In this article,a new optimization system that uses few features to recognize locomotion with high classification accuracy is proposed.The optimization system consists of three parts.First,the features of the mixed me...In this article,a new optimization system that uses few features to recognize locomotion with high classification accuracy is proposed.The optimization system consists of three parts.First,the features of the mixed mechanical signal data are extracted from each analysis window of 200 ms after each foot contact event.Then,the Binary version of the hybrid Gray Wolf Optimization and Particle Swarm Optimization(BGWOPSO)algorithm is used to select features.And,the selected features are optimized and assigned different weights by the Biogeography-Based Optimization(BBO)algorithm.Finally,an improved K-Nearest Neighbor(KNN)classifier is employed for intention recognition.This classifier has the advantages of high accuracy,few parameters as well as low memory burden.Based on data from eight patients with transfemoral amputations,the optimization system is evaluated.The numerical results indicate that the proposed model can recognize nine daily locomotion modes(i.e.,low-,mid-,and fast-speed level-ground walking,ramp ascent/decent,stair ascent/descent,and sit/stand)by only seven features,with an accuracy of 96.66%±0.68%.As for real-time prediction on a powered knee prosthesis,the shortest prediction time is only 9.8 ms.These promising results reveal the potential of intention recognition based on the proposed system for high-level control of the prosthetic knee.展开更多
Based on the regularity nature of lower-limb motion,an intent pattern recognition approach for above-knee prosthesis is proposed in this paper. To remedy the defects of recognizer based on electromyogram(EMG), we deve...Based on the regularity nature of lower-limb motion,an intent pattern recognition approach for above-knee prosthesis is proposed in this paper. To remedy the defects of recognizer based on electromyogram(EMG), we develop a pure mechanical sensor architecture for intent pattern recognition of lower-limb motion. The sensor system is composed of an accelerometer, a gyroscope mounted on the prosthetic socket, and two pressure sensors mounted under the sole. To compensate the delay in the control of prosthesis, the signals in the stance phase are used to predict the terrain and speed in the swing phase. Specifically, the intent pattern recognizer utilizes intraclass correlation coefficient(ICC) according to the Cartesian product of walking speed and terrain. Moreover, the sensor data are fused via DempsterShafer's theory. And hidden Markov model(HMM) is used to recognize the realtime motion state with the reference of the prior step. The proposed method can infer the prosthesis user's intent of walking on different terrain, which includes level ground,stair ascent, stair descent, up and down ramp. The experiments demonstrate that the intent pattern recognizer is capable of identifying five typical terrain-modes with the rate of 95.8%. The outcome of this investigation is expected to substantially improve the control performance of powered above-knee prosthesis.展开更多
Online accurate recognition of target tactical intention in beyond-visual-range (BVR) air combat is an important basis for deep situational awareness and autonomous air combat decision-making, which can create pre-emp...Online accurate recognition of target tactical intention in beyond-visual-range (BVR) air combat is an important basis for deep situational awareness and autonomous air combat decision-making, which can create pre-emptive tactical opportunities for the fighter to gain air superiority. The existing methods to solve this problem have some defects such as dependence on empirical knowledge, difficulty in interpreting the recognition results, and inability to meet the requirements of actual air combat. So an online hierarchical recognition method for target tactical intention in BVR air combat based on cascaded support vector machine (CSVM) is proposed in this study. Through the mechanism analysis of BVR air combat, the instantaneous and cumulative feature information of target trajectory and relative situation information are introduced successively using online automatic decomposition of target trajectory and hierarchical progression. Then the hierarchical recognition model from target maneuver element, tactical maneuver to tactical intention is constructed. The CSVM algorithm is designed for solving this model, and the computational complexity is decomposed by the cascaded structure to overcome the problems of convergence and timeliness when the dimensions and number of training samples are large. Meanwhile, the recognition result of each layer can be used to support the composition analysis and interpretation of target tactical intention. The simulation results show that the proposed method can effectively realize multi-dimensional online accurate recognition of target tactical intention in BVR air combat.展开更多
Recognizing target intent is crucial for making decisions on the battlefield.However,the imperfect and ambiguous character of battlefield situations challenges the validity and causation analysis of classical intent r...Recognizing target intent is crucial for making decisions on the battlefield.However,the imperfect and ambiguous character of battlefield situations challenges the validity and causation analysis of classical intent recognition techniques.Facing with the challenge,a target intention causal analysis paradigm is proposed by combining with an Intervention Retrieval(IR)model and a Hybrid Intention Recognition(HIR)model.The target data acquired by the sensors are modelled as Basic Probability Assignments(BPAs)based on evidence theory to create uncertain datasets.Then,the HIR model is utilized to recognize intent for a tested sample from uncertain datasets.Finally,the intervention operator under the evidence structure is utilized to perform attribute intervention on the tested sample.Data retrieval is performed in the sample database based on the IR model to generate the intention distribution of the pseudo-intervention samples to analyze the causal effects of individual sample attributes.The simulation results demonstrate that our framework successfully identifies the target intention under the evidence structure and goes further to analyze the causal impact of sample attributes on the target intention.展开更多
The battlefield environment is changing rapidly,and fast and accurate identification of the tactical intention of enemy targets is an important condition for gaining a decision-making advantage.The current Intention R...The battlefield environment is changing rapidly,and fast and accurate identification of the tactical intention of enemy targets is an important condition for gaining a decision-making advantage.The current Intention Recognition(IR)method for air targets has shortcomings in temporality,interpretability and back-and-forth dependency of intentions.To address these problems,this paper designs a novel air target intention recognition method named STABC-IR,which is based on Bidirectional Gated Recurrent Unit(Bi GRU)and Conditional Random Field(CRF)with Space-Time Attention mechanism(STA).First,the problem of intention recognition of air targets is described and analyzed in detail.Then,a temporal network based on Bi GRU is constructed to achieve the temporal requirement.Subsequently,STA is proposed to focus on the key parts of the features and timing information to meet certain interpretability requirements while strengthening the timing requirements.Finally,an intention transformation network based on CRF is proposed to solve the back-and-forth dependency and transformation problem by jointly modeling the tactical intention of the target at each moment.The experimental results show that the recognition accuracy of the jointly trained STABC-IR model can reach 95.7%,which is higher than other latest intention recognition methods.STABC-IR solves the problem of intention transformation for the first time and considers both temporality and interpretability,which is important for improving the tactical intention recognition capability and has reference value for the construction of command and control auxiliary decision-making system.展开更多
In mixed and dynamic traffic environments,accurate long-term trajectory forecasting of surrounding vehicles is one of the indispensable preconditions for autonomous vehicles to accomplish reasonable behavioral decisio...In mixed and dynamic traffic environments,accurate long-term trajectory forecasting of surrounding vehicles is one of the indispensable preconditions for autonomous vehicles to accomplish reasonable behavioral decisions and guarantee driving safety.In this paper,we propose an integrated probabilistic architecture for long-term vehicle trajectory prediction,which consists of a driving inference model(DIM)and a trajectory prediction model(TPM).The DIM is designed and employed to accurately infer the potential driving intention based on a dynamic Bayesian network.The proposed DIM incorporates the basic traffic rules and multivariate vehicle motion information.To further improve the prediction accuracy and realize uncertainty estimation,we develop a Gaussian process-based TPM,considering both the short-term prediction results of the vehicle model and the driving motion characteristics.Afterward,the effectiveness of our novel approach is demonstrated by conducting experiments on a public naturalistic driving dataset under lane-changing scenarios.The superior performance on the task of long-term trajectory prediction is presented and verified by comparing with other advanced methods.展开更多
The intelligent knee prosthesis is capable of human-like bionic lower limb control through advanced control systems and artificial intelligence algorithms that will potentially minimize gait limitations for above-knee...The intelligent knee prosthesis is capable of human-like bionic lower limb control through advanced control systems and artificial intelligence algorithms that will potentially minimize gait limitations for above-knee amputees and facilitate their reintegration into society.In this paper,we sum up the control strategies corresponding to the prevailing control objectives(position and impedance)of the current intelligent knee prosthesis.Although these control strategies have been successfully implemented and validated in relevant experiments,the existing deficiencies still fail to achieve optimal performance of the controllers,which complicates the definition of a standard control method.Before a mature control system can be developed,it is more important to realize the full potential for the control strategy,which requires upgrading and refining the relevant key technologies based on the existing control methods.For this reason,we discuss potential areas for improvement of the prosthetic control system based on the summarized control strategies,including intent recognition,sensor system,prosthetic evaluation,and parameter optimization algorithms,providing future directions toward optimizing control strategies for the next generation of intelligent knee prostheses.展开更多
基金This research was supported in part by the National Key Research and Development Program of China under Grant 2018YFC2001300in part by the National Natural Science Foundation of China under Grant 91948302,Grant 91848204,and Grant 52021003the Project of Scientific and Technological Development Plan of Jilin Province under Grant 20220508130RC.
文摘In this article,a new optimization system that uses few features to recognize locomotion with high classification accuracy is proposed.The optimization system consists of three parts.First,the features of the mixed mechanical signal data are extracted from each analysis window of 200 ms after each foot contact event.Then,the Binary version of the hybrid Gray Wolf Optimization and Particle Swarm Optimization(BGWOPSO)algorithm is used to select features.And,the selected features are optimized and assigned different weights by the Biogeography-Based Optimization(BBO)algorithm.Finally,an improved K-Nearest Neighbor(KNN)classifier is employed for intention recognition.This classifier has the advantages of high accuracy,few parameters as well as low memory burden.Based on data from eight patients with transfemoral amputations,the optimization system is evaluated.The numerical results indicate that the proposed model can recognize nine daily locomotion modes(i.e.,low-,mid-,and fast-speed level-ground walking,ramp ascent/decent,stair ascent/descent,and sit/stand)by only seven features,with an accuracy of 96.66%±0.68%.As for real-time prediction on a powered knee prosthesis,the shortest prediction time is only 9.8 ms.These promising results reveal the potential of intention recognition based on the proposed system for high-level control of the prosthetic knee.
基金supported in part by the National Nature Science Fundation(61174009,61203323)Youth Foundation of Hebei Province(F2016202327)+3 种基金the Colleges and Universities in Hebei Province Science and Technology Research Project(ZC2016020)supported in part by Key Project of NSFC(61533009)111 Project(B08015)Research Project(JCYJ20150403161923519)
文摘Based on the regularity nature of lower-limb motion,an intent pattern recognition approach for above-knee prosthesis is proposed in this paper. To remedy the defects of recognizer based on electromyogram(EMG), we develop a pure mechanical sensor architecture for intent pattern recognition of lower-limb motion. The sensor system is composed of an accelerometer, a gyroscope mounted on the prosthetic socket, and two pressure sensors mounted under the sole. To compensate the delay in the control of prosthesis, the signals in the stance phase are used to predict the terrain and speed in the swing phase. Specifically, the intent pattern recognizer utilizes intraclass correlation coefficient(ICC) according to the Cartesian product of walking speed and terrain. Moreover, the sensor data are fused via DempsterShafer's theory. And hidden Markov model(HMM) is used to recognize the realtime motion state with the reference of the prior step. The proposed method can infer the prosthesis user's intent of walking on different terrain, which includes level ground,stair ascent, stair descent, up and down ramp. The experiments demonstrate that the intent pattern recognizer is capable of identifying five typical terrain-modes with the rate of 95.8%. The outcome of this investigation is expected to substantially improve the control performance of powered above-knee prosthesis.
基金The authors gratefully acknowledge the support of the National Natural Science Foundation of China under Grant No.62076204 and Grant No.61612385in part by the Postdoctoral Science Foundation of China under Grants No.2021M700337in part by the Fundamental Research Funds for the Central Universities under Grant No.3102019ZX016.
文摘Online accurate recognition of target tactical intention in beyond-visual-range (BVR) air combat is an important basis for deep situational awareness and autonomous air combat decision-making, which can create pre-emptive tactical opportunities for the fighter to gain air superiority. The existing methods to solve this problem have some defects such as dependence on empirical knowledge, difficulty in interpreting the recognition results, and inability to meet the requirements of actual air combat. So an online hierarchical recognition method for target tactical intention in BVR air combat based on cascaded support vector machine (CSVM) is proposed in this study. Through the mechanism analysis of BVR air combat, the instantaneous and cumulative feature information of target trajectory and relative situation information are introduced successively using online automatic decomposition of target trajectory and hierarchical progression. Then the hierarchical recognition model from target maneuver element, tactical maneuver to tactical intention is constructed. The CSVM algorithm is designed for solving this model, and the computational complexity is decomposed by the cascaded structure to overcome the problems of convergence and timeliness when the dimensions and number of training samples are large. Meanwhile, the recognition result of each layer can be used to support the composition analysis and interpretation of target tactical intention. The simulation results show that the proposed method can effectively realize multi-dimensional online accurate recognition of target tactical intention in BVR air combat.
基金partially supported by the National Natural Science Foundation of China(No.62173272)。
文摘Recognizing target intent is crucial for making decisions on the battlefield.However,the imperfect and ambiguous character of battlefield situations challenges the validity and causation analysis of classical intent recognition techniques.Facing with the challenge,a target intention causal analysis paradigm is proposed by combining with an Intervention Retrieval(IR)model and a Hybrid Intention Recognition(HIR)model.The target data acquired by the sensors are modelled as Basic Probability Assignments(BPAs)based on evidence theory to create uncertain datasets.Then,the HIR model is utilized to recognize intent for a tested sample from uncertain datasets.Finally,the intervention operator under the evidence structure is utilized to perform attribute intervention on the tested sample.Data retrieval is performed in the sample database based on the IR model to generate the intention distribution of the pseudo-intervention samples to analyze the causal effects of individual sample attributes.The simulation results demonstrate that our framework successfully identifies the target intention under the evidence structure and goes further to analyze the causal impact of sample attributes on the target intention.
基金supported by the National Natural Science Foundation of China(Nos.62106283 and 72001214)。
文摘The battlefield environment is changing rapidly,and fast and accurate identification of the tactical intention of enemy targets is an important condition for gaining a decision-making advantage.The current Intention Recognition(IR)method for air targets has shortcomings in temporality,interpretability and back-and-forth dependency of intentions.To address these problems,this paper designs a novel air target intention recognition method named STABC-IR,which is based on Bidirectional Gated Recurrent Unit(Bi GRU)and Conditional Random Field(CRF)with Space-Time Attention mechanism(STA).First,the problem of intention recognition of air targets is described and analyzed in detail.Then,a temporal network based on Bi GRU is constructed to achieve the temporal requirement.Subsequently,STA is proposed to focus on the key parts of the features and timing information to meet certain interpretability requirements while strengthening the timing requirements.Finally,an intention transformation network based on CRF is proposed to solve the back-and-forth dependency and transformation problem by jointly modeling the tactical intention of the target at each moment.The experimental results show that the recognition accuracy of the jointly trained STABC-IR model can reach 95.7%,which is higher than other latest intention recognition methods.STABC-IR solves the problem of intention transformation for the first time and considers both temporality and interpretability,which is important for improving the tactical intention recognition capability and has reference value for the construction of command and control auxiliary decision-making system.
基金This work was supported by the National Natural Science Foundation of China(51975310 and 52002209).
文摘In mixed and dynamic traffic environments,accurate long-term trajectory forecasting of surrounding vehicles is one of the indispensable preconditions for autonomous vehicles to accomplish reasonable behavioral decisions and guarantee driving safety.In this paper,we propose an integrated probabilistic architecture for long-term vehicle trajectory prediction,which consists of a driving inference model(DIM)and a trajectory prediction model(TPM).The DIM is designed and employed to accurately infer the potential driving intention based on a dynamic Bayesian network.The proposed DIM incorporates the basic traffic rules and multivariate vehicle motion information.To further improve the prediction accuracy and realize uncertainty estimation,we develop a Gaussian process-based TPM,considering both the short-term prediction results of the vehicle model and the driving motion characteristics.Afterward,the effectiveness of our novel approach is demonstrated by conducting experiments on a public naturalistic driving dataset under lane-changing scenarios.The superior performance on the task of long-term trajectory prediction is presented and verified by comparing with other advanced methods.
基金The authors would liketo thank the support of the National Natural Science Foundation of China(grant no.62073224)National Key Research and Development Program of China(grant no.2018YFB1307303).
文摘The intelligent knee prosthesis is capable of human-like bionic lower limb control through advanced control systems and artificial intelligence algorithms that will potentially minimize gait limitations for above-knee amputees and facilitate their reintegration into society.In this paper,we sum up the control strategies corresponding to the prevailing control objectives(position and impedance)of the current intelligent knee prosthesis.Although these control strategies have been successfully implemented and validated in relevant experiments,the existing deficiencies still fail to achieve optimal performance of the controllers,which complicates the definition of a standard control method.Before a mature control system can be developed,it is more important to realize the full potential for the control strategy,which requires upgrading and refining the relevant key technologies based on the existing control methods.For this reason,we discuss potential areas for improvement of the prosthetic control system based on the summarized control strategies,including intent recognition,sensor system,prosthetic evaluation,and parameter optimization algorithms,providing future directions toward optimizing control strategies for the next generation of intelligent knee prostheses.
