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Fusion of Time-Frequency Features in Contrastive Learning for Shipboard Wind Speed Correction

Journal of Ocean University of China 2025年第2期24卷 377-386页

作者： SONG Jian HUANG Meng LI Xiang ZHANG Zhenqiang WANG Chunxiao ZHAO Zhigang Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center(National Supercomputer Center in Jinan)Qilu University of Technology(Shandong Academy of Sciences)Jinan 250000China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer ScienceJinan 250000China

Accurate wind speed measurements on maritime vessels are crucial for weather forecasting,sea state prediction,and safe ***,vessel motion and challenging environmental conditions often affect measurement *** address this issue,this study proposes an innovative framework for correcting and predicting shipborne wind *** integrating a main network with a momentum updating network,the proposed framework effectively extracts features from the time and frequency domains,thereby allowing for precise adjustments and predictions of shipborne wind speed *** using real sensor data collected at the Qingdao Oceanographic Institute demonstrates that the proposed method outperforms existing approaches in single-and multi-step predictions compared to existing methods,achieving higher accuracy in wind speed *** proposed innovative approach offers a promising direction for future validation in more realistic maritime onboard scenarios.

关键词： time series prediction wind speed correction comparative learning shipborne sensor

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key Management and Authentication Scheme Forward Trusted Pervasive Container computing 2

Key Management and Authentication Scheme Forward Trusted Per...

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2nd International Conference on Big Data and Privacy computing, BDPC 2024

作者： Hei, XinHong Xue, YuZhan Wang, YiChuan Liu, YiFan Peng, Chen Sun, Xiang School of Computer Science and Engineering Xi'an University of Technology Xi'an China School of Computer Science and Engineering Xi'an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China

ISBN: (纸本)9798350307597

Cloud computing technology is an internet-based computing model that provides high reliability and security measures, including data backup, secure access control, etc. As Docker containers play a increasingly important role in traditional cloud computing architectures, the communication needs between containers become prominent. However, existing container to container communication mainly relies on network protocols for file transfer, making it difficult to achieve the expected results in cloud cluster security. Therefore, this paper primarily focuses on container based keys and proposes a container identity trusted key management method based on Pervasive container computing architecture. The experimental results demonstrate that the container identity trusted key management method not only meets the security transmission characteristics required in the container cloud architecture, but also adopts a strategy combined with trusted hardware, providing security guarantees. In addition, the method proposed in this article that combines trusted hardware for signature verification can defend against multiple network attacks compared to traditional container architectures without using security policies. © 2024 IEEE.

关键词： Cloud computing architecture

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Glare-SNet: Unsupervised Glare Suppression Balance network 27th

Glare-SNet: Unsupervised Glare Suppression Balance Network

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Li, Pei Zuo, Chengyu Wei, Wangjuan Pan, Xiaoying Wang, Zhanhao School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an China Shaanxi Key Laboratory of Intelligent Media Computing and interaction Xi’an China Xi’an Key Laboratory of Big Data and Intelligent Computing Xi’an China

ISBN: (纸本)9783031781681

In light of the problems associated with glare and halo effects in low-light images, as well as the inadequacy of existing processing algorithms in handling details, a glare suppression balance network based on unsupervised learning has been designed and implemented in this study. A network that perceives brightness through light decomposition has been proposed, and an illumination learning network has been designed. The idea of integration has been employed to form an unsupervised glare suppression balance network, aiming to tackle the issues of glare and the accompanying halo effects. Through objective and subjective evaluations of real images, it has been proven that the method introduced in this paper surpasses other methods in suppressing glare and enhancing the overall quality of low-light images. Furthermore, it has been shown to exhibit good performance and effectiveness in target detection. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image enhancement

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A Method for Removing Reflections from Water Surface Images Based on Pre-trained Image Restoration

A Method for Removing Reflections from Water Surface Images ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Minghua Zhao Rui Zhi Shuangli Du Jing Hu Cheng Shi Lin Wang Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an University of Technology Xi’an China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Reflections on the water surface hinder the extraction of valuable information from water surface images. To remove reflections from water surface images, we construct a synthetic dataset and propose a multi-task network for water surface reflection detection and removal. Specifically, we first use a U-Net-based reflection detection module to generate a reflection mask, followed by a GAN-based network to remove the reflection. To extract multi-level features from the images, we design a color feature extraction network and a detail feature extraction network. Finally, to enhance the model's ability to remove large-area reflections, we pre-train the reflection removal network on an image restoration dataset. Experimental results on the proposed synthetic dataset and real water surface reflection images from the Internet show that our method significantly outperforms other methods in water surface reflection detection and removal.

关键词： Image color analysis Signal processing Feature extraction Multitasking Reflection Image restoration Speech processing Surface treatment Synthetic data Sports

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Diffusion Model with Multi-layer Wavelet Transform for Low-Light Image Enhancement

Diffusion Model with Multi-layer Wavelet Transform for Low-L...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Haiyan Jin Jing Wang Fengyuan Zuo Haonan Su Zhaolin Xiao Bin Wang Yuanlin Zhang Xi’an University of Technology China Shaanxi Key Laboratory for Network Computing and Security Technology China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Low-light image enhancement methods based on diffusion models, though effective in improving image quality, often overrely on noise sensitivity and neglect the reconstruction deviations due to the naive up- and down-sampling operations. To address this issue, we propose a novel diffusion model, MWT-Diff, which utilizes multi-layer wavelet transforms to replace up-and down-sampling based on convolutions for extracting high-order features of different scales while mitigating representation degradations. Specifically, MWT-Diff is based on the U-Net architecture; it encodes four local features after the frequency-based down-sampling at each layer and fuses the enhanced four components during the up-sampling process. Additionally, we incorporate global refinement branches to mitigate information loss and employ efficient soft gate aggregation for feature fusion and reconstruction. Extensive quantitative and qualitative experiments demonstrate that our model achieves state-of-the-art performance on benchmark datasets. Code is available at: https://***/lalalulao/MWT-Diff.

关键词： Wavelet transforms Sensitivity Noise Logic gates Speech enhancement Feature extraction Diffusion models Image enhancement Image reconstruction Optimization

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Edge computing Task Scheduling with Joint Blockchain and Task Caching in Industrial Internet

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Computers, Materials & Continua 2023年第4期75卷 2101-2117页

作者： Yanping Chen Xuyang Bai Xiaomin Jin Zhongmin Wang Fengwei Wang Li Ling School of Computer Science and Technology Xi’an University of Posts and TelecommunicationsXi’an710121China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an710121China Xi’an Key Laboratory of Big Data and Intelligent Computing Xi’anShaanxi710121China ZTE Corporation Shenzhen51805China

Deploying task caching at edge servers has become an effectiveway to handle compute-intensive and latency-sensitive tasks on the industrialinternet. However, how to select the task scheduling location to reduce taskdelay and cost while ensuring the data security and reliable communicationof edge computing remains a challenge. To solve this problem, this paperestablishes a task scheduling model with joint blockchain and task cachingin the industrial internet and designs a novel blockchain-assisted cachingmechanism to enhance system security. In this paper, the task schedulingproblem, which couples the task scheduling decision, task caching decision,and blockchain reward, is formulated as the minimum weighted cost problemunder delay constraints. This is a mixed integer nonlinear problem, which isproved to be nonconvex and NP-hard. To solve the optimal solution, thispaper proposes a task scheduling strategy algorithm based on an improvedgenetic algorithm (IGA-TSPA) by improving the genetic algorithm initializationand mutation operations to reduce the size of the initial solutionspace and enhance the optimal solution convergence speed. In addition,an Improved Least Frequently Used algorithm is proposed to improve thecontent hit rate. Simulation results show that IGA-TSPA has a faster optimalsolution-solving ability and shorter running time compared with the existingedge computing scheduling algorithms. The established task scheduling modelnot only saves 62.19% of system overhead consumption in comparison withlocal computing but also has great significance in protecting data security,reducing task processing delay, and reducing system cost.

关键词： Edge computing task scheduling blockchain task caching industrial security

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A Semi-supervised Protein Complex Identification Algorithm Based on Sparseness Constraint

A Semi-supervised Protein Complex Identification Algorithm B...

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2024 International Conference on Applied Mathematics, Modelling and Statistics Application, AMMSA 2024

作者： Liu, Guangming Dang, Fengqing Wang, Bin Peng, Yonghong Wang, Xiaofan Tan, Li School of Computer Science and Engineering Shaanxi Key Laboratory for Network Computing and Security Technology Xi'An University of Technology Shaanxi Xi'an China Faculty of Science and Engineering Computing and Information Science Cambridge United Kingdom School of Computer and Information Engineering Beijing Technology and Business University Beijing China

Recognising protein complexes in protein interaction networks is crucial, but poses a major challenge due to the frequency of noisy interactions. These networks typically involve numerous protein complexes, with each protein generally only participating in a few complexes. Current recognition models often ignore this aspect. To address this problem, we present a semi-supervised protein complex identification algorithm that extends non-negative matrix factorization (NMF) with sparsity constraints. In contrast to conventional approaches that apply a global sparsity constraint to the entire matrix, our method imposes individual sparsity constraints on protein membership indicator vectors. This targeted strategy controls the algorithm more effectively. Our experimental results with yeast and human protein interaction networks show that our algorithm achieves higher accuracy in identifying protein complexes than leading contemporary methods. © Published under licence by IOP Publishing Ltd.

关键词： Non-negative matrix factorization

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An Adaptive Residual Coordinate Attention-Based network for Hat and Mask Wearing Detection in Kitchen Environments

An Adaptive Residual Coordinate Attention-Based Network for ...

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

作者： Zhao, Ying Wu, Xiaoming Liu, Xiangzhi Chen, Hao Qi, Bei Dong, Huomin Key Laboratory of Computing Power Network and Information Security Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9781665410205

In order to ensure food safety, it is required for personnel to wear hats and masks during food handling processes. To accurately detect the wearing status of kitchen staff, the ARP-YOLO model is proposed. Firstly, images are obtained from multiple kitchens and angles to construct a dataset reflecting the wearing status of hats and masks. To simulate more complex kitchen environments, Gaussian noise is added to the data and lighting conditions are adjusted for data augmentation. Lighting conditions in the kitchen can affect detection, causing the same target to exhibit different shapes and features under different lighting conditions, leading to missed detections. To address the above issues, we propose ARCA (Adaptive-Residual-Coordinate-Attention), which uses residual connections to strengthen attention to important features while preserving original features, and employs adaptive convolution reduction to reduce module parameters. To improve target localization accuracy, P2 detection layers are added in the Neck to obtain more accurate target position information. The ARP-YOLO model demonstrates significant improvements over the baseline model, with a 13.6% increase in Recall, allowing for more effective target capture and reduced missed detection risk. Additionally, mAP@0.5 has increased by 10%, enhancing target localization accuracy. The F1-Score has also increased by 7.4%, better balancing the relationship between Precision and Recall. To validate the effectiveness of the model, comparative experiments with other models are conducted, showing that ARP-YOLO model's Recall and Average Precision(AP) are higher than those of other models. © 2024 IEEE.

关键词： Lighting

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A Lightweight Node Verification and Protection Strategy for Edge computing 5

A Lightweight Node Verification and Protection Strategy for ...

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5th International Seminar on Artificial Intelligence, networking and Information Technology, AINIT 2024

作者： Du, Jiayuan Zhang, Guowei Qufu Normal University School of Cyber Science and Engineering Qufu China Key Laboratory of Computing Power Network and Information Security Jinan China

ISBN: (纸本)9798350385557

To alleviate the burden on the core network, edge computing has rapidly advanced to support distributed computing. However, as sensitive information is delegated to the edge layer closer to endpoints, the privacy protection challenges faced by edge nodes intensify while striving to deliver high-quality services. Existing studies predominantly rely on various encryption algorithms to safeguard privacy information, such as identity, location, and offloading routes privacy. Unfortunately, these approaches exhibit high overhead and often neglect the potential maliciousness of the nodes themselves. To address these issues, this paper introduces a lightweight edge node verification and protection strategy. This strategy not only verifies the reliability of edge nodes but also enables anonymized uploading of private information. Numerical simulation results demonstrate the superiority of this strategy over existing schemes, showcasing reduced computational overhead while achieving enhanced protection. © 2024 IEEE.

关键词： Computation offloading

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Offloading Mode Hidden for Delay Sensitive Tasks in Edge computing networks 5

Offloading Mode Hidden for Delay Sensitive Tasks in Edge Com...

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5th International Seminar on Artificial Intelligence, networking and Information Technology, AINIT 2024

作者： Zhang, Guowei Du, Jiayuan Qufu Normal University School of Cyber Science and Engineering Qufu China Key Laboratory of Computing Power Network and Information Security Jinan China

ISBN: (纸本)9798350385557

Edge computing has been developed to support local services with lower burden to the core network. However, privacy issues deteriorate when terminal users enjoy the high-quality services with the price of sending their data to multiple service nodes (SNs) scattered throughout the network. Existing researches concerning this problem are almost all based on the traditional security technologies including encryption and authentication, which are too complicated to be well supported by the terminal nodes (TNs) in edge networks due to the poor local processing capabilities. In view of the above, this article proposes an offloading mode hidden (OMH) algorithm, which reduces the risk level by equalizing the task offloading modes of different TNs. Gaming process is adopted to achieve the risk-minimized offloading solutions for TNs. Numerical results demonstrate that comparing with the Greedy algorithm, the proposed OMH algorithm can achieve lower task offloading risk with higher efficiency and provide more TNs with satisfied services. © 2024 IEEE.

关键词： Edge computing

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