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Classification of Sailboat Tell Tail Based on Deep Learning

Journal of Ocean University of China 2024年第3期23卷 710-720页

作者： CHANG Xiaofeng YU Jintao GAO Ying DING Hongchen LIU Yulong YU Huaming Department of Physical Education Ocean University of ChinaQingdao 266100China College of Big Data Qingdao University of Science and TechnologyQingdao 266061China College of Oceanic and Atmospheric Sciences Ocean University of ChinaQingdao 266100China

The tell tail is usually placed on the triangular sail to display the running state of the air flow on the sail *** is of great significance to make accurate judgement on the drift of the tell tail of the sailboat during sailing for the best sailing *** it is difficult for sailors to keep an eye for a long time on the tell sail for accurate judging its changes,affected by strong sunlight and visual *** this case,we adopt computer vision technology in hope of helping the sailors judge the changes of the tell tail in ease with *** paper proposes for the first time a method to classify sailboat tell tails based on deep learning and an expert guidance system,supported by a sailboat tell tail classification data set on the expert guidance system of interpreting the tell tails states in different sea wind conditions,including the feature extraction *** the expression capabilities that vary with the computational features in different visual tasks,the paper focuses on five tell tail computing features,which are recoded by an automatic encoder and classified by a SVM *** experimental samples were randomly divided into five groups,and four groups were selected from each group as the training set to train the *** remaining one group was used as the test set for *** highest resolution value of the ResNet network was 80.26%.To achieve better operational results on the basis of deep computing features obtained through the ResNet network in the *** method can be used to assist the sailors in making better judgement about the tell tail changes during sailing.

关键词： tell tail sailboat classification deep learning

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Beamforming Design for RIS-Aided THz Wideband Communication Systems 24

Beamforming Design for RIS-Aided THz Wideband Communication ...

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24th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2023

作者： Jiang, Yihang Zhou, Ziqin Li, Xiaoyang Gong, Yi Southern University of Science and Technology Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China

ISBN: (纸本)9781665496261

Benefiting from tens of GHz of bandwidth, terahertz (THz) communications has become a promising technology for future 6G networks. However, the conventional hybrid beamforming architecture based on frequency-independent phase-shifters is not able to cope with the beam split effect in THz massive multiple-input multiple-output (MIMO) systems. Despite some work introducing the frequency-dependent phase shifts via the time delay network to mitigate the beam splitting in THz wideband communications, the corresponding issue in reconfigurable intelligent surface (RIS)-aided communications has not been well investigated. In this paper, the beam split effect in THz massive MIMO is quantified by analyzing the array gain loss. A new beamforming architecture has been proposed to mitigate this effect under RIS-aided communications scenarios. Simulations are performed to evaluate the effectiveness of the proposed system architecture in combating the array gain loss. © 2023 IEEE.

关键词： Beamforming

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A momental bound algorithm based on precise stepsize adjustment for training deep neural networks 43

A momental bound algorithm based on precise stepsize adjustm...

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43rd Chinese Control Conference, CCC 2024

作者： Zhang, Haixia Li, Dequan Qian, Zhen Anhui University of Science and Technology School of Mathematics and Big Data Huainan232000 China Anhui University of Science and Technology School of Artificial Intelligence Huainan232000 China

ISBN: (纸本)9789887581581

Adaptive algorithms are extensively employed in the field of deep learning owing to their rapid convergence properties. Adam is the most common adaptive algorithm among them. However, it has revealed that Adam has a poor generalization ability. AdaBelief is an algorithm based on Adam with exact stepsize adjustment. It introduces a new second-order momentum that corrects Adam's second-order moments. These algorithms still fail to converge during training due to the presence of instability and extreme learning rates. In this paper, we propose a new adaptive and momental bounded algorithm, called AdaBMod, which can effectively mitigate the sudden large learning rate problem and is especially suitable for training deep neural networks. By setting an adaptive finite learning rate in AdaBelief algorithm, the obtained AdaBMod can effectively eliminate the problem of high learning rate in the late training of neural networks, so as to make the training process more stable. Numerical simulation experiments on deep neural network tasks also show that our proposed AdaBMod algorithm eliminates the large learning rate during the training process. The results are also better than other current state-of-the-art optimizers. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Adaptive Algorithms Adaptive and Momental Bound Deep Neural Networks Image Classification Robustness

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Robust Multi-Object Tracking Using Vision Sensor with Fine-Grained Cues in Occluded and Dynamic Scenes

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IEEE Sensors Journal 2025年第11期25卷 20547-20560页

作者： Hu, Yaoqi Sun, Jinqiu Jin, Hao Niu, Axi Yan, Qingsen Zhu, Yu Zhang, Yanning School of Computer Science China School of Astronautics China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Northwestern Polytechnical University Dongxiang Road Shaanxi Xi'an710129 China

Multi-object tracking (MOT) using vision sensors remains a challenging problem, particularly in dynamic backgrounds and severe occlusions. Existing methods, relying on holistic appearance or spatial cues, fail to capture detailed information within object regions and the background, resulting in inaccurate and inconsistent tracking. To address these issues, we propose a novel Detail-Driven Multi-Object Tracking (DD-MOT) method that revisits and leverages fine-grained cues to discover the dynamics of both object regions and the background, facilitating the recovery and association of trajectories. Specifically, the proposed method consists of three key modules: (i) Points Trajectories Generator, (ii) Camera Motion and Occlusion Compensation, and (iii) Fine- and Coarsegrained Association. The Points Trajectories Generator is responsible for generating fine-grained cues by sampling an initial set of points, generating point trajectories, and refining the initial set of points. The Camera Motion and Occlusion Compensation module utilizes the background and object point trajectories to correct background motion and recover occluded object bounding boxes. Finally, the Fine- and Coarse-grained Association module leverages point trajectory cues to assist in establishing a more effective association strategy, combining both fine-grained and coarse-grained spatial cues from the object bounding boxes. We evaluate our DD-MOT method on several benchmark datasets, including MOT17, MOT20, demonstrating that it consistently outperforms state-of-the-art (SOTA) methods in key metrics such as HOTA (64.2, 62.6), MOTA (80.8, 78.1), and IDF1 (78.8, 77.1). © 2025 IEEE.

关键词： Motion compensation

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Dual-Enhanced High-Order Self-Learning Tensor Singular Value Decomposition for Robust Principal Component Analysis

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3564-3578页

作者： Xu, Honghui Fang, Chuangjie Wang, Renfang Chen, Shengyong Zheng, Jianwei Zhejiang University of Technology College of Computer Science and Technology Hangzhou310023 China Zhejiang Wanli University College of Big Data and Software Engineering Ningbo315200 China Tianjin University of Technology College of Computer Sciences and Engineering Tianjin300384 China

Recently, tensor singular value decomposition (TSVD) within high-order (Ho) algebra framework has shed new light on tensor robust principal component analysis (TRPCA) problem. However, HoTSVD lacks flexibility in handling the hidden correlations along different modes of large-scale multidimensional data. Moreover, the utilization of fixed or data-independent transformations in HoTSVD may result in suboptimality. For a relief, we propose a dual-enhanced self-learning TSVD along all modes to address computational flaws and learn a lossless transformation that induces a lower average-rank tensor. Specifically, we multiply the learnable semiorthogonal matrices obtained through Tucker compression with the original tensor along all modes, thus obtaining a core tensor with more inherent low rankness. Building upon this foundation, a new TNN is introduced by generalizing HoTSVD to Mode-k TSVD, followed by the facilitation to the core tensor, achieving dual-enhancement. Moreover, a reweighting scheme is imposed on the Mode-k HoTSVD to learn the global low-rank correlation and provide an efficient numerical solution. Finally, an alternating direction method of multipliers (ADMM)-based algorithm is developed as a solver. Experimental results on several types of multidimensional visual data, including light field images (LFI) and color videos, demonstrate the superiority of the proposal over previous state-of-the-art methods. © 2020 IEEE.

关键词： Discrete Fourier transforms

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Adoption of Medical AI and its Effect on Speciesism 3

Adoption of Medical AI and its Effect on Speciesism

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Abangbila, Linda Li, Xianmiao Agordzo, George K. Fomude, Akwi Helene Anhui University of Science and Technology School of Economics and Management Huainan232000 China Anhui University of Science and Technology School of Mathematics and Big Data Huainan232000 China

ISBN: (纸本)9798350375190

AI can change healthcare by boosting medical decision-making and patient outcomes. However, the use of AI in medicine creates ethical issues, notably with speciesism. Participants' awareness of AI in healthcare, perceptions of speciesism, ethical considerations, and willingness to use A I technologies were all gathered through the questionnaire. Descriptive statistics, chi-square tests, and correlation analyses established the relationship between speciesism and AI adoption. 70 % of participants were aware of healthcare AI technologies, but just 45 % showed a willingness to utilize them. 60 % of those hesitant cited ethical concerns regarding speciesism in AI decision-making. The investigation also found a substantial association between participants' perceptions of speciesism and their willingness to adopt AI technologies, demonstrating that speciesist attitudes affected AI adoption. The study emphasizes ethical considerations and non-human animal interests in medical AI system development and implementation. This research contributes to the ethical use of AI in healthcare by highlighting the effect of speciesism on AI adoption and emphasizing the need to overcome speciesist prejudices to achieve fairness, inclusion, and equity. This study highlights the importance of speciesism awareness and education in medical AI adoption. This study illuminates the relationship between speciesism and medical AI adoption. To enable more inclusive, ethical, and equitable use of AI technologies in healthcare, it addresses ethical considerations and obstacles. © 2024 IEEE.

关键词： Decision making

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Soft-GNN:towards robust graph neural networks via self-adaptive data utilization

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Frontiers of Computer science 2025年第4期19卷 1-12页

作者： Yao WU Hong HUANG Yu SONG Hai JIN National Engineering Research Center for Big Data Technology and System Service Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China College of Information and Communication National University of Defense TechnologyWuhan 430019China Department of Computer Science and Operations Research Universitéde MontréalMontreal H3C 3J7Canada

Graph neural networks(GNNs)have gained traction and have been applied to various graph-based data analysis tasks due to their high ***,a major concern is their robustness,particularly when faced with graph data that has been deliberately or accidentally polluted with *** presents a challenge in learning robust GNNs under noisy *** address this issue,we propose a novel framework called Soft-GNN,which mitigates the influence of label noise by adapting the data utilized in *** approach employs a dynamic data utilization strategy that estimates adaptive weights based on prediction deviation,local deviation,and global *** better utilizing significant training samples and reducing the impact of label noise through dynamic data selection,GNNs are trained to be more *** evaluate the performance,robustness,generality,and complexity of our model on five real-world datasets,and our experimental results demonstrate the superiority of our approach over existing methods.

关键词： graph neural networks node classification label noise robustness

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Design of Subpixel Computer Vision Detection Algorithm Based on Texture Image

Design of Subpixel Computer Vision Detection Algorithm Based...

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2023 International Conference on Networking, Informatics and Computing, ICNETIC 2023

作者： Wang, Junxian Mao, Kaihui Zhang, Xiaolong Lv, Wei Gao, Junhan Zhuhai College of Science and Technology Alibaba Cloud Big Data Application College Zhuhai519041 China

ISBN: (纸本)9798350313314

Testing technology based on computer vision is a new testing technology. It uses the image as a means or carrier to detect and transmit information, extracts useful signals from the image, and obtains various required parameters by processing the measured image. It has the characteristics of non-contact, full-field measurement, high precision and high degree of automation. In this paper, an optimal design scheme of sub-pixel computer vision detection algorithm based on texture image is proposed. The sub-pixel image texture is extracted by texture database, and then the sub-pixel accuracy is grayed by computer vision detection to achieve the optimal design of sub-pixel accuracy. Finally, the sub-pixel of texture image is analyzed experimentally. Finally, the experiment shows that the sub-pixel gray level is improved by 8.45% compared with the traditional algorithm in the case of texture primitives. It is of great practical significance to carry out the research of computer vision inspection design with sub-pixel as the core to speed up the improvement of vision inspection technology. © 2023 IEEE.

关键词： Image texture

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Efficient Relaxed Functional Dependency Discovery with Minimal Set Cover 40

Efficient Relaxed Functional Dependency Discovery with Minim...

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40th IEEE International Conference on data Engineering, ICDE 2024

作者： Ding, Xiaoou Liu, Yida Wang, Hongzhi Wang, Chen Song, Yichen Yang, Donghua Wang, Jianmin School of Computer Science and Technology Harbin Institute of Technology China National Engineering Research Center for Big Data Software EIRI Tsinghua University China

ISBN: (纸本)9798350317152

Assessing data quality through Functional Depen-dencies (FDs) is a crucial aspect of data governance. However, with the diverse range of data sources and the exponential growth in data volume, exact FDs can sometimes be impractical for real-world applications. In contrast, relaxed functional dependencies (RFDs), which allows for some flexibility in attribute value comparisons, demonstrates greater adaptability and flexibility for big data scenarios. To address the efficient discovery of RFDs, this paper proposes a novel mining method to supplement the current research gaps. By establishing a difference table for tuples, we transform the problem into a specialized minimal set covering problem. Additionally, we introduce two optimization strategies: reducing the time complexity of enumerating the left-hand side of the base RFDs to 0 (1) and decreasing the search complexity for feasible LHS attributes and threshold candidates from O(2m-l) to O(1.5m-1). We rigorously proof that our mining approach guarantees the identification of validity and minimal RFDs. Experiments on nine real-world datasets reveal that our method significantly improves efficiency compared to existing techniques. Furthermore, it uncovers more concise and higher-quality RFDs. Importantly, the RFDs extracted through our methodology exhibit better performance in downstream cleaning tasks. © 2024 IEEE.

关键词： Quality management

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Design and Application of a Traffic Light System at Airport Level Crossing Based on Video Surveillance technology 5

Design and Application of a Traffic Light System at Airport ...

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5th IEEE International Conference on Civil Aviation Safety and Information technology, ICCASIT 2023

作者： Miao, Jiahe Le, Ningning Zhang, Xinyue Li, Qian Shi, Hongfang Li, Yang China Academy of Civil Aviation Science & Technology Big Data Analysis and Application Center Beijing China

ISBN: (纸本)9798350310603

This paper presents a design scheme of traffic light system at airport level crossing based on video surveillance technology. The system uses cameras and edge computing to detect the aircraft about to pass through the intersection with signals transmitted through data radio stations, and controls the traffic lights to alert vehicle drivers, to reduce the risk of conflict or collision between vehicles and the aircraft. The overall framework of the system and the design scheme of each functional module are introduced in detail, and the practical application of the system at a domestic airport is shown in the end. The system has the characteristics of low cost, easy deployment, high reliability, low delay, and strong expansibility, which can provide help for the safety management of airport surface operation. © 2023 IEEE.

关键词： Object detection

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