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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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A Degeneracy-Aware LiDAR-Inertial-Visual SLAM for Degenerate Environments

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IEEE Sensors Journal 2025年第11期25卷 19376-19385页

作者： Zhu, Hongwei Zhang, Guobao Huang, Yongming Southeast University School of Automation Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China

Different sensors may experience degeneracy in specific scenarios, which can lead to failures in multi-sensor fusion optimization. To address the challenges of localization and mapping under such degeneracy conditions, this paper proposes a LiDAR-Inertial-Visual SLAM system with integrated degeneracy awareness. In the frontend, the system utilizes the ESIKF for tracking and incorporates a sensor degeneracy detection method to ensure that degraded sensor data do not affect the state error update. In the backend, we replace the conventional point cloud map with an adaptive voxel map to accelerate the retrieval of planar point cloud, and merge LiDAR planar features within a sliding window via region growing algorithm. Furthermore, the point-to-plane distance formula is optimized via the Rayleigh quotient theorem, which reduces the dimensionality of the optimization variables and simplifies the optimization process. Finally, we evaluate the proposed system on several challenging datasets and compare it with various state-of-the-art sensor fusion systems. The experimental results demonstrate that the proposed system achieves high localization accuracy in the majority of challenging scenarios. © 2001-2012 IEEE.

关键词： Inertial navigation systems

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Regional Multi-Agent Cooperative Reinforcement Learning for City-Level Traffic Grid Signal control

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IEEE/CAA Journal of Automatica Sinica 2024年第9期11卷 1987-1998页

作者： Yisha Li Ya Zhang Xinde Li Changyin Sun School of Automation Southeast UniversityNanjing 210096 Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of EducationSoutheast UniversityNanjing 210096China

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.

关键词： Human-machine cooperation mixed domain attention mechanism multi-agent reinforcement learning spatio-temporal feature traffic signal control

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Sensorless control of PMSM Drives Using Octant Newton-Raphson Method and Reduced-Order EKF Considering Speed Reversal and Noise

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IEEE Transactions on Power Electronics 2025年第8期40卷 10793-10803页

作者： Xiang, Fuyuan Yu, Ke Chen, Congyan Li, Shihua Southeast University School of Automation and the Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China

For back electromotive force-based sensorless control in permanent magnet synchronous motor (PMSM) drives, the conventional phase-locked loop (PLL) exhibits a ±π position estimation error during speed reversal. This paper presents an octant Newton-Raphson method (ONRM) based PLL, which employs a newly defined objective function and a position octant method. It not only ensures the position estimation converges correctly during speed reversal but also improves dynamic performance compared to the conventional PLL. The applicability condition of the ONRM in the presence of noise is also analyzed in detail. Subsequently, due to the noise sensitivity of the ONRM, a reduced-order extended Kalman filter is designed to filter out the non-Gaussian noise in the position estimation while simultaneously estimating the speed. The effectiveness of the proposed sensorless control scheme is verified on a 1.5 kW PMSM drive platform. © 1986-2012 IEEE.

关键词： Extended Kalman filters

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Disturbance Observer-based Pointing control of Leighton Chajnantor Telescope

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Research in Astronomy and Astrophysics 2024年第1期24卷 135-150页

作者： Weirui Chen Zheng Wang School of Automation Southeast UniversityNanjing 210096China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of EducationSoutheast UniversityNanjing 210096China

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.

关键词： instrumentation:miscellaneous methods:analytical methods:numerical telescopes

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Balanced prioritized experience replay in off-policy reinforcement learning

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Neural Computing and Applications 2024年第25期36卷 15721-15737页

作者： Lou, Zhouwei Wang, Yiye Shan, Shuo Zhang, Kanjian Wei, Haikun Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China School of Automation Southeast University Nanjing210096 China

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 the learning process are unevenly distributed in the state space, resulting in the agent’s inability to accurately estimate the value of each potential state. This problem is typically caused by environments with high-dimensional state and action spaces, as well as the exploration–exploitation mechanism inherent in RL. This article proposes a balanced prioritized experience replay (BPER) algorithm based on experience rarity. First, an evaluation metric to quantify experience rarity is defined. Then, the sampling priority of each experience is calculated according to this metric. Finally, prioritized experience replay is performed according to the sampling priority. BPER increases the sampling frequency of high-rarity experiences and decreases the sampling frequency of low-rarity experiences, enabling the agent to learn more comprehensive knowledge. We evaluate BPER on a series of MuJoCo continuous control tasks. Experimental results show that BPER can effectively improve the learning performance while mitigating the impact of the experience imbalance problem. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Reinforcement learning

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Efficient Circuit Diagram Retrieval via Graph Representation

Efficient Circuit Diagram Retrieval via Graph Representation

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2024 China Automation Congress, CAC 2024

作者： Gao, Ming Chen, Hongcai Zhang, Kanjian Wei, Haikun Key Laboratory of Measurement and Control of Complex System Ministry of Education School of Automation Southeast University Nanjing China

ISBN: (纸本)9798350368604

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 and in the identification of design plagiarism. Existing image retrieval techniques, which are proficient in managing natural images, typically involve the conversion of images into feature vectors and retrieve similar images according to the closeness of these vectors. However, these methods encounter limitations when adapted to the more specialized and complex domain of circuit diagrams. This paper introduces an innovative approach to the retrieval of circuit diagrams through the use of a graph representation of these diagrams. This novel approach effectively transforms the retrieval task into a graph retrieval problem. The methodology proposed in this paper is comprised of two main components: A recognition algorithm for circuit diagrams designed to accurately extract the components and the topological structure of the circuit and a retrieval strategy based on graph similarity. This methodology is pioneering in its use of graph representation for circuit diagram retrieval, incorporating topological features that are often not considered by conventional image retrieval methods. The experimental results presented in this paper validate the effectiveness of the proposed approach. © 2024 IEEE.

关键词： Circuit Diagram Recognition Circuit Diagram Retrieval Circuit Graph Representation Graph Retrieval

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Parallel system Based Quantitative Assessment and Self-evolution for Artificial Intelligence of Active Power Corrective control

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CSEE Journal of Power and Energy systems 2024年第1期10卷 13-28页

作者： Tianyun Zhang Jun Zhang Feiyue Wang Peidong Xu Tianlu Gao Haoran Zhang Ruiqi Si School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072HubeiChina State Key Laboratory for Management and Control of Complex System Institute of AutomationChinese Academy of SciencesBeijing 100190China

In artificial intelligence(AI)based-complex power system management and control technology,one of the urgent tasks is to evaluate AI intelligence and invent a way of autonomous intelligence ***,there is,currently,nearly no standard technical framework for objective and quantitative intelligence *** this article,based on a parallel system framework,a method is established to objectively and quantitatively assess the intelligence level of an AI agent for active power corrective control of modern power systems,by resorting to human intelligence evaluation *** this basis,this article puts forward an AI self-evolution method based on intelligence assessment through embedding a quantitative intelligence assessment method into automated reinforcement learning(AutoRL)systems.A parallel system based quantitative assessment and self-evolution(PLASE)system for power grid corrective control AI is thereby constructed,taking Bayesian Optimization as the measure of AI evolution to fulfill autonomous evolution of AI under guidance of their intelligence assessment *** results exemplified in the power grid corrective control AI agent show the PLASE system can reliably and quantitatively assess the intelligence level of the power grid corrective control agent,and it could promote evolution of the power grid corrective control agent under guidance of intelligence assessment results,effectively,as well as intuitively improving its intelligence level through selfevolution.

关键词： AI quantitative intelligence assessment and self-evolution automated reinforcement learning Bayesian optimization corrective control parallel system

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BeltLineNet: A Shape-Prior-Guided Lightweight Network for Real-Time Deviation Detection in Circular Pipe Conveyors

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IEEE Sensors Journal 2025年第11期25卷 20024-20036页

作者： Zhao, Long Su, Jinhui Zhong, Yusheng Xie, Weiwei Su, Jinya Chen, Xisong Chen, Congyan Li, Shihua Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Nanjing210096 China Fujian Longking Intelligent Conveying Engineering Co. Ltd Longyan China

Belt deviation in circular pipe conveyor systems could lead to material spillage, environmental contamination, reduced efficiency, and accelerated belt wear. Real-time belt deviation detection is crucial for ensuring safe and efficient operation. However, existing methods, primarily developed for flat-belt systems, heavily rely on distinct belt-background semantics, making them unsuitable for circular conveyors where indistinct target features, motion blur, and occlusions pose significant challenges. To address these issues, we reformulate the deviation detection task as an object detection problem, proposing BeltLineNet, a lightweight, real-time deviation detection network specifically designed for circular pipe conveyors. Our method integrates explicit feature learning with a coarse-to-fine global feature fusion mechanism, enhancing belt line representation under complex conditions. Additionally, a shape-prior loss strategy is introduced to improve supervision during training, ensuring more accurate detection of elongated targets. The model is also optimized for real-time deployment through layer-adaptive pruning, achieving a balance between accuracy and computational efficiency. Extensive comparative experiments involving 8 state-of-the-art (SOTA) feature extraction networks and 6 feature enhancement strategies on a self-collected dataset under industrial conditions demonstrate that BeltLineNet surpasses SOTA feature extraction and fusion networks, improving average precision (AP) by 8.23% before pruning and 6.33% after pruning. © 2001-2012 IEEE.

关键词： Belt conveyors

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Self-supervised Depth Completion with Adaptive Online Adaptation 23

Self-supervised Depth Completion with Adaptive Online Adapta...

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7th International Conference on Machine Learning and Soft Computing, ICMLSC 2023

作者： Chen, Yang Tan, Yang Key Laboratory of Measurement and Control of Complex System of Engineering Ministry of Education School of Automation Southeast University Nanjing210096 China

ISBN: (纸本)9781450398633

Although depth completion has achieved remarkable performance relying on deep learning in recent years, these models tend to suffer a performance degradation when exposed to new environments. Online adaptation, where the model is trained in a self-supervised manner during testing, seems a promising technique to alleviate the drop. However, continuous online adaptation may cause the model to over-adapt and miss the optimal parameters, resulting in oscillation or even degradation of the model performance, in addition to wasting computational resources. Therefore, this paper proposes an adaptive online adaptation framework to make model adaptively trigger online adaptation when encountering novel environments and stop adaptation when model has adapted to the current environment. In detail, we design a trigger to detect the familiarity of model to the current scenario based on image similarity and then launch online adaptation when the scenario is novel. Besides, we elaborate a stopper to monitor the error between prediction and depth input and convert online adaptation to inference when online adaptation does not bring improvement for model. Experimental results demonstrate that our method improves the accuracy of model prediction and increases average running speed of the model on each frame in online adaptation. © 2023 ACM.

关键词： Deep learning

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