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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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Management-Oriented Operating systems: Harnessing the Power of DAOs and Foundation Models

Management-Oriented Operating Systems: Harnessing the Power ...

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IEEE International Conference on systems, Man and Cybernetics

作者： Rui Qin Juanjuan Li Xiaolong Liang Fei-Yue Wang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Faculty of Innovation Engineering Macau University of Science and Technology Macau China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

This paper presents Management-Oriented Operating systems (M2OS) that leverages the power of parallel intelligence theory, Decentralized Autonomous Organizations (DAOs) and foundation models to revolutionize the manner of management in Cyber-Physical-Social systems (CPSS). The parallel architecture on M2OS is proposed, including the parallel interactive actual M2OS and artificial M2OS. Among them, the artificial M2OSs provide digital infrastructures for organizations to operate, collaborate, and make decisions in the virtual space, and conduct computational experiments to evaluate management decisions and predict future states of the actual M2OS. Through parallel execution and closed-loop feedback between the artificial and actual M2OSs, the management and control, experimentation and evaluation, as well as learning and training of the actual M2OS can be realized. Moreover, the functional layers of M2OS, including the infrastructure layer, data layer, scenario layer, modeling layer, decision layer, and application layer, are discussed. These layers work together to support the intelligent, autonomous, collaborative, and adaptive nature of the M2OS, and facilitate data-driven decision-making, optimize business operations, and empower managers with real-time actionable insights. The proposed M2OS paradigm has great potential to transform the management paradigm and opens up new possibilities for intelligent and collaborative decision-making.

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Bounded Extremum Seeking for Single-Variable Static Map with Large Measurement Delay via Time-Delay Approach to Averaging

Bounded Extremum Seeking for Single-Variable Static Map with...

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IEEE Conference on Decision and control

作者： Xuefei Yang Emilia Fridman Bowen Zhao Center for Control Theory and Guidance Technology Harbin Institute of Technology Harbin China National Key Laboratory of Modeling and Simulation for Complex Systems School of Electrical Engineering Tel-Aviv University Israel

In this paper, we present a time-delay approach to gradient-based bounded extremum seeking (ES) with large measurement constant delay, for an unknown single-input static quadratic map. We assume that the extremum point and the Hessian $H$ belong to known intervals, whereas the sign of $H$ is known. We apply a time-delay approach to the bounded ES system and arrive at the neutral type system with a nominal linear delayed system. We present the latter system as a retarded one and employ variation of constants formula for practical stability analysis. Explicit conditions in terms of simple scalar inequalities depending on tuning parameters and delay are established to guarantee the practical stability of the bounded ES control systems. Given any delay and neighborhood of the extremum point and through the solution of the constructed inequalities, we find lower bounds on the dither period that ensures the practical stability.

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Finite-Time Observer Based Integral Terminal SMC for Converter-Driven Motor systems with Mismatched Disturbances

Finite-Time Observer Based Integral Terminal SMC for Convert...

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Chinese Automation Congress (CAC)

作者： Zhongding Zhang Zeyu Guo Zuo Wang Shihua Li School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China Shenzhen Research Institute Southeast University Shenzhen China

Converter-driven motor systems play crucial roles in contemporary industrial applications owning to the advantages of smooth starting and stepless speed regulation. For such fourth-order systems with both immeasurable states and mismatched disturbances, a finite-time control solution is proposed to address the speed tracking problem in this paper. Firstly, a finite-time observer is constructed to precisely estimate the immeasurable states and mismatched disturbances. Then, based on integral terminal sliding mode control (ITSMC), a continuous finite-time controller is designed for the systems to achieve both rapid response and superior speed tracking performance. Furthermore, a rigorous global finite-time stability analysis for the closed-loop system is presented. Finally, simulation results validate the effectiveness of the proposed control method. As compared with existing methods, the composite ITSMC algorithm design demonstrates reliable finite-time speed tracking performance and exceptional capability in disturbance rejection.

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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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MFANet: Mixed Feature Attention Network for Port Lane Detection

MFANet: Mixed Feature Attention Network for Port Lane Detect...

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Chinese control Conference (CCC)

作者： Jinwei Zhang Jinya Su Dewei Yi Jun Yang Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education and the School of Automation Southeast University Nanjing China Department of Computing Science University of Aberdeen Aberdeen U.K.

With the advancement of industrial automation and artificial intelligence technology, unmanned port autonomy has gained increasing attention. Port automatic driving technology is a critical component of unmanned autonomous systems, enabling rubber tire gantry cranes (RTGs) to autonomously navigate by detecting port lanes and implementing control commands. In this study, a deep learning-based method is proposed for detecting port lanes in complex port scenes. The method employs the Inception module and attention mechanism to enhance lane feature extraction, and a structural loss is introduced to explicitly constrain the detection results and incorporate the prior characteristics of port lanes. A novel Mixed Feature Attention Network (MFANet) is proposed to implement the method for port lane detection. Experimental results demonstrate that MFANet effectively improves the accuracy of port lane detection while maintaining high computational efficiency. Furthermore, the performance of MFANet is evaluated in a real-world application scenario.

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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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A Robust Time-Delay Approach to Continuous-Time Extremum Seeking for Multi-Variable Static Map

A Robust Time-Delay Approach to Continuous-Time Extremum See...

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IEEE Conference on Decision and control

作者： Xuefei Yang Emilia Fridman Center for Control Theory and Guidance Technology Harbin Institute of Technology Harbin China National Key Laboratory of Modeling and Simulation for Complex Systems School of Electrical Engineering Tel-Aviv University Israel

In this article, we introduce a time-delay approach to gradient-based extremum seeking (ES) in the continuous domain for n-dimensional (nD) static quadratic maps. As in the recently introduced (for 2D maps in the continuous domain), we transform the system to the time-delay one (neutral type system). This system is $\mathbf{O}(\varepsilon)$ . ‘perturbation of the averaged linear ODE system. We further explicitly present the neutral system as the linear ODE, where $\mathrm{O}(\varepsilon)$ -terms are considered as disturbances with distributed delays of the length of the small parameter $\varepsilon$ . Quantitative (for uncertain map) and qualitative (for unknown map) practical stability analyses are provided by employing a variation of constants formula that greatly simplifies the results compared to the previously used Lyapunov-Krasovskii (L-K) method. The new approach also simplifies the conditions and improves the results. Examples from the literature illustrate the efficiency of the new approach, allowing essentially large uncertainty of the Hessian matrix with bounds on $\varepsilon$ that are not too small.

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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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Deep Learning-Based LecturePosture Evaluation

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Wuhan University Journal of Natural Sciences 2024年第4期29卷 315-322页

作者： YANG Yifan ZHANG Tao LI Weiyu Key Laboratory of Multidisciplinary Management and Control of Complex Systems of Anhui Higher Education Institutes Anhui University of TechnologyMaanshan 243002AnhuiChina School of Microelectronics and Data Science Anhui University of TechnologyMaanshan 243032AnhuiChina

Computer vision,a scientific discipline enables machines to perceive visual information,aims to supplant human eyes in tasksencompassing object recognition,localization,and *** traditional educational settings,instructors or evaluators evaluate teachingperformance based on subjective ***,with the continuous advancements in computer vision technology,it becomes increasinglycrucial for computers to take on the role of judges in obtaining vital information and making unbiased *** thisbackdrop,this paper proposes a deep learning-based approach for evaluating lecture ***,feature information is extracted fromvarious dimensions,including head position,hand gestures,and body posture,using a human pose estimation ***,a machinelearning-based regression model is employed to predict machine scores by comparing the extracted features with expert-assigned *** correlation between machine scores and human scores is investigated through experiment and analysis,revealing a robustoverall correlation(0.6420)between predicted machine scores and human *** ideal scoring conditions(100 points),approximately51.72%of predicted machine scores exhibited deviations within a range of 10 points,while around 81.87%displayed deviationswithin a range of 20 points;only a minimal percentage of 0.12%demonstrated deviations exceeding the threshold of 50 ***,tofurther optimize performance,additional features related to bodily movements are extracted by introducing facial expression recognitionand gesture recognition *** fusion of multiple models resulted in an overall average correlation improvement of 0.0226.

关键词： deep learning human pose estimation object detection correlation

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