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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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Low-Complexity Cooperative Payload Transportation for Nonholonomic Mobile Robots Under Scalable Constraints

arXiv

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arXiv 2025年

作者： Guan, Renhe Wang, Yuanzhe Liu, Tao Wang, Yan Mechanical Electrical Engineering and Automation Harbin Institute of Technology Shenzhen Shenzhen China School of Control Science and Engineering Shandong University Shandong Jinan China School of Automation and Intelligent Manufacturing Southern University of Science and Technology Shenzhen518055 China

Cooperative transportation, a key aspect of logistics cyber-physical systems (CPS), is typically approached using distributed control and optimization-based methods. The distributed control methods consume less time, but poorly handle and extend to multiple constraints. Instead, optimization-based methods handle constraints effectively, but they are usually centralized, time-consuming and thus not easily scalable to numerous robots. To overcome drawbacks of both, we propose a novel cooperative transportation method for nonholonomic mobile robots by improving conventional formation control, which is distributed, has a low time-complexity and accommodates scalable constraints. The proposed control-based method is testified on a cable-suspended payload and divided into two parts, including robot trajectory generation and trajectory tracking. Unlike most time-consuming trajectory generation methods, ours can generate trajectories with only constant time-complexity, needless of global maps. As for trajectory tracking, our control-based method not only scales easily to multiple constraints as those optimization-based methods, but reduces their time-complexity from polynomial to linear. Simulations and experiments can verify the feasibility of our method. © 2025, CC BY.

关键词： Mobile robots

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Evaluating the Influence of Object Detection Models and Dataset Selection on Traffic Accident Prediction Performance

Evaluating the Influence of Object Detection Models and Data...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Wen-Hui Chen Yu-Hung Huang Yu-Chen Lin Graduate Institute of Automation Technology National Taipei University of Technology Taipei Taiwan Department of Automatic Control Engineering Feng Chia University Taichung Taiwan

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Traffic accidents pose significant social and economic challenges, making effective accident prediction systems essential. Traditional machine learning methods often struggle to capture the complex, nonlinear patterns in visual accident data. Recent advancements suggest that deep learning offers a promising solution to this challenge. These vision-based systems rely on object detection as a foundational step, identifying key entities in traffic scenes captured by cameras to improve prediction accuracy. Despite its importance, the impact of object detection accuracy on overall accident prediction effectiveness remains underexamined. This study investigates this relationship through cross-dataset analysis. Experimental results show that object detection accuracy directly impacts accident prediction performance, with training dataset choice playing a critical role. Enhancing model generalization is essential to stabilizing accident prediction systems.

关键词： Training Economics Visualization Accuracy Transportation Object detection Predictive models Safety Object recognition Accidents

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An FE-S-BiLSTM and Heatmap-Based State-of-Health Estimation Method for Lithium-Ion Batteries

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IEEE Sensors Journal 2025年第10期25卷 17727-17738页

作者： Kui Shao Chao Zhai Chaolong Zhang Yigang He Bolun Du Ji Wu School of Future Technology China University of Geosciences Wuhan China School of Automation China University of Geosciences Wuhan China College of Intelligent Science and Control Engineering Jinling Institute of Technology Nanjing China China Electric Power Research Institute Wuhan China School of Electrical Engineering and Automation Wuhan University Wuhan China School of Automotive and Transportation Engineering Hefei University of Technology Hefei Anhui China

Accurate battery state-of-health (SOH) estimation can improve battery reliability and ensure its safe and efficient operation. Therefore, this study proposes a novel method for battery SOH estimation. First, a thermocouple temperature sensor monitors the battery’s operating temperature and provides feedback to the thermostat for precise temperature control during experiments. The battery’s charging voltage and current are measured using voltage transmitters and Hall current sensors, respectively, and the two are fused to obtain the battery’s charging power. Next, 1-D charging power data are converted into 2-D heatmaps using image encoding techniques. The heatmap corresponding to the first cycle is selected as the reference image, and the difference between the heatmaps of subsequent cycles and the reference image is quantified using structural similarity (SSIM). The final results serve as an indicator for battery health. In addition, this study proposes a novel battery SOH estimation model, the feature enhancement-simplification-bidirectional long short-term memory (FE-S-BiLSTM). The feature enhancement layer in the FE-S-BiLSTM model enriches global static features through enhancement learning. Based on the model’s bidirectional long short-term memory (BiLSTM) layer and simplification layer, dynamic features in the time-space domain are double captured. Finally, this study utilizes six batteries and designed a variety of experiments to validate the effectiveness of the proposed method. The experimental design comprises three tasks: SOH estimation based on full-charging data, SOH estimation based on random SOC interval charge data, and cross-battery SOH estimation. The experimental results demonstrate that the proposed SOH estimation method for batteries exhibits significant potential for practical applications.

关键词： Batteries Estimation Heating systems Sensors Aging Voltage Feature extraction Temperature measurement Image coding Voltage measurement

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Oral Cancer Classification Using a Hybrid Attention-Aided Deep Learning Model 5th

Oral Cancer Classification Using a Hybrid Attention-Aided D...

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5th International Conference on Medical Imaging and Computer Aided Diagnosis, MICAD 2024

作者： Dutta, Srinjan Roy, Jibesh Mandal, Diptarka Kaplun, Dmitrii Shpakovskaya, Irina Sarkar, Ram Department of Computer Science and Engineering Jalpaiguri Government Engineering College Jalpaiguri India School of Computer Engineering and Mathematical Sciences Defence Institute of Advanced Technology Pune India Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Automation and Control Process Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia Department of Computer Science and Engineering Jadavpur University West Bengal Kolkata India I.P. Pavlov Saint Petersburg First State Medical University St. Petersburg Russia

ISBN: (纸本)9789819638628

Oral cancer remains a critical global health challenge, characterized by high morbidity and mortality due to late-stage diagnosis. This paper addresses the need for improved diagnostic accuracy by introducing a novel hybrid-attention aided deep learning model for oral cancer classification. Our approach integrates the EfficientNetB0 model for feature extraction with a customized hybrid attention module, combining self, spatial, and soft attention mechanisms to emphasize crucial features. The model has been trained and evaluated on both microscopic and macroscopic oral cancer datasets, demonstrating its generalizability. Our proposed model achieves an accuracy of 96.97% on a macroscopic dataset and 92% on a microscopic dataset, with respective F1-scores of 0.97 and 0.89, outperforming baseline models. This hybrid attention-aided deep learning model shows significant potential in improving early detection and treatment planning of oral cancer. Source code and additional results of this work can be found at the GitHub repository. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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