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7th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： Liang, Xi Yu, Ye Chen, Fengxin Wang, Zilong Li, Xin Lu, Qiang School of Computer Science and Information Hefei University of Technology Hefei China School of Electrical Engineering and Automation Hefei University of Technology Hefei China

ISBN: (纸本)9798350350890

Sewing thread segmentation can help locate the defects in fabric sewing process, which is a challenging problem in factory quality control. In this paper, we propose a twin U-shaped Transformer Network (TUTNet) for sewing thread segmentation. To learn high-dimensional abstract features and capture the sewing direction while ensuring the smoothness of segmentation, we propose a Twin-Attention Module (TAM), which is composed of Sewing Thread Direction (STD) and Sewing Thread Smoothness (STS) blocks. To better combine low-dimensional features with fine-grained features, and generate more continuous results, we propose a Scaled Feature Fusion Module (SFFM), which is used to calculate losses at multiple levels. We also propose a large-scale sewing thread segmentation (STSeg) dataset, which is collected from real clothing production factory. Finally, we conduct experiments on both STSeg and DGRE datasets to verify the effectiveness of our method for sewing thread and other linear object segmentation. © 2024 IEEE.

关键词： Large datasets

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Simulation Analysis of Indoor Electromagnetic Environment Based on Correlation Coefficient

Simulation Analysis of Indoor Electromagnetic Environment Ba...

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2024 Photonics and Electromagnetics Research Symposium, PIERS 2024

作者： Gu, Jianfeng Mo, Yuncheng Tang, Wanchun Nanjing Normal University School of Computer and Electronic Information Nanjing China Nanjing Normal University School of Electrical and Automation Engineering Nanjing China

ISBN: (纸本)9798350375909

In this paper, the indoor electromagnetic environment of a certain campus is simulated and analyzed, providing the distribution of electromagnetic radiation field in the indoor environment of the campus. Fully considering the randomness of radiation sources in geographical space in terms of time, space, and frequency, the electromagnetic radiation field distribution in empty rooms and equipped rooms was compared and analyzed. A large number of simulation results showed that the electromagnetic radiation field distribution in empty rooms and equipped rooms had a good correlation. This could provide some reference for the layout of indoor micro base stations and WIFI. © 2024 IEEE.

关键词： Indoor air pollution

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Learning-based Big Data Sharing Incentive in Mobile AIGC Networks

Learning-based Big Data Sharing Incentive in Mobile AIGC Net...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Wen, Jinbo Zhang, Yang Chen, Yulin Zhong, Weifeng Huang, Xumin Liu, Lei Niyato, Dusit Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology China Guangdong University of Technology School of Automation China Xidian University School of Telecommunications Engineering China Nanyang Technological University School of Computer Science and Engineering Singapore

ISBN: (纸本)9798350351255

Rapid advancements in wireless communication have led to a dramatic upsurge in data volumes within mobile edge networks. These substantial data volumes offer opportunities for training Artificial Intelligence-Generated Content (AIGC) models to possess strong prediction and decision-making capabilities. AIGC represents an innovative approach that utilizes sophisticated generative AI algorithms to automatically generate diverse content based on user inputs. Leveraging mobile edge networks, mobile AIGC networks enable customized and real-time AIGC services for users by deploying AIGC models on edge devices. Nonetheless, several challenges hinder the provision of high-quality AIGC services, including issues related to the quality of sensing data for AIGC model training and the establishment of incentives for big data sharing from mobile devices to edge devices amidst information asymmetry. In this paper, we initially define a Quality of Data (QoD) metric based on the age of information to quantify the quality of sensing data. Subsequently, we propose a contract theoretic model aimed at motivating mobile devices for big data sharing. Furthermore, we employ a Proximal Policy Optimization (PPO) algorithm to determine the optimal contract. Numerical results demonstrate the efficacy and reliability of the proposed PPO-based contract model. © 2024 IEEE.

关键词： age of information big data sharing contract theory Mobile AIGC networks proximal policy optimization

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Graph Attention Transformer with Dilated Causal Convolution and Laplacian Eigenvectors for Long-term Water Quality Prediction

Graph Attention Transformer with Dilated Causal Convolution ...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Bi, Jing Chen, Danqing Yuan, Haitao College of Computer Beijing University of Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China

ISBN: (纸本)9781665410205

Water quality prediction has emerged as a prominent research problem in recent years, which entails a spatiotemporal prediction task. However, several challenges are associated with water quality prediction: 1) water quality time series exhibit complex nonlinear relationships, making prediction challenging;2) water quality sensors are distributed across river networks, leading to strong spatial dependencies in water quality prediction;3) current methods like traditional machine learning methods have poor accuracy in long-term water quality prediction. To solve these problems, this work proposes a spatiotemporal prediction model named Graph Attention Transformer with Dilated Causal Convolution and Laplacian Eigenvectors (GTDL). First, dilated causal convolution is adopted to extract temporal features of input sequences. Second, a transformer with Laplacian eigenvectors is designed to extract spatial dependencies of river networks. Third, residual connections are utilized at the output stage to enhance the accuracy of the long-term prediction. Finally, GTDL is evaluated with two real-world water quality datasets and experimental results prove that GTDL outperforms other baseline methods regarding the prediction accuracy. Specifically, compared with five state-of-the-art prediction models, GTDL improves the prediction accuracy by 11.3%-63.5% and 6%-52.4% on two datasets, respectively. © 2024 IEEE.

关键词： Prediction models

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Resource Allocation and Trajectory Optimization in Unmanned Aerial Vehicle-assisted Mobile Edge Computing

Resource Allocation and Trajectory Optimization in Unmanned ...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Bi, Jing Cheng, Xiangshuai Yuan, Haitao Niu, Siyu Zhai, Jiahui College of Computer Beijing University of Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China

ISBN: (纸本)9781665410205

Edge computing offers a groundbreaking architecture for supplying computing, storage, and networking resources to propel the Internet of Things forward. By situating them at the network's edge, this model makes computational power more accessible to users. If tasks are executed entirely at the edge, energy and resource constraints of edge nodes may lead to poor performance. Therefore, it is widely recognized that offloading certain tasks to cloud data centers (CDCs), which possess abundant execution resources, is advantageous. However, implementing CDCs is not widespread and lacks flexibility in isolated regions. This presents challenges and high costs for reliably completing tasks quickly. Consequently, employing more adaptable unmanned aerial vehicles (UAVs) as CDCs in specific scenarios is crucial. The work presents the idea of mobile edge computing supported by the UAV. By considering the needs of user services, we enhance the energy efficiency of the UAV by optimizing their trajectories, transmission power, and computational load distribution. Furthermore, the work introduces an improved algorithm called GeneticSimulated-annealing-based Particle Swarm Optimizer (GSPSO) to optimize the energy efficiency of the UAV. Experimental simulations show that regarding the energy efficiency of the UAV, GSPSO exhibits superior search efficiency, surpassing genetic algorithm, simulated annealing, and particle swarm optimization by 7.39%, 15.03%, and 27.93%, respectively. © 2024 IEEE.

关键词： Simulated annealing

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Water Quality Anomaly Detection with Dual Sliding Windows and Convolutional LSTM

Water Quality Anomaly Detection with Dual Sliding Windows an...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Bi, Jing Yuan, Ming Yuan, Haitao Wang, Ziqi Qiao, Junfei College of Computer Beijing University of Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China

ISBN: (纸本)9781665410205

Water pollution is continuously increasing in water ecosystems across all continents. Surface water sensors can record data on water quality indicators at regular intervals, and the associated water quality sequences show abnormal trends when extreme weather or unusual industrial discharges occur. Therefore, governments can take timely actions to minimize damage and protect the water environment by detecting these abnormal trends promptly. However, current methods make it difficult to interpret different correlations among water quality parameters effectively. To solve this problem, this work proposes a parameter correlation-aware anomaly detection model, which integrates Dual sliding windows, Convolutional LSTM, and a Deep neural network with dropout, called for DCLD short. First, DCLD designs dual sliding windows to capture local and global patterns within the sequence of water quality. Second, DCLD adopts a stacked long short-term memory with a convolutional neural network to capture complex features and long-term dependencies in the time series. Third, DCLD uses a deep neural network incorporating the dropout algorithm to extract abstract features. DCLD mitigates overfitting risks and enhances the model's generalization capacity. Finally, DCLD is evaluated with two real-world water quality datasets, and its anomaly detection accuracy is improved by 5.41% and 0.79% on average over its peers. © 2024 IEEE.

关键词： Convolutional neural networks

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Research on a Simplified Ray Tracing Method for Indoor Radio Wave Propagation Calculation

Research on a Simplified Ray Tracing Method for Indoor Radio...

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2024 Photonics and Electromagnetics Research Symposium, PIERS 2024

作者： Mo, Yuncheng Gu, Jianfeng Tang, Wanchun Nanjing Normal University School of Computer and Electronic Information Nanjing China Nanjing Normal University School of Electrical and Automation Engineering Nanjing China

ISBN: (纸本)9798350375909

With the development of indoor wireless communication technology, ray tracing method is widely used for indoor radio wave propagation analysis. This paper proposes a simplified method for indoor ray tracing. Referring to the Motley-Keenan model, the indoor environment and objects are considered as a combination of planes. The attenuation of rays after reflection and transmission through the plane is determined by the Fresnel coefficient. By identifying and labeling all non-coplanar planes (walls, doors, windows, tables) in the structure, the coplanar structures are divided into one group, this can reduce the number of mirror sources when using the image theory to determine the reflection path, thereby reducing the computational complexity of ray and panel test intersections. Numerical results demonstrate the ability of the proposed method. © 2024 IEEE.

关键词： Radio waves

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PI-funnel and inverse hysteresis compensation cascade control of smart soft dielectric elastomer actuator

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Control Theory and Technology 2024年第4期22卷 552-567页

作者： Peng Huang Yawu Wang Yue Zhang Jundong Wu Chun-Yi Su School of Automation China University of GeosciencesWuhan430074HubeiChina Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074HubeiChina Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of EducationWuhan430074HubeiChina Gina Cody School of Engineering and Computer Science Concordia UniversityMontrealQCH3G 1M8Canada

Smart soft dielectric elastomer actuators(SSDEAs)possess wide applications in soft robotics due to their properties similar to natural muscles,including large deformation ratio,high energy density,and fast response ***,the complicated asymmetric and rate-dependent hysteresis property,creep property and quadratic input property of the SSDEA pose enormous challenges to its dynamic modeling and motion *** this paper,first,we construct the dynamic model of the SSDEA by connecting a square module,a one-sided Prandtl–Ishlinskii(OSPI)model and a linear system in series to describe the above *** key and innovative aspect of the dynamic modeling lies in cascading the square module in series with the OSPI model to construct the asymmetric hysteresis ***,a PI-funnel and inverse hysteresis compensation(PIFIHC)cascade control method of the SSDEA is proposed to actualize its tracking control *** performing the inversion operation on the asymmetric hysteresis model,the inverse hysteresis compensation controller(IHCC)is designed to compensate the asymmetric hysteresis property and quadratic input property of the *** addition,a PI-funnel controller is designed to cascade with the IHCC to construct the PIFIHC cascade controller to obtain a good tracking ***,the stability analysis of the PIFIHC cascade control system of the SSDEA is performed to theoretically prove that the tracking error can be controlled within the performance funnel and the steady-state error converges to ***,several practical tracking control experiments of the SSDEA are conducted,and RRMSEs are less than 2.30%for all *** experimental results indicate the effectiveness and feasibility of the proposed PIFIHC cascade control method of the SSDEA.

关键词： Smart soft dielectric elastomer actuator Dynamic modeling Inverse hysteresis compensation controller PI-funnel controller Cascade controller

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Two Stages Segmentation Algorithm of Breast Tumor in DCE-MRI Based on Multi-Scale Feature and Boundary Attention Mechanism

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computers, Materials & Continua 2024年第7期80卷 1543-1561页

作者： Bing Li Liangyu Wang Xia Liu Hongbin Fan Bo Wang Shoudi Tong School of Automation Harbin University of Science and TechnologyHarbin150080China Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration Harbin150080China School of Computer Engineering Technology Guangdong Institute of Science and TechnologyZhuhai519090China

Nuclearmagnetic resonance imaging of breasts often presents complex *** tumors exhibit varying sizes,uneven intensity,and indistinct *** characteristics can lead to challenges such as low accuracy and incorrect segmentation during tumor ***,we propose a two-stage breast tumor segmentation method leveraging multi-scale features and boundary attention ***,the breast region of interest is extracted to isolate the breast area from surrounding tissues and ***,we devise a fusion network incorporatingmulti-scale features and boundary attentionmechanisms for breast tumor *** incorporate multi-scale parallel dilated convolution modules into the network,enhancing its capability to segment tumors of various sizes through multi-scale convolution and novel fusion ***,attention and boundary detection modules are included to augment the network’s capacity to locate tumors by capturing nonlocal dependencies in both spatial and channel ***,a hybrid loss function with boundary weight is employed to address sample class imbalance issues and enhance the network’s boundary maintenance capability through additional *** was evaluated using breast data from 207 patients at RuijinHospital,resulting in a 6.64%increase in Dice similarity coefficient compared to the *** results demonstrate the superiority of the method over other segmentation techniques,with fewer model parameters.

关键词： Dynamic contrast-enhanced magnetic resonance imaging(DCE-MRI) breast tumor segmentation multi-scale dilated convolution boundary attention the hybrid loss function with boundary weight

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An Infrared and Visible Image Fusion Approach of Self-calibrated Residual Networks and Feature Embedding

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Recent Advances in computer Science and Communications 2023年第2期16卷 2-13页

作者： Dai, Jinpeng Luo, Zhongqiang Li, Chengjie School of Automation and Information Engineering Sichuan University of Science and Engineering Yibin644000 China Artificial Intelligence Key Laboratory of Sichuan University of Science and Engineering Yibin644000 China School of Computer Science and Technology Southwest Minzu University Chengdu610041 China

Background: The fusion of infrared images and visible images has been a hot topic in the field of image fusion. In the process of image fusion, different methods of feature extraction and processing will directly affect the fusion performance. Objectives: Low resolution (small size) of high-level features will lead to the loss of spatial information. On the other side, the low-level features are not significant due to their insufficient filtering of background and noise. Methods: In order to solve the problem of insufficient feature utilization in existing methods, a new fusion approach (SC-Fuse) based on self-calibrated residual networks (SCNet) and feature embedding has been proposed. The method improves the quality of image fusion from two aspects: feature extraction and feature processing. Results: First, self-calibrated modules are applied to the field of image fusion for the first time, which enlarged the receptive field to make feature maps contain more information. Second, we use ZCA (Zero-phase Component Analysis) and l1-norm to process features, and propose a feature embedding operation to realize the complementarity of feature information at different levels. Conclusion: Finally, a suitable strategy is given to reconstruct the fused image. After ablation experiments and comparison with other representative algorithms, the results show the effectiveness and superiority of SC-Fuse. © 2023 Bentham Science Publishers.

关键词： Image reconstruction

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