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4th International Conference on Energy engineering and Power Systems, EEPS 2024

作者： Liu, Hanhan Su, Jun School of Electrical Engineering and Automation Xiamen University of Technology Xiamen China Xiamen University of Technology Xiamen Key Laboratory of Frontier Electric Power Equipment and Intelligent Control Xiamen China

ISBN: (纸本)9798350366914

The State of Charge of lithium batteries is a crucial parameter for the battery management system in electric vehicles and energy storage applications. Accurate estimation of the State of Charge is of significant importance for the design of the battery management system. To address the problem of accurately estimating the State of Charge, this paper employs the square root cubature Kalman filter algorithm in conjunction with a second-order resistance-capacitance equivalent circuit model of lithium batteries for precise State of Charge estimation. This study utilizes the open-source experimental dataset of the Panasonic NCR18650PF lithium battery and identifies the parameters of the second-order resistance-capacitance equivalent circuit model through the forgetting factor recursive least squares method. To verify the estimation accuracy of the Square Root Cubature Kalman Filter algorithm, the algorithm is simulated and tested using the Urban Dynamometer Driving Schedule conditions within the experimental dataset. The estimation accuracy of the Square Root Cubature Kalman Filter algorithm is compared with that of the Extended Kalman Filter algorithm and the Unscented Kalman Filter algorithm under the same conditions. Simulation test results indicate that the Square Root Cubature Kalman Filter algorithm achieves higher accuracy in estimating the State of Charge of lithium batteries. © 2024 IEEE.

关键词： State of charge

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Infrared Thermal Image Fault Detection of Electric Motor Based on Improved VGG16 Network Model 6

Infrared Thermal Image Fault Detection of Electric Motor Bas...

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6th International Conference on Communications, Information System and Computer engineering, CISCE 2024

作者： Zhu, Xianghua Hu, Shan Bing, Zhigang Zhao, Di School of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin China Key Laboratory of Information Sensing & Intelligent Control Tianjin China Engineering Teaching Practice Training Center of Tiangong University Tianjin China

ISBN: (纸本)9798350353174

As a core component of modern industry, electric motors are widely used in important fields such as electric power, machinery and national defense, and the fault detection of their thermal images faces problems such as diverse fault types, insufficient complex data processing capability and long training time. To address the above problems, this paper proposes a method for fault detection of infrared thermal images of electric motors based on an improved VGG16 network model. In the model construction and implementation, based on the VGG16 network model, the significant optimization of training time and accuracy is achieved by improving the structure of the latter layers of the model and introducing the migration learning strategy, as well as adding the callback function, the AdamW optimizer and the focal point loss function to the model. The experimental results show that the improved VGG16 model increases the average accuracy by 12.96%, reduces the average loss rate by about 23 times, and shortens the training time by half in infrared thermal image fault detection of induction motors. In the model performance evaluation, whether adjusting the test conditions or conventional model evaluation methods, the results reflect the superiority of the improved model in the realm of infrared thermal image anomaly identification of induction motors, which has practical application value. © 2024 IEEE.

关键词： Electric motors

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Dynamical pattern recognition for univariate time series and its application to an axial compressor

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control Theory and Technology 2024年第1期22卷 39-55页

作者： Jingtao Hu Weiming Wu Zejian Zhu Cong Wang Center for Intelligent Medical Engineering School of Control Science and TechnologyShandong UniversityJinan250061ShandongChina School of Automation Science and Engineering South China University of TechnologyGuangzhou510641GuangdongChina

In this paper, a learning and recognition approach is proposed for univariate time series composed of output measurements of general nonlinear dynamical systems. Firstly, a class of dynamical systems in the canonical form is derived to describe the univariate time series by introducing coordinate transformation. An observer-based deterministic learning technique is then adopted to achieve dynamical modeling of the associated transformed systems of the training univariate time series, and the modeling results in the form of radial basis function network (RBFN) models are stored in a pattern library. Subsequently, multiple observer-based dynamical estimators containing the RBFN models in the pattern library are constructed for a test univariate time series, and a recognition decision scheme is proposed by the derived recognition indicator. On this basis, more concise recognition conditions are provided, which is beneficial for verifying the recognition results. Finally, simulation studies on the Rossler system and aero-engine stall warning verify the effectiveness of the proposed approach.

关键词： Dynamical pattern recognition Deterministic learning Stall warning Radial basis function network Sampled-data observer

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Toward quantum unmanned systems

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Science China(Technological Sciences) 2024年第7期67卷 2277-2280页

作者： CHENG ShuMing QI HongSheng SUN Yue LIU LiJun XIE ShaoRong REN XiaoQiang Department of Control Science and Engineering Tongji UniversityShanghai 200092China Academy of Mathematics and Systems Science Chinese Academy of SciencesBeijing 100190China Cipher Cloud Core Ltd. Beijing 100085China College of Mathematics and Computer Science Shanxi Normal UniversityLinfen 041081China School of Computer Engineering and Science Shanghai UniversityShanghai 200444China School of Mechatronic Engineering and Automation Shanghai UniversityShanghai 200444China

1 Quantum information technology Quantum information technology utilizes physical systems at the microscopic level, such as photon, atom, ion, and superconducting, to accomplish information-processing tasks that are impossible for the classical macroscopic world. During the past decade, significant process has been achieved in the pursuit of quantum technology into practical applications,generating great research interest from various domains, with the potential to radically change our information infrastructure [1–3].

关键词： quantum utilize generating

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Detection of inter row navigation lines in corn fields based on improved CenterNet

Detection of inter row navigation lines in corn fields based...

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International Conference on Computer Vision, Robotics, and automation engineering, CRAE 2024

作者： Zhang, Zhipeng Li, Huiyan Meng, Qingkuan Zhang, Ruitong Zhang, Wenkang School of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin China Tianjin Key Laboratory of Information Sensing and Intelligent Control Tianjin University of Technology and Education Tianjin China

ISBN: (纸本)9781510682283

In complex corn orchard environments, traditional machine vision algorithms often struggle to handle changes in light, occlusion between crops, and interference from weeds, which leads to inaccurate navigation line extraction. This article proposes a corn field navigation route detection method based on improved CenterNet to address this issue. Firstly, we replaced the CenterNet backbone network with ResNeSt, which can effectively improve detection performance when facing small targets and complex hierarchical image content. At the same time, we added CBMA attention mechanism in CenterNet to enhance the recognition of target features of corn seedlings and weaken background interference. We use the midpoint of corn seedlings as the navigation positioning base point, use an improved CenterNet model to detect corn seedlings on both sides, use the center point of corn seedlings as the positioning reference point, and then use the least squares method to fit the center points of corn seedlings on both sides. The experimental results show that the improved CenterNet model has a detection accuracy of 76.44% and a detection speed of 42.09 frames per second. Compared with the original CenterNet model, it has improved by 4.28 percentage points and 1.51 frames per second, respectively. From this, it can be seen that the model can accurately identify corn seedlings, and the average processing time for one frame of image is 0.082 seconds, which fully meets the needs of navigation in corn fields. © 2024 SPIE.

关键词： Seed

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Spin Hall Effect of Light (SHEL) Ellipsometry for surface measurement with different models of optical interface

Spin Hall Effect of Light (SHEL) Ellipsometry for surface me...

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Optics and Photonics International Congress 2024, OPIC 2024

作者： Zahra, Naila Mizutani, Yasuhiro Uenohara, Tsutomu Takaya, Yasuhiro Graduate School of Engineering Osaka University 2-1 Yamadaoka Osaka Suita Japan Instrumentation Control & Automation Research Group Institut Teknologi Bandung Ganesha 10 Bandung Indonesia

ISBN: (数字)9781510687646

ISBN: (纸本)9781510687639

The demonstration of the Spin Hall Effect of Light (SHEL) observation by weak measurement has enabled exploration of the phenomenon in various fields including its application for precision measurement. The sensitivity of SHEL to the optical properties of the medium opens the possibility for precision measurement. Previously, SHEL ellipsometry has shown its potential as surface measurement. In this paper, SHEL ellipsometry is utilized to reconstruct flat optical windows fabricated from different material. Two different models of optical interface are used to solve the SHEL ellipsometry formula, based on air-glass model to retrieve pseudo refractive index value and based on effective medium approximation model to retrieve roughness approximation as effective thickness. This paper further exhibits SHEL potential for surface measurement of smooth surface objects. © 2024 SPIE.

关键词： Surface measurement

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Research on Speech Recognition Methods Based on Spectral Subtraction and Improved BP Neural Network Algorithm 7

Research on Speech Recognition Methods Based on Spectral Sub...

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7th IEEE Information Technology, Networking, Electronic and automation control Conference, ITNEC 2024

作者： Ma, Yuqi Zhang, Yunxi Wang, Zihan Zhao, Ya Liu, Jia School of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin China Tianjin University of Technology and Education Tianjin Key Laboratory of Information Sensing and Intelligent Control Tianjin China

ISBN: (纸本)9798350370782

This paper presents a speech recognition method combining spectral subtraction with improved BP neural network algorithm. The speech samples containing unstable noise are collected in the real environment. Firstly, the noise from speech signals is removed using spectral subtraction method, and the speech feature parameters after noise removal are extracted. Then the speech is recognized effectively by the improved BP neural network algorithm. Finally, some comparative experiments are given to verify the effectiveness of the proposed method. © 2024 IEEE.

关键词： Speech enhancement

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A new Interleaved Full-Bridge Converter for AC battery 12

A new Interleaved Full-Bridge Converter for AC battery

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12th IEEE International Conference on control, automation and Information Sciences, ICCAIS 2023

作者： Do, Tuan Anh Le, Quang Nghia Nguyen, Thang Long Dich, Nguyen Quang Hoang, Phuong Vu Hanoi University of Science and Technology School of Electrical and Electronic Engineering Hanoi Viet Nam Hanoi University of Science and Technology Institute for Control Engineering and Automation Hanoi Viet Nam

ISBN: (纸本)9798350328783

Home battery storage systems using Second-life time batteries (SLBs) are of recent research interest for resilient homes. The AC Battery is chosen for this application due to its flexibility, in which the Interleaved Full Bridge (IFB) converter is studied in this paper for the Power Correction Factor (PFC) stage. The pulse width modulation and control strategy are introduced for the IFB converter in this research. To verify the operation and the feasibility of the proposed structure in the grid-tied mode, a real-time simulation is carried out with the results meeting a good agreement with the theoretical analyst. © 2023 IEEE.

关键词： Electric power factor correction

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Pavement Cracks Coupled With Shadows:A New Shadow-Crack Dataset and A Shadow-Removal-Oriented Crack Detection Approach

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IEEE/CAA Journal of Automatica Sinica 2023年第7期10卷 1593-1607页

作者： Lili Fan Shen Li Ying Li Bai Li Dongpu Cao Fei-Yue Wang School of Information and Electronics Beijing Institute of TechnologyBeijing 100081China School of Civil Engineering Tsinghua UniversityBeijing 100084China School of Mechanical Engineering Beijing Institute of TechnologyBeijing 100081China State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body and the College of Mechanical and Vehicle Engineering Hunan UniversityChangsha 410082China School of Vehicle and Mobility Tsinghua UniversityBeijing 100084China State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

Automatic pavement crack detection is a critical task for maintaining the pavement stability and driving *** task is challenging because the shadows on the pavement may have similar intensity with the crack,which interfere with the crack detection *** to the present,there still lacks efficient algorithm models and training datasets to deal with the interference brought by the *** fill in the gap,we made several contributions as ***,we proposed a new pavement shadow and crack dataset,which contains a variety of shadow and pavement pixel size *** also covers all common cracks(linear cracks and network cracks),placing higher demands on crack detection ***,we designed a two-step shadow-removal-oriented crack detection approach:SROCD,which improves the performance of the algorithm by first removing the shadow and then detecting *** addition to shadows,the method can cope with other noise ***,we explored the mechanism of how shadows affect crack *** on this mechanism,we propose a data augmentation method based on the difference in brightness values,which can adapt to brightness changes caused by seasonal and weather ***,we introduced a residual feature augmentation algorithm to detect small cracks that can predict sudden disasters,and the algorithm improves the performance of the model *** compare our method with the state-of-the-art methods on existing pavement crack datasets and the shadow-crack dataset,and the experimental results demonstrate the superiority of our method.

关键词： Automatic pavement crack detection data augmentation compensation deep learning residual feature augmentation shadow removal shadow-crack dataset

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Machine Learning Based Anomaly Clustering Study for Bolt Tightening System 5

Machine Learning Based Anomaly Clustering Study for Bolt Tig...

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5th International Conference on Electronic Communication and Artificial Intelligence, ICECAI 2024

作者： Liu, Tiehui Hu, Shan Bing, Zhigang Zhao, Di School of Automation and Electrical-Engineering Tianjin University of Technology and Education Tianjin China Key Laboratory of Information Sensing & Intelligent Control Tianjin China Engineering Teaching Practice Training Center of Tiangong University Tianjin China

ISBN: (纸本)9798350386943

In the context of Industry 4.0, the tightening quality of bolts is still critical to product performance and safety. In order to improve the abnormal data identification and classification ability of bolt tightening system, this paper adopts principal component analysis to reduce the dimension and adds residual network structure on the basis of deep self-encoder to improve the feature expression. The k-means++ algorithm is used to optimize the initialization of the clustering center, which in turn improves the k-means clustering algorithm. The experimental results show that the improved algorithm achieves significant improvement in contour coefficient and David Bunting index, which effectively improves the automatic identification accuracy of bolt tightening state. © 2024 IEEE.

关键词： K-means clustering

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