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Geometry-aware 3D pose transfer using transformer autoencoder

Computational Visual Media 2024年第6期10卷 1063-1078页

作者： Shanghuan Liu Shaoyan Gai Feipeng Da Fazal Waris Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of EducationSoutheast UniversityNanjing210096China

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.

关键词： 3D pose transfer geometry-aware transfor-mer autoencoder cross-covariance attention

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Recursive estimation with compensation strategies under random access protocol and deception attacks

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IEEE Transactions on Signal Processing 2025年

作者： Li, Jiaxing Caballero-Aguila, Raquel Hu, Jun Linares-Perez, Josefa Harbin University of Science and Technology Department of Applied Mathematics Harbin150080 China University of Jaén Department of Statistics and Operations Research Jaén23071 Spain Harbin University of Science and Technology Heilongjiang Provincial Key Laboratory of Optimization Control and Intelligent Analysis for Complex Systems Harbin150080 China Harbin University of Science and Technology School of Automation Harbin150080 China University of Granada Department of Statistics and Operations Research Granada18071 Spain

In this paper, recursive least-squares linear estimation algorithms are proposed for stochastic systems influenced by uniform quantization, random access protocol (RAP) and deception attacks. With the purpose of enhancing communication efficiency and reducing unnecessary data collisions, RAP is adopted to schedule data signal transmissions that are also subject to deception attacks. In order to alleviate the side effect of missing information caused by RAP, three compensation strategies (zero-input, zero-order hold and prediction-compensation) are utilized. By resorting to an innovation method, covariance-based filters are designed and then fixed-point smoothers are obtained in light of available observations. Finally, a simulation experiment with comparisons is employed to demonstrate the effectiveness of the developed recursive estimation schemes, where the influence of attack probabilities on estimation accuracy is evaluated. © 1991-2012 IEEE.

关键词： Least squares approximations

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Hybrid Beamforming Design for Bistatic Integrated Sensing and Communication systems

arXiv

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arXiv 2025年

作者： Mao, Tianhao Yang, Jie Liang, Le Jin, Shi National Mobile Communications Research Laboratory Frontiers Science Center for Mobile Information Communication and Security Southeast University Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China Frontiers Science Center for Mobile Information Communication and Security Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China

Integrated sensing and communication (ISAC) in millimeter wave is a key enabler for next-generation networks, which leverages large bandwidth and extensive antenna arrays, benefiting both communication and sensing functionalities. The associated high costs can be mitigated by adopting a hybrid beamforming structure. However, the well-studied monostatic ISAC systems face challenges related to full-duplex operation. To address this issue, this paper focuses on a three-dimensional bistatic configuration that requires only half-duplex base stations. To intuitively evaluate the error bound of bistatic sensing using orthogonal frequency division multiplexing waveforms, we propose a positioning scheme that combines angle-of-arrival and time-of-arrival estimation, deriving the closed-form expression of the position error bound (PEB). Using this PEB, we develop two hybrid beamforming algorithms for joint waveform design, aimed at maximizing achievable spectral efficiency (SE) while ensuring a predefined PEB threshold. The first algorithm leverages a Riemannian trust-region approach, achieving superior performance in terms of global optima and convergence speed compared to conventional gradient-based methods, but with higher complexity. In contrast, the second algorithm, which employs orthogonal matching pursuit, offers a more computationally efficient solution, delivering reasonable SE while maintaining the PEB constraint. Numerical results are provided to validate the effectiveness of the proposed designs. Copyright © 2025, The Authors. All rights reserved.

关键词： Orthogonal frequency division multiplexing

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Feature Selection and Feature Learning for High-dimensional Batch Reinforcement Learning: A Survey

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International Journal of Automation and computing 2015年第3期12卷 229-242页

作者： De-Rong Liu Hong-Liang Li Ding Wang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences

Tremendous amount of data are being generated and saved in many complex engineering and social systems every *** is significant and feasible to utilize the big data to make better decisions by machine learning techniques. In this paper, we focus on batch reinforcement learning(RL) algorithms for discounted Markov decision processes(MDPs) with large discrete or continuous state spaces, aiming to learn the best possible policy given a fixed amount of training data. The batch RL algorithms with handcrafted feature representations work well for low-dimensional MDPs. However, for many real-world RL tasks which often involve high-dimensional state spaces, it is difficult and even infeasible to use feature engineering methods to design features for value function approximation. To cope with high-dimensional RL problems, the desire to obtain data-driven features has led to a lot of works in incorporating feature selection and feature learning into traditional batch RL algorithms. In this paper, we provide a comprehensive survey on automatic feature selection and unsupervised feature learning for high-dimensional batch RL. Moreover, we present recent theoretical developments on applying statistical learning to establish finite-sample error bounds for batch RL algorithms based on weighted Lpnorms. Finally, we derive some future directions in the research of RL algorithms, theories and applications.

关键词： Intelligent control reinforcement learning adaptive dynamic programming feature selection feature learning big data.

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Sliding Mode control for Flexible-link Manipulators Based on Adaptive Neural Networks

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International Journal of Automation and computing 2018年第2期15卷 239-248页

作者： Hong-Jun Yang Min Tan State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

This paper mainly focuses on designing a sliding mode boundary controller for a single flexible-link manipulator based on adaptive radial basis function （RBF） neural network. The flexible manipulator in this paper is considered to be an Euler-Bernoulli beam. We first obtain a partial differential equation （PDE） model of single-link flexible manipulator by using Hamiltons approach. To improve the control robustness, the system uncertainties including modeling uncertainties and external disturbances are compensated by an adaptive neural approximator. Then, a sliding mode control method is designed to drive the joint to a desired position and rapidly suppress vibration on the beam. The stability of the closed-loop system is validated by using Lyapunov＇s method based on infinite dimensional model, avoiding problems such as control spillovers caused by traditional finite dimensional truncated models. This novel controller only requires measuring the boundary information, which facilitates implementation in engineering practice. Favorable performance of the closed-loop system is demonstrated by numerical simulations.

关键词： Sliding mode control adaptive control neural network flexible manipulator partial differential equation （PDE）.

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Remembering Dave Waltz (1943-2012) IN MEMORIAM

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IEEE INTELLIGENT systems 2012年第4期27卷 86-88页

作者： Wang, Fei-Yue State Key Laboratory of Management and Control for Complex Systems

IEEE Intelligent systems remembers Dave Walz, a longtime member of its advisory board and a great friend.

关键词： Obituaries Waltz Dave IEEE Intelligent systems Dave Walz Advisory Board

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Finite-time control for a class of hybrid systems via quantized intermittent control

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Science China(Information Sciences) 2020年第9期63卷 140-155页

作者： Xiaolin XIONG Xinsong YANG Jinde CAO Rongqiang TANG Cunjin College Guangdong Ocean University School of Mathematical Sciences Chongqing Normal University Key Laboratory of Optimization and Control Ministry of Education School of Mathematics and Research Center for Complex Systems and Network SciencesSoutheast University

This paper considers the finite-time drive-response synchronization of stochastic nonlinear systems consisting of continuous-time and discrete-time subsystems. To save communication resources and reduce control cost, quantized controllers, which only work on continuous-time intervals, are designed. Owing to the hybrid characteristics of continuous-and discrete-time subsystems, existing finite-time stability theorems are not applicable. By developing novel analytical techniques, three criteria are derived to guarantee the finite-time synchronization. Moreover, the settling time is explicitly estimated. It is shown that the settling time is dependent not only on the control gains and systems’ initial conditions, but also on the control width and uncontrolled width. Numerical examples demonstrate the effectiveness of the theoretical analysis.

关键词： hybrid system finite-time synchronization quantized intermittent control stochastic perturbation

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Voltage-controlled robust topological states in perforated beams

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Engineering Structures 2025年 338卷

作者： Hong, Fang Zhang, Kai Yao, Yuxin Tang, Lihua Deng, Zichen School of Mechanics Civil Engineering and Architecture Northwestern Polytechnical University Xi'an710072 China MIIT Key Laboratory of Dynamics and Control of Complex Systems Northwestern Polytechnical University Xi'an710072 China Department of Mechanical and Mechatronics Engineering University of Auckland 20 Symonds Street Auckland1010 New Zealand

Topological metamaterials possess waveguiding and energy concentration properties even under perturbations such as defects or disorders. Tunable topological materials offer enhanced flexibility to these properties. In this study, a voltage-controlled strategy is presented to realize the tunable robust topological states. First, a perforated beam with piezoelectric patches and additional steel columns is proposed to analyze the topological properties, and the corresponding numerical model based on Timoshenko beam theory is introduced to analyze the band gap properties and transmission spectra by spectral element method (SEM). Then the effects of the applied voltage on the resonance frequency and equivalent stiffness of piezoelectric patches are characterized. Two new band gaps emerge around the locally resonant band gap and topological phase transition can be achieved by altering the collective displacement of the perforations and resonators. Subsequently, the finite element simulations are presented to validate the accuracy of SEM results, and the equivalent voltage-controlled stiffness is used in SEM to simplify the relationship between voltage and topological properties. The Zak phase calculations demonstrate the topological properties of opened band gaps and further indicate the existence of topologically protected interface states (TPISs). Finally, the transmission spectra are used to analyze the characteristics of TPISs, and the robustness of TPISs is examined by introducing different perturbation factors. The results show that the frequency of topological states can be simply tuned by varying the voltage on the piezoelectric patch, and the topological rainbow trapping can be realized in a designed gradient system without changing the geometric configuration. © 2025 Elsevier Ltd

关键词： Metamaterials

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Multi-objective Evolutionary Algorithm Based Graph Neural Network Architecture Search 11th

Multi-objective Evolutionary Algorithm Based Graph Neural Ne...

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11th International Symposium on Computational Intelligence and Industrial Applications, ISCIIA 2024

作者： He, Lianyi Liu, Xiaobo Xiang, Hongbo Wang, Guangjun School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China Key Laboratory of Geological Survey and Evaluation of Ministry of Education China University of Geosciences Wuhan430074 China

ISBN: (纸本)9789819647521

Graph Neural Networks (GNN) has become a powerful graph data processing method, which has been widely used in node classification, link prediction, and other graph analysis tasks. Due to the diversity and complexity of graph structures and information propagation, as well as the handling of heterogeneous graphs, etc., the design of GNNs presents many challenges. The existing methods have high classification accuracy, but their structures are very complex. In this paper, a graph neural network architecture search framework named MO-GNN, which is based on multi-objective evolutionary algorithm is proposed. Furthermore, a weight sharing strategy MO-GNN-WS is proposed to reduce the resource consumption caused by weight training of different architectures. To verify the performance of the proposed algorithm, experiments on four popular graph datasets are used for transduction and induction tasks. The experimental results show that the MO-GNN-WS algorithm outperforms the most advanced neural network architecture search methods in terms of classification accuracy and resource consumption. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data Sharing

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Path Planning for Distribution Network Robots with Adaptive Simulated Annealing-Enhanced Artificial Potential Field Method 26

Path Planning for Distribution Network Robots with Adaptive ...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Zhang, Baichao Chen, Xin Chen, Jinge Li, Jinbin Wang, Xiaokai Jian, Xu School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China State Grid Hubei Electric Power Co. Ltd. Electric Power Science Research Institute Wuhan430077 China Hubei Fangyuan Dongli Electric Power Science Research Co. Ltd Wuhan430077 China

ISBN: (纸本)9798331521950

In this paper, we propose a hybrid algorithm that combines an improved Artificial Potential Field (APF) method with the Simulated Annealing (SA) algorithm for path planning of an electric power operation robot manipulator in complex distribution grid environments. To address the unreachable target issue inherent in traditional APF, we introduce a distance regulation factor to optimize the repulsive function. This modification allows the manipulator to smoothly approach the target point as it nears, while the repulsion from obstacles gradually decreases. Additionally, to overcome the limitations of the traditional SA algorithm, such as its tendency to get trapped in local minimum solutions and its inefficiency in complex environments, we propose an adaptive temperature rise strategy. This strategy increases the temperature, enhancing the probability of escaping local optimal solutions. When the APF algorithm becomes trapped in a local optimum, the improved SA algorithm is applied to escape the local minimum. Once the local optimum is avoided, the algorithm switches back to APF to continue the path planning process. Simulation results demonstrate that the proposed improved APF-SA algorithm adapts effectively to various complex environments, achieving shorter planning times and higher success rates compared to traditional APF and SA algorithms. It successfully resolves the unreachable target and local minimum problems associated with APF. Finally, the feasibility of the proposed APF-SA fusion algorithm is validated through experiments conducted on an electric power operation robot experimental platform for distribution grid applications. © 2025 IEEE.

关键词： Motion planning

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