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SSRN 2025年

作者： Yang, Jiaying Qian, Yiman Yan, Jingqi Li, Jiayin Liu, Pengfei College of Chemistry and Chemical Engineering Hunan University of Science and Technology Xiangtan411201 China

Advanced oxidation processes (AOPs) are more energy-efficient than photocatalytic methods, as they degrade organic dye pollutants without light under mild conditions. In this work,we developed an effective Zr(WO4)2 catalyst for AOPs, characterized by SEM, TEM, TG-DTA, N2-sorption, XRD, and XPS. The catalyst achieved a 95.62% removal rate of Phenol (2 mg/L) using 0.1 g/L Zr(WO4)2 and 1 mM PMS in 60 minutes, with singlet oxygen (1O2) as the main reactive species. The redox cycles of Zr4+ and WO42- on Zr(WO4)2's surface were key for PMS activation. Zr(WO4)2 also demonstrated excellent reusability, versatility, and produced low-toxicity treated water. In addition, the ideal reusability and versatility of Zr(WO4)2 as well as low-toxicity of treated water endowed the Zr(WO4)2 system with a wide range of applications. © 2025, The Authors. All rights reserved.

关键词： Reusability

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Passive on-chip isolators based on the thin-film lithium niobate platform

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Chinese Physics B 2025年第3期34卷 408-413页

作者： Jiacheng Liu Gongyu Xia Qilin Hong Pingyu Zhu Kai-Kai Zhang Keyu Xia Ping Xu Shiqiao Qin Zhihong Zhu College of Advanced Interdisciplinary Studies&Hunan Provincial Key Laboratory of Novel Nano Optoelectronic Information Materials and Devices National University of Defense TechnologyChangsha 410073China Institute for Quantum Information and State Key Laboratory of High-Performance Computing College of Computer Science and TechnologyNational University of Defense TechnologyChangsha 410073China College of Engineering and Applied Sciences National Laboratory of Solid State Microstructuresand Collaborative Innovation Center of Advanced MicrostructuresNanjing UniversityNanjing 210023China

Optical isolators,the photonic analogs of electronic diodes,are essential for ensuring the unidirectional flow of light in optical systems,thereby mitigating the destabilizing effects of back ***-film lithium niobate(TFLN),hailed as“the silicon of photonics,”has emerged as a pivotal material in the realm of chip-scale nonlinear optics,propelling the demand for compact optical *** report a breakthrough in the development of a fully passive,integrated optical isolator on the TFLN platform,leveraging the Kerr effect to achieve an impressive 10.3 dB of isolation with a minimal insertion loss of 1.87 *** theoretical simulations have demonstrated that our design,when applied to a microring resonator with a Q factor of 5×10^(6),can achieve 20 dB of isolation with an input power of merely 8 *** advancement underscores the immense potential of lithium niobate-based Kerr-effect isolators in propelling the integration and application of high-performance on-chip lasers,heralding a new era in integrated photonics.

关键词： thin-film lithium niobate Kerr effect optical isolator

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Highly efficient norm preserving numerical schemes for micromagnetic energy minimization based on SAV method

arXiv

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arXiv 2025年

作者： Zhan, Jiajun Yang, Lei He, Jiayun Cui, Zixuan School of Computer Science and Engineering Faculty of Innovation Engineering Macau University of Science and Technology 999078 China School of Mathematics and Computational Science Xiangtan University Hunan Xiangtan411105 China

In this paper, two efficient and magnetization norm preserving numerical schemes based on the scalar auxiliary variable (SAV) method are developed for calculating the ground state in micromagnetic structures. The first SAV scheme is based on the original SAV method for the gradient flow model, while the second scheme features an updated scalar auxiliary variable to better align with the associated energy. To address the challenging constraint of pointwise constant magnetization length, an implicit projection method is designed, and verified by both SAV schemes. Both proposed SAV schemes partially preserve energy dissipation and exhibit exceptional efficiency, requiring two linear systems with constant coefficients to be solved. The computational efficiency is further enhanced by applying the Discrete Cosine Transform during the solving process. Numerical experiments demonstrate that our SAV schemes outperform commonly used numerical methods in terms of both efficiency and stability. Copyright © 2025, The Authors. All rights reserved.

关键词： Discrete cosine transforms

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DESIGN AND ANALYSIS OF WIND TURBINE FAULT DIAGNOSIS SYSTEM BASED ON CONVOLUTIONAL NEURAL NETWORK

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Diagnostyka 2025年第1期26卷

作者： Luo, Xiaoli Ning, Jinye Wu, Min Scientific Research Department Hunan Electrical College of Technology Xiangtan411101 China Institute of Big Data and Artificial Intelligence Application Technology Hunan Electrical College of Technology Xiangtan411101 China School of Mechanical Engineering Hunan University of Science and Technology Xiangtan City411201 China

Wind turbines are apt to diverse faults during long-term operation in natural environments, which affect their power generation efficiency and lifespan. Therefore, based on convolutional neural networks, gradient descent method was introduced to optimize their parameter training. Meanwhile, synchronous compressed wavelet transform was utilized to enhance the fault signal's time-frequency information. The fault detection correlation operation was optimized through Pearson correlation coefficient. Finally, a new type of fan fault detection model was proposed. The average fault detecting accuracy of this model was the highest at 98.98%, the average loss value was the lowest at 0.08%, and the average time consumption was the shortest at 16.52s. The minimum mean square error for detecting inner and outer ring pitting of fan bearings was 0.016 and 0.018, respectively. As a result, the proposed new model performs excellently in terms of accuracy and reliability in fault detection, with detection accuracy generally superior to other existing models. This model can significantly improve wind turbine fault detection, reduce false alarm and false alarm rates, and provide effective guarantees for wind turbines' stable operation. © 202x by the Authors.

关键词： Convolutional neural networks

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Efficient Layer Sharing for Application Deployment in Edge Computing Environment

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IEEE Transactions on Consumer Electronics 2025年

作者： Tang, Bing Ma, Yahui Liu, Yu Yang, Qing Cao, Buqing Tang, Mingdong Guangzhou Maritime University School of Computer Science Guangzhou510725 China Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan411201 China Hunan University of Technology School of Computer Zhuzhou412007 China Guangdong University of Foreign Studies School of Information Science and Technology Guangzhou510006 China

Edge computing provides consumer electronics with faster and smarter responses, thereby enhancing user experience. Based on container technology, in a cloud-edge collaborative environment, edge servers pull images from image registries in the cloud and deploy them locally to serve various smart devices and consumer electronics. Leveraging image layer sharing can reduce the amount of image transmission, as locally cached layers on edge servers need not be repeatedly pulled. This paper investigates the rational deployment of applications on edge servers under a layer sharing mechanism. Considering a scenario with distributed multiple image registries, the objective is to minimize the total deployment time under the condition of image layer sharing. In this scenario, the application deployment problem can be modeled as a variable-value 0-1 multiple knapsack problem with partial item sharing. To address this improved knapsack problem, two algorithms named greedy minimum value based on layer sharing (GD-LSV) and greedy minimum value density based on layer sharing (GD-LSVD) are proposed. Experimental simulations using real layer dataset have proved the effectiveness of the proposed algorithms. Compared with representative algorithms, as the number of applications increases, the proposed algorithms shorten the total deployment time by up to 51.78% and reduce the total storage consumption by up to 22.79%. © 2025 IEEE.

关键词： Application deployment container edge computing greedy algorithm layer sharing

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Design method of wood scrimber beams: Strength and stability analysis for failure prevention

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Structures 2025年 76卷

作者： Xiang, Siyu Chen, Haibing He, Yongjun Chen, Lin Li, Xingong Gong, Meng College of Materials Science and Engineering Central South University of Forestry and Technology Changsha China College of Civil Engineering Hunan University Changsha China Wood Science and Technology Center University of New Brunswick Fredericton Canada School of Civil Engineering Hunan University of Science and Technology Xiangtan411201 China

Wood scrimber is a novel material that addresses the shortage of large-diameter trees and the inconsistency in the mechanical properties of natural wood resources. However, the application of wood scrimber in construction is limited by the insufficient understanding of its structural performance. This study investigated the failure behaviour of wood scrimber beams from the perspectives of both material strength and structural stability, aiming to develop a design method for controlling failure modes. Initially, a finite element (FE) model was developed and validated against a previous bending test of scrimber beams. Based on the FE model, the strength failure mechanism of the scrimber beam was revealed. Subsequently, the instability failure of the beam was numerically and analytically investigated. The relationship between material parameters and geometric parameters was established, and then a design method was developed. It was found that the strength failure of wood-based materials can be classified into three distinct types in both quantitative and qualitative ways, according to the ratio of material tensile strength to compressive strength (ρ). Type II failure (0.38 © 2025 Institution of Structural Engineers

关键词： Buckling modes

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scGANCL: Bidirectional Generative Adversarial Network for Imputing scRNA-Seq Data With Contrastive Learning

IEEE Transactions on Computational Biology and Bioinformatic...

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IEEE Transactions on Computational Biology and Bioinformatics 2025年第2期22卷 661-671页

作者： Wanwan Shi Yahui Long Jiawei Luo Ying Liu Zehao Xiong Bo Wang Zhongyuan Xu College of Computer Science and Electronic Engineering Hunan University Changsha China Agency for Science Technology and Research (A*STAR) Singapore School of Computer Science and Engineering Central South University Changsha China

The advent of single-cell RNA sequencing (scRNA-seq) has offering unprecedented insights at the single-cell level. This groundbreaking technology has opened new pathways for understanding cellular diversity and revealing novel insights into disease mechanisms. However, the analysis of scRNA-seq data is challenging, primarily due to dropout events caused by technical noise. Developing effective imputation methods is crucial for the reliable and informative analysis of scRNA-seq data. While deep learning-based approaches have been proposed for scRNA-seq data imputation, they often fall short of optimal performance, especially in identifying rare cell types. Here we propose a novel self-supervised deep learning model named scGANCL for scRNA-seq data imputation. scGANCL combines bidirectional generative adversarial network (BiGAN) with contrastive learning (CL) to enhance imputation performance. To fully exploit gene expression profiles, a contrastive learning module is introduced to enhance the representation learning of cells by minimizing the discrepancy between the distributions of real and generated data. Comprehensive experiments have been conducted on ten simulated and seven real datasets to validate scGANCL’s effectiveness. The results demonstrated scGANCL consistently outperformed seven state-of-the-art methods across various downstream tasks. Ablation studies further validated the contribution of each component to the overall performance of the model.

关键词： Gene expression Data models Imputation Contrastive learning Generative adversarial networks Training Sequential analysis Generators computer architecture Noise

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RGB-Based Category-Level Object Pose Estimation via Depth Recovery and Adaptive Refinement

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IEEE Robotics and Automation Letters 2025年第6期10卷 5377-5384页

作者： Yang, Hui Sun, Wei Liu, Jian Zheng, Jin Zeng, Zhiwen Mian, Ajmal Hunan University National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering Changsha410082 China Hunan University State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Changsha410082 China Central South University School of Architecture and Art Changsha410082 China University of Western Australia Department of Computer Science and Software Engineering CrawleyWA6009 Australia

Category-level pose estimation methods have received widespread attention as they can be generalized to intra-class unseen objects. Although RGB-D-based category-level methods have made significant progress, reliance on depth image limits practical application. RGB-based methods offer a more practical and cost-effective solution. However, current RGB-based methods struggle with object geometry perception, leading to inaccurate pose estimation. We propose depth recovery and adaptive refinement for category-level object pose estimation from a single RGB image. We leverage DINOv2 to reconstruct the coarse scene-level depth from the input RGB image and propose an adaptive refinement network based on an encoder-decoder architecture to dynamically improve the predicted coarse depth and reduce its gap from the ground truth. We introduce a 2D–3D consistency loss to ensure correspondence between the point cloud obtained from depth projection and the objects in the 2D image. This consistency supervision enables the model to maintain alignment between the depth image and the point cloud. Finally, we extract features from the refined point cloud and feed them into two confidence-aware rotation regression branches and a translation and size prediction residual branch for end-to-end training. Decoupling the rotation matrix provides a more direct representation, which facilitates parameter optimization and gradient propagation. Extensive experiments on the REAL275 and CAMERA25 datasets demonstrate the superior performance of our method. Real-world estimation and robotic grasping experiments demonstrate our model robustness to occlusion, clutter environments, and low-textured objects. Our code and robotic grasping video are available at DA-Pose. © 2016 IEEE.

关键词： Depth perception

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Towards Privacy-Enhanced and Robust Clustered Federated Learning

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IEEE Transactions on Mobile Computing 2025年

作者： Xu, Yang Tan, Yunlin Zhang, Cheng Sun, Peng Zhang, Yibang Ren, Ju Jiang, Hongbo Zhang, Yaoxue Hunan University College of Cyber Science and Technology Changsha410082 China Tsinghua University Department of Computer Science and Technology BNRist Beijing100084 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

Clustered federated learning (CFL) leverages data distribution similarities to cluster clients, facilitating personalized model training under data heterogeneity. However, most existing CFL schemes pose potential privacy risks for clients (e.g., gradient inversion attacks) as they rely on individual gradients for clustering. This also renders them incompatible with secure aggregation mechanisms that are widely employed in federated learning for privacy protection. Moreover, CFL introduces the risk of malicious clients dominating several clusters and conducting poisoning attacks therein, thereby threatening secure model training. To address these issues, we propose ProCFL, a Privacy-Enhanced and Robust CFL framework incorporating gradient-free clustering and peer validation. Specifically, we first design a new protocol for measuring data distribution similarity among clients without using their gradient information. Then, we transform the client clustering process into a weighted set covering problem and introduce a diversity-optimized clustering algorithm to achieve near-optimal clustering results while eliminating any need for prior knowledge. Furthermore, we develop a post-hoc detection mechanism that employs peer validation to identify and discard malicious client models. Extensive experimental evaluation of ProCFL validates its superior model robustness and accuracy performance compared to existing schemes. © 2025 IEEE. All rights reserved.

关键词： Federated learning

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Improving Maritime Data: A Machine Learning-based Model for Missing Vessel Trajectories Reconstruction

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IEEE Transactions on Vehicular technology 2025年

作者： Chen, Jin Liang, Maohan Peng, Chang Zhang, Jixin Huo, Shengxu Sanya Institute Hunan University of Science and Technology China Department of Civil and Environmental Engineering National University of Singapore Singapore Singapore College of Computer Science and Electronic Engineering Hunan University China School of Computer Science Hubei University of Technology China

Advancements in maritime satellite technology have significantly impacted the maritime industry, enhancing both communication and safety at sea. These technological improvements have enabled Automatic Identification Systems (AIS) to transmit data through robust maritime communication networks. However, despite these advancements, AIS data often contain significant missing data due to limitations in both devices and network coverage. To overcome these limitations, this research presents an innovative approach for reconstructing missing points. In this paper, we first extracted the geometric shape and motion characteristics of vessels and constructed a decision tree using an adaptive sparse constraint mechanism to classify four types of vessel trajectories. Then, the vessel classification findings serve as input and the vessel acceleration features are constructed using the heading and velocity features of the trajectories at both ends. Finally, We employ a bidirectional Gate Recurrent Unit (GRU) network to simultaneously train on historical trajectory data. The method progressively shortens the reconstructed trajectory, thereby enhancing the accuracy of the reconstruction. In this paper, comparative experiments are conducted with several algorithms across various dimensions, including algorithm performance, trajectory types, and navigation scenarios. It is indicated by the experimental results that the algorithm discussed in this study outperforms baselines. Significant methodological advancements in vessel trajectory reconstruction are represented by this study, which also shows promising potential for application in intelligent maritime transportation systems. © 1967-2012 IEEE All rights reserved.

关键词： Waterway transportation

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