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14th International Conference on Quality, Reliability, Risk, Maintenance, and Safety engineering, QR2MSE 2024

作者： Zhou, Ningxu Lyu, Yi Shen, Zaichen Xiao, Xu Fan, Ruhui School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Computer University of Electronic Science and Technology of China Zhongshan Institutes Zhongshan China School of Automation Guangdong University of Technology Guangzhou China

ISBN: (纸本)9781837242672

Aero engines, with their complex structures and intricate gas paths, are a crucial component of the aviation system. Traditional machine learning-based methods for predicting the remaining useful life (RUL) of rolling bearings often struggle to provide adequate feature representation and adaptive extraction due to complex operating conditions and challenges in obtaining degradation data. Given that traditional machine learning methods require substantial amounts of labeled data, they frequently fail to yield satisfactory RUL prediction results. To overcome these limitations, domain adaptation methods have been employed, facilitating the transfer of knowledge from a well-labeled source domain to an unlabeled target domain. Additionally, to augment the available data, a multisource domain transfer approach is proposed, which transfers data from multiple source domains to the target domain intended for prediction. To enhance the effectiveness of this multisource domain transfer method, a dual alignment strategy is introduced for aligning features at both the feature and semantic levels, thereby improving predictive performance. The model incorporates a convolutional neural network (CNN) and long short-term memory (LSTM) as its backbone, leveraging CNN's robust feature extraction capabilities combined with LSTM’s proficiency in processing time-dependent data. Experiments conducted on the CMAPSS dataset, along with comparisons and ablation studies against current popular multisource domain adaptation methods, demonstrate the superiority of the proposed approach. © The Institution of engineering & technology 2024.

关键词： Convolutional neural networks

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A New High-Precision Micro-Accelerometer Based on Optomechanical System

A New High-Precision Micro-Accelerometer Based on Optomechan...

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

作者： Zhang, Senyu Li, Zhe Li, Xinwei Huang, Wenyi Chen, Dingwei Huang, Yongjun School of Information and Communication Engineering Sichuan Provincial Engineering Research Center of Communication Technology for Intelligent IoT University of Electronic Science and Technology of China Chengdu611731 China School of Automation Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9798350312614

In this paper, a new linear accelerometer based on the optomechanical system of silicon photonic crystal cavity is designed. The structure has a large mass of mechanical oscillator, which effectively reduces the thermal noise of the optomechanical system. And the silicon based photonic crystal is used for detection, which has the advantages of high sensitivity and high detection precision. Firstly, the noise which affects the detection accuracy of the optomechanical accelerometer is analyzed and the numerical expression is given. Then the mechanical structure design is described in detail and the mechanical characteristics of the accelerometer sensitive structure are analyzed by finite element method. A photonic crystal with a thickness of 500 nm, high optical Q value and optical resonance frequency in the communication C-band is simulated by FTDT simulation method. This paper introduces a new accelerometer based on optomechanical system with high sensitivity and low noise, which can be widely used in future high-precision inertial measurement, inertial navigation, micro-vibration detection and other technical fields. © 2023 IEEE.

关键词： accelerometer optomechanical system photonic crystal

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Towards in X-Ray Induced Acoustic Imaging for Nasopharyngeal Carcinoma Radiotherapy with a Multidimensional Information Fusion Network 3

Towards in X-Ray Induced Acoustic Imaging for Nasopharyngeal...

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3rd International Conference on Computer Applications technology, CCAT 2024

作者： Jiang, Xin Chen, Zhi-Chao Sun, Zheng Liu, Ming-Zhe Chongqing University of Technology School of Artificial Intelligence Chongqing401135 China Chongqing University Cancer Hospital Department of Radiology Chongqing400030 China Chengdu University of Technology College of Nuclear Technology and Automation Engineering Chengdu610000 China Wenzhou University of Technology School of Data Science and Artificial Intelligence Wenzhou325000 China

ISBN: (纸本)9798331539795

X-ray-induced acoustic computed tomography (XACT) imaging is an emerging noninvasive imaging technique with wide applications in various clinical medicine fields. However, traditional XACT imaging methods often suffer from low imaging quality and increased noise artifacts, particularly notable under sparse sampling conditions. To overcome these challenges, we propose a hybrid supervised reconstruction method based on deep learning. Specifically, we extract and integrate information from XACT thermoacoustic signals and XACT images reconstructed by traditional methods to leverage complementary feature information from both modalities. To adapt to imaging under sparse sampling, we define multiple sparse conditions based on real-world imaging environments and validate them using a real radiotherapy dataset of head and neck cancer patients. The results demonstrate the adaptability of our method to sparse conditions and its capability to enhance XACT imaging quality under such circumstances. Additionally, we validate the feasibility of X-ray thermoacoustic imaging for 3D dose monitoring. © 2024 IEEE.

关键词： Radiotherapy

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Receding Horizon Control for UAV Formation Reconfiguration: An Enhanced Marine Predators Strategy

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IEEE Transactions on automation Science and engineering 2025年 22卷 15904-15915页

作者： Zixiang Zhou Xiaoxue Feng Feng Pan Hang Geng School of Automation Beijing Institute of Technology Beijing China School of Automation Engineering University of Electronic Science and Technology of China Chengdu China

The uncrewed aerial vehicle (UAV) formation reconfiguration problem, due to its various limitations and high nonlinearity, has become a significant challenge in UAV formation. The existing methods mainly convert the problem into a constrained global optimization problem using control parameterization and time discretization (CPTD). This method, however, overlooks possible constraints on global optimization and the fact that solving the optimization problem itself can be difficult and time-consuming. In this paper, we firstly comprehensively considered the constraints in the UAV formation reconfiguration and proposed a problem model closer to real-world conditions. Then, we propose a new heuristic algorithm, the enhanced marine predators algorithm (EMPA) to address the constrained optimization problem, which has stronger optimization capability and greater stability when solving high-dimensional nonlinear problems. Finally, to decrease the computation cost, a more accurate and real-time receding horizon controller is designed based on the proposed EMPA, where the global optimal control problem of UAV formation reconfiguration is decomposed into a series of online local optimization problems. Simulation experiments verify the feasibility and effectiveness of the proposed control method. Note to Practitioners—This paper was motivated by the formation reconfiguration problem of uncrewed aerial vehicles (UAV). Existing approaches convert the problem into a constrained global optimization one using control parameterization and time discretization, which not only overlooks possible constraints on global optimization, but also has great difficulty in finding a feasible solution. This paper comprehensively takes into account such optimization constraints to build a problem model closer to real-world conditions, resulting in an enhanced marine predators algorithm of stronger optimization capability, greater stability as well as lower complexity. In this paper, a new heuristic

关键词： Autonomous aerial vehicles Optimization Optimal control Mathematical models Heuristic algorithms Real-time systems Complexity theory Collision avoidance Transforms Particle swarm optimization

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Landslide Recognition in High Resolution Remote Sensing Images Based on Semantic Segmentation 10

Landslide Recognition in High Resolution Remote Sensing Imag...

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10th International Conference on Intelligent Computing and Wireless Optical Communications, ICWOC 2022

作者： Zhang, Qin Zhang, Jie Sun, Wencheng Qin, ZhangJian Chengdu University of Technology Mechanical and Electrical Engineering Chengdu China State Grid Corporation of China Southwest Division Chengdu China Chengdu University of Technology Nuclear Technology and Automation Engineering Chengdu China

ISBN: (纸本)9781665471534

In order to ensure the stable operation of high voltage transmission network, DeepLab V3+_SDF is proposed based on DeepLab V3+ for rapid and intelligent landslide detection from high resolution remote sensing images. Firstly, the backbone network is replaced by ResNet with squeeze-and-excitation (SE) attention mechanism to enhance the extraction of useful features. Secondly, astrous spatial pyramid pooling (ASPP) is reconstructed based on dense connection to expand the receptive field. More low-level features are then added to the decoder with feature pyramid networks plus (FPNP) to enhance detail recovery. Finally, a mixed loss function is proposed based on the pixel distribution to solve the sample imbalance problem. DeepLabV3+ _SDF is trained with self-made landslide remote sensing dataset. The experimental results show that the mean pixel accuracy(mPA) and mean intersection over union (mIoU) of DeepLab V3+_SDF on the landslide dataset reach 95.38 % and 85.27 %, which are 2.90 % and 7.76 % higher than those of DeepLabV3+. Finally, the trained DeepLab V3+_SDF is applied to Sichuan-Chongqing region in China, and the comparison results with manual interpretation show that the algorithm can be used for rapid identification of landslides in large-scale mountainous areas. © 2022 IEEE.

关键词： Semantic Segmentation

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Adaptive Thermal Management Strategy of Fuel Cell Vehicle Systems with Environmental Adaptability 11

Adaptive Thermal Management Strategy of Fuel Cell Vehicle Sy...

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11th International Conference on Power and Energy Systems engineering, CPESE 2024

作者： Cao, Jishen Shi, Chengyin Deng, Bo Song, Yating Yin, Cong Tang, Hao University of Electronic Science and Technology of China School of Automation Engineering Chengdu China China Automotive Engineering Research Institute Co. Ltd Chongqing China

ISBN: (纸本)9798350377439

Thermal management of proton exchange membrane fuel cells plays a crucial role in the long-term stable operation of fuel cell vehicle systems. Existing thermal management strategies ignore the key role of system component characteristic constraints in reducing the influence of thermal perturbations from the vehicle's exterior environment, making it challenging to achieve the desired control results. In this study, an active adaptive coupled thermal management strategy is proposed. It considers the changing thermal environment and the component characteristics of the thermal management system. The approach combines particle filter estimation, closed-loop model predictive control, and fuzzy control to regulate the critical components of the thermal management system and act in concert to optimize the thermal management of the fuel cell. Compared with the closed-loop model predictive control alone, the strategy proposed in this study reduces the average errors of the stack's coolant inlet and outlet temperatures by 39.9% and 30.5%, respectively. The proposed active thermal management strategy exhibits excellent performance in an external thermal environment with unpredictable disturbances. © 2024 IEEE.

关键词： Predictive control systems

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Neural Adaptive Fixed-Time Consensus Control for Nonlinear Multi-Agent Systems with Prescribed Performance 13

Neural Adaptive Fixed-Time Consensus Control for Nonlinear M...

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13th International Conference on Information Science and technology, ICIST 2023

作者： Yan, Yancheng Li, Tieshan Yang, Hanqing Li, Yaolun Zhang, Yunhao Long, Yue Chen, C.L. Philip School of Automation Engineering University of Electronic Science and Technology of China Chengdu611731 China School of Computer Science and Engineering South China University of Technology Guangzhou510641 China

ISBN: (纸本)9798350313925

This paper proposes a neural adaptive fixed-time consensus control approach for the nonlinear multi-agent system (MAS) with prescribed performance. First, by employing an error transformation, a prescribed performance consensus control strategy is presented, which can steer the consensus errors to the predetermined range in a fixed time. Then, the unknown nonlinear functions are approximated by utilizing neural networks and the adaptive backstepping technique. Meanwhile, a first-order filter is adopted to avoid taking derivatives of the virtual controller. With the proposed strategy, all signals of the MAS remain bounded and the fixed-time leader-following consensus control performance is ensured. Finally, these results are illustrated by simulations. © 2023 IEEE.

关键词： Multi agent systems

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A Dead Time Compensation Method for Three-Level Inverters Based on Current Ripple Prediction 49

A Dead Time Compensation Method for Three-Level Inverters Ba...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Song, Xilu Shen, Zewei Zhou, Dehong Zou, Jianxiao School of Automation Engineering Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350331820

This paper proposes a dead time compensation method based on current ripple prediction (CRP) for three-level inverters. First, the dead time effect in three-level inverters is analyzed, including the influence of dead time freewheeling and switching process caused by parasitic capacitors, and the time delay variation curves of the phase voltage switching edges with the phase current are obtained. Then, the actual phase current is reconstructed using the CRP method and the sampled low-frequency current. Thus, the compensation time of the switching edges can be accurately calculated. Simulation and experimental results are provided to validate the effectiveness of the proposed method in both reducing phase current distortion and improving the common-mode voltage elimination effect. © 2023 IEEE.

关键词： Forecasting

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Research on Regularization Network Optimization Based on Wavelet Function 8th

Research on Regularization Network Optimization Based on Wav...

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8th International Conference on Computing, Control and Industrial engineering, CCIE 2024

作者： Bai, Hongbao Mao, Weiwei Zhang, Zhiguo Shen, Xiangzhong Pang, Teng The Fifth Department Naval Petty Officer Academy Bengbu China School of Automation Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9789819769339

In order to solve overfitting of modeling in noisy circumstance, a normal function with corresponding training algorithm is proposed for wavelet networks based on wavelet sampling theory. Since such an algorithm can use sample distributions and errors respectively to train input and output weights, learning efficiencies of wavelet networks are improved greatly. In addition, the related theorems have also been given to demonstrate that the novel cost function can ensure minimization of the approximation error, the theories and experiments show that this novel cost function can ensure generalizations of wavelet networks. Data management module is responsible for data communication in the test process. It can obtain data from external or instrument drive module and transmit data to signal processing module for processing. When completion of the test, the test results are edited and printed through the affiliated function modules. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cost functions

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A Brain Tumor Segmentation Approach with Adaptive Threshold Optimization Numerical Spiking Neural P Systems

A Brain Tumor Segmentation Approach with Adaptive Threshold ...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Dong, Jianping Zhang, Gexiang Rong, Haina Fortin, Giancarlo Chen, Min School of Automation Chengdu University of Information Technology Chengdu610225 China School of Electrical Engineering Southwest Jiaotong University Chengdu611756 China University of Calabria CS Rende87036 Italy School of Computer Science and Engineering South China University of Technology Guangzhou510640 China Pazhou Laboratory Guangzhou510640 China

ISBN: (纸本)9781665410205

Magnetic resonance imaging (MRI) with the high-resolution in computer-aided diagnostic technology is widely used to provide doctors with diagnostic advice, especially in brain tumor segmentation. In addition, MRI multi-sequence images of brain tumors also provide better image data support for studying brain tumor segmentation. In this paper, an adaptive threshold segmentation numerical optimization spiking neural P system (ATONSNPS or ATONSN P system) is designed to dynamically adjust the threshold quantity. In addition, the ATONSN P system and connectivity algorithm are combined to finish multi-sequence brain tumor segmentation. Experimental results on BraTS2019 show that the multi-sequence brain tumor segmentation approach can achieve more effective segmentation of brain tumor images comparing with several benchmark algorithms. © 2024 IEEE.

关键词： Magnetic resonance imaging

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