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IEEE International Conference on Automation, Electronics and Electrical engineering (AUTEEE)

作者： Chen Yumu Su Jingjing Zhang Zihan Song Chengpu Chen Xin Sheng Jiancheng College of Computer and Control Engineering Minjiang University Fuzhou China

Based on the substation outdoor box cabinet in the operation process, which is prone to condensation and overheating, this paper designs a temperature and humidity control device for outdoor box cabinet of substation based on STM32 micro-controller. The device uses STM32 as the main control unit, and uses DHT 11 temperature and humidity sensor to detect the temperature and humidity of the box cabinet, and compare the threshold value to judge the temperature and humidity in the box cabinet and control the heater and fan to effectively drive moisture and heat.

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EEG-GNet: GCN-Based EEG Signal Fatigue Detection Network for Edge Devices

EEG-GNet: GCN-Based EEG Signal Fatigue Detection Network for...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhang, Fuquan Wu, Yu Xu, Xinxin Wang, Chuansheng Ma, Minjia Grau, Antoni Huang, Jiayan School of Computer and Big Data Minjiang University Fuzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China College of New Engineering Industry Putian University Putian China Department of Automatic Control Polytechnic University of Catalonia Barcelona Spain School of Robotics Xi'an Jiaotong-Liverpool University Suzhou China College of New Engineering Industry Putian University Fujian Ruituo Information Technology Limited Company Putian China

ISBN: (纸本)9798350386226

Fatigue driving is a common potential traffic safety hazard. To detect and analysis the driver Fatigue degree in real-time, more attention have been increasing paid to application research of EEG fatigue detection device. However, existing methods suffer from complex model structure or large-scale model parameters. In the other words, they requires high computational consumption and large memory footprint, which creates a contradiction between model and portable EEG machine with limited hardware resources. In this work, we take both time and frequency domains of EEG signal into account and design an lightweight and efficient driver fatigue detection network based on GCN (EEG-GNet), which is available edge-end device application. Experiments on 4th Raspberry Pi on a public baseline dataset show that our EEG-GNet outperforms the state-of-the-art methods and takes good tradeoff between detection accuracy and resource consumption. Our EEG-GNet provides a new potential opportunity for future applications in practical portable/wearable (P/W) devices. © 2024 IEEE.

关键词： Electrocardiography

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Prediction Model of Marine Drifting Trajectory Based on Multilayer Perceptron

Prediction Model of Marine Drifting Trajectory Based on Mult...

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International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Zeng, Ziyue He, Wei Wang, Zhiyuan Zhang, Feng Zhong, Yaozhao Fuzhou Institute of Oceanography Minjiang University Fuzhou35021 China Fujian Engineering Research Center of Safety Control for Ship Intelligent Navigation College of Physics and Electronic Information Engineering Minjiang University Fujian Fuzhou350121 China School of Optoelectronics and Information Engineering Fujian Normal University Fujian Fuzhou350108 China

ISBN: (纸本)9789819639687

This study introduces a trajectory prediction method for the drift of marine buoys, utilizing the Multilayer Perceptron (MLP) model, designed to enhance maritime search and rescue operations as well as ocean resource development. We preprocessed marine buoy drift data to build an MLP model for trajectory prediction. The model excels in forecasting linear and large curvature paths and shows accuracy in more complex trajectories. It outperformed 1D CNN, CNN-MLP hybrids, SVR, and LSTM in metrics like MSE and R2. The MLP's simplicity and efficiency make it ideal for real-time predictions, though improvements are needed for complex trajectory changes. This research provides a practical tool and foundation for future studies in maritime search, rescue, and ocean resource management. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Trajectories

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Efficient water disinfection with ball milled Mg-biochar: The key role of trace Cu

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Chinese Chemical Letters 2024年第1期35卷 412-417页

作者： Yanchao Jin Suixiaochen Chen Peiwen Huang Xiongjian Chen Chun-Yan Lin Li-Ping Li Xiao Chen Rui Ding Jianxi Liu Riyao Chen College of Environmental and Resource Sciences Fujian Normal UniversityFuzhou 350117China School of Materials and Chemical Engineering Minjiang UniversityFuzhou 350108China Research and Development Center for Watershed Environmental Eco-Engineering Beijing Normal UniversityZhuhai 519087China Fujian Key Laboratory of Pollution Control&Resource Reuse Fuzhou 350007China

In this study, magnesium and coconut shell carbon (CSC) were prepared by a ball milled process and used for water disinfection with adsorbing tiny amounts of copper(II). Dissolved oxygen (DO) was reduced to hydrogen peroxide (H_(2)O_(2)) via a two-electron pathway by Mg corrosion. Cu(II) in the wastewater will be enriched on the CSC surface and efficiently catalyzes H2O2 for inactivating E. coli. The results show that E. coli with an initial concentration of approximately 106 CFU/mL was under the detection limit (<4 CFU/mL) within 15 min. All of the Cu(II) could be adsorbed by the composite and catalyzed H2O2 to different active species. The quenching experiments, electron spin resonance (ESR) capture measurements and the UV-vis spectroscopy detection confirmed the present of the hydroxyl radicals (·OH), superoxide radicals (·O_(2)^(-)) and Cu(III). Different with tradition Fenton like process, Cu(III), rather than radicals, played the major role during the Mg-CSC/Cu(II) process. In addition to the cellular membrane damage, most of the bacterial genomic DNA was also be degraded and the bacterial reactivation was avoided. The Mg-CSC/Cu(II) process also showed a satisfied disinfection performance in real wastewater treatment. Overall, this study provides a new strategy for water disinfection.

关键词： Disinfection Magnesium Cu Hydrogen peroxide Biochar

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Polyp Segmentation Neural Network with Simple Encoding Structure and Reverse Attention Module 13

Polyp Segmentation Neural Network with Simple Encoding Struc...

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13th International Conference on Information Technology in Medicine and Education, ITME 2023

作者： Wang, Jie Li, Zuoyong Xu, Haiping Cheng, Xuesong Teng, Shenghua College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao266590 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China Fujian Key Laboratory of Medical Big Data Engineering Fujian Provincial Hospital Fuzhou350001 China College of Mathematics and Data Science Minjiang University Fuzhou350121 China

ISBN: (纸本)9798350319156

Colorectal cancer is a common malignancy. In colonoscopy images, computer-assisted polyp segmentation helps doctors diagnose and treat disorders more precisely. In recent years, some methods based on deep convolutional neural networks have made significant breakthroughs in this task. However, only a few algorithms explicitly consider the effect of light intensity on segmentation performance, which results in algorithms needing to be more robust for complex samples. This paper proposes a neural network combining a simple encoding structure with a reverse attention module. Specifically, the model uses HarDNet68 as the backbone network, adds a camouflage identification module (CIM) to enhance the capture of polyp details on the underlying features, and finally utilizes adaptive feature fusion with the reverse attention module (RAM). The proposed model effectively addresses the challenge of poor segmentation quality in colonoscopy images caused by inconsistent illumination. Extensive experiments on five datasets show that the proposed model performs well in polyp segmentation. © 2023 IEEE.

关键词： Diseases

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A Novel Visual Transformer for Long-Distance Pipeline Pattern Recognition in Complex Environment

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2933-2945页

作者： Zhu, Chengyuan Pu, Yanyun Yang, Kaixiang Yang, Qinmin Chen, C.L. Philip Zhejiang University College of Control Science and Engineering Zhejiang Hangzhou310007 China Huzhou Institute of Zhejiang University Zhejiang Huzhou313000 China South China University of Technology College of Computer Science and Engineering Guangdong Guangzhou510006 China

The effective warning of dangerous events along long-distance pipelines is critical to ensure the safety of oil and gas transportation. Distributed optical fiber sensing (DOFS) technology can assist operators to identify threat vibration signals. However, due to the complex and changeable environmental background noise along long-distance pipeline, most of the existing methods only extract one-dimensional features of the signal, making it difficult to distinguish various types of environmental noise, strong interference, and dangerous events. Besides, the samples of different classes in the actual scene are unbalanced. The sample size of dangerous events is often smaller than others. To address these problems, we use image encoding to transform the time series signals collected by the DOFS system into image data, and fully extract the time dependence and the correlation between different elements in the signal. Moreover, a visual transformer model PipelineADWinT is proposed. The self-attention mechanism of diagonal-axial window designed in this model can perfectly combine image encoding features, and obtain local to global multiscale features through hierarchical structure. By optimizing the loss function, the model's ability to handle the class imbalance problem is enhanced. The experimental results show that PipelineADWinT has more comprehensive classification performance and fewer false alarms than all the baseline models, which proves the effectiveness and superiority of the model. © 2020 IEEE.

关键词： Pattern recognition

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Distortion Evaluation of EMP Sensors Using Associated-Hermite Functions

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computers, Materials & Continua 2023年第1期74卷 1093-1105页

作者： Rupo Ma Siping Gao College of Command and Control Engineering Army Engineering UniversityNanjing210007China Department of Computer Information and Cyber Security Jiangsu Police InstituteNanjing210031China Department of Electrical and Computer Engineering National University of Singapore119260Singapore

Electromagnetic pulse(EMP)is a kind of transient electromagnetic phenomenon with short rise time of the leading edge and wide spectrum,which usually disrupts communications and damages electronic equipment and *** is challenging for an EMP sensor to measure a wideband electromagnetic pulse without distortion for the whole ***,analyzing the distortion of EMP measurement is crucial to evaluating the sensor distortion characteristics and correcting the measurement *** fidelity is usually employed to evaluate the distortion of an ***,this metric depends on specific signal waveforms,thus is unsuitable for evaluating and analyzing the distortion of EMP *** this paper,an associated-hermite-function based distortion analysis method including system transfer matrices and distortion rates is proposed,which is general and independent from individual *** system transfer matrix and distortion rate can be straightforwardly calculated by the signal orthogonal transformation coefficients using associated-hermite *** of a sensor *** is then visualized via the system transfer matrix,which is convenient in quantitative analysis of the *** of a current probe,a coaxial pulse voltage probe and a B-field sensor were performed,based on which the feasibility and effectiveness of the proposed distortion analysis method is successfully verified.

关键词： Electromagnetic pulse associated-hermite functions system transfer matrix distortion rate

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EFFNet: Semantic Segmentation Network for Enhanced Feature Fusion in Traffic Scenes 3

EFFNet: Semantic Segmentation Network for Enhanced Feature F...

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3rd International Conference on computer Graphics, Image, and Virtualization, ICCGIV 2023

作者： Zhang, Shiwei Liu, Qicheng College of Computer and control engineering Yantai University Yantai China

ISBN: (纸本)9781510671720

Semantic segmentation of high-resolution traffic scene images is a challenging task due to complex backgrounds, diverse object shapes, similar appearances of multiple objects, and multi-scale characteristics of the same object. Many existing semantic segmentation networks only perform simple feature fusion when dealing with different object shapes and multi-scale properties of the same object, often failing to provide satisfactory results. To address these issues, we propose an end-to-end multi-scale feature fusion network called EFFNet for traffic scenes semantic segmentation. EFFNet adopts an encoder-decoder structure, where ResNet-34 is used as the backbone network for feature extraction. At each stage of feature extraction, we introduce the Feature Fusion Module for multi-scale information. The Feature Fusion Module fuses the feature maps extracted by the backbone network at different stages, enriching the spatial and semantic information of the feature maps. Through end-to-end training, the accuracy of the segmentation results is significantly improved. We evaluated EFFNet on the traffic datasets Camvid and Cityscapes using a device equipped with a GTX 1650Ti graphics card. The results show that EFFNet achieves Miou scores of 55.3% and 50% on these two datasets, respectively. This indicates that EFFNet outperforms existing methods and demonstrates excellent segmentation accuracy in traffic scenes semantic segmentation tasks. © 2023 SPIE.

关键词： Semantic Segmentation

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Decentralized Game-based Auction Algorithm for Scheduling Robotic Chargers to Service EVs with Uncertain Demands

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-14页

作者： Fang, Qiuyang Zhang, Jianlei Wang, Chen Xie, Guangming Zhang, Chunyan Department of Automation College of Computer and Control Engineering Nankai University Tianjin China National Engineering Research Center of Software Engineering Peking University Beijing China State Key Laboratory for Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing China

In this study, we introduce a novel auction-based algorithm modeled as a decentralized coalition formation game, designed for the complex requirements of large-scale multi-robot task allocation under uncertain demand. This context is particularly illustrative in scenarios where robots are tasked to charge electric vehicles. The algorithm begins by partitioning a composite task sequence into distinct subsets based on spatial similarity principles. Subsequently, we employ a coalition formation game paradigm to coordinate the assembly of robots into cooperative coalitions focused on these distinct subsets. To mitigate the impact of unpredictable task demands on allocations, our approach utilizes the conditional value-at-risk to assess the risk associated with task execution, along with computing the potential revenue of the coalition with an emphasis on risk-related outcomes. Additionally, integrating consensus auctions into the coalition formation framework allows our approach to accommodate assignments for individual robot-task pairings, thus preserving the stability of individual robotic decision autonomy within the coalition structure and assignment distribution. Simulative analyses on a prototypical parking facility layout confirm that our algorithm achieves Nash equilibrium within the coalition structure in polynomial time and demonstrates significant scalability. Compared to competing algorithms, our proposal exhibits superior performance in resilience, task execution efficiency, and reduced overall task completion times. The results demonstrate that our approach is an effective strategy for solving the scheduling challenges encountered by multi-robot systems operating in complex environments. IEEE

关键词： Polynomial approximation

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Temporal Dynamics in instrumental Learning: Integrating Entropy Measures and Computational Models 31

Temporal Dynamics in instrumental Learning: Integrating Entr...

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31st National and 9th International Iranian Conference on Biomedical engineering, ICBME 2024

作者： Hassanzadeh, Zakieh Bahrami, Fariba University College of Engineering University of Tehran Human Motor Control and Computational Neuroscience Lab School of Electrical and Computer Engineering Tehran Iran University College of Engineering University of Tehran School of of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798331529710

This research explores the dynamics of decision-making within an instrumental learning framework, combining analyses of response times, entropy measures, and computational modeling. We conducted a study using an RLWM task, involving 27 participants and varying set sizes to examine the relationship between task complexity and cognitive functions. Our methodology incorporates Shannon entropy, Cumulative Residual Entropy (CRE), and Instantaneous CRE to assess uncertainty and mental effort across different task conditions. We evaluate two computational models - RLWM and its enhanced version, ~ p i H - to interpret both choice patterns and reaction times. Our findings indicate that set size significantly impacts response times and entropy measures, with increased complexity leading to higher entropy and slower responses. The temporal analysis of iCRE suggests a dynamic shift in cognitive strategies throughout the task. Model comparisons highlight distinct capabilities of RLWM and piH in capturing various aspects of cognitive processes. This comprehensive approach provides novel viewpoints into how the brain balances complex learning mechanisms with simpler rules for efficient decision-making under limited mental resources, contributing to our understanding of adaptive behavior in complex environments. © 2024 IEEE.

关键词： Adversarial machine learning

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