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第41届中国控制会议

作者： Yujie Cao Sheng Du Chengda Lu Min Wu Jinhua She School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Engineering Tokyo University of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Safe and efficient deep-sea drilling is inseparable from accurate and timely recognition of the ocean and geological *** refers to ocean dynamics,meteorology,hydrology,ecology,chemistry,ocean sound,light physical characteristics,ocean geology and geography.A complex ocean environment is one of the challenges facing ocean engineering at *** is difficult to establish an independent ocean or geological model for controller of the deep-sea drilling *** the face of complex and changeable ocean environment,modeling and analysis of wave environmental factors alone is not suitable for the control of deep-sea drilling *** paper analyzes the influence of various marine environmental factors on the offshore drilling platform and studies the internal relationship between each ***,the models of ocean breeze,wave and ocean current were designed to realize the simulation of ocean environment and parameterized description of ocean environment for deep-sea drilling *** has laid the foundation for the design of a deep-sea drilling control system.

关键词： Marine environment model Offshore drilling process Multi-model coupled

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Multi-stage Dynamic Post-disaster Recovery Strategy for Distribution Networks Considering Integrated Energy and Transportation Networks

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CSEE Journal of Power and Energy systems 2021年第2期7卷 408-420页

作者： Xinyi Jiang Jian Chen Ming Chen Zhen Wei Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong UniversityJinan 250061China State Grid Qingdao Power Supply Company Qingdao 266002China

To improve the resilience of distribution networks(DNs),a multi-stage dynamic recovery strategy is proposed in this paper,which is designed for post-disaster DN considering an integrated energy system(IES)and transportation network(TN).First,the emergency response quickly increases the output of gas turbines(GTs)in the natural gas network(NGN),and responsively reconfigures the DN in microgrids,to maximize the amount of loads to be *** single-commodity flow model is adopted to construct spanning tree ***,in the second stage of energy storage recovery,mobile energy storage systems(MESSs)are deployed to cover the shortages of power demands,i.e.,to further restore the loads after evaluating the load recovery *** Floyd algorithm based dynamic traffic assignment(DTA)is selected to obtain the optimal path of the *** the third stage,the outputs of various post-disaster recovery measures are adjusted to achieve an economically optimized *** studies demonstrate the effectiveness of the proposed dynamic post-disaster recovery strategy.

关键词： Dynamic post-disaster recovery dynamic transportation network integrated energy system resilience distribution

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Analysis and State Feedback control Bifurcation of Fractional-Order Cyclic Gene Regulatory Networks with Time Delay 41

Analysis and State Feedback Control Bifurcation of Fractiona...

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第41届中国控制会议

作者： Feng Liu Shujiang Sun Juan Zhao Feiyue Zhuo 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 Department of Mechanical Engineering Boston University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In this paper,a fractional-order cyclic gene regulatory network model with time delay is ***,the total time delay of the system is selected as the bifurcation parameter,and the condition for the existence of Hopf bifurcation is derived by analyzing its characteristic *** is found that the time delay affects the stability of the system,and the order affects the position of the bifurcation *** the time delay is greater than the critical time delay,the system loses ***,a state feedback controller is designed for the unstable *** is proved that the control method has good control effect for the system instability caused by Hopf ***,the correctness of the theoretical derivation is verified by simulation.

关键词： Fractional-order cyclic gene regulatory network Hopf bifurcation feedback control time delay

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Spatially Dense Multi-Target Track Segment Association Algorithm

Spatially Dense Multi-Target Track Segment Association Algor...

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第42届中国控制会议

作者： Yan Yao Liping Yan Yuanqing Xia Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology

In the field of radar data processing, track interruption seriously affects target tracking, track fusion, and other *** existing track segment association algorithms have low correlation accuracy in dense distributed or long-time interruption situations. To this purpose, a dense multi-target track segment association(DMTTSA) algorithm is proposed. Firstly, two identical networks based on the multi-head probability sparse(ProbSparse) self-attention are used to capture the long-term dependencies of the tracks. Then, the bidirectional quadruplet hard sample loss(BiQuaHard loss) is constructed to make the tracks belonging to the same targets closer and the tracks belonging to the different targets farther. Finally, DMTTSA takes the closest track pairs in the feature space as the associated tracks and divides the unassociated tracks into the birth and dead tracks in chronological order. Some comparative experiments are carried out to show the anti-noise performance of the DMTTSA, as well as the effectiveness of solving the problem of dense multi-target track interruption.

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A Test Suite and An Optimizer for Dietary Nutrition Optimization Problem: From Constrained Many-Objective Perspective

A Test Suite and An Optimizer for Dietary Nutrition Optimiza...

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2023 IEEE Congress on Evolutionary Computation, CEC 2023

作者： Ti, Yaqi Li, Changhe Yang, Shengxiang Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China School of Computer Science and Informatics De Montfort University Leicester United Kingdom

ISBN: (纸本)9798350314588

With increasing people who suffer from diet-related diseases, providing suggestions for personal daily nutrient-dense intake is highly expected. However, current dietary nutrition models are less precise, and dietary nutrition optimizers usually fail to give satisfactory solutions. Therefore, we construct a constrained many-objective nutrition model with more precise nutrient assessments and a scalable constrained many-objective benchmark set. This test suite has great flexibility in evaluating algorithms' performance on high dimensional search and objective spaces with some feasible region fragments. We also propose a kd-tree based dynamic constrained many-objective evolutionary algorithm to search for customized food combinations according to personal daily consumption and intake preference. Experiments show that our algorithm has better diversity maintenance ability in high dimension space. © 2023 IEEE.

关键词： Nutrition

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A Soft-Sensor Modeling Method Based on Gated Stacked Isomorphic Autoencoder 41

A Soft-Sensor Modeling Method Based on Gated Stacked Isomorp...

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第41届中国控制会议

作者： Jianjun Yang Xiaolu Xu Xiaohui Yin Wenxing Liu Yi Yuan Jian Peng School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Huaxin Cement Co. Ltd

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Soft sensing mainly studies the real-time prediction of some key performance indicators or quality variables in the actual production process,which has the role of guiding production in the actual production *** stacked autoencoder is a multi-layer autoencoder *** input variables will be encoded and decoded through each layer of autoencoders,and the obtained hidden features will be retained as the input of the next *** this way,high-level data features can be successfully learned from the input layer to the intermediate *** isomorphic autoencoder reconstruct an identical data input layer,and the reconstruction is performed by minimizing the error of the decoded data from the original input ***,for a soft measurement model,some data information may also reduce the accuracy or generalization of the *** order to overcome the above shortcomings,this paper proposes a gated stacked isomorphic autoencoder,which evaluates the contribution of each hidden layer feature through the gating unit,and then integrates the information of different hidden layers to complete the prediction and estimation of related main ***,the effectiveness and feasibility of the method are verified in practical industrial cases.

关键词： Deep learning soft sensor stacked autoencoder gating unit

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Real-Time Semantic Segmentation of Maritime Navigation Scene Based on Improved Fast-SCNN

Real-Time Semantic Segmentation of Maritime Navigation Scene...

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International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Chen, Li-Jia Zou, Jia-Min Zhou, Yang Hao, Guo-Zhu Wang, Yang School of Shipping Wuhan University of Technology Wuhan430070 China Hubei Provincial Key Laboratory of Inland Waterway Shipping Technology Wuhan University of Technology Wuhan430070 China Intelligent Transportation System Research Center Wuhan University of Technology Wuhan430070 China National Key Laboratory of Waterway Traffic Control Wuhan University of Technology Wuhan430070 China

ISBN: (纸本)9789819639762

Waterway navigation scene segmentation is crucial for safe and efficient autonomous vessel operation. To address the issues of large parameter sizes in current high-resolution semantic segmentation models, the difficulty in deployment on embedded devices, and inaccurate segmentation in navigation scenarios, this study proposes an improved Fast-SCNN-based real-time semantic segmentation network for waterway scenes. The proposed network is enhanced by incorporating a dual attention mechanism in the context branch, which allows feature extraction along spatial and channel dimensions to be guided more effectively, thereby improving accuracy. A pyramid pooling module based on decomposed dilated convolution is designed to enhance the model's multi-scale feature representation capabilities while ensuring real-time performance is maintained. Additionally, a loss function combining Focal Loss and Dice Loss is introduced to enhance focus on hard-to-classify samples and improve prediction accuracy for target regions, thereby significantly enhancing the model's ability to recognize minority-class targets. Experimental results have demonstrated that the improved model achieves a segmentation accuracy (mIoU) of 80.6% on the constructed On_Water_Dataset, with an inference speed of 90.1 frames per second, Segmentation accuracy increased by 16.9% relative to the original model. Deployment and experimental verification on embedded devices have confirmed that real-time performance is maintained while effectively segmenting complex backgrounds and multi-scale objects in waterway navigation scenarios. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Semantic Segmentation

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Transformer based Online Continuous Multi-Target Tracking with State Regression 12

Transformer based Online Continuous Multi-Target Tracking wi...

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12th IEEE International Conference on control, Automation and Information Sciences, ICCAIS 2023

作者： Wei, Xinwei Chen, Linxiu Zhang, Chenyu Lin, Yiru Zhang, Linao Liu, Xiaokai Jiang, Jixiang Yi, Wei University of Electronic Science and Technology of China School of Information and Communication Engineering China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory China University of Electronic Science and Technology of China Research Institute of Electronic Science and Technology China

ISBN: (纸本)9798350328783

Multi-target Tracking (MTT) is the process of processing received measurements to maintain estimates of the current status of multiple targets, with important applications to autonomous driving, aerial reconnaissance, underwater operations, and others. In the model-based setting, Bayesian filtering can provide the theoretical optimal estimate in a single target scenario. However, in complex situations, uncertain factors such as changes in the number of targets will cause the amount of calculation to increase exponentially, resulting in a decline in tracking accuracy. To solve that problem, model-free methods based on deep-learning provide an attractive alternative, especially the state-of-the-art architecture Transformer based encoder-decoder prediction model, which outperforms the Bayesian filters in the single frame prediction tasks. However, when switching to continuous tracking, these algorithms need to be trained separately frame by frame to adapt to the new tasks. Still, there is no correlation between their predictions from different frames, which prevents them from fully utilizing all the measurements. In this paper, we propose an end-to-end Transformer based MTT method with state autoregression, which allows the model to have the capability of online continuous tracking and make total use of the entire trajectory. The results show that the proposed model is a great extension from single-frame prediction to online continuous tracking. © 2023 IEEE.

关键词： Forecasting

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Parametric Study on Performance of Parallel Asymmetric Nonlinear Energy Sinks

Parametric Study on Performance of Parallel Asymmetric Nonli...

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International Conference on Applied Nonlinear Dynamics, Vibration, and control, ICANDVC 2023

作者： Li, Huiyang Chen, Jianen Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin300384 China

ISBN: (纸本)9789819705535

The complexification-averaging method is utilized to study the amplitude-frequency response of a system connected to a parallel asymmetric nonlinear energy sink (NES) under harmonic excitation, and the Runge-Kutta method is used to analyze the change law of the frequency band of the higher branches of response and the strongly modulated responses with the assistance of the complexification-averaging method. Under shock loading, the effect of parallel asymmetric NES parameters on its vibration absorption efficiency is investigated. The results demonstrate that the frequency band of higher branches of response is widened by reducing the stiffness ratio, mass ratio, and damping ratio of the two NES, emphasizing the fact that the frequency band of the strongly modulated response can be widened or narrowed with the reduction of these parameters. Moreover, reducing the mass ratio and damping ratio of the two NESs decreases the vibration absorption efficiency when the system is subjected to a shock load. However, the vibration absorption efficiency increases by reducing the stiffness ratio. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Energy dissipation

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A Novel Rolling Bearing Vibration Impulsive Signals Detection Approach Based on Dictionary Learning

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IEEE/CAA Journal of Automatica Sinica 2021年第6期8卷 1188-1198页

作者： Chuan Sun Hongpeng Yin Yanxia Li Yi Chai School of Big Data&Software Engineering Chongqing UniversityChongqing 401331China Key Laboratory of Complex System Safety and Control Ministry of EducationChongqing UniversityChongqing 400044China

The localized faults of rolling bearings can be diagnosed by its vibration impulsive ***,it is always a challenge to extract the impulsive feature under background noise and non-stationary *** paper investigates impulsive signals detection of a single-point defect rolling bearing and presents a novel data-driven detection approach based on dictionary *** overcome the effects harmonic and noise components,we propose an autoregressive-minimum entropy deconvolution model to separate harmonic and deconvolve the effect of the transmission *** address the shortcomings of conventional sparse representation under the changeable operation environment,we propose an approach that combines K-clustering with singular value decomposition(K-SVD)and split-Bregman to extract impulsive components *** experiments on synthetic signals and real run-to-failure signals,the excellent performance for different impulsive signals detection verifies the effectiveness and robustness of the proposed ***,a comparison with the state-of-the-art methods is illustrated,which shows that the proposed approach can provide more accurate detected impulsive signals.

关键词： Dictionary learning impulsive signals detection Kclustering with singular value decomposition(K-SVD) minimum entropy deconvolution rolling bearing signal processing

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