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2022 Chinese Automation Congress, CAC 2022

作者： Ma, Aoyun Li, Dewei Xi, Yugeng Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Key Laboratory of System Control and Information Processing Department of Automation Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781665465335

The hydrogen gas turbine can be the key technology for carbon-neutral in the future. The flexibility of gas turbine under various operation conditions is an important issue. A state-space model of hydrogen gas turbine with different hydrogen volume ratio mixed fuel is constructed based on Rowen model, and a full-dimensional state observer is designed to obtain the states of state-space model. The model predictive control method based on amplitude decaying aggregation strategy is presented to stabilize the rotational speed of hydrogen gas turbine under various constraints, which are to ensure the safety of operation. The number of variables contained in the optimization problem to be solved online is reduced by aggregation strategy. The simulation results confirmed that the presented method works well, and the effects of the hydrogen volume ratio for the performance of hydrogen gas turbine are discussed. © 2022 IEEE.

关键词： Model predictive control

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A novel feature enhancement module for small object detection

A novel feature enhancement module for small object detectio...

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2022 IEEE International Conference on Industrial Technology, ICIT 2022

作者： Wang, Ruogu Li, Ning Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai China

ISBN: (纸本)9781728119489

Generic object detection has made great progress in recent years, yet small object detection is still facing a serious problem of missed detection. The problem is caused by two reasons: low resolution in small targets and feature loss during deep convolution downsampling. Some previous studies used feature pyramid structures and multi-scale training predictions to alleviate this problem, but still cannot efficiently utilise the feature information of small targets. In this paper, firstly we propose a novel and effective feature enhancement module to extract more features;secondly, a filter size design method is presented to extract features of matching objects and incorporate context information;finally, an adaptive fusion method is used to process outputs of our module and improve model robustness. We select YOLOv3 as base model and carry out several experiments on a special Small Object Dataset and the results show that our work can achieve an increase of about seven percentage points (35.73%) compared to 28.78% on baseline. © 2022 IEEE.

关键词： Object detection

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An Expansion-based Document Ranking Framework Incorporated with Core Concern Capturing 5

An Expansion-based Document Ranking Framework Incorporated w...

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5th International Conference on Intelligent Autonomous Systems, ICoIAS 2022

作者： Qi, Yinqiao Xin, Jinghao Li, Ning Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai China

ISBN: (数字)9781665498388

ISBN: (纸本)9781665498388

Entity-based ranking is a popular research area in document ranking as knowledge graphs can introduce additional evidence for ranking systems. In most entity-based ranking framework, the original query is transformed into a set of entities and candidate documents are ranked according to their relevance to the entity set. However, mistakes can easily occur during the transformation process, which introduce noise to the ranking system. This paper represents an expansion-based ranking framework, which expand the original query with high-quality candidate entities to mitigate the impact of transformation mistakes and enrich the semantics of the query in the meanwhile. Compared with existing query expansion methods, our expansion method is lightweight as it reduces much computational burden and requests little extra information. However, the core concerns of the users might be covered up after query expansion. To alleviate this dilemma, a core concern capturing method is pro-posed, wherein the attention weight of each inter-entity relation is measured to capture the core entity pairs in the expanded query. Experiments on the S2-CS dataset demonstrate the effectiveness of our lightweight query expansion method and the superiority of our core concern capturing method. Compared with a classical retrieval model, our method provides 11% improvements in terms of NDCG@15. © 2022 IEEE.

关键词： Semantics

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Integrated Power and Hydrogen Trading in Multimicrogrid Coupled with Offsite Hydrogen Refueling Stations 6

Integrated Power and Hydrogen Trading in Multimicrogrid Coup...

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6th IEEE Conference on Energy Internet and Energy System Integration, EI2 2022

作者： Li, Longyan Ning, Chao Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (纸本)9798350347159

This paper proposes a novel two-level electricity and hydrogen market framework for multi-microgrids (MMGs) coupled with offsite hydrogen refueling stations (HRSs), aiming to strengthen the synergy between electricity and hydrogen. The local hydrogen market among the MMG and HRSs is modeled by multi-leader multi-follower Stackelberg game theory, in which microgrids play the role of leaders to adjust hydrogen prices while HRSs participate as followers to determine their hydrogen purchase scheme. Moreover, to deal with economic and technical issues of the MMG in a holistic manner, Nash bargaining theory is used to model the electricity transaction among microgrids. A distributed algorithm is developed to solve such games, thus reducing the cost of information communication and the risk of privacy exposure. The case study demonstrates that this market mechanism guarantees benefits for all parties and boosts the independence of MMGs by reducing transactions with the main grid. © 2022 IEEE.

关键词： Game theory

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An Integrated Intelligent Autonomous Driving Bulldozer System: Pose Estimation, Object Detection, and Work Planning for Dumping Operations

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Journal of Field Robotics 2025年第0期

作者： Peng, Gang Gao, Qiang Liu, Xingyu Zhou, Yicheng Duan, Hangqi Wu, ZhanGang Hu, Bin Zhu, Xukang Xu, Daosheng School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Key Laboratory of Image Processing and Intelligent Control Ministry of Education Wuhan China Engineering Research Center of Autonomous Intelligent Unmanned Systems Ministry of Education Wuhan China Shantui Construction Machinery Co. Ltd. Jining China

In recent years, the rapid advancement of automation control and intelligent sensing technologies has positioned autonomous driving as a focal point of interest for both academia and industry. As core equipment in modern construction and industrial production, engineering machinery urgently requires intelligent transformation. To promote the intelligent development of engineering machinery, we have designed an integrated intelligent autonomous driving bulldozer system, which can be extended to various types of engineering machinery. For the specific mine dumping operation environment, we propose a multisensor fusion pose estimation algorithm framework based on the Global Navigation Satellite System, inertial measurement unit, visual cameras, and light detection and ranging (LiDAR) to address issues arising from sensor failures under harsh construction conditions. Furthermore, to ensure safety during task execution, we design a three-dimensional object detection method based on the optimal observation plane from LiDAR data. By integrating pose estimation and environmental perception results, we develop a comprehensive work planning and path-tracking algorithm to maintain task efficiency. Experimental results demonstrate that our intelligent autonomous driving bulldozer system performs excellently under various working conditions. The accuracy of its pose estimation, object detection, and path tracking meets the requirements of actual construction environments, showcasing its significant potential in engineering applications. © 2025 Wiley Periodicals LLC.

关键词： Construction equipment

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Containment control Protocol Design for PDE Agents Subject to External Disturbances

Containment Control Protocol Design for PDE Agents Subject t...

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第42届中国控制会议

作者： Yining Chen School of Artificial Intelligence Tiangong University Key Laboratory of System Control and Information Processing Ministry of Education

This paper mainly solves the containment control issue for a network of infinite-dimensional agents described by wave equations. To eliminate influences caused by external disturbances, observers based on partial differential equations are proposed. A novel interaction protocol is then designed to reach containment control under a fixed directed communication topology. Different from the existing works, disturbances with unbounded derivatives are allowed in this paper. A proper Lyapunov function is constructed and the sufficient condition assuring containment control is obtained. Finally, the theoretical results are verified via numerical examples.

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An Intelligent Evaluation Method of Root Canal Therapy Quality Based on Deep Learning

An Intelligent Evaluation Method of Root Canal Therapy Quali...

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2022 Chinese Automation Congress, CAC 2022

作者： Liu, Jie Peng, Gang Yan, Shiqian Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Hubei Eya Medical Investment Management Co. Ltd Wuhan430061 China

ISBN: (纸本)9781665465335

Dentists judge the quality of root canal therapy for each patient very time-consuming, and inefficient, lack of quantitative evaluation criteria, easy to cause judgment errors. At the same time, the traditional method of extracting the root canal image features based on experience is difficult to accurately extract the filling area of the root canal and the tooth area where it is located, resulting in low accuracy of root canal target segmentation, and thus affecting the accuracy of root canal treatment quality evaluation. In this paper, for the real root canal treatment images of patients, the CenterNet network was used to detect the root canal target of root canal images. Then, according to the test results, the U-Net full convolution neural network was used to segment the root canal filling region and its tooth region. Finally, the segmented images were quantitatively evaluated according to the professional evaluation index of doctors. The experimental results show that the coincidence rate between the evaluation results of this method and the evaluation results of professional doctors is 83.3 %, which can better complete the quality evaluation of root canal therapy for patients, effectively improve the work efficiency of doctors, and has reference significance for the application of artificial intelligence in medical field. © 2022 IEEE.

关键词： Patient treatment

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control Input Inference of Mobile Agents under Unknown Objective

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IFAC-PapersOnLine 2023年第2期56卷 2134-2139页

作者： Chendi Qu Jianping He Xiaoming Duan Shukun Wu The Dept. of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

Trajectory and control secrecy is an important issue in robotics security. This paper proposes a novel algorithm for the control input inference of a mobile agent without knowing its control objective. Specifically, the algorithm first estimates the target state by applying external perturbations. Then we identify the objective function based on the inverse optimal control, providing the well-posedness proof and the identifiability analysis. Next, we obtain the optimal estimate of the control horizon using binary search. Finally, the agent's control optimization problem is reconstructed and solved to predict its input. Simulation illustrates the efficiency and the performance of the algorithm.

关键词： Mobile Agents control Input Inference Optimal control Trajectory Secrecy

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Research on High-Precision Contouring control of Gantry Dual-Drive H-Type Planar Motion Platform

Research on High-Precision Contouring Control of Gantry Dual...

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IEEE International Conference on Real-time Computing and Robotics (RCAR)

作者： Chao Chen Haoyu Li Chaochen Gu Department of Automation Ministry of Education of China Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Shanghai China

ISBN: (数字)9798350372601

ISBN: (纸本)9798350372618

The H-type two-axis linear plane motion platform with dual gantry drive is one of the most crucial components in contour processing equipment. The dual gantry structure employs independently controlled dual-side motors to collectively drive the motion of the gantry crossbeam axis. While the introduction of dual gantry drive brings higher single-axis positioning accuracy and greater operational speed to contour processing *** these issues, this paper starts with the dynamic modeling of the dual gantry H-type plane motion platform. It modifies and extends the task coordinates within the framework of the Global Task Coordinate Frame (GTCF). Subsequently, it effectively addresses the coordination between axes and the synchronization of dual drive in the task space, obtaining both motion coordination and dual-axis synchronization capabilities, leading to more accurate contour error calculation and evaluation. The paper includes a comprehensive design of experiments to assess the dual gantry synchronization performance in contour control. The proposed control method is compared with the traditional GTCF-based contour control method, demonstrating the effectiveness and superiority of the integrated coordination and synchronization contour control method.

关键词： Accuracy Uncertainty Tracking Robot kinematics Dynamics Aerospace electronics Motors

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Short-Term Building Load Forecast Based on Patch Learning with Long Short-Term Memory Network and Support Vector Regression 1

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2nd International Conference on Neural Computing for Advanced Applications, NCAA 2021

作者： Dan, Zhaohui Wang, Bo Fan, Huijin Liu, Lei Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Artificial Intelligence and Automation School Huazhong University of Science and Technology Wuhan430074 China

ISBN: (数字)9789811651885

ISBN: (纸本)9789811651878

This paper proposes a novel short-term building load forecasting approach under the framework of patch learning, a novel data-driven model that aggregates a global model and several patch models to further reduce forecasting errors. A PL-LSTM-SVR model is hereby employed to address such a time-series based forecasting problem, where the long short-term memory network is considered as the global model and the support vector regression is selected as the patch model. To obtain satisfying performances, an infinity norm measurement is selected to evaluate load forecasting errors and identify patch locations. Furthermore, a genetic algorithm with elitist preservation strategy is introduced for hyperparameter tuning. The performances of the proposed PL-LSTM-SVR model are tested and verified on two different data sets, and compared with four advanced building load forecasting models on several common metrics. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

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