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Subspace Identification for Closed-Loop Systems With Unknown Deterministic Disturbances

IEEE/CAA Journal of Automatica Sinica 2023年第12期10卷 2248-2257页

作者： Kuan Li Hao Luo Yuchen Jiang Dejia Tang Hongyan Yang IEEE Shanghai Aerospace Control Technology Institute Shanghai 201109China the Control and Simulation Center School of AstronauticsHarbin Institute of TechnologyHarbin 150006China the Faculty of Information Technology Engineering Research Center of Digital CommunityMinistry of EducationBeijing University of TechnologyBeijing 100124China

This paper presents a subspace identification method for closed-loop systems with unknown deterministic *** deal with the unknown deterministic disturbances,two strategies are implemented to construct the row space that can be used to approximately represent the unknown deterministic disturbances using the trigonometric functions or Bernstein polynomials depending on whether the disturbance frequencies are *** closed-loop identification,CCF-N4SID is extended to the case with unknown deterministic disturbances using the oblique *** addition,a proper Bernstein polynomial order can be determined using the Akaike information criterion(AIC)or the Bayesian information criterion(BIC).Numerical simulation results demonstrate the effectiveness of the proposed identification method for both periodic and aperiodic deterministic disturbances.

关键词： Bernstein polynomial closed-loop system subspace identification unknown deterministic disturbances

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Adaptive Optimal Output Regulation of Interconnected Singularly Perturbed Systems With Application to Power Systems

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IEEE/CAA Journal of Automatica Sinica 2024年第3期11卷 595-607页

作者： Jianguo Zhao Chunyu Yang Weinan Gao Linna Zhou Xiaomin Liu the Engineering Research Center of Intelligent Control for Underground Space Ministry of EducationChina University of Mining and TechnologyXuzhou 221116 the School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116 IEEE the State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819China

This article studies the adaptive optimal output regulation problem for a class of interconnected singularly perturbed systems(SPSs) with unknown dynamics based on reinforcement learning(RL).Taking into account the slow and fast characteristics among system states,the interconnected SPS is decomposed into the slow time-scale dynamics and the fast timescale dynamics through singular perturbation *** the fast time-scale dynamics with interconnections,we devise a decentralized optimal control strategy by selecting appropriate weight matrices in the cost *** the slow time-scale dynamics with unknown system parameters,an off-policy RL algorithm with convergence guarantee is given to learn the optimal control strategy in terms of measurement *** combining the slow and fast controllers,we establish the composite decentralized adaptive optimal output regulator,and rigorously analyze the stability and optimality of the closed-loop *** proposed decomposition design not only bypasses the numerical stiffness but also alleviates the *** efficacy of the proposed methodology is validated by a load-frequency control application of a two-area power system.

关键词： Adaptive optimal control decentralized control output regulation reinforcement learning(RL) singularly perturbed systems(SPSs)

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Structure-performance relationship between denitration performance and catalytic interface morphologies of MnCeO_(x)/P84 catalytic filters

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Journal of Rare Earths 2025年第3期43卷 516-525,I0004页

作者： Bo Yang Yujie Lei Maosen Ni Liuying Wang Qiong Huang Mindong Chen Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution ControlSchool of Environmental Science and EngineeringNanjing University of Information Science&TechnologyNanjing210044China

MnCeO_(x)/P84 catalytic filters with spherical,flower-like,cubic and rod-like catalytic interfaces were synthesized respectively,and their catalytic activities in the NH_(3)-SCR reaction were *** MnCeO_(x)/P84 catalytic filter with spherical catalytic interfaces(recorded as S-MnCeO_(x)/P84)exhibits the best catalytic denitration *** NO_(x)removal efficiency of S-MnCeO_(x)/P84 reaches the highest value of 98.6%at 160℃when the catalyst loading is 100 g/m^(2).At the same time,S-MnCeO_(x)/P84 exhibits good SO_(2)resistance and stability,achieving a NO_(x)removal rate of 83%at 190℃with 30 ppm SO_(2).The characterization results illustrate that the MnCeO_x active component in S-MnCeO_(x)/P84 is present in weak crystalline states,tightly wrapped around the surface of the filter fiber,and uniformly dispersed,and the mesopore is the main pore structure of the S-MnCeO_(x)/P84,which can provide a channel for the catalytic reaction to *** the same time,transmission electron microscopy(TEM)characterization shows that y-MnO_(2)is the main form of MnO_(2)in the S-MnCeO_(x)/*** analysis of H_(2)temperature programmed reduction(H_(2)-TPR).NH_(3)temperature programmed desorption(NH_(3)-TPD)and in-situ diffuse reflectance infrared spectra(DRIFTS)show that S-MnCeO_(x)/P84 has good redox ability at 100-200℃and has abundant Lewis acid sites and Bronsteds acid sites,which provides an important guarantee for its superior low-temperature NH_(3)-SCR denitration performance.

关键词： Catalytic filter Nitrogen oxides(NO_x) Denitration Catalytic interface Rare earths

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MAO-DBN based membrane fouling prediction

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 10923-10939页

作者： Wang, Zhiwen Zhao, Yibin Shi, Yaoke Ling, Guobi College of Electrical and Information Engineering Lanzhou University of Technology Lanzhou China Key Laboratory of Gansu Advanced Control for Industrial Processes Lanzhou University of Technology Lanzhou China National Demonstration Center for Experimental Electrical and Control Engineering Education Lanzhou University of Technology Lanzhou China

Due to the complexity of the factors influencing membrane fouling in membrane bioreactors (MBR), it is difficult to accurately predict membrane fouling. This paper proposes a multi-strategy of integration aquila optimizer deep belief network (MAO-DBN) based membrane fouling prediction method. The method is developed to improve the accuracy and efficiency of membrane fouling prediction. Firstly, partial least squares (PLS) are used to reduce the dimensionality of many membrane fouling factors to improve the algorithm's generalization ability. Secondly, considering the drawbacks of deep belief network (DBN) such as long training time and easy overfitting, piecewise mapping is introduced in aquila optimizer (AO) to improve the uniformity of population distribution, while adaptive weighting is used to improve the convergence speed and prevent falling into local optimum. Finally, the prediction of membrane fouling is carried out by utilizing membrane fouling data as the research object. The experimental results show that the method proposed in this paper can achieve accurate prediction of membrane fluxes, with an 88.45% reduction in RMSE and 87.53% reduction in MAE compared with the DBN model before improvement. The experimental results show that the model proposed in this paper achieves a prediction accuracy of 98.61%, both higher than other comparative models, which can provide a theoretical basis for membrane fouling prediction in the practical operation of membrane water treatment. © 2024-IOS Press. All rights reserved.

关键词： Bioreactors

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Bearing fault diagnosis method for unbalance data based on Gramian angular field

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Journal of Intelligent and Fuzzy Systems 2024年第1-2期47卷 45-54页

作者： Yu, Ping Li, Rong-Bin Cao, Jie Qin, Jun-Hua College of Electrical and Information Engineering Lanzhou University of Technology Lanzhou China Key Laboratory of Gansu Advanced Control for Industrial Processes Lanzhou University of Technology Lanzhou China National Demonstration Center for Experimental Electrical and Control Engineering Education Lanzhou University of Technology Lanzhou China

In the application of deep learning-based fault diagnosis, more often than not, the network model could perform better with a balanced dataset input, whereby the number of fault data is equivalent to that of normal data. However, in the context of real-world applications, the number of fault data is generally insufficient compared to the normal data. In this study, a new approach for fault diagnosis in unbalanced data sets is proposed using the Gramian angular field (GAF) method. Firstly, the GAF method is employed to convert one-dimensional data into two-dimensional data, which enhances the feature extraction process. Secondly, to balance the sample distribution, fault data is generated using Generative Adversarial Networks (GANs). Finally, the Residual neural network (ResNet) with an attention mechanism is utilized to improve the accuracy of fault diagnosis. The proposed method is experimentally validated using open-source bearing datasets that are published by Case Western Reserve University and the University of Ottawa. The experimental results show that the proposed method has greatly improved fault diagnosis performance in cases of data distribution imbalance, surpassing that of the compared methods. © 2024 - IOS Press. All rights reserved.

关键词： Generative adversarial networks

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Efficient Real-Time Recognition Model of Plant Diseases for Low-Power Consumption Platform

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第5期5卷 2040-2054页

作者： Deng, Songyun Wu, Wanneng Zou, Kunlin Qin, Hai Cheng, Lekai Liang, Qiaokang Hunan University College of Electrical and Information Engineering Changsha410082 China Changsha University of Science and Technology School of Electrical and Information Engineering Changsha410114 China Hunan University National Engineering Research Center for Robot Vision Perception and Control College of Electrical and Information Engineering Changsha410082 China

Recognition and early warning of plant diseases is one of the keys to agricultural disaster prevention and mitigation. Deep learning-based image recognition methods give us a new idea for plant disease identification. Due to the harsh conditions in agricultural environment, recent research has focused on exploring ways to lightweight the recognition model for deployment on low-power devices. In this article, we propose an efficient and feature-guided real-time plant disease recognition model with a multiclassifier architecture, specifically designed for low-power devices. By comparing with other advanced methods, our model reaches the state of the art in the combined metrics of recognition accuracy, the number of parameters, and inference speed. First, we propose AMI-NanoNet based on Roofline theory to significantly reduce the number of parameters and computational complexity. This model can achieve 99.8343% accuracy on PlantVillage by using a feature-guided curriculum learning with stepwise training strategy. Moreover, we design another training strategy suitable for lightweight ensemble models. Based on this strategy, our model only needs to integrate the classifiers at the end of the network to achieve 99.8708% identification accuracy, and it hardly increases the number of operations and parameters of the network. Extensive evaluations on this dataset demonstrate the effectiveness of our ensemble learning method. Furthermore, we tested our proposed methods on another dataset from other domains to validate its applicability to different scenarios. Overall, our research provides a basis for rapid and intelligent identification of plant diseases. © 2023 IEEE.

关键词： Real time systems

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An improved 3D U-Net-based deep learning system for brain tumor segmentation using multi-modal MRI

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Multimedia Tools and Applications 2024年第37期83卷 85027-85046页

作者： Ali, Saqib Khurram, Rooha Rehman, Khalil ur Yasin, Anaa Shaukat, Zeeshan Sakhawat, Zareen Mujtaba, Ghulam Faculty of Information Technology Beijing University of Technology Beijing100124 China College of Mechatronics and Control Engineering Shenzhen University Shenzhen China Department of Radiology Advance Diagnostic Center Islamabad Pakistan

Cancer is a lethal illness that requires an initial stage prognosis to enhance patient survival rate. Accurate brain tumor and their sub-structure segmentation through Magnetic Resonance Images (MRIs) is a tough endeavor. Owing to the heterogeneous tumor areas, automatically segmenting brain tumors has proved to be a critical task even for neural network-based algorithms, some tumor regions remain unidentified due to their small size and the variation in area occupancy among tumor sub-classes. Current progress in the area of neural networks has been employed to enhance the segmentation performance. This study designed an intelligent 3D U-Net encoder-decoder-based system for automatic detection and brain tumor sub-structure segmentation. Our proposed 3D model constitutes neural units (the basic building blocks) followed by transition layer blocks and skip connections. BraTS 2018 and private local datasets are used to evaluate the proposed model which segments the Whole Tumor (WT), Tumor Core (TC), and the Enhancing Tumor (ET). The training accuracy, validation accuracy, dice score, sensitivities, and specificities of WT, CT, and ET zones are computed. The experimental results demonstrate that dice scores are 0.913, 0.874, and 0.801 for the BraTS 2018 dataset. The developed models performance was further evaluated by utilizing the dataset from a local hospital containing 71 subjects. The dice scores of 0.891, 0.834, and 0.776 are achieved by the proposed model on the private dataset. The practicability of the proposed model was assessed by the comparative studies of our model with existing literature. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Magnetic resonance

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Energy-Efficient Data Collection over Underwater MI-Assisted Acoustic Cooperative MIMO WSNs

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China Communications 2023年第11期20卷 96-110页

作者： Qingyan Ren Yanjing Sun Song Li Bin Wang Zhengda Yu School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China Department of Information Technology and Electrical Engineering ETH ZurichZurich 8092Switzerland School of Communication and Information Engineering Xi’an University of Science and TechnologyXi’an 710054China Xuzhou Engineering Research Center of Intelligent Industry Safety and Emergency Collaboration Xuzhou 221000China

Underwater magnetic induction(MI)-assisted acoustic cooperative multiple-input-multipleoutput(MIMO) has been recently proposed as a promising technique for underwater wireless sensor networks(UWSNs).For the more,the energy utilization of energy-constrained sensor nodes is one of the key issues in UWSNs,and it relates to the network *** this paper,we present an energy-efficient data collection for underwater MI-assisted acoustic cooperative MIMO wireless sensor networks(WSNs),including the formation of cooperative MIMO and relay link ***,the cooperative MIMO is formed by considering its expected transmission range and the energy balance of nodes with ***,from the perspective of the node’s energy consumption,the expected cooperative MIMO size and the selection of master node(MN) are ***,to improve the coverage of the networks and prolong the network lifetime,relay links are established by relay selection algorithm that using matching ***,the simulation results show that the proposed data collection improves its efficiency,reduces the energy consumption of the master node,improves the networks’ coverage,and extends the network lifetime.

关键词： cooperative MIMO cooperative MIMO size data collection heterogeneous UWSNs relay link establishment

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Graph Dual Adversarial Network for Hyperspectral Image Classification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第4期4卷 922-932页

作者： Cheng, Yuhu Chen, Yang Kong, Yi Philip Chen, C.L. Wang, Xuesong China University of Mining and Technology Engineering Research Center of Intelligent Control for Underground Space Ministry of EducationSchool of Information and Control Engineering Xuzhou221116 China South China University of Technology School of Computer Science and Engineering Guangzhou510006 China

An end-to-end unsupervised domain adaptation method for hyperspectral image (HSI) classification based on a graph dual adversarial network is proposed in this article. First, in order to extract the domain-invariant features of the source and target domains, the rich spectral information and spatial position of HSI are used to construct a spectral-spatial nearest neighbor graph, which is input into the graph convolutional network. Then, a prototype adversarial strategy is proposed, which uses the labeled data of the source domain to reliably calculate the feature prototypes of different classes. Through the prototype adversarial strategy, the distances between different prototypes are appropriately extended, so that the clusters of different classes are far away from their respective decision boundaries, and the discriminability of features are also enhanced. The dual adversarial strategy is composed of the prototype adversarial strategy and the domain adversarial strategy. It is worth noting that the dual adversarial strategy does not require feature extractor and discriminator to work in turn, which can be implemented through a gradient reversal layer. Finally, on the basis of adapting the overall features of both the domains via the domain adversarial strategy, the source- and target-domain features are further adapted by minimizing the correlation alignment loss of each class of samples. Experimental results on two real HSI datasets of Botswana and Kennedy Space center show the effectiveness of our proposed method. © 2020 IEEE.

关键词： Spectroscopy

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Effectiveness of Precursor Emission Reductions for the control of Summertime Ozone and PM2.5 in the Beijing–Tianjin–Hebei Region under Different Meteorological Conditions

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Advances in Atmospheric Sciences 2025年第4期42卷 653-669页

作者： Jing QIAN Hong LIAO Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment TechnologySchool of Environmental Science and EngineeringNanjing University of Information Science&TechnologyNanjing 210044JiangsuChina

We used observed concentrations of air pollutants,reanalyzed meteorological parameters,and results from the Goddard Earth Observing System Chemical Transport Model to examine the relationships between concentrations of maximum daily 8-h average ozone(MDA8 O_(3)),PM_(2.5)(particulate matter with diameter of 2.5μm or less),and PM_(2.5)components and 2-m temperature(T2)or relative humidity(RH),as well as the effectiveness of precursor emission reductions on the control of O_(3) and PM_(2.5) in Beijing–Tianjin–Hebei(BTH)under different summertime temperature and humidity *** observed(simulated)MDA8 O_(3) and PM_(2.5) concentrations increased as T2 went up,with linear trends of 4.8(3.2)ppb℃^(−1) and 1.9(1.5)μg m^(−3)℃^(−1),*** results showed that the decreases in MDA8 O_(3) from precursor emission reductions were more sensitive to T2 than to *** a larger proportion of volatile organic compound(VOC)emissions at higher T2 was more effective for the control of summertime O_(3) in *** the control of summertime PM_(2.5) in BTH,reducing nitrogen oxides(NOx)combined with a small proportion of VOCs was the best *** magnitude of reduction in PM_(2.5) from reducing precursor emissions was more sensitive to RH than to T2,with the best efficiency at high *** from this study are helpful for formulating effective policies to tackle O_(3) and PM_(2.5) pollution in BTH.

关键词： ozone PM_(2.5) emission reductions meteorological conditions

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