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第41届中国控制会议

作者： Chaoling Zhang Fan Wei Xiongbo Wan 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

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In this paper,we focus on dynamic event-triggered model predictive control(MPC) problem for interval type-2 Takagi-Sugeno(IT2 T-S) fuzzy systems with hard constraints on the state.A dynamic event-triggered mechanism(DETM) with two adjusting variables is devised to reduce data transmission *** MPC problem is formulated as a "min-max" problem over the infinite *** purpose is to find a series of desired sub-controllers in terms of MPC such that the underlying IT2 T-S fuzzy system is asymptotically *** taking the T-S fuzzy nonlinearities and the DETM into consideration,we give the design method of the controller through an online auxiliary optimization problem with several constraint conditions.A simulation example is used to demonstrate the effectiveness and the validity of the proposed methods.

关键词： Dynamic event-triggered mechanism adjusting variables model predictive control interval type-2 T-S fuzzy systems

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Research on Energy Storage control Strategy Considering Economy 41

Research on Energy Storage Control Strategy Considering Econ...

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第41届中国控制会议

作者： Zihan Zhang Zhijian Fang Jie Cai Wenhe 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

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Energy storage system is generally used to ensure the stability and reliability of *** the system generally contains multiple energy storage units,and due to the process,material and other aspects of the limitation,the initial state of charge(SOC) of each energy storage unit cannot be guaranteed to be completely equal,which leads to the shortening of the life of energy storage unit,and affect the economic benefits of energy storage system ***,this paper analyzes the influence conditions between energy storage units according to the calculated formula of energy storage unit *** value of charge between energy storage systems is analyzed through the active power output of the system,and then the difference is obtained by comparing the value of charge of each energy storage unit with the average value of charge of each *** the iterative algorithm is used to update the state of each node,and the balance factor is introduced to gradually reduce the difference on the basis of droop control to optimize and control the output of these energy storage nodes to achieve SOC *** the economic benefits such as maintenance cost of energy storage system are ***,a working model of microgrid is established and the feasibility and economy of this method are verified by simulation.

关键词： Economy Micro grid Energy storage system Droop control SOC balance

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Prediction Method of Blast Furnace Fuel Ratio Based on Spectral Clustering and Extreme Learning Machine Model 41

Prediction Method of Blast Furnace Fuel Ratio Based on Spect...

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第41届中国控制会议

作者： Jingjie Liang Yan Yuan Shuqiao Wang Zhuofu Zhang 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

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

The blast furnace fuel ratio,as the main index reflecting the blast furnace fuel consumption,plays an important role in guiding blast furnace *** order to solve the problem of insufficient stability of the existing fuel ratio prediction model under the condition of process parameters fluctuation,a prediction method of blast furnace fuel ratio based on spectral clustering and extreme learning machine model is ***,on the basis of analyzing the influencing factors of fuel ratio,the classification results after clustering are visualized with t-SNE dimension reduction downscaling and spectral clustering method;Secondly,for the classification results after clustering,the extreme learning machine is used to establish the sub-models,and the PSO algorithm was used to optimize the input weights of hidden layer and the bias of hidden layer *** results show that the proposed method has high prediction accuracy.

关键词： Fuel Ratio Spectral Clustering Extreme Learning Machine(ELM) t-SNE Particle Swarm optimization(PSO)

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An online drilling rate of penetration(ROP) optimization method 41

An online drilling rate of penetration(ROP) optimization met...

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第41届中国控制会议

作者： Tuo Wang Chao Gan Weihua Cao School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex System Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Drilling rate of penetration(ROP) optimization is crucial due to its vital role in increasing the drilling efficiency and decreasing the non-productive *** existing methods seldom pay attention to updating model parameter during drilling,which is actually important for ROP optimization under dynamical and uncertain downhole *** this paper,an online drilling ROP optimization method is proposed via three stages(drilling data pre-processing,online ROP modelling and online optimal operational parameters optimization).In the first stage,drilling data is pre-processed by resampling and Savitzky-Golay *** this,support vector regression and moving-window strategy are introduced together to establish an online ROP model in the next ***,particle swarm optimization algorithm is applied in the online ROP optimization framework to obtain the optimal operational parameters in the last *** results indicated the effectiveness of the proposed method.

关键词： Rate of penetration Drilling optimization Online optimization Moving-window strategy

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Multi-Time Scale Analysis of The Influence of Blast Furnace Operations on Permeability Index 41

Multi-Time Scale Analysis of The Influence of Blast Furnace ...

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第41届中国控制会议

作者： Weijun Li Jianqi An Xin Chen Qingyi Wang 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

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In the smelting process of a blast furnace(BF),BF operations can regulate the permeability index(PI),but different operations affect the PI on different time *** at the problem that BF operations have multi-time-scale effects on the PI,this paper uses a multi-time scale method to analyze the influence of each operation on PI at different time ***,the influence of BF operations on the PI is analyzed through the ironmaking mechanism,and three mechanism chains of BF operations affecting the PI were *** this basis,the time scale of the mechanism chains is ***,a crosscorrelation analysis is used to calculate the time-delay relationship between each operation and the ***,the influence of different operation parameters on the PI is verified by real-world production data,and the multi-time scale characteristics of the influence of BF operations on the PI are proved.

关键词： Blast Furnace Multi-Time Scale Permeability Index Cross-Correlation Analysis

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Unbalanced few-shot classification based on ME-MAML framework

Unbalanced few-shot classification based on ME-MAML framewor...

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第43届中国控制会议

作者： Zherun Liao Zheng Lv Qiao Sun Jun Zhao Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Electric Operations Control Center State Grid Dalian Power Supply Company

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Aiming at the few-shot problem of lack of sufficient samples and uneven samples, which is common in medical image classification tasks, this paper proposes a meta-learning framework ME-MAML(Medical ModelAgnostic Meta-Learning) for few-shot medical image classification tasks. The framework aims to train a set of parameters with good generalization performance for the multi-class medical image classification task by learning the base class of medical images, and complete the generalization for the new class of medical image classification task by learning a small number of new classes of medical images. ME-MAML improves the MAML(ModelAgnostic Meta-Learning) meta-learning framework from the aspects of the feature extraction network, the loss function and the algorithmic framework, which makes the framework more suitable for few-shot medical image classification tasks. The experimental results demonstrate that for few-shot medical image classification task, the framework has better classification performance compared to supervised learning frameworks as well as advanced meta-learning frameworks.

关键词： Few-shot Learning Meta-learning Medical Image Classification

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Time Series Prediction with Heteroscedastic Input Noise and Its Industrial Applications

Time Series Prediction with Heteroscedastic Input Noise and ...

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第34届中国过程控制会议

作者： Wange Li Long Chen Jun Zhao Wei Wang Quanli Liu Xinye Cai The Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology The School of Control Science and Engineering Dalian University of Technology

Industrial time series data,with the characteriscs of strong fluctuation,are often corrupted by high level noise,posing a great challenge to make accurate prediction for industrial *** study proposes a general time series prediction framework with heteroscedastic input noise,where it is assumed that each data point has its Gaussian white noise with independent *** this framework,the heteroscedasticity is formulated by the regularity between the inputs and outputs constructed in an autoregressive manner,and a non-linear function between the noise variance of inputs and outputs is derived to improve the estimation of the predictive *** this framework is general,the Gaussian processes model with heteroscedastic input noise,as a particular specialization,is built for time series *** verify the performance of the proposed method,two synthetic time series,a benchmark dataset and two real-life datasets are employed to conduct the comparative *** experimental results demonstrate that the proposed method exhibits better performance on prediction than other methods.

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Hybrid Deep Learning Model for Arch of Aorta Classification Based on Slime Mould Algorithm

Hybrid Deep Learning Model for Arch of Aorta Classification ...

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IEEE International Conference on Bioinformatics and Computational Biology (ICBCB)

作者： Shi-Hang Cheng Jun-Zhe Cao Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education School of Control Science and Engineering Dalian University of Technology Dalian China

As an important anatomical structure, the mediastinum can develop a variety of lesions. Endoscopic ultrasonography-guided fine needle aspiration is a novel auxiliary diagnostic method for these diseases at present. Since the images presented by endoscopic ultrasonography are not direct views, even experienced doctors may have certain difficulties in identifying the exact location of lesions in clinical diagnosis. Therefore, deep learning can be used to classify endoscopic images to assist doctors in the positioning of endoscopic ultrasound (EUS). In this paper, a hybrid model combining the pre-trained deep learning network-ResNet and the metaheuristic algorithm called Slime Mould Algorithm (SMA) is proposed to identify the structure of the aortic arch by distinguishing the aortic arch from non-aortic arch to assist doctors in positioning rapidly. The hybrid model utilizes ResNet50 for a raw feature extraction, adopts SMA for a further feature selection based on the extracted features, and finally uses the Support Vector Machine classifier to classify the filtered features. The hybrid model takes advantage of the excellent feature extraction ability of the deep network and the good search characteristics in the solution space of the SMA. This model achieves an average accuracy of 92.21% for classification on practical data of 14 patients’ EUS images. Experimental results show that the proposed model outperforms other deep networks and metaheuristic algorithms. It shows that this model has achieved good results in the aortic arch classification problem.

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Integrated metabolomics and artificial intelligence to predict the dietary-derived compound alleviates cognitive impairment by regulating ferroptosis

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Food Science and Human Wellness 2025年

作者： Song Liu Meng-jia Sun Jian-wen Gan Yuan-li Li Huan-huan Qin Xin-ran Ji Hong-xia Chen Rui Liu Guang-nian Zhao Bing-xin Ma Institute of Pharmaceutical Process， Hubei Province Key Laboratory of Occupational Hazard Identification and Control， School of Medicine， Wuhan University of Science and Technology Reproductive Medicine Center， Tongji Hospital， Tongji Medical College， Huazhong University of Science and Technology Department of Obstetrics and Gynecology， Tongji Hospital， Tongji Medical College， Huazhong University of Science and Technology Cancer Biology Research Center (Key Laboratory of the Ministry of Education)， Tongji Hospital， Tongji Medical College， Huazhong University of Science and Technology

Long term high-fat diet (HFD) can damage the central nervous system and lead to cognitive impairment (CI). Compound with anti-CI activity and safety was screened from a dietary-derived compound database based on pathological targets identified by metabolomics integrated with deep learning， and validated by established in vivo and in vitro assays. Ferroptosis was found to be highly associated with HFD-induced brain damage in metabolomic studies. Two deep learning models were trained to screen for ferroptosis related target arachidonate-5-Lipoxygenase (ALOX5) inhibitory activity and safety evaluation， respectively. The trained models screened new potentially active chemicals from approximately 70，000 dietary compounds. Linarin was selected from 143 predicted ALOX5 inhibitors because of its high safety， high oral bioavailability， druglike properties， high blood-brain barrier permeability， and novel chemical structure characteristics. The single-dose acute toxicity study demonstrated the safety of linarin. In vivo and in vitro assays further demonstrated that linarin could alleviate CI by reducing ferroptosis in HFD-induced CI neurons， thereby inhibiting ALOX5 and activating the endogenous antioxidant SLC7A11/GPX4 axis. Overall， linarin can be used as a functional food compound for the treatment of CI. The verification of metabolomics and deep learning screening can assist us in predicting potentially active compounds and revealing their pharmacological mechanisms more efficiently and accurately.

关键词： Deep learning Metabolomics Cognitive impairment Linarin Ferroptosis High-fat diet

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Robustness of gross primary production estimation from long-term reconstructed solar-induced chlorophyll fluorescence varies with greenness on a global scale

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Journal of Applied Remote Sensing 2025年第1期19卷

作者： Fu, Lijiang Xia, Qian Tang, Hao Chen, Junqing Tan, Jinglu Guo, Ya Jiangnan University Ministry of Education Key Laboratory of Advanced Process Control for Light Industry Wuxi China Changshu Institute of Technology School of Electrical Engineering and Automation Changshu China University of Missouri Department of Chemical and Biomedical Engineering ColumbiaMO United States

Long-term reconstructed solar-induced chlorophyll fluorescence (SIF) derived from raw gridded SIF has been used for the estimation of gross primary production (GPP), but the robustness of the spatial relationship may vary from location to location. We examined the often-used linear relationship between GPP and SIF in terms of R2 values for varied locations globally using three GPP datasets (FLUXCOM, VPM, PML) and three long-term reconstructed monthly SIF datasets (CSIF, SIF005, and RTSIF). The results show that the R2 value is a concave function of vegetation greenness level (NDVI) on an annual or seasonal basis. The average R2 is over 0.8 in areas where the annual average NDVI is in the range of 0.4 to 0.6, whereas the R2 is much lower where the annual average NDVI is less than 0.2 or greater than 0.8. Prediction of GPP or SIF by three methods from five major environmental variables revealed greater uncertainties in GPP and/or SIF at low or high greenness levels as an apparent cause of the low R2. The results offer useful insights into how global GPP may be effectively estimated from multi-satellite measured SIF. © 2025 Society of Photo-Optical Instrumentation Engineers (SPIE).

关键词： Optical correlation

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