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IEEE International Conference on Power, intelligent Computing and Systems (ICPICS)

作者： Shuzhi Yang Chunhua Zhu Xue Li Key Laboratory of Grain Information Processing and Control (Henan University of Technology) Ministry of Education Henan Key Laboratory of Grain Photoelectric Detection and Control Henan University of Technology Henan Engineering Laboratory of Grain Condition Intelligent Detection and Application Henan University of Technology Zhengzhou China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

Class activation mapping (CAM) is a post-hoc explanation method for Convolutional neural networks (CNNs), aiming to visually explain the decision-making process of CNNs. CAM demonstrates superior categorical discrimination capabilities compared to visual explanation methods rooted in backpropagation and perturbation. Five CAM methods have been comprehensively analyzed and discussed, revealing their applications in explaining CNN decision-making. Additionally, the evaluation principles and metrics for CAM and other explainable methods are summarized. A comparative analysis highlights their characteristics, applicability, and inherent limitations. Finally, the challenges faced by CAM methods in the field of explainable research are explored, and future development trends are anticipated.

关键词： Measurement Backpropagation Visualization Perturbation methods Decision making Market research Convolutional neural networks

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BokehMe: When Neural Rendering Meets Classical Rendering

arXiv

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arXiv 2022年

作者： Peng, Juewen Cao, Zhiguo Luo, Xianrui Lu, Hao Xian, Ke Zhang, Jianming Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Adobe Research

We propose BokehMe, a hybrid bokeh rendering framework that marries a neural renderer with a classical physically motivated renderer. Given a single image and a potentially imperfect disparity map, BokehMe generates high-resolution photo-realistic bokeh effects with adjustable blur size, focal plane, and aperture shape. To this end, we analyze the errors from the classical scattering-based method and derive a formulation to calculate an error map. Based on this formulation, we implement the classical renderer by a scattering-based method and propose a two-stage neural renderer to fix the erroneous areas from the classical renderer. The neural renderer employs a dynamic multi-scale scheme to efficiently handle arbitrary blur sizes, and it is trained to handle imperfect disparity input. Experiments show that our method compares favorably against previous methods on both synthetic image data and real image data with predicted disparity. A user study is further conducted to validate the advantage of our method. Copyright © 2022, The Authors. All rights reserved.

关键词： Rendering (computer graphics)

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Hierarchical finite-time optimization regulation of multiple autonomous surface vehicles for time-varying target cooperative tracking

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Kongzhi yu Juece/control and Decision 2025年第1期40卷 279-284页

作者： Ge, Ming-Feng Li, Yi-Fan Liu, Zhi-Wei Gu, Zhi-Wei Chen, Qian School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan430074 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Imaging Processing and Intelligent Control Ministry of Education Huazhong University of Science and Technology Wuhan430074 China

We propose a novel hierarchical finite-time optimization regulation algorithm for multiple autonomous surface vehicles (ASVs) to achieve cooperative tracking of a time-varying target. An objective function reflecting the energy consumption of the multiple ASVs system in the tracking process is designed, and a finite-time optimal estimator (FTOE) is developed to minimize the objective function in the case that ASVs form a specified formation while following the desired trajectory. Meanwhile, the target trajectory can be variable instead of constant. In the finite-time local controller (FTLC), the sailing states of the multiple ASVs system can be stabilized to the vicinity of the multiple various predefined time-varying trajectories in finite time in a way that minimizes the objective function by tracking the FTOE. The proof of the finite-time stability is provided and the settling time is calculated. Finally, simulation results demonstrate the effectiveness of the proposed hierarchical algorithm. © 2025 Northeast University. All rights reserved.

关键词： Target tracking

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Two-Stage Robust Optimization Under Decision Dependent Uncertainty

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IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1295-1306页

作者： Yunfan Zhang Feng Liu Yifan Su Yue Chen Zhaojian Wang João P.S.Catalão State Key Laboratory of Power System and Generation Equipment the Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Department of Mechanical and Automation Engineering the Chinese University of Hong KongHong Kong SARChina Ministry of Education Key Laboratory of System Control and Information Processing the Department of AutomationShanghai Jiao Tong Universityand also with Shanghai Engineering Research Center of Intelligent Control and ManagementShanghai 200240China Faculty of Engineering of the University of Porto and Institute for Systems and Computer Engineering Technology and Science(INESC TEC)Porto 4200-465Portugal IEEE

In the conventional robust optimization(RO)context,the uncertainty is regarded as residing in a predetermined and fixed uncertainty *** many applications,however,uncertainties are affected by decisions,making the current RO framework *** paper investigates a class of two-stage RO problems that involve decision-dependent *** introduce a class of polyhedral uncertainty sets whose right-hand-side vector has a dependency on the here-and-now decisions and seek to derive the exact optimal wait-and-see decisions for the second-stage problem.A novel iterative algorithm based on the Benders dual decomposition is proposed where advanced optimality cuts and feasibility cuts are designed to incorporate the uncertainty-decision *** computational tractability,robust feasibility and optimality,and convergence performance of the proposed algorithm are guaranteed with theoretical *** motivating application examples that feature the decision-dependent uncertainties are ***,the proposed solution methodology is verified by conducting case studies on the pre-disaster highway investment problem.

关键词： Benders decomposition decision-dependent uncertainty endogenous uncertainty robust optimization(RO)

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Two-stage Train Components Defect Detection Based on Prior Knowledge

Two-stage Train Components Defect Detection Based on Prior K...

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IEEE/RSJ International Conference on intelligent Robots and Systems (IROS)

作者： Gang Peng Zhiyong Li Shaowei Wan Zhang Deng Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Wuhan Lisai Technology Co. Ltd. Wuhan China

The existing method of detecting defects in train components, which relies on visual identification, requires extensive involvement from inspectors and presents certain limitations. In this study, a two-stage defect detection based on prior knowledge was developed, which first detects the types and positions of components, and then conducts targeted detection of possible existing defect types. The algorithm introduces the prior knowledge of the relative spatial position relationship of components and optimizes the detection of sub-components by cascaded convolutional neural networks and local scale-up. In this study, three methods were used, including deep learning, template matching, and quantitative evaluation based on prior knowledge, to perform targeted detection of defect types that may occur in components. Experiments have verified the adaptability and accuracy of the method, demonstrating its high value for engineering applications.

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Data selection strategy in data-driven model predictive control of uncertain systems

Data selection strategy in data-driven model predictive cont...

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第34届中国过程控制会议

作者： Lingyi Yang dewei Li Department of Automation Shanghai Jiao Tong UniversityKey Laboratory of System Control and Information ProcessingMinistry of Education of ChinaShanghai Engineering Research Center of Intelligent Control and Management

In this paper,a novel data-driven methodology for MPC with data selection strategies is *** novel selection strategy involves two ***,we check the similarity between the current data and the past data items already existing in the data *** enables the controller to pick a set of informative data items which contain sufficient information on each direction of the signal ***,for the data items within the data oracle,we integrate multiple similar data vectors into a single vector to cut down amount of *** simulation examples displays the upgraded performance and smaller amount of calculation of selection strategies.

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Prescribed Performance control of Mobile Wheeled Inverted Pendulum Systems Under Arbitrary Initial Conditions

Prescribed Performance Control of Mobile Wheeled Inverted Pe...

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Mengshi Zhang Yu Cao Bo Yang Jian Huang Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Shenzhen Huazhong University of Science and Technology Research Institute ShenZhen China

Mobile wheeled inverted pendulum (MWIP) is a typical naturally unstable underactuated system and it is very necessary to constrain its transient response to improve the overall safety. Prescribed performance control (PPC) is an effective method, however the system initial conditions are usually required to be known and constraint, otherwise it can lead to singularity problems. In practical systems, it is often difficult to obtain the exact initial state leading to limitations in the application of PPC. To overcome this issue, this paper presented a composite error transformation function to address the problem of initial condition dependence. A continuous bounded function was designed to compress the arbitrary initial error into the predefined bound to avoid singularities. After a set time, it was degraded to an original system error. Based on this composite transformation, a state feedback controller was proposed for the underactuated MWIP system to achieve the constraint outputs. Then, by employing the Lyapunov theorem, it was guaranteed that the closed-loop system would remain stable. Various numerical simulations under different initial conditions were carried out to prove the validity of the proposed controller.

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A Real-time Local Planner Design for Smart Wheelchairs Based on Koopman Bilinear Model Predictive control

A Real-time Local Planner Design for Smart Wheelchairs Based...

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IEEE International Conference on Real-time Computing and Robotics (RCAR)

作者： Guanfeng Yu Han Zhang Zehao Wang Jingchuan Wang Department of Automation Institute of Medical Robotics Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (数字)9798350372601

ISBN: (纸本)9798350372618

The paper introduces a Koopman bilinear model predictive control (KBMPC) as a local planner for smart wheelchair systems. This approach leverages the Koopman Operator to streamline the nonlinear terms in MPC, aiming to achieve superior real-time performance. In particular, we employ the Extended Dynamic Mode Decomposition (EDMD) method to approximate the infinite dimensional Koopman operator. To tackle the challenge of designing lifting functions, we systematically utilize the derivatives of known nonlinear structures to methodically derive lifting functions. This systematic approach facilitates the identification of a precise bilinear model. Next, we formulate the corresponding Koopman bilinear MPC problem. Finally, an iterative algorithm is employed to efficiently solve the KBMPC problem. This method linearizes the bilinear model around estimations and iteratively solves the resulting quadratic programming problem to refine estimations. This allows KBMPC to function as a real-time local planner for smart wheelchairs. The prediction of nonlinear terms shows that the identified Koopman bilinear model increases the prediction accuracy. Furthermore, experiments demonstrate the advantages of the designed KBMPC local planner, highlighting its superior real-time performance and predictive capabilities.

关键词： Pedestrians Accuracy Systematics Wheelchairs Estimation Predictive models Real-time systems

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K-BMPC: Derivative-based Koopman Bilinear Model Predictive control For Tractor-trailer Trajectory Tracking With Unknown Parameters

K-BMPC: Derivative-based Koopman Bilinear Model Predictive C...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Zehao Wang Han Zhang Jingchuan Wang Department of Automation Institute of Medical Robotics Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China and Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Nonlinear dynamics bring difficulties to controller design for control-affine systems such as tractor-trailer vehicles, especially when the parameters in the dynamics are unknown. To address this constraint, we propose a derivative-based lifting function construction method, show that the corresponding infinite dimensional Koopman bilinear model over the lifting function is equivalent to the original control-affine system. Further, we analyze the propagation and bounds of state prediction errors caused by the truncation in derivative order. The identified finite dimensional Koopman bilinear model would serve as predictive model in the next step. Koopman Bilinear Model Predictive control (K-BMPC) is proposed to solve the trajectory tracking problem. We linearize the bilinear model around the estimation of the lifted state and control input. Then the bilinear Model Predictive control problem is approximated by a quadratic programming problem. Further, the estimation is updated at each iteration until the convergence is reached. Moreover, we implement our algorithm on a tractor-trailer system, taking into account the longitudinal and side slip effects. The open-loop simulation shows the proposed Koopman bilinear model captures the dynamics with unknown parameters and has good prediction performance. Closed-loop tracking results show the proposed K-BMPC exhibits elevated tracking precision with the commendable computational efficiency. The experimental results demonstrate the feasibility of K-BMPC.

关键词： Trajectory tracking Computational modeling Estimation Predictive models control systems Data models Nonlinear dynamical systems

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CLIP-guided Prototype Modulating for Few-shot Action Recognition

arXiv

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arXiv 2023年

作者： Wang, Xiang Zhang, Shiwei Cen, Jun Gao, Changxin Zhang, Yingya Zhao, Deli Sang, Nong Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Alibaba Group China The Hong Kong University of Science and Technology Hong Kong

Learning from large-scale contrastive language-image pre-training like CLIP has shown remarkable success in a wide range of downstream tasks recently, but it is still under-explored on the challenging few-shot action recognition (FSAR) task. In this work, we aim to transfer the powerful multimodal knowledge of CLIP to alleviate the inaccurate prototype estimation issue due to data scarcity, which is a critical problem in low-shot regimes. To this end, we present a CLIP-guided prototype modulating framework called CLIP-FSAR, which consists of two key components: a video-text contrastive objective and a prototype modulation. Specifically, the former bridges the task discrepancy between CLIP and the few-shot video task by contrasting videos and corresponding class text descriptions. The latter leverages the transferable textual concepts from CLIP to adaptively refine visual prototypes with a temporal Transformer. By this means, CLIP-FSAR can take full advantage of the rich semantic priors in CLIP to obtain reliable prototypes and achieve accurate few-shot classification. Extensive experiments on five commonly used benchmarks demonstrate the effectiveness of our proposed method, and CLIP-FSAR significantly outperforms existing state-of-the-art methods under various settings. The source code and Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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