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2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022

作者： Wang, Dongxu Xu, Kai-Da Xi'an Jiaotong University School of Information and Communications Engineering Xi'an China Science and Technology on Electromechanical Dynamic Control Laboratory Xi'an China Shaanxi Key Laboratory of Artificially-Structured Functional Materials and Devices Xi'an China

ISBN: (纸本)9781665452366

In this paper, a D-band dual-band substrate integrated waveguide (SIW) cavity-backed antenna based on low temperature co-fired ceramic (LTCC) fed by standard Flange of WR-6.5 rectangular waveguide (RWG) has been proposed. The dual-band cavity-backed antenna operates at its two high-order radiation modes through the four radiating slots etched on the top of its surface coupled by an hourglass-shaped inductive window. In addition, the design of SIW-RWG vertical transition is in order to transmit the power from the waveguide to the SIW cavity-backed antenna efficiently. The 10-dB impedance bandwidths of the proposed antenna are over 3% at both radiation frequencies of 146.6 and 167.2 GHz, while the maximum realized gains are 8.73 and 9.29 dBi, respectively. According to the LTCC process, the structure of the antenna is able to be stable and well performed. © 2022 IEEE.

关键词： Substrate integrated waveguides

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MULTI-ROBOT PATH PLANNING AND COOPERATIVE OBSTACLE AVOIDANCE BASED ON MONTE-CARLO TREE SEARCH 10

MULTI-ROBOT PATH PLANNING AND COOPERATIVE OBSTACLE AVOIDANCE...

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10th International Symposium on Test Automation and Instrumentation, ISTAI 2024

作者： Dai, Juan Jing, Yanzhang Su, Zhong Zhu, Cui Beijing Key Laboratory of High Dynamic Navigation Technology Beijing Information Science & Technology University Beijing China Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing China School of Automation Beijing Information Science & Technology University Beijing China School of Information and Communication Engineering Beijing Information Science & Technology University Beijing China

ISBN: (纸本)9781837242672

With the rapid development of artificial intelligence technologies, multi-robot cooperative processing tasks are becoming more and more common in various applications. However, traditional single-robot planning algorithms show obvious limitations in dealing with complex social interactions and dynamic environments in the real world. To address this challenge, this paper applies Monte Carlo Tree Search (MCTS) to multi-robot path planning and proposes a collaborative obstacle avoidance motion planning algorithm based on MCTS. Specifically, the algorithm prunes the Monte Carlo tree by excluding cases that do not meet the robot speed constraints and potential collisions, which significantly improves the computational speed and success rate of the algorithm, making it applicable to the multi-robot path conflict problem. In addition, we design a policy space that conforms to the robot's operation and a cost function that meets the requirements of robot path planning to plan an efficient cooperative path for each robot. Through a series of simulation experiments, we demonstrate the algorithm's path planning and obstacle avoidance performance in a variety of conflict scenarios. The results show that the method is highly cooperative and effective in complex environments. © The Institution of Engineering & technology 2024.

关键词： Cost functions

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Research on Rolling Bearing Fault Diagnosis Based on Classification Generative Adversarial Network

Research on Rolling Bearing Fault Diagnosis Based on Classif...

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UNIfied Conference of International Workshop on Defence Applications of Multi-Agent Systems, DAMAS 2023, International Conference on Maintenance Engineering, IncoME-V 2023, International conference on the Efficiency and Performance Engineering Network, TEPEN 2023

作者： Kang, Ma Hongjun, Wang School of Mechanical and Electrical Engineering Beijing Information Science and Technology University Beijing100192 China Beijing International Science and Technology Cooperation Base for Intelligent Sensing and Control of High-End Equipment Beijing100192 China Beijing Key Laboratory of Measurement and Control of Electromechanical System Beijing100192 China

ISBN: (纸本)9783031494123

In practical engineering, it is usually difficult to label monitoring data, and the fault diagnosis accuracy is not high in strong noise environment. To solve the above problems, this paper uses Cwt-CatGAN method to process signals and diagnose faults. In this method, CWT is first used to convert one-dimensional original vibration signals into two-dimensional time–frequency graphs as the input of classification generation admixture network. Then, the CatGAN model is obtained through the adversarial training process, and fake samples similar to the extraction distribution of CWT are generated. Finally, the input samples are clustered into certain categories. In addition, the performance of the Cwt-CatGAN method is verified by using the classical rotating machine data set, the experiment is compared with the unsupervised fault diagnosis model CatAAE. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Failure analysis

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Composite Fault Diagnosis of VMD-FastICA Bearing Based on Parameter Optimization

Composite Fault Diagnosis of VMD-FastICA Bearing Based on Pa...

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作者： Zhang, Chao Fei, Hongbo Wu, Le Da, L.V. School of Mechanical Engineering Inner Mongolia University of Science and Technology Baotou014000 China Key Laboratory of Intelligent Diagnosis and Control of Electromechanical Systems in Inner Mongolia Autonomous Region Baotou014000 China

ISBN: (纸本)9783031494123

Rolling bearings are one of the most commonly used bearing components in rotating machinery and are also one of the most prone to failure. Therefore, the diagnosis of the condition of rolling bearings is of significant importance for safety and economy. In practical engineering, bearings are often interfered with by vibration signals from other components, which makes it difficult to extract the fault characteristic information of bearings due to noise or other signals. In this paper, to address the problem of difficulty in extracting compound fault characteristic information of rolling bearings under strong noise background, a feature extraction method based on sparrow algorithm optimized VMD parameters and energy entropy and envelope entropy mean minimization is proposed, and combined with FastICA to achieve signal separation, denoising, and feature enhancement. This method is applied to the fault feature extraction of simulated signals, achieving denoising and feature enhancement of separated signals. The results of simulation and experimental analysis demonstrate the feasibility of this method. Furthermore, a comparison with other methods is conducted to validate the superiority of this method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Roller bearings

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Calibration of Space Robot Parameters Based on Base Coordinate System Errors 3

Calibration of Space Robot Parameters Based on Base Coordina...

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3rd IEEE International Conference on Electrical Engineering, Big Data and Algorithms, EEBDA 2024

作者： Yao, Zhen Liu, Chuankai Chen, Li Chen, Gang Han, Fang Hu, Xiaodong Fuzhou University Fuzhou China Beijing Aerospace Control Center Key Laboratory of Science and Technology on Aerospace Flight Dynamic Beijing China Beijing University of Posts and Telecommunications Beijing China Beijing Aerospace Control Center Beijing China

ISBN: (纸本)9798350380989

A motion error parameter identification method is proposed to address the issue of insufficient absolute positioning accuracy of a seven-degree-of-freedom space robot based on the geometric parameter errors of the robotic arm and the position errors of the base coordinate system. Firstly, the kinematic model of the space robot is established based on a modified complete and parametrically continuous model, and the mapping relationship between the geometric parameter error of the robotic arm link and the end-effector pose error is obtained using the pose differential transformation method. Then, taking into account the base coordinate system's positional error, a novel model for the space robot is developed, based on the errors associated with the base coordinate system. Finally, the identification of kinematic parameters is accomplished using the Levenberg-Marquardt optimization algorithm. Simulation results validate the effectiveness of the proposed method. © 2024 IEEE.

关键词： error model kinematic parameters space robot the position errors of the base coordinate system

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Residual Life Prediction of Rolling Bearings Based on Transformer-BiGRU-Attention Model with Improved Sparrow Optimization Algorithm 1

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TEPEN International Workshop on Fault Diagnostics and Prognostics, TEPEN-IWFDP 2024

作者： An, Xunmeng Zhang, Chao Liu, Caiye Liu, Guiyi Hao, Jinzhang College of Mechanical Engineering Inner Mongolia University of Science and Technology Baotou014010 China Inner Mongolia Autonomous Region Key Laboratory of Intelligent Diagnosis and Control of Electromechanical Systems Baotou014010 China

ISBN: (数字)9783031694837

ISBN: (纸本)9783031694820

Remaining useful life (RUL) prediction is one of the core problems of PHM, and the traditional deep learning method has low accuracy in predicting the remaining useful life of rolling bearings. A hybrid deep learning model of Transformer-BiGRU-Attention based on improved sparrow optimization algorithm is proposed for rolling bearing remaining useful life prediction. The current sparrow optimization algorithm has the problems of slow convergence, lack of robustness and falling into local optimum. Therefore, an enhanced version of the sparrow search algorithm, termed the Chaotic Mapping-Sparrow Search Algorithm (CSSA), is introduced. The utilization of chaotic cube mapping for initializing the population enhances the quality and diversity of distribution within the population. This approach effectively prevents the algorithm from getting trapped in local optima and optimizes both the speed of convergence and the accuracy of the solutions. Tests on RUL prediction of rolling bearings show that the proposed method can more accurately achieve the RUL prediction of bearings under variable operating conditions, and the accuracy of RUL prediction results is higher than that of current methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Mapping

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Variational Mode Decomposition Guided by Time-Frequency Domain Difference Information 1

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TEPEN International Workshop on Fault Diagnostics and Prognostics, TEPEN-IWFDP 2024

作者： Fei, Hongbo Zhang, Chao Xu, Shuai Zhang, Jing Wu, Le School of Mechanical Engineering Inner Mongolia University of Science and Technology Baotou014010 China Key Laboratory of Intelligent Diagnosis and Control of Electromechanical Systems in Inner Mongolia Autonomous Region Baotou014010 China

ISBN: (数字)9783031694837

ISBN: (纸本)9783031694820

To address the difficulty of extracting fault features of rolling bearings in noisy environments, and the issues where the main effects of Variational Mode Decomposition (VMD) are influenced by the decomposition level and penalty factor, a method based on time-frequency domain difference information-guided Variational Mode Decomposition for rolling bearing fault feature extraction is proposed. Initially, the constraints in VMD decomposition are utilized as discrimination conditions, constructing similarity coefficient differences and energy difference ratios as convergence criteria to find the optimal parameter combination. The IMF component with the maximum envelope peak factor is selected for envelope demodulation to determine the fault type. Simulation and experimental results demonstrate that this method can extract fault features from noise interference, confirming its effectiveness. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Variational mode decomposition

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Semi-LLIE: Semi-supervised Contrastive Learning with Mamba-based Low-light Image Enhancement

arXiv

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arXiv 2024年

作者： Li, Guanlin Zhang, Ke Wang, Ting Li, Ming Zhao, Bin Li, Xuelong Northwestern Polytechnical University Xi’an710072 China Science and Technology on Electromechanical Dynamic Control Laboratory Xi’an China The School of Astronautics Northwestern Polytechnical University Xi’an China Science and Technology on Electromechanical Dynamic Control Laboratory Xi’an China Northwestern Polytechnical University Xi’an710072 China The Key Laboratory of Intelligent Interaction and Applications Northwestern Polytechnical University Ministry of Industry and Information Technology Xi’an710072 China

Despite the impressive advancements made in recent low-light image enhancement techniques, the scarcity of paired data has emerged as a significant obstacle to further advancements. This work proposes a mean-teacher-based semi-supervised low-light enhancement (Semi-LLIE) framework that integrates the unpaired data into model training. The mean-teacher technique is a prominent semi-supervised learning method, successfully adopted for addressing high-level and low-level vision tasks. However, two primary issues hinder the naive mean-teacher method from attaining optimal performance in low-light image enhancement. Firstly, pixel-wise consistency loss is insufficient for transferring realistic illumination distribution from the teacher to the student model, which results in color cast in the enhanced images. Secondly, cutting-edge image enhancement approaches fail to effectively cooperate with the mean-teacher framework to restore detailed information in dark areas due to their tendency to overlook modeling structured information within local regions. To mitigate the above issues, we first introduce a semantic-aware contrastive loss to faithfully transfer the illumination distribution, contributing to enhancing images with natural colors. Then, we design a Mamba-based low-light image enhancement backbone to effectively enhance Mamba’s local region pixel relationship representation ability with a multi-scale feature learning scheme, facilitating the generation of images with rich textural details. Further, we propose novel perceptive loss based on the large-scale vision-language Recognize Anything Model (RAM) to help generate enhanced images with richer textual details. The experimental results indicate that our Semi-LLIE surpasses existing methods in both quantitative and qualitative metrics. The code and models are available at https://***/guanguanboy/Semi-LLIE. © 2024, CC BY.

关键词： Contrastive Learning

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Error Modeling Method for BeiDou Satellite Receivers Based on Long Short-Term Memory Neural Networks 24

Error Modeling Method for BeiDou Satellite Receivers Based o...

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Proceedings of the 2024 4th International Conference on Computational Modeling, Simulation and Data Analysis

作者： Boda Li Hongsen Ji Guotai Xu Keyu Qi Ziyang Lin Shuo Zhang Xi'an Institute of Electromechanical Information Technology Xi'an Shaanxi China Science and Technology on Electromechanical Dynamic Control Laboratory Xi'an Institute of Electromechanical Information Technology Xi'an Shaanxi China School of Mechatronical Engineering of Beijing Institute of Technology Beijing China Liao Shen Industries Group Co LTD Shenyang Liaoning China

ISBN: (纸本)9798400711831

In response to the influence of errors in the BeiDou satellite receiver on the precision correction fuze in two-dimensional correction munitions during accurate weapon strikes, this study proposes a BeiDou satellite receiver positioning error compensation model based on Long Short-Term Memory (LSTM) networks. By analyzing radar data from flight tests and the measurement data retrieved from recovered projectiles, the receiver error sequence is isolated. Considering the relationship between the receiver and its dynamic characteristic parameters, an error prediction model based on LSTM is established. The model takes the dynamic characteristic parameters of Beidou satellite receiver as input and the error sequence of each dynamic parameter as output to supplement the missing error sequence and compensate the error. The results show that the model can effectively predict receiver errors and reduce the maximum errors of position and speed. Through Monte Carlo simulation, error compensation effectively improves the accuracy of projectile correction, and verifies the effectiveness of the model. This error compensation method can effectively improve the accuracy and combat effectiveness of two-dimensional trajectory correction control

关键词： BeiDou satellite receiver

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Research on Surface Defect Detection technology of Railway Tracks Based on Image Processing

Research on Surface Defect Detection Technology of Railway T...

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作者： Liu, Guiyi Zhang, Chao Wang, Jianguo Chi, Yuliang Xu, Shuai School of Mechanical Engineering Inner Mongolia University of Science and Technology Baotou014000 China Key Laboratory of Intelligent Diagnosis and Control of Electromechanical Systems in Inner Mongolia Autonomous Region Baotou014000 China

ISBN: (纸本)9783031494123

Railroad transportation is an essential component of national economic development, providing crucial support for safe and efficient transportation. The quality of rail infrastructure directly impacts the efficiency and safety of train operation. Therefore, the timely and accurate detection of rail surface defects is a critical element of railroad operation and safety management. This paper examines the technology of railroad track surface detection and proposes a novel defect detection method based on image processing. The proposed method utilizes a high-resolution digital camera to capture images of the rail surface and eliminates noise and lighting effects using pre-processing techniques. Next, a feature extraction algorithm is employed to extract the features of rail surface defects, and is used to classify and identify them. Experimental results demonstrate that the proposed method accurately detects various defects on the rail surface. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Surface defects

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