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Axial Assembled Correspondence Network for Few-Shot Semantic Segmentation

IEEE/CAA Journal of Automatica Sinica 2023年第3期10卷 711-721页

作者： Yu Liu Bin Jiang Jiaming Xu School of Automation Science and Engineering South China University of TechnologyGuangzhou 510640 R&D Center of Precision Electronic Manufacturing Technology Guangzhou Institute of Modern Industrial Technologyand also with the High Performance Motor and Intelligent Control Engineering Technology Research Center of Guangdong ProvinceGuangzhou 511458China School of Automation Science and Engineering South China University of TechnologyGuangzhou 510640China IEEE

Few-shot semantic segmentation aims at training a model that can segment novel classes in a query image with only a few densely annotated support *** remains a challenge because of large intra-class variations between the support and query *** approaches utilize 4D convolutions to mine semantic correspondence between the support and query ***,they still suffer from heavy computation,sparse correspondence,and large *** propose axial assembled correspondence network(AACNet)to alleviate these *** key point of AACNet is the proposed axial assembled 4D kernel,which constructs the basic block for semantic correspondence encoder(SCE).Furthermore,we propose the deblurring equations to provide more robust correspondence for the aforementioned SCE and design a novel fusion module to mix correspondences in a learnable *** on PASCAL-5~i reveal that our AACNet achieves a mean intersection-over-union score of 65.9%for 1-shot segmentation and 70.6%for 5-shot segmentation,surpassing the state-of-the-art method by 5.8%and 5.0%respectively.

关键词： Artificial intelligence computer vision deep convolutional neural network few-shot semantic segmentation

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Event-Triggered Extended Dissipative FTB for T-S Fuzzy Switched Systems With Mismatched Phenomena and Deception Attacks: A Multidomain Framework

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IEEE Transactions on Cybernetics 2024年第9期54卷 4928-4938页

作者： Chen, Haiyang Zong, Guangdeng Su, Shun-Feng Gao, Fangzheng Tiangong University School of Control Science and Engineering Tianjin300387 China Nanjing Institute of Technology School of Automation Jiangsu Collaborative Innovation Center for Smart Distribution Network Nanjing211167 China National Taiwan University of Science and Technology Department of Electrical Engineering Taipei106 Taiwan Nanjing Institute of Technology School of Automation Nanjing211167 China

This article investigates the extended dissipative finite-time boundedness (ED-FTB) problem for fuzzy switched systems under deception attacks. To improve the network resource efficiency, a multidomain probabilistic event-triggered mechanism (MDPETM) is proposed. The mode mismatched phenomenon is modeled based on the switching delay information between the controller mode and the system mode. To extract the true signal generated by the MDPETM, a virtual delay concept is developed. The constraint that the controller and the system must have the same premise variables is removed. Based on the MDPETM, mismatched fuzzy state feedback controllers are first devised which may not share the same modes with the system. Then, by establishing fuzzy basis and controller mode-dependent Lyapunov functionals, sufficient criteria free of nonlinear terms existing in the literature are derived, which ensure the ED-FTB of the closed-loop system under admissible delays and deception attacks. Finally, an application-oriented one-link robotic arm system is utilized to validate the theoretical results. © 2013 IEEE.

关键词： controllers

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Generic and flexible self-correction method for nonlinearity-induced phase error in three-dimensional imaging

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Chinese Optics Letters 2024年第6期22卷 129-135页

作者： Jianhua Wang Peng Xu Yanxi Yang School of Information and Control Engineering Qingdao University of TechnologyQingdao 266520China School of Automation and Information Engineering Xi’an University of TechnologyXi’an 710048China

In three-dimensional imaging employing phase-shifting profilometry(PSP), the nonlinear response of projector and camera makes the fringe gray distribution non-sinusoidal, which further leads to phase error. Although the double 3-step phaseshifting method is simple and effective, it needs to add an additional set of fringe sequences, which reduces the measurement efficiency. To this end, this paper introduces a generic and flexible self-correction method for nonlinearity-induced phase ***, according to the nonlinearity-induced phase error model, we introduce an additional wrapped phase with a phase difference of π/3. The error waveform of the two wrapped phases is opposite but not coincident. Then, we introduce an estimation algorithm for the additional wrapped phase offset. Finally, we fuse the two wrapped phases to correct the phase error. Experiments confirm that the root mean squared error of the proposed method is 64.1% lower than that of the traditional method and 13.3% lower than that of the Hilbert transform method. The proposed method does not require any additional fringes or hardware assistance and can be easily extended to 4-step or 5-step PSP.

关键词： three-dimensional imaging phase-shifting profilometry nonlinear response self-correction Hilbert transform

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A Prediction Optimization Method with Federated Learning and Neural Architecture Search for Distributed Renewable Energy Sources

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Distributed Generation and Alternative Energy Journal 2025年第2期40卷 213-237页

作者： Su, Jun Tang, Chaolong Liu, Zhiquan School of Electrical Engineering and Automation Xiamen University of Technology Fujian Province Xiamen361012 China Xiamen Key Laboratory of Frontier Electric Power Equipment and Intelligent Control Fujian Province Xiamen361012 China

This paper proposes a prediction optimization method with federated learning and neural architecture search for distributed renewable energy sources, which is suitable for intelligent management of multi node distributed energy networks. The federated learning technology is used to enable each energy node to independently train local neural network models without sharing raw data, ensuring data privacy and security, and achieving collaborative optimization among nodes through the aggregation of global model parameters. By combining neural architecture search (NAS) technology, the system can automatically design the optimal neural network architecture and dynamically adapt to the energy demands and environmental changes of different nodes. This method improves the efficiency and robustness of distributed renewable energy systems through real-time scheduling optimization and load forecasting, and is suitable for various renewable energy generation scenarios such as wind power and photovoltaics. Finally, the experiment results have shown that the method proposed in this paper improves the intelligence of energy scheduling and the accuracy of prediction, ensuring data security and the adaptive optimization capability of the system. © 2025 River Publishers.

关键词： Distributed Wind

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Combined Estimation of Vehicle Dynamic State and Inertial Parameter for Electric Vehicles Based on Dual Central Difference Kalman Filter Method

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Chinese Journal of Mechanical engineering 2023年第4期36卷 339-354页

作者： Xianjian Jin Junpeng Yang Liwei Xu Chongfeng Wei Zhaoran Wang Guodong Yin School of Mechatronic Engineering and Automation Shanghai UniversityShanghai 200072China State Key Laboratory of Automotive Simulation and Control Jilin UniversityChangchun 130025China School of Mechanical Engineering Southeast UniversityNanjing 211189China Mechanical and Construction Engineering Northumbria UniversityNewcastle NE18STUK

Distributed drive electric vehicles(DDEVs)possess great advantages in the viewpoint of fuel consumption,environment protection and traffic *** the effects of inertial parameter variation in DDEV control system become much more pronounced due to the drastic reduction of vehicle weights and body size,and inertial parameter has seldom been tackled and systematically *** paper presents a dual central difference Kalman filter(DCDKF)where two Kalman filters run in parallel to simultaneously estimate vehicle different dynamic states and inertial parameters,such as vehicle sideslip angle,vehicle mass,vehicle yaw moment of inertia,the distance from the front axle to centre of *** proposed estimation method only integrates and utilizes real-time measurements of hub torque information and other in-vehicle sensors from standard *** four-wheel nonlinear vehicle dynamics estimation model considering payload variations,Pacejka tire model,wheel and motor dynamics model is developed,the observability of the DCDKF observer is analysed and derived via Lie derivative and differential geometry *** address system nonlinearities in vehicle dynamics estimation,the DCDKF and dual extended Kalman filter(DEKF)are also investigated and *** with various maneuvers are carried out to verify the effectiveness of the proposed method using Matlab/*** results show that the proposed DCDKF method can effectively estimate vehicle dynamic states and inertial parameters despite the existence of payload variations and variable driving *** research provides a boot-strapping procedure which can performs optimal estimation to estimate simultaneously vehicle system state and inertial parameter with high accuracy and real-time ability.

关键词： Distributed drive Electric vehicle State observation Inertial parameter Dual central difference Kalman filter

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Analysis of light propagation characteristic in the aero-optic flow field of cone-headed vehicle with side window

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Optoelectronics Letters 2024年第1期20卷 58-64页

作者： XU Liang ZHOU Liye WANG Luyang ZHAO Shiwei WANG Tao Tianjin Key Laboratory of Complex Control Theory and Application School of Electrical Engineering and AutomationTianjin University of TechnologyTianjin 300384China School of Intelligent Engineering Sun Yat-sen UniversityGuangzhou 510275China Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai) Zhuhai 519000China

Aero-optic imaging deviation research is carried out for infrared-guided vehicle with cone-head side window,with a focus on the propagation characteristics of light in an aero-optic flow *** the light entering the aero-optic flow field from the free-stream should be close to the normal,numerous data indicate that the light is refracted away from the *** paper divides the aero-optic flow field into two parts and uses the gas density distribution in the aero-optic flow field to propose the hypothesis that there are two modes of refraction when light propagates through the flow *** results show that light propagates from the optically denser medium to the optically thinner medium after passing through the shock wave and eventually leads to refraction away from the normal when the light enters the aero-optic flow field.

关键词： flow propagation light

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Pattern Matching of Industrial Alarm Floods Using Word Embedding and Dynamic Time Warping

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 1096-1098页

作者： Wenkai Hu Xiangxiang Zhang Jiandong Wang Guang Yang Yuxin Cai School of Automation China University of GeosciencesWuhan 430074Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex SystemsWuhan 430074 Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of EducationWuhan 430074China College of Electrical Engineering and Automation Shandong University of Science and TechnologyQingdao 266590China

Dear Editor,This letter proposes a new pattern matching method based on word embedding and dynamic time warping(DTW)to identify groups of similar alarm ***,alarm messages are transformed into numeric values that represent alarms and also reflect the relationships between alarm ***,similarities between numerically encoded alarm flood sequences are calculated by DTW and groups of similar floods are identified via *** effectiveness of the proposed method is demonstrated by a case study with alarm&event data obtained from a public industrial simulation model.

关键词： Word Alarm DTW

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Detecting physical laws from data of stochastic dynamical systems perturbed by non-Gaussianα-stable Lévy noise

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Chinese Physics B 2023年第5期32卷 337-342页

作者：陆凌弘志李扬刘先斌 State Key Laboratory of Mechanics and Control for Mechanical Structures College of Aerospace EngineeringNanjing University of Aeronautics and AstronauticsNanjing 210016China School of Automation Nanjing University of Science and TechnologyNanjing 210094China

Massive data from observations,experiments and simulations of dynamical models in scientific and engineering fields make it desirable for data-driven methods to extract basic laws of these *** present a novel method to identify such high dimensional stochastic dynamical systems that are perturbed by a non-Gaussianα-stable Lévy *** explicitly,firstly a machine learning framework to solve the sparse regression problem is established to grasp the drift terms through one of nonlocal Kramers–Moyal *** the jump measure and intensity of the noise are disposed by the relationship with statistical characteristics of the *** examples are then given to demonstrate the *** approach proposes an effective way to understand the complex phenomena of systems under non-Gaussian fluctuations and illuminates some insights into the exploration for further typical dynamical indicators such as the maximum likelihood transition path or mean exit time of these stochastic systems.

关键词： data-driven modelling noise-induced transitions Lévy noise Kramers–Moyal formuas

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Multiobjective Differential Evolution for Higher-Dimensional Multimodal Multiobjective Optimization

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1458-1475页

作者： Jing Liang Hongyu Lin Caitong Yue Ponnuthurai Nagaratnam Suganthan Yaonan Wang School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou 450001 School of Electrical Engineering and Automation Henan Institute of TechnologyXinxiang 453003China School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou 450001China KINDI Center for Computing Research Qatar UniversityDohaQatarand also with the Nanyang Technology UniversitySingapore College of Electrical and Information Engineering and also with the National Engineering Research Center for Robot Visual Perception and Control TechnologyHunan UniversityChangsha 410082China IEEE

In multimodal multiobjective optimization problems(MMOPs),there are several Pareto optimal solutions corre-sponding to the identical objective *** paper proposes a new differential evolution algorithm to solve MMOPs with higher-dimensional decision *** to the increase in the dimensions of decision variables in real-world MMOPs,it is diffi-cult for current multimodal multiobjective optimization evolu-tionary algorithms(MMOEAs)to find multiple Pareto optimal *** proposed algorithm adopts a dual-population framework and an improved environmental selection *** utilizes a convergence archive to help the first population improve the quality of *** improved environmental selection method enables the other population to search the remaining decision space and reserve more Pareto optimal solutions through the information of the first *** combination of these two strategies helps to effectively balance and enhance conver-gence and diversity *** addition,to study the per-formance of the proposed algorithm,a novel set of multimodal multiobjective optimization test functions with extensible decision variables is *** proposed MMOEA is certified to be effective through comparison with six state-of-the-art MMOEAs on the test functions.

关键词： Benchmark functions diversity measure evolution-ary algorithms multimodal multiobjective optimization.

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Fault diagnosis of rolling bearings with noise signal based on modified kernel principal component analysis and DC‐ResNet

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CAAI Transactions on Intelligence Technology 2023年第3期8卷 1014-1028页

作者： Yunji Zhao Menglin Zhou Xiaozhuo Xu Nannan Zhang School of Electrical Engineering and Automation Henan Polytechnic UniversityJiaozuoChina Henan Key Laboratory of Intelligent Detection and Control of Coal Mine Equipment Henan Polytechnic UniversityJiaozuoChina

In view of the influence of aliasing noise on the effectiveness and accuracy of bearing fault diagnosis,a bearing fault diagnosis algorithm based on the spatial decoupling method of modified kernel principal component analysis(MKPCA)and the residual network with deformable convolution(DC‐ResNet)is innovatively ***,the Gaussian noise with different signal‐to‐noise ratios(SNRs)is added to the data to simulate the different degrees of noise in the actual data acquisition *** MKPCA is used to project the fault signal with different SNRs in the kernel space to reduce the data dimension and eliminate some noise ***,the DC‐ResNet model is used to further filter the noise effects and fully extract the fault features through the training of the preprocessed *** proposed algorithm is tested on the Case Western Reserve University(CWRU)and Xi'an Jiaotong University and Changxing Sumyoung Technology Co.,Ltd.(XJTU‐SY)bearing data sets with different SNR *** fault diagnosis accuracy can reach 100%within 30 min,which has better performance than most of the existing *** experimental results show that the algorithm has an excellent effect on accuracy and computation complexity under different noise levels.

关键词： artificial intelligence classification deep learning fault feature extraction

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