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Generalized-Extended-State-Observer and Equivalent-Input-Disturbance Methods for Active Disturbance Rejection: Deep Observation and Comparison

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 957-968页

作者： Jinhua She Kou Miyamoto Qing-Long Han Min Wu Hiroshi Hashimoto Qing-Guo Wang School of Engineering Tokyo University of TechnologyHachiojiTokyo 192-0982Japan K.Miyamoto is with the Institute of Technology Shimizu CorporationKotoTokyo 135-0044Japan School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia School of Automation China University of GeosciencesWuhan 430074 Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of EducationWuhan 430074China School of Industrial Technology Advanced Institute of Industrial TechnologyTokyo 140-0011Japan Institute of Artificial Intelligence and Future Networks Beijing Normal UniversityZhuhai 519087 Guangdong Key Lab of AI and Multi-Modal Data Processing Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNUHKBU United International College Zhuhai 519087China

Active disturbance-rejection methods are effective in estimating and rejecting disturbances in both transient and steady-state *** paper presents a deep observation on and a comparison between two of those methods:the generalized extended-state observer(GESO)and the equivalent input disturbance(EID)from assumptions,system configurations,stability conditions,system design,disturbance-rejection performance,and extensibility.A time-domain index is introduced to assess the disturbance-rejection performance.A detailed observation of disturbance-suppression mechanisms reveals the superiority of the EID approach over the GESO method.A comparison between these two methods shows that assumptions on disturbances are more practical and the adjustment of disturbance-rejection performance is easier for the EID approach than for the GESO method.

关键词： Active disturbance-rejection control(ADRC) disturbance observer(DOB) equivalent input disturbance(EID) extendedstate observer(ESO) generalized extended-state observer(GESO)

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UAV-Assisted Joint Mobile Edge Computing and data Collection via Matching-Enabled Deep Reinforcement Learning

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IEEE Internet of Things Journal 2025年

作者： Wang, Boxiong Kang, Hui Li, Jiahui Sun, Geng Sun, Zemin Jilin University College of Computer Science and Technology Changchun130012 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China Nanyang Technological University College of Computing and Data Science 639798 Singapore

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) and data collection (DC) have been popular research issues. Different from existing works that consider MEC and DC scenarios separately, this paper investigates a multi-UAV-assisted joint MEC-DC system. Specifically, we formulate a joint optimization problem to minimize the MEC latency and maximize the collected data volume. This problem can be classified as a non-convex mixed integer programming problem that exhibits long-term optimization and dynamics. Thus, we propose a deep reinforcement learning-based approach that jointly optimizes the UAV movement, user transmit power, and user association in real time to solve the problem efficiently. Specifically, we reformulate the optimization problem into an action space-reduced Markov decision process (MDP) and optimize the user association by using a two-phase matching-based association (TMA) strategy. Subsequently, we propose a soft actor-critic (SAC)-based approach that integrates the proposed TMA strategy (SAC-TMA) to solve the formulated joint optimization problem collab.ratively. Simulation results demonstrate that the proposed SAC-TMA is able to coordinate the two subsystems and can effectively reduce the system latency and improve the data collection volume compared with other benchmark algorithms. © 2025 IEEE. All rights reserved,

关键词： Unmanned aerial vehicles (UAV)

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Virtual scanning: unsupervised non-line-of-sight imaging from irregularly undersampled transients 24

Virtual scanning: unsupervised non-line-of-sight imaging fro...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Xingyu Cui Huanjing Yue Song Li Xiangjun Yin Yusen Hou Yun Meng Kai Zou Xiaolong Hu Jingyu Yang School of Electrical and Information Engineering Tianjin University China School of Precision Instrument and Optoelectronic Engineering Tianjin University China and Key Lab. of Optoelectronic Information Science and Technology Ministry of Education China

ISBN: (纸本)9798331314385

Non-line-of-sight (NLOS) imaging allows for seeing hidden scenes around corners through active sensing. Most previous algorithms for NLOS reconstruction require dense transients acquired through regular scans over a large relay surface, which limits their applicability in realistic scenarios with irregular relay surfaces. In this paper, we propose an unsupervised learning-based framework for NLOS imaging from irregularly undersampled transients (IUT). Our method learns implicit priors from noisy irregularly undersampled transients without requiring paired data, which is difficult and expensive to acquire and align. To overcome the ambiguity of the measurement consistency constraint in inferring the albedo volume, we design a virtual scanning process that enables the network to learn within both range space and null space for high-quality reconstruction. We devise a physics-guided SURE-based denoiser to enhance robustness to ubiquitous noise in low-photon imaging conditions. Extensive experiments on both simulated and real-world data validate the performance and generalization of our method. Compared with the state-of-the-art (SOTA) method, our method achieves higher fidelity, greater robustness, and remarkably faster inference times by orders of magnitude. The code and model are availab.e at https://***/XingyuCuii/Virtual-Scanning-NLOS.

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Using Depth-Enhanced Spatial Transformation for Student Gaze Target Estimation in Dual-View Classroom Images

Using Depth-Enhanced Spatial Transformation for Student Gaze...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Miao, Haonan Zhao, Peizheng Sun, Yuqi Nan, Fang Zhang, Xiaolong Wu, Yaqiang Tian, Feng School of Computer Science and Technology Xi'an Jiaotong University Xi'an710049 China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi'an Jiaotong University Xi'an710049 China School of Advanced Technology Xi'an Jiaotong-Liverpool University Suzhou215123 China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Xi'an710049 China

ISBN: (纸本)9798350368741

Dual-view gaze target estimation in classroom environments has not been thoroughly explored. Existing methods lack consideration of depth information, primarily focusing on 2D image information and neglecting the latent 3D spatial context, which could lead to suboptimal transformation and cause the gaze cone to intersect with an incorrect object. This paper introduces a novel dual-view gaze target estimation method tailored for classroom settings, leveraging depth-enhanced spatial transformations. By formulating a depth-enhanced 2D space, our method uses depth-enhanced spatial transformation to accurately project students' gaze cones to the teacher-oriented image. Additionally, we collected a dataset named DVSGE, specifically for student gaze target estimation in dual-view classroom images. Experimental results demonstrate significant performance improvements of 9.8% in AUC and 19.9% in L2-Distance for our method, surpassing existing methods. © 2025 IEEE.

关键词： classroom depth-enhanced 2D space dual-view gaze target estimation spatial transformation

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An information model for highway operational risk management based on the IFC-Brick schema

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International Journal of Transportation Science and Technology 2023年第3期12卷 878-890页

作者： Bencheng Zhu Fujin Hou Tao Feng Tao Li Cancan Song The Key Laboratory of Road and Traffic Engineering of the Ministry of Education Tongji UniversityShanghai 201804China Shandong Hi-Speed Construction Management Group Co. Ltd.Jinan 250014ShandongChina Urban and Data Science Lab Graduate School of Advanced Science and EngineeringHiroshima University7390046 Higashi-HiroshimaJapan Department of the Built Environment Eindhoven University of TechnologyEindhoven 5600 MBthe Netherlands School of Civil Engineering Shanghai Normal UniversityNo.100Haisi RoadShanghai 201418China

With the development of highways,new technologies should be continuously introduced to improve highway traffic *** twin(DT)has been an emerging field of research in recent *** develop a digital twin management system,a data model is *** the field of highway operational risk management(HORM),however,the development of data models is still in its *** by the concept of linked data,in this paper,we attempt to propose an information model for *** main achievements of this paper include data architecture,identification and classification code methods,data interaction method,and the developed *** on data needs analysis,the highway information model architecture for risk management is defined as five layers:basic highway products,traffic sensors and equipment,traffic rules,traffic flow,and ***,according to the concepts of semantic data,these five layers can be classified into three categories:highway product data,topology data,and sensor *** the Industry Foundation Classes(IFC)standard and Brick schema were first proposed and applied in the building domain,some of their entities and relationships can also be applied to *** this end,we defined some new classes,a specific ontology,and an integrated framework for ***,a case study was carried *** such information model to highways has broad *** changes the file-based exchange method to the data-based one,which can promote highway data exchange and *** proposed information model could be of great significance for HORM.

关键词： Highway Information Model Operational Risk Management IFC-Brick Digital Twin

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Multi-lab.l classification with Missing lab.ls by Preserving Feature-lab.l Space Consistency

Multi-Label classification with Missing Labels by Preserving...

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IEEE International Conference on Big knowledge (ICBK)

作者： Zan Zhang Depeng Zhang Gongqing Wu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

Multi-lab.l classification deals with the problem where an instance is associated with multiple lab.ls simultaneously. Most existing multi-lab.l classification algorithms assume that the lab.ls of the training data are complete. However, we can obtain only a partial lab.l set of each instance in some real applications since lab.lling data is difficult or costly. Some existing works on multi-lab.l classification with missing lab.ls focus on exploiting lab.l correlations to complete the original lab.l space and simultaneously build a multi-lab.l learning model using lab.l specific features. However, these methods may be suboptimal since they do not preserve feature-lab.l space consistency. In this paper, we propose a Space Consistency-based Multi-lab.l classification algorithm named SCML to address this issue. First, lab.l correlation in lab.l space is learned to augment the incomplete original lab.l matrix to a new supplementary lab.l matrix, and the multi-lab.l classifier is constructed simultaneously based on the new supplementary lab.l matrix. Then, correlation information in feature space is learned based on the probabilistic neighborhood similarities to preserve feature-lab.l space consistency. Moreover, the proposed algorithm has an effective mechanism for learning lab.l-specific features to improve the multi-lab.l classification with missing lab.ls. Extensive experiments on twelve benchmark data sets validate the effectiveness of the proposed approach for improving the generalization performance of state-of-the-art algorithms of multi-lab.l learning with missing lab.ls.

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Dispersion-managed dual-wavelength fiber laser based on nonlinear polarization evolution 20

Dispersion-managed dual-wavelength fiber laser based on nonl...

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20th International Conference on Optical Communications and Networks, ICOCN 2022

作者： Liu, Runmin Niu, Shuang Zou, Defeng Song, Youjian Hu, Minglie School of Precision Instruments and Opto-electronics Engineering Tianjin University Ultrafast Laser Laboratory Key Lab. of Opto-electronic Information Science and Technology of Ministry of Education Tianjin300072 China

ISBN: (数字)9781665458986

ISBN: (纸本)9781665458986

We report the generation of dual-wavelength dispersion-managed solitons in a near-zero anomalous-dispersion Er-doped mode-locked fiber laser. Transmissive dual channels are provided by birefringence filtering effect. The dual-wavelength mode-locking centers at 1539.1 and 1578.8 nm with the repetition-rate difference of 430 Hz. Furthermore, coexistence of the soliton and the bound state is also observed. © 2022 IEEE.

关键词： Dispersion (waves)

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AAV-Assisted Joint Mobile Edge Computing and data Collection via Matching-Enabled Deep Reinforcement Learning

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IEEE Internet of Things Journal 2025年第12期12卷 19782-19800页

作者： Boxiong Wang Hui Kang Jiahui Li Geng Sun Zemin Sun Jiacheng Wang Dusit Niyato College of Computer Science and Technology Jilin University Changchun China College of Computer Science and Technology and the Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China College of Computing and Data Science Nanyang Technological University Jurong West Singapore

Autonomous aerial vehicle (AAV)-assisted mobile edge computing (MEC) and data collection (DC) have been popular research issues. Different from existing works that consider MEC and DC scenarios separately, this article investigates a multi-AAV-assisted joint MEC-DC system. Specifically, we formulate a joint optimization problem to minimize the MEC latency and maximize the collected data volume. This problem can be classified as a nonconvex mixed integer programming problem that exhibits long-term optimization and dynamics. Thus, we propose a deep reinforcement learning-based approach that jointly optimizes the AAV movement, user transmit power, and user association in real time to solve the problem efficiently. Specifically, we reformulate the optimization problem into an action space-reduced Markov decision process (MDP) and optimize the user association by using a two-phase matching-based association (TMA) strategy. Subsequently, we propose a soft actor-critic (SAC)-based approach that integrates the proposed TMA strategy (SAC-TMA) to solve the formulated joint optimization problem collab.ratively. Simulation results demonstrate that the proposed SAC-TMA is able to coordinate the two subsystems and can effectively reduce the system latency and improve the DC volume compared with other benchmark algorithms.

关键词： Internet of Things Optimization Autonomous aerial vehicles Interference Wireless networks Sun data collection Real-time systems Production facilities Performance evaluation

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Improved Generative Adversarial Network with Hybrid Attention Mechanism for Path Planning 42

Improved Generative Adversarial Network with Hybrid Attentio...

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42nd Chinese Control Conference, CCC 2023

作者： Zhang, Yi-Fan Sun, Tao Ma, Song Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian116024 China Tsinghua University Department of Automation Beijing100084 China School of Control Science and Engineering Dalian University of Technology Dalian116024 China

ISBN: (纸本)9789887581543

Multi-agent path planning is one of the most challenging problems in the field of artificial intelligence. In this paper, an improved generative adversarial network model with hybrid attention mechanism for data-driven path planning is proposed. Firstly, by introducing spatial attention mechanism, positional attention mechanism and channel attention mechanism, a hybrid attention module is designed. This module not only establishes feature relationship between different locations, but also provides adaptive feature weights to highlight key information. Next, by embedding the hybrid attention module into the generative adversarial network, an improved generative adversarial network model is developed. The proposed model can capture effective feature information in the image and ensures that the generated path can evade obstacles accurately. Finally, some simulation results are provided to verify the superiority and accuracy of the model in path planning. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Motion planning

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Area Expansion Effect of Glare Suppression with Low-transmittance Eigenchannels of Transmission Matrices 14

Area Expansion Effect of Glare Suppression with Low-transmit...

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14th International Conference on Information Optics and Photonics, CIOP 2023

作者： Zhang, Hengkang Liu, Kaige Wang, Li Chen, Mingce Guo, Shaogang Liu, Qiang Space Optoelectronic Measurement and Perception Lab. Beijing Institute of Control Engineering Beijing100190 China Ministry of Education Beijing100084 China Tsinghua University State Key Laboratory of Precision Measurement Technology and Instruments Department of Precision Instrument Beijing100084 China

ISBN: (纸本)9781510671744

Wavefront shaping technique has been applied to actively control the intensity profile of optical speckles, and a recent progress is to realize glare suppression with the low-transmittance eigenchannels of the transmission matrices (TMs). This makes it possible to custom-tailor the speckle fields. For the purpose of suppressing glare at will, research about the properties of low-transmittance eigenchannels in glare suppression is demanded. In this paper, we report the area expansion effect of glare suppression with low-transmittance eigenchannels. With numerical simulations, we found the actual darkened area was larger than the set target area due to the transverse correlation of the speckle field. Their difference is related to the measuring noise of the TM and gets smaller with the increase of the noise intensity. We believe our research could help to design structured speckle fields, and it will be of significant for advanced applications of optical speckles. © 2023 SPIE.

关键词： Glare

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