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Multi-objective optimisation design of cross-shaft based on Kriging response surface optimisation model

International Journal of Wireless and Mobile Computing 2024年第2期26卷 207-214页

作者： Xie, Yuanmin Xiong, Sijie Yun, Juntong Mao, Yanfeng Li, Boao Tang, Xinjie Cao, Yongcheng Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Hubei Wuhan China Precision Manufacturing Research Institute Wuhan University of Science and Technology Hubei Wuhan China Research Centre for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Hubei Wuhan China Technology Research and Development Hubei Jingmen Wusan Machinery Equipment Manufacturing Co. Ltd. Hubei Jingmen China

Cross-universal coupling is a key component of the mechanical transmission system and the cross-shaft is the core component of the coupling for torque transmission. Under normal circumstances, cross-shafts are most susceptible to fatigue and deformation, mainly due to the large torques they carry and the irrationality of their structure. Traditional design methods rely on practical experience to determine the key dimensions of the cross-shaft, resulting in long design cycles and low reliability. To address this problem, parametric modelling of the cross-shaft is carried out in this paper and imported into ANSYS Workbench. In addition, static and finite element analyses are carried out to find the weak parts of the cross shaft as the objective function. Finally, sensitivity analysis is carried out using the main structural parameters of the cross-shaft as design variables. Based on the linear correlation matrix and sensitivity graph, the three design variables that have the greatest impact on the objective function, journal height, thickness and body length, are selected. © 2024 Inderscience Enterprises Ltd.

关键词： Sensitivity analysis

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Gesture Recognition Using FMCW Radar Based on Deep Neural Network

Gesture Recognition Using FMCW Radar Based on Deep Neural Ne...

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2023 China Automation Congress, CAC 2023

作者： Wang, Rui Xue, Wei Peng, Jiao Yang, Xinya School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

ISBN: (纸本)9798350303759

In recent years, radar based gesture recognition technology has attracted more and more attention. Among different types of radars, frequency modulated continuous wave (FMCW) radar has the greatest application potential due to its high range and velocity resolution and low cost. However, insufficient gesture information extraction and interference signals affect the performance of FMCW radar gesture recognition. To address the problems, a gesture recognition method based on multi-dimensional features and deep neural network is proposed in this paper. First, the range-time map (RTM) and velocity-time map (VTM) of the gesture are constructed from the range-Doppler map (RDM) with two-dimensional fast Fourier transform (2D-FFT). Then, the angle-time map (ATM) is constructed with the multiple signal classification (MUSIC) algorithm. Subsequently, RTM, VTM and ATM are normalized and adaptively filtered to suppress interference signals, and the three maps are fused to construct the range-velocity-angle-time map (RV ATM) of the gesture. Finally, an improved VGG16 network with self-attention mechanism module is used for feature extraction and recognition. The experimental results show that the proposed method achieves an average accuracy of 98.3% for six gestures, and the improved VGG16 network outperforms other traditional convolutional neural networks. © 2023 IEEE.

关键词： Deep neural networks

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Multi-Level Features Fusion for Zero-Shot Object Pose Estimation 26

Multi-Level Features Fusion for Zero-Shot Object Pose Estima...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Huang, Nanxin Xu, Chi School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (纸本)9798331521950

Driven by advancements in industrial production and artificial intelligence, the need for pose estimation of new ob-jects in areas like robotic manipulation and virtual reality is increasing. We introduce a zero-shot object pose estimation approach that identifies the poses of objects excluded from the training dataset, removing the requirement for re-modeling. The method is built around a multi-level features fusion framework de-signed to enhance generalization. First, a trainable feature extraction module filters and selects multi-level features extracted by the backbone network. Unlike traditional convolutional ker-nels, we incorporate a dynamic convolution kernel to enhance the feature extraction capability. Second, in the feature fusion module, we adopt a dynamic weight generation strategy to perform weighted fusion of multi-level features. This method enhances template matching by effectively describing similarities between unseen objects (those absent from the training set) and templates, leveraging robust and adaptive feature representations to narrow the gap with seen objects. Experimental results demonstrate that our approach achieves state-of-the-art performance on two popu-lar benchmark datasets, LineMod and LineMod-Occlusion, proves that our method has better generalization than previous models. © 2025 IEEE.

关键词： Industrial robots

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Detection of Termite Nests in Dams Using Ground Penetrating Radar Based on Deep Learning 20

Detection of Termite Nests in Dams Using Ground Penetrating ...

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20th International Conference on Ground Penetrating Radar, GPR, 2024

作者： Peng, Jiao Xue, Wei Wang, Wenkai Song, Junlei Zhou, Feng Huang, Pengchun Zhang, Zhixuan Zhang, Feng Zhang, Xu Chen, Longjia Sun, Li School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China Dam Safety Monitoring and Termite Control Center of Hubei Provincial Department of Water Resources China Key Laboratory of Termite Control Ministry of Water Resources China School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan China

Termites have always been one of the major hidden dangers to the operational safety of dams. Traditional detection methods such as manual inspection and termite baiting have limitations in terms of efficiency and accuracy. To solve the problems, paper proposes a termite nest detection method based on ground penetrating radar (GPR) and deep learning. First, the simulated GPR data are generated using gprMax software, and the real data are collected using a GPR at several dams. Subsequently, the YOLOv8 deep neural network is employed for termite nest recognition in GPR images. The experimental results show that the proposed model can achieve the mean average precision (mAP) of 0.96 and a detection speed of 53.76 frames per second (FPS). © Published under licence by IOP Publishing Ltd.

关键词： Deep neural networks

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A Review on 3D Hand Pose and Shape Reconstruction from Color Images 43

A Review on 3D Hand Pose and Shape Reconstruction from Color...

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43rd Chinese control Conference, CCC 2024

作者： Leng, Huajian Ge, Yuting Xu, Chi China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China

ISBN: (纸本)9789887581581

The task of hand reconstruction aims to estimate the shape and pose of hands from the input images. Deep learning algorithms have shown remarkable performance in the field of computer vision in recent years, promoting the development of related algorithms. This article reviews the 3D hand reconstruction technologies, introducing the research background, value, and directions, summarizing the characteristics of datasets and evaluation metrics, and discussing the types of parametric, non-parametric, and joint mesh representation methods;it then outlines research directions in single-hand, hand-object interaction, and two-hand reconstruction, and analyzes their progress. Finally, it summarizes both the hand datasets and the algorithms, and provides a perspective on the future development of hand reconstruction. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： 3D reconstruction

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Pre-trained Language Model - Based Real-time Formation Lithology Identification Method for Drilling Process

Pre-trained Language Model - Based Real-time Formation Litho...

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2024 China Automation Congress, CAC 2024

作者： Liu, Jianyang Gan, Chao Cao, Weihua 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 Wuhan430074 China

ISBN: (纸本)9798350368604

Geological exploration is essential for economic and social progress, with lithology identification being a key technology that improves exploration precision and resource development efficiency. This paper presents a real-time identification method of formation lithology in drilling process based on pre-training language model, which is divided into three stages. In this first stage, the drilling data undergoes rigorous cleaning, feature selection, and normalization to eliminate noise and anomalies. In the second stage, key model inputs such as weight on bit (WOB), rate of penetration (ROP), rotation speed (RPM), torque, and pump volume (P) are identified through detailed drilling mechanism analysis. In the last stage, a real-time identification method of formation lithology in drilling process based on pre-training language model is proposed, which realizes high-precision real-time identification in drilling process. The effectiveness of the proposed method is proved by comparison with common methods. © 2024 IEEE.

关键词： Lithology

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A Lightweight Steel Strip Defect Detection Method Based on YOLO 43

A Lightweight Steel Strip Defect Detection Method Based on Y...

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43rd Chinese control Conference, CCC 2024

作者： Qian, Hao Xu, Chi China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China

ISBN: (纸本)9789887581581

As an essential component of modern industry, steel strips play an indispensable role in various fields and serve as a crucial raw material in industrial production. However, due to various factors such as production technology, the surface of steel strips often exhibits unavoidable defects like scratches and crazings. Therefore, implementing online surface quality inspection for steel strips is one effective way to enhance product quality and production efficiency. This paper proposes a detection method for surface defects in steel strips based on YOLO. By optimizing the network structure of YOLOv5s, the performance on the NEU-DET dataset has improved by nearly 4 % compared to the original network's mAP value. Experimental results indicate that the detection performance of this method is significantly superior to the current mainstream lightweight detection networks. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Strip metal

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High-spatial-resolution phase-chaos Brillouin optical correlation domain sensor: theoretical analysis and experimental demonstration

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Journal of Lightwave Technology 2025年第12期43卷 5957-5966页

作者： Niu, Lintao Wang, Yahui Tian, Yixiu Ma, Jinglian Zhang, Mingjiang Taiyuan University of Technology Key Laboratory of Advanced Transducers and Intelligent Control System Ministry of Education Taiyuan030024 China Taiyuan University of Technology College of Physics and Optoelectronic Engineering Taiyuan030024 China Shanxi-Zheda Institute of Advanced Materials and Chemical Engineering Taiyuan030032 China

The predicaments of micro-scale gain motivation and its high signal-to-noise ratio (SNR) extraction primarily restrict the practical measurement of a superior spatial resolution (SR) for stimulated Brillouin scattering sensors. A high-SNR phase-chaos laser Brillouin optical correlation domain analysis (PCL-BOCDA) is introduced to breakthrough the physical bottlenecks. The fundamental principle of gain excitation scale based on PCL linewidth is proposed, and a novel SR evolvement model is constructed. The significant impact of edge effects on SR measurement is thoroughly analyzed, and this approach is theoretically capable of achieving higher SR benefiting from the narrower gain range and feebler accumulation noise. Experiments with different linewidth PCLs are conducted and the results are confirmed to the simulated ones. Ultimately, an SR of 4.8 mm with a relative error of 4.3% is achieved over a 1.14 km sensing distance, representing a tenfold improvement over previous results. This advancement provides a competent approach for high-SR sensors and their application. © 1983-2012 IEEE.

关键词： Stimulated Brillouin scattering

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Alarm Prediction with Variational Output Dimension for Alarm Floods Using Transformer 43

Alarm Prediction with Variational Output Dimension for Alarm...

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43rd Chinese control Conference, CCC 2024

作者： Pang, Long Jiang, Wenbin Hu, Wenkai Wang, Zhuang Cao, Weihua China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China

ISBN: (纸本)9789887581581

Industrial alarm systems play crucial roles in assessing the production status and ensuring process safety. However, the presence of alarm floods can make alarm systems partially or fully fail, and thus would cause serious consequences. Alarm prediction, as an advanced approach to address alarm floods, offers operators decision supports when an alarm flood occurs. Existing alarm prediction methods requires that the output alarm sequences are of a fixed dimension, making such methods unable to adapt to the changing alarm rate. Accordingly, this paper proposes an alarm prediction model based on Transformer. The proposed model can predict alarm sequences with variational dimensions and can also achieve remarkable accuracy. The effectiveness of the proposed method is demonstrated by a case study with data from the Vinyl Acetate Monomer (VAM) simulation model. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Polyvinyl acetates

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Non-Convex Operating Zone Establishment and Alarm Limit Design for Multivariate Alarm Systems Based on Grid Clustering 43

Non-Convex Operating Zone Establishment and Alarm Limit Desi...

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43rd Chinese control Conference, CCC 2024

作者： Wei, Yujie Hu, Wenkai Li, Yupeng Cao, Weihua China University of Geosciences School of Automation 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 Wuhan430074 China

ISBN: (纸本)9789887581581

Alarm systems are core components in large complex industrial facilities to ensure safe and efficient operation. Traditional univariate alarm system design methods ignore the correlation of process variables and thus may generate massive false alarms and missed alarms. An effective way of designing a multivariate alarm system is to establish the normal operating zones (NOZs) based on historical data under normal conditions. These NOZs serve as the basis for setting alarm limits in multivariate alarm systems. Given that the actual correlations between related variables are frequently unknown, a robust modeling approach is necessary to effectively handle both convex and non-convex NOZs. Thus, this paper presents a systematic method for nonconvex operating zone establishment and alarm limit design for multivariate alarm systems. A multivariate non-convex NOZ is established based on grid clustering. Then, the multidimensional grid boundary is extracted and used to determine the alarm limits for real-time samples. A numerical example is used to demonstrate the effectiveness of the proposed method. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Alarm systems

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