuevering tracking models. It is shown that the MF-Singer model outperforms these algorithms in several examples.
基金Supported by the National Natural Science Foundation of China Youth Science Fund Project(Nos.62101405,61372185)
文摘This paper proposed a robust method based on the definition of Mahalanobis distance to track ground moving target. The feature and the geometry of airborne ground moving target tracking systems are studied at first. Based on this feature, the assignment relation of time-nearby target is calculated via Mahalanobis distance, and then the corresponding transformation formula is deduced. The simulation results show the correctness and effectiveness of the proposed method.
基金supported by the National Basic Research Program of China(973 Program),No.2006CB504505,2012CB518504the National Natural Science Foundation of China,No.90709027+1 种基金the Student's Platform for Innovation and Entrepreneurship Training Program of Southern Medical University of China,No.201512121165the Doctoral Foundation of Guangdong Medical University of China,No.2XB13058
文摘The acute effect of acupuncture on Alzheimer’s disease,i.e.,on brain activation during treatment,has been reported.However,the effect of long-term acupuncture on brain activation in Alzheimer’s disease is unclear.Therefore,in this study,we performed long-term needling at Zusanli(ST36)or a sham point(1.5 mm lateral to ST36)in a rat Alzheimer’s disease model,for 30 minutes,once per day,for 30 days.The rats underwent 18F-fluorodeoxyglucose positron emission tomography scanning.Positron emission tomography images were processed with SPM2.The brain areas activated after needling at ST36 included the left hippocampus,the left orbital cortex,the left infralimbic cortex,the left olfactory cortex,the left cerebellum and the left pons.In the sham-point group,the activated regions were similar to those in the ST36 group.However,the ST36 group showed greater activation in the cerebellum and pons than the sham-point group.These findings suggest that long-term acupuncture treatment has targeted regulatory effects on multiple brain regions in rats with Alzheimer’s disease.
基金Project supported by the National High Technology Research and Development Program of China (Grant No. 2008AA03Z402)the National Natural Science Foundation of China (Grant No. 61076044)the Natural Science Foundation of Beijing,China(Grant Nos. 4092007 and 4102003)
文摘A multi-hole vertical-cavity surface-emitting laser(VCSEL) operating in stable single mode with a low threshold current was produced by introducing multi-leaf scallop holes on the top distributed Bragg-reflector of an oxidationconfined 850 nm VCSEL.The single-mode output power of 2.6 mW,threshold current of 0.6 mA,full width of half maximum lasing spectrum of less than 0.1 nm,side mode suppression ratio of 28.4 dB,and far-field divergence angle of about 10 are obtained.The effects of different hole depths on the optical characteristics are simulated and analysed,including far-field divergence,spectrum and lateral cavity mode.The single-mode performance of this multi-hole device is attributed to the large radiation loss from the inter-hole spacing and the scattering loss at the bottom of the holes,particularly for higher order modes.
基金Project(61101185) supported by the National Natural Science Foundation of ChinaProject(2011AA1221) supported by the National High Technology Research and Development Program of China
文摘In order to improve the performance of the probability hypothesis density(PHD) algorithm based particle filter(PF) in terms of number estimation and states extraction of multiple targets, a new probability hypothesis density filter algorithm based on marginalized particle and kernel density estimation is proposed, which utilizes the idea of marginalized particle filter to enhance the estimating performance of the PHD. The state variables are decomposed into linear and non-linear parts. The particle filter is adopted to predict and estimate the nonlinear states of multi-target after dimensionality reduction, while the Kalman filter is applied to estimate the linear parts under linear Gaussian condition. Embedding the information of the linear states into the estimated nonlinear states helps to reduce the estimating variance and improve the accuracy of target number estimation. The meanshift kernel density estimation, being of the inherent nature of searching peak value via an adaptive gradient ascent iteration, is introduced to cluster particles and extract target states, which is independent of the target number and can converge to the local peak position of the PHD distribution while avoiding the errors due to the inaccuracy in modeling and parameters estimation. Experiments show that the proposed algorithm can obtain higher tracking accuracy when using fewer sampling particles and is of lower computational complexity compared with the PF-PHD.
基金Projects(61002022,61471370)supported by the National Natural Science Foundation of China
文摘Detection and tracking of multi-target with unknown and varying number is a challenging issue, especially under the condition of low signal-to-noise ratio(SNR). A modified multi-target track-before-detect(TBD) method was proposed to tackle this issue using a nonstandard point observation model. The method was developed from sequential Monte Carlo(SMC)-based probability hypothesis density(PHD) filter, and it was implemented by modifying the original calculation in update weights of the particles and by adopting an adaptive particle sampling strategy. To efficiently execute the SMC-PHD based TBD method, a fast implementation approach was also presented by partitioning the particles into multiple subsets according to their position coordinates in 2D resolution cells of the sensor. Simulation results show the effectiveness of the proposed method for time-varying multi-target tracking using raw observation data.
基金Project partially supported by NSF (No. CCR0306438) and theBoeing Company, USA
文摘We propose a high-performance path planning algorithm for automatic target tracking in the applications of real-time simulation and visualization of large-scale terrain datasets, with a large number of moving objects (such as vehicles) tracking multiple moving targets. By using a modified Dijkstra’s algorithm, an optimal path between each vehicle-target pair over a weighted grid-presented terrain is computed and updated to eliminate the problem of local minima and losing of tracking. Then, a dynamic path re-planning strategy using multi-resolution representation of a dynamic updating region is proposed to achieve high-performance by trading-off precision for efficiency, while guaranteeing accuracy. Primary experimental results showed that our algorithm successfully achieved 10 to 96 frames per second interactive path-replanning rates during a terrain simulation scenario with 10 to 100 vehicles and multiple moving targets.
基金Supported by the Program for Technology Innovation Team of Ningbo Government (No. 2011B81002)the Ningbo University Science Research Foundation (No.xkl11075)
文摘This paper introduces an approach for visual tracking of multi-target with occlusion occurrence. Based on the author's previous work in which the Overlap Coefficient (OC) is used to detect the occlusion, in this paper a method of combining Bhattacharyya Coefficient (BC) and Kalman filter innovation term is proposed as the criteria for jointly detecting the occlusion occurrence. Fragmentation of target is introduced in order to closely monitor the occlusion development. In the course of occlusion, the Kalman predictor is applied to determine the location of the occluded target, and the criterion for checking the re-appearance of the occluded target is also presented. The proposed approach is put to test on a standard video sequence, suggesting the satisfactory performance in multi-target tracking.