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5th International Conference on Industrial Artificial Intelligence, IAI 2023

作者： Gu, Shiyi Zhang, Tingting Zhang, Ya Southeast University School of Automation Nanjing210096 China Ministry of Education Key Laboratory of Measurement and Control of Complex Systems of Engineering Nanjing China Army Engineering University College of Command and Control Engineering Nanjing210017 China

ISBN: (纸本)9798350325294

Traffic Signal control (TSC) optimization is vital for efficient traffic flow in urban intersections. Recent research has explored the application of Reinforcement Learning (RL) agents to tackle TSC challenges. However, the selection of an appropriate reward function in RL agent training still relies on subjective judgment and experience. To address this issue, an Inverse Reinforcement Learning (IRL) integrated RL algorithm is proposed. In the IRL learning section, expert trajectory data are collected and analyzed by Relative Entropy IRL (REIRL). The latent reward of expert policy is reconstructed and utilized in RL agent training process. In the RL control section, a Double Dueling Deep Q Network is applied under a cycle control framework. As verified in simulations, the introduction of expert experience improves the performance of the RL agent to the expert level and concurrently enables robustness to expert policy noises. © 2023 IEEE.

关键词： Reinforcement learning

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A distributed finite-time economic dispatch algorithm in smart grid

A distributed finite-time economic dispatch algorithm in sma...

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2022 Jiangsu Annual Conference on Automation, JACA 2022

作者： Lin, Luxin Wang, Xiangyu School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education China

ISBN: (纸本)9781839538476

This paper focuses on the design of an initialization-free distributed finite-time algorithm for the economic dispatch problem in smart grid, where the generation units only communicate with its neighbors to minimize the total cost cooperatively. Firstly, the optimal solution of the economic dispatch problem is analyzed in accordance with the saddle-point dynamics. Then, an initialization-free finite-time algorithm is presented to guarantee the active power generation and the incremental cost converge to the optimal solution in a distributed manner. At last, some numerical simulations are preformed to validate the proposed method. Copyright© JACA 2022 All rights reserved.

关键词： Smart power grids

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A novel task-oriented framework for dual-arm robotic assembly task

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Frontiers of Mechanical Engineering 2021年第3期16卷 528-545页

作者： Zhengwei WANG Yahui GAN Xianzhong DAI School of Automation Southeast UniversityNanjing 210096China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of EducationNanjing 210096China

In industrial manufacturing,the deployment of dual-arm robots in assembly tasks has become a ***,making the dual-arm robots more intelligent in such applications is still an open,challenging *** paper proposes a novel framework that combines task-oriented motion planning with visual perception to facilitate robot deployment from perception to execution and finish assembly problems by using dual-arm *** this framework,visual perception is first employed to track the effects of the robot behaviors and observe states of the workpieces,where the performance of tasks can be abstracted as a high-level state for intelligent *** assembly task and manipulation sequences can be obtained by analyzing and reasoning the state transition trajectory of the environment as well as the ***,the corresponding assembly manipulation can be generated and parameterized according to the differences between adjacent states by combining with the prebuilt knowledge of the *** are set up with a dual-arm robotic system(ABB YuMi and an RGB-D camera)to validate the proposed *** results demonstrate the effectiveness of the proposed framework and the promising value of its practical application.

关键词： dual-arm assembly Al reasoning intelligent system task-oriented motion planning visual perception

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keypoints-guided Lightweight Network for Single-view 3D Human Reconstruction

Keypoints-guided Lightweight Network for Single-view 3D Huma...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Yuhang Chen Chenxing Wang School of Automation Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Single-view 3D human reconstruction has been a hot topic due to the potential of wide applications. To achieve high accuracy, existing works usually take computationally intensive models as backbone for exhaustive underlying features and then directly estimate human mesh vertices. These factors lead to redundant parameters, large calculations and low efficiency, while lightweight solutions to address these challenges are relatively scarce. In this work, based on the problems studied above, we propose a keypoints-guided lightweight network with an encoding-decoding framework. As the input is an image, a lightweight backbone named multi-stage and global feature enhanced network is designed for 2D encoding, where some operations of multi-scale fusion and frequency domain filtering are performed to extract more informative but low-resolution features. As the output is mesh of human body, we construct a keypoints-based 3D human template, with which the 2D low-resolution features can be mapped to 3D space to guide the 3D decoding with high efficiency and high accuracy. Extensive experiments on popular benchmarks 3DPW and Human3.6M illustrate the favorable trade-off between the accuracy and complexity of our method. Our code is publicly available at https://***/ChrisChenYh/***.

关键词： Three-dimensional displays Accuracy Image coding Frequency-domain analysis Estimation Benchmark testing Feature extraction

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Distributed Finite-Time Anti-Disturbance Secondary Voltage Regulation for Islanded Alternating-Current Microgrids 41

Distributed Finite-Time Anti-Disturbance Secondary Voltage R...

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第41届中国控制会议

作者： Xuechao Qiu Guanjun Li Xiangyu Wang School of Automation Southeast Universityand Key Laboratory of Measurement and Control of Complex Systems of EngineeringMinistry of Education China Electric Power Research Institude Company Ltd.

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In this paper,a distributed finite-time anti-disturbance secondary voltage regulation problem is investigated for islanded alternating-current(AC) microgrids with unmeasurable states and *** combining the integral-type nonsingular terminal sliding-mode control and finite-time extended-state observer technique together,distributed finite-time anti-disturbance secondary voltage regulation protocols are *** order to estimate the unmeasurable states and disturbances,some finite-time extended-state observers are constructed at ***,according to the estimates of disturbances and unmeasurable states,some integral-type nonsingular terminal sliding-mode surfaces are designed for the ***,secondary voltage regulation protocols are proposed based on the *** the islanded AC microgrid system,these protocols achieve the output voltage consensus in finite *** demonstrate the effectiveness of the proposed secondary voltage regulation protocols.

关键词： Islanded Alternating-Current Microgrids Disturbance Rejection control Secondary Voltage Regulation Output-Feedback control

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Context-Aided Semantic-Aware Self-Alignment for Video-Based Person Re-Identification

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IEEE Transactions on Circuits and systems for Video Technology 2025年第6期35卷 5307-5317页

作者： Ran, Zhidan Xiao, Zhiyao Lu, Xiaobo Wei, Xuan Liu, Wei Southeast University School of Automation Nanjing210096 China Ministry of Education Key Laboratory of Measurement and Control of Complex Systems of Engineering Nanjing210096 China

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-ID. Existing alignment-based methods generally rely on off-the-shelf semantic parsing models to locate visible human parts, which ignore identifiable personal belongings and cannot handle various interferences (e.g., pedestrian detection errors and occlusions) in video clips. In this work, we propose a novel framework termed Context-Aided Semantic-Aware Self-Alignment (CSSA) for video-based person Re-ID. First, we propose to jointly learn pixel-level part-aligned representations and semantic-aligned global-level representations in an end-to-end manner. Unlike most existing approaches that depend on prior information in terms of pose for part estimation, CSSA can locate different body parts and achieve the pixel-level semantic alignment without extra human topology semantics. Second, a Context-Aided Region Enhancement (CARE) module is proposed to efficiently highlight macro-visual patterns associated with the target pedestrian and suppress noise caused by factors like background clutters and occlusions. Third, we propose a Semantic-Aware Global Feature Alignment (SGFA) method for generating pair-wise semantic-aligned global representations, which play an essential role in both the training and inference phases. Extensive experimental results on multiple challenging benchmarks indicate the superiority and effectiveness of the proposed CSSA. © 1991-2012 IEEE.

关键词： Semantic Segmentation

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Sensorless control of SPMSM Based on Generalized Super-Twisting Observer and Non-Smooth controller 42

Sensorless Control of SPMSM Based on Generalized Super-Twist...

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42nd Chinese control Conference, CCC 2023

作者： Chen, Dejin Yu, Ke Ding, Shihong Wang, Xiangyu Li, Shihua School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China School of Electrical and Information Engineering Zhenjiang212013 China

ISBN: (纸本)9789887581543

To enhance the estimation accuracy and dynamic performance of sensorless surface-mounted permanent magnet synchronous motor (SPMSM) drives, a sensorless control scheme based on generalized super-twisting observer (GSTO) and non-smooth controller is proposed. Firstly, a GSTO for back electromotive force (back-EMF) estimation is proposed. Compared with the conventional super-twisting observer, the GSTO has a faster convergence rate and stronger robustness due to the additional terms. Then a linear extended state observer (LESO) is adopted to estimate the position, speed, and lumped disturbance at the same time. Moreover, a non-smooth composite speed controller is designed by combining the disturbance feed-forward compensation. Compared with the conventional PI speed controller, the non-smooth controller has a shorter settling time and a better disturbance rejection ability. Finally, the effectiveness of the proposed method is verified by simulation results. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： controllers

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RSD-Diff: Boundary-Frequency Feature-Enhanced Rail Surface Defect Inspection with Diffusion-Based Transformer Decoder

RSD-Diff: Boundary-Frequency Feature-Enhanced Rail Surface D...

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International Conference on systems and Informatics (ICSAI)

作者： Dezhou Wang Shixiang Su Xiaobo Lu Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing China

ISBN: (数字)9798331510138

ISBN: (纸本)9798331510145

Rail surface defect inspection is an essential task for the railway system. However, due to the similarity of the background and defect foreground pixels, uneven textures, irregular shapes and multiple scales of the existing defects, the inspection accuracy is still required to be further improved. In this paper, we first expand our dataset by GAN-based method for the scarcity of the defect samples. Then, we propose a diffusion-based model to perform the classification and segmentation task. The diffusion model predicts the mask from the Gaussian noise broken ground truth by successive designed transformer decoder. Meanwhile, to better distinguish the pixels in the boundary or other illegible regions, a boundary-frequency feature enhancement module (BFM) is proposed to make the model pay more attention to the defect-related features. Experiments on the rail surface defect dataset revealed that the proposed diffusion-based transformer decoder and BFM can work well together and efficiently improve the rail surface defect segmentation accuracy.

关键词： Rails measurement Accuracy Shape Inspection Transformers Rail transportation Decoding Surface texture Informatics

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Long-term Solar Irradiance Forecasting Based on TimesNet with Multiple Weather Parameters

Long-term Solar Irradiance Forecasting Based on TimesNet wit...

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Chinese control and Decision Conference, CCDC

作者： Fei Gong Shuo Shan Haikun Wei Kanjian Zhang Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

In renewable energy grids, solar irradiance prediction is significant for electric operation planning. The emphasis of our work is forecasting irradiance changes over multiple days ahead using hourly irradiance data in association with weather parameters such as temperature, humidity, barometric pressure, wind speed, and cloud cover. Specifically, a framework based on TimesNet was proposed for 4-day global horizontal irradiance (GHI) forecasting and a comparative experiment was conduct with three well-performing models in long-term forecasting tasks. The experimental results indicate that the suggested method outperformed the accuracy when compared to other models, with MSE 0.198, MAE 0.259, RMSE 0.445 and R 2 0.787.

关键词： Solar irradiance Temperature distribution Computational modeling Wind speed Time series analysis Weather forecasting Predictive models

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Guided Transformative GAN: Joint Face Image Super-Resolution and Frontalization

Guided Transformative GAN: Joint Face Image Super-Resolution...

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International Conference on systems and Informatics (ICSAI)

作者： Minghao Mu Wei Wang Xiaobo Lu Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing China

ISBN: (数字)9798331510138

ISBN: (纸本)9798331510145

Joint face super-resolution and frontalization of non-frontal low-resolution faces is of significant importance for many face analysis applications. However, at the same time, it is a challenging task. In this paper, a novel Guided Transformative Generative Adversarial Network (GT-GAN) is proposed to address the challenge of generating frontal high-resolution face images from non-frontal low-resolution face images. Existing methods usually treat super-resolution and frontalization as two separate tasks, leading to unsatisfactory processing results. GTG AN innovatively achieves both goals within a unified frame-work. Specifically, we propose Profile Attention Guidance (PAG) to generate landmark heatmaps that provide a priori pose information to the generative network and direct its attention to critical facial regions. In addition, an Attention Fusion Transformation Module (AFTM) is proposed to significantly improve the hallucination performance of the network by utilizing the attention mechanism and STNs, combined with sequential feature enhancement strategies. The experimental results show that GT-GAN exhibits superior performance in the joint face super-resolution and frontalization task on the public face dataset Multi-PIE, which provides new ideas and references for this challenging topic.

关键词： Heating systems Attention mechanisms Superresolution Generative adversarial networks Informatics Faces

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