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4th IEEE International Conference on Information technology, Big Data and Artificial Intelligence, ICIBA 2024

作者： Du, Jiayi Liu, Jia Cao, Runxi Zhang, Yijing Zhang, Yunxi Tianjin University of Technology and Education School of Automation and Electrical Engineering Tianjin China Tianjin University of Technology and Education Tianjin Key Laboratory of Information Sensing and Intelligent Control Tianjin China

ISBN: (纸本)9798350363586

This study proposes a consensus control strategy for heterogeneous multi-agent systems characterized by uncertain parameters and external disturbances, to address the intricate robust control challenges posed by time-varying delays and dynamic network topologies. A distributed control protocol is design to make the states of the heterogeneous multi-agent system be consistent. Then the stability of the control protocol is analyzed by Lyapunov-Krasovskii stability theory and integral inequality techniques, and the sufficient conditions for the system to achieve robust H∞ consistency are obtained. Finally, the results of the simulations verify the accuracy of the theoretical analysis presented in this paper. © 2024 IEEE.

关键词： Multi agent systems

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Prescribed-Time Stabilization of Singularly Perturbed Systems

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 569-571页

作者： Yan Lei Yan-Wu Wang Xiao-Kang Liu Wu Yang the School of Artificial Intelligence and Automation Huazhong University of Science and Technology(HUST)Wuhan 430074 Key Laboratory of Image Processing and Intelligent Control Ministry of EducationWuhan 430074China the School of Electrical Engineering Guangxi UniversityNanning 530004China

Dear Editor, This letter investigates the prescribed-time stabilization of linear singularly perturbed systems. Due to the numerical issues caused by the small perturbation parameter, the off-the-shelf control design techniques for the prescribed-time stabilization of regular linear systems are typically not suitable here. To solve the problem, the decoupling transformation techniques for time-varying singularly perturbed systems are combined with linear time-varying high gain feedback design techniques.

关键词： prescribed stabilization perturbed

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Substation Insulator Inspection Robot System Based on Deep Learning 7

Substation Insulator Inspection Robot System Based on Deep L...

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7th IEEE Information technology, Networking, Electronic and Automation control Conference, ITNEC 2024

作者： Li, Meng Tian, Liguo Hou, Shidong College of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin China Tianjin University of Technology and Education Tianjin Key Laboratory of Information Sensing & Intelligent Control Tianjin China

ISBN: (纸本)9798350370782

Substation plays an important role in power system As the key electrical isolation component in substation, the state of insulator is directly related to the safe operation of power grid In order to improve the inspection efficiency and safety of substation insulators, this paper presents a substation insulator inspection robot system based on ResNet18 model in order to improve the inspection efficiency and safety of substation insulators The system consists of visual inspection module, navigation module and main control system The visual inspection module adopts Hi3516 visual identification terminal to accurately inspect insulators;The navigation module uses lidar and Rising Sun X3 to collect map data and SLAM algorithm to realize accurate positioning and path planning;The main control system takes STM32 core board as the center and is responsible for motor control network communication and other tasks In addition, a man-machine interface is designed to display the inspection status and inspection results of the robot in real time Experiments show that the system has high accuracy and robustness in practical application, which provides technical support for automatic inspection of insulators in substations. © 2024 IEEE.

关键词： Motion planning

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Plant Growth Status Analyser based on WOA-LSTM Prediction Model 6

Plant Growth Status Analyser based on WOA-LSTM Prediction Mo...

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6th IEEE Advanced Information Management, Communicates, Electronic and Automation control Conference, IMCEC 2024

作者： Hu, Zhiming Tian, Liguo Li, Meng Tianjin University of Technology and Education College of Automation and Electrical Engineering Tianjin China Tianjin University of Technology and Education Tianjin Key Laboratory of Information Sensing and Intelligent Control Tianjin China

ISBN: (纸本)9798350316520

Plant electrical signal is a kind of non-smooth and non-linear weak signal, and it is a low-frequency random signal. The plant growth state analyzer of the plant electrical signal is to receive the data from the plant electrical signal acquisition board first, and then preprocess the electrical signal data collected from the acquisition board to finally obtain the plant electrical signal with high signal-to-noise ratio, extract the maximum value, minimum value, peak value, average value, area, rising speed, falling speed as the feature value, and then get the deep learning model through the training of WOA-LSTM neural network to obtain the collected plant electrical signal and a relationship with the influences on the plant growth state. model to get the collected plant electrical signals and a relationship with the influence of plant growth state. The experimentally trained WOA-LSTM neural network model has a mean square error MSE and correlation coefficient R of 0.0232 and 0.9408, respectively. which is a good judgment, proving that the model has a high judgment accuracy. Finally, the model was deployed to the development board and the plant growth status was displayed on the feedback interface using QT. The plant growth state analyzer speculates the current growth state of the plant through the change of the electrical signal of the plant in order to adjust the environment of the plant in time. Through the electrical signal to determine whether the plant grows in a good direction or a bad direction to control it, it helps to promote the development of agriculture in a more accurate, efficient and sustainable direction. © 2024 IEEE.

关键词： Neural network models

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State of Charge Estimation for Lithium Batteries Based on the Square Root Cubature Kalman Filter Algorithm 4

State of Charge Estimation for Lithium Batteries Based on th...

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4th International Conference on Energy Engineering and Power Systems, EEPS 2024

作者： Liu, Hanhan Su, Jun School of Electrical Engineering and Automation Xiamen University of Technology Xiamen China Xiamen University of Technology Xiamen Key Laboratory of Frontier Electric Power Equipment and Intelligent Control Xiamen China

ISBN: (纸本)9798350366914

The State of Charge of lithium batteries is a crucial parameter for the battery management system in electric vehicles and energy storage applications. Accurate estimation of the State of Charge is of significant importance for the design of the battery management system. To address the problem of accurately estimating the State of Charge, this paper employs the square root cubature Kalman filter algorithm in conjunction with a second-order resistance-capacitance equivalent circuit model of lithium batteries for precise State of Charge estimation. This study utilizes the open-source experimental dataset of the Panasonic NCR18650PF lithium battery and identifies the parameters of the second-order resistance-capacitance equivalent circuit model through the forgetting factor recursive least squares method. To verify the estimation accuracy of the Square Root Cubature Kalman Filter algorithm, the algorithm is simulated and tested using the Urban Dynamometer Driving Schedule conditions within the experimental dataset. The estimation accuracy of the Square Root Cubature Kalman Filter algorithm is compared with that of the Extended Kalman Filter algorithm and the Unscented Kalman Filter algorithm under the same conditions. Simulation test results indicate that the Square Root Cubature Kalman Filter algorithm achieves higher accuracy in estimating the State of Charge of lithium batteries. © 2024 IEEE.

关键词： State of charge

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Support Vector Regression based Spectral Calibration Model Building for In-situ Process Measurement via Near-infrared Spectroscopy 41

Support Vector Regression based Spectral Calibration Model B...

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第41届中国控制会议

作者： Xiaoyu Qu Tao Liu Yang Wu Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Institute of Advanced Control Technology Dalian University of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

For in-situ measurement of biochemical process variables such as the multi-component concentrations of a fermentation process via near-infrared spectroscopy,an improved spectral calibration model building method is proposed in this paper,based on the support vector regression(SVR) in combination with a grey wolf optimizer for parameter *** address the nonlinear properties and high-dimensional problem of infrared spectral variables,the Gaussian kernel function is adopted to establish an SVR based spectral calibration model so as to improve fitting accuracy with respect to the training ***,a grey wolf optimization algorithm is given to determine the model parameters for obtaining good prediction *** application to a fluidized bed drying(FBD) process for prediction of moisture content and another lactic acid fermentation process for multi-component prediction are performed,well demonstrating the effectiveness and superiority of the proposed modelling method over the traditional partial least-squares modeling algorithms.

关键词： spectral calibration model near-infrared spectroscopy in-situ measurement support vector regression(SVR) grey wolf optimizer multi-component prediction

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Simulation analysis of erosion characteristics of slurry pipelines based on CFD-DDPM pipeline parameters 43

Simulation analysis of erosion characteristics of slurry pip...

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43rd Chinese control Conference, CCC 2024

作者： Yuan, Haoran Wu, Jiande Xiong, Xin Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan Kunming650500 China Kunming University of Science and Technology Faculty of Information Engineering and Automation Yunnan Kunming650500 China Kunming University of Science and Technology Yunnan Key Laboratory of Intelligent Control and Application Yunnan Kunming650500 China

ISBN: (纸本)9789887581581

In order to explore the impact of pipe diameter, bend-diameter ratio and bend angle on the erosion of slurry pipe, a numerical simulation method approach grounded in Computational Fluid Dynamics - Dense Discrete Phase Model (CFD-DDPM) is proposed. Firstly, the geometric models of bends with different pipe diameters, bend-diameter ratios, and bend angles are constructed;secondly, the slurry pipe erosion model based on Dense Discrete Phase Model (DDPM) is constructed;then, model validation is conducted on the slurry pipe erosion model to affirm the accuracy of the model;finally, the computational fluid dynamics method is employed to simulate the erosion and wear behavior of the pipeline, to investigate the impact of pipe diameter, bend-diameter ratio and bend angle on the erosion and wear of the slurry pipeline. At the same time, gray correlation analysis was also used to analyze the significant relationship between the three pipeline factors and erosion and wear. The results show that the main erosion area is not determined, with the change of the pipeline parameters, the most serious erosion area will move;the effect of different pipeline parameter variations on the erosion rate is different, in which the pipe diameter of the largest impact, the second largest bend-diameter ratio, the smallest impact of the bend angle. Therefore, in the design of slurry pipeline, should first consider the influence of pipe diameter factors. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Slurry pipelines

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BKA-SVR Based Wireless Charging Location Efficiency Prediction for Electric Vehicles 19th

BKA-SVR Based Wireless Charging Location Efficiency Predicti...

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19th Annual Conference of China Electrotechnical Society, ACCES 2024

作者： Lin, Sha Yingjie, Zhang Ming, Xue Jiangang, Liu Tianjin Key Laboratory of Intelligent Control of Electrical Equipment Tiangong University Tianjin300387 China State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin300401 China

ISBN: (纸本)9789819647996

As the popularity of electric vehicles and the development of wireless charging technology have prompted a focus on charging efficiency, determining the optimal charging location has become an important issue to improve charging efficiency and user experience. The aim of this study is to develop a method for predicting the efficiency prediction of wireless charging locations for electric vehicles using Black Kite Algorithms (BKA) and Support Vector Regression (SVR). First, we screened the factors affecting charging efficiency by Spearman’s correlation coefficient to identify the most relevant factors. Then, this paper compares four common prediction methods, including BP, RBF, RF and SVR, and identifies SVR as the benchmark model due to its better performance in charging efficiency prediction. Subsequently, this paper further optimizes the SVR model, examines the effects of two optimization algorithms, BKA and PSO, and finally selects BKA as the optimal algorithm. The effectiveness and practicality of the proposed method are verified through simulation and experiment, and the results show that it has a high prediction accuracy and provides new ideas and methods for the design and optimization of wireless charging systems for electric vehicles. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Black-winged Kite algorithm Electric vehicle wireless charging Location efficiency prediction Support vector regression

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Research on Speech Recognition Methods Based on Spectral Subtraction and Improved BP Neural Network Algorithm 7

Research on Speech Recognition Methods Based on Spectral Sub...

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7th IEEE Information technology, Networking, Electronic and Automation control Conference, ITNEC 2024

作者： Ma, Yuqi Zhang, Yunxi Wang, Zihan Zhao, Ya Liu, Jia School of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin China Tianjin University of Technology and Education Tianjin Key Laboratory of Information Sensing and Intelligent Control Tianjin China

ISBN: (纸本)9798350370782

This paper presents a speech recognition method combining spectral subtraction with improved BP neural network algorithm. The speech samples containing unstable noise are collected in the real environment. Firstly, the noise from speech signals is removed using spectral subtraction method, and the speech feature parameters after noise removal are extracted. Then the speech is recognized effectively by the improved BP neural network algorithm. Finally, some comparative experiments are given to verify the effectiveness of the proposed method. © 2024 IEEE.

关键词： Speech enhancement

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Memristor-based multi-synaptic spiking neuron circuit for spiking neural network

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Chinese Physics B 2022年第4期31卷 225-233页

作者： Wenwu Jiang Jie Li Hongbo Liu Xicong Qian Yuan Ge Lidan Wang Shukai Duan College of Artificial Intelligence Southwest UniversityChongqing 400715China National&Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology Chongqing 400715China Brain-inspired Computing and Intelligent Control of Chongqing Key Laboratory Chongqing 400715China Chongqing Brain Science Collaborative Innovation Center Chongqing 400715China

Spiking neural networks(SNNs) are widely used in many fields because they work closer to biological ***,due to its computational complexity,many SNNs implementations are limited to computer ***,this paper proposes a multi-synaptic circuit(MSC) based on memristor,which realizes the multi-synapse connection between neurons and the multi-delay transmission of pulse *** synapse circuit participates in the calculation of the network while transmitting the pulse signal,and completes the complex calculations on the software with ***,a new spiking neuron circuit based on the leaky integrate-and-fire(LIF) model is designed in this *** amplitude and width of the pulse emitted by the spiking neuron circuit can be adjusted as *** combination of spiking neuron circuit and MSC forms the multi-synaptic spiking neuron(MSSN).The MSSN was simulated in PSPICE and the expected result was obtained,which verified the feasibility of the ***,a small SNN was designed based on the mathematical model of *** the SNN is trained and optimized,it obtains a good accuracy in the classification of the IRIS-dataset,which verifies the practicability of the design in the network.

关键词： memristor multi-synaptic circuit spiking neuron spiking neural network(SNN)

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