Acoustic source localization(ASL)and sound event detection(SED)are two widely pursued independent research fields.In recent years,in order to achieve a more complete spatial and temporal representation of sound field,...Acoustic source localization(ASL)and sound event detection(SED)are two widely pursued independent research fields.In recent years,in order to achieve a more complete spatial and temporal representation of sound field,sound event localization and detection(SELD)has become a very active research topic.This paper presents a deep learning-based multioverlapping sound event localization and detection algorithm in three-dimensional space.Log-Mel spectrum and generalized cross-correlation spectrum are joined together in channel dimension as input features.These features are classified and regressed in parallel after training by a neural network to obtain sound recognition and localization results respectively.The channel attention mechanism is also introduced in the network to selectively enhance the features containing essential information and suppress the useless features.Finally,a thourough comparison confirms the efficiency and effectiveness of the proposed SELD algorithm.Field experiments show that the proposed algorithm is robust to reverberation and environment and can achieve higher recognition and localization accuracy compared with the baseline method.展开更多
Automated detection of military people based on the images in different environments plays an important role in accurately completing military missions.With the equipment gradually moving towards intelligence,unmanned...Automated detection of military people based on the images in different environments plays an important role in accurately completing military missions.With the equipment gradually moving towards intelligence,unmanned aerial vehicles(UAVs)will be widely used for integrated reconnaissance/attack in the future.The lightweight and compact design of the small UAV allows it to travel through dense forests and other environments to capture images with its convenient mobility.However,as the camouflage has been designed to blend in with surroundings,which greatly reduces the probability of the target being discovered.Moreover,the lack of training data on camouflaged people detection will inhibit the training of a deep model.To address these problems,a novel semi-supervised camouflaged military people detection network is proposed to automatically detect the target from the images.In this paper,the camouflaged object detection dataset(COD10K)is first supplemented according to our mission requirements,then the edge attention is utilized to enhance the boundaries based on search identification network.Further,a semi-supervised learning strategy is presented to take advantage of the unlabeled data which can alleviate insufficient data and improve the detection accuracy.Experiments demonstrate that the proposed semi-supervised search identification network(Semi-SINet)performs well in camouflaged people detection compared with other object detection methods.展开更多
With the development of technology, the relevant performance of unmanned aerial vehicles(UAVs) has been greatly improved, and various highly maneuverable UAVs have been developed, which puts forward higher requirement...With the development of technology, the relevant performance of unmanned aerial vehicles(UAVs) has been greatly improved, and various highly maneuverable UAVs have been developed, which puts forward higher requirements on target tracking technology. Strong maneuvering refers to relatively instantaneous and dramatic changes in target acceleration or movement patterns, as well as continuous changes in speed,angle, and acceleration. However, the traditional UAV tracking algorithm model has poor adaptability and large amount of calculation. This paper applies support vector regression(SVR)to the interacting multiple model(IMM) algorithm. The simulation results show that the improved algorithm has higher tracking accuracy for highly maneuverable targets than the original algorithm, and can adjust parameters adaptively, making it more adaptable.展开更多
This work addresses the problem of self-excited vibration,which degrades the stability of the levitation control,decreases the ride comfort,and restricts the construction cost of maglev system.Firstly,a minimum model ...This work addresses the problem of self-excited vibration,which degrades the stability of the levitation control,decreases the ride comfort,and restricts the construction cost of maglev system.Firstly,a minimum model containing a flexible bridge and a single levitation unit is presented.Based on the simplified model,the principle underlying the self-excited vibration is explored.After investigations about the energy transmission between the levitation system and bridge,it is concluded that the increment of modal damping can dissipate the accumulated energy by the bridge and the self-excited vibration may be avoided.To enlarge the equivalent modal damping of bridge,the sky-hooked damper is adopted.Furthermore,to avoid the hardware addition of real sky-hooked damper,considering the fact that the electromagnet itself is an excellent actuator that is capable of providing sufficiently fast and large force acting on the bridge to emulate the influence of the real sky-hooked damper,the technique of the virtual sky-hooked damper is proposed.The principle underlying the virtual sky-hooked damper by electromagnet is explored and the vertical velocity of bridge is estimated.Finally,numerical and experimental results illustrating the stability improvement of the vehicle-bridge interaction system are provided.展开更多
基金supported by the National Natural Science Foundation of China(61877067)the Foundation of Science and Technology on Near-Surface Detection Laboratory(TCGZ2019A002,TCGZ2021C003,6142414200511)the Natural Science Basic Research Program of Shaanxi(2021JZ-19)。
文摘Acoustic source localization(ASL)and sound event detection(SED)are two widely pursued independent research fields.In recent years,in order to achieve a more complete spatial and temporal representation of sound field,sound event localization and detection(SELD)has become a very active research topic.This paper presents a deep learning-based multioverlapping sound event localization and detection algorithm in three-dimensional space.Log-Mel spectrum and generalized cross-correlation spectrum are joined together in channel dimension as input features.These features are classified and regressed in parallel after training by a neural network to obtain sound recognition and localization results respectively.The channel attention mechanism is also introduced in the network to selectively enhance the features containing essential information and suppress the useless features.Finally,a thourough comparison confirms the efficiency and effectiveness of the proposed SELD algorithm.Field experiments show that the proposed algorithm is robust to reverberation and environment and can achieve higher recognition and localization accuracy compared with the baseline method.
基金the Project of Science and Technology on Near-Surface Detection Laboratory (Grant no.TCGZ2019A006) to provide fund for conducting experiments
文摘Automated detection of military people based on the images in different environments plays an important role in accurately completing military missions.With the equipment gradually moving towards intelligence,unmanned aerial vehicles(UAVs)will be widely used for integrated reconnaissance/attack in the future.The lightweight and compact design of the small UAV allows it to travel through dense forests and other environments to capture images with its convenient mobility.However,as the camouflage has been designed to blend in with surroundings,which greatly reduces the probability of the target being discovered.Moreover,the lack of training data on camouflaged people detection will inhibit the training of a deep model.To address these problems,a novel semi-supervised camouflaged military people detection network is proposed to automatically detect the target from the images.In this paper,the camouflaged object detection dataset(COD10K)is first supplemented according to our mission requirements,then the edge attention is utilized to enhance the boundaries based on search identification network.Further,a semi-supervised learning strategy is presented to take advantage of the unlabeled data which can alleviate insufficient data and improve the detection accuracy.Experiments demonstrate that the proposed semi-supervised search identification network(Semi-SINet)performs well in camouflaged people detection compared with other object detection methods.
基金supported by the Foundation of Key Laboratory of Near-Surface。
文摘With the development of technology, the relevant performance of unmanned aerial vehicles(UAVs) has been greatly improved, and various highly maneuverable UAVs have been developed, which puts forward higher requirements on target tracking technology. Strong maneuvering refers to relatively instantaneous and dramatic changes in target acceleration or movement patterns, as well as continuous changes in speed,angle, and acceleration. However, the traditional UAV tracking algorithm model has poor adaptability and large amount of calculation. This paper applies support vector regression(SVR)to the interacting multiple model(IMM) algorithm. The simulation results show that the improved algorithm has higher tracking accuracy for highly maneuverable targets than the original algorithm, and can adjust parameters adaptively, making it more adaptable.
基金Projects(11302252,11202230) supported by the National Natural Science Foundation of China
文摘This work addresses the problem of self-excited vibration,which degrades the stability of the levitation control,decreases the ride comfort,and restricts the construction cost of maglev system.Firstly,a minimum model containing a flexible bridge and a single levitation unit is presented.Based on the simplified model,the principle underlying the self-excited vibration is explored.After investigations about the energy transmission between the levitation system and bridge,it is concluded that the increment of modal damping can dissipate the accumulated energy by the bridge and the self-excited vibration may be avoided.To enlarge the equivalent modal damping of bridge,the sky-hooked damper is adopted.Furthermore,to avoid the hardware addition of real sky-hooked damper,considering the fact that the electromagnet itself is an excellent actuator that is capable of providing sufficiently fast and large force acting on the bridge to emulate the influence of the real sky-hooked damper,the technique of the virtual sky-hooked damper is proposed.The principle underlying the virtual sky-hooked damper by electromagnet is explored and the vertical velocity of bridge is estimated.Finally,numerical and experimental results illustrating the stability improvement of the vehicle-bridge interaction system are provided.
