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25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Liu, Xiao Chen, Luefeng Wu, Min Cao, Weihua Lu, Chengda Pedrycz, Witold School of Automation China University of Geosciences Wuhan China School of Automation The Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems The Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Istinye University Department of Computer Engineering Sariyer Istanbul34396 Turkey

Changes in coal seam hardness cause fluctuations in the feed resistance at the drill bit during the drilling process, leading to unstable feeding speed. This paper proposes a robust dynamic output feedback controller to suppress disturbances caused by the variations in coal seam hardness in the feed system. Firstly, an unknown parameter measuring coal seam hardness is introduced, and an uncertain model of the feeding system is established based on the finite element model of the drill string. By designing weighted functions based on industrial field requirements and constructing a generalized plant, the controller achieves loop shaping, reducing the low-frequency impact of coal seam hardness variations on the feed system and suppressing the systems resonance peak. Simulation results demonstrate that the controller effectively suppresses parameter variations and external disturbances caused by changes in coal seam hardness, achieving stable control of the drilling speed. © 2024 IEEE.

关键词： Robust control

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Pre-Trained Language Model - Based Real-Time Formation Lithology Identification Method for Drilling Process

Pre-Trained Language Model - Based Real-Time Formation Litho...

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Chinese automation Congress (CAC)

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

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Geological exploration is essential for economic and social progress, with lithology identification being a key technology that improves exploration precision and resource development efficiency. This paper presents a real-time identification method of formation lithology in drilling process based on pre-training language model, which is divided into three stages. In this first stage, the drilling data undergoes rigorous cleaning, feature selection, and normalization to eliminate noise and anomalies. In the second stage, key model inputs such as weight on bit (WOB), rate of penetration (ROP), rotation speed (RPM), torque, and pump volume (P) are identified through detailed drilling mechanism analysis. In the last stage, a real-time identification method of formation lithology in drilling process based on pre-training language model is proposed, which realizes high-precision real-time identification in drilling process. The effectiveness of the proposed method is proved by comparison with common methods.

关键词： Drilling Solid modeling Accuracy Torque Geology Transforms Reliability engineering Data models Real-time systems Robustness

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Distributed Power Source Planning Based on Improved Particle Swarm Optimization

Distributed Power Source Planning Based on Improved Particle...

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Power and Electrical Technology Conference (APET), Asia

作者： Jiahui Wang Zhili Tan School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (数字)9798350367690

ISBN: (纸本)9798350367706

The multitude of advantages offered by distributed power generation units are recognized as crucial factors in enhancing the security of distribution networks. Ensuring the optimal size and placement of distributed generation (DG) units not only enhances system security but also increases profitability. Haphazard placement of DG units may lead to adverse impacts on the power system, resulting in increased power losses and diminished voltage distribution. Therefore, to ensure the optimal size and placement of DG units, this paper proposes a method to optimize DG scheduling and operation, thereby improving voltage distribution, reducing power losses, and enhancing the overall reliability of the entire grid. To reduce power loss (PL) and improve voltage distribution (VP), an adaptive chaotic particle swarm optimization (ACPSO) algorithm is employed to determine the optimal locations and sizes of DG units. By comparing this algorithm with traditional approaches, its efficacy in enhancing performance can be discerned.

关键词： Profitability Voltage Distributed power generation Power system reliability Planning Security Reliability Particle swarm optimization Optimization Testing

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Semantic Coherence Guided Multiview Similarity for Image Classification with Varied Supervision

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 3072-3082页

作者： Zhang, Chunjie Cao, Ping Bai, Huihui Zheng, Xiaolong Beijing Jiaotong University Institute of Information Science Beijing100044 China Beijing Jiaotong University Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China University of Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems The State of Key Laboratory of Management and Control for Complex System Institute of Automation Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100190 China

Pairwise similarity has been widely used for image classification by propagating the class information from labeled images to unlabeled images and predicting the classes of unlabeled images accordingly. Although widely used, pairwise similarity based classification methods have two drawbacks. On one hand, visual-semantic similarity consistency does not always hold due to semantic gap. On the other hand, the reliability of pairwise similarity is also influenced by the number and underlying distribution of labeled images. A well-designed classification model cannot be easily generalized to few-shot classification tasks. To solve these two problems, in this article, we propose a semantic coherence guided multiview similarity (SCG-MV) for image classification method with varied supervision levels. We measure the semantic coherence between image pairs for classifier learning. This is achieved by first conducting line search along the line segment between each image pair, and then measure the semantics of each interval. The similarity of one image pair can then be obtained by aggregating the semantic coherence of all the intervals. In this way, we can model image similarities more finely and discriminatively. To make use of multiview information, we also use the semantic coherence guided image similarities to learn multiview classifiers. The proposed semantic coherence guided method can be combined with other similarity based methods for image classification with various levels of supervision. We conduct both few-shot and fully supervised classification experiments on four datasets with the results well prove the discriminative power and generalization ability of the proposed method. © 2020 IEEE.

关键词： Semantics

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Fault-Tolerant Model Predictive Load Frequency control for Power systems with Electrolytic Aluminum Load Under New Event-triggered Mechanism

Fault-Tolerant Model Predictive Load Frequency Control for P...

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Fully Actuated System Theory and Applications (CFASTA), Conference on

作者： Shisen Zhou Ziqian Wang Xiongbo Wan School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (数字)9798350373691

ISBN: (纸本)9798350373707

In this paper, the problem of event-based fault-tolerant model predictive load frequency control is investigated for the power system where the electrolytic aluminum load participates in frequency modulation and actuator faults. A dynamic event-triggered mechanism containing an internal dynamic variable (IDV) and an adjustable variable is designed to reduce the data transmission burden. The fault-tolerant model predictive control (FTMPC) problem is expressed as a "min-max" optimization problem (OP). According to the Lyapunov-like function that depends on the IDV, an auxiliary OP with constrained linear matrix inequalities is constructed. By solving the auxiliary OP, the FTMPC controller gains that ensure the input-to-state stability of the closed-loop system can be obtained. The effectiveness of the proposed algorithm is verified through a simulation example.

关键词： Fault tolerance Actuators Aluminum Fault tolerant systems Power system dynamics Power system stability Predictive models

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Fuel Ratio Prediction Model Based on Attention Mechanism

Fuel Ratio Prediction Model Based on Attention Mechanism

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Asian control Conference

作者： Yihan Li Weihua Cao Wenkai Hu Yan Yuan School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

One of the key concerns in today's steel business is how to effectively save energy and reduce emissions, since the concepts of green environmental preservation, energy conservation, and emission reduction are deeply ingrained in people's hearts. Fuel ratio is a key parameter in blast furnace steelmaking. Identifying the factors that influence the fuel ratio and being able to model and predict it can greatly help in guiding the stable operation of the blast furnace, reducing fuel ratio, saving energy, and cutting emissions. In this paper, the fuel ratio mechanism of blast furnace is analyzed, and the parameters affecting the fuel ratio are initially selected. Subsequently, the parameters with the highest correlation to the fuel ratio are identified using the maximum correlation coefficient method. A CNN-LSTM-Attention-based fuel ratio prediction model was developed, and its accuracy and efficacy were demonstrated through comparison with three other models.

关键词： Attention mechanisms Accuracy Energy conservation Steel industry Predictive models Blast furnaces Data models

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Feature-Prescribed Iterative Learning control of Waggle Dance Movement for Social Motor Coordination in Joint Actions

arXiv

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arXiv 2024年

作者： Guo, Bowen Zhai, Chao School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-exploration Ministry of Education Wuhan430074 China

Extensive experiments suggest that motor coordination among human participants may contribute to social affinity and emotional attachment, which has great potential in the clinical treatment of social disorders or schizophrenia. Mirror game provides an effective experimental paradigm for studying social motor coordination. Nevertheless, the lack of movement richness prevents the emergence of high-level coordination in the existing one-dimensional experiments. To tackle this problem, this work develops a two-dimensional experimental paradigm of mirror game by playing waggle dance between two participants. In particular, an online control architecture of customized virtual player is created to coordinate with human player. Therein, an iterative learning control algorithm is proposed by integrating position tracking and behavior imitation with prescribed kinematic feature. Moreover, convergence analysis of control algorithm is conducted to guarantee the online performance of virtual player. Finally, the proposed control strategy is validated by matching experimental data and compared with other control methods using a set of performance indexes. Copyright © 2024, The Authors. All rights reserved.

关键词： Mirrors

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An Error Correcting Output Code Algorithm Based on Hierarchical Clustering for Lithology Identification

An Error Correcting Output Code Algorithm Based on Hierarchi...

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Chinese control Conference (CCC)

作者： Ao Li Xi Chen School of Rail Transit Hunan University of Technology Zhuzhou China 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)9789887581581

ISBN: (纸本)9798350366907

Lithology identification serves as a crucial foundation for the precise exploration and safe exploitation of geological resources. However, challenges arise from the inherent imbalance and overlap present in logging data, making the accurate identification of lithology a complex task. Addressing these challenges effectively, this paper introduces a novel algorithm, the Error Correcting Output Code algorithm based on Hierarchical Clustering(HC-ECOC). The proposed algorithm initially formulates a coding matrix using distance and overlap measures between class clusters. Subsequently, it extends the length of the coding matrix by considering both the accuracy of the integrated classifier and the diversity among base classifiers. To evaluate the efficacy of the proposed method, comparisons were conducted with alternative algorithms using UCI datasets and an authentic lithology dataset. The results unequivocally demonstrate the superior performance of our algorithm.

关键词： Codes Accuracy Geology Clustering algorithms Binary trees Encoding Classification algorithms

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Integrated Prediction of Unfavorable Geology in Main Tunnel Considering the Geological Conditions of Parallel Tunnel

Integrated Prediction of Unfavorable Geology in Main Tunnel ...

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Chinese automation Congress (CAC)

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

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Currently, the advanced geological prediction for the main tunnel from the parallel tunnel often employs parallel deduction of detection conclusions, which faces issues such as a lack of diverse geological exploration information, a lack of analysis of geological structures and poor accuracy. In this paper, an integrated prediction method for unfavorable geology in main tunnel considering the geological conditions of parallel tunnel is proposed, which consists of three stages. In the first stage, based on the forecast mileage range and tunnel excavation conditions, the process is divided into four specific cases. In the second stage, it deduces the geology of the main tunnel along the orientation of rock strata occurrence or joint to parallel tunnel and selects the results of the rock strata occurrence or joint deduction according to the distance of the deduction. In the last stage, it deduces the geological conditions of parallel tunnel face and the integration of geophysical and drilling information for predictive conclusions based on the four specific cases. The proposed method offers higher prediction accuracy for rock mass integrity and surrounding rock grades compared to parallel deduction method.

关键词： Drilling Accuracy automation Prediction methods Rocks Excavation Faces

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Simplified outlier detection for improving the robustness of a fuzzy model

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Science China(Information Sciences) 2020年第4期63卷 218-220页

作者： Yali JIN Weihua CAO Min WU Yan YUAN School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China University of Geosciences

Dear editor,Fuzzy models have been widely employed in various fields, such as complex industrial process modeling, classification, function approximation, and time series prediction. Extracting fuzzy rules is very important as they determine the capabilities of the fuzzy model. In recent years, data-based methods such as fuzzy clustering, genetic algorithm, and neural networks [1–3], have been pro-

关键词： Simplified outlier detection for improving the robustness of a fuzzy model Gaussian

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