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Chinese control and decision Conference, CCDC

作者： Zhuang Li Jianqi An Yunpeng Guo Guoyu Zhao Min Wu 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 School of Future Technology China University of Geosciences Wuhan China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

The gas utilization ratio (GUR) in a blast furnace is directly linked to the efficiency, cost-effectiveness and environmental impact on the blast furnace ironmaking process. However, The stability of the published GUR time series prediction models need to be improved. This paper presents an improved particle swarm optimization (PSO) incorporating linearly decreasing inertia weights (LDIW) to optimize the kernel-based extreme learning machine (KELM) for single-step prediction. This paper uses singular spectrum analysis (SSA) to preprocess the data and extract the key components from the GUR time series to solve the problem of high volatility of the GUR time series. In addition, this paper introduces LDIW to improve the optimization ability of particle swarm optimization algorithm, which enhances the stability of a single-step prediction model. Then this paper uses the improved PSO algorithm to extract the optimal parameters of KELM, and establishes a single-step GUR prediction model based on the improved PSO-KELM. Finally, this paper uses the actual production process data of blast furnace to verify the prediction model. The results show that the prediction accuracy of GUR and the overall stability of the model are significantly improved, providing important guidance for the blast furnace ironmaking process.

关键词： Extreme learning machines Time series analysis Production Predictive models Blast furnaces Prediction algorithms Stability analysis

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Improving the Adaptability of sEMG-Based Gesture Recognition Using Variational Mode Decomposition and Transfer Learning

Improving the Adaptability of sEMG-Based Gesture Recognition...

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Pervasive and intelligent Computing (PICom), IEEE Conference on

作者： Hao Wu Feng Wang Juan Zhao Seiichi Kawata Jinhua She 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 School of Engineering Tokyo University of Technology Hachioji Tokyo Japan

ISBN: (数字)9798331522742

ISBN: (纸本)9798331522759

Surface electromyography (sEMG) based gesture recognition has received broad attention and application in rehabilitation areas. sEMG signals exhibit strong user dependence properties among users with different physiology, causing the inapplicability of the recognition model on new users. Transfer learning (TL) is a representative method to reducing user gaps by utilizing features already learned by pre-trained models. However, TL uses a large amount of training data due to the discrepancy of sEMG among different users, which increases the training burden. In this paper, a multi-user adaptive network (MUAN) is devised to decompose the insensitive features among different users to improve gesture recognition accuracy for new users, which is based on variational modal decomposition (VMD), convolutional neural network (CNN), and TL. Ninapro dataset is used to evaluate the adaptability of MUAN and the training burden on new users. Experimental results show that MUAN outperforms CNN and TL, and reduces the training burden for new users. MUAN has the potential to provide a robust and generalized HMI system for clinical applications.

关键词： Training Adaptation models Adaptive systems Transfer learning Training data Gesture recognition Electromyography Physiology Convolutional neural networks Signal resolution

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Fully Decentralized DC Optimal Power Flow Model And Its Solution 6

Fully Decentralized DC Optimal Power Flow Model And Its Solu...

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6th IEEE International Electrical and Energy Conference, CIEEC 2023

作者： Hao, Guangtao Han, Xueshan Chen, XueJun Luo, Sibei Han, Shifeng Ye, Pingfeng School of Mechanical Electrical & Information Engineering Putian University Putian351100 China Shandong University Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Jinan250061 China New Engineering and Industry College Putian University Putian351100 China Intelligent Manufacturing of Huzhou College Huzhou313000 China School of Management of Putian University Putian351100 China College of Energy Storage Technology Shandong University of Science and Technology Qingdao266590 China

ISBN: (纸本)9798350346671

In view of the fact that renewable energy power generations such as wind and solar energy have been integrated into the power system in a high proportion in a distributed manner, driven by factors such as privacy and security of various power supply stakeholders, the power supply of the power system presents a decentralized management and control situation, making the traditional centralized DC optimal power flow difficult to achieve. In this paper, a fully decentralized DC optimal power flow model, its solution method and application are proposed. Firstly, based on the economic dispatch model of the traditional consensus algorithm, the node phase decentralized calculation method is established and the active power flow decentralized calculation method of the transmission line is given to determine the system overload line. Secondly, the improved Lagrangian optimal model of the centralized DC optimal power flow is proposed and the optimization equations are derived. Thirdly, on this basis, combined with the improved traditional consensus algorithm, the fully distributed DC optimal power flow model and its solution method are realized. Finally, a real system is taken as an example to demonstrate the effectiveness of the proposed method. © 2023 IEEE.

关键词： Solar energy

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Nonlinear Underwater Robot system Environmental Disturbance Suppression Based on Feedback Linearization and EID Method

Nonlinear Underwater Robot System Environmental Disturbance ...

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Asian control Conference

作者： Chengyu Li Xin Chen Yang Zhou Wei Tan Wenpeng He School of Future Technology 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 School of Automation China University of Geosciences Wuhan China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

A disturbance suppression approach combining feedback linearization and equivalent input disturbance (EID) method is proposed to control nonlinear underwater robots' position. Firstly, the complex nonlinear model of the underwater robot is transformed into a linear model by feedback linearization. Then, to enhance the system's ability to reject unknown disturbances in the marine environment, the EID method is utilized for estimation and compensation. This helps improve the system's ability to handle disruptions. Additionally, a linear quadratic regulator (LQR) is employed to ensure system stability and achieve rapid convergence. Finally, the proposed approach is validated by demonstrating its effectiveness through simulation results.

关键词： Autonomous underwater vehicles Regulators Simulation Position control Mathematical models Stability analysis Feedback linearization

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Distributed Generalized Nash Equilibria Learning for Online Stochastic Aggregative Games

arXiv

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arXiv 2025年

作者： Du, Kaixin Meng, Min Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai200092 China Department of Control Science and Engineering College of Electronics and Information Engineering National Key Laboratory of Autonomous Intelligent Unmanned System Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Shanghai Research Institute for Intelligent Autonomous Systems Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai China

This paper investigates online stochastic aggregative games subject to local set constraints and time-varying coupled inequality constraints, where each player possesses a time-varying expectation-valued cost function relying on not only its own decision variable but also an aggregation of all the players’ variables. Each player can only access its local individual cost function and constraints, necessitating partial information exchanges with neighboring players through time-varying unbalanced networks. Additionally, local cost functions and constraint functions are not prior knowledge and only revealed gradually. To learn generalized Nash equilibria of such games, a novel distributed online stochastic algorithm is devised based on push-sum and primal-dual strategies. Through rigorous analysis, high probability bounds on the regret and constraint violation are provided by appropriately selecting decreasing stepsizes. Moreover, for a time-invariant stochastic strongly monotone game, it is shown that the generated sequence by the designed algorithm converges to its variational generalized Nash equilibrium (GNE) almost surely, and the time-averaged sequence converges sublinearly with high probability. Finally, the derived theoretical results are illustrated by numerical simulations. Copyright © 2025, The Authors. All rights reserved.

关键词： Nash equilibrium

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Information Extraction of Bionic Camera-Based Polarization Navigation Patterns Under Noisy Weather Conditions

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Journal of Shanghai Jiaotong university(Science) 2020年第1期25卷 18-26页

作者： AH SAN Muhammad CAI Yunze ZHANG Weidong Department of Automation Ministry of Education of ChinaShanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of System Control and Information Processing Ministry of Education of ChinaMinistry of Education of ChinaShanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of Marine Intelligent Equipment and System Ministry of Education of ChinaShanghai Jiao Tong UniversityShanghai 200240China

Light polarization is the phenomenon that describes the oscillations and orientations of the light *** light is a source of substantial cue for navigation in many marine and land-dwelling *** work investigates the challenges of obtaining information regarding polarization under various conditions by observing the phenomenology of the ommatidium in many *** conditions can be because of haze or the presence of clouds in the *** molecules are greater in volume and scale in such atmospheric *** sunlight crosses through these molecules of aerosol,its polarization information is *** distorted pattern has a little or no information on neutral points of light *** the basis of the relationship bet ween wavelength of sunlight and polarization,we propose a novel hue based color mixing(HBCM)model to calculate the polarization information and orientation information more accurately and *** method improves the symmetries of polarization patterns and eliminates the effect of *** of polarization patterns are compared with red,green and blue(RGB)spectrum and these differences are quantified and compared especially under high noisy weather conditions.

关键词： light polarization ommatidium aerosols neutral points hue based color mixing(HBCM)

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Human pose recognition based on multiple features and random forest algorithm 2

Human pose recognition based on multiple features and random...

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2nd International Conference on Computer Vision, Image, and Deep Learning

作者： Fang, Di Wang, Hong Meng, Fanglong School of Automation China University of Geosciences Wuhan430074 China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

ISBN: (数字)9781510646827

ISBN: (纸本)9781510646810

Human pose recognition based on bone node data collected by depth camera is a key problem in the field of human-computer interaction. To improve the accuracy of human pose recognition, a new algorithm based on multiple features and random forest model is proposed. Firstly, a 93-dimensional vector is defined, which contains the coordinate feature of the joint and the distance feature, and the distance feature is selected according to the spatial position of the joint. Then, in the process of body pose recognition, the random forest model is combined with Bagging algorithm to ensure the balance of samples, so as to improve the classification performance of the classifier for different samples. Finally, the performance test of the constructed classifier is carried out on the UTKinect-action3D Dataset. The experimental result shows that the algorithm can effectively identify a variety of human posture, and the recognition rate reaches more than 90%. The fusion of multiple features is of great significance to improve the accuracy of human posture recognition. © 2021 SPIE.

关键词： decision trees

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Accurate Distance Measurement Method of Single-Phase Ground Fault of Power Grid Feeder with Load Division Based on Fault Secondary Discrimination

Accurate Distance Measurement Method of Single-Phase Ground ...

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International Conference on Power and Energy Technology (ICPET)

作者： Yunxiang Cheng Xiangbo Liu Sen Wang Fang Gao Hao Li Zhengchen Tao Power Supply Service Command Center State Grid Rizhao Power Supply Company Rizhao China Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Shandong University Jinan China

In order to solve the problem of low accuracy of traditional fault location method when the feeder of distribution network contains load branches, this paper proposes an accurate fault location method for single-phase grounding fault of feeder in distribution network with load branches based on fault secondary discrimination. By analyzing the feeder fault recording data, the fault point is identified twice to determine whether the fault point occurs on the load branch. According to different fault location, the voltage of the fault point is calculated by using the voltage and current of the two ends of the fault section. The voltage of the calculated fault point should be equal. The objective function of the minimum voltage difference of the fault point is written, and the fault distance x under the minimum voltage difference is obtained. The simulation model of 10KV distribution network is built on PSCAD, which verifies the accuracy of the ranging algorithm and has certain ability to resist transition resistance, which has certain engineering practice value.

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Aerial battlefield targets grouping based on DTW-DBSCAN algorithm

Aerial battlefield targets grouping based on DTW-DBSCAN algo...

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第40届中国控制会议

作者： Shao Zhuang Chen Chen School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex System

Targets grouping is the basis of second-level information fusion,which can effectively assist commanders to make *** aerial targets grouping algorithm only takes the radar acquisition data at the current time as the object and cannot update the clustering results automatically.A grouping method combining dynamic time warping(DTW) and the algorithm Density-Based Spatial Clustering of Applications with Noise(DBSCAN) is *** DTW distance of each attribute historical time-series data is used to measure the similarity between ***,an improved DBSCAN algorithm is used for *** simulation results show that the method has better grouping effect and can automatically cluster regularly.

关键词： targets grouping dynamic time warping density clustering

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Weakly Supervised and Semi-Supervised Semantic Segmentation Makes Full Use of a Small Amount of Labeled Data

SSRN

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SSRN 2023年

作者： Zhang, Wenming Li, Yaqian Li, Haibin Wang, Zhe Engineering Research Center Ministry of Education for Intelligent Control System and Intelligent Equipment Yanshan University Qinhuangdao China Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao China School of Electrical Engineering Yanshan University Qinhuangdao066004 China

Semi-supervised and weakly supervised semantic segmentation are very challenging tasks and have been studied in recent years. The purpose is to reduce the high cost of semantic segmentation label production. In this paper, we propose a weakly supervised assistant supervision strategy (ASS), which introduces more semantic segmentation for the shallow network by introducing additional classification tasks to the classification network, and enhances the fusion of shallow features and deep features. In addition, we propose a multi-scale global correlation module (MSG). This module uses the correlation degree of different scale regions to improve the weight of the nondiscriminative part, which solves the problem of insufficient CAMs activation. In most scenarios, weak supervision and semi-supervision are inseparable. We creatively propose a training strategy that integrates semi-supervision and weak supervision, adding pixel-level supervision in the training process of weak supervision to give full play to the role of pixel-level annotation. Extensive experiments on PASCAL VOC 2012 dataset show that our weakly supervised training strategy achieves the state of the art, and the combination of weakly supervised and semi-supervised training strategy can make more effective use of pixel-level labels. © 2023, The Authors. All rights reserved.

关键词： Semantic Segmentation

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