作者:
Qiming LiuXinru CuiZhe LiuHesheng WangDepartment of Automation
Shanghai Jiao Tong UniversityShanghai 200240China MoE Key Laboratory of Artificial Intelligence
AI InstituteShanghai Jiao Tong UniversityShanghai 200240China Department of Automation
Key Laboratory of System Control and Information Processing of Ministry of EducationKey Laboratory of Marine Intelligent Equipment and System of Ministry of EducationShanghai Engineering Research Center of Intelligent Control and ManagementShanghai Jiao Tong UniversityShanghai 200240China
Autonomous navigation for intelligent mobile robots has gained significant attention,with a focus on enabling robots to generate reliable policies based on maintenance of spatial *** this paper,we propose a learning-b...
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Autonomous navigation for intelligent mobile robots has gained significant attention,with a focus on enabling robots to generate reliable policies based on maintenance of spatial *** this paper,we propose a learning-based visual navigation pipeline that uses topological maps as memory *** introduce a unique online topology construction approach that fuses odometry pose estimation and perceptual similarity *** tackles the issues of topological node redundancy and incorrect edge connections,which stem from the distribution gap between the spatial and perceptual ***,we propose a differentiable graph extraction structure,the topology multi-factor transformer(TMFT).This structure utilizes graph neural networks to integrate global memory and incorporates a multi-factor attention mechanism to underscore elements closely related to relevant target cues for policy *** from photorealistic simulations on image-goal navigation tasks highlight the superior navigation performance of our proposed pipeline compared to existing memory *** validation through behavior visualization,interpretability tests,and real-world deployment further underscore the adapt-ability and efficacy of our method.
This paper studies a novel distributed optimization problem that aims to minimize the sum of the non-convex objective functionals of the multi-agent network under privacy protection, which means that the local objecti...
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This paper studies a novel distributed optimization problem that aims to minimize the sum of the non-convex objective functionals of the multi-agent network under privacy protection, which means that the local objective of each agent is unknown to others. The above problem involves complexity simultaneously in the time and space aspects. Yet existing works about distributed optimization mainly consider privacy protection in the space aspect where the decision variable is a vector with finite dimensions. In contrast, when the time aspect is considered in this paper, the decision variable is a continuous function concerning time. Hence, the minimization of the overall functional belongs to the calculus of variations. Traditional works usually aim to seek the optimal decision function. Due to privacy protection and non-convexity, the Euler-Lagrange equation of the proposed problem is a complicated partial differential ***, we seek the optimal decision derivative function rather than the decision function. This manner can be regarded as seeking the control input for an optimal control problem, for which we propose a centralized reinforcement learning(RL) framework. In the space aspect, we further present a distributed reinforcement learning framework to deal with the impact of privacy protection. Finally, rigorous theoretical analysis and simulation validate the effectiveness of our framework.
Real-time six degrees-of-freedom(6D)object pose estimation is essential for many real-world applications, such as robotic grasping and augmented reality. To achieve an accurate object pose estimation from RGB images i...
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Real-time six degrees-of-freedom(6D)object pose estimation is essential for many real-world applications, such as robotic grasping and augmented reality. To achieve an accurate object pose estimation from RGB images in real-time, we propose an effective and lightweight model, namely high-resolution 6D pose estimation network(HRPose). We adopt the efficient and small HRNetV2-W18 as a feature extractor to reduce computational burdens while generating accurate 6D poses. With only 33% of the model size and lower computational costs, our HRPose achieves comparable performance compared with state-of-the-art models. Moreover, by transferring knowledge from a large model to our proposed HRPose through output and feature-similarity distillations, the performance of our HRPose is improved in effectiveness and efficiency. Numerical experiments on the widely-used benchmark LINEMOD demonstrate the superiority of our proposed HRPose against state-of-the-art methods.
Aiming at the problem that the intermediate potential part of the traditional bistable stochastic resonance model cannot be adjusted independently, a new composite stochastic resonance(NCSR) model is proposed by combi...
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Aiming at the problem that the intermediate potential part of the traditional bistable stochastic resonance model cannot be adjusted independently, a new composite stochastic resonance(NCSR) model is proposed by combining the Woods–Saxon(WS) model and the improved piecewise bistable model. The model retains the characteristics of the independent parameters of WS model and the improved piecewise model has no output saturation, all the parameters in the new model have no coupling characteristics. Under α stable noise environment, the new model is used to detect periodic signal and aperiodic signal, the detection results indicate that the new model has higher noise utilization and better detection ***, the new model is applied to image denoising, the results showed that under the same conditions, the output peak signal-to-noise ratio(PSNR) and the correlation number of NCSR method is higher than that of other commonly used linear denoising methods and improved piecewise SR methods, the effectiveness of the new model is verified.
Preserving the topology from being inferred by external adversaries has become a paramount security issue for network systems (NSs), and adding random noises to the nodal states provides a promising way. Nevertheless,...
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作者:
Chen, YiyangHua, YongzhaoFeng, ZhiDong, XiwangBeihang University
School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control Laboratory Shenyuan Honors College Beijing100191 China Beihang University
School of Artificial Intelligence Institute of Artificial Intelligence Beijing100191 China Beihang University
School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control Laboratory Beijing100191 China Beihang University
Institute of Unmanned System School of Artificial Intelligence Institute of Artificial Intelligence School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control Laboratory Beijing100191 China
This paper investigates the adaptive prescribed-time distributed Nash equilibrium (NE) seeking problems for networked games with heterogeneous dynamics and unknown uncertainties. The proposed algorithms are based on t...
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Bolt connection is one of the main fixing methods of cylindrical shell structures.A typical bolted connection model is considered as a tuned ***,in the actual working conditions,due to the manufacturing error,installa...
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Bolt connection is one of the main fixing methods of cylindrical shell structures.A typical bolted connection model is considered as a tuned ***,in the actual working conditions,due to the manufacturing error,installation error and uneven materials of bolts,there are always random errors between different *** investigate the influence of non-uniform parameters of bolt joint,including the stiffness and the distribution position,on frequency complexity characteristics of cylindrical shell through a statistical method is the main aim of this *** bolted joints considered here were simplified as a series of springs with random *** vibration equation of the bolted joined cylindrical shell was derived based on Sanders’thin shell *** Monte Carlo simulation and statistical theory were applied to the statistical analysis of mode characteristics of the ***,the frequency and mode shape of the tuned system were investigated and compared with ***,the effect of the random distribution and the random constraint stiffness of the bolts on the frequency and mode shape were *** the statistical analysis on the natural frequencies was evaluated for different mistuned *** some special cases were presented to help understand the effect of random *** research introduces random theory into the modeling of bolted joints and proposes a reference result to interpret the complexity of the modal characteristics of cylindrical shells with non-uniform parameters of bolt joints.
This paper proposes the analysis of the material can be protected from transformer testing and presents a mathematical model of the power transformer. In this research, transformers were study in four protective mater...
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This paper considers the problem of approximating the infinite-horizon value function of the discrete-time switched LQR *** particular,the authors propose a new value iteration method to generate a sequence of monoton...
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This paper considers the problem of approximating the infinite-horizon value function of the discrete-time switched LQR *** particular,the authors propose a new value iteration method to generate a sequence of monotonically decreasing functions that converges exponentially to the value *** method facilitates us to use coarse approximations resulting from faster but less accurate algorithms for further value iteration,and thus,the proposed approach is capable of achieving a better approximation for a given computation time compared with the existing *** numerical examples are presented in this paper to illustrate the effectiveness of the proposed method.
The recently proposed ambient signal-based load modeling approach offers an important and effective idea to study the time-varying and distributed characteristics of power ***,it also brings new *** the load model par...
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The recently proposed ambient signal-based load modeling approach offers an important and effective idea to study the time-varying and distributed characteristics of power ***,it also brings new *** the load model parameters of power loads can be obtained in real-time for each load bus,the numerous identified parameters make parameter application *** order to obtain the parameters suitable for off-line applications,load model parameter selection(LMPS)is first introduced in this ***,the convolution neural network(CNN)is adopted to achieve the selection purpose from the perspective of short-term voltage *** begin with,the field phasor measurement unit(PMU)data from China Southern Power Grid are obtained for load model parameter identification,and the identification results of different substations during different times indicate the necessity of ***,the simulation case of Guangdong Power Grid shows the process of LMPS,and the results from the CNNbased LMPS confirm its effectiveness.
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