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16th International Conference on Photonics and Imaging in Biology and Medicine, PIBM 2023

作者： Zhang, Yameng Chen, Yang Qi, Xinping Li, Weitao Department of Computer Engineering Nanjing Institute of Technology No.1 Hongjing Avenue Nanjing Jiangsu211167 China College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing Jiangsu211106 China

ISBN: (纸本)9781510667341

Photoacoustic microscopy (PAM) is a novel medical imaging technique developed rapidly in recent years with the advantages of high resolution, high contrast, and no ionizing radiation. In this study, we developed a one-shot functional photoacoustic microscopy (OS-fPAM) system for assessing traumatic brain injury (TBI). The OS-fPAM system only employed a single 532 nm nanosecond pulse laser with Raman delay optical path control, which can produce both 532 nm and 553 nm laser beams. The dual-wavelength beams were then coupled via a dichroic mirror and focused on the surface of the experimental sample. Then, the photoacoustic signal was collected and amplified in the ultrasonic signal acquisition module, and the blood oxygen saturation (sO2) image was calculated by two wavelength photoacoustic images. The feasibility of the OS-fPAM system was performed through the calibration experiments with different ratios of red ink and blue ink. Meanwhile, the OS-fPAM system can monitor microvascular structures and sO2 in the TBI model on mice for 96 hours in vivo with a spatial resolution of ∼40 μm, demonstrating the potential to evaluate rehabilitation and cerebral vascular damage. © 2023 SPIE.

关键词： Photoacoustic microscopy

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Liquid refractive index measurement method based on orthogonal polarization laser self-mixing interference

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Optoelectronics Letters 2025年

作者： ZHAO Yan WANG Haipeng DU Shengzhi TONG Jigang KANG Jun School of Electrical Engineering and Automation， Tianjin University of Technology Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems Department of Electrical Engineering， Tshwane University of Technology

In this paper， a novel method combining orthogonal polarization laser self-mixing interference is proposed. The method utilizes a rotating cuvette to measure the refractive index of liquids at different concentrations. The cuvette is filled with the liquid to be measured and rotated by a certain angle. The change in the number of interference fringes， caused by comparing an empty cuvette with a liquid-filled cuvette， is used to calculate the refractive index of the liquid. A four-fold logic subdivision algorithm is then used to improve measurement resolution. The experimental results show that for pure water and different NaCl and glucose solutions concentrations， the average relative errors are 0.47%， 0.59%， and 2.17%， respectively， with the maximum relative error within ±2.54%. The standard deviation of all solutions is less than 3.4%.

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PS-DFL: A Parallel Sequential Decentralized Federated Learning Framework to Facilitate DGA Interpretation Related to Transformer Fault Diagnosis

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IEEE Transactions on Dielectrics and Electrical Insulation 2025年

作者： Dong, Chen Wu, Qi Li, Shengwei Guo, Fanghong Wang, Lei Zhang, Wen-An Zhejiang University of Technology Department of Automation China Zhejiang University College of Control Science and Engineering Hangzhou China

Recently, data-driven methods have demonstrated excellent performance in diagnosing faults in power transformers. However, concerns about data privacy and computational resources arise due to the requirement of collecting and training large numbers of labeled samples in a centralized manner. Although centralized federated learning (CFL) provides optimal solutions, the dependence on central servers engenders issues associated with reliability and increased communication expenditures. Meanwhile, existing decentralized federated learning (DFL) frameworks are not specifically designed for industrial applications with unbalanced data and high communication costs. To tackle these issues, this paper proposes a novel parallel sequential DFL (PS-DFL) framework that exchanges information directly between client pairs, thus overcoming the limitation caused by the central server in CFL. In particular, by combining parallel training with sequential communication techniques, the impact of unbalanced data on model performance is mitigated, as well as communication costs are reduced. The experiment conducted on IEC TC 10 and other public datasets demonstrate that, despite handling tasks involving unbalanced data, the diagnostic accuracy is maintained at 97.09%. Furthermore, the communication overhead is significantly lower than that of existing methods, thereby proving its feasibility for industrial deployment within the power grid. © 1994-2012 IEEE.

关键词： Data privacy

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AeroGTO: An Efficient Graph-Transformer Operator for Learning Large-Scale Aerodynamics of 3D Vehicle Geometries 39

AeroGTO: An Efficient Graph-Transformer Operator for Learnin...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Liu, Pengwei Wang, Pengkai Ren, Xingyu Yuan, Hangjie Hao, Zhongkai Xu, Chao Cai, Shengze Ni, Dong College of Control Science and Engineering Zhejiang University Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China Department of Computer Science and Technology Tsinghua University Beijing China State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China

ISBN: (纸本)157735897X

Obtaining high-precision aerodynamics in the automotive industry relies on large-scale simulations with computational fluid dynamics, which are generally time-consuming and computationally expensive. Recent advances in operator learning for partial differential equations offer promising improvements in terms of efficiency. However, capturing intricate physical correlations from extensive and varying geometries while balancing large-scale discretization and computational costs remains a significant challenge. To address these issues, we propose AeroGTO, an efficient graph-transformer operator designed specifically for learning large-scale aerodynamics in engineering applications. AeroGTO combines local feature extraction through message passing and global correlation capturing via projection-inspired attention, employing a frequency-enhanced graph neural network augmented with k-nearest neighbors to handle three-dimensional (3D) irregular geometries. Moreover, the transformer architecture adeptly manages multi-level dependencies with only linear complexity concerning the number of mesh points, enabling fast inference of the model. Given a car's 3D mesh, AeroGTO accurately predicts surface pressure and estimates drag. In comparisons with five advanced models, AeroGTO is extensively tested on two industry-standard benchmarks, Ahmed-Body and DrivAerNet, achieving a 7.36% improvement in surface pressure prediction and a 10.71% boost in drag coefficient estimation, with fewer FLOPs and only 1% of the parameters used by the prior leading method. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Graph neural networks

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An Efficient Privacy-Preserving Asynchronous Federated Approach for Intelligent Decision Making in Equipment Maintenance 7

An Efficient Privacy-Preserving Asynchronous Federated Appro...

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7th IEEE International Conference on Big Data and Artificial Intelligence, BDAI 2024

作者： Dou, Kangkang He, Fan Shang, Fanshu Li, Xingyi Dong, Yuhao Liu, Jiayu Jiangsu Automation Research Institute Nanjing University of Aeronautics and Astronautics Department of Information Technology Research Lianyungang China Harbin Engineering University College of Computer Science and Technology Harbin China

ISBN: (纸本)9798350352009

Federated learning enables collaborative training of a high-quality global model across multiple participants without sharing raw local data. Its advantages, such as decentralization, data isolation, and high computational performance, have made it a popular research direction in various fields. However, federated learning faces privacy risks during model parameter transmission. Additionally, reducing communication overhead and computational resource consumption while ensuring data privacy protection remains a critical challenge. To address these issues, this paper proposes an efficient privacy-preserving asynchronous federated learning method tailored for equipment maintenance scenarios. Leveraging asynchronous federated learning techniques, this method enables efficient collaborative training among multiple participants. It also employs homomorphic encryption to protect local data information. By utilizing time-series models, we construct a predictive model for equipment spare parts consumption, providing valuable insights for intelligent maintenance decision-making. Experimental results demonstrate that our proposed method has faster convergence. More importantly, the root mean square error decreases by at least 0.004, the mean square error decreases by at least 0.024, and the relative error decreases by at least 0.008 compared to the baseline. © 2024 IEEE.

关键词： Federated learning

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Inverse design and accurate optimization of layered structured seeding mechanism for sugarcane planters

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International Journal of Agricultural and Biological engineering 2023年第6期16卷 107-115页

作者： Jiaodi Liu Qingli Chen Hongzhen Xu Yong Hua Xiaoman Wu Key Laboratory of Advanced Manufacturing and Automation Technology(Guilin University of Technology) Education Department of Guangxi Zhuang Autonomous RegionGuilin 541006GuangxiChina College of Mechanical and Control Engineering Guilin University of TechnologyGuilin 541004GuangxiChina

Existing sugarcane planters are difficult to have ideal seeding trajectory and motion attitude at the same time, and the speed is difficult to meet the requirements at the critical stage, resulting in poor stability, which ultimately makes it impossible to ensure that the sugarcane seeding is carried out in accordance with the agronomic requirements to ensure that the cane buds are oriented toward the wall of the seeding trench. Aiming at the second-order non-circular planetary gear system pendulum seeding mechanism of the planter, the paper innovatively adopts the combination of inverse design and multi-objective layered accurate optimization to solve the problems of attitude, speed and trajectory that do not meet the requirements of fixed-attitude seeding that still exists in the process of sugarcane seeding. The second-order non-circular planetary gear system is simplified into a three-rod two-degree-of-freedom mechanism, and the radius of the pitch curve of each non-circular gear is solved inversely by actively preplanning the static trajectory of the cane seed motion and analyzing the law of motion of the rod assembly. Determining the range of cane seed attitude angles in different motion phases as the first layer optimization objective, and fine-tuning the position of static trajectory key type value points to achieve the first layer optimization. Based on the non-circular gear pitch curve obtained from optimization, the interpolation points are marked on each non-circular gear pitch curve of the second-order non-circular planetary gear system, and based on the parameter optimization method of human-computer interaction, the radius values corresponding to the interpolation points of the non-circular gear pitch curve are fine-tuned to optimize the pitch curves, so as to satisfy the speed requirements of the cane species in each stage, and at the same time to make the convexity of non-circular gears in line with the principle of gear mesh, so as to complete the

关键词： sugarcane planting seeding mechanism inverse design layered structure optimization

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A Novel Hybrid Transformer-Based Framework for Solar Irradiance Forecasting under Incomplete Data Scenarios

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IEEE Transactions on Industrial Informatics 2024年第6期20卷 8605-8615页

作者： Zhang, Hanjin Li, Bin Su, Shun-Feng Yang, Wankou Xie, Liping Southeast University School of Economics and Management Nanjing210096 China National Taiwan University of Science and Technology Department of Electrical Engineering Taipei106 Taiwan Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Nanjing210096 China Southeast University Shenzhen Research Institute Shenzhen518063 China

Accurate prediction of solar irradiance is crucial for the effective utilization of solar energy. However, in real-world scenarios, complex irradiance patterns and prevalent incomplete data pose challenges to precise forecasting, resulting in additional uncertainties and instability. To address these issues, this study proposes a novel irradiance forecasting model that integrates a Mask-Transformer data imputation module and a prediction module centered around the typical patterns representation mechanism. The Mask-Transformer leverages a mask modeling mechanism to model the context of missing data, facilitating accurate estimation of missing values and reducing noise and uncertainty in the input data. The typical patterns representation mechanism comprises a series decomposition module and a feature fusion module, providing the module with the capability to mitigate nonlinearity and nonstationarity in solar irradiance data. This enhancement leads to improved short-term forecasting performance while maintaining long-term forecasting capabilities. Experimental results on two datasets demonstrate that the proposed model exhibits sufficient robustness and accuracy, making it effective in scenarios with incomplete data. © 2005-2012 IEEE.

关键词： Forecasting

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Comparative Analysis of SVM and CNN Classifiers for EEG Signal Classification in Response to Different Auditory Stimuli

Comparative Analysis of SVM and CNN Classifiers for EEG Sign...

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2024 International Conference on Telecommunications and Intelligent Systems, ICTIS 2024

作者： Tibermacine, Ahmed Akrour, Djouher Khamar, Rofeida Tibermacine, Imad Eddine Rabehi, Abdelaziz University of Biskra Department of Computer Science Biskra Algeria Sapienza University of Rome Department of Computer Automation and Management Engineering Rome Italy Zian Achour University of Djelfa Laboratory of Telecommunications and Smart Systems Faculty of Sciences and Technology Djelfa17000 Algeria

ISBN: (纸本)9798331527396

This study aims to classify brainwave patterns using electroencephalogram (EEG) signals in response to various auditory stimuli, specifically Quran recitation, participants' favorite music, and Interstellar's main theme. By analyzing the unique EEG responses elicited by these stimuli, the study explores cognitive and emotional processing differences. Two prominent classifiers, Support Vector Machine (SVM) and Convolutional Neural Networks (CNN), were utilized. SVM, combined with Power Spectral Density (PSD) features, achieved the highest accuracy of 95% when distinguishing Quran recitation from rest. CNN, particularly with Kernel Density Estimation (KDE) features, exhibited strong performance in distinguishing between different auditory stimuli, with an average accuracy exceeding 90%. These results highlight the critical role of feature extraction techniques, such as PSD and KDE, in improving classification performance and deepening our understanding of EEG-based emotional and cognitive responses to auditory stimuli. © 2024 IEEE.

关键词： Convolutional neural networks

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Bifurcation control and circuit simulation of a fractional-order synthetic gene networks 41

Bifurcation control and circuit simulation of a fractional-o...

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第41届中国控制会议

作者： Feng Liu Feiyue Zhuo Shujiang Sun Zhi-Hong Guan Hua O Wang School of Automation China University of Geosciences Engineering Research Center of Ministry of education for intelligent technology of earth exploration Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Artificial Intelligence and Automation Department of Mechanical Engineering Boston University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This paper presents a ring synthetic gene network model described by fractional differential equations,studies the conditions of Hopf bifurcation in the model,and discusses the limit cycle oscillation phenomenon due to the existence of bifurcation,which causes the system to become unstable,and then We propose a method to control the bifurcation behavior in the system,so that the system can be stable over a large *** addition,the dynamic behavior of the fractional-order model is simulated by triggering a genetic oscillator(a single inhibitory gene) composed of a transistor composed of linear and nonlinear electronic *** circuit simulation results well verify the theoretical analysis conclusion,and the circuit simulation model can well demonstrate the biological characteristics of gene regulatory networks.

关键词： Synthetic gene networks fractional-order bifurcation control circuit simulation

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Condition-Based Maintenance for Performance Degradation Under Nonperiodic Unreliable Inspections

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第4期4卷 709-721页

作者： Li, Yang Shi, Yan Zhang, Zhiyao Lu, Ningyun Wang, Xiuli Zio, Enrico Nanjing University of Aeronautics and Astronautics College of Automation Engineering Key Laboratory of Internet of Things and Control Technologies Nanjing211106 China Politechnico di Milano Energy Department Milan20156 Italy Northwestern Polytechnical University School of Aeronautics Xi'an710072 China Chongqing University State Key Laboratory of Mechanical Transmission Chongqing400044 China Nanjing University of Aeronautics and Astronautics College of Automation Engineering Nanjing211106 China Politecnico di Milano Energy Department Milan20156 Italy Aramis Srl Milano20121 Italy Kyung Hee University Department of Nuclear Engineering College of Engineering Seoul02447 Korea Republic of

With the development of monitoring technology, increasing attention has been paid to condition-based maintenance (CBM). Also, if condition information for maintenance can be obtained with fewer inspections, the cost of the entire maintenance process could be reduced. However, in application, the monitoring equipment cannot maintain always a reliable operating state, for example, due to various uncertain factors, such as sensor errors, component tolerances, and environment disturbances, the inspections from the sensors are often unreliable. Motivated by these simple observations, we propose a nonperiodic CBM policy under unreliable inspections. The time interval of the nonperiodic inspections is obtained via an inspection scheduling function. The unknown parameters of the component degradation process are updated by GD, while simultaneously the maintenance decision variables are adjusted. A catastrophe strategy-based particle swarm optimization is used to set the optimal decision variables by minimizing the long-run cost rate. Application to laser degradation data illustrates the effectiveness of the proposed method. © 2020 IEEE.

关键词： Condition based maintenance

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