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43rd Chinese control Conference, CCC 2024

作者： Yan, Xinyue Feng, Yiyu Zhang, Xianfu Shandong University School of Control Science and Engineering Jinan250061 China

ISBN: (纸本)9789887581581

Differences between individual dynamic functional networks have stable spatial distributions, and whether such differences can be used for identification has not been fully studied. In this paper based on a set of public near-infrared spectroscopy datasets, we preprocessed the data of 30 subjects, constructed brain networks under different time windows, and identified them as 'brainprint' features. Next, we compared the impact of traditional classification algorithms and deep learning classification algorithms on 'brainprint' recognition results. For the 30 classification predictions of different feature experimental groups, the one-dimensional convolutional neural network (1DCNN) model constructed in this paper can achieve an average recognition accuracy of 98.67 %. This shows that the CNN model constructed in this study is feasible for the classification of near-infrared 'brainprint' and provides a potential biometric technology for future identity recognition. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Convolutional neural networks

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Distributed Observer for Descriptor Linear System: Summary of Results 1

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7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Xu, Haotian Liu, Shuai School of Control Science and Engineering Shandong University Jinan250061 China

ISBN: (数字)9789819733323

ISBN: (纸本)9789819733316

This paper concerns the distributed observer for descriptor linear system. The structure of distributed observer is constructed and the observer parameters are presented through skillfully design. As a result, each local observer has the ability to reconstruct the states of the underlying system including its impulse phenomenon. Furthermore, the case of distributed observer for normally linear system can be directly obtained from the developed result as a particular case. Finally, a simulation result verifies the validity of our results. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Linear systems

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Recursive Subspace Least Squares Estimation for Data-Driven Model Predictive control and Its Application to Aeroengines 22

Recursive Subspace Least Squares Estimation for Data-Driven ...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Wang, Na Wen, Si-Xin Wu, Yuhu School of Control Science and Engineering Dalian University of Technology Dalian China

ISBN: (纸本)9798331527471

Model predictive control (MPC) is subject to limitations, including stringent model accuracy requirements and time-consuming modeling, which impede its broader application. To address these challenges, this article proposes a predictive controller based on Data-Driven Recursive Subspace Least Squares (DD-RSPC), obviating the need for any prior model identification. Unlike traditional MPC, DD-RSPC dynamically updates the prediction model using real-time measured input-output data, thereby significantly enhancing system adaptability. Furthermore, this article introduces a rigorous stability theorem that employs the Lyapunov function to ensure the stability of the DD-RSPC approach. Finally, the effectiveness of DD-RSPC is validated through aeroengine simulation experiments, effectively substantiating its adaptability, stability, and practical applicability. © 2024 IEEE.

关键词： Predictive control systems

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End-to-End On-Orbit Objects Detection with ConvNets

End-to-End On-Orbit Objects Detection with ConvNets

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Ru, Bo Hou, Pengrong Li, Xiang Chu, Qinghao Zeng, Zikang Zhang, Chenming Wang, Zhelong School of Control Science and Engineering Dalian University of Technology Dalian China

ISBN: (纸本)9781665410205

As space activities expand, the quantity of space debris also increases, posing significant risks to spacecraft and infrastructure. Space situational awareness (SSA) is essential for avoiding collisions and limiting the generation of extra debris. Accurate and efficient detection of space objects plays a critical role in achieving this goal. Our research focuses on the development of detection algorithms that are both precise and quick, taking into account the real-time and safety of spacecraft operations in orbit. For the first time, we take a fully Convolutional Neural Networks (ConvNets) to run the query-based end-to-end object detection for SSA. We further compare its performance with the newest YOLOv9 algorithm. This is an innovative attempt at SSA. First of all, it does not require predefined a priori anchor boxes or complex post-processing strategies such as Non-Maximum Suppression (NMS), and can directly achieve end-to-end target detection. Secondly, the fully ConvNets are selected as the basic framework, which not only retains the advantages of self-attention mechanism, but also greatly improves the computing efficiency. These methods show outstanding performance on the challenging SPARK data set. The fully ConvNets approach achieves end-to-end detection by utilizing the query attention mechanism, excluding the need for complicated post-processing in traditional object detection methods and with higher efficiency. YOLOv9 involves an enhanced feature pyramid fusion and a more powerful detection head, potentially resulting in higher precision. Following that, we will thoroughly assess the speed, accuracy, and trade-offs of the two algorithms using actual data sets in order to deliver an efficient and dependable solution for detecting targets in aerospace sensing missions. © 2024 IEEE.

关键词： Orbits

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Hierarchical Neural Network Ensemble Modeling Based on Parallel and Serial Structure 16

Hierarchical Neural Network Ensemble Modeling Based on Paral...

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16th International Conference on Advanced Computational Intelligence, ICACI 2024

作者： Li, Jie Wang, Degang Dalian University of Technology School of Control Science and Engineering Dalian China

ISBN: (纸本)9798350308501

Hierarchical models have superiority in solving the data dimension explosion problem. An appropriate hierarchical structure is an important factor affecting the robustness of model. A hierarchical structure is constructed, which combines the parallel learning and serial connection manner. Meanwhile, a hierarchical neural network based on parallel and serial structure (PSHNN) is proposed. Firstly, the hierarchical clustering method is used to determine feasible divisions of the data. In this way, the data can be represented as a set of several subsets. These subsets are input into the serial hierarchical structure with layer by layer manner. Each serial hierarchical structure is constructed by several sublayers. These sublayers are connected via serial manner. Secondly, several parallel layers are established to perform the parallel learning, and each parallel layer contains a serial hierarchical structure. Each parallel layer can be considered as a parallel learning channel. Finally, the Bayesian information criterion (BIC) is used to integrate the learning results of each parallel channel, and determine final output of model. Performance of PSHNN was verified on several benchmark datasets, and an actual collected fatty liver medical dataset. Simulation results prove the superiority of the designed PSHNN. © 2024 IEEE.

关键词： Benchmarking

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The Investigation of Data Voting Algorithm for Train Air-Braking System Based on Multi-Classification SVM and ANFIS

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Chinese Journal of Electronics 2024年第1期33卷 274-281页

作者： Juhan WANG Ying GAO Yuan CAO Tao TANG School of Electronics and Information Engineering Beijing Jiaotong University Graduate Department China Academy of Railway Science National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University

The pressure data of the train air braking system is of great significance to accurately evaluate its operation state. In order to overcome the influence of sensor fault on the pressure data of train air braking system, it is necessary to design a set of sensor fault-tolerant voting mechanism to ensure that in the case of a pressure sensor fault, the system can accurately identify and locate the position of the faulty sensor, and estimate the fault data according to other normal data. A fault-tolerant mechanism based on multi-classification support vector machine(SVM) and adaptive network-based fuzzy inference system(ANFIS) is introduced. Multi-classification SVM is used to identify and locate the system fault state, and ANFIS is used to estimate the real data of the fault sensor. After estimation, the system will compare the real data of the fault sensor with the ANFIS estimated data. If it is similar,the system will recognize that there is a false alarm and record it. Then the paper tests the whole mechanism based on the real data. The test shows that the system can identify the fault samples and reduce the occurrence of false alarms.

关键词： Multi-classification support vector machine Adaptive network-based fuzzy inference system Train air braking system Fault-tolerant voting Multi-sensors

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Multi-Scale Patch Transformer Network for Satellite Image Time Series Classification 14

Multi-Scale Patch Transformer Network for Satellite Image Ti...

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14th International Conference on Information science and Technology, ICIST 2024

作者： Suo, Jifeng Wang, Degang School of Control Science and Engineering Dalian University of Technology Dalian China

ISBN: (纸本)9798350353334

With the increasing availability of high-quality earth observation data, satellite image time series (SITS) classification has become a hot topic. In this paper, a multi-scale patch Transformer model (PatchSITS) for SITS classification is proposed. First, SITS samples are segmented into patch sequences of varying patch lengths using k-means clustering. Subsequently, the enhanced Transformer is proposed to capture temporal features at various scales. To capture inter-band relationships and enhance critical band information, the gated channel attention mechanism is applied to obtain dynamic weights between bands. Furthermore, a multi-scale weighted fusion strategy is proposed to integrate these multi-scale features. And broad learning system (BLS) is utilized for SITS classification. Numerical simulations demonstrate that the PatchSITS model exhibits excellent classification performance on the BreizhCrops dataset. © 2024 IEEE.

关键词： Satellite imagery

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Coupled interactive effects of ammonia and hydrogen additions on ethylene diffusion flames: A detailed kinetic study

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science China(Technological sciences) 2024年第3期67卷 930-948页

作者： XIAO JiaYi XU TingTing SHU ZhiMei DENG QiGe ZHAO Xuan YING YaoYao LI TianJiao LIU Dong MIIT Key Laboratory of Thermal Control of Electronic Equipment School of Energy and Power EngineeringNanjing University of Science and TechnologyNanjing 210094China Advanced Combustion Laboratory School of Energy and Power EngineeringNanjing University of Science and TechnologyNanjing210094China

In order to create effective combustion technologies and fuels with low or no carbon emissions,the research was conducted to assess the coupled interactive effects of NH_(3) and H_(2) additions on ethylene counterflow diffusion flames from a kinetic *** effects of the NH_(3)/H_(2) combination on flame temperatures,major species,key radicals,important intermediate species,representative oxygenated species and NO_xwere *** results of the study utilizing fictitious inert NH_(3) and/or H_(2) revealed the chemical effects of the two *** was found that the NH_(3)/H_(2) coupled effects had a more effective inhibitory effect on soot precursors than the effects of corresponding sum of single NH_(3) or H_(2) *** production of soot precursors was promoted by the coupled chemical effects of NH_(3) and H_(2),but the coupled dilution and thermal effects were observed to have a greater impact,resulting in a decrease of the mole fractions of soot *** for the interaction of NH_(3) and H_(2) effects,the presence of H_(2) decreased the chemical effects of NH_(3) on the augmentation of C_(2)H_(2),A1,A2,and CH_(3)CHO mole *** NH_(3) addition alleviated the H_(2) chemical effects on increasing C_(2)H_(2),C_(3)H_(3),A1 and A2 ***,the NH_(3)chemical effects on C_(3)H_(3),OH and CH_(3)CHO were enhanced when H_(2) was *** presence of NH_(3) augmented the chemical effects of H_(2) on the growth of OH mole ***,the H_(2) chemical effects hindered the production of NO and NO_(2) in the presence of NH_(3).

关键词： NH_(3)/H_(2)addition kinetic analysis coupled interactive effects chemical effects

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PSO-based Modified control Barrier Function to Avoid Collision of Vehicle

PSO-based Modified Control Barrier Function to Avoid Collisi...

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2024 China Automation Congress, CAC 2024

作者： Wu, Zhenyu Huang, Tao Gao, Liang Yu, Donghui School of Control Science and Engineering Dalian University of Technology Dalian China

ISBN: (纸本)9798350368604

An efficient and stable algorithm for vehicle path following and obstacle avoidance is proposed in this paper. Firstly, Particle Swarm Optimization (PSO) is used as the solver for the controller, combined with the modified control Barrier Function (CBF), to propose the PSO-CBF controller. To ensure the safety of vehicle obstacle avoidance and solve the deadlock issue, we insert an additional local path planning layer after the global path planning, which enhances the stability of the controller. In addition, we optimize the initial state of the PSO solver, reduce the population size of PSO under the conditions of satisfying basic obstacle avoidance requirements and path following. This approach effectively improving the efficiency of the algorithm. Finally, the performance of the PSO-CBF controller is verified through simulation experiments. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Hierarchical Informative Path Planning for Active Sensing and Field Reconstruction 24

Hierarchical Informative Path Planning for Active Sensing an...

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3rd International Symposium on control engineering and Robotics, ISCER 2024

作者： Yang, Bingyu Yang, Guang Yang, Kaibo Zhang, Hao Li, Teng School of Control Science and Engineering Shandong University Jinan250061 China

ISBN: (纸本)9798400709951

This paper investigates the informative path planning problem for robotic sensing and environmental field monitoring. A novel hierarchical path planner is proposed to achieve active sensing and field reconstruction. In particular, a global-local hierarchical path planner is proposed to adaptively sense, characterize and reconstruct the environmental field of interest with limited observations. In particular, the environmental field is defined as a Gaussian random field (GRF) for a mobile sensing robot to capture critical information in the field for further field reconstruction. The informative path is planned hierarchically based on the mutual information (MI) metric with a global planner in terms of a Gaussian Markov random field (GMRF) and a local planner with respect to a Gaussian process (GP). More importantly, persistent monitoring can be achieved by the proposed model updating and planning strategy. Finally, the experiments from both a simulation and a real-world field test are conducted, which demonstrate the superior performance of the proposed path planning methodology for active sensing and field reconstruction. © 2024 ACM.

关键词： Markov processes

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