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9th IEEE International Conference on Cyber Security and Cloud Computing and 8th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2022

作者： Gu, Haonan Wu, Jing Guo, Yi Wuhan University of Science and Technology College of Computer Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China

ISBN: (数字)9781665480666

ISBN: (纸本)9781665480666

This paper proposed a soft real-time scheduling algorithm 'time-deterministic dynamic partitioning scheduling' for multimedia tasks in embedded devices. We established a new model based on the characteristics of embedded devices. We developed a pre-scheduling mechanism to select a suitable scheduling strategy based on the actual situation of the task set in the offline cache and to rearrange the task order so as to guarantee the reliability and real-time performance of the system while having higher flexibility and stability. The mechanism also completes the tasks assigned by the system in a stable manner with high utilization. © 2022 IEEE.

关键词： Scheduling algorithms

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SMWT: A Universal and Lightweight SDN-Based Meter Table Watermarking Traceback Scheme 24

SMWT: A Universal and Lightweight SDN-Based Meter Table Wate...

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24th Asia-Pacific Network Operations and Management Symposium, APNOMS 2023

作者： He, Heng Yang, Sen Zhou, Xiaohu Wang, Jia School of Computer Science and Technology Wuhan University of Science and Technology Wuhan430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan430081 China Guilin Medical University Guilin541001 China

ISBN: (纸本)9788995004395

The use of anonymous network has increased dramatically due to the development of network technology and people focus on the network security and privacy. However, anonymous networks are often used for illegal purposes, which has caused a significant increase in illegal transactions in the network. Therefore, it is necessary to investigate methods that can trace illegal transactions in anonymous networks. In this paper, we propose a universal and lightweight Software Defined Network (SDN)-based Meter table Watermarking Traceback scheme (SMWT), which is an active watermarking technology. SMWT uses the Bose-Chaudhuri-Hocquenghem (BCH) code as the encoding method and the flow rate as the carrier for the watermark to effectively trace flows of different communication protocols in various anonymous network environments. It utilises the SDN controller to install flow table entries and meter entries to the SDN switch, which can efficiently change the flow rate and embed the watermark in the flow in a stealthily manner. After the flow passes through the anonymous network, the SDN switch can extract the watermark using the flow rate and identify the two communicating parties. We have conducted experiments to evaluate SMWT in a real anonymous network Tor. Compared with other relevant existing schemes, SMWT demonstrated a higher detection rate of 96.7% and a lower false positive rate of 0.3%, as well as high flexibility and stealthiness, low deployment difficulty and controller load. Copyright 2023 KICS.

关键词： Network security

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LT-1 Differential Tomography: First Results and Comparison between Monostatic and Bistatic Systems

LT-1 Differential Tomography: First Results and Comparison b...

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Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Shimeng Zhou Zhiyang Chen Yuanhao Li Cheng Hu Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing Institute of Technology Beijing China Advanced Technology Research Institute Beijing Institute of Technology Beijing China School of Information and Electronic Beijing Institute of Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Differential Tomography SAR technology integrates the advantages of Differential Interferometric SAR and Tomographic SAR, enabling four-dimensional imaging in areas with SAR layover to obtain elevation and deformation information of the target area. Compared to the traditional monostatic mode, the space-based bistatic observation mode has the advantages of interference immunity and richer information acquisition. This paper uses Differential Tomography SAR technology to perform deformation inversion on the LuTan-1 monostatic and space-based bistatic data. While obtaining the elevation information and linear deformation rate of the observed scene, it also compares the results and performance of Differential Tomography under the two different *** final conclusion is that LuTan-1 demonstrates strong capabilities in differential tomography. Additionally, the differential tomography performance of space-based bistatic systems is superior to that of monostatic systems.

关键词： Point cloud compression Accuracy Deformation Buildings Interference Tomography Data processing Root mean square Standards Monitoring

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Cascaded mixed pyramid pooling for surface defect segmentation in solid oxide fuel cell 3

Cascaded mixed pyramid pooling for surface defect segmentati...

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3rd International Conference on Electronic information Engineering, Big Data, and Computer technology, EIBDCT 2024

作者： Wang, Yaokun Fu, Xiaowei Li, Xi Xu, Wei College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Hubei Wuhan430065 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Wuhan430074 China

ISBN: (纸本)9781510680449

To address the issues of defect leakage and mis-segmentation in the surface defect segmentation of solid oxide fuel cells, we propose a cascaded mixed pyramid pooling segmentation method. In the encoding part, attention-based depth-wise separable convolution is implemented to replace the traditional convolution. This reduces the computational complexity and achieves hierarchical feature extraction. Secondly, cascaded sampling is applied to the last three layers of the encoder to promote multi-level feature fusion, and mixed pyramid pooling consisting of atrous convolution and strip pooling is adopted to achieve feature extraction for long-range band defects. Lastly, an adaptive loss function is constructed to supervise the training process, balancing the relative importance among the losses and improving the learning of defect features by the network. The experiments demonstrate that the proposed method improves segmentation accuracy, as evidenced by better results in mean absolute error, F1 score, intersection over union, and Pratt's figure of merit. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Convolution

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Fine grained image recognition algorithm based on CNN-Transformer and paired interaction

Fine grained image recognition algorithm based on CNN-Transf...

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Neural Networks, information and Communication Engineering (NNICE), International Conference on

作者： Yu Wang Hong Zhang Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology Wuhan China

A new fine-grained image recognition algorithm (P-ResViT, Paired Interaction Res50-ViT) based on CNN-Transformer is proposed to address the issue of existing fine-grained image recognition methods based on convolutional neural networks (CNN) lacking attention mechanisms and difficulty in accurately locating key regions, as well as weak ability to extract contextual information and global features. This algorithm introduces paired interaction attention mechanisms to supplement external attention. This algorithm can utilize the self attention mechanism of the Vision Transformer and the external attention mechanism formed by paired image inputs during training to achieve precise positioning, while preserving the inductive bias and local feature extraction capabilities of CNN. Firstly, the fine-tuning improved ResNet-50 completes local feature extraction and obtains high-resolution feature maps. Secondly, the Transformer encodes the tokenized image blocks of the feature map as input sequences for extracting global context. Finally, the Transformer completes global feature extraction and outputs the classification sequence. In addition, two external attention mechanisms were introduced by inputting image pairs during training, including collaborative attention mechanisms for similar images and cross attention mechanisms for heterogeneous images, fully utilizing the correlation information between images. After experimental evaluation, the P-ResViT proposed in this article has reached advanced levels in three fine-grained image datasets CUB, CAR, and AIR.

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Robust Adaptive OS-CFAR Detector for Maneuvering Group Target Based on Formation Prior Knowledge

Robust Adaptive OS-CFAR Detector for Maneuvering Group Targe...

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Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Mengxin Shi Qi Jiang Rui Wang Radar Technology Research Institute School of Information and Electronics Beijing Institute of Technology Beijing China Beijing Key Laboratory of Real-Time Information Processing Technology of Embedded Beijing China The Advanced Technology Research Institute Beijing Institute of Technology Jinan China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Detecting the individuals such like the bird flocks or a UAV swarm is a great challenge for radar systems. The lack of prior knowledge about the characteristics of dynamic group targets leads to the performance degradation of conventional detectors. In this study, we present a robust adaptive OS-CFAR detector for maneuvering group targets, leveraging prior knowledge of their formation. The group target’s state is updated and predicted using the random matrix approach. The projection length of the group target in the radar beam direction is computed using the predicted information during the group target tracking process. Using the projection length, the number of reference units occupied by the group target within the reference window is determined, and this prior knowledge is applied to update the detection parameters of the OS-CFAR detector. The simulation result shows that the algorithm can update the detection parameters timely and avoid target missed detection.

关键词： Target tracking Simulation Radar detection Detectors Radar Radar tracking Prediction algorithms time measurement real-time systems Motion measurement

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Metric Learning with Distillation for Overcoming Catastrophic Forgetting 16th

Metric Learning with Distillation for Overcoming Catastrop...

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16th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2021

作者： Yu, Piaoyao He, Juanjuan Min, Qilang Zhu, Qi College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China

ISBN: (纸本)9789811912528

In incremental learning, reducing catastrophic forgetting always adds additional weights to the classification layer when a new task comes. Moreover, it focuses on prediction accuracy, ignores mislabeling differences, and causes the learned features to be scattered. This paper removes the softmax and integrates depth metric learning to compute image embeddings, which doesn’t need to add extra weights or additional space for new classes. The distances of the data mapped in the old and new task spaces are calculated separately. Then, the distillation of depth metric learning is used to make the two distances as similar as possible to improve the performance of the old knowledge. The experimental results on Cifar10 and Tiny-ImageNet show that the proposed method can effectively alleviate catastrophic forgetting and enhance the effectiveness of incremental learning. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Distillation

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An end-to-end instance segmentation method based on improved ConvNeXt V2

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International Journal of Wireless and Mobile Computing 2024年第4期26卷 384-392页

作者： Wang, Wenlu Sun, Ying Xu, Manman Bai, Dongxu Huang, Li Zou, Chunlong Chen, Baojia Tang, Dalai Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Hubei Wuhan China Research Centre for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Hubei Wuhan China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Hubei Wuhan China College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Hubei Wuhan China College of Mechanical Engineering Hubei University of Automotive Technology Hubei Shiyan China Hubei Key Laboratory of Hydroelectric Machinery Design and Maintenance China Three Gorges University Hubei Yichang China School of Computer Information Management Inner Mongolia University of Finance and Economics Hohhot China

In order to improve the efficiency of indoor mobile robots in locating and segmenting environmental instances, an instance segmentation method based on RTMDet is proposed. Firstly, the more powerful ConvNeXt V2 is used as the backbone network of the model, which improves the performance of the existing RTMDet model. Subsequently, NAS-FPN is used as a converged network to detect the network at any given time. Finally, AdamW is used as the model optimiser, which effectively solves the problem of excessive memory occupation when updating parameters and improves the performance of the model. The proposed method obtains the highest evaluation index in all cases. After training and testing on the Cityscapes data set, the recognition accuracy reaches 65.0%. © 2024 Inderscience Enterprises Ltd.

关键词： Statistical tests

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Steel Surface Defect Detection Based on Improved YOLOv8 20

Steel Surface Defect Detection Based on Improved YOLOv8

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20th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2024

作者： Zhang, Jichen Zhang, Zhi College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Hubei Wuhan430065 China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Hubei Wuhan430065 China

ISBN: (纸本)9798350356328

There are some defects on the surface of steel that are difficult to detect, so we propose an improved algorithm based on YOLOv8 for detecting defects that are difficult to be detected. This improvement includes the introduction of an SPD module to minimize the problem of information loss due to downsampling;the addition of a small object detection head combined with EMA attention to further enhance the detection capabilities for small targets;the use of BiFPN to replace the original PANet, applying different weights to different features to strengthen feature fusion. Experiments were conducted on the NEU-DET dataset, and the algorithm improved mAP to 80.2%, an improvement of 3.9% over the baseline model. © 2024 IEEE.

关键词： Change detection

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Research on Enhanced Perception and Attention-Driven Person Re-Identification 20

Research on Enhanced Perception and Attention-Driven Person ...

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20th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2024

作者： Wu, Cancan Li, Dong Zhang, Zhi College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Hubei Wuhan430065 China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Hubei Wuhan430065 China

ISBN: (纸本)9798350356328

When matching similarity among pedestrians in images, pedestrian re-identification algorithms are often disturbed by occlusions. A typical tactic is to improve the robustness of occlusion features in the model. However, this approach neglects the noise introduced by other pedestrians in the image, leading to limited learning of relevant information between images. To address these issues, this paper introduces a pedestrian re-identification model that leverages perception-enhanced and attention mechanisms. By enhancing the perception of non-pedestrian noise and erasing non-pedestrian features, we then employ a cross-attention mechanism to subtly embed global and local features for finer feature recognition. Finally, through context attention, we learn the interaction between pedestrian images to improve re-identification accuracy. Results from studies performed on the Market-1501, CUHK03-D, and Duke-ReID datasets indicate that the suggested model surpasses leading contemporary models regarding Rank-1 scores and mean Average Precision (mAP), while maintaining good robustness. © 2024 IEEE.

关键词： Reamers

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