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Encapsulating Ru doped Co/CO_(2)P nanoparticles into delignified and TEMPO oxidized wood carbon enabling efficient pH-universal hydrogen evolution reaction

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Journal of Energy Chemistry 2025年第5期104卷 452-461页

作者： Jiahui Li Peng Huang Zhijie Zhang Cuihua Tian Yu Liao Tai Yang Yan Qing Yiqiang Wu College of Materials Science and Engineering Central South University of Forestry and TechnologyChangsha 410004HunanChina

High-performance catalyst is significant for the sustainable hydrogen(H_(2))production by electrocatalytic water *** porous structure and active groups of substrate can promote the interaction of substrate and active metal particles,enabling excellent catalytic properties and ***,the optimization strategy of delignification and 2,2,6,6-tetramethylpyperidine-1-oxyl(TEMPO)oxidization was developed to modify the porous structure and active groups of wood substrate,and Ru doped Co/CO_(2)P(Ru-Co/CO_(2)P)nanoparticles were encapsulated into the optimized wood carbon substrate(Ru-Co/CO_(2)P@TDCW)for the efficient pH-universal hydrogen evolution reaction(HER).The nanopore and carboxyl groups were produced by delignification and TEMPO oxidation,which accelerated the dispersion and deposition of Ru-Co/CO_(2)P *** RuCo alloy and RuCoP nanoparticles were produced with the doping of Ru,and more Ru-Co/CO_(2)P nanoparticles were anchored by the delignified and TEMPO oxidized wood carbon(TDCW).As anticipated,the Ru-Co/CO_(2)P@TDCW catalyst exhibited excellent pH-universal HER activity,and only 16.6,93,and 43 mV of overpotentials were required to deliver the current density of 50 mA cm^(-2)in alkaline,neutral,and acidic electrolytes,outperforming the noble Pt/C/TDCW catalyst *** addition,Ru-Co/CO_(2)P@TDCW catalyst presented excellent stability for more than 600 h working at 100 mA cm^(-2)in alkaline solution(1.0 M KOH).Density function theory(DFT)results revealed that energy barriers for the dissociation of H_(2)O and the formation of H_(2)were decreased by the doping of Ru,and the conductivity and efficiency of electron migration were also *** work demonstrated a strategy to optimize the structure and properties of wood carbon substrate,providing a promising strategy to synthesize high-efficiency catalyst for H_(2)production.

关键词： Carbonized wood substrate Delignification TEMPO oxidization Hydrogen evolution reaction DFT calculation

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UAV-supported intelligent truth discovery to achieve low-cost communications in mobile crowd sensing

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Digital Communications and Networks 2024年第4期10卷 837-852页

作者： Jing Bai Jinsong Gui Guosheng Huang Shaobo Zhang Anfeng Liu School of Computer Science and Engineering Central South UniversityChangSha410083China School of Information Science and Engineering Hunan First Normal UniversityChangsha410205China School of Computer Science and Engineering Hunan University of Science and TechnologyXiangTan411100China

Unmanned and aerial systems as interactors among different system components for communications,have opened up great opportunities for truth data discovery in Mobile Crowd Sensing(MCS)which has not been properly solved in the *** this paper,an Unmanned Aerial Vehicles-supported Intelligent Truth Discovery(UAV-ITD)scheme is proposed to obtain truth data at low-cost communications for *** main innovations of the UAV-ITD scheme are as follows:(1)UAV-ITD scheme takes the first step in employing UAV joint Deep Matrix Factorization(DMF)to discover truth data based on the trust mechanism for an Information Elicitation Without Verification(IEWV)problem in MCS.(2)This paper introduces a truth data discovery scheme for the first time that only needs to collect a part of n data samples to infer the data of the entire network with high accuracy,which saves more communication costs than most previous data collection schemes,where they collect n or kn data ***,we conducted extensive experiments to evaluate the UAV-ITD *** results show that compared with previous schemes,our scheme can reduce estimated truth error by 52.25%–96.09%,increase the accuracy of workers’trust evaluation by 0.68–61.82 times,and save recruitment costs by 24.08%–54.15%in truth data discovery.

关键词： Unmanned aerial systems Trust computing Truth discovery Deep matrix factorization Low-cost communications

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Starlet:Network defense resource allocation with multi-armed bandits for cloud-edge crowd sensing in IoT

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Digital Communications and Networks 2024年第3期10卷 586-596页

作者： Hui Xia Ning Huang Xuecai Feng Rui Zhang Chao Liu College of Computer Science and Technology Ocean University of ChinaQingdao266100China

The cloud platform has limited defense resources to fully protect the edge servers used to process crowd sensing data in Internet of *** guarantee the network's overall security,we present a network defense resource allocation with multi-armed bandits to maximize the network's overall ***,we propose the method for dynamic setting of node defense resource thresholds to obtain the defender(attacker)benefit function of edge servers(nodes)and ***,we design a defense resource sharing mechanism for neighboring nodes to obtain the defense capability of ***,we use the decomposability and Lipschitz conti-nuity of the defender's total expected utility to reduce the difference between the utility's discrete and continuous arms and analyze the difference ***,experimental results show that the method maximizes the defender's total expected utility and reduces the difference between the discrete and continuous arms of the utility.

关键词： Internet of things Defense resource sharing Multi-armed bandits Defense resource allocation

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A Survey of Reliability Issues Related to Approximate Circuits

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Journal of computer science & technology 2023年第2期38卷 273-288页

作者：王真徐荣臣陈嘉诚肖杰 College of Computer Science and Technology Shanghai University of Electric PowerShanghai 200090China College of Computer Science and Technology Zhejiang University of TechnologyHangzhou 310000China

As one of the most promising paradigms of integrated circuit design,the approximate circuit has aroused widespread concern in the scientific *** takes advantage of the inherent error tolerance of some applications and relaxes the accuracy for reductions in area and power *** paper aims to provide a comprehensive survey of reliability issues related to approximate circuits,which covers three concerns:error characteristic analysis,reliability and test,and reliable design involving approximate *** error characteristic analysis is used to compare the outputs of the approximate circuit with those of its precise counterpart,which can help to find the most appropriate approximate design for a specific application in the large design *** the approximate design getting close to physical realization,manufacturing defects and operational faults are inevitable;therefore,the reliability prediction and vulnerability test become increasingly ***,the research on approximate circuit reliability and test is insufficient and needs more ***,although there is some existing work combining the approximate design with fault tolerant techniques,the reliability-enhancement approaches for approximate circuits are lacking.

关键词： approximate circuit error metric fault tolerance test reliability

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RayMVSNet++: Learning Ray-Based 1D Implicit Fields for Accurate Multi-View Stereo

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2023年第11期45卷 13666-13682页

作者： Shi, Yifei Xi, Junhua Hu, Dewen Cai, Zhiping Xu, Kai National University of Defense Technology College of Intelligence Science and Technology Hunan Changsha410073 China National University of Defense Technology College of Computer Science Hunan Changsha410073 China

Learning-based multi-view stereo (MVS) has by far centered around 3D convolution on cost volumes. Due to the high computation and memory consumption of 3D CNN, the resolution of output depth is often considerably limited. Different from most existing works dedicated to adaptive refinement of cost volumes, we opt to directly optimize the depth value along each camera ray, mimicking the range (depth) finding of a laser scanner. This reduces the MVS problem to ray-based depth optimization which is much more light-weight than full cost volume optimization. In particular, we propose RayMVSNet which learns sequential prediction of a 1D implicit field along each camera ray with the zero-crossing point indicating scene depth. This sequential modeling, conducted based on transformer features, essentially learns the epipolar line search in traditional multi-view stereo. We devise a multi-task learning for better optimization convergence and depth accuracy. We found the monotonicity property of the SDFs along each ray greatly benefits the depth estimation. Our method ranks top on both the DTU and the Tanks & Temples datasets over all previous learning-based methods, achieving an overall reconstruction score of 0.33 mm on DTU and an F-score of 59.48% on Tanks & Temples. It is able to produce high-quality depth estimation and point cloud reconstruction in challenging scenarios such as objects/scenes with non-textured surface, severe occlusion, and highly varying depth range. Further, we propose RayMVSNet++ to enhance contextual feature aggregation for each ray through designing an attentional gating unit to select semantically relevant neighboring rays within the local frustum around that ray. This improves the performance on datasets with more challenging examples (e.g., low-quality images caused by poor lighting conditions or motion blur). RayMVSNet++ achieves state-of-the-art performance on the ScanNet dataset. In particular, it attains an AbsRel of 0.058m and produces accura

关键词： Deep neural networks

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Adaptive ROI Optimization Pyramid Network: Lane Detection for FSD under Data Uncertainty

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Engineering Letters 2025年第2期33卷 282-291页

作者： Cao, Xu Liu, Weisheng Wang, Zhijian College of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China College of Computer Science and Software Engineering University of Science and Technology Liaoning Co Anshan114051 China

To mitigate the challenges posed by data uncertainty in Full-Self Driving (FSD) systems. This paper proposes a novel feature extraction learning model called Adaptive Region of Interest Optimized Pyramid Network (ARO). Specifically, ARO introduces a novel cross-layer fusion attention mechanism that dynamically assigns weighted attention to feature maps across different levels, facilitating deep feature fusion and enabling the effective extraction and utilization of salient feature information. Furthermore, ARO incorporates a feature replication layer to duplicate and refine feature maps at multiple levels, thereby enhancing its capability to capture fine-grained details and generate richer feature representations. Additionally, a multi-path upsampling strategy preserves fine-grained features during upsampling. Extensive experimental evaluations conducted on the benchmark CuLane and Tusimple datasets demonstrate that ARO achieves an F1 score of 80.60% on the CuLane dataset, outperforming state-of-the-art methods, showcasing the effectiveness of the proposed approach in handling data uncertainty for robust lane detection in autonomous driving. Our code are available at https://***/caoxu0109/lane detect ldfr. © 2025, International Association of Engineers. All rights reserved.

关键词： Attention mechanisms Culane Lane detection Multi-scale fusion network Tusimple

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A New Industrial Intrusion Detection Method Based on CNN-BiLSTM

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computers, Materials & Continua 2024年第6期79卷 4297-4318页

作者： Jun Wang Changfu Si Zhen Wang Qiang Fu College of Computer Science and Technology Shenyang University of Chemical TechnologyShenyang110142China

Nowadays,with the rapid development of industrial Internet technology,on the one hand,advanced industrial control systems(ICS)have improved industrial production ***,there are more and more cyber-attacks targeting industrial control *** ensure the security of industrial networks,intrusion detection systems have been widely used in industrial control systems,and deep neural networks have always been an effective method for identifying cyber *** intrusion detection methods still suffer from low accuracy and a high false alarm ***,it is important to build a more efficient intrusion detection *** paper proposes a hybrid deep learning intrusion detection method based on convolutional neural networks and bidirectional long short-term memory neural networks(CNN-BiLSTM).To address the issue of imbalanced data within the dataset and improve the model’s detection capabilities,the Synthetic Minority Over-sampling Technique-Edited Nearest Neighbors(SMOTE-ENN)algorithm is applied in the preprocessing *** algorithm is employed to generate synthetic instances for the minority class,simultaneously mitigating the impact of noise in the majority *** approach aims to create a more equitable distribution of classes,thereby enhancing the model’s ability to effectively identify patterns in both minority and majority *** the experimental phase,the detection performance of the method is verified using two data *** results show that the accuracy rate on the CICIDS-2017 data set reaches 97.7%.On the natural gas pipeline dataset collected by Lan Turnipseed from Mississippi State university in the United States,the accuracy rate also reaches 85.5%.

关键词： Intrusion detection convolutional neural network bidirectional long short-term memory neural network multi-head self-attention mechanism

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Fake Face Detection Based on Fusion of Spatial Texture and High-Frequency Noise

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Chinese Journal of Electronics 2025年第1期34卷 212-221页

作者： Dengyong Zhang Feifan Qi Jiahao Chen Jiaxin Chen Rongrong Gong Yuehong Tian Lebing Zhang Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of Computer and Communication Engineering Changsha University of Science and Technology School of Computer and Communication Engineering Changsha University of Science and Technology Changsha Social Work College Changkuangao Beijing Technology Co. Ltd. School of Computer and Artificial Intelligence Huaihua University

The rapid development of the Internet has led to the widespread dissemination of manipulated facial images, significantly impacting people's daily lives. With the continuous advancement of Deepfake technology, the generated counterfeit facial images have become increasingly challenging to distinguish. There is an urgent need for a more robust and convincing detection method. Current detection methods mainly operate in the spatial domain and transform the spatial domain into other domains for analysis. With the emergence of transformers, some researchers have also combined traditional convolutional networks with transformers for detection. This paper explores the artifacts left by Deepfakes in various domains and, based on this exploration, proposes a detection method that utilizes the steganalysis rich model to extract high-frequency noise to complement spatial features. We have designed two main modules to fully leverage the interaction between these two aspects based on traditional convolutional neural networks. The first is the multi-scale mixed feature attention module, which introduces artifacts from high-frequency noise into spatial textures, thereby enhancing the model's learning of spatial texture features. The second is the multi-scale channel attention module, which reduces the impact of background noise by weighting the features. Our proposed method was experimentally evaluated on mainstream datasets, and a significant amount of experimental results demonstrate the effectiveness of our approach in detecting Deepfake forged faces, outperforming the majority of existing methods.

关键词： Deepfakes Adaptation models Attention mechanisms Noise Transforms Transformers Feature extraction Internet Convolutional neural networks Faces

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A survey on cross-user federated recommendation

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science China(Information sciences) 2025年第4期68卷 7-32页

作者： Enyue YANG Yudi XIONG Wei YUAN Weike PAN Qiang YANG Zhong MING College of Computer Science and Software Engineering Shenzhen University School of Electrical Engineering and Computer Science The University of Queensland WeBank AI Lab WeBank Department of Computer Science and Engineering Hong Kong University of Science and Technology College of Big Data and Internet Shenzhen Technology University Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)

Recommender systems are effective in mitigating information overload, yet the centralized storage of user data raises significant privacy concerns. Cross-user federated recommendation(CUFR) provides a promising distributed paradigm to address these concerns by enabling privacy-preserving recommendations directly on user devices. In this survey, we review and categorize current progress in CUFR, focusing on four key aspects: privacy, security, accuracy, and efficiency. Firstly,we conduct an in-depth privacy analysis, discuss various cases of privacy leakage, and then review recent methods for privacy protection. Secondly, we analyze security concerns and review recent methods for untargeted and targeted *** untargeted attack methods, we categorize them into data poisoning attack methods and parameter poisoning attack methods. For targeted attack methods, we categorize them into user-based methods and item-based methods. Thirdly,we provide an overview of the federated variants of some representative methods, and then review the recent methods for improving accuracy from two categories: data heterogeneity and high-order information. Fourthly, we review recent methods for improving training efficiency from two categories: client sampling and model compression. Finally, we conclude this survey and explore some potential future research topics in CUFR.

关键词： cross-user federated recommendation federated recommendation federated learning recommender systems user privacy

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LP-CRI:Label Propagation Immune Generation Algorithm Based on Clustering and Rebound Mechanism

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computers, Materials & Continua 2025年第6期83卷 5373-5391页

作者： Hao Huang Kongyu Yang College of Computer Science Beijing Information Science and Technology UniversityBeijing100096China College of Communication Arts and Sciences Beijing Information Science and Technology UniversityBeijing100096China

Many existing immune detection algorithms rely on a large volume of labeled self-training samples,which are often difficult to obtain in practical scenarios,thus limiting the training of detection ***,noise inherent in the samples can substantially degrade the detection accuracy of these *** overcome these challenges,we propose an immune generation algorithm that leverages clustering and a rebound mechanism for label propagation(LP-CRI).The dataset is randomly partitioned into multiple subsets,each of which undergoes clustering followed by label propagation and *** rebound mechanism assesses the model’s performance after propagation and determines whether to revert to its previous state,initiating a subsequent round of propagation to ensure stable and effective *** results demonstrate that the proposed method is both computationally efficient and easy to train,significantly enhancing detector performance and outperforming traditional immune detection algorithms.

关键词： Artificial immunity label propagation detector generation unsupervised clustering

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