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检索条件"任意字段=13th IEEE International Conference on Computer Research and Development, ICCRD 2021"

共 1327 条记录，以下是1-10 订阅

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2021 ieee 13th international conference on computer research and development, iccrd 2021

2021 IEEE 13th International Conference on Computer Research...

引用

13th ieee international conference on computer research and development, iccrd 2021

ISBN: (纸本)9780738110387

the proceedings contain 25 papers. the topics discussed include: surgical action and instrument detection based on multiscale information fusion;trusted data management for e-learning system based on blockchain;application of Yolo on mask detection task;a convolutional neural network with background exclusion for crowd counting in non-uniform population distribution scenes;a solution for concurrency and cyclic reference of DI container;dynamic multi-path and multi-protocol encrypted communication mechanism;research on RFID virtual tag location algorithm based on Monte Carlo;and optimization of indoor positioning algorithm based on LANDMARC.

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100 years of EEG: unleashing the brain’s secrets and motor of brain-computer interface (BCI) research 13

100 years of EEG: unleashing the brain’s secrets and motor ...

引用

13th international Winter conference on Brain-computer Interface, BCI 2025

作者： Kübler, Andrea Institute of Psychology Department of Psychology I Section Intervention Psychology University of Würzburg Würzburg Germany

ISBN: (纸本)9798331521929

Electroencephalography (EEG) has come a long way since its discovery, marking over a hundred years of groundbreaking progress. Hans Berger, the scientist behind this invention, revolutionized the study of brain activity by developing a non-invasive way to measure the brain’s electrical currents. His work laid the foundation for modern neuroscience, enabling advancements in both clinical applications and cutting-edge technologies like brain-computer interfaces (BCIs). In this small flashlight on its history, I will delve into the history of EEG, its development from a tool of clinical diagnosis to an established and highly sophisticated research tool, culminating in its integration into brain-computer interface (BCI) technology and its relation to consciousness. © 2025 ieee.

关键词： Electroencephalography

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Adaptive Particle Swarm Optimization of U-net Parameters for Marine Aquaculture Image Segmentation 13

Adaptive Particle Swarm Optimization of U-net Parameters for...

引用

13th international conference on Intelligent Control and Information Processing, ICICIP 2025

作者： Zhao, Jin Wang, Jun Han, Min Fan, Jianchao School of Computer Science and Engineering Chongqing Three Gorges University Chongqing China Department of Computer Science Department of Data Science City University of HongKong Hong Kong School of Control Science and Engineering Dalian University of Technology Dalian China

ISBN: (纸本)9798331516147

Marine aquaculture image segmentation plays a crucial role in managing aquatic resources and environmental protection. Traditional deep learning models rely on manual parameter tuning for image segmentation, which limits their efficiency and accuracy. this paper proposes an adaptive Particle Swarm Optimization (PSO) algorithm to optimize the parameters of the U-net model automatically. the algorithm dynamically adjusts the PSO parameters based on the population’s entropy and clustering metrics and employs a hill-climbing algorithm to address the issue of the PSO easily falling into local optima, enhancing the algorithm’s adaptability. the proposed algorithm updates and iterates particles to automatically find suitable model parameters. the remote sensing data used in this experiment were from the Yellow Sea aquaculture area near Dalian, China, with a segmentation accuracy of 91.8%. this method improves segmentation accuracy, reduces the burden of manual parameter tuning, and provides an effective solution for optimizing deep learning models. © 2025 ieee.

关键词： Aquaculture

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research on Mango Ripeness Based on Yolo 13

Research on Mango Ripeness Based on Yolo

引用

13th international conference on Intelligent Control and Information Processing, ICICIP 2025

作者： Qiao, Tiezheng Luo, Xiaonan Zhu, Xiaoshu School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9798331516147

Mangoes are a crucial agricultural crop in southern China, especially in the Guangxi region, where their production significantly contributes to the local economy. However, chal¬lenges in ripeness identification lead to quality control issues in storage, transportation, and sales. this research proposes a detection method for mango ripeness based on an improved YOLOv5 model. Traditional fruit ripeness detection methods have certain limitations in terms of accuracy and efficiency. To improve detection accuracy, this research replaces the backbone network of the YOLOv5 model with ResNet50 and introduces the Coordinate Attention (CA) mechanism to enhance the models feature extraction ability and the accuracy of ripeness judgment. Experimental results show that the improved model demonstrates higher precision and robustness in detecting mangoes at different stages of ripeness. the research results indicate that the improved YOLOv5 model can effectively identify and distinguish mango ripeness, providing technical support for fruit quality manage¬ment and harvesting in the agricultural field. © 2025 ieee.

关键词： Fruits

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Pseudoinverse Learning Algorithm As a Unified Framework For Normalization In Deep Learning 13

Pseudoinverse Learning Algorithm As a Unified Framework For ...

引用

13th international conference on Intelligent Control and Information Processing, ICICIP 2025

作者： Guo, Ping Deng, Xiyan Wang, Yuping International Academic Center of Complex Systems Beijing Normal University Zhuhai China School of Computer Science and Technology Xidian University Xi’an China

ISBN: (纸本)9798331516147

In recent years, several normalization methods have been proposed in order to train neural networks, including batch normalization, layer normalization, weight normalization, and group normalization. However, it does not exist a unified framework which can unify these normalization techniques for easy using to most researchers. In this paper, we proved that most of the normalization methods can be realized in a unified framework, namely PseudoInverse Learning (PIL) algorithm. When training neural network with PIL algorithm, no other normalization methods are needed further since normalization is natural embedded in PIL algorithm. And like normalization techniques, PIL algorithm not only can speed up training deep neural networks, but also can act as a regularizer to improve generalization when utilizing low rank constraint PIL for autoencoders. Hence, our proposed unified framework can help researchers to tackle the difficult of choosing proper normalization methods, and this can greatly facilitate their research process. © 2025 ieee.

关键词： Adversarial machine learning

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A Discrete-Time Collaborative Neurodynamic Approach to Distributed Global Optimization 13

A Discrete-Time Collaborative Neurodynamic Approach to Distr...

引用

13th international conference on Intelligent Control and Information Processing, ICICIP 2025

作者： Huang, Haoen Zeng, Zhigang Wang, Jun School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Department of Computer Science Department of Data Science City University of HongKong Kowloon Hong Kong

ISBN: (纸本)9798331516147

In this paper, a discrete-time projection neural network with an adaptive step size (DPNN) is proposed for distributed global optimization. the DPNN is proven to be convergent to a Karush-Kuhn-Tucker point. Several DPNNs are utilized in a collaborative neurodynamic framework for solving distributed global optimization problem. the efficacy of the collaborative neurodynamic approach with DPNNs is demonstrated through simulation results. © 2025 ieee.

关键词： Neural network models

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A Defect Detection Method for Artificial Leather Based on YOLOv5 13

A Defect Detection Method for Artificial Leather Based on YO...

引用

13th international conference on Intelligent Control and Information Processing, ICICIP 2025

作者： Chen, Wenbin Luo, Xiaonan Zhu, Xiaoshu School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9798331516147

In recent years, the demand for high-quality artificial leather has increased, highlighting the need for accurate and efficient defect detection methods. To address issues of inefficiency and a high leakage rate in manually inspecting defects in artificial leather, this study introduces a defect detection approach using an enhanced *** network. In this method, the Ghost convolution module replaces the CNN convolution module within the CSP structure of the backbone network. this adjustment simplifies the network architecture, enhances model feature extraction comprehensiveness, and reduces the parameter count in the backbone network. Additionally, the SE attention mechanism is incorporated into the backbone network, allowing it to learn the texture and edge features of small target leather defects more effectively and rapidly, thereby improving the network's sensitivity to minor defects. Finally, by substituting the original CIoU loss function with the EIoU loss function, the method intensifies the penalty on discrepancies between the predicted and actual bounding boxes, specifically in width and height Experimental results indicate that the modified model achieves an overall average accuracy (mAP) of 87.1% on the tested dataset, offering a more efficient solution for detecting defects in artificial leather. © 2025 ieee.

关键词： Leather

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Application of the CTPN-CRNN Model with Attention Mechanism in Scene Text Recognition 13

Application of the CTPN-CRNN Model with Attention Mechanism ...

引用

13th international conference on Intelligent Control and Information Processing, ICICIP 2025

作者： Zhang, Weijie Luo, Xiaonan Zhu, Xiaoshu School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9798331516147

With the rapid development of the internet, textual information contained in online images has become a key resource for automated information extraction. However, this information is often embedded in images with complex backgrounds or irregular layouts, posing significant challenges to traditional text recognition methods. this paper proposes a method based on CTPN (Connectionist Text Proposal Network) and attention mechanism-enhanced CRNN (Convolutional Recurrent Neural Network) for extracting key information from online images, effectively supporting information extraction and data analysis tasks. the method first uses CTPN to accurately detect text areas in the image and generate text box candidates, then employs the CRNN enhanced with an attention mechanism for character recognition within the candidate boxes. We conducted experimental validation on the ICPR MTWT 2018 dataset, which includes a variety of real-world application scenarios. the results show that the CRNN model combined with CTPN and the attention mechanism outperforms traditional OCR methods on this dataset. the high precision and robustness of this method provide strong technical support for automated information extraction, product data analysis, and intelligent search, offering broad application potential. © 2025 ieee.

关键词： Convolutional neural networks

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Underwater Image Restoration Based on Improved SwinIR Model 13

Underwater Image Restoration Based on Improved SwinIR Model

引用

13th international conference on Intelligent Control and Information Processing, ICICIP 2025

作者： Lu, Yanke Li, Fang Luo, Xiaonan Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin541004 China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China Guilin541004 China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China

ISBN: (纸本)9798331516147

Image restoration is a classic foundational visual task, aimed at recovering damaged images, such as those affected by compression, blurring, or noise, to high-definition clarity. Although current image enhancement techniques largely rely on convolutional neural networks, the efficient capabilities of Transformers demonstrated in complex visual tasks have not yet been extensively applied in the field of image restoration. this paper presents an optimized image restoration strategy based on the SwinIR model, which is applied to the task of underwater image restoration. In the shallow feature extraction phase, deep separable convolution technology is employed;while in the deep feature extraction phase, a Spatial Gated Feedforward Network (SGFN) is utilized. the experimental data suggests that the novel approach surpasses conventional techniques in effectiveness and has effectively minimized both the model's parameter quantity and computational demands. © 2025 ieee.

关键词： Convolution

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NewsCap: Audio-guided Context-aware Network for News Video Captioning 24

NewsCap: Audio-guided Context-aware Network for News Video C...

引用

13th international conference on Computing and Pattern Recognition, ICCPR 2024

作者： Zeng, Zijian Zheng, Yue Li, Yali Wang, Shengjin Beijing National Research Center for Information Science and Technology Beijing China Department of Electronic Engineering Tsinghua University Beijing China

ISBN: (纸本)9798400717482

As a task connects computer vision and natural language processing, video captioning aims to generate natural language descriptions of video content. Because of the rapid development of deep learning, recent research has greatly promoted the progress in video captioning. Due to the limitations of datasets and application scenarios which mainly focus on the visual-centric content, most methods concentrate on how to better extract visual information, while ignoring other modal information such as audio. However, in our view, these methods may be limited in the news scenario. Because in addition to visual information, news videos are often accompanied by audio information, such as voice, background music or ambient sounds. these audio elements provide an additional modality for the semantic description of videos, which makes captions richer and more comprehensive. In this paper, we propose a method called Audio-guided Context-aware Network for news video captioning (NewsCap). It consists of two modules named context-aware module which considers comprehensive audio information and audio-guided module which exploits audio information to guide the generation of captions. We conduct extensive experiments on News-11k, a multi-modal video captioning dataset based on news video, and results demonstrate the effectiveness and priority of NewsCap, which outperforms the existing methods by 2.3 in CIDEr-D. © 2024 Copyright held by the owner/author(s).

关键词： Semantics

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