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8th Optoelectronics Global Conference, OGC 2023

作者： Liu, Jie Ye, Mengyuan Ma, Hong Zeng, Duowei Chen, Yang Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Automation China University of Geosciences Wuhan430074 China

We propose and demonstrate a polarization-independent dual mode spot size converter (SSC) on silicon integrated platform. By utilizing gradual index distributed subwavelength gratings (GRIN-SWG). The proposed device c... 详细信息

ISBN: (纸本)9798350325324

关键词： Silicon

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An Adaptive Physics-Informed Neural Network with Two-Stage Learning Strategy to Solve Partial Differential Equations

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Numerical Mathematics(Theory,Methods and Applications) 2023年第2期16卷 298-322页

作者： Shuyan Shi Ding Liu Ruirui Ji Yuchao Han School of Automation and Information Engineering Xi‘an University of TechnologyXi‘an 710048P.R.China National&Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Xi‘an 710048P.R.China Xi’an University of Technology Shannxi Key Laboratory of Complex System Control and Intelligent Information ProcessingXi‘an 710048P.R.China

Physics-Informed Neural Network(PINN)represents a new approach to solve Partial Differential Equations(PDEs).PINNs aim to solve PDEs by integrating governing equations and the initial/boundary conditions(I/BCs)into a loss ***,the imbalance of the loss function caused by parameter settings usually makes it difficult for PINNs to converge,*** they fall into local *** other words,the presence of balanced PDE loss,initial loss and boundary loss may be critical for the *** addition,existing PINNs are not able to reveal the hidden errors caused by non-convergent boundaries and conduction errors caused by the PDE near the ***,these problems have made PINN-based methods of limited use on practical *** this paper,we propose a novel physics-informed neural network,*** adaptive physics-informed neural network with a two-stage training *** algorithm adds spatio-temporal coefficient and PDE balance parameter to the loss function,and solve PDEs using a two-stage training process:pre-training and formal *** pre-training step ensures the convergence of boundary loss,whereas the formal training process completes the solution of PDE by balancing various loss *** order to verify the performance of our method,we consider the imbalanced heat conduction and Helmholtz equations often appearing in practical *** Klein-Gordon equation,which is widely used to compare performance,reveals that our method is able to reduce the hidden *** results confirm that our algorithm can effectively and accurately solve models with unbalanced loss function,hidden errors and conduction *** codes developed in this manuscript are publicy available at https://***/callmedrcom/ATPINN.

关键词： Physics informed neural networks partial differential equations two-stage learning scientific computing

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Fault diagnosis for small samples based on VMR

Fault diagnosis for small samples based on VMR

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2024 China Automation Congress, CAC 2024

作者： Wu, Xuejin Wu, Yali Hu, Yanghu Department of Information and Control Engineering Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an710048 China State Key Laboratoiy of Metal Extrusion and Forging Equipment Technology China National Heavy Machinery Research Institute Co. Ltd Xi'an710016 China

ISBN: (纸本)9798350368604

The scarcity of fault samples presents a significant challenge for fault diagnosis in industrial processes. Conventional methods often fail to deliver satisfactory results when dealing with small sample sizes. In addressing the issue of small sample fault diagnosis in industrial processes, this paper utilizes the Variational Mode Reconstruction(VMR) algorithm to screen the best components decomposed by the Variational Mode Decomposition(VMD) through the envelope entropy then random weighting them and signal reconstruction to achieve data enhancement, to enrich the sample size and to prevent the model overfitting phenomenon due to the scarcity of samples. Meanwhile, this paper integrates Convolutional Neural Network (CNN) and Long Short-term Memory Neural Network (LSTM) to propose a network model of spatio-temporal feature fusion for feature extraction and classification. The experiments demonstrate that this model not only enhances data diversity but also ensures consistency before and after enhancement, effectively mitigating overfitting. Furthermore, it guarantees high recognition and generalization rates in fault diagnosis. © 2024 IEEE.

关键词： Convolutional neural networks

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Wavelength Selective Mode Converter Using Grating Assisted Anti-reflection Contra-asymmetric Directional Couplers 8

Wavelength Selective Mode Converter Using Grating Assisted A...

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8th Optoelectronics Global Conference, OGC 2023

作者： Zeng, Duowei Ye, Mengyuan Chen, Yang Ma, Hong Liu, Jie China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Automation Wuhan430074 China

ISBN: (纸本)9798350325324

We propose and demonstrate a wavelength selective mode converter on silicon integrated platform. By utilizing grating assisted anti-reflection contra-asymmetric directional couplers (CADCs), wavelength selective TE0-TE1 mode conversion could be achieved. Simulation results show that the operation wavelength is 1544.8 nm and 3 dB bandwidth is 8.3 nm. Insertion loss of the proposed device is simulated to be 0.29dB and the crosstalk is less than -50dB. The proposed device can be used in mode division multiplexing (MDM) wavelength division multiplexing (WDM) communication network. © 2023 IEEE.

关键词： Directional couplers

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Design and Experimental Validation of a Worm-Like Tensegrity Robot for In-Pipe Locomotion

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Journal of Bionic Engineering 2023年第2期20卷 515-529页

作者： Xiaolin Dai Yixiang Liu Wei Wang Rui Song Yibin Li Jie Zhao School of Control Science and Engineering Shandong UniversityJinan250061China Engineering Research Center of Intelligent Unmanned System of Ministry of Education Jinan250061China Shandong Research Institute of Industrial Technology Jinan250061China State Key Laboratory of Robotics and System Harbin Institute of TechnologyHarbin150080China

Traditional rigid-body in-pipe robots usually have complex and heavy structures with limited flexibility and *** soft in-pipe robots have great improvements in flexibility,they still have manufacturing difficulties due to their reliance on high-performance soft *** structure is a kind of self-stressed spatial structure consisting discrete rigid struts connected by a continuous net of tensional flexible strings,which combines the advantages of both rigid structures and soft *** applying tensegrity structures into robotics,this paper proposes a novel worm-like tensegrity robot for moving inside ***,a robot module capable of body deformation is designed based on the concept of tensegrity and its deformation performance is ***,the optimal parameters of the module are obtained based on the tensegrity *** deformation ability of the tensegrity module is tested ***,the worm-like tensegrity robot that can crawl inside pipes is developed by connecting three modules in *** performance and load capacity are tested on the prototype of the worm-like tensegrity robot by experiments of moving in horizontal pipe,vertical pipe,and elbow *** results demonstrate the effectiveness of the proposed design and suggest that the robot has high compliance,mobility,and adaptability although with simple structure and low cost.

关键词： Bio-inspired robot Soft robot application Tensegrity structure Pipe crawling

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Lightweight Neural Network-Based Infrared Image and Anomalous Heat Region Recognition for Electrical Equipment 18th

Lightweight Neural Network-Based Infrared Image and Anomalou...

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18th Annual Conference of China Electrotechnical Society, ACCES 2023

作者： Han, Zhikai Zhang, Jinglei Jia, Xin School of Electrical Engineering and Automation Tianjin University of Technology Tianjin300384 China Tianjin Key Laboratory of Complex System Control Theory and Application Tianjin300384 China

ISBN: (纸本)9789819710676

In order to accurately and quickly recognize electrical devices and abnormal heating parts under infrared conditions, we propose a lightweight neural network-based method for identifying electrical devices and their abnormal heating parts. First, we construct a fast target detection network for real-time detection. Second, we propose an efficient feature retention module to avoid the feature loss problem in multi-layer convolutional operations. In addition, we introduce an attention mechanism based on the coordinate information interaction strategy to localize the target more accurately. Finally, we use a lightweight weighted bidirectional feature pyramid network to improve the detection accuracy at different scales. Experimental results on a dataset of 12109 electrical infrared images containing six types of electrical devices and anomalously heated areas show that the proposed method achieves good results, with an average recognition accuracy of 93.9% and a processing speed of 222 frames/second. © Beijing Paike Culture Commu. Co., Ltd. 2024.

关键词： Infrared imaging

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Grid Strength Assessment Method for Evaluating Small-signal Synchronization Stability of Grid-following and Grid-forming Converters Integrated systems

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Journal of Modern Power systems and Clean Energy 2025年第1期13卷 55-65页

作者： Qianhong Shi Wei Dong Guanzhong Wang Junchao Ma Chenxu Wang Xianye Guo Vladimir Terzija Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education School of Electrical EngineeringShandong UniversityJinan 250061China Electric Power Research Institute State Grid Zhejiang Electric Power Co.Ltd.Hangzhou 310014China School of Engineering Newcastle UniversityNewcastle upon TyneUK

Oscillations caused by small-signal instability have been widely observed in AC grids with grid-following(GFL)and grid-forming(GFM)*** generalized short-circuit ratio is commonly used to assess the strength of GFL converters when integrated with weak AC systems at risk of *** paper provides the grid strength assessment method to evaluate the small-signal synchronization stability of GFL and GFM converters integrated ***,the admittance and impedance matrices of the GFL and GFM converters are analyzed to identify the frequency bands associated with negative damping in oscillation modes dominated by heterogeneous synchronization ***,based on the interaction rules between the short-circuit ratio and the different oscillation modes,an equivalent circuit is proposed to simplify the grid strength assessment through the topological transformation of the AC *** risk of sub-synchronization and low-frequency oscillations,influenced by GFL and GFM converters,is then reformulated as a semi-definite programming(SDP)model,incorporating the node admittance matrix and grid-connected device *** effectiveness of the proposed method is demonstrated through a case analysis.

关键词： Grid-following converter grid-forming converter grid strength assessment small-signal stability

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Fault Diagnosis Method For Autonomous Underwater Vehicles Based On Neural Network With Attention Mechanism 14

Fault Diagnosis Method For Autonomous Underwater Vehicles Ba...

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14th IEEE Global Reliability and Prognostics and Health Management Conference, PHM-Hangzhou 2023

作者： Yang, Yin Wang, Hongchao Gao, Junjie Gong, Xiaoyun Du, Wenliao Henan Provincial Key Laboratory of Intelligent Manufacturing of Mechanical Equipment Henan International Joint Laboratory of Complex Mechanical Equipment Intelligent Monitoring and Control Zhengzhou University of Light Industry Zhengzhou China

ISBN: (纸本)9798350301359

Aiming at the autonomous underwater vehicle (AUV), each system module has the characteristics of strong coupling and nonlinearity, which leads to the existence of strong nonlinear relationship between various types of faults and fault features, and the information content of a single signal is limited, which can not meet the diagnosis requirements. For this reason, a fault diagnosis model based on the attention mechanism convolutional neural network (SE-CNN) is proposed. Firstly, the SE attention mechanism module is introduced to fuse the vibration acceleration features in different directions. Second, the convolutional neural network is combined for further feature extraction and fault identification. Finally, experimental validation is carried out by the public dataset of AUV and compared with other state-of-the-art methods. The results show that the SE-CNN model has a good diagnostic effect on the fault data of AUVs and can successfully detect different fault types with high accuracy and stability. © 2023 IEEE.

关键词： attention mechanisms autonomous underwater vehicles deep learning intelligent fault diagnosis

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Master-slave game-based optimal operation of microgrids containing customer-owned photovoltaics 8

Master-slave game-based optimal operation of microgrids cont...

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8th International Conference on Energy system, Electricity, and Power, ESEP 2023

作者： Liu, Xinyue Ji, Yuehui Shi, Shaobo Liu, Junjie Gao, Qiang Xu, Zhao Tianjin Key Laboratory of Complex System Control Theory and Application TJ 300384 China School of Electrical Engineering and Automation Tianjin University of Technology TJ 300384 China

ISBN: (数字)9781510679801

ISBN: (纸本)9781510679795

Due to the dispersed and disorderly feathers of distributed photovoltaic (PV) power generation on the user side, the difficulties of the low-carbon transformation of the power grid and sustainable development of large-scale distributed PV power generation is increased. Based on a master-slave game, this article proposes an optimizing scheme for the operation of microgrids, where the users built PV and shared energy storages. Firstly, this paper introduces the operational framework of the system and the functions of micro-network operator (MNO) and load aggregator (LA). The optimization operation models for MNO and LA are modelled in the framework of Stackelberg master-slave game. The game relationship between MNO and LA is analyzed. Yalmip tool with the CPLEX solver are introduced to solve the differential evolution algorithm. Final, the effectiveness of the proposed method is verified in numerical examples. © 2024 SPIE.

关键词： Energy storage

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Research on operation optimization of rural hybrid renewable energy systems based on Nash bargaining 8

Research on operation optimization of rural hybrid renewable...

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8th International Conference on Energy system, Electricity, and Power, ESEP 2023

作者： Zhuang, Xin Gao, Qiang Xu, Zhao Chen, Jiaqi Zhang, Zhengyang Liu, Junjie College of Electrical Engineering and Automation Tianjin University of Technology Tianjin300384 China Tianjin Key Laboratory of Complex System Control Theory and Applications Tianjin300384 China

ISBN: (数字)9781510679801

ISBN: (纸本)9781510679795

The Rural Hybrid Renewable Energy system (RHRES) holds great significance as a prospective energy system development. Ensuring its economic viability and low-carbon operation is crucial in order to fully harness its potential. Consequently, this research paper presents a multi-party cooperation operation model for RHRES, based on the Nash bargaining theory. To effectively differentiate between the various parties involved in carbon trading and guarantee the system's low-carbon operation, a ladder-type multi-party carbon trading model is formulated. The proposed RHRES cooperation operation model, grounded in Nash bargaining, is approached in two steps: determining the transaction volume between parties and resolving the transaction price through the concept of equivalent transformation. To address concerns regarding party privacy, the alternating direction multiplier method is employed for solution. The numerical findings demonstrate that the suggested cooperation operation model substantially reduces RHRES operating costs and carbon emissions, while enhancing the utilization of wind and solar power. Consequently, this model holds significant practical and promotional value. © 2024 SPIE.

关键词： Solar energy

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