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检索条件"机构=Key Laboratory of Measurements and Control of Complex Systems of Engineering"

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Robust control of Feeding Speed for Coal Mine Tunnel Drilling Machines 25

Robust Control of Feeding Speed for Coal Mine Tunnel Drillin...

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25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Liu, Xiao Chen, Luefeng Wu, Min Cao, Weihua Lu, Chengda Pedrycz, Witold School of Automation China University of Geosciences Wuhan China School of Automation The Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems The Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Istinye University Department of Computer Engineering Sariyer Istanbul34396 Turkey

Changes in coal seam hardness cause fluctuations in the feed resistance at the drill bit during the drilling process, leading to unstable feeding speed. This paper proposes a robust dynamic output feedback controller to suppress disturbances caused by the variations in coal seam hardness in the feed system. Firstly, an unknown parameter measuring coal seam hardness is introduced, and an uncertain model of the feeding system is established based on the finite element model of the drill string. By designing weighted functions based on industrial field requirements and constructing a generalized plant, the controller achieves loop shaping, reducing the low-frequency impact of coal seam hardness variations on the feed system and suppressing the systems resonance peak. Simulation results demonstrate that the controller effectively suppresses parameter variations and external disturbances caused by changes in coal seam hardness, achieving stable control of the drilling speed. © 2024 IEEE.

关键词： Robust control

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Adaptive Neural Network Asymptotic Tracking control for Autonomous Surface Vehicles

Adaptive Neural Network Asymptotic Tracking Control for Auto...

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Industrial Cyber-Physical systems (ICPS)

作者： Yongchao Liu Qingzhi Wang Baozeng Fu Institute of Complexity Science School of Automation Qingdao University Qingdao China Shandong Key Laboratory of Industrial Control Technology Qingdao China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China

In this paper, an adaptive neural network asymptotic tracking control method is presented for autonomous surface vehicles (ASV) with unknown uncertainties. In the control design, a smooth function is integrated into backstepping construction, which can achieve asymptotic convergence. The neural networks are borrowed to approximate the lumped nonlinear functions encompassing the unknown dynamics. It can be proved that the tracking errors of ASV can asymptotically converge to zero and all signals of the ASV closed-loop system are bounded. We offer simulation images to exhibit the validity of the devised asymptotic control method of ASV.

关键词： Adaptation models Backstepping Uncertainty Adaptive systems control design Process control Artificial neural networks

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Correction of Images Projected on Non-White Surfaces Based on Deep Neural Network 14

Correction of Images Projected on Non-White Surfaces Based o...

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14th International Conference on Digital Image Processing, ICDIP 2022

作者： Wang, Zewei Zhang, Jingjing Du, Xingzhuo Cao, Sihua Wei, Wenxuan 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 China

ISBN: (纸本)9781510657564

When projecting onto a non-white surface, the projected image is distorted or color mixing by complex luminance and chrominance information, which makes the projection result different from the visual perception of the human eye. The purpose of projection image correction is to remove these effects, and traditional solutions usually estimate parameters from the collected projection samples, compute an inverse model of the projection imaging process, and try to fit a correction function. In this paper, a deep neural network-based projection image correction network (PICN) is designed to implicitly learn complex correction functions. PICN consists of a U-shaped backbone network, a convolutional neural network that extracts projected surface features, and a perceptual loss network that optimizes the correction results. Such a structure can not only extract the deep features and surface interference features of the projected image, but also make the corrected projected image more in line with human visual perception. In addition, we built a projector-camera system under the condition of a fixed global illumination environment for verification experiment, and proved the effectiveness of the proposed method by calculating the evaluation metrics of projected images before and after correction. © 2022 SPIE.

关键词： Deep neural networks

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An Error Correcting Output Code Algorithm Based on Hierarchical Clustering for Lithology Identification

An Error Correcting Output Code Algorithm Based on Hierarchi...

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第43届中国控制会议

作者： Ao Li Xi Chen School of Rail Transit Hunan University of Technology Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems.Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

Lithology identification serves as a crucial foundation for the precise exploration and safe exploitation of geological resources. However, challenges arise from the inherent imbalance and overlap present in logging data, making the accurate identification of lithology a complex task. Addressing these challenges effectively, this paper introduces a novel algorithm,the Error Correcting Output Code algorithm based on Hierarchical Clustering(HC-ECOC). The proposed algorithm initially formulates a coding matrix using distance and overlap measures between class clusters. Subsequently, it extends the length of the coding matrix by considering both the accuracy of the integrated classifier and the diversity among base classifiers. To evaluate the efficacy of the proposed method, comparisons were conducted with alternative algorithms using UCI datasets and an authentic lithology dataset. The results unequivocally demonstrate the superior performance of our algorithm.

关键词： Lithology Identification Error Correcting Output Code Hierarchical Clustering

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Drug-target interaction prediction using high order nonlinear features via neural factorization machines 4

Drug-target interaction prediction using high order nonlinea...

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2022 4th International Conference on Artificial Intelligence Technologies and Applications, ICAITA 2022

作者： Qian, Quan Zhu, Yuan School of Automation China University of Geosciences Wuhan430074 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

Medication design and repositioning are sped up by the prediction of drug-target interactions (DTIs). Two main kinds of prediction methods are commonly used, which are based on chemical structure feature extraction and deep learning methods. However, on the one hand, the DTI prediction approaches based on chemical structural feature extraction may not wholly explore the possible network characteristics in the data. On the other hand, many deep learning methods call for numerous layers of neural networks to be layered to learn higher-order feature interaction information. To sum up, the existing computation methods often have the limitations of gradient disappearance and overfitting. This study presents a novel method (JRD-NFM) by calculating Jaccard similarities, getting an eigenvector through Restarted random walk (RWR), and generating low-dimensional feature vectors by Disposition Component Analysis (DCA). Besides decoding the topological features and similarity information of target and drug node, it can also get the context information of a single network. Considering the advantages of Neural Factorization Machines (NFM) in extracting high-order nonlinear features and processing sparse data, this study use NFM to classifier the collection data to integrate drug and target biochemical structure information. The experimental results demonstrate that JRD-NFM can outperform widely used deep learning methods and conventional chemical structure approaches. It could provide fresh ideas for combining molecular structure and heterogeneous network data to predict DTIs. © Published under licence by IOP Publishing Ltd.

关键词： Feature extraction

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Dynamics Modeling Method of Offshore Drilling Experimental System Based on Double-Layer Stewart Parallel Mechanism 42

Dynamics Modeling Method of Offshore Drilling Experimental S...

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42nd Chinese control Conference, CCC 2023

作者： Wang, Chenxuan Ma, Zhejiaqi Lu, Chengda Du, Sheng Wu, Min School of Future Technology China University of Geosciences Wuhan430074 China School of Automation China University of Geosciences 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)9789887581543

Establishing the dynamics model of the offshore drilling experimental system can better complete the offshore drilling test in the laboratory environment and reduce the cost of testing. A dynamical modeling method for the offshore drilling experimental system built on the double-layer Stewart parallel mechanism is proposed. Firstly, the kinematic and dynamical characteristics of the double-layer Stewart parallel mechanism are combined with the Lagrange method and the virtual work method to establish the dynamics model of the mechanism. Then a parameter identification scheme is designed using a nonlinear gray system estimation method based on the trust-domain reflection algorithm, and the model parameters are identified. The model is downscaled to improve the feasibility of the identification scheme and the accuracy of the identified parameters. The actual experimental system data verify this model's correctness and the model parameters' accuracy. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Dynamics

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Simulation and optimization of inchworm-like pneumatic crawling actuator based on ABAQUS software

Simulation and optimization of inchworm-like pneumatic crawl...

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Robotics, control and Automation engineering (RCAE), International Conference on

作者： Junqi Mu Xiaofeng Zong Yiyang Wei Xuping Hou 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: (数字)9798350355642

ISBN: (纸本)9798350355659

The actuator, serving as the fundamental power source of a soft robot, functions as its central component. The field of soft robotics has garnered increasing research attention since its inception. Pneumatic crawling robots, being an essential branch within the realm of soft robotics, possess notable characteristics such as rapid response time, cost-effectiveness, and human-computer interaction safety. Consequently, they hold significant research value. Among various types of crawling robots that have garnered considerable interest from researchers is the inchworm crawling robot. This paper aims to address the design and development aspects pertaining to inchworm actuators. By utilizing ABAQUS simulation software and taking displacement as the reference parameter, an analysis was conducted to investigate the impact of various geometric parameters on actuator displacement. Additionally, optimization of these geometric parameters was performed. The values of aspect ratio, length-to-width ratio, vertex angle and gap depth suitable for inchworm-like actuator are obtained. These findings are presented for the first time in the field of crawling soft robots and provide a theoretical basis for the development of pneumatic inchworm-like actuators.

关键词： Pneumatic actuators Transportation Pneumatic systems Soft robotics Bending Solids Software Safety Time factors Optimization

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Norm-Integrated Softmax Loss For Deep Face Recognition

Norm-Integrated Softmax Loss For Deep Face Recognition

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IEEE International Conference on Image Processing

作者： Jun Chen Yiwei Wang Haiyan Zhang School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

The key to face recognition lies in how to improve the model’s ability to extract facial features. To this end, numerous loss functions based on different metrics have been proposed to increase the margin of feature distinction between different classes. Methods based on Cosine distance significantly enhance face recognition performance by focusing on angular constraints between samples and classes, demonstrating their superiority over those based on Euclidean distance. However, a significant oversight in these methods is the neglect of feature magnitude’s importance in representing facial features. To address this gap, our study introduces the NormIntegrated Softmax loss (NIface loss), a novel loss function that amalgamates feature norms with angular information. This integration offers a comprehensive perspective for feature classification, augmenting the compactness of intra-class features. Extensive evaluations on large-scale public datasets have demonstrated the efficacy of NIface loss in enhancing recognition accuracy and stability.

关键词： Measurement Technological innovation Image recognition Accuracy Face recognition Focusing Euclidean distance

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An Online Education Effect Assessing Method Based on YOLOv8 and Vision Transformer

An Online Education Effect Assessing Method Based on YOLOv8 ...

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2023 China Automation Congress, CAC 2023

作者： Zhang, Bo-Yang Liu, Zhen-Tao Zeng, Xiang-Yu School of Future Technology 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 School of Automation China University of Geosciences Wuhan430074 China

ISBN: (纸本)9798350303759

Due to the rapid growth of online education worldwide, assessing the learning effectiveness of students during online classes has become increasingly challenging for teachers. In this paper, a method of assessing online education effect based on YOLOv8 and Vision Transformer is proposed. Firstly, the drowsiness state of students in an online teaching unconstraint environment is estimated by using YOLOv8, and then the gaze direction of awake students is estimated by using Vision Transformer to assess the students' attention levels during online education. The results from YOLOv8 are determined based on the calculated probabilities of drowsiness or wakefulness. And then gaze estimation method proposed in this paper was compared with state-of-the-art methods on the MPIIFaceGaze and Gaze360 datasets in which the angular errors of gaze estimation are 4.58° and 12.27°, respectively. We conducted experiments and analysis on a self-made dataset, from which the results demonstrate the feasibility of our method in an unconstrained environment. © 2023 IEEE.

关键词： Students

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Multi-UAV Coverage Path Planning Method Based on Improved Particle Swarm Optimization

Multi-UAV Coverage Path Planning Method Based on Improved Pa...

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Chinese Automation Congress (CAC)

作者： Yan Zhou Yonghua Xiong Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Automation China University of Geosciences Wuhan China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

The widespread application of unmanned aerial vehicle (UAV) has made the coverage path planning problem for multiple UAV systems a research hotspot. In this paper, we propose a multi-UAV solution to achieve complete coverage of multi-region. Based on this solution, we present an improved particle swarm optimization (PSO) algorithm that combines PSO algorithm, greedy strategy, and back-and-forth (BAF) algorithm. Its purpose is to plan the optimal paths for multi-UAV while achieving the shortest total distance and the highest UAV cooperation efficiency with the minimum number of UAVs, thereby enhancing the multi-UAV coverage cooperation and path optimization capabilities. Simulation results demonstrate the feasibility and superiority of this algorithm.

关键词： Automation Simulation Autonomous aerial vehicles Path planning Particle swarm optimization Collision avoidance Optimization

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