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IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Wu, Wenjing Fu, Hanjing Cui, Kaixin Liu, Zhigang Yang, Dong Shi, Dawei Beijing Institute of Technology MIIT Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing100081 China Beijing Institute of Spacecraft System Engineering Beijing100094 China

Data limitation caused by the difficulty of data acquisition, the high cost of data collection, and the changes of working conditions is a serious obstacle to the accurate lithium-ion battery SOH estimation for spacecrafts system. To achieve accurate few-shot lithium-ion battery state of health(SOH) estimation and enhance the adaptability of learning methods across varying working conditions, a domain similarity model agnostic meta-learning method (DS-MAML) is proposed. First, we design a dynamic-static feature extraction method to adequately exploit information from the limited battery data and link the obtained features by gray correlation coefficients to collect sufficient information. Then, by calculating the domain similarity between the training tasks through the maximum mean discrepancy algorithm, the training tasks are ranked to reduce the re-training time. Finally, an LSTM model is added to the meta-learning framework to capture the long-term dependency relationships between SOH and time series of the voltage, and the ranked tasks are embedded in the meta-training process to improve adaptability in different working conditions. The effectiveness of the proposed method is validated by NASA and MIT datasets, and comparative experimental results illustrate that the proposed method average error is reduced by 73%, and the running speed is increased by 20% compared with traditional MAML in few-shot scenarios. © 1965-2011 IEEE.

关键词： State of charge

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Enhancing Transparent Object Pose Estimation: A Fusion of GDR-Net and Edge Detection

arXiv

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arXiv 2025年

作者： Pulli, Tessa Hönig, Peter Thalhammer, Stefan Hirschmanner, Matthias Vincze, Markus Automation and Control Institute TU Wien Vienna Austria Department of Industrial Engineering University of Applied Sciences Technikum Wien Vienna Austria

Object pose estimation of transparent objects remains a challenging task in the field of robot vision due to the immense influence of lighting, background, and reflections. However, the edges of clear objects have the highest contrast, which leads to stable and prominent features. We propose a novel approach by incorporating edge detection in a pre-processing step for the tasks of object detection and object pose estimation. We conducted experiments to investigate the effect of edge detectors on transparent objects. We examine the performance of the state-of-the-art 6D object pose estimation pipeline GDR-Net and the object detector YOLOX when applying different edge detectors as pre-processing steps (i.e., Canny edge detection with and without color information, and holistically-nested edges (HED)). We evaluate the physically-based rendered dataset Trans6D-32 K of transparent objects with parameters proposed by the BOP Challenge. Our results indicate that applying edge detection as a pre-processing enhances performance for certain objects. © 2025, CC BY.

关键词： Robot vision

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A hybrid estimation of distribution algorithm for agile earth observing satellite task scheduling problem

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Swarm and Evolutionary Computation 2025年 96卷

作者： Ma, Chunchun Huang, Panxing Liu, Xiaoze Wu, Chu-ge Xu, Rui School of Aerospace Engineering Beijing Institute of Technology Beijing100081 China Beijing Institute of Control Engineering Beijing100094 China National Key Laboratory of Space Intelligent Control Beijing100094 China School of Automation Beijing Institute of Technology Beijing100081 China

Agile Earth Observing Satellites (AEOSs) represent a new generation of Earth observation satellites, widely used for various observation tasks. To efficiently utilize the visible and observing durations of the orbiting AEOS, the AEOS scheduling problem (AEOSSP) is formulated to maximize the overall observation profit while satisfying the complex operational constraints. In this paper, a hybrid Estimation of Distribution Algorithm (EDA) that incorporates three knowledge-oriented local search operators is proposed to efficiently solve the AEOSSP. The multiple multidimensional knapsack problem with conflicts (MMdKPC) is first modeled and used to formulate AEOSSP. An EDA probability model as well as its updating and sampling mechanisms, is designed to generate solutions to explore the solution space and generate potential solutions. In addition, based on the characteristics of MMdKPC, three knowledge-oriented local search operators are developed to improve the solution. Based on the benchmark instances and simulation data provided sampled from Satellite Tool Kit, the comparison simulation experiments are carried out. The results validate the effectiveness of three knowledge-oriented local search operators, respectively. Additionally, the proposed hybrid EDA performs better compared to the existing state-of-the-art algorithms in terms of overall observation profit. © 2025 Elsevier B.V.

关键词： Scheduling algorithms

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Automatic Nonlinear MPC Approximation with Closed-loop Guarantees

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IEEE Transactions on Automatic control 2025年

作者： Tokmak, Abdullah Fiedler, Christian Zeilinger, Melanie N. Trimpe, Sebastian Kohler, Johannes Aalto University Department of Electrical Engineering and Automation Espoo02150 Finland RWTH Aachen University Institute for Data Science in Mechanical Engineering Aachen52068 Germany ETH Zurich Institute for Dynamic Systems and Control ZurichCH-8092 Switzerland

Safety guarantees are vital in many control applications, such as robotics. Model predictive control (MPC) provides a constructive framework for controlling safety-critical systems but is limited by its computational complexity. We address this problem by presenting a novel algorithm that automatically computes an explicit approximation to nonlinear MPC schemes while retaining closed-loop guarantees. Specifically, the problem can be reduced to a function approximation problem, which we then tackle by proposing Alkia-x, the Adaptive and Localized Kernel Interpolation Algorithm with eXtrapolated reproducing kernel Hilbert space norm. Alkia-x is a non-iterative algorithm that ensures well-conditioned computations, a fast-to-evaluate approximating function, and the guaranteed satisfaction of any desired bound on the approximation error. Hence, Alkia-x automatically computes an explicit function that approximates the MPC, yielding a controller suitable for safety-critical systems and high sampling rates. We apply Alkia-x to approximate two nonlinear MPC schemes, demonstrating reduced computational demand and applicability to realistic problems. © 1963-2012 IEEE.

关键词： Predictive control systems

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Insulators Defect Detection for Power Grid Based on Improved Yolov7 Algorithm 13

Insulators Defect Detection for Power Grid Based on Improved...

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13th International Conference on Intelligent control and Information Processing, ICICIP 2025

作者： Zhang, Zhiwei Zhuang, Yan Liang, Zhida Kong, Shuai Zhu, Shengyi School of Control Science and Engineering Dalian University of Technology Dalian China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Special Equipment Safety Testing Technology of Zhejiang Province Zhejiang Academy of Special Equipment Science Hangzhou China

ISBN: (纸本)9798331516147

High-current out-of-phase closed bus is an essential link in the transmission of electric energy and is widely used in thermal power stations, hydropower stations and nuclear power plants. Although these busbars have the advantages of excellent dustproof effect and strong magnetic shielding, they are susceptible to insulator aging and damage in long-term operation, posing a potential safety risk to the power grid. Due to the enclosed space inside the bus and the complexity of external maintenance tasks, manual inspection has problems such as inefficiency and low safety. In order to solve these problems, this paper proposes an improved detection algorithm based on Yolov7 for insulator defect detection in hydropower station scenarios. Because of the particularity of Yolov7 network structure and application scenarios, this paper proposes a Channel Feature Enhancement module to enhance the channel representation ability of the network. In addition, an Edge Feature Enhancement module is introduced to improve the boundary detection of insulators by enhancing the contrast between the target and the background. Considering the limitations of existing studies in specific scenarios and the scarcity of dataset, this paper constructs and accurately annotates an insulator defect detection dataset applied to hydropower station scenarios. Experimental results show that the improved-Yolov7-tiny algorithm in this paper achieves a balance between accuracy and inference speed. © 2025 IEEE.

关键词： Magnetic shielding

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An Advanced Deep Learning Detection of Rice Plant Diseases Based on Residual Neural Networks 8th

An Advanced Deep Learning Detection of Rice Plant Diseases B...

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8th International Conference on Advances in Computing and Data Sciences, ICACDS 2024

作者： Huong, Nguyen Thanh Lan, Nguyen Dang Dong, Trinh Cong Thanh, Bui Dang School of Electrical and Electronic Engineering Hanoi University of Science and Technology Hanoi Viet Nam Institute for Control Engineering and Automation Hanoi University of Science and Technology Hanoi Viet Nam

ISBN: (纸本)9783031709050

For countries with the large rice export in the world, taking care of rice plants for them to grow healthily is very necessary. One of the techniques is to detect diseases promptly, at the right time, on the right diseases in order that necessary treatment can be taken to minimize the harmful effects of diseases on rice plants as well as prevent them from breaking out into epidemics. Previously, disease detection was carried out manually, which took a lot of time and labor. Nowadays, automated methods have been increasingly applied for precision agriculture applications. In the article, a method to automatically detect rice diseases based on machine learning, especially deep learning concentrated method is proposed. In this article, our study concentrates on detecting four most common rice diseases: brown spot, hispa, blast and blight. The dataset for training, testing and validation collected and constructed from Kaggle and Google dataset includes approximately 400 images for each type of rice leaf diseases. From the experiment to assess the performance of the proposed learning method, the accuracy can reach approximately 97%. Thereby, the method shows feasibility and reasonableness, with extensively potential usage for practical precision agriculture application. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Fault Dictionary Based Optimal Sensor Allocation Strategy Considering Fault Undetectability

Fault Dictionary Based Optimal Sensor Allocation Strategy Co...

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Robotics, control and automation (ICRCA), International Conference on

作者： Jian Sun Yuanjin Wang Wen Shi Ningyun Lu College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing China National Key Laboratory of Science and Technology on Aerospace Intelligent Control Beijing Aerospace Automatic Control Institute Beijing China

ISBN: (数字)9798331544577

ISBN: (纸本)9798331544584

With the increasing complexity of equipment systems, the requirements for testability design are becoming more demanding. This paper proposes using an integer-encoded fault dictionary as the testability model, integrating fault propagation and test detectability for sensor allocation. The proposed approach is carried out in three main steps. First, an integer-encoded fault dictionary model is developed to represent the relationship between faults and tests. Second, the model’s ability to diagnose faults is evaluated by defining the performance index, which considers fault propagation and the detectability of tests. Third, the Discrete Cuckoo Search (DCS) algorithm is applied to optimize the sensor allocation based on the defined performance index. Experimental results demonstrate that this approach can achieve more accurate testability models under imperfect conditions, thereby enhancing fault diagnosis performance.

关键词： Fault diagnosis Dictionaries Costs Fault detection Robot sensing systems Birds Performance analysis Complexity theory Resource management Complex systems

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SGDR-YOLOv8: Training Method for Rice Diseases Detection Using YOLOv8 8th

SGDR-YOLOv8: Training Method for Rice Diseases Detection Usi...

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8th International Conference on Advances in Computing and Data Sciences, ICACDS 2024

作者： Thanh, Bui Dang Anh, Mac Tuan Khanh, Giap Dang Dong, Trinh Cong Huong, Nguyen Thanh School of Electrical and Electronic Engineering Hanoi University of Science and Technology Hanoi Viet Nam Institute for Control Engineering and Automation Hanoi University of Science and Technology Hanoi Viet Nam

ISBN: (纸本)9783031709050

In Vietnam, rice stands as a vital food source for the whole population, and because of that timely identification and management of rice plant diseases is extremely crucial. Among the diseases that appear in Vietnam, blast leaf, leaf folder, and brown spot are among the most prevalent ailments that harm rice plants, directly impacting cultivation. To address this problem, deep learning, a cutting-edge solution can be used to detect plant diseases. In this paper, we present an approach utilizing Deep learning to identify illnesses affecting rice leaves. YOLOv8 is employed and trained with various strategies of Stochastic Gradient Descent with Warm Restart (SGDR) to identify diseases on rice leaves. The result of the proposed method is assessed using 309 images. The experimental findings reveal an accuracy of 88.6%, indicating superior performance compared to alternative methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Plant diseases

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Influence Analysis of Rotor Topologies on Power Density of PMSM Used in Large-Scale EMP

Influence Analysis of Rotor Topologies on Power Density of P...

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International Conference on Computer and automation engineering, ICCAE

作者： Dong Li Ruining Jin Bohan Dong Junci Cao Yi Wang Chenwei Yang School of Electrical Engineering Beijing Jiaotong University Beijing China Control Equipment Beijing Institute of Automation Beijing China Mechatronics and Controls Beijing Institute of Precision Beijing China

ISBN: (数字)9798331533816

ISBN: (纸本)9798331533823

Electro-hydrostatic direct drive system integrates servo motor, pump and actuator, which has the advantages of high integration, large power to weight ratio and high reliability, has been applied in aerospace, high-end robots, large underwater equipment and high-end construction machinery. Aiming at the problems of low power density and mismatch of high efficiency zone of motor pump, a high power density drive motor is designed to meet the high power density requirement of existing electrohydrostatic direct drive system of high-end construction machinery. The electromagnetic performance of the drive motor under different rotor topologies is calculated and analyzed. On this basis, the effect of permanent magnet size on motor performance is studied. The adaptive response surface method (ARSM) is used to optimize the motor, and the power density of the motor is improved.

关键词： Power system measurements Torque Density measurement Pumps Rotors Motors Permanent magnets Topology Response surface methodology Optimization

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BnB-Based Robust PnP Pose Estimation Method for Outliers

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IEEE Robotics and automation Letters 2025年第7期10卷 6856-6863页

作者： Chenrong Long Qinglei Hu Cuicui Jiang Dongyu Li Zhenchao Ouyang School of Automation Science and Electrical Engineering Beihang University Beijing China Beijing Institute of Control Engineering Beijing China Tianmushan Laboratory Hangzhou China Zhongfa Aviation Institute of Beihang University Hangzhou China School of Cyber Science and Technology Beihang University Beijing China

The Perspective-n-Point (PnP). problem is a fundamental issue in the field of computer vision. However, the PnP problem is susceptible to the influence of outliers. In this letter, we introduce a robust PnP pose estimation method based on the Branch and Bound (BnB) algorithm. Considering that the computational complexity of the BnB method is related to the dimensionality of the parameter space, we construct a cost function based on the maximization of the consensus set for the parameter $\bm {R}^{T}\bm {t}$ . This approach reduces the search from a 6D space to a 3D space, thereby decreasing computational complexity and reducing computation time. Subsequently, we derive the upper and lower bounds for the BnB algorithm and perform pruning until convergence to obtain the global optimal solution for this parameter. After identifying corresponding point pairs that meet the inlier threshold, we employ Singular Value Decomposition (SVD) to solve for the translation and rotation parameters from this parameter. Finally, extensive comparative studies across synthetic and real datasets reveal that our method delivers accurate pose estimation and demonstrates robustness against noise and outliers, offering a viable solution for PnP issues in practical applications.

关键词： Pose estimation Iterative methods Accuracy Cost function Three-dimensional displays Noise Translation Robustness Computational complexity Vectors

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