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Chinese automation Congress (CAC)

作者： Wei Liu Yisheng Lu Tao Zhang Jun Chen School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Ministry of Education Engineering Research Center of Intelligent Technology for Geoexploration Wuhan China

In recent years, the semantic segmentation of 3D point cloud has received increasing attention the field of computer vision, because 3D point cloud can better reflect our 3D space. Because of the unstructured and disordered characteristics of 3D point cloud data, semantic segmentation of point cloud is still a difficult task. Our network automatically learns the importance of feature channels by adding a channel attention module, which enables the network to obtain better training results. After the channel attention module is fused, the important channels in the features are enhanced, and the unimportant channels are suppressed, making the network training more efficient. In this paper, we propose an indoor point cloud semantic segmentation method combined with channel attention mechanism. The experimental results show that our method achieves better results than other methods.

关键词： Point cloud compression Training Computer vision Three-dimensional displays automation Statistical analysis Semantic segmentation

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Design of audio to image cross-modal learning and generation based on single-layer CoPt spin-orbit torque devices

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Journal of Magnetism and Magnetic Materials 2025年 629卷

作者： Qian, Likun Yang, Liu Zuo, Chao Tao, Ying Li, Wendi Jin, Fang Li, Huihui Dong, Kaifeng 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 Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China Wuhan Second Ship Design and Research Institute Wuhan430064 China Beijing Superstring Academy of Memory Technology Beijing100176 China

Despite remarkable achievements in neural network applications using spin–orbit torque (SOT) devices, current research predominantly focuses on unimodal classification tasks, while cross-modal learning tasks often suffer from suboptimal generation quality. To address this limitation, we employ generative adversarial networks (GANs) based on CoPt-SOT to achieve cross-modal learning and generation from speech to handwritten digit images. Initially, we explore the field-free switching characteristics of single-layer CoPt devices, developing spin-based implementations of Scaled Rectified Linear Unit (SReLu), Sigmoid, and Tanh neuronal functions. Simultaneously, we leverage the nonvolatile multistate characteristics of CoPt devices to realize excitatory-inhibitory synaptic plasticity. Subsequently, for the first time, we construct half-spin and full-spin GAN networks using these spin-based neurons and synapses, enabling cross-modal learning and generation between speech and image domains. Finally, we evaluate the performance of these networks through handwritten digit recognition tasks, achieving impressive recognition accuracies of 93.78 % and 88.61 % for half-spin and full-spin implementations, respectively. Notably, this work overcomes the existing bottleneck in SOT device applications, which have been largely confined to unimodal classification tasks, and demonstrates significant potential for expanding the scope of SOT-based technologies. © 2025 Elsevier B.V.

关键词： Generative adversarial networks

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An improved dissipativity criterion for singular systems with time-varying delays via a state decomposition method

An improved dissipativity criterion for singular systems wit...

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第40届中国控制会议

作者： Yang Li Yong He School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

This paper is concerned with the dissipativity analysis of singular systems with time-varying delays. Firstly, an improved augmented Lyapunov-Krasovsii functional(ALKF) is constructed based on the state decomposition method. Then, the derivative of the ALKF is estimated by applying the auxiliary function integral inequality and the extended reciprocally convex inequality. As a result, an improved dissipativity criterion with less conservativeness is established. Finally, numerical examples are given to show the superiority of the proposed method.

关键词： Singular systems dissipativity analysis time-varying delays state decomposition method

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Fast control Strategy of Grid-forming Inverter Based on Power Feedforward

Fast Control Strategy of Grid-forming Inverter Based on Powe...

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第42届中国控制会议

作者： Sheng Zhang Zhijian Fang Qine Liu Muchao Xiang Dingbang Huang 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 State Grid Hubei Electric Power Co. LtdXiangyang Power Supply Company Electric Power Scientific Research Institute of State Grid Hubei Electric Power Co. Ltd

Grid-forming inverter is widely used in grid-connected systems of distributed generation because of its frequency and voltage support capacity and good stability in microgrid,but its large inertia will affect the dynamic response speed of grid-forming *** order to solve this problem,this paper introduces the loop that affects the dynamic response of grid-forming inverter,and carries out small signal modeling for active loop,analyzes the dynamic performance indicators and determinants of typical second-order ***,a method of adding power feedforward coefficient to the forward channel of the power loop is designed,and the response speed of the system with or without feedforward coefficient under the unit step response is ***,the simulation results show that adding the power feedforward coefficient can improve the response speed of the grid-forming inverter during startup and power switching,then achieves the effect of fast control.

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Prediction Method of Truck Travel Time in Open Pit Mines Based on LSTM Model

Prediction Method of Truck Travel Time in Open Pit Mines Bas...

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Chinese control Conference (CCC)

作者： Mengting Ao Changhe Li Shengxiang Yang School of Automation China University of Geosciences Wuhan 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 School of Computer Science and Informatics De Montfort University Leicester United Kingdom

Aiming at the prediction of truck travel time in open pit mines, we established a prediction model based on long short-term memory(LSTM). This model fully accounts for 11 factors, including the nature of trucks, weather, road conditions, and driver's behaviors, as well as the influence of neighbor road segments in the route on the current predicted road segment. The experiment shows that the error of the LSTM prediction model is significantly reduced compared with SVR and BP models. In addition, the maximum absolute mean error under different conditions is less than 12 seconds.

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A Test Suite and An Optimizer for Dietary Nutrition Optimization Problem: From Constrained Many-Objective Perspective

A Test Suite and An Optimizer for Dietary Nutrition Optimiza...

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Congress on Evolutionary Computation

作者： Yaqi Ti Changhe Li Shengxiang Yang School of Automation China University of Geosciences Wuhan 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 School of Computer Science and Informatics De Montfort University Leicester United Kingdom

With increasing people who suffer from diet-related diseases, providing suggestions for personal daily nutrient-dense intake is highly expected. However, current dietary nutrition models are less precise, and dietary nutrition optimizers usually fail to give satisfactory solutions. Therefore, we construct a constrained many-objective nutrition model with more precise nutrient assessments and a scalable constrained many-objective benchmark set. This test suite has great flexibility in evaluating algorithms' performance on high dimensional search and objective spaces with some feasible region fragments. We also propose a kd-tree based dynamic constrained many-objective evolutionary algorithm to search for customized food combinations according to personal daily consumption and intake preference. Experiments show that our algorithm has better diversity maintenance ability in high dimension space.

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Surface Defect Detection of Sanitary Ceramic Product Utilizing High-resolution Image on Two Different Scales

Surface Defect Detection of Sanitary Ceramic Product Utilizi...

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Chinese control and Decision Conference, CCDC

作者： Xing-Ao Wang Jianqi An Yisheng Mei Xin Chen Min Wu School of Automation China University of Geosciences Wuhan 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

ISBN: (纸本)9781665478977

Surface defect detection of sanitary ceramic products is an important part of the production process. The deep learning method is the mainstream research direction in the field of defect detection. Since there are many small defects on the surface of sanitary ceramic products, it is an effective means to use a high-resolution camera to obtain images. The detection speed of high-resolution pictures and the interference of background are the difficult problems in detection. Therefore, this paper designs a two-scale detection framework to detect and locate defects of sanitary ceramics utilizing high-resolution images. The two-scale detection framework speeds up the detection of high-resolution images and weakens the interference of the background by dividing the defect detection task into two parts, a workpiece region recognition task on large scale and a defect detection task on small scale. The experiments show that the two-scale detection method has a better detection speed and accuracy rate compared with the single-scale method, which proves the superiority of the proposed method.

关键词： Deep learning Image recognition Ceramic products Interference Production Cameras Ceramics

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Design of intelligent monitoring and dynamic early warning system for geological hazards

Design of intelligent monitoring and dynamic early warning s...

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Chinese automation Congress (CAC)

作者： Ziliang Yu Weihua Cao Chao Gan School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China

intelligent monitoring system is crucial to the process of geological disaster prevention and control, and plays a vital role in improving prevention and control efficiency and early warning accuracy. An intelligent monitoring and dynamic early warning system for geological hazards is designed. First, four main functional requirements of data fusion and visualization, single landslide dynamic warning, regional landslide susceptibility evaluation and risk level inquiry are analyzed in detail. Then, the system network architecture is analyzed and a six-layer software framework is designed. Finally, a new online data architecture is proposed. The designed system provides a feasible solution for geological disaster prevention and control.

关键词： Geology Scalability Software algorithms Computer architecture Network architecture Alarm systems Terrain factors

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Image Super-Resolution of Residual-Dense Connected Networks in Unmanned Deep-Sea Exploration Equipment

Image Super-Resolution of Residual-Dense Connected Networks ...

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Chinese control Conference (CCC)

作者： Jiajun Chu Weihua Cao Chao Gan Yulong Yang School of Automation China University of Geosciences Wuhan P. R. China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan P. R. China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan P. R. China

Deep-sea unmanned exploration equipment is an important tool for exploring and developing the resources in the ocean, and it can survey the deep-sea environment more visually with the help of visual images. However, the complex and variable environment and the low resolution of the underwater lens lead to the poor resolution of the images acquired by the equipment. In this paper, we propose a residual-dense connected method applied to unmanned deep-sea exploration equipment to improve it's image resolution. The method uses dense connections within the residual structure to improve the model detail information acquisition to ensure accuracy and model stability of model. Secondly, through the study of the model performance, a high precision residual-dense connected model with less computational effort is designed. Finally, the model is trained and tested using environmental images in deep-sea conditions, and it is demonstrated that the method can be applied to deep-sea unmanned exploration equipment for fast, accurate, and stable image super-resolution processing.

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Emotion Recognition via Environmental Context and Human Body

Emotion Recognition via Environmental Context and Human Body

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Industrial Cyber-Physical systems (ICPS)

作者： Cheng-Shan Jiang Zhen-Tao Liu School of Automation China University of Geosciences Wuhan P. R. China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan P. R. China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan P. R. China

To promote the humanized interactive experience of the intelligent device and system, emotional intelligence has become a popular research field in the human-machine interaction. The previous research on emotion recognition based on computer vision has mostly been carried out by analysing facial expression or body posture, and psychological studies show that scene context also contributes some important information on emotion recognition. In addition, most context-aware emotion recognition studies focus on exploring the relevance analysis of environmental semantics, but the influence of feature encoder on semantic information embedding has not been fully discussed. In this paper, we proposed a Global Semantic Feature Enhancement-Dual Stream Densely Connected Network (GSFE-DSDCN) to enhance global semantic information learning from the perspectives of dimension and spatial. Densely connected pattern is introduced to concatenate the shallow and deep layers output, which fuses the semantic information of low-dimensional geometric features and high-dimensional abstract context features together. The Global Multi-Scale Feature Recalibration (GMSFR) module expands the receptive field in spatial, which effectively improves the global semantic features extraction capability of feature encoder. We evaluate the proposed method on the EMOTIC data set, and experimental results are shown to be competitive with the state-of-the-art algorithms.

关键词： Human computer interaction Emotion recognition Computer vision Fuses Biological system modeling Semantics Psychology

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