3D pose transfer over unorganized point clouds is a challenging generation task,which transfers a source’s pose to a target shape and keeps the target’s *** deep models have learned deformations and used the target...
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3D pose transfer over unorganized point clouds is a challenging generation task,which transfers a source’s pose to a target shape and keeps the target’s *** deep models have learned deformations and used the target’s identity as a style to modulate the combined features of two shapes or the aligned vertices of the source ***,all operations in these models are point-wise and independent and ignore the geometric information on the surface and structure of the input *** disadvantage severely limits the generation and generalization *** this study,we propose a geometry-aware method based on a novel transformer autoencoder to solve this *** efficient self-attention mechanism,that is,cross-covariance attention,was utilized across our framework to perceive the correlations between points at different ***,the transformer encoder extracts the target shape’s local geometry details for identity attributes and the source shape’s global geometry structure for pose *** transformer decoder efficiently learns deformations and recovers identity properties by fusing and decoding the extracted features in a geometry attentional manner,which does not require corresponding information or modulation *** experiments demonstrated that the geometry-aware method achieved state-of-the-art performance in a 3D pose transfer *** implementation code and data are available at https://***/SEULSH/Geometry-Aware-3D-Pose-Transfer-Using-Transfor mer-Autoencoder.
This article studies the effective traffic signal control problem of multiple intersections in a city-level traffic system.A novel regional multi-agent cooperative reinforcement learning algorithm called RegionSTLight...
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This article studies the effective traffic signal control problem of multiple intersections in a city-level traffic system.A novel regional multi-agent cooperative reinforcement learning algorithm called RegionSTLight is proposed to improve the traffic *** a regional multi-agent Q-learning framework is proposed,which can equivalently decompose the global Q value of the traffic system into the local values of several regions Based on the framework and the idea of human-machine cooperation,a dynamic zoning method is designed to divide the traffic network into several strong-coupled regions according to realtime traffic flow *** order to achieve better cooperation inside each region,a lightweight spatio-temporal fusion feature extraction network is *** experiments in synthetic real-world and city-level scenarios show that the proposed RegionS TLight converges more quickly,is more stable,and obtains better asymptotic performance compared to state-of-theart models.
In the field of electronic engineering, the retrieval of circuit diagrams has become an area of considerable research interest. This is attributed to the critical role it plays in consulting legacy electronic designs ...
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Video-based person re-identification (Re-ID) aims at associating the video sequences of the identical person across multiple cameras. The ubiquitous appearance misalignment poses a major obstacle for video person Re-I...
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Leighton Chajnantor Telescope(LCT), i.e., the former Caltech Submillimeter Observatory telescope, will be refurbished at the new site in Chajnantor Plateau, Chile in 2023. The environment of LCT will change significan...
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Leighton Chajnantor Telescope(LCT), i.e., the former Caltech Submillimeter Observatory telescope, will be refurbished at the new site in Chajnantor Plateau, Chile in 2023. The environment of LCT will change significantly after its relocation, and the telescope will be exposed to large wind disturbances directly because its enclosure will be completely open during observation. The wind disturbance is expected to be a challenge for LCT's pointing control since the existing control method cannot reject this disturbance very well. Therefore, it is very necessary to develop a new pointing control method with good capability of disturbance rejection. In this research, a disturbance observer—based composite position controller(DOB-CPC) is designed, in which an H∞feedback controller is employed to compress the disturbance, and a feedforward linear quadratic regulator is employed to compensate the disturbance precisely based on the estimated disturbance signal. Moreover, a controller switching policy is adopted, which applies the proportional controller to the transient process to achieve a quick response and applies the DOB-CPC to the steady state to achieve a small position error. Numerical experiments are conducted to verify the good performance of the proposed pointing controller(i.e., DOB-CPC) for rejecting the disturbance acting on LCT.
In Off-Policy reinforcement learning (RL), the experience imbalance problem can affect learning performance. The experience imbalance problem refers to the phenomenon that the experiences obtained by the agent during ...
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Dear editor, In the past two decades, scholars have begun to use system dynamical models and state motions to study the safety of a system to realize the analysis, diagnosis, and control of system safety. This safety ...
Dear editor, In the past two decades, scholars have begun to use system dynamical models and state motions to study the safety of a system to realize the analysis, diagnosis, and control of system safety. This safety is called state safety, which is different but closely related to state stability.
This paper investigates the problem of traffic signal control in large-scale road networks. A deep reinforcement learning model based on graph meta-learning using local subgraphs is proposed to control the traffic sig...
This paper investigates the problem of traffic signal control in large-scale road networks. A deep reinforcement learning model based on graph meta-learning using local subgraphs is proposed to control the traffic signal. The entire traffic network is represented as a graph by defining traffic lights as nodes and treating connections between intersections as edges. A graph neural network is used to enhance cooperation and communications between agents since information about neighbors is aggregated. To overcome the challenges in large-scale road networks, the proposed model employs a graph neural network on local subgraphs to reduce the difficulty of training in large-scale road networks. The model trained in small-scale traffic networks is transferred to a large-scale traffic network. Agent knowledge acquired from local subgraphs during the training of a small-scale road network confers advantages to the training of large-scale road networks under the resemblance between the structures of local subgraphs in small-and large-scale road networks. Furthermore, meta-learning is used to facilitate the model's rapid adaptability to unseen large-scale road networks. The advantage of the double Q-learning network is taken to reduce overestimation. In experiments, real-world road networks and synthetic road networks comprising more than 1000 intersections are given to evaluate the effectiveness of the proposed model.
A differential game guidance scheme with obstacle avoidance,based on the formulation of a combined linear quadratic and norm-bounded differential game,is designed for a three-player engagement scenario,which includes ...
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A differential game guidance scheme with obstacle avoidance,based on the formulation of a combined linear quadratic and norm-bounded differential game,is designed for a three-player engagement scenario,which includes a pursuer,an interceptor,and an *** confrontation between the players is divided into four phases(P1-P4)by introducing the switching time,and proposing different guidance strategies according to the phase where the static obstacle is located:the linear quadratic game method is employed to devise the guidance scheme for the energy optimization when the obstacle is located in the P1 and P3 stages;the norm-bounded differential game guidance strategy is presented to satisfy the acceleration constraint under the circumstance that the obstacle is located in the P2 and P4 ***,the radii of the static obstacle and the interceptor are taken as the design parameters to derive the combined guidance strategy through the dead-zone function,which guarantees that the pursuer avoids the static obstacle,and the interceptor,and attacks the ***,the nonlinear numerical simulations verify the performance of the game guidance strategy.
The Chaotic Baseband Wireless Communication system(CBWCS)is expected to eliminate the Inter-Symbol Interference(ISI)caused by multipath propagation by using the optimal decoding threshold that is the sum of the ISI ca...
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The Chaotic Baseband Wireless Communication system(CBWCS)is expected to eliminate the Inter-Symbol Interference(ISI)caused by multipath propagation by using the optimal decoding threshold that is the sum of the ISI caused by past decoded bits and the ISI caused by future transmitting ***,the current technique is only capable of removing partial effects of the ISI,because only past decoded bits are available for the suboptimal decoding threshold *** unavailability of the future information needed for the optimal decoding threshold is an obstacle to further improve the Bit Error Rate(BER)*** contrast to the previous method using Echo State Network(ESN)to predict one future bit,the proposed method in this paper predicts the optimal decoding threshold directly using *** proposed ESN-based threshold prediction method simplifies the symbol decoding operation by avoiding the iterative prediction of the output waveform points using ESN and accumulated error caused by the iterative *** this approach,the calculation complexity is reduced compared to the previous ESN-based *** proposed method achieves better BER performance compared to the previous *** reason for this superior result is ***,the proposed ESN is capable of using more future symbols information conveyed by the ESN input to obtain more accurate threshold rather than the previous method in which only one future symbol was ***,the proposed method here does not need to estimate the channel information using Least Squared(LS)method,which avoids the extra error caused by inaccurate channel information *** results and experiment based on a wireless open-access research platform under a practical wireless channel show the effectiveness and superiority of the proposed method.
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