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Scale‐wise interaction fusion and knowledge distillation network for aerial scene recognition

CAAI Transactions on Intelligence technology 2023年第4期8卷 1178-1190页

作者： Hailong Ning Tao Lei Mengyuan An Hao Sun Zhanxuan Hu Asoke K.Nandi School of Computer Science and Technology Xi'an University of Posts and TelecommunicationsShaanxi Key Laboratory of Network Data Analysis and Intelligent ProcessingXi'anChina Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'anChina School of Electronic Information and Artificial Intelligence Shaanxi University of Science and TechnologyXi'anChina School of Computer Central China Normal UniversityWuhanChina Department of Electronic and Electrical Engineering Brunel University LondonLondonUK Xi'an Jiaotong University Xi'anChina

Aerial scene recognition(ASR)has attracted great attention due to its increasingly essential *** of the ASR methods adopt the multi‐scale architecture because both global and local features play great roles in ***,the existing multi‐scale methods neglect the effective interactions among different scales and various spatial locations when fusing global and local features,leading to a limited ability to deal with challenges of large‐scale variation and complex background in aerial scene *** addition,existing methods may suffer from poor generalisations due to millions of to‐belearnt parameters and inconsistent predictions between global and local *** tackle these problems,this study proposes a scale‐wise interaction fusion and knowledge distillation(SIF‐KD)network for learning robust and discriminative features with scaleinvariance and background‐independent *** main highlights of this study include two *** the one hand,a global‐local features collaborative learning scheme is devised for extracting scale‐invariance features so as to tackle the large‐scale variation problem in aerial scene ***,a plug‐and‐play multi‐scale context attention fusion module is proposed for collaboratively fusing the context information between global and local *** the other hand,a scale‐wise knowledge distillation scheme is proposed to produce more consistent predictions by distilling the predictive distribution between different scales during *** experimental results show the proposed SIF‐KD network achieves the best overall accuracy with 99.68%,98.74%and 95.47%on the UCM,AID and NWPU‐RESISC45 datasets,respectively,compared with state of the arts.

关键词： deep learning image analysis image classification information fusion

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Jamming-Resilient Consensus for Wireless Blockchain Networks

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Tsinghua science and technology 2025年第1期30卷 262-278页

作者： Yifei Zou Meng Hou Li Yang Minghui Xu Libing Wu Dongxiao Yu xiuzhen Cheng School of Computer Science and Technology Shandong UniversityQingdao 266237China School of Cyber Science and Engineering Wuhan UniversityWuhan 430072China

As the device complexity keeps increasing,the blockchain networks have been celebrated as the cornerstone of numerous prominent platforms owing to their ability to provide distributed and immutable ledgers and data-driven autonomous *** distributed consensus algorithm is the core component that directly dictates the performance and properties of blockchain ***,the inherent characteristics of the shared wireless medium,such as fading,interference,and openness,pose significant challenges to achieving consensus within these networks,especially in the presence of malicious jamming *** cope with the severe consensus problem,in this paper,we present a distributed jamming-resilient consensus algorithm for blockchain networks in wireless environments,where the adversary can jam the communication channel by injecting jamming *** on a non-binary slight jamming model,we propose a distributed four-stage algorithm to achieve consensus in the wireless blockchain network,including leader election,leader broadcast,leader aggregation,and leader announcement *** high probability,we prove that our jamming-resilient algorithm can ensure the validity,agreement,termination,and total order properties of consensus with the time complexity of O(n).Both theoretical analyses and empirical simulations are conducted to verify the consistency and efficiency of our algorithm.

关键词： consensus in blockchain jamming attacks distributed algorithm

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Virtual Reality Based Shading Pattern Recognition and Interactive Global Maximum Power Point Tracking in Photovoltaic Systems

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Journal of Modern Power Systems and Clean Energy 2024年第6期12卷 1849-1858页

作者： Kangshi Wang Jieming Ma xiao Lu Jingyi Wang Ka Lok Man Kaizhu Huang xiaowei Huang School of Advanced Technology Xi'an Jiaotong-Liverpool UniversitySuzhou 215123China Data Science Research Center Duke Kunshan UniversitySuzhou 215316China School of Computer Science the University of LiverpoolLiverpoolU.K

The performance of photovoltaic(PV)systems is in-fluenced by various factors,including atmospheric conditions,geographical locations,and spatial and temporal ***,the optimization of PV systems relies heavily on the global maximum power point tracking(GMPPT)*** this paper,we adopt virtual reality(VR)technology to visual-ize PV entities and simulate their *** integra-tion of VR technology introduces a novel spatial and temporal dimension to the shading pattern recognition(SPR)of PV sys-tems,thereby enhancing their descriptive ***-more,we introduce an interactive GMPPT(IGMPPT)method based on VR *** method leverages interactive search techniques to narrow down search regions,thereby en-hancing the search *** results demonstrate the effectiveness of the proposed IGMPPT in representing the spatial and temporal characteristics of PV systems and improv-ing the efficiency of GMPPT.

关键词： Photovoltaic(PV)system virtual reality(VR) shading pattern recognition(SPR) global maximum power point tracking(GMPPT)

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Modifying Lyapunov exponent of chaotic map by self-cascading

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science China(Technological sciences) 2024年第7期67卷 2203-2214页

作者： YI ChenLong LI ChunBiao LI Yongxin xiA Ming HUA ZhongYun School of Electronic and Information Engineering Nanjing University of Information Science&TechnologyNanjing 210044China School of Artificial Intelligence Nanjing University of Information Science&TechnologyNanjing 210044China School of Computer Science and Technology Harbin Institute of Technology Shenzhen Graduate SchoolShenzhen 518055China

The self-cascade(SC) method is an effective technique for chaos enhancement and complexity increasing in chaos ***, the controllable self-cascade(CSC) method allows for more accurate control of Lyapunov exponents of the discrete map. In this work, the SC and CSC systems of the original map are derived, which enhance the chaotic performance while preserving the fundamental dynamical characteristics of the original map. Higher Lyapunov exponent of chaotic sequences corresponding to higher frequency are obtained in SC and CSC systems. Meanwhile, the Lyapunov exponent could be linearly controlled with greater flexibility in the CSC system. The verification of the numerical simulation and theoretical analysis is carried out based on the platform of CH32.

关键词： self-cascade controllable self-cascade chaos enhancement chaos control

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An Approach for Privacy-Aware Mobile App Package Recommendation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6240-6252页

作者： Liu, Shanpeng Cao, Buqing Liu, Jianxun Kang, Guosheng Shi, Min Li, xiong Fletcher, Kenneth K. Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan411201 China Hunan University of Technology School of Computer Zhuzhou412007 China Harvard University Harvard Medical School BostonMA02138 United States School of Computer Science and Engineering Chengdu611731 China University of Massachusetts School of Computer Science BostonMA02125 United States

With the prosperity of the mobile Internet, the abundance of data makes it difficult for users to choose their favorite app. Thus, mobile app recommendation as an emerging topic attracts lots of attention. However, existing methods for app recommendation rarely consider recommendation accuracy under the privacy representation of user preferences. To address this problem, we propose a privacy-aware app package recommendation method named APP-Rec. Specifically, in this method: 1) treat an app and its associated heterogeneous entities (APP-Rec considers not only the apps themselves but also a variety of related factors-collectively referred to as heterogeneous entities, such as app category and app neighbors) as an app package and extract comprehensive features from the app package using an intrapackage attention network and an interpackage attention network to improve app recommendation;and 2) design a privacy module utilizing Laplace noise to achieve privacy preservation of user preferences. Experimental results show that APP-Rec outperforms the state-of-the-art methods in terms of area under the curve (AUC). Moreover, the privacy preservation of user preferences in APP-Rec is proved by theoretical analysis and experimental results. © 2024 IEEE.

关键词： Differential privacy

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Composite spectral spatial pixel CNN for land-use hyperspectral image classification with hybrid activation function

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Multimedia Tools and Applications 2025年第12期84卷 10527-10550页

作者： Banerjee, Anasua Swain, Satyajit Rout, Minakhi Bandyopadhyay, Mainak School of Computer Engineering Kalinga Institute of Industrial Technology-Deemed-to-be-University Bhubaneswar India Department of Computer Science and Engineering National Institute of Technology Silchar India

Deep learning methods have played a prominent role in the development of computer visualization in recent years. Hyperspectral imaging (HSI) is a popular analytical technique based on spectroscopy and visible imaging which examines the pattern of light in a target and recognizes objects based on varying spectral properties. However, in remote sensing, detecting surface material via HSI analysis is a critical and difficult task. The performance of spectral-spatial data exploitation is well established to outperform typical spectral pixel-wise techniques. Because of its great feature extraction ability, convolutional neural networks (CNN) have emerged as a potent deep learning approach. CNN translates the input features of an image into an equivalent CNN feature map, in addition to naturally combining spectral and spatial information. However, spectral-spatial properties when combined with pixel-wise extraction can learn more minute details of objects present on the earth’s terrain. In this paper, a noble Composite Spectral Spatial Pixel CNN model for the classification of hyperspectral data is presented which is an amalgamation of 3D-2D-1D CNN. While the 3D and 2D CNN exploit the spectral-spatial features effectively, 1D CNN works on pixel-wise feature extraction. Further, to optimize the classification performance of the proposed model, a new hybrid activation function Flatten-T Swish is also used which is the combination of ReLU and Swish function. The proposed model is compared with other state-of-the-art models based on three popular HSI datasets, and it is found that the proposed model performs better among others in terms of classification and computation time, giving 98.87% accuracy for Indian Pines, 99.92% accuracy for Pavia university, and 99.99% accuracy for Salinas Valley dataset. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Extraction

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Supramolecular Macrocycle Regulated Single-Atom MoS_(2)@Co Catalysts for Enhanced Oxygen Evolution Reaction

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Energy & Environmental Materials 2024年第5期7卷 283-291页

作者： Shuai Cao Wenzhuo Wu Chaozhong Liu Leqian Song Qun Xu Huacheng Zhang Yanli Zhao School of Chemical Engineering and Technology Xi'an Jiaotong UniversityXi'an 710049China Henan Institute of Advanced Technology Zhengzhou UniversityZhengzhou 450052China Department of Materials Science and Engineering Zhengzhou University Zhengzhou 450001China School of Chemistry Chemical Engineering and BiotechnologyNanyang Technological University21 Nanyang Link Singapore 637371Singapore

The development of active water oxidation catalysts for water splitting has stimulated considerable ***,the design and building of single atom Co sites using a supramolecular tailoring strategy are reported,that is,the introduction of pillar[4]arene[1]quinone(P4A1Q)permits mononuclear Co species stereoelectronically assembled on MoS_(2)matrix to construct an atomically dispersed MoS_(2)@Co catalyst with modulated local electronic structure,definite chemical environment and enhanced oxygen evolution reaction *** calculations indicate that immsobilized single-Co sites exhibit an optimized adsorption capability of oxygen-containing intermediates,endowing the catalyst an excellent electrocatalytic oxygen evolution reaction activity,with a low overpotential of 370 mV at 10 mA cm^(-2)and a small Tafel slope of 90 mV dec^(-1).The extendable potential of this strategy to other electrocatalysts such as MoS_(2)@Ni and MoS_(2)@Zn,and other applications such as the hydrogen evolution reaction was also *** study affords new insights into the rational design of single metal atom systems with enhanced electrocatalytic performance.

关键词： electrocatalysis oxygen evolution reaction pillar[4]arene[1]quinone single atoms transition-metal dichalcogenides

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Revolutionizing Image Captioning: Integrating Attention Mechanisms with Adaptive Fusion Gates

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第3期51卷 212-221页

作者： Sheng, Shou-Jun Zhou, Zi-Wei School of Computer and Software Engineering University of Science and Technology Liaoning AnShan114051 China

In order to dynamically create a sequence of textual descriptions for images, image description models often make use of the attention mechanism, which involves an automatic focus on different regions within an image. However, a prevalent issue with current attention mechanisms is their tendency to overlook essential elements within the image, prioritizing contextual aspects of the object when generating descriptive text. This constraint results in a decrease in the precision of the textual descriptions produced. To address this issue and improve the accuracy of image interpretation, a proposed model for image description utilizes an attention-based approach and includes a multi-layer decoder and a fusion gate. This model is based on an encoder-decoder architecture and utilizes Residual Network (ResNet) framework for feature extraction during the encoding phase, thereby extending the encoder-decoder structure into the decoding phase. Within this framework, an adaptive fusion gate mechanism is introduced and combined with multi-layer cascade decoders to facilitate the generation of utterances. This allows decoders from lower layers to actively contribute to the final text prediction phase, thereby incrementally improving the accuracy of the predicted text and generating more precise descriptions. MS COCO 2014 dataset has been utilized for the purpose of training and validating the effectiveness of this model in understanding images. The results clearly and unequivocally establish the model’s capacity to generate exceptional predictions. When compared with the top-performing models, it has demonstrated significant enhancements, as indicated by a 0.096 rise in BLEU_1 metric, a 0.153 improvement in ROUGE_L metric, and a remarkable 0.32 increase in CIDEr metric on MS COCO dataset. The overall improvement in performance across all evaluation criteria highlights the model’s alignment with the requirements of image understanding applications. © (2024), (International Associ

关键词： computer vision

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A Spatial Modes Filtering FETD Method for 3-D Periodic Structures

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Applied Computational Electromagnetics Society Journal 2025年第2期40卷 96-102页

作者： Wang, Yixin Wei, Bing Fan, Kaihang He, xinbo Zhao, Sihan Department of Information Technology Shaanxi Police College Xi’an710021 China School of Physics Xidian University Xi’an710071 China School of Information and Communications Engineering Xi’an Jiaotong University Xi’an710049 China Science and Technology on Electromechanical Dynamic Control Laboratory Xi’an710065 China

– A spatial modes filtering (SMF) finite-element time-domain (FETD) method with periodic boundary condition (PBC) is proposed for efficiently analyzing the electromagnetic characteristics of 3-D periodic structures with partial fine structures. The system matrices of FETD become asymmetrical because of the introduction of PBC, which prevents the system eigenvalue analysis. By decomposing the system matrix into PBC-independent and PBC-related parts, the unstable spatial modes under the given large time step can be found and removed from the symmetrical PBC-independent system matrices. Then the system matrix equation and time marching of the SMF-FETD and SMF-FETD method based on local eigenvalue solution (LES-SMF-FETD) with PBC are obtained. Numerical results illustrate the efficiency and effectiveness of the SMF-FETD method with PBC based on non-uniform mesh for analyzing the transport properties of 3-D periodic structures. © ACES.

关键词： Eigenvalues and eigenfunctions

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An Industrial Micro Parts Recognition technology Based on Improved Yolov8 7

An Industrial Micro Parts Recognition Technology Based on Im...

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7th International Conference on computer Network, Electronic and Automation, ICCNEA 2024

作者： Hao, Yafei Jia, Feng School of Computer Science and Engineering Xi'an Technological University Xi'an China School of Mechanical and Electrical Engineering Xi’an Technological University Xi’an China

Aiming at the recognition and classification problem of micro assembly parts in industrial production, the Yolov8 object detection algorithm was used as the base model to improve the C2f class in the backbone. And apply the improved object detection algorithm to the recognition field of detecting assembly parts. By collecting images of gears, nails, and screws from various angles on Roboflow, a training set and a validation set containing three types of gears, nails, and screws are formed. At the same time, in order to improve the robustness of the Yolov8 object detection algorithm, data processing methods such as adding noise and image rotation were adopted for the samples in the data. Due to the use of Yolov8 as the base model, a TXT text file containing label prediction box position information was formed after labelling the dataset on Roboflow. Based on Python (3.10.12) and cloud platform, improved Yolov8 object detection algorithm was trained and network validation was carried out. Use TXT files as input for the model to obtain and learn features of gears, nails, and screws. After 200 epochs, the mAP for detecting gears, nails, and screws increased by about 3% compared to the base model. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Screws

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