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Chinese Automation Congress (CAC)

作者： Mingxuan Cai Qi Liu Yuxiang Liu Bo Yang Department of Automation Shanghai Jiao Tong University shanghai China Key Laboratory of System Control and Information Processing Shanghai Engineering Research Center of Intelligent Control and Management Ministry of Education of China Shanghai China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Accurate fault diagnosis plays a key role in the safe and efficient operation of electrical motors. Aiming at multiclass motor fault diagnosis under complex working conditions, a signal fusion-based fault diagnosis framework is proposed. measurement parameters from different sensors are first transformed by wavelet denoising and fast Fourier transform (FFT). Then, multiple parameters spectrum and working condition information are integrated and converted into high-dimension matrices. Finally, a channel-attention-based convolution neural network (CA-CNN) is proposed to extract fault patterns from a reconstructed matrix and output diagnosis results. Experiment results on both simulated and experimental datasets verify the effectiveness of the proposed method.

关键词： Fault diagnosis Employee welfare Wavelet transforms Accuracy Fast Fourier transforms Motors Sensors Numerical models Velocity measurement Matrix converters

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A CL-MDT Method of Multi-energy Load Forecasting in Integrated Energy system 4

A CL-MDT Method of Multi-energy Load Forecasting in Integrat...

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4th International Conference on Power and Energy Technology, ICPET 2022

作者： Zheng, Tao Liu, Gang Cheng, Wei Hu, Pingzhao Wang, Ying State Grid Electric Power Research Institute Nari Group Corporation Nanjing China NARI-TECH Nanjing Control System Ltd Nanjing China Nari Technology Co. Ltd Nanjing China Ministry of Education Southeast University Key Laboratory of Measurement and Control of Cse Nanjing China

ISBN: (数字)9781665480796

ISBN: (纸本)9781665480796

Source-load scheduling is based on multi-energy-load forecasting. The integrated energy system mainly includes three types of energy: electricity, cooling and heating. Studying the correlation among electricity, cooling and heating may improve the accuracy of multi-energy-load forecasting. This paper considers the correlation of three energy sources, fully analyzes the correlation, and applies the correlation of the three in the forecasting model. This paper proposes a CL-MDT (CNN-LSTM-Multi-Decoder-Transformer) model for multi-energy-load forecasting. The model is based on the Transformer, and the Multi-Head Attention part in the Encoder is replaced by a 2dimensional 3*3 CNN (Convolutional Neural Network) feature extraction module for feature extraction of data. And a 1dimensional CNN feature extraction module and LSTM structure are added to the Decoder. The structure of single Encoder and multiple Decoders is used in this paper to realize the application of the correlation of the three in the forecasting model. Finally, the model is tested on public datasets and the forecasting results of CL-MDT are compared with that of LSTM model for multi-energy-load joint forecasting. The results show that the CL-MDT model proposed in this paper has better forecasting accuracy. © 2022 IEEE.

关键词： Forecasting

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SC-NeRF: Self-Correcting Neural Radiance Field with Sparse Views

arXiv

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arXiv 2023年

作者： Song, Liang Wang, Guangming Liu, Jiuming Fu, Zhenyang Miao, Yanzi Wang, Hesheng Engineering Research Center of Intelligent Control for Underground Space Ministry of Education School of Information and Control Engineering Advanced Robotics Research Center China University of Mining and Technology Xuzhou221116 China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China

In recent studies, the generalization of neural radiance fields for novel view synthesis task has been widely explored. However, existing methods are limited to objects and indoor scenes. In this work, we extend the generalization task to outdoor scenes, trained only on object-level datasets. This approach presents two challenges. Firstly, the significant distributional shift between training and testing scenes leads to black artifacts in rendering results. Secondly, viewpoint changes in outdoor scenes cause ghosting or missing regions in rendered images. To address these challenges, we propose a geometric correction module and an appearance correction module based on multi-head attention mechanisms. We normalize rendered depth and combine it with light direction as query in the attention mechanism. Our network effectively corrects varying scene structures and geometric features in outdoor scenes, generalizing well from object-level to unseen outdoor scenes. Additionally, we use appearance correction module to correct appearance features, preventing rendering artifacts like blank borders and ghosting due to viewpoint changes. By combining these modules, our approach successfully tackles the challenges of outdoor scene generalization, producing high-quality rendering results. When evaluated on four datasets (Blender, DTU, LLFF, Spaces), our network outperforms previous methods. Notably, compared to MVSNeRF, our network improves average PSNR from 19.369 to 25.989, SSIM from 0.838 to 0.889, and reduces LPIPS from 0.265 to 0.224 on Spaces outdoor scenes. © 2023, CC BY.

关键词： Rendering (computer graphics)

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Concept-Level Semantic Transfer and Context-Level Distribution Modeling for Few-Shot Segmentation

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IEEE Transactions on Circuits and systems for Video Technology 2025年

作者： Luo, Yuxuan Chen, Jinpeng Cong, Runmin Ip, Horace Ho Shing Kwong, Sam City University of Hong Kong Department of Computer Science Hong Kong Shandong University School of Control Science and Engineering Jinan250061 China Ministry of Education Key Laboratory of Machine Intelligence and System Control Jinan250061 China Lingnan University Hong Kong

Few-shot segmentation (FSS) methods aim to segment objects using only a few pixel-level annotated samples. Current approaches either derive a generalized class representation from support samples to guide the segmentation of query samples, which often discards crucial spatial contextual information, or rely heavily on spatial affinity between support and query samples, without adequately summarizing and utilizing the core information of the target class. Consequently, the former struggles with fine detail accuracy, while the latter tends to produce errors in overall localization. To address these issues, we propose a novel FSS framework, CCFormer, which balances the transmission of core semantic concepts with the modeling of spatial context, improving both macro and micro-level segmentation accuracy. Our approach introduces three key modules: (1) the Concept Perception Generation (CPG) module, which leverages pre-trained category perception capabilities to capture high-quality core representations of the target class;(2) the Concept-Feature Integration (CFI) module, which injects the core class information into both support and query features during feature extraction;and (3) the Contextual Distribution Mining (CDM) module, which utilizes a Brownian Distance Covariance matrix to model the spatial-channel distribution between support and query samples, preserving the fine-grained integrity of the target. Experimental results on the PASCAL-5i and COCO-20i datasets demonstrate that CCFormer achieves state-of-the-art performance, with visualizations further validating its effectiveness. Our code is available at ***/lourise/ccformer. © 1991-2012 IEEE.

关键词： Semantic Segmentation

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An Integrated Smart Manufacturing Platform Powered by Microservices

An Integrated Smart Manufacturing Platform Powered by Micros...

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Asian control Conference

作者： Yuxiang Liu Mengqi Han Annan Xu Qi Liu Bo Yang Department of Automation Shanghai Jiao Tong University Shanghai China 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)9789887581598

ISBN: (纸本)9798331540845

With the rapid development of the manufacturing industry, traditional control systems can no longer meet the requirements of flexibility, efficiency, and intelligence for smart manufacturing. In order to fully utilize technologies such as microservices and enhance the flexibility of smart manufacturing, we propose a microservice-based architecture integrating sensing, communication, computation, and control, dedicated to achieving integrated management and configuration of the production process. In this architecture, each production stage is designed as an independent microservice, capable of being flexibly deployed in various edge controllers. Therefore, a universal edge controller can simultaneously act as a PLC, router, data acquisition device, and edge server, greatly simplifying the complexity of equipment deployment. The microservice-based integrated architecture not only improves the efficiency of data routing but also helps simplify the integration of new sensors and actuators, making adjustments of production lines more convenient, thereby enhancing the scalability and flexibility of the system. We implemented the proposed architecture based on a validation scenario, and the experimental results demonstrate the enormous development potential of this architecture.

关键词： Scalability Microservice architectures Restful API Computer architecture Production Routing Sensor systems

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MorNet: Asymmetric UNet-like Network with Morphological Opening and Closing for Image Segmentation 17th

MorNet: Asymmetric UNet-like Network with Morphological Open...

引用

17th International Conference on Intelligent Robotics and Applications, ICIRA 2024

作者： Li, Meng Yun, Juntong Jiang, Du Tao, Bo Li, Gongfa Liu, Rong Liu, Ying Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Wuhan430081 China Research Center for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan430081 China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan430081 China Precision Manufacturing Research Institute Wuhan University of Science and Technology Wuhan430081 China School of Medicine Wuhan University of Science and Technology No. 1 Huangjia Lake University Town Wuhan430065 China School of Mechanical Engineering Hubei Engineering University Xiaogan432000 China

ISBN: (纸本)9789819607761

Image segmentation plays a crucial role in target recognition as it provides an essential prerequisite for identifying targets. However, UNet cannot acquire comprehensive information about the image, which limits its ability to effectively segment small and discrete objects. A new network structure, called MorNet, is proposed. It applies dilation and erosion operations in morphological opening and closing for connected and discrete region segmentation. MorNet effectively integrates global segmentation images, emphasizing small and discrete area segmentation and enhancing the segmentation of large continuous areas. This method enhances the understanding of the location of organs and diseased sites, enabling accurate segmentation. A side-out (SO) monitoring mechanism is introduced into the decoder to provide monitoring information for the original image and the predicted results. Compared with current UNet-like networks, our proposed method achieved the highest Dice scores (96.4635%, 94.0514%, 94.3444%) and PPV scores (94.2226%, 90.2361%, 91.5582%) for liver tumor, brain tumor, and liver segmentation tasks, respectively. The experimental results show that MorNet successfully combines the segmentation of connected and discrete regions. MorNet locates organs and lesions more accurately than current state-of-the-art medical image segmentation models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image segmentation

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Risk Prediction of Cavitation Erosion of Venturi Tube Based on a New Erosion Indicator

SSRN

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SSRN 2024年

作者： Ma, Junqi Wang, Xiaoyu Wang, Tian Sun, Qiang School of Mechanical Engineering and Automation Northeastern University Shenyang110819 China Key Laboratory of Vibration and Control of Aero-Propulsion System Ministry of Education Northeastern University Shenyang110819 China

Cavitation erosion would reduce the performance of the fluid machinery. In order to improve the reliability and prolong the life span of fluid machinery, it is necessary to study the mechanism of cavitation erosion and predict the possible erosion aera. Since the erosion power to be measured and calculated is closer to the actual state of cavitation, a new cavitation erosion indicator model based on erosion power is proposed, which can reflect the size and region of the erosion generated by cavitation more precisely. Based on the two models of axisymmetric nozzles and venturi tubes, the prediction of cavitation erosion location based on the newly proposed indicator is illustrated. It is found that the location of cavitation erosion mainly occurs near the maximum location boundary of the cavitation region. This research lays a foundation for the study of possible erosion state of fluid machinery in cavitation environment. © 2024, The Authors. All rights reserved.

关键词： Cavitation

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A 4-Channel Ultrafast Wavelength-Swept REC-DFB Laser Array for Fiber Bragg Grating Interrogation

A 4-Channel Ultrafast Wavelength-Swept REC-DFB Laser Array f...

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2023 Asia Communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings, ACP/POEM 2023

作者： Meng, Lingxin Dai, Pan Ban, Qilu Xu, Kaichuan Wang, Jiacheng Li, Zhen Wang, Feng Zeng, Jie Li, Shaobo Ma, Xiang Chen, Xiangfei Res. Center of Precision Photonics Integration and System Application of the Ministry of Education College of Engineering and Applied Sciences Nanjing University Key Lab. of Intelligent Optical Sensing and Manipulation of the Ministry of Education Engineering National Laboratory of Solid State Microstructures Nanjing210023 China Nanjing University of Aeronautics and Astronautics State Key Laboratory of Mechanics and Control for Aerospace Structures Nanjing210016 China The 54th Research Institute of China Electronics Technology Group Corporation Optical Communication Research and Development Center Shijiazhuang050081 China

ISBN: (纸本)9798350312614

We present a four-channel ultrafast wavelength-swept distributed feedback (DFB) semiconductor laser array based on the reconstruction-equivalent-chirp (REC) technique using instantaneous injection current modulation. With the aid of our designed laser driving circuit system consists of the microcontroller unit (MCU), field-programmable gate array (FPGA) and negative metal-oxide-semiconductor field effect transistors (n-MOSFETs), the precise pulse driving current for the 4-channel wavelength-swept REC-DFB laser array can be generated and a fast tuning of 16.424 nm within 10 μs is achieved. To further investigate the tuning performance of the proposed wavelength-swept laser source, we then applied it to the fiber Bragg grating (FBG) interrogation system and four FBG temperature sensor arrays were successfully demodulated. The development of high-efficient optical sensing systems heavily relies on the significance of the ultrafast wavelength-swept laser source, which has the potential to greatly enhance FBG interrogator performance in the future. © 2023 IEEE.

关键词： DFB laser array fast wavelength-swept fiber Bragg grating interrogation REC technique

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Towards Non-Virtual Inertia control of Renewable Energy for Frequency Regulation: Modeling, Analysis and New control Scheme

SSRN

引用

SSRN 2024年

作者： Gao, Haishu Zhang, Feng Ding, Lei Zhang, Gang Salimu, Athuman State Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan250061 China Yantai Research Institute Harbin Engineering University Yantai China

Currently, when renewable generation participates in frequency regulation, the traditional control method is to emulate synchronous generators through virtual inertia control. However, virtual inertia has a time delay, so essentially it is a kind of fast power response. Meanwhile, virtual inertia control is likely to be affected by frequency fluctuation, since it responds to the derivative of frequency. Hence, it’s worth exploring non-virtual inertia control for renewable energy when participating in frequency regulation. For this reason, a novel non-virtual inertia control scheme for renewable energy frequency regulation is proposed in this research. Firstly, the extended model of system frequency regulation (SFR), which contains virtual inertia with time delay, is built and analytically solved by order decrement on basis of Routh approximation method. Afterwards, according to analytical solution, it shows time delay really has an effect on the frequency response of renewable energy, and it can also be analytically proved that non-virtual inertia control, e.g., sole droop control, was capable to replace virtual inertia under the same frequency deviation, but more energy for frequency regulation may be needed. Furthermore, a novel two-segment droop control is presented, and to analytically prove its ability to replace virtual inertia, the impulse function balancing principle and the integration by parts algorithm were adopted to address the initial conditions of the differential equation. Based on the analytical expression, it can be analytically proved that under the same energy of frequency regulation, a lower frequency deviation can be obtained. Accordingly, a parameter setting method for two-segment droop control was proposed. Finally, the effectiveness of the proposed method is verified by using a two-area system frequency response model, and the results reveal that it can be used to replace virtual inertia and has better performance. © 2024, The Authors. All r

关键词： Time delay

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A Robot Coordinate measurement system Based on Pull Wire Sensor and Its Parameter Identification Method 12

A Robot Coordinate Measurement System Based on Pull Wire Sen...

引用

12th International Conference on CYBER Technology in Automation, control, and Intelligent systems, CYBER 2022

作者： Gao, Shuang Ma, Kaiwei Gao, Yang Shen, Xin Yang, Mingxing Xu, Fengyu College of Automation and College of Artificial Intelligence Nanjing University of Posts and Telecommunications Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Jiangsu Nanjing China Anhui Province Key Laboratory of Special Heavy Load Robot Anhui Maanshan China College of Automation and College of Artificial Intelligence Nanjing University of Posts and Telecommunications Jiangsu Nanjing China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

Aiming at the problems of large robot error and difficult coordinate detection, a robot coordinate measurement system and parameter identification method based on pull wire sensor are proposed. Firstly, a robot coordinate measurement system based on pull wire sensor is established. Secondly, Levenberg-Marquarelt algorithm is used to solve the kinematics parameters of the robot. Finally, the neural network of the whole connection layer is used to predict the optimal compensation amount for secondary compensation. In order to verify the above theory, the robot coordinate measurement system is built and verified on the five degree of freedom robot experimental platform. The results show that the robot coordinate measurement system can measure the robot coordinates quickly and accurately. The secondary compensation method controls the accuracy of the robot to about 2mm, and the optimal error is controlled within 0.012mm. The absolute positioning accuracy of the robot is effectively improved. © 2022 IEEE.

关键词： Robots

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