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High-speed train positioning based on a combination of Beidou navigation, inertial navigation and an electronic map

Science China(Information Sciences) 2023年第7期66卷 237-252页

作者： Hui YANG Shuai-Qiang DONG Chun-Hua XIE School of Electrical and Automation Engineering East China Jiaotong University Key Laboratory of Advanced Control & Optimization of Jiangxi Province East China Jiaotong University State Key Laboratory of Performance Monitoring Protecting of Rail Transit Infrastructure East China Jiaotong University

To date, the studies on the combination positioning of high-speed trains have made great progress, but the positioning accuracy of these methods is relatively low. Map-matching positioning can improve positioning accuracy, but information transmission is time-consuming. Few studies incorporate it into combination positioning. To solve the problem, this paper proposes a high-speed train positioning method based on combining a Beidou navigation system, an inertial navigation system, and an electronic map. First, the combination positioning problem is transformed into a multi-objective optimization problem, which weights the direction similarity and distance error to form a fitness function and converts the railway line and the maximum error range of each positioning system into constraints. Second, an improved differential evolution algorithm is proposed to solve this problem. By referencing the gray wolf algorithm,the differential evolution algorithm updates individuals by varying toward the direction of multiple optimal values. Then, a new combination positioning algorithm for high-speed trains is proposed. In the simulation,the increase in positioning speed and accuracy is analyzed and validated. Compared to the current algorithms, the proposed algorithm performs better. The proposed method has practical value for improving the reliability and safety of train operations.

关键词： electronic map multi-objective optimization differential evolution algorithm Beidou navigation system high-speed train positioning

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Static-feedback guaranteed cost control for linear systems with outage and loss of effectiveness actuator faults

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Journal of control and Decision 2022年第4期9卷 407-419页

作者： Chun-Hua Xie Zhe Li Yanli Zhou School of Electrical and Automation Engineering East China Jiaotong UniversityNanchangPeople’s Republic of China Key Laboratory of Advanced Control&Optimization of Jiangxi Province NanchangPeople’s Republic of China cCollege of Electrical and Information Engineering Hunan UniversityChangshaPeople’s Republic of China

This paper investigates the static-feedback guaranteed cost control problem for linear systems with actuator faults including outage and loss of *** the actuator redundancy condition,theoretical analysis shows that a static-feedback guaranteed cost controller can always be well designed to ensure that the resulting closed-loop system is stable with desirable quadratic *** particular,the feedback gain can be determined through the solution of a modified algebraic Riccati ***,extension to the system with uncertainties is further *** with the dynamic feedback controller,the static-feedback controller consists only of logical gates/modules and it does not require any memory element,and hence it is simplest in a design *** from the existing results,the severe and timevarying actuator outage faults can be handled very well by the proposed control ***,simulation on a linearised reduced-order aircraft system is provided for verifying the theoretical results.

关键词： Static-feedback guaranteed cost control linear systems actuator outage faults algebraic Riccati equation

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Prediction of Element Component Content Based on Mechanism Analysis and Error Compensation 4

Prediction of Element Component Content Based on Mechanism A...

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4th International Conference on Industrial Artificial Intelligence, IAI 2022

作者： Lu, Rongxiu Deng, Biao Ding, Kanghao Yang, Hui Zhu, Jianyong Liu, Hongliang School of Electrical and Automation Engineering East China Jiaotong University Key Laboratory of Advanced Control and Optimization of Jiangxi Province Jiangxi Nanchang330013 China

ISBN: (数字)9781665451208

ISBN: (纸本)9781665451208

To solve the difficulty of rapid and accurate detection of component content in the rare earth extraction process, a component content modeling method combining mechanism model and error compensation model based on just-in-time learning (JITL) was proposed. Considering the different dynamic characteristics of each section, the extraction section is simplified using the segmented-aggregation method, and the mechanism model of the rare earth extraction process based on material balance is established;in view of the error caused by the simplification of the model and the characteristics of some rare earth solutions with color features, the color features of rare earth solution samples are extracted by machine vision technology, and the error compensation model of the mechanism model is established by the just-in-time learning algorithm. Through the experimental verification of the field sample data of the praseodymium/neodymium (Pr/Nd) extraction process, the results show that the modeling method proposed in this paper is suitable for rapid and accurate detection of elemental component content in the rare earth extraction process with ionic color features. © 2022 IEEE.

关键词： Extraction

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A Fault Detection Method and System for Highway Tunnel dome light Based on Improved YOLO with Locatization Loss Function 4

A Fault Detection Method and System for Highway Tunnel dome ...

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4th International Conference on Industrial Artificial Intelligence, IAI 2022

作者： Dai, Lizhen Tang, Cailing Yang, Gang Yang, Hui Luo, Jiang Chen, Zhaozhang East China Jiaotong University Key Laboratory of Advanced Control and Optimization of Jiangxi Province School of Electrical and Automation Engineering Jiangxi Nanchang330013 China

ISBN: (数字)9781665451208

ISBN: (纸本)9781665451208

Sufficient light intensity in the tunnel plays an extremely important role in ensuring the safety of driving in the tunnel. Tunnel dome light is the basic facility to ensure tunnel lighting. The traditional fault detection method is manual inspection, and the discovery of the problem is not timely. In this paper, a tunnel dome light fault detection method and system based on video monitoring is proposed. Constructing a tunnel dome light detection data set. The original positioning loss function in YOLOv5 is changed from CIOD_Loss function to SCALoss function, which is composed of side overlap (SO), corner distance (CD) and aspect ratio (AR) loss. So that the network can generate more penalties for low overlap positioning frames, and the network model has better positioning performance and faster convergence speed, it is more suitable for the dense and small target such as tunnel headlight. After improving the model, the recognition accuracy is improved by 13.1 %, and the positioning loss is also reduced. So that it can quickly and accurately locate the target to be detected. Finally, the fault lamp detection model is constructed to locate the specific location of the fault lamp. The experiment shows that the model has good performance, and it can effectively detect the state of tunnel dome light in real time and detect abnormal working conditions. © 2022 IEEE.

关键词： Location

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Improvement and Application of Discrete State Transition Algorithm 4

Improvement and Application of Discrete State Transition Alg...

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4th International Conference on Industrial Artificial Intelligence, IAI 2022

作者： Lu, Rongxiu Liu, Hongliang Yang, Hui Dai, Wenhao School of Electrical and Automation Engineering East China Jiaotong University Key Laboratory of Advanced Control and Optimization of Jiangxi Province Jiangxi Nanchang330013 China

ISBN: (数字)9781665451208

ISBN: (纸本)9781665451208

Discrete state transition algorithm relies on the initial solution and can easily fall into the local optimum. This paper proposes an improved discrete state transition algorithm (CDSTA) for the above problem. Firstly, the genetic algorithm is used to initialize to obtain the initial solution with high quality and quickly approximate the optimal value. Secondly, the optimal recovery strategy reduces the number of iterations to accelerate the algorithm's convergence rate. Finally, the chaotic perturbation strategy is introduced. When the algorithm falls into the stagnation state, a chaotic sequence is generated by Tent mapping to get rid of the local extremum. Two single-peaked and three multi-peaked functions are used to experiment with the improved algorithm, and the performance is compared and analyzed with other algorithms. The results show that the improved algorithm's solution accuracy and convergence rate are better than other comparative algorithms. The application of the improved algorithm to the traveling salesman problem demon-strates that CDSTA has good practical engineering application potential. © 2022 IEEE.

关键词： Traveling salesman problem

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iCycleGAN: An Improved CycleGAN for Rain Streak Removal From Single Image 4

iCycleGAN: An Improved CycleGAN for Rain Streak Removal From...

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4th International Conference on Industrial Artificial Intelligence, IAI 2022

作者： Weng, Yuyu Yang, Gang Tang, Cailing Yang, Hui Lu, Rongxiu Xu, Fangping Luo, Jiang School of Electrical and Automation Engineering East China Jiaotong University Key Laboratory of Advanced Control and Optimization of Jiangxi Province Jiangxi Nanchang330013 China Jiangxi Fangxing Technology Co. Ltd. Jiangxi Nanchang330025 China

ISBN: (数字)9781665451208

ISBN: (纸本)9781665451208

On the one hand, although the supervised learning methods have been used for image rain removal task, such methods have obvious limitations because maybe there is no or only few paired images with-without rain. On the other hand, problems such as color distortion and the inpainting of background information is not clear enough also limit the processing effect of unsupervised methods for image rain removal. An improved CycleGAN (iCycleGAN) was proposed to remove rain streak from a single image. First of all, CycleGAN's transfer learning ability and cyclic structure can solve the problem of the lack of paired data sets. Secondly, a densely connected convolutional network (DenseNet) was added to the generator backbone network to improve the protection of high-frequency information such as background textures, and a CBAM attention mechanism was added to the generator to focus on the repaired area near the rain streak and obtain a clearer repaired image. Finally, feature perceptual loss was introduced to strengthen the constraint of image feature restoration and obtain more realistic results. In order to verify the effectiveness of the proposed method, training was conducted on Rain100L and Rian800 data sets. The comparison of experimental results shows that the model is superior to the existing unsupervised methods in the overall repair effect, and also has comparable inpainting effect compared with mainstream supervised methods. © 2022 IEEE.

关键词： Rain

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optimization Setting of Reagent Dosage in Rare Earth Extraction Process Based on JITL 4

Optimization Setting of Reagent Dosage in Rare Earth Extract...

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4th International Conference on Industrial Artificial Intelligence, IAI 2022

作者： Dai, Wenhao Yang, Hui Lu, Rongxiu Zhu, Jianyong Chen, Pengzhan School of Electrical and Automation Engineering East China Jiaotong University Key Laboratory of Advanced Control and Optimization of Jiangxi Province Jiangxi Nanchang China School of Intelligent Manufacture Taizhou University Zhejiang Taizhou China

ISBN: (数字)9781665451208

ISBN: (纸本)9781665451208

The mechanism of the rare earth extraction process is complex, and the extraction efficiency is greatly affected by the environment. The optimal dose setting value determined by the mechanism model is not the optimal setting value for the actual extraction process. In order to make the rare earth extraction process always run in the most economical state, this paper proposes an optimal setting approach for the dosage of the rare earth extraction process based on just-in-time learning (JITL). Firstly, according to the mechanism model of the rare earth extraction process, an optimization model of the rare earth extraction process with the goal of maximizing economic benefits is established, and the theoretical optimal dose value is obtained;Then, a local model of reagent dosage increment and economic benefit increment is established utilizing the JITL, and the dosage increment that maximizes the economic benefit increment is then obtained;Finally, the increment is applied to the rare earth production process, and the approach is iterated continuously to maximize the economic benefit. The simulation result of the CePr/Nd extraction process demonstrates the effectiveness of the proposed method. © 2022 IEEE.

关键词： Economic and social effects

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Identification of Abnormal Operating Conditions in Fused Magnesium Furnaces Using a Deep Multiview TSK Fuzzy System

Identification of Abnormal Operating Conditions in Fused Mag...

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2024 China Automation Congress, CAC 2024

作者： Xi, Chao Wu, Gaochang Fan, Zizhu Xu, Feng Wang, Hui Tang, Xiaokun School of Electrical and Automation Engineering East China Jiaotong University Nanchang China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China College of Computer Science and Technology Shanghai University of Electric Power Shanghai China Key Laboratory of Advanced Control and Optimization of Jiangxi Province East China Jiaotong University Nanchang China

ISBN: (纸本)9798350368604

The smelting process of the fused magnesium furnace(FMF) is prone to semi-molten abnormal conditions, which may lead to low product quality and production efficiency, and is also prone to safety accidents. At present, the operating conditions of FMF mainly rely on manual observation of the furnace shell features for diagnosis, and it is hard to timely and effectively identify abnormalities in the smelting process. Given the difficulty in identifying abnormal operating conditions of FMF, this paper proposes a deep multiview fuzzy classification method(DMFC) that can automatically identify abnormal operating conditions. First, based on the three-phase current data, CNN and Transformer are used to extract features as multiple views. Secondly, the Takagi-Sugeno-Kang(TSK) fuzzy model is used as a single-view model, and the evidence theory is used for dynamic fusion of multiple views. This method can effectively fuse multiple deep features, and has strong scalability and a certain degree of interpretability. Through verification of the real historical data of multiple FMFs, our model has relatively high accuracy, false detection rate(FDR), and missed detection rate(MDR). © 2024 IEEE.

关键词： Smelting

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A Novel Extraction Model with Effective Separation Coefficient for Rare Earth Extraction Process

SSRN

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SSRN 2024年

作者： Xu, Fangping Yang, Hui Chang, Wenjia Zhu, Jianyong School of Electrical and Electronic East China Jiaotong University Nanchang330013 China Key Laboratory of Advanced Control & Optimization of Jiangxi Province Nanchang330013 China

For the rare earth extraction process is complicated, and its mechanistic model is usually ideal, which fails to fully consider the actual separation effect in the field extraction process. This oversight results in a significant discrepancy between the theoretical solute concentration at each stage and the empirical data from on-site production. To rectify this issue, we introduce an effective separation coefficient, thereby enabling the creation of a model capable of accurately determining the concentration of rare earth elements (REE) within each extraction extractor. This model also facilitates the construction of an optimized objective function for determining the effective separation coefficient. Taking into account the multi-peak and multi-variable nature of the optimized objective function, we put forth an enhanced version of the chaotic double mutation adaptive differential evolution algorithm: the Linear-Chaos and Two Mutation Strategies of Adaptive Differential Evolution with Covariance Matrix and Cauchy Perturbation (CC-LCTADE). Initially, a chaotic sequence is integrated into the improved algorithm to generate the initial population, thereby amplifying population diversity. Subsequently, a Covariance Matrix and Cauchy perturbation framework is incorporated into a novel characteristic coordinate system, established during the mutation and crossover processes, thereby improving its capability to prevent premature convergence. Given the variable performance requirements of the mutation strategy at different evolutionary stages, the proposed method employs a DE/current-to pbest/1 and DE/rand/1 double mutation strategy. Ultimately, adaptive techniques are utilized to determine the values of F, CR, and NP. Simulation results tested on the function CEC2017 indicate a clear superiority of CC-LCTADE over other algorithms. CC-LCTADE is then used to solve the effective separation coefficient based objective function for rare earth extraction process. The experimen

关键词： Covariance matrix

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Adaptive Backstepping Sliding Mode control of High-speed Train Based on Fuzzy Regulation 5

Adaptive Backstepping Sliding Mode Control of High-speed Tra...

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5th International Conference on Industrial Artificial Intelligence, IAI 2023

作者： Fu, Yating Tian, Junhao Yang, Hui East China Jiaotong University State Key Laboratory of Performance Monitoring and Protecting of Rail Transit Infrastructure Nanchang330013 China East China Jiaotong University Key Laboratory of Advanced Control and Optimization of Jiangxi Province Nanchang330013 China East China Jiaotong University School of Electrical and Automation Engineering Nanchang330013 China East China Jiaotong University Nanchang330013 China

ISBN: (纸本)9798350325294

In order to meet the operational requirements of the high-speed train in terms of safety, accuracy and smoothness, an adaptive backstepping sliding mode control approach based on fuzzy regulation is put forward in this paper. Firstly, to solve the problem of insufficient accuracy of the single point model, this paper establishes a mass point dynamics model of the high-speed train and sets a compensation term for the model considering the uncertainties in the operation process. Secondly, the adaptive law is designed to estimate the compensation term and the new backstepping sliding mode control strategy is developed. Finally, fuzzy rules are designed to adjust the switching gain of the sliding mode term to suppress jitter. The results of simulation and comparison experiments demonstrate the effectiveness and feasibility of the proposed modeling and control method. © 2023 IEEE.

关键词： Railroads

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