检索结果-内蒙古大学图书馆

Multi-scale network for single image deblurring based on ensemble learning module

Multimedia Tools and Applications 2025年第11期84卷 9045-9064页

作者： Wu, WenBo Pan, Yun Su, Na Wang, JingTao Wu, ShaoChuan Xu, ZeKun Yu, YouJian Liu, YaPeng State Key Laboratory of Media Convergence and Communication Communication University of China Beijing100024 China School of Computer and Cyberspace Security Communication University of China Beijing100024 China School of Electronics and Information Engineering Harbin Institute of Technology Harbin150080 China College of Computer and Control Engineering Northeast Forestry University Harbin150040 China School of Computer and Information Engineering Tianjin Chengjian University Tianjin300384 China School of Computer Science and Technology Anhui University Hefei230601 China

In this paper, we have identified two primary issues with current multi-scale image deblurring methods. On the one hand, the blurring scale is ignored. On the other hand, the context information of images is not fully utilized, and the spatial detail information of images is lost. To better solve the above problems, on top of that, this paper proposes a multi-scale network for single image deblurring. The designed network contains two parts: 1) Ensemble Learning Model: The Weak Learning Model in this module connects multiple weak learners in series to increase network depth. Moreover, through multi-scale feature ensemble module, the output features and intermediate features of different weak learner are integrated, and finally strong learner is used to enhance the features with semantic information at different scales;2) Spatial Feature Enhancement Module: This module can preserve spatial detail information of images and compensate for the disadvantages of losing image spatial details due to continuous downsampling in the Ensemble Learning Model structure. Our proposed deblurring model has been extensively evaluated on several benchmark datasets, and the model achieves superior performance compared to the most advanced deblurring methods. In particular, the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) values are 33.57 dB and 0.965 on the GoPro dataset, respectively, with PSNR and SSIM improving by 0.49 dB and 0.002 over NAFNet, and 0.65 dB and 0.004 over Restormer. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Learning systems

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High-Sensitivity and External RI-Independent Mode-Selective Michelson Interferometer Using a Single-Ended Helical Graded-Index Fiber

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Journal of Lightwave technology 2025年第11期43卷 5336-5344页

作者： Zhu, Chengliang Meng, Xinyue Shi, Mengshuo Cheng, Tonglei Zhao, Yong Northeastern University College of Information Science and Engineering Shenyang110004 China Northeastern University School of Control Engineering Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao066004 China State Key Laboratory of Synthetical Automation for Process Industries Shenyang China Northeastern University School of Control Engineering Qinhuangdao066004 China

We have theoretically and experimentally proposed and demonstrated an all-in-fiber-core mode-selective Michelson interferometer (MS-MI) sensor utilizing a single-ended helical graded-index few-mode fiber (SE-HGIFMF). The SE-HGIFMF effectively eliminates multi-mode interference, remains insensitive to external refractive index (RI) variations, and significantly enhances sensing sensitivity due to the minimal disparity in group RI between the two guided core modes. We investigated the torsion, strain, and temperature characteristics of the sensor, achieving sensitivities of 1.785 nm/(rad/m) for directional torsion, 0.0316 nm/μϵ for strain, and 0.624 nm/°C for temperature. These sensitivities are competitive within the fiber optic sensor family and represent the highest values reported for mode-selective MI and Mach-Zehnder interferometer (MZI) sensors to our knowledge. The proposed sensor not only demonstrates exceptional sensitivity but also offers directionality identification for torsion, ease of use, flexible installation, low loss, and high reliability, showcasing robustness against environmental fluctuations. © 1983-2012 IEEE.

关键词： Michelson interferometers

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Deformation Mechanism in Central China Revealed by Regional Earthquake Tomography

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Seismological Research Letters 2025年第3期96卷 1561-1572页

作者： Hua, Yujin Zhang, Shuangxi Liu, Yu Liu, Li Wu, Tengfei Wei, Yu Li, Bin Department of Earth Sciences and Engineering Taiyuan University of Technology Taiyuan China Department of Geophysics School of Geodesy and Geomatics Wuhan University Wuhan China School of Geophysics and Measurement-Control Technology East China University of Technology Nanchang China College of Civil Engineering Taiyuan University of Technology Taiyuan China School of Civil Engineering and Architecture Hubei Polytechnic University Huangshi China China Huadian Corporation Ltd. Beijing China

The central China region (CCN) has experienced significant deformation and complex structural arrangements. We conducted research on the deformation pattern of the CCN through the 3D velocity structure of the crust and uppermost mantle obtained via body-wave tomography. This velocity structure strongly correlates with the region’s topographic features. The relationship between velocity distributions and topography indicates that compression in the east–west direction, which can lead to crustal and mantle shortening, may play a crucial role in shaping topography. The various structures between the Qinling mountain range in the west and the Tongbai–Dabie mountain range in the east highlight distinct geological processes occurring along the Qinling–Dabie orogenic belt. In addition, we propose that reservoir water has infiltrated the crystalline basement rock layer up to 15–20 km beneath the Three Gorges Reservoir. Furthermore, the Three Gorges Dam serves as a solid foundation to prevent reservoir water from seeping downstream underground. © Seismological Society of America.

关键词： Crystalline rocks

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Curve-Suppression-Based Event-Triggered Mechanisms for Quasi-Synchronization of Fuzzy Delayed Neural Networks on Time Scales

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3174-3187页

作者： Wan, Peng Zhou, Yufeng Zeng, Zhigang Lai, Jingang Wuhan University of Science and Technology School of Artificial Intelligence and Automation Engineering Research Center of Metallurgical Automation and Measurement Technology Wuhan430081 China Huazhong University of Science and Technology School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent control of Education Ministry of China Wuhan430074 China

The vast majority of published event-triggered mechanisms (ETMs) are constructed based on measurement errors, which introduces a problem naturally that they are updated when the measurement errors exceed the thresholds although the current obtained sampling states can make systems converge well. With this problem in mind, we redesign ETMs for quasi-synchronization of T-S fuzzy neural networks (FNNs) with time delays on time scales. First, a novel ETM is designed for continuous-time FNNs with time-varying delays to achieve quasi-synchronization, with which synchronization errors is suppressed to globally exponentially converge to a ball. Second, we introduce the ETM for continuous-time FNNs to discrete-time FNNs, owing to the existence of discrete-time states, the Lypunov function of synchronization errors run over the exponentially decay curve, but it can be suppressed to evolve under another exponentially decay curve. Third, for FNNs on time scales with constant and time-varying delays, we estimate the forward jump operator of the Lyapunov functions and design ETMs to guarantee that the Lypunov functions evolve under the exponentially decay curves, so quasi-synchronization can be achieved. Last but not least, we prove that Zeno behavior will not happen and four numerical examples are introduced to verify the validity and the superiority of the proposed ETMs in reducing information transmission. © 2013 IEEE.

关键词： Continuous time systems

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Deterministic Event-Based Physical Watermarks Against Replay Attacks in Cyber-Physical Systems

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 61-69页

作者： Zhao, Xudong Xing, Wei Zhang, Junfeng Wang, Xinyu Zong, Guangdeng Zhao, Ning Hainan University School of Information and Communication Engineering Haikou570288 China Dalian University of Technology Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education Dalian116622 China Tiangong University School of Control Science and Engineering Tianjin300387 China Bohai University College of Control Science and Engineering Jinzhou121013 China

Cyber-physical systems (CPSs) are of great importance in various applications and have garnered significant attention. Ensuring their security is a crucial concern, while this paper focuses on the security of CPSs against replay attacks during the transmission of sensor measurements over a wireless network. It is widely recognized that incorporating physical watermark signals is an effective approach to mitigate replay attacks. However, traditional physical watermarks are time-based and result in a notable degradation in the performance of Linear Quadratic Gaussian systems. With this in mind, a novel event-based physical watermarking technique is devised to address the performance degradation and detect replay attacks. The approach involves a deterministic physical watermark schedule that utilizes the innovation sequence to determine whether to incorporate physical watermarks or not. We provide evidence that the probability of successfully embedding physical watermarks increases when the system is targeted by malicious attacks. Additionally, we demonstrate that the deterministic event-based watermarking approach is more effective than the traditional time-based watermarking method. Finally, simulation examples are included to validate and illustrate the theoretical results. © 2023 IEEE.

关键词： Cyber attacks

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Fast resistivity imaging of transient electromagnetic using an extreme learning machine

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Soft Computing 2025年第2期29卷 1121-1131页

作者： Li, Ruiyou Zhang, Yong Li, Guang Li, Ruiheng Hu, Jia Li, Min School of Software and Internet of Things Engineering Jiangxi University of Finance and Economics NanChang330013 China School of Geophysics and Measurement Control Technology East China University of Technology NanChang330013 China School of Information Engineering Hubei University of Economics Wuhan430205 China The State Key Laboratory of Power Transmission Equipment and System Security and New Technology Chongqing University Chongqing400044 China

The transient electromagnetic method (TEM) is widely used in geophysical exploration. In TEM data interpretation, nonlinear inversion plays an important role. However, traditional TEM nonlinear inversion adopts the OCCAM imaging method, which merely presents the approximate shape of the stratum model, with poor inversion accuracy and much iteration time. To solve the above problems, a novel nonlinear inversion approach based on extreme learning machine (ELM) is proposed in this paper. The ELM is required to establish the input–output mapping relationship of the inversion network only once through the analytical method, which is different from the traditional neural network method that demands iterative gradient learning and is prone to fall into the local optimum. Moreover, the ELM inversion network by randomly assigning the hidden layer parameters is capable of mapping the observed TEM data and quickly producing resistivity images, which avoids time-consuming iteration and inversion calculations. The presented approach is applied to both synthetic and field examples. The results show that compared with the traditional nonlinear inversion algorithms (BP and OCCAM), the proposed method achieves better inversion accuracy and significantly reduces the calculation time, which verifies the effectiveness of the ELM algorithm for TEM data interpretation. Additionally, the research provides a new method and technology for TEM data inversion. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Electromagnetic transients

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Infrared image and circular polarization image processing methods for high-concentration scattering environments

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Applied Optics 2025年第12期64卷 3140-3149页

作者： Zhao, Dengke Wei, Yongqing Hong, Lu Xia, Haojie Huang, Liang Zhang, Jin School of Instrument Science and Opto-electronics Engineering Hefei University of Technology Hefei230009 China Anhui Province Key Laboratory of Measuring Theory and Precision Instrument Hefei230009 China Engineering Research Center of Safety Critical Industrial Measurement and Control Technology Ministry of Education Hefei230009 China

In high-concentration scattering media, low contrast often obscures the surface details and textures of target objects. This paper proposes a method to enhance imaging quality under such conditions using polarization imaging theory. The method involves image registration, estimation of environmental transmittance, calculation of the degree of polarization and polarization difference intensity, and application of the CLAHE method to improve detail visibility and obtain de-scattered images. Finally, a weighted adaptive wavelet transform based on edge detection fuses the de-scattered and infrared images. Experimental results show that the proposed method outperforms traditional approaches, particularly in high-concentration scattering environments, offering insights into combining infrared and polarized imaging for challenging scenarios. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Circular polarization

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Terahertz time-of-flight tomography algorithm for different curvature samples

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Optics Express 2025年第7期33卷 15095-15109页

作者： Zhou, Yu Li, Quanyong Cui, Zongyu Zhang, Jiyang Ren, Jiaojiao Zhang, Dandan Gu, Jian Li, Haoxiang Lin, Zihao Key Laboratory of Optoelectronic Measurement and Control and Optical Information Transmission Technology Ministry of Education Changchun University of Science and Technology Changchun130022 China School of Optoelectronic Engineering Changchun University of Science and Technology Changchun130022 China Zhongshan Research Institute Changchun University of Science and Technology Zhongshan528403 China

Traditional guide rail-based terahertz reflection tomography works well with planar samples and complements transmission tomography. Most imaging methods perform effectively with plane-like or simple curvature samples. However, these methods are ineffective for complex shapes due to the challenges of dealing with internal corrections and stitching errors. To address the above issues, terahertz time-of-flight tomography is applied to the prepared samples with abnormal curvature using a rotating scanning array and "point cloud splicing" method. This approach enables comprehensive sample information collection and resolves the inner layer point cloud position correction challenge. A radial distance point cloud down-sampling method is proposed to reduce the impact of overlapping point clouds on reconstruction accuracy. After down-sampling, the outer layer reconstruction error reduces to 0.22%. Poisson surface reconstruction further enhances the intuitiveness of imaging results, and the reconstruction accuracy is 0.01 mm. This study broadens the application of terahertz time-of-flight tomography and offers a different approach for reflection tomography. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Surface reconstruction

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The Client-Level GAN-Based Data Reconstruction Attack and Defense in Clustered Federated Learning 18th

The Client-Level GAN-Based Data Reconstruction Attack and D...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Wu, Han Shi, Lei Ye, Junyu Fan, Yuqi Lv, Zengwei School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China Engineering Research Center of Safety Critical Industrial Measurement and Control Technology Ministry of Education Hefei230009 China

ISBN: (纸本)9783031714634

Clustered Federated Learning (CFL) is an innovative architecture that effectively tackles the challenge of data heterogeneity in Federated Learning (FL). However, such a setup also introduces vulnerabilities that can be exploited by attackers to execute more severe attacks. This paper compares the efficacy of data reconstruction attacks utilizing Generative Adversarial Nets (GANs) in FL and CFL, respectively. It also proposes a general client-level data reconstruction attack scheme whose key is in that the attackers can eavesdrop the victim’s partial uploaded parameters. Experimental results demonstrate that the sample data generated in this kind of attack within the scenario of CFL exhibit a higher degree of similarity with the targeted data compared to FL. And it is observed that the attacks involving eavesdropping in CFL perform more effectively with a client-level attack accuracy. In addition, we design a lightweight and adaptive local differential privacy (LDP) mechanism to defend against this kind of attack. In this method, under the premise that the differential privacy budget decreases as the number of training round adds, each client dynamically adjusts the probability of adding differential privacy noise and the size of DP’s parameters according to the training accuracy and the resemblance to the reconstructed data generated by attackers. Our defense method can effectively alleviate the loss of training accuracy for each client caused by the accumulation of the differential privacy budget while ensuring the defense effect, and it also reduces clients’ computational burden. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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A Method for Abnormal Detection and Poisoned Data Recovery in Clustered Federated Learning 18th

A Method for Abnormal Detection and Poisoned Data Recovery...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Chen, Yingying Shi, Lei Xu, Hao Ye, Junyu Xu, Juan School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China Engineering Research Center of Safety Critical Industrial Measurement and Control Technology Ministry of Education Hefei230009 China

ISBN: (纸本)9783031714665

Clustered Federated Learning (CFL), as a type of Federated Learning (FL) paradigm, divides the clients into multiple clusters through the clustering process and trains them within the clusters, thus improving the overall model training accuracy. But the clustering process is also the weak link, which can lead to the failure of the whole model training if it is interfered or destroyed by the attacker. In this paper, we study the attack methods and defense strategies for attackers to implement data poisoning by tampering with client data, resulting in overall clustering failure. Our defense approach is designed to identify anomalous clients and recover their poisoned data. In order to resist the malicious behavior of attackers, we propose a dual-threshold pixel difference and watermark authentication defense method for detecting the presence of anomalous clients. The method can accurately identify the abnormal fluctuations in the data and effectively screen out the abnormal clients. Meanwhile, we propose a self-embedding watermarking defense algorithm based on shuffling idea for recovering poisoned data. Among them, our self-embedding watermarking algorithm not only accurately locates and recovers the tampered region of the image, but also improves the watermark security to prevent attackers from cracking it easily. Simulation results show that our proposed algorithm can accurately identify the abnormal client and recover the data. Even if the client’s data is massively tampered with, we can recover the tampered data images with high quality and ensure that the CFL clustering is not corrupted. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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