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2023 3rd International Conference on Electrical, Electronics, and Computing technology, EECT 2023

作者： Tong, Di Guo, Zhen Zhao, Yan-Cheng Chen, Shuang Nai, Ji-Qiu Ye, Ming-Hao School of Information and Control Engineering Qingdao University of Technology Qingdao266520 China

Asynchronous motors are commonly utilized in industrial and agricultural production due to their inexpensive manufacturing cost and reliable structure. However, due to the demands of production and everyday usage, these motors may be operated at no or light load for long periods of time, resulting in a decrease in the efficiency of the motor system. A model-referenced adaptive parameter identification in combination with a particle swarm algorithm is proposed in this paper to enhance the operating efficiency of asynchronous motors. This control strategy can reduce motor power loss and improve efficiency. Simulation model is established and simulation results are provided to verify the feasibility of the proposed control strategy for optimizing the efficiency of asynchronous motors. © Published under licence by IOP Publishing Ltd.

关键词： Induction motors

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Salient Object Detection via Hybrid Encoder and Feature Feedback Fusion Mechanism 4

Salient Object Detection via Hybrid Encoder and Feature Feed...

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4th International Conference on Computer Vision, Image and Deep Learning, CVIDL 2023

作者： Li, Xueliang School of Information and Control Engineering Qingdao University of Technology Qingdao266000 China

ISBN: (纸本)9798350326444

Deep encoder-decoder networks have been adopted for saliency detection and achieved the state-of-the-art performance. However, most existing saliency methods usually fail to process images with complex background and small objects. In this paper, we propose and apply a new method for saliency detection using hybrid encoder and feature feedback fusion mechanism. The proposed network architecture is composed of hybrid encoder based feature extraction module, feature feedback fusion mechanism based decoder module and multi observation point supervised learning module. In the feature extraction module, CNN encoder and transformer encoder are used to extract the local and global features of the image respectively. The decoder module optimizes the salient features of each layer by effectively fusing the visual features of the two layers or three layers. The multi observation point supervised learning module sets multiple observation points at each layer of the decoder, and each observation point considers the binary cross entropy loss and intersection over union loss. A large number of experimental results on five public data sets show that the method proposed in this paper is significantly better than other methods in different evaluation metrics. © 2023 IEEE.

关键词： Feature extraction

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Maneuvering Target Imaging and Motion Parameter Estimation Based on Improved Maximum Likelihood Estimation

Maneuvering Target Imaging and Motion Parameter Estimation B...

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IET International Radar Conference 2023, IRC 2023

作者： Zhang, Mengdi Lu, Hongyi Li, Shiyin Li, Zhiwei School of Information and Control Engineering China University of Mining and Technology Xuzhou China

ISBN: (纸本)9781839539954

In synthetic aperture radar (SAR), long-time coherent integration may induce range migration and Doppler frequency migration, resulting in the defocusing of the moving target. To this end, a new method to estimate motion parameters of maneuvering target is proposed in this paper. In this method, the second-order keystone transform is utilized to correct the range curvature. Then, the Radon transform is used to estimate the slope of the range migration, from which radial velocity of the target can be obtained. Next, an improved algorithm based on Maximum likelihood (ML) is applied to estimate motion parameter of the maneuvering target. Finally, a well-focused image of a moving target can be accomplished by compensating Doppler frequency migration according to the estimated motion parameters. Compared with ML, this method can reduce computational complexity. Compared with other traditional method, this method can estimate motion parameter of the target more accurately. © The Institution of engineering & technology 2023.

关键词： Radar imaging

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Defect Detection in Computer Motherboard Assembly through Fusion of Multi-Scale Features and Attention Mechanisms 9

Defect Detection in Computer Motherboard Assembly through Fu...

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9th International Conference on Systems and Informatics, ICSAI 2023

作者： Li, Zhaoyang Chen, Junying Huang, Hantao Dong, Xuze Xi'an University of Architecture and Technology College of Information and Control Engineering Xi'an China

ISBN: (纸本)9798350383706

To meet the growing demands for automation and intelligence in the quality inspection of computer motherboard assembly, this study proposes an improved defect detection method based on RetinaNet. The method integrates multi-scale features and attention mechanisms. A path aggregation network is introduced to construct a feature fusion network, combining low-level positional features with high-level semantic information to capture features of different scales in target objects. Furthermore, an attention mechanism is introduced after feature fusion to weight feature maps, enhancing the representation capability of foreground defect targets and mitigating interference challenges in the motherboard defect detection process. Experimental validation on a self-built dataset for computer motherboard assembly defect detection demonstrates an average detection accuracy of 84.5%. The results underscores the effectiveness of the proposed method, providing a promising solution for the automation and intelligence enhancement of assembly inspection tasks. © 2023 IEEE.

关键词： Semantics

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Hybrid text classification model based on graph convolution network and neural network

Hybrid text classification model based on graph convolution ...

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2023 International Conference on Cyber Security, Artificial Intelligence, and Digital Economy, CSAIDE 2023

作者： Dong, Zhaohe Zhai, Zhengli Yang, Jitao School of Information and Control Engineering Qingdao University of Technology Qingdao266525 China

ISBN: (纸本)9781510666597

With the rapid development of graph neural network technology, its application in the field of natural language processing is more and more extensive, text classification is one of the important applications, everyday life will produce a large number of non-Euclidean text data, while the traditional classification methods in the graphic structure of text data has been a great challenge. Graph convolutional neural network(GCN) is considered to be able to model the structural attributes and node feature information of graphs well, and is gradually becoming a good choice for text classification of graph data. This paper proposes a text classification model based on graph convolution network and neural network local enhancement. On the basis of using GCN to extract features, Bi-LSTM method is used to balance the experimental results, enrich the feature information by capturing local information, integrate the attention mechanism, and fuse the evaluation values to improve the accuracy of classification. It is verified that this method has achieved better results than the existing classification methods in many classical data sets such as 20NG and OHSUMED. © 2023 SPIE.

关键词： Convolution

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A New Hybrid Model for Segmentation of the Skin Lesion Based on Residual Attention U-Net

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Computers, Materials & Continua 2023年第6期75卷 5177-5192页

作者： Saleh Naif Almuayqil Reham Arnous Noha Sakr Magdy M.Fadel Department of Information Systems College of Computer and Information SciencesJouf UniversitySakakaSaudi Arabia Department of Communications and Electronics Delta Higher Institute for Engineering and Technology(DHIET)MansouraEgypt Computers Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansouraEgypt

Skin segmentation participates significantly in various biomedical applications,such as skin cancer identification and skin lesion *** paper presents a novel framework for segmenting the *** framework contains two main stages:The first stage is for removing different types of noises from the dermoscopic images,such as hair,speckle,and impulse noise,and the second stage is for segmentation of the dermoscopic images using an attention residual U-shaped Network(U-Net).The framework uses variational Autoencoders(VAEs)for removing the hair noises,the Generative Adversarial Denoising Network(DGAN-Net),the Denoising U-shaped U-Net(D-U-NET),and Batch Renormalization U-Net(Br-U-NET)for remov-ing the speckle noise,and the Laplacian Vector Median Filter(MLVMF)for removing the impulse *** the second main stage,the residual attention u-net was used for *** framework achieves(35.11,31.26,27.01,and 26.16),(36.34,33.23,31.32,and 28.65),and(36.33,32.21,28.54,and 27.11)for removing hair,speckle,and impulse noise,respectively,based on Peak Signal Noise Ratio(PSNR)at the level of(0.1,0.25,0.5,and 0.75)of *** framework also achieves an accuracy of nearly 94.26 in the dice score in the process of segmentation before removing noise and 95.22 after removing different types of *** experiments have shown the efficiency of the used model in removing noise according to the structural similarity index measure(SSIM)and PSNR and in the segmentation process as well.

关键词： Skin tumor speckle noise impulse noise hair noise deep learning segmentation

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Design of De-tumbling Device for Improving the De-tumbling Performance of Uncooperative Space Target

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Space(Science & technology) 2024年第1期4卷 10-19页

作者： Lei Du Zhen Chen Hengzai Hu Xiangdong Liu Youguang Guo School of Automation Beijing Institute of TechnologyBeijingChina Key Laboratory of Drive and Control of Servo Motion System Ministry of Industry and Information TechnologyBeijingChina School of Electrical and Data Engineering University of Technology SydneySydneyAustralia

This article presents the design of an optimal coil structure for 2 de-tumbling devices, each is carried by a de-tumbling robot. The design is based on electromagnetic eddy current method and aims to reduce the angular velocity of uncooperative space targets. It proposes an optimization framework with the advantages of safety and high performance. The magnetic field analytical model is established by the designed coil’s structure parameters, and the optimal structure parameters of the coil are determined. To further ensure the maximum magnetic field at the target, the electromagnetic characteristics under different current directions in the 2 coils are analyzed based on magnetic field analytical model, and their accuracy is verified using finite element method (FEM). Additionally, an improved Maxwell’s stress tensor method is proposed to calculate the de-tumbling torque, and its accuracy is assessed using traditional Maxwell’s stress tensor and virtual displacement method. The proposed optimal coil structure and its optimization framework can de-tumble over 1 million targets of various sizes, demonstrating universality.

关键词： uncooperative space target optimal structure parameters detumbling optimization framework electromagnetic eddy current reduce angular velocity electromagnetic eddy current method coil structure

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Rethinking Early-Fusion Strategies for Improved Multimodal Image Segmentation

Rethinking Early-Fusion Strategies for Improved Multimodal I...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Shen, Zhengwen Li, Yulian Zhang, Han Weng, Yuchen Wang, Jun School of Information and Control Engineering China University of Mining and Technology Jiangsu Xuzhou221116 China

ISBN: (纸本)9798350368741

RGB and thermal image fusion have great potential to exhibit improved semantic segmentation in low-illumination conditions. Existing methods typically employ a two-branch encoder framework for multimodal feature extraction and design complicated feature fusion strategies to achieve feature extraction and fusion for multimodal semantic segmentation. However, these methods require massive parameter updates and computational effort during the feature extraction and fusion. To address this issue, we propose a novel multimodal fusion network (EFNet) based on an early fusion strategy and a simple but effective feature clustering for training efficient RGB-T semantic segmentation. In addition, we also propose a lightweight and efficient multiscale feature aggregation decoder based on Euclidean distance. We validate the effectiveness of our method on different datasets and outperform previous state-of-the-art methods with lower parameters and computation. © 2025 IEEE.

关键词： feature aggregation feature clustering RGB-T semantic segmentation

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Object recognition and grasp detection in complex scenarios for home service robot

Object recognition and grasp detection in complex scenarios ...

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2024 China Automation Congress, CAC 2024

作者： Xu, Shengjun Bai, Liang Zhong, Huangwei Hu, Yuji Sun, Xiangcai Lyu, Hongqiang School of Information and Control Engineering Xi'an University of Architecture and Technology Xi’an China School of Automation Science and Engineering Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an China

ISBN: (纸本)9798350368604

With the development of artificial intelligence, home service robots have been widely applied in daily life. As one of the fundamental functions of service robots, object grasping helps improve the level of unmanned and intelligent household. However, traditional robots still face challenges in flexibility and accuracy in complex home environments. In this paper, an object recognition and grasp detection system in complex scenarios for home service robots is proposed. The system consists of four modules: image acquisition, human-machine interaction software, RGB-D image processing, and robot control. The image acquisition module captures RGB and depth images of the work area and sends them to the software. The RGB-D image processing module begins with object recognition to obtain object categories and bounding box with the help of YOLOv8. The target point cloud is produced by combining the target subimage and corresponding depth information, followed by depth completion and plane expansion. Then the processed point cloud is fed into a Graspnet pose estimation network to calculate the 6D grasping pose for service robot. And finally, the robot control module is invoked to carry out the corresponding target grasping action. To evaluate the performance, an experimental platform for home service robot is built to grasp various daily necessities. The results tell that the average accuracy for object recognition reaches up to 98.3%, the success rate for single-target grasping is 94.4%, and the success rate for multi-target grasping can achieve 80%. © 2024 IEEE.

关键词： Image acquisition

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Improved-GCN based Collaborative Filtering Recommendation Model 3

Improved-GCN based Collaborative Filtering Recommendation Mo...

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3rd International Conference on Computer Vision and Pattern Analysis, ICCPA 2023

作者： Yang, Jitao Zhai, Zhengli Dong, Zhaohe School of information and control engineering Qingdao University of Technology QingDao266520 China

ISBN: (纸本)9781510667563

Machine learning (ML) graph convolution network (GCN) has been applied in various fields, including recommendation systems, and has achieved great success. The core goal of the recommendation system is to infer user preferences so as to actively provide users with valuable and targeted information. Collaborative Filtering (CF) is a classic and widely used recommendation method. However, GCN-based recommendation methods still have many problems. For example, GCN recursively merges messages from different order neighborhoods, it is difficult to distinguish between different node messages, which leads to training difficulties, and problems such as over-smoothing have great constraints on the recommendation model. Different from the current GCN-based method, in view of the above problems, it is proposed to use a simple GCN model to separately aggregate neighbor messages in different orders for collaborative filtering, and then aggregate them in a hierarchical manner without introducing other model parameters. After that, the dropout idea was transferred to the model, by generating multiple views of the same node from the three dimensions of nodes, edges and graphs to reduce the effect of over-smoothing, which prevented over-fitting and improved the performance of the model. The experimental results on three datasets demonstrate the effectiveness of the proposed model. © 2023 SPIE.

关键词： Convolution

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