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共 68658 条记录，以下是341-350 订阅

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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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Comprehensive Deadlock Prevention for GPU Collective Communication 25

Comprehensive Deadlock Prevention for GPU Collective Communi...

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20th European conference on computer Systems, EuroSys 2025, co-located 30th ACM international conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS 2025

作者： Pan, Lichen Liu, Juncheng Fu, Yongquan Yuan, Jinhui Zhang, Rongkai Li, Pengze Xiao, Zhen School of Computer Science Peking University China OneFlow Research China National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology China

ISBN: (纸本)9798400711961

Distributed deep neural network training necessitates efficient GPU collective communications, which are inherently susceptible to deadlocks. GPU collective deadlocks arise easily in distributed deep learning applications when multiple collectives circularly wait for each other. GPU collective deadlocks pose a significant challenge to the correct functioning and efficiency of distributed deep learning, and no general effective solutions are currently available. Only in specific scenarios, ad-hoc methods, making an application invoke collectives in a consistent order across GPUs, can be used to prevent circular collective dependency and deadlocks. This paper presents DFCCL, a novel GPU collective communication library that provides a comprehensive approach for GPU collective deadlock prevention while maintaining high performance. DFCCL achieves preemption for GPU collectives at the bottom library level, effectively preventing deadlocks even if applications cause circular collective dependency. DFCCL ensures high performance with its execution and scheduling methods for collectives. Experiments show that DFCCL effectively prevents GPU collective deadlocks in various situations. Moreover, extensive evaluations demonstrate that DFCCL delivers performance comparable to or superior to NCCL, the state-of-the-art collective communication library highly optimized for NVIDIA GPUs. © 2025 Copyright held by the owner/author(s).

关键词： Deep neural networks

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Parallel solution and optimization of microphysics module in GRAPES physics process 2

Parallel solution and optimization of microphysics module in...

引用

2nd international conference on Big Data, Computational Intelligence, and Applications, BDCIA 2024

作者： Xu, Aokang Jia, Jinfang School of Computer Technology and Applications Qinghai University Qinghai Xining810016 China Intelligent Computing and Application Laboratory of Qinghai Province Qinghai Xining810016 China

ISBN: (纸本)9781510689053

Global and Regional Assimilation and Prediction System (GRAPES) is a domestically developed numerical weather prediction system in China. The microphysical process is the key physical process of cloud formation and precipitation and is also one of the hot spots in the calculation of the whole model. Despite the parallel processing capabilities of MPI and OpenMP, the computation time of the microphysics process remains relatively long. In order to achieve finer-grained parallelization, the code is ported to Graphics Processing Unit (GPU) using a combination of memory coalescing and array scalarization methods. In addition, to address the issue of underutilization of GPU resources when multiple processes share a single GPU, the Compute Unified Device Architecture (CUDA) Multi-Processing Service (MPS) is introduced in the microphysics module to improve GPU utilization. Experimental results show that for a resolution of 50km, excluding data transfer time, the speedup achieved by using 4 GPUs in conjunction with 36 CPU cores reaches 11.59, and the speedup excluding data transfer time is 18.59. It has been verified that the parallel computation of the microphysics process on GPUs is stable and correct. © 2025 SPIE.

关键词： Graphics processing unit

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MFE-Net: A remote sensing image dehazing model for the visual system of a flight simulator 6

MFE-Net: A remote sensing image dehazing model for the visua...

引用

6th international conference on Geoscience and Remote Sensing Mapping, GRSM 2024

作者： Ge, Wenyi Wang, Bo Liu, Qi Qin, Xiaolin Tan, Shihan Wang, Shengjie Yuan, Xia College of Computer Science Chengdu University of Information Technology Chengdu610255 China Chengdu Institute of Computer Applications Chinese Academy of Sciences Chengdu610213 China Jiuzhou Air Trafic Control Sichuan Jiuzhou Investment Holding GroupCo. Ltd Mianyang621000 China The Key Laboratory of Flight Techniques and Flight Safety Civil Aviation Flight University of China Guanghan618307 China

ISBN: (纸本)9781510688018

Obtaining high-quality remote sensing images is crucial in generating a three-dimensional terrain for flight simulators. However, due to the presence of haze and other impact factors, collected remote sensing images usually suffer from blurred details, which limits its applicability real-world applications. To address this issue a deep learning-based dehazing model is proposed to advance hazy remote sensing images to build flight simulator visual systems. The encoder-decoder architecture is proposed to perform the image dehazing, in which an improved feature extraction module is designed to extract both global and local semantic features across multi-resolutions. In addition, the pixel-pixel and region-region mechanism are innovatively designed to achieve deep mapping relationships and further enhance image dehazing. Experimental results demonstrate that the proposed model outperforms other comparative baselines, achieving high-confidence dehazing in multi-scene remote sensing images, and with good generality and applicability in multi-resolution flight simulators and multi-terrain remote sensing. © 2025 SPIE.

关键词： Flight simulators

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Pluggable Compensation Module for Twofold Propagation-based Hologram Generation 4

Pluggable Compensation Module for Twofold Propagation-based ...

引用

4th international Computational Imaging conference, CITA 2024

作者： Yuan, Ganzhangqin Tsinghua Shenzhen International Graduate School Tsinghua University China

ISBN: (纸本)9781510688834

Recent advancements in learning-based computer-generated holography (CGH) have enabled sophisticated methods for generating phase-only holograms (POHs). However, a key challenge remains unaddressed: ringing artifacts, which arise due to the Gibbs phenomenon during the diffraction propagation process, significantly degrade the quality of reconstructed holographic images. To address this, we introduce a novel pluggable compensation module designed to predict and mitigate these artifacts by adjusting for residual values during propagation. This pluggable module, easily integrated with existing CGH networks, alleviates the network's burden by aligning the diffraction process more closely with ideal conditions. Our results demonstrate that embedding this module in state-of-the-art CGH frameworks, such as HoloNet and CCNN, elevates the PSNR by up to 3.9 dB and 0.6 dB, respectively. Notably, our method achieves considerable artifact reduction in real-world scenarios, thereby enhancing the overall fidelity of holographic displays. This pluggable module represents a substantial advancement, offering versatility and improved performance across diverse CGH algorithms. © 2025 SPIE.

关键词： Holograms

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Digital Cockpit Construction Based on Cloud Platform 15th

Digital Cockpit Construction Based on Cloud Platform

引用

15th China Academic conference on Printing and Packaging, CACPP 2024

作者： Lu, Ji Liu, Linlin Zhou, Yuwen Lu, Ming Xiao, Chenyang Jia, Junjie Faculty of Printing Packaging Engineering and Digital Media Technology Xi’an University of Technology Xi’an China Institute of Automation Gansu Academy of Sciences Gansu China

ISBN: (纸本)9789819644667

With the development of the industrial Internet, the printing industry, as a discrete manufacturing industry, gradually attaches importance to the concept and strategy of using data to promote the digital and intelligent transformation of enterprises. For printing enterprises, the value content of the production data of printing equipment is extremely high. If the equipment production data can be automatically collected and further mined, it is of great significance to improve the production efficiency of enterprises and realize the visualization and intelligence of the whole production process. In this study, a set of digital cockpit system is built for data acquisition and monitoring of the printing equipment, and for data processing and analysis of the equipment. Digital cockpit is a set of highly collaborative human-computer interaction system that can realize central empowerment. With the help of digital cockpit, printing enterprises are expected to improve production efficiency, improve the quality of printing products, and improve the decision-making of managers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Smart manufacturing

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Innovations and Transformations in Teaching Systems from the Perspective of Human-computer Interaction: A Case Study on College Students' Mental Health Education Courses 3

Innovations and Transformations in Teaching Systems from the...

引用

3rd international conference on Artificial Intelligence and Education, ICAIE 2024

作者： Min, Min Cheng, Junlin School of Foreign languages Jiangsu University of Science and Technology Jiangsu Zhenjiang China School of Energy and Power Jiangsu University of Science and Technology Jiangsu Zhenjiang China

ISBN: (纸本)9798400712692

The arrival of human-computer interaction (HCI) technology has mainly transformed all areas of education. This study discusses the use of HCI technology in college mental health education and its impact on teaching method. This study explores the current challenges of traditional mental health education, such as limited interaction, lack of individualization, and low participation of students. By combining HCI technologies such as interactive platforms, virtual reality (VR), and personalized feedback systems, this research proposes and implements a novel teaching model. In this study, the effectiveness of HCI technology-based teaching model was evaluated through a survey involving 200 students and the results showed that the HCI technology-based program effectively increased the participation and satisfaction of mental health education among college students and also help improve their mental heath level. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Higher Education Human-computer Interaction technology Mental Health Education Personalized Learning Teaching Systems Innovation

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Siamese Network for EEG Classification of Motor Execution and Imagery 5

Siamese Network for EEG Classification of Motor Execution an...

引用

5th IEEE international conference on Power, Electronics and computer Applications, ICPECA 2025

作者： Li, Zhi Chen, Cai Peng, Fulai Lv, Danyang Tian, Chongxuan Meng, Xiangwei Zhang, Ningling Wu, Fengxia Shandong Institute of Advanced Technology Chinese Academy of Sciences Jinan250000 China Biomedical Engineering Institute School of Control Science and Engineering Shandong University Jinan250061 China School of Basic Medical Sciences Shandong University Jinan250000 China

ISBN: (纸本)9798331533694

Motor imagery (MI), the mental simulation of movement without physical execution, is a pivotal aspect of brain-computer interface (BCI) research, facilitating direct communication between the brain and external devices. Non-invasive BCIs, particularly those utilizing electroencephalogram (EEG) signals, face inherent challenges, including high-dimensional data, temporal dependencies, and variability across subjects. This study investigates the potential of Siamese networks-A deep learning framework grounded in contrastive learning-for single-channel EEG classification in MI tasks. Through a series of comparative experiments employing identical parameter settings, including ten-fold cross-validation, we demonstrate that Siamese networks outperform convolutional neural networks (CNNs), yielding an average classification accuracy of approximately 10% higher. These findings underscore the superior capability of Siamese networks in capturing subtle temporal patterns and mitigating inter-subject variability, positioning them as a promising alternative to traditional CNN-based approaches in EEG-based MI classification. © 2025 IEEE.

关键词： Electroencephalography

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Learning Simultaneous Facial Canonical Correlation Representation for Face Hallucination

Learning Simultaneous Facial Canonical Correlation Represent...

引用

2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Yuan, Yun-Hao Li, Jin Qiang, Jipeng Zhu, Yi Shen, Xiaobo Li, Yun School of Information Engineering Yangzhou University Yangzhou China School of Computer Science Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798350368741

The low resolution (LR) problem is rather challenging in face analysis. Most existing face hallucination methods assume that LR face images have only one resolution, but multiple resolutions may be available from different sources. To solve this issue, we propose a novel simultaneous facial canonical correlation representation learning method for face hallucination, which seeks latent correlation subspaces for multi-resolution views. Our method jointly solves multiple linear transformations by optimizing a correlation summation criterion of all pairs of resolutions. The neighborhood reconstruction is used to infer the HR facial canonical correlation representation of LR face inputs. Extensive experimental results show the superiority of our proposed method in terms of quantitative and qualitative evaluations. © 2025 IEEE.

关键词： canonical correlation analysis face hallucination latent representation multiple facial resolutions

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Scene Graph and Dependency Grammar Enhanced Remote Sensing Change Caption Network (SGD-RSCCN) 31

Scene Graph and Dependency Grammar Enhanced Remote Sensing C...

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31st international conference on Computational Linguistics, COLING 2025

作者： Sun, Qiaoli Wang, Yan Song, Xiaoyu College of Computer Science Inner Mongolia University Hohhot010021 China Portland State University Portland97207 United States

ISBN: (纸本)9798891761964

With the continuous advancement of remote sensing technology, it is easier to obtain high-resolution, multi-temporal and multi-spectral images. The images carry rich information of ground objects. However, how to effectively extract useful information from the complex image data and convert it into understandable semantic descriptions remains a challenge. To deal with the challenges, we propose a Scene Graph and Dependency Grammar Enhanced Remote Sensing Change Caption Network (SGD-RSCCN) to improve the accuracy and naturalness of extracting and describing change information from remote sensing images. By combining advanced visual analysis technology and natural language processing technology, the network not only optimizes the problem of insufficient understanding of complex scenes, but also enhances the ability to capture dynamic changes, thereby generating more accurate and smooth natural language description. In addition, we also proposes the decoder based on prior knowledge, which further improves the readability and comprehensibility of the description. Extensive experiments on LEVIR-CC and Dubai-CC datasets verify the advantages of the proposed method in generating accurate and true descriptions. © 2025 Association for Computational Linguistics.

关键词： Semantics

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