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第41届中国控制会议

作者： Feng Liu Feiyue Zhuo Shujiang Sun Zhi-Hong Guan Hua O Wang School of Automation China University of Geosciences Engineering Research Center of Ministry of education for intelligent technology of earth exploration Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Artificial Intelligence and Automation Department of Mechanical Engineering Boston University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This paper presents a ring synthetic gene network model described by fractional differential equations,studies the conditions of Hopf bifurcation in the model,and discusses the limit cycle oscillation phenomenon due to the existence of bifurcation,which causes the system to become unstable,and then We propose a method to control the bifurcation behavior in the system,so that the system can be stable over a large *** addition,the dynamic behavior of the fractional-order model is simulated by triggering a genetic oscillator(a single inhibitory gene) composed of a transistor composed of linear and nonlinear electronic *** circuit simulation results well verify the theoretical analysis conclusion,and the circuit simulation model can well demonstrate the biological characteristics of gene regulatory networks.

关键词： Synthetic gene networks fractional-order bifurcation control circuit simulation

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Analysis and State Feedback control Bifurcation of Fractional-Order Cyclic Gene Regulatory Networks with Time Delay 41

Analysis and State Feedback Control Bifurcation of Fractiona...

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第41届中国控制会议

作者： Feng Liu Shujiang Sun Juan Zhao Feiyue Zhuo Hua O Wang School of Automation China University of Geosciences Engineering Research Center of Ministry of education for intelligent technology of earth exploration Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Department of Mechanical Engineering Boston University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In this paper,a fractional-order cyclic gene regulatory network model with time delay is ***,the total time delay of the system is selected as the bifurcation parameter,and the condition for the existence of Hopf bifurcation is derived by analyzing its characteristic *** is found that the time delay affects the stability of the system,and the order affects the position of the bifurcation *** the time delay is greater than the critical time delay,the system loses ***,a state feedback controller is designed for the unstable *** is proved that the control method has good control effect for the system instability caused by Hopf ***,the correctness of the theoretical derivation is verified by simulation.

关键词： Fractional-order cyclic gene regulatory network Hopf bifurcation feedback control time delay

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Output Regulation Using Bilinear Lifting Linearization

Output Regulation Using Bilinear Lifting Linearization

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control Technology and Applications (CCTA),

作者： Danaiwat Sriwajana Masaki Yamakita Department of Systems and Control Engineering School of Engineering Tokyo Institute of Technology Tokyo Japan

ISBN: (数字)9798350370942

ISBN: (纸本)9798350370959

This paper presents a computationally efficient MPC approach to achieve a nonlinear output regulation using lifting bilinearization. The method uses an internal model to anticipate the future effects of the disturbance. The output regulation MPC formulation is formulated into an augmented reference scheme to reduce computational complexity. By using lifting bilinearization, the MPC formulation transforms into a linear optimal control problem that can be solved efficiently by well-established convex optimization techniques. The effectiveness of the proposed method is demonstrated in a simulation of a quadrotor system operating in three-dimensional space. The method shows adequate performance in improving the input delay time after observing the system state by approximately 90% and overall computational time by approximately 60%.

关键词： Computational modeling Optimal control Transforms Aerospace electronics Regulation Convex functions Delays

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Instance-Wise MRI Reconstruction Based on Self-Supervised Implicit Neural Representation

Instance-Wise MRI Reconstruction Based on Self-Supervised Im...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Song-Xiao Yang Yi-Zhou Li Masatoshi Okutomi Department of Systems and Control Engineering School of Engineering Tokyo Institute of Technology Tokyo Japan

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Accelerated MRI involves a trade-off between sampling sufficiency and acquisition time. Supervised deep learning methods have shown great success in MRI reconstruction from under-sampled measurements, but they typically require a large set of fully-sampled MR images for training, which can be difficult to obtain. In this paper, we present a novel fully self-supervised method based on implicit neural representation, which requires only a single under-sampled MRI instance for training. To effectively guide the self-supervised learning process, we introduced multiple novel supervisory signals in both the image and frequency domains. Experimental results indicate that the proposed method outperforms existing self-supervised methods and even a supervised method, demonstrating its strong reliability and flexibility. Our code is publicly available at https://***/YSongxiao/*** relevance— The proposed method can significantly enhance the image quality of under-sampled MR images without the need of ground-truth fully-sampled MR images for supervision and additional prior images for guidance.

关键词： Training Image quality Deep learning Codes Magnetic resonance imaging Frequency-domain analysis Self-supervised learning Reliability engineering in medicine and biology Image reconstruction

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Safe distancing in the time of COVID-19

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European Journal of Operational Research 2023年第1期304卷 139-149页

作者： Fischetti, Martina Fischetti, Matteo Stoustrup, Jakob Vattenfall BA Wind Havneholmen 29 København1561 Denmark Department of Information Engineering University of Padua via Gradenigo 6/A Padova35100 Italy Automation & Control Department of Electronic Systems Aalborg University Fredrik Bajers Vej 7C Aalborg9220 Denmark

The spread of viruses such as SARS-CoV-2 brought new challenges to our society, including a stronger focus on safety across all businesses. Many countries have imposed a minimum social distance among people in order to ensure their safety. This brings new challenges to many customer-related businesses, such as restaurants, offices, theaters, etc., on how to locate their facilities (tables, seats etc.) under distancing constraints. We propose a parallel between this problem and that of locating wind turbines in an offshore area. The discovery of this parallel allows us to apply Mathematical Optimization algorithms originally designed for wind farms, to produce optimized facility layouts that minimize the overall risk of infection among customers. In this way we can investigate the structure of the safest layouts, with some surprising outcomes. A lesson learned is that, in the safest layouts, the facilities are not equally distanced (as it is typically believed) but tend to concentrate on the border of the available area—a policy that significantly reduces the overall risk of contagion. © 2021 Elsevier B.V.

关键词： COVID-19

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LiDAR Point Cloud Map Coding Framework Based on Quadric Surface Fitting and Scene Construction 5

LiDAR Point Cloud Map Coding Framework Based on Quadric Surf...

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5th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2024

作者： Sun, Xuebin Wang, Jinxi Yang, Xiaofei School of Electrical Automation and Information Engineering Tianjin University Tianjin China School of Information and Control Engineering China University of Mining and Technology Xuzhou China Shanghai Institute of Optics and Fine Mechanics Department of Aerospace Laser Technology and System Shanghai China

ISBN: (纸本)9798350355925

Due to rapid 3D acquisition advancements, LiDAR point clouds are prevalent in emerging applications like heritage preservation, mobile robotics, and remote sensing. Yet, the sheer data volume poses challenges for transmission and storage, especially in autonomous driving. This paper introduces an efficient coding framework for compressing large dense LiDAR point cloud maps. By recording timestamps and quaternion matrices during map construction, we reduce map compression to sequence compression. The scans are segregated into intra-frames and inter-frames. For intra-frames, a quadric surface fitting intra-prediction technique eliminates spatial redundancy. For inter-frames, by constructing the 3D scene using encoded intra-frames, the inter-frame can be simulated according to its posture and LiDAR intrinsic parameters. Coding focuses on the difference between actual and predicted point cloud data. Decoding reverses this process, reconstructing the point cloud map from the decoded sequence and quaternion matrices. KITTI dataset experiments show the point cloud map can be averagely compressed to 3.43% of its original size while maintaining centimeter-level precision. This outperforms both octree and Google Draco methods. © 2024 IEEE.

关键词： Mobile robots

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CFDNet: A Generalizable Foggy Stereo Matching Network with Contrastive Feature Distillation

arXiv

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arXiv 2024年

作者： Liu, Zihua Li, Yizhou Okutomi, Masatoshi The Department of Systems and Control Engineering School of Engineering Tokyo Institute of Technology Tokyo Japan

Stereo matching under foggy scenes remains a challenging task since the scattering effect degrades the visibility and results in less distinctive features for dense correspondence matching. While some previous learning-based methods integrated a physical scattering function for simultaneous stereo-matching and dehazing, simply removing fog might not aid depth estimation because the fog itself can provide crucial depth cues. In this work, we introduce a framework based on contrastive feature distillation (CFD). This strategy combines feature distillation from merged clean-fog features with contrastive learning, ensuring balanced dependence on fog depth hints and clean matching features. This framework helps to enhance model generalization across both clean and foggy environments. Comprehensive experiments on synthetic and real-world datasets affirm the superior strength and adaptability of our method. © 2024, CC0.

关键词： Distillation

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LIFTEDCL: LIFTING CONTRASTIVE LEARNING FOR HUMAN-CENTRIC PERCEPTION 11

LIFTEDCL: LIFTING CONTRASTIVE LEARNING FOR HUMAN-CENTRIC PER...

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11th International Conference on Learning Representations, ICLR 2023

作者： Chen, Ziwei Li, Qiang Wang, Xiaofeng Yang, Wankou School of Automation Southeast University China Key Lab of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China Y-tech Kuaishou Technology China Institute of Automation Chinese Academy of Sciences China

Human-centric perception targets for understanding human body pose, shape and ***-training the model on large-scale datasets and fine-tuning it on specific tasks has become a well-established paradigm in human-centric ***, self-supervised learning methods have re-investigated contrastive learning to achieve superior performance on various downstream *** handling human-centric perception, there still remains untapped potential since 3D human structure information is neglected during the task-agnostic *** this paper, we propose the Lifting Contrastive Learning (LiftedCL) to obtain 3D-aware human-centric representations which absorb 3D human structure *** particular, to induce the learning process, a set of 3D skeletons is randomly sampled by resorting to 3D human kinematic *** this set of generic 3D samples, 3D human structure information can be learned into 3D-aware representations through adversarial *** results demonstrate that LiftedCL outperforms state-of-the-art self-supervised methods on four human-centric downstream tasks, including 2D and 3D human pose estimation (0.4% mAP and 1.8 mm MPJPE improvement on COCO 2D pose estimation and Human3.6M 3D pose estimation), human shape recovery and human parsing. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Large datasets

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CFDNet: A Generalizable Foggy Stereo Matching Network with Contrastive Feature Distillation

CFDNet: A Generalizable Foggy Stereo Matching Network with C...

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IEEE International Conference on Robotics and automation (ICRA)

作者： Zihua Liu Yizhou Li Masatoshi Okutomi Department of Systems and Control Engineering School of Engineering Tokyo Institute of Technology Tokyo Japan

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

关键词： Training Learning systems Adaptation models Scattering Estimation Contrastive learning Task analysis

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Real-Time Adaptation of Drone Altitude and Object Detection Model for Moving Target Tracking

Real-Time Adaptation of Drone Altitude and Object Detection ...

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SICE Annual Conference

作者： Muhammad Hanif Takeshi Hatanaka Department of Systems and Control Engineering School of Engineering Tokyo Institute of Technology Tokyo Japan

ISBN: (数字)9784907764838

ISBN: (纸本)9798331544461

This paper addresses the scenario of tracking a moving target by drones. By employing a so-called persistent coverage control strategy, drones efficiently search and monitor the target in the field. Expanding upon previous work utilizing the concept of constraint-based control to keep the target within the field of view of the drone, this research extends the approach by dynamically adjusting drone's altitude and adapting the object detection model based on the moving target's velocity. Experimental validation demonstrates the superior efficacy of the proposed algorithm in tracking a moving target compared to its predecessor, highlighting its potential for enhanced performance in dynamic target tracking scenarios.

关键词： Adaptation models Target tracking Heuristic algorithms Object detection Real-time systems Iron Monitoring Drones

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