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42nd Chinese control Conference, CCC 2023

作者： Zuo, Zhiqiang Du, Yunhao Wang, Yijing Zhang, Zhicheng Li, Peng School of Electrical and Information Engineering Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System Tianjin University Tianjin300072 China Ministry of Education Key Laboratory of System Control and Information Processing Shanghai200240 China

ISBN: (纸本)9789887581543

State-of-charge (SoC) balancing is crucial for improving the efficiency and lifetime of the battery energy storage system in near-space vehicles. In this paper, the SoC balancing control problem is investigated by a coupling battery model with electric-thermal-aging dynamics. Firstly, a system identification experiment is carried out to obtain the internal parameters of the battery. Secondly, a weight optimization index is designed by integrating the balancing speed and battery state-of-health (SoH). Then, a receding horizon control algorithm is proposed to realize multi-unit battery SoC balancing with partial swarm optimization (PSO). Finally, the effectiveness of the proposed strategy is verified by simulation results. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Battery management systems

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Impact of Evaporation Duct on Electromagnetic Wave Propagation During a Typhoon

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Journal of Ocean University of China 2022年第5期21卷 1069-1083页

作者： WANG Shuwen YANG Kunde SHI Yang YANG Fan ZHANG Hao School of Marine Science and Technology Northwestern Polytechnical UniversityXi’an 710072China Key Laboratory of Ocean Acoustics and Sensing Northwestern Polytechnical UniversityMinistry of Industry and Information TechnologyXi’an 710072China National Key Laboratory of Underwater Information Processing and Control Xi’an 710072China

The evaporation duct,a result of evaporation from the ocean,is a region above the sea surface in which radio waves are refracted *** duct has strong effects on microwave *** cause huge anomalies in marine meteorological parameters that influence the evaporation duct distribution and structure,which in turn affects the propagation of electromagnetic(EM)***,EM wave propagation under the typhoon process has seldom been ***,taking Typhoon Phanfone(201929)as an example,this study uses a dataset from the European Centre for Medium-Range Weather Forecasts,combined with the Naval Atmospheric Vertical Surface Layer Model and the parabolic equation model,to study the evaporation duct’s impact on EM wave propagation during a *** spatial and temporal path loss distributions reveal that large amounts of EM wave energy are emitted from the evaporation duct when the EM wave passes through a typhoon *** average,a typhoon eye causes an approximately 20 dB increase in path loss for EM wave propagation at low antenna ***,the effects of a typhoon eye on EM wave propagation at different signal frequencies and antenna heights are *** results show that a typhoon has a larger impact on EM wave propagation with low signal frequency and high antenna height.

关键词： evaporation duct typhoon ECMWF wave glider electromagnetic wave propagation

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A Depression Level Classification Model Based on EEG and GCN Network with Domain Generalization 43

A Depression Level Classification Model Based on EEG and GCN...

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43rd Chinese control Conference, CCC 2024

作者： Zhang, Zhongyi Xu, Chenyang Jin, Licheng Hou, Huirang Meng, Qinghao Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin Key Laboratory of Process Measurement and Control Tianjin300072 China

ISBN: (纸本)9789887581581

Electroencephalography (EEG) contains a wealth of information, including neuron activity, allowing for a partial understanding of the brain's state. As a reliable tool, EEG, combined with deep neural networks, has been increasingly utilized to assist in the detection of depression and has achieved satisfactory results. However, there are still some limitations in the current models. Firstly, it is worth noting that many existing models are binary classification models. These models can determine whether a subject is depressed or not, but they lack the capability to differentiate the severity of depression. Besides, the variations in EEG signals among various individuals have not been thoroughly taken into account, leading to subpar performance when applying the trained model to new subjects. To address the aforementioned issues, we suggest implementing a multi-classification model that utilizes a graph convolutional neural network (GCN) to identify levels of depression. Additionally, to mitigate the problem of imbalanced samples in multi-classification tasks, we introduce a penalty coefficient for the smaller categories. At the same time, the issue of signal discrepancies among subjects was taken into account, leading to the introduction of the domain generalization module. Finally, the depression level identification task on the MODMA and PRED+CT dataset achieved an accuracy of 75.47% and 77.97% respectively, surpassing the performance of the state-of-the-art (SOTA) model. In addition, numerous ablation experiments were conducted to validate the efficacy of each module. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Convolution

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Lidar-Visual-Inertial Odometry with Online Extrinsic Calibration

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Journal of Shanghai Jiaotong university(Science) 2023年第1期28卷 70-76页

作者：茅天阳赵文韬王景川陈卫东 Department of Automation Institute of Medical RoboticsShanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai 200240China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240China

To achieve precise localization,autonomous vehicles usually rely on a multi-sensor perception system surrounding the mobile *** is a time-consuming process,and mechanical distortion will cause extrinsic calibration ***,we propose a lidar-visual-inertial odometry,which is combined with an adapted sliding window mechanism and allows for online nonlinear optimization and extrinsic *** the adapted sliding window mechanism,spatial-temporal alignment is performed to manage measurements arriving at different *** nonlinear optimization with online calibration,visual features,cloud features,and inertial measurement unit(IMU)measurements are used to estimate the ego-motion and perform extrinsic *** experiments were carried out on both public datasets and real-world *** indicate that the proposed system outperforms state-of-the-art open-source methods when facing challenging sensor-degenerating conditions.

关键词： autonomous vehicles multi-sensor fusion online calibration

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A Multi-Gas Detection System Based on Convolutional Neural Network 43

A Multi-Gas Detection System Based on Convolutional Neural N...

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43rd Chinese control Conference, CCC 2024

作者： Chen, Si-Wen Li, Hong-Yue Hou, Hui-Rang Meng, Qing-Hao Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin Key Laboratory of Process Measurement and Control Tianjin300072 China

ISBN: (纸本)9789887581581

The emission of carbon oxides (COx), nitrogen oxides (NOx), sulfur compounds, and volatile organic compounds (VOCs) from vehicles has significantly impacted the air quality, thus posing threats to the environmental resources and human health. This work presents the development of a non-dispersive infrared (NDIR) multi-gas detection system featuring three broadband light sources, a sealed gas chamber, and three multi-channel pyroelectric detectors with a response time shorter than 32 ms. The proposed system is used for constructing an odor dataset comprising five target gases, including carbon dioxide (CO2), carbon monoxide (CO), nitric oxide (NO), sulfur dioxide (SO2), and propane (C3 H8). We perform odor recognition experiments using the traditional machine learning methods, as well as one-dimensional convolutional neural network (1D-CNN) and depthwise separable convolutional neural network (DS-CNN). The results show that the proposed DS-CNN model achieves an accuracy of 94.75% in recognizing different odors, thus outperforming other classification algorithms. This work demonstrates that the proposed detection system exhibits rapid response and precise recognition, thus establishing it as an effective approach for analyzing the primary components of automobile exhaust. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Air quality

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Online Identification of Boost Converter Components with Multiple Updating RLS Algorithm 42

Online Identification of Boost Converter Components with Mul...

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42nd Chinese control Conference, CCC 2023

作者： Zhou, Tong Zhang, Zhicheng Zuo, Zhiqiang Wang, Yijing Li, Peng School of Electrical and Information Engineering Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System Tianjin University Tianjin300072 China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China

ISBN: (纸本)9789887581543

In this paper, component parameters of the boost converter are identified online using a multiple updating recursive least squares (MURLS) algorithm. The component parameters, such as resistance, inductor inductance and capacitor capacitance, are obtained directly through the identification procedure rather than transfer function coefficients. The MURLS algorithm is applied to improve the rapidity of system identification compared to the traditional recursive least squares (RLS) algorithm, which is verified by a comparative simulation between MURLS and RLS and the simulation of a load-switching scenario. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Parameter estimation

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Research on Grain Yield Prediction Model Based on Wavelet transform and LSTM 3

Research on Grain Yield Prediction Model Based on Wavelet tr...

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3rd International Seminar on Artificial Intelligence, Networking, and information Technology, AINIT 2022

作者： Zhu, Chunhua Li, Pengle Key Laboratory of Grain Information Processing and Control Henan University of Technology Ministry of Education China College of Information Science and Engineering Henan University of Technology China Henan Key Laboratory of grain photoelectric detection and control Henan University of Technology Zhengzhou Henan China

ISBN: (数字)9781510662971

ISBN: (纸本)9781510662964

To improve the accuracy of grain yield prediction, a grain yield prediction model based on wavelet transform and long short-term memory (LSTM) is proposed. Firstly, the original data is decomposed by wavelet transform algorithm to obtain a series of sub-sequences of different scales, and then LSTM prediction models are built for the sub-sequences, finally wavelet reconstruction is used to obtain the predicted yield and analyze the model performance. The article uses China's 1999-2018 grain yield as experimental data. The experiment shows that the method proposed in this article has excellent performance in both short-term and medium-term predictions compared to the existing methods. © 2023 SPIE.

关键词： Forecasting

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Modeling and optimization design for RF humidity perception in storage environment 3

Modeling and optimization design for RF humidity perception ...

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3rd International Seminar on Artificial Intelligence, Networking, and information Technology, AINIT 2022

作者： Wang, Lihong Zhu, Chunhua Key Laboratory of Grain Information Processing and Control Henan University of Technology Ministry of Education China College of Information Science and Engineering Henan University of Technology China Henan Key Laboratory of grain photoelectric detection and control Henan University of Technology Zhengzhou Henan China

ISBN: (数字)9781510662971

ISBN: (纸本)9781510662964

Radio Frequency (RF) perception of humidity is the current research hotspot of grain condition monitoring for "three temperature and three humidity". In order to analyze the optimal detection parameters on humidity of storage environment, the Wireless Insite test platform is built to analyze the effects of antenna height, carrier frequency and grain and with different moisture content on Channel State information (CSI) such as received power, path loss and time delay. The mathematical relations between the humidity and path loss, the humidity and time delay are deduced respectively. In simulation experiments, the frequency bands of 2.4GHz, 5GHz, 28GHz, 45GHz, 60GHz are selected. Study the changes of path loss and delay spread with humidity in the storage environment at different frequencies and antenna height. When the height of the transceiver antenna is set to 1m, the 28GHz, 45GHz and 60GHz millimeter wave signals are more sensitive to humidity changes, thereby which can provide more higher perception resolution of detecting humidity in the storage environment, compared with the 2.4GHz and 5GHz frequency bands. Besides, when space humidity is constant and the moisture content of wheat is changed, the arrival time of each ray in the channel at 45GHz is longer than that at 60GHz. The above work can provide reference for the application of RF sensing technology in the humidity detection scenario. © 2023 SPIE.

关键词： Millimeter waves

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Reinforcement Learning with Hierarchical Graph Structure for Flexible Job Shop Scheduling

Reinforcement Learning with Hierarchical Graph Structure for...

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2023 International Annual Conference on Complex systems and Intelligent Science, CSIS-IAC 2023

作者： Zhang, Linli Li, Dewei Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Dept. of Automation Shanghai China

ISBN: (纸本)9798350309003

The flexible job shop scheduling problem (FJSP) is a NP-hard combinatorial optimization challenge, having extensive real-world industrial applications. For a given instance of FJSP, a scheduling solution need to be devised, which entails determining the appropriate machine for each operation and establishing the sequence in which operations are processed on a given machine, all while adhering to certain constraints. However, many of the methods are lacking in flexibility, unable to cope effectively with multi-relational operations and machine selection scenarios. This can make the representation of state and action challenging in practice, leading to a significant increase in decision complexity. This paper considers these issues by a hierarchical graph structure with a model that synergistically integrates two modules: the environment and the decision module. The environment module is characterized by a hierarchical graph neural network which defines the multi-relational FJSP instance where all machine and operation data are stored. The decision module incorporates the Actor-Critic networks, designed to train reasoning strategies and enhance decision performance, based on the states and rewards provided at each step by the environment module. The objective is to derive optimal scheduling strategies through interactions between the decision agent and the environment, targeting the minimum resources consumed or makespan. Numerical simulations validate the efficacy of the proposed approach, showing its potential to yield superior results and thereby enhance performance. © 2023 IEEE.

关键词： Reinforcement learning

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E-Nose-Based Domain-Generalization Recognition of Ignitable Liquids 43

E-Nose-Based Domain-Generalization Recognition of Ignitable ...

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43rd Chinese control Conference, CCC 2024

作者： Song, Tian-Shu Chen, Zhen-Peng Hou, Hui-Rang Li, Hong-Yue Meng, Qing-Hao Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin Key Laboratory of Process Measurement and Control Tianjin300072 China

ISBN: (纸本)9789887581581

Detecting ignitable liquids (ILs) at the scene of a fire is crucial for fire investigation. The electronic nose (e-nose) is crucial for detecting ILs due to its affordability and rapid response time. Process limitations and changes in environmental conditions can cause variations in the reactions of multiple e-noses with the same sensor array to the same input gas. This can significantly reduce the accuracy of identifying ILs when deploying a model trained on one e-nose to other e-noses. In order to tackle the issue of cross-platform identification of ILs using e-noses, we developed a feature mute gated network (FMG-Net). This network is capable of maintaining consistency in the shared components of different e-noses at the low-level feature level while effectively muting the distinct low-level features. FMG-Net is comprised of a main network and multiple branch networks that are specific to different domains. Initially, a subsidiary domain-specific convolutional neural network (SDS-CNN) is trained to learn the network parameters on various e-nose platforms with known labels. Additionally, the backbone network generates low-level features of the e-nose responses. Furthermore, the SDS-CNN incorporates random input of low-level features and generates a mask using the gradient. This mask is then used to explicitly remove the non-overlapping parts of the low-level features. At last, the low-level features are sent back to the backbone network for further training once the non-overlapping parts have been removed. Experiments were conducted to identify four types of ILs (diesel, gasoline, ethanol, and thinner) using five self-made e-nose platforms with identical sensor arrays. The results indicated that the proposed FMG-Net consistently outperforms other domain-generalization algorithms in terms of recognition accuracy and parameter sizes. FMG-Net has the ability to detect volatile gases in ILs using an e-nose, even without the need for a dataset. © 2024 Technical Committ

关键词： Convolutional neural networks

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