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4th International Conference on Industrial Artificial Intelligence, IAI 2022

作者： Zhou, Guangze Luo, Zhong Zhu, Yunpeng Gao, Yi Wang, Zhiao Northeastern University School of Mechanical Engineering and Automation Shenyang110819 China Northeastern University Key Laboratory of Vibration and Control of Aero-Propulsion Systems Ministry of Education of China Shenyang110819 China Dalian University of Technology State Key Laboratory of Structural Analysis for Industrial Equipment Dalian116024 China University of Sheffield Department of Automatic Control and System Engineering SheffieldS13JD United Kingdom

ISBN: (数字)9781665451208

ISBN: (纸本)9781665451208

Model based online fault detection often conducted by extracting features from models driven by system input and output data under various working conditions. The efficiency of online system modelling is therefore significant to improve the performance of online fault detections. In this study, a novel fast data-driven modelling approach, known as the OMP (Orthogonal Matching Pursuit)- ERR (Error Reduction Ratio) method is proposed to improve the efficiency of online fault detections. The new system identification method is motivated by noticing that the traditional OMP algorithm is much faster but usually less accurate than the OLS (Orthogonal least squares) algorithm in the identification of system NARX (Nonlinear Auto-Regressive with Exogenous inputs) models. The problem is first illustrated by the identification of a Single Degree of Freedom (SDoF) system. After that, the OMP-ERR algorithm is developed to improve the NARX modelling efficiency for the purpose of system model-based online fault detections. A case study on the crack detection of a cantilever beam shows that the new approach is over 10 times faster than the traditional OLS modelling process, demonstrating the promising applications of the new approach in online fault detections in engineering practice. © 2022 IEEE.

关键词： Least squares approximations

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Influence of Ring Gear Flexibility on the Fatigue Reliability of Planetary Gear systems in Heavy Helicopters

SSRN

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SSRN 2023年

作者： Li, Ming Luo, Yuan Qu, Ligang Xie, Liyang Zhao, Bingfeng School of Mechatronics Engineering Shenyang Aerospace University Shenyang110136 China Key Laboratory of Vibration Control of Aero-propulsion System Ministry of Education Northeastern University Shenyang110819 China

Flexibility of ring gear rims significantly affects the mechanical properties of planetary transmissions at both levels of gear tooth meshing and system power sharing, and is one of the important design factors determining the service reliability of large aviation planetary systems. Therefore, to analyze the mechanism by which the flexibility of ring gear rims affects the fatigue reliability of planetary systems, in the framework of stress-strength interference theory, a reliability assessment model for the planetary system is constructed based on a calculation logic extension for the full probability formula. Starting from balancing the contradiction between evaluation accuracy and evaluation costs, a hybrid finite element method and a gear tooth probabilistic life transformation test are employed to provide load and strength input variables for the reliability model respectively. Finally, a mapping relationship from ring gear rim flexibility to planetary system fatigue reliability is established. Accordingly, a ring gear rim thickness range that is sensitive to the system fatigue reliability, as well as a design point in this range with the fastest reliability growth rate, is captured. © 2023, The Authors. All rights reserved.

关键词： Gear teeth

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NeSLAM: Neural Implicit Mapping and Self-Supervised Feature Tracking With Depth Completion and Denoising

arXiv

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arXiv 2024年

作者： Deng, Tianchen Wang, Yanbo Xie, Hongle Wang, Hesheng Wang, Jingchuan Wang, Danwei Chen, Weidong Institute of Medical Robotics Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China School of Electrical and Electronic Engineering Nanyang Technological University Singapore

In recent years, there have been significant advancements in 3D reconstruction and dense RGB-D SLAM systems. One notable development is the application of Neural Radiance Fields (NeRF) in these systems, which utilizes implicit neural representation to encode 3D scenes. This extension of NeRF to SLAM has shown promising results. However, the depth images obtained from consumer-grade RGB-D sensors are often sparse and noisy, which poses significant challenges for 3D reconstruction and affects the accuracy of the representation of the scene geometry. Moreover, the original hierarchical feature grid with occupancy value is inaccurate for scene geometry representation. Furthermore, the existing methods select random pixels for camera tracking, which leads to inaccurate localization and is not robust in real-world indoor environments. To this end, we present NeSLAM, an advanced framework that achieves accurate and dense depth estimation, robust camera tracking, and realistic synthesis of novel views. First, a depth completion and denoising network is designed to provide dense geometry prior and guide the neural implicit representation optimization. Second, the occupancy scene representation is replaced with Signed Distance Field (SDF) hierarchical scene representation for high-quality reconstruction and view synthesis. Furthermore, we also propose a NeRF-based self-supervised feature tracking algorithm for robust real-time tracking. Experiments on various indoor datasets demonstrate the effectiveness and accuracy of the system in reconstruction, tracking quality, and novel view synthesis. © 2024, CC BY-NC-SA.

关键词： Geometry

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Reinforcement Learning based Data-driven Optimal control Strategy for systems with Disturbance

Reinforcement Learning based Data-driven Optimal Control Str...

引用

Data Driven control and Learning systems (DDCLS)

作者： Zhong-Xin Fan Shihua Li Rongjie Liu School of Automation Southeast University Nanjing P. R. China Ministry of Education Key Laboratory of Measurement and Control of Complex Systems of Engineering Nanjing P. R. China Department of Statistics Florida State University Tallahassee FL USA

This paper proposes a partially model-free optimal control strategy for a class of continuous-time systems in a data-driven way. Although a series of optimal control have achieving superior performance, the following challenges still exist: (i) The controller designed based on the nominal system is difficult to cope with sudden disturbances. (ii) Feedback control is highly dependent on system dynamics and generally requires full state information. A novel composite control method combining output feedback reinforcement learning and input-output disturbance observer for these two challenges is concluded in this paper. Firstly, an output feedback policy iteration (PI) algorithm is given to acquire the feedback gain iteratively. Simultaneously, the observer continuously provides estimates of the disturbance. system dynamic information and states information are not needed to be known in advance in our approach, thus offering a higher degree of robustness and practical implementation prospects. Finally, an example is given to show the effectiveness of the proposed controller.

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Distributed Power Source Planning Based on Improved Particle Swarm Optimization

Distributed Power Source Planning Based on Improved Particle...

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Power and Electrical Technology Conference (APET), Asia

作者： Jiahui Wang Zhili Tan School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (数字)9798350367690

ISBN: (纸本)9798350367706

The multitude of advantages offered by distributed power generation units are recognized as crucial factors in enhancing the security of distribution networks. Ensuring the optimal size and placement of distributed generation (DG) units not only enhances system security but also increases profitability. Haphazard placement of DG units may lead to adverse impacts on the power system, resulting in increased power losses and diminished voltage distribution. Therefore, to ensure the optimal size and placement of DG units, this paper proposes a method to optimize DG scheduling and operation, thereby improving voltage distribution, reducing power losses, and enhancing the overall reliability of the entire grid. To reduce power loss (PL) and improve voltage distribution (VP), an adaptive chaotic particle swarm optimization (ACPSO) algorithm is employed to determine the optimal locations and sizes of DG units. By comparing this algorithm with traditional approaches, its efficacy in enhancing performance can be discerned.

关键词： Profitability Voltage Distributed power generation Power system reliability Planning Security Reliability Particle swarm optimization Optimization Testing

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Traversability estimation based on vehicle characteristics and environmental geometry

Traversability estimation based on vehicle characteristics a...

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10th International Symposium on Test Automation & Instrumentation (ISTAI 2024)

作者： Naixiang He Qinghua Su Liyong Wang Yue Song Haodong Wang Haoyuan Zhang Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing Information Science & Technology University Beijing People's Republic of China School of Mechanical Engineering and Vehicles Beijing Institute of Technology Beijing People's Republic of China

ISBN: (数字)9781837241910

In unstructured environments, ensuring the safe operation of autonomous vehicles necessitates precise perception and thorough analysis of the surrounding terrain. Existing traversability analysis methods frequently rely on data from a single sensor and fail to integrate vehicle characteristics with environmental geometric features. This limitation results in weak environmental perception and poor performance in traversability analysis. To address these challenges, this paper proposes an innovative approach. By fusing data from multiple LiDAR sensors, the method constructs a locally dense traversability map, significantly enhancing the completeness and reliability of terrain ***, this approach combines vehicle characteristics with multiple environmental geometric factors to generate a traversability layer. This integration leads to a more holistic understanding of the terrain, improving the vehicle's ability to navigate complex environments safely. The proposed method demonstrates outstanding performance in traversability estimation, enabling a more accurate evaluation of traversability. This, in turn, provides a solid guarantee for the safe driving of autonomous vehicles in various challenging *** system underwent rigorous testing in unstructured environments to validate its effectiveness. The test results indicate that the elevation maps and traversability layer generated by this method exhibit significant improvements in both detail and accuracy. These advancements ensure that autonomous vehicles can better perceive and navigate their surroundings, ultimately contributing to safer and more reliable autonomous driving in unstructured and complex terrains.

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A Swing-Up and Stable control Strategy for Pendubot Based on Nonlinear Model Predictive Method

A Swing-Up and Stable Control Strategy for Pendubot Based on...

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2023 China Automation Congress, CAC 2023

作者： Wang, Lejun Liu, Yiqin Zhang, Ruihuan Huang, Zixin Cai, Zhen School of Automation School of Industrial Internet Chongqing University of Posts and Telecommunications Chongqing400065 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China Hubei Key Laboratory of Digital Textile Equipment Wuhan Textile University Wuhan430200 China School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan430205 China School of Information Engineering Wuhan City Polytechnic Wuhan430064 China

ISBN: (纸本)9798350303759

The control objective of Pendubot is to stabilise the system in a vertically upward position by swinging up from a vertically downward position. Facing various physical constraints in practical applications, this paper proposes a swing-up stabil-isation control strategy based on a nonlinear model prediction method to accomplish the above control objective. On the basis of the system dynamics model, the forward Euler method is used to construct a nonlinear prediction model of the system. According to the control objectives and physical constraints of the system, the performance index function is designed, and then the control inputs of each sampling cycle are obtained through rolling optimisation and feedback correction, so that the system state gradually converges to the target trajectory, thus achieving the motion control objective. Based on the simulation results, it can be seen that the strategy proposed in this paper is efficient and robust under external disturbances. © 2023 IEEE.

关键词： Model predictive control

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Size-Invariance Matters: Rethinking Metrics and Losses for Imbalanced Multi-object Salient Object Detection 41

Size-Invariance Matters: Rethinking Metrics and Losses for I...

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41st International Conference on Machine Learning, ICML 2024

作者： Li, Feiran Xu, Qianqian Bao, Shilong Yang, Zhiyong Cong, Runmin Cao, Xiaochun Huang, Qingming Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Science Beijing Jiaotong University Beijing China School of Control Science and Engineering Shandong University Jinan China Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Cyber Science and Tech. Sun Yat-sen University Shenzhen Campus China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper explores the size-invariance of evaluation metrics in Salient Object Detection (SOD), especially when multiple targets of diverse sizes co-exist in the same image. We observe that current metrics are size-sensitive, where larger objects are focused, and smaller ones tend to be ignored. We argue that the evaluation should be size-invariant because bias based on size is unjustified without additional semantic information. In pursuit of this, we propose a generic approach that evaluates each salient object separately and then combines the results, effectively alleviating the imbalance. We further develop an optimization framework tailored to this goal, achieving considerable improvements in detecting objects of different sizes. Theoretically, we provide evidence supporting the validity of our new metrics and present the generalization analysis of SOD. Extensive experiments demonstrate the effectiveness of our method. The code is available at https://***/Ferry-Li/SI-SOD. Copyright 2024 by the author(s)

关键词： Object detection

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Local Structural Health Monitoring system in Aircraft Based on Fiber Bragg Grating Array

SSRN

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SSRN 2024年

作者： Liu, Zhichao Shao, Taoping Yin, Lixin Hou, Maosheng Changchun University of Science and Technology School of Optoelectronic Engineering Jilin Changchun130000 China Key Laboratory of Optoelectronic Measurement and Control and Optical Information Transmission Technology of the Ministry of Education Changchun University of Science and Technology Jilin Changchun130022 China

In this study, a structural health monitoring system for aircraft wings based on a fiber-optic grating sensing network was designed, and a three-dimensional position correction algorithm based on fiber Bragg grating (FBG) array test data was developed. Solving the deformation parameters using mathematical models of strain-position offset introduces errors, leading to inaccurate measurements and affecting the preciseness of three-dimensional-reconstructed surfaces. Two fitting algorithms (bitone and spline with linear interpolations) are proposed to improve this method in analyzing errors at the measured points of the wing structure. The relative error of each measurement point of the reconstructed wing structure using the bitone and spline interpolation methods was lower than 4.96%. Furthermore, the absolute error was within 0.014 mm, the average relative error was lower than 3.47%, and the root-mean-square error was lower than 0.01 mm. Compared to the linear interpolation, the average relative error decreased by up to 5.84%. The experimental results show that the measurement accuracy of bitone and spline interpolation was significantly higher than that of linear interpolation. FBG array measurements using bitone and spline interpolations for the local structural health monitoring system of aircraft improve the fitting reconstruction effect for the wing structure and stabilize the compensation effect. © 2024, The Authors. All rights reserved.

关键词： Wings

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A Secure Parameter Self-Tuning control Scheme for Vehicle Platoons Under DoS Attacks

A Secure Parameter Self-Tuning Control Scheme for Vehicle Pl...

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IEEE International Conference on Cyber Technology in Automation, control, and Intelligent systems

作者： Qiaoni Han Guowen Cheng Zhenning Tang Chang Liu Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System School of Electrical and Information Engineering Tianjin University Tianjin China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Center for Biomedical Engineering School of Information Science and Technology Fudan University Shanghai China

In order to mitigate the adverse impact of denial of service (DoS) attacks, this work designs a parameter self-tuning controller based on fuzzy rules for vehicle platoons with vehicle-to-vehicle (V2V) communications. Aiming at achieving the cooperative adaptive cruise control (CACC) of the vehicles with a required inter-vehicle distance and the same velocity, while considering the possible DoS attacks, we propose a parameter self-tuning longitudinal controller combining linear control and fuzzy control to enhance the stability and resilience of the platoon systems. Firstly, by analyzing the dynamic characteristics of vehicles and the mechanism of DoS attacks, we establish the dynamic model of vehicle platoons and model the impact of random DoS attacks as a constant data transmission delay, whose probability distribution can be defined using the Bernoulli random variable. Then, a linear controller based on fuzzy control is designed for longitudinal control to guarantee that the platoon keeps a reference velocity and the specified inter-vehicle distance under DoS attacks. Finally, thorough simulations are performed and the detailed results show the performance of the secure longitudinal control scheme for vehicle platoons in terms of stability and security.

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