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5th International Conference on Smart Computing and Communication, SmartCom 2020

作者： Guo, Yuwen Zhang, Xin Yang, Qi Guo, Hong 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 Beijing China

ISBN: (纸本)9783030747169

Handwritten digital image recognition is a special problem in digital recognition, which is much more difficult due to uncertainties such as writer and writing environment. But the accuracy of handwritten digital recognition is particularly important in some critical areas such as public security and finance. In this paper, we used machine learning and deep learning technology, to identify MNIST handwritten digital data sets. And the support vector machine and convolutional neural network were used to image recognition, among them, the support vector machine compares the accuracy of the identification of the two kernel functions. Convolutional neural networks are improved based on the classic LeNet-5 model. The Experiments result shown that the identification accuracy of Gaussian kernel function model and linear kernel function model is 94.03% and 90.47% for support vector machines, respectively. In contrast, Gaussian kernel function performs better, but the overall accuracy is low. The best performance is based on the classic convolutional neural network LeNet-5 improved model, the test set recognition rate can reach 99.05%. © 2021, Springer Nature Switzerland AG.

关键词： Support vector machines

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Multi-scale object detection by top-down and bottom-up feature pyramid network

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Journal of Systems Engineering and Electronics 2019年第1期30卷 1-12页

作者： ZHAO Baojun ZHAO Boya TANG Linbo WANG Wenzheng WU Chen School of Information and Electronics Beijing Institute of Technology Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology

While moving ahead with the object detection technology, especially deep neural networks, many related tasks, such as medical application and industrial automation, have achieved great success. However, the detection of objects with multiple aspect ratios and scales is still a key problem. This paper proposes a top-down and bottom-up feature pyramid network(TDBU-FPN),which combines multi-scale feature representation and anchor generation at multiple aspect ratios. First, in order to build the multi-scale feature map, this paper puts a number of fully convolutional layers after the backbone. Second, to link neighboring feature maps, top-down and bottom-up flows are adopted to introduce context information via top-down flow and supplement suboriginal information via bottom-up flow. The top-down flow refers to the deconvolution procedure, and the bottom-up flow refers to the pooling procedure. Third, the problem of adapting different object aspect ratios is tackled via many anchor shapes with different aspect ratios on each multi-scale feature map. The proposed method is evaluated on the pattern analysis, statistical modeling and computational learning visual object classes(PASCAL VOC)dataset and reaches an accuracy of 79%, which exhibits a 1.8% improvement with a detection speed of 23 fps.

关键词： convolutional neural network (CNN) feature pyramid network (FPN) object detection deconvolution.

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Consecutive Pretraining: A Knowledge Transfer Learning Strategy with Relevant Unlabeled Data for Remote Sensing Domain

arXiv

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arXiv 2022年

作者： Zhang, Tong Gao, Peng Dong, Hao Zhuang, Yin Wang, Guanqun Zhang, Wei Chen, He Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing100081 China The Shang Hai AI laboratory Shanghai100024 China Peking University Beijing100871 China

Currently, under supervised learning, a model pretrained by a large-scale nature scene dataset and then fine-tuned on a few specific task labeling data is the paradigm that has dominated the knowledge transfer learning. It has reached the status of consensus solution for task-aware model training in remote sensing domain (RSD). Unfortunately, due to different categories of imaging data and stiff challenges of data annotation, there is not a large enough and uniform remote sensing dataset to support large-scale pretraining in RSD. Moreover, pretraining models on large-scale nature scene datasets by supervised learning and then directly fine-tuning on diverse downstream tasks seems to be a crude method, which is easily affected by inevitable labeling noise, severe domain gaps and task-aware discrepancies. Thus, in this paper, considering the self-supervised pretraining and powerful vision transformer (ViT) architecture, a concise and effective knowledge transfer learning strategy called ConSecutive PreTraining (CSPT) is proposed based on the idea of not stopping pretraining in natural language processing (NLP), which can gradually bridge the domain gap and transfer knowledge from the nature scene domain to the RSD. The proposed CSPT also can release the huge potential of unlabeled data for task-aware model training. Finally, extensive experiments are carried out on twelve datasets in RSD involving three types of downstream tasks (e.g., scene classification, object detection and land cover classification) and two types of imaging data (e.g., optical and SAR). The results show that by utilizing the proposed CSPT for task-aware model training, almost all downstream tasks in RSD can outperform the previous method of supervised pretraining-then-fine-tuning and even surpass the state-of-the-art (SOTA) performance without any expensive labeling consumption and careful model design. Copyright © 2022, The Authors. All rights reserved.

关键词： Supervised learning

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Budget Constraint Task Allocation for Mobile Crowd Sensing with Hybrid Participant 16th

Budget Constraint Task Allocation for Mobile Crowd Sensing w...

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16th EAI International Conference on Collaborative Computing: Networking, Applications, and Worksharing, CollaborateCom 2020

作者： Wang, Xin Li, Peng Xiao, Junlei College of Computer Science and Technology Wuhan University of Science and Technology Hubei China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Hubei China

ISBN: (纸本)9783030675394

In the Mobile crowdsensing (MCS) system, the task allocation problem is a crucial problem. In this paper, we focus on the task allocation problem for hybrid participants with a budget-constrained MCS system. There are two types of participants: mobile participants and static participants. Mobile participants have low cost, large numbers, and flexibility. However, most of the sensing data they submitted are of low quality. On the other hand, static participants, such as city cameras, roadside infrastructure, have high-quality sensing data. Despite the benefit of high quality, static participants have less coverage and high cost. Given a budget, the problem is how to assign the task to the two types of participants, such that the social welfare is maximized. To solve the problem, we propose a reverse auction-based task allocation method (ORA) to select winning bids round by round. Then, a Shapley value based online algorithm (OAA) is proposed to ensure the task is finished. Moreover, we consider the different types of participants to have a different probability to finish tasks. We exploit the semi-Markov model to calculate the probability that participants finished tasks. We prove that the proposed task allocation method has truthfulness and individual rationality. We conduct extensive experiments to evaluate the performance of our system, and the evaluation results demonstrate the remarkable effect of the proposed task allocation method. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Budget control

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A Novel Two-Stage Method for Texture Defect Detection

A Novel Two-Stage Method for Texture Defect Detection

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Consumer Electronics and Computer Engineering (ICCECE), 2021 IEEE International Conference on

作者： Lu Liu Min Jiang Lifa Xu School of Computer Science and Technology Wuhan University of Science and Technology WuHan Hubei P.R. China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan Hubei China

Texture defect detection has wide applications in both textiles and industry, where traditional defect detection methods focus on costly manual labeling and defect samples are complex to collect in real-world industrial scenarios. Furthermore, labeling errors are common in datasets from industrial scenarios, and the problem still needs to be explored. Therefore, we propose a novel texture defect detection method. It has two stages; the defective samples are synthesized in the first stage. In the second stage, we represent normal samples using a self-supervised learning method. Then, we construct a single classifier based on this learned representation. Subsequently, the model is trained to distinguish between normal and defective samples within the embedding space. To increase the robustness of our method, we adopt a novel strategy to construct defective samples and compute the feature similarity through a similarity-weighted aggregation method to achieve defect detection, which is more robust to noise. Experimental results on the DAGM2007 dataset show that our method performs comparably to the leading texture defect detection methods without label noise. In contrast, in the presence of noise, our method can effectively minimize the impact of noise on model performance.

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FedCross: Intertemporal Federated Learning Under Evolutionary Games 39

FedCross: Intertemporal Federated Learning Under Evolutionar...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Lu, Jianfeng Zhang, Ying Jia, Riheng Cao, Shuqin Liu, Jing Fu, Hao School of Computer Science and Technology Wuhan University of Science and Technology China Key Laboratory of Social Computing and Cognitive Intelligence (Dalian University of Technology) Ministry of Education China School of Computer Science and Technology Zhejiang Normal University China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology China

ISBN: (纸本)157735897X

Federated Learning (FL) mitigates privacy leakage in decentralized machine learning by allowing multiple clients to train collaboratively locally. However, dynamic mobile networks with high mobility, intermittent connectivity, and bandwidth limitation severely hinder model updates to the cloud server. Although previous studies have typically addressed user mobility issue through task reassignment or predictive modeling, frequent migrations may result in high communication overhead. Addressing this challenge involves not only dealing with resource constraints, but also finding ways to mitigate the challenges posed by user migrations. We therefore propose a intertemporal incentive framework, FedCross, which ensures the continuity of FL tasks by migrating interrupted training tasks to feasible mobile devices. FedCross comprises two distinct stages: Specifically, in Stage 1, we address the task allocation problem across regions under resource constraints by employing a multi-objective migration algorithm to quantify the optimal task receivers. Moreover, we adopt evolutionary game theory to capture the dynamic decision-making of users, forecasting the evolution of user proportions across different regions to mitigate frequent migrations. In Stage 2, we utilize a procurement auction mechanism to allocate rewards among base stations, ensuring that those providing high-quality models receive optimal compensation. This approach incentivizes sustained user participation, thereby ensuring the overall feasibility of FedCross. Finally, experimental results validate the theoretical soundness of FedCross and demonstrate its significant reduction in communication overhead. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Federated learning

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TRAIL: Trust-Aware Client Scheduling for Semi-Decentralized Federated Learning 39

TRAIL: Trust-Aware Client Scheduling for Semi-Decentralized ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Hu, Gangqiang Lu, Jianfeng Han, Jianmin Cao, Shuqin Liu, Jing Fu, Hao School of Computer Science and Technology Zhejiang Normal University China School of Computer Science and Technology Wuhan University of Science and Technology China Key Laboratory of Social Computing and Cognitive Intelligence Dalian University of Technology Ministry of Education China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology China

ISBN: (纸本)157735897X

Due to the sensitivity of data, Federated Learning (FL) is employed to enable distributed machine learning while safeguarding data privacy and accommodating the requirements of various devices. However, in the context of semi-decentralized FL, clients’ communication and training states are dynamic. This variability arises from local training fluctuations, heterogeneous data distributions, and intermittent client participation. Most existing studies primarily focus on stable client states, neglecting the dynamic challenges inherent in real-world scenarios. To tackle this issue, we propose a TRust-Aware clIent scheduLing mechanism called TRAIL, which assesses client states and contributions, enhancing model training efficiency through selective client participation. We focus on a semi-decentralized FL framework where edge servers and clients train a shared global model using unreliable intra-cluster model aggregation and inter-cluster model consensus. First, we propose an adaptive hidden semi-Markov model to estimate clients’ communication states and contributions. Next, we address a client-server association optimization problem to minimize global training loss. Using convergence analysis, we propose a greedy client scheduling algorithm. Finally, our experiments conducted on real-world datasets demonstrate that TRAIL outperforms state-of-the-art baselines, achieving an improvement of 8.7% in test accuracy and a reduction of 15.3% in training loss. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Federated learning

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Tomographic imaging method of GB-ArcSAR with arc-shaped interferometric baseline

Tomographic imaging method of GB-ArcSAR with arc-shaped inte...

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

作者： Hanpu Zhou Kai Yan Jian Wang Xiaoxiao Li Weiming Tian Yunkai Deng Radar Technology Research Institute Bejing Institute of Technology Beijing China Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing Institute of Technology Beijing China Beijing Building Research Institute Corporation Limited of CSCEC Beijing China Advanced Technology Research Institute Beijing Institute of Technology Jinan China Chongqing Innovation Center Beijing Institute of Technology China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

When observing building scenarios, there exists the layover problem. Synthetic aperture radar (SAR) tomography can use multiple two-dimensional (2D) SAR images acquired from different heights to inverse the range-azimuth-height 3D information of the targets. Ground-based arc-scanning synthetic aperture radar (GB-ArcSAR) can obtain a series of SAR images at different heights by performing azimuthal scanning on horizontal planes with different pitch angles, which makes it has tomography capabilities. However, the baseline of GB-ArcSAR is arc-shaped, which makes the existing tomographic imaging methods suitable for linear tomographic baselines cannot be directly applied to GB-ArcSAR. This paper proposes a tomographic imaging method of GB-ArcSAR with arc-shaped interferometric baseline. Through Taylor expansion of the distance between the target and the sampling position, a mapping relationship between the target pitch angle and interferometric phase was established. Based on the mapping relationship, the measurement matrix was constructed. Validation of the proposed method is achieved through simulations and measured data.

关键词： Point cloud compression Three-dimensional displays Two-dimensional displays Superresolution Tomography Apertures Radar imaging Taylor series Radar polarimetry Synthetic aperture radar

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An Adaptive Phase Noise Filtering Approach For Multi-Frequency Interferometric SAR

An Adaptive Phase Noise Filtering Approach For Multi-Frequen...

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IEEE International Conference on Radar

作者： Zhen Wang Zegang Ding Yan Wang Xinnong Ma Linghao Li Tao Zeng School of Information and electronics Beijing Institute of Technology Beijing China Beijing Key Laboratory of Embedded Real-time Information Processing Technology

ISBN: (纸本)9781665468893

Multi-frequency (MF) interferometric SAR (InSAR) provides an effective way for the surveying of steep terrains. Phase noise filtering is a key step in MF-InSAR processing which determines the quality of the interferograms and affects the accuracy of inversed elevation. Different from the traditional filtering method of single frequency (SF) InSAR, an adaptive filtering approach for MF-InSAR based on MF phase fusion is proposed in this paper. The main contributions of this method is summarized into two aspects. First, the local fringe frequency estimation (LFFE) is extended from SF to MF case. Second, the non-linear model is introduced in the adaptive phase filtering in the local window based on MF-LFFE to adapt to complex terrains. The computer simulation and the real dual-frequency airborne experiment validate the proposed approach.

关键词： Phase noise Adaptation models Filtering Computer simulation Computational modeling Atmospheric modeling Estimation

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Offloading Algorithm for Edge Computing Tasks Based on Energy Optimization

Offloading Algorithm for Edge Computing Tasks Based on Energ...

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IEEE International Conference on Smart Cloud (SmartCloud)

作者： Xing Gao Haonan Gu Fang Liu Wei Hu Wuhan City College Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China

Current research in computation offloading encompasses various aspects, including task partitioning and allocation, resource management, communication, and data management. This paper focuses on developing new algorithms and techniques to enhance the efficiency and effectiveness of the offloading process while addressing challenges arising from dynamic and heterogeneous mobile environments. Researchers are also examining the trade-off between offloading benefits like improved performance and energy efficiency, and the associated overhead costs such as communication and data management. This paper proposes an optimization strategy for computational offloading to enhance the Grey Wolf algorithm. The solution aims to quickly optimize system energy consumption while considering the arithmetic power constraint of the mobile edge computing server and time delay constraints. Simulation experiments demonstrate that the algorithm in this paper significantly outperforms the traditional Grey Wolf and PSO algorithms in terms of both performance and computational speed.

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