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2023 International Conference on Power system Technology, PowerCon 2023

作者： Li, Cuiping Liu, Yang Zhang, Tianxun Zhao, Zhiqiang Liu, Haojun Zhu, Xingxu Li, Zhi An, Chenyu Li, Junhui Northeast Electric Power University Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Jilin132012 China Beijing Technology and Business University Beijing China State Grid Hami Power Supply Company Xinjiang Uygur Autonomous Region Hami Hami839000 China

ISBN: (纸本)9798350300222

With the rise of the application of sharing economy in various fields of power system, As a typical application of shared economy in the field of energy storage, the optimal allocation of shared energy storage on the source-network-load side has been a great topic. The problem dealt with in this paper is the configuration result of the source-grid-load energy storage system under the same control strategy. This paper designs an optimization method for the source-network-load side configuration of generalized shared energy storage in regional power grid: Firstly, according to the extensional usage scenario and demand of energy storage, the alternative set of shared energy storage configuration is constructed. Secondly, based on the profit model of shared energy storage, a power system source-network-load optimization scheduling strategy is proposed. Finally, a numerical example is carried out to prove the feasibility and efficacy of the suggested method. © 2023 IEEE.

关键词： Profitability

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River Turbidity Monitoring Based on Semi-supervised Transfer Learning 9th

River Turbidity Monitoring Based on Semi-supervised Transfe...

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9th International Forum on Digital Multimedia Communication, IFTC 2022

作者： Xie, Shuangyi Liao, Xin Bai, Ruxue Zhou, Chengxu Gu, Ke Faculty of Information Technology Beijing University of Technology Beijing China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing China Beijing Laboratory of Smart Environmental Protection Beijing China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China Beijing Artificial Intelligence Institute Beijing China School of Electronic and Information Engineering Liaoning University of Technology Liaoning China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian China

ISBN: (纸本)9789819908554

River turbidity is an important index to evaluate the quality of water environment, which is of great significance to environmental monitoring. Most of the existing monitoring methods rely on turbidity labels measured by river water quality monitoring stations. However, due to the limitation of spatial distribution of water quality monitoring stations, those turbidity data outside the monitoring range are difficult to obtain and the correlation of turbidity data between monitoring stations is poor. Using such data directly might weaken the generalization ability that the model has. In this paper, we propose a new river turbidity monitoring model based on semi-supervised transfer learning (RTMM-SSTL) to solve these problems. First, in the pre-training stage, we innovatively propose a semi-supervised training method combined with big data with pseudo labels for river turbidity monitoring to solve the problem of weakened model generalization ability. Then, the model is fine-tuned using GEE hyper spectral data with ground truth to further improve the monitoring capability of the model. Experiments conducted on the river turbidity monitoring task demonstrate that the proposed model is superior to the advanced learning models, and further proves that our semi-supervised transfer learning method performs better than the state-of-the-art supervision models. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Rivers

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A Novel Hybrid Method for Fault Diagnosis of Industrial Equipment Based on Vibration Signals 14

A Novel Hybrid Method for Fault Diagnosis of Industrial Equi...

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14th IEEE Global Reliability and Prognostics and Health Management Conference, PHM-Hangzhou 2023

作者： Tao, Sen Wang, Kai Zeng, Peng Yu, Tianda Wu, Hao Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110169 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110169 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Nuclear Power Institute of China Chengdu610213 China State Key Laboratory of Power Transmission Equipment & System Security and New Technology Chongqing University Chongqing400044 China

ISBN: (纸本)9798350301359

Industrial equipment reliability is a critical issue that smart manufacturing systems must address. Implementation of online fault diagnosis for industrial equipment is a promising technology that can improve equipment reliability. Vibration analysis is a popular technique for equipment condition monitoring. For the non-linear and non-stationary features of industrial vibration signals, a hybrid model based on wavelet transform (WT), support vector machine (SVM), and grey wolf optimization algorithm (GWO) is proposed. The time-frequency domain features of fault signals are extracted by wavelet transform to solve the non-linearity of signals, and the obtained feature vectors are input into SVM for classification. Aiming at the problem of setting penalty coefficient and kernel function parameters, GWO is introduced to seek hyperparameters by using the hunting principle of wolves. In the experimental part, firstly, the comparison of the GWO-WT-SVM, GWO-SVM and SVM models was carried out. Secondly, Comparison with classical classification algorithms is made. Apparently, the GWO-WT-SVM model achieves higher accuracy than other models in bearing fault diagnosis. © 2023 IEEE.

关键词： component Fault diagnosis GWO PHM SVM

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New Impact-Time-control Guidance Law for Maneuvering Targets with Field-of-View Constraint

New Impact-Time-Control Guidance Law for Maneuvering Targets...

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第42届中国控制会议

作者： Bin Yuru Wang Hui Guo Kaiyang Dai Xin Yang Haorui School of Aerospace Engineering Beijing Institute of Technology Beijing Key Laboratory of UAV Autonomous Control Beijing Institute of Technology Beijing Institute Of Electronic System Engineering

In this paper,a novel guidance law aimed at intercepting targets of various motion forms with impact time and additional field-of-view(FOV) constraints is *** polynomial function in range domain is designed to derive the guidance command in new-defined virtual relative frame,and the function of seeker's virtual FOV is introduced in the form of additional feedback term to achieve simultaneous constraints on impact time and *** with existing methods based on numerical routines with open-loop formulations,the proposed guidance method has the potential to gain better performance in practical applications due to its analytical *** availability and practicability of the guidance method are demonstrated by simulations in various combat scenarios.

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Design of Yaw controller for a Small Unmanned Helicopter Based on Improved ADRC

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Guidance, Navigation and control 2021年第4期1卷 7-25页

作者： Linjie Huang Hailong Pei Key Laboratory of Autonomous Systems and Networked Control Ministry of EducationUnmanned Aerial Vehicle Systems Engineering and Technology Research Center of GuangdongSouth China University of TechnologyGuangzhou 510640P.R.China

Yaw control is signi¯cant to the attitude control of small unmanned helicopters(SUHs).Since the existing robust control method cannot be applied to the SUH with unknown dynamics and disturbances,this paper proposes an improved active disturbance rejection control(IADRC)to solve the *** IADRC obtains the optimal solution of the actuator gain(b0)by gradient ***,this paper summarizes some experiences during the tuning process of ADRC,which signi¯cantly reduces the di±culty of designing ***,the experimental results show that the proposed method is better than the traditional PID in robust and tracking control performance.

关键词： Yaw control small unmanned helicopter active disturbance rejection control gradient descent

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Uncertainty and Reliability Analysis of Neural Networks Based Soft-sensors With Applications in Wastewater Treatment*

Uncertainty and Reliability Analysis of Neural Networks Base...

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Chinese Automation Congress (CAC)

作者： Yiqi Liu Jingyi Yuan Daoping Huang Key Laboratory of Autonomous Systems and Networked Control Ministry of Education the School of Automation Science & Engineering South China University of Technology Guangzhou China

To overcome the transparency and interpretability issues of neural network-based soft sensing, a data-driven framework by combining neural network prediction models and sensitivity analysis methods is constructed in this paper, with application in wastewater treatment plant analysis. The results show that readily biodegradable substrate effluent (SS-e) and ${\text{N}}{{\text{H}}_4}^ + + {\text{N}}{{\text{H}}_3}$ nitrogen effluent (SNH-e) are both important input factors affecting the difficult-to-measured effluent quality, chemical oxygen demand (COD), by resorting to PAWN sensitivity analysis and four neural network models. In addition, four neural network models worked together with bilateral sensitivity analysis for the input perturbation analysis, which shows that the addition of Gaussian perturbation to SS-e and flow rate influent (Q-in) has a greater effect on the prediction which results large fluctuations of output results. Finally, the effectiveness of this framework is confirmed based on above results.

关键词： Analytical models Uncertainty Sensitivity analysis Biological system modeling Perturbation methods Neural networks Effluents

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Sampled-Data Semi-Global Practical Tracking for Uncertain Euler-Lagrange systems

Sampled-Data Semi-Global Practical Tracking for Uncertain Eu...

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Chinese Automation Congress (CAC)

作者： Kaiji Zeng Wei Liu Key Laboratory of Autonomous Systems and Networked Control Ministry of Education School of Automation Science and Engineering South China University of Technology Guangzhou China

We investigate a sampled-data tracking control for uncertain Euler-Lagrange systems in this paper. What makes our result interesting is that our system and exosystem encompass some uncertain parameters that belong to some compact domain that can be specified arbitrarily large. To handle the uncertainties in both the Euler-Lagrange systems and the exosystem, we need to resort to not only the adaptive control technique but also the internal model method. By proposing a sampled-data control law, we solve the problem with guaranteed tracking performance, that is, the tracking errors can converge exponentially, by any fast specified rate, to any small specified neighborhood of the origin.

关键词： Adaptation models Upper bound Uncertainty Automation control systems Adaptive control

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Incremental Nonlinear Dynamic Inversion for a Dual-Ducted-Fan UAV Attitude control under In-Ground-Effect

Incremental Nonlinear Dynamic Inversion for a Dual-Ducted-Fa...

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Intelligent Robotics and Automatic control (IRAC), International Conference on

作者： Zixiao Cheng Hailong Pei Kai Fang College of Automation Science and Engineering South China University of Technology Guangzhou China Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Guangzhou China Guangdong Engineering Technology Research Center of Unmanned Aerial Vehicle Systems Guangzhou China

ISBN: (数字)9798350389807

ISBN: (纸本)9798350389814

This paper is aimed at modeling and attitude control of a dual-ducted-fan UAV under in-ground-effect(IGE). As the UAV flies and hovers over ground or above obstacles, the IGE can cause changes in thrust/torque that drastically affects flight behavior, especially during takeoff/landing. To address this problem, the paper derives a six degree-of-freedom model of the aircraft dynamics firstly. Then, key parameters and in-ground-effect features are obtained through measurements. In order to achieve stable attitude tracking control with in-ground-effect adapting, an incremental nonlinear dynamic inversion(INDI) based cascade controller is designed. Finally, the performance of the designed INDI controller is verified by stability proof, simulations and real flight experiments.

关键词： Adaptation models Fans Attitude control Atmospheric modeling autonomous aerial vehicles Stability analysis Data models Nonlinear dynamical systems Motion measurement Aircraft

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Aspect-Ratio and Scale Aware Rotated Defect Detection for Fiber-reinforced Composites

Aspect-Ratio and Scale Aware Rotated Defect Detection for Fi...

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Computer Vision, Image and Deep Learning (CVIDL), International Conference on

作者： Yuanye Xu Yuanhao Liu Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Key Laboratory of Networked Control System Shenyang Institute of Automation Chinese Academy of Sciences Shenyang Institute of Automation University of Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798350373820

ISBN: (纸本)9798350373837

Detecting surface defects in carbon fiber-reinforced composites (FRCs) during prepreg stacking is crucial for ensuring product quality. Traditional methods using horizontal bounding boxes (HBBs) for defect detection often lead to overlapping boxes and inaccurate results. This paper proposes an Aspect-Ratio and Scale Aware Detector network (ARSADet) specifically designed for rotated defect detection in FRCs. The network incorporates a Gabor convolutional network for feature extraction, an aspect-ratio and scale-aware label assignment method, and a newly established FRCs Oriented Defect Detection dataset (FODD). Evaluation on the FODD dataset demonstrates superior performance compared to existing methods.

关键词： Deep learning Stacking Detectors Optical fiber networks Feature extraction Product design Quality assessment

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Artificial Intelligence-based Fault Detection and Diagnosis: Towards Application in a Chemical Process

Artificial Intelligence-based Fault Detection and Diagnosis:...

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Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), CAA Symposium on

作者： Yiqi Liu Zhi Li Hongtian Chen Key Laboratory of Autonomous Systems and Networked Control Ministry of Education The School of Automation Science & Engineering South China University of Technology Guangzhou China Department of Automation Shanghai Jiao Tong University Shanghai China

As the chemical processes become more and more complicated, it is crucial but also difficult to detect and diagnose faults in the system than ever. With the development of machine learning algorithms and cloud computing, to monitor chemical processes completely with the help of big data become true. However, most machine learning methods suffer from unexplainable problem. By employing Explainable Artificial Intelligence(XAI) techniques, this paper applies two kinds of unsupervised machine learning methods, One-Class Support Vector Machine and Depth-based Isolation Forest Feature Importance, to detect and diagnosis faults in a chemical process. By combining two different fault explanation methodologies, accuracy and reliability of the fault diagnosis results can be achieved by the validation in a simulated continuous stirred tank reactor.

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