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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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CAMG: Context-Aware Moment Graph Network for Multimodal Temporal Activity Localization via Language 12th

CAMG: Context-Aware Moment Graph Network for Multimodal Tem...

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12th National CCF Conference on Natural Language processing and Chinese computing, NLPCC 2023

作者： Hu, Yuelin Xu, Yuanwu Zhang, Yuejie Feng, Rui Zhang, Tao Lu, Xuequan Gao, Shang School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University Shanghai200433 China School of Information Management and Engineering Shanghai Key Laboratory of Financial Information Technology Shanghai University of Finance and Economics Shanghai200433 China School of Information Technology Deakin University Waurn PondsVIC3216 Australia

ISBN: (纸本)9783031446924

Temporal Activity Localization via Language (TALL) is a challenging task for language based video understanding, especially when a video contains multiple moments of interest and the language query has words describing complex context dependencies between the moments. Latest studies have proposed various ways to exploit the temporal context of adjacent moments, but two apparent limitations remained. First, only limited context information was encoded based on RNNs or 2-D convolutions, which highly depended on the pre-sorting of proposals and lacked flexibility. Second, semantically correlated content in different moments was ignored, i.e., semantic context. To address these limitations, we propose a novel GCN-based framework, i.e., Context-Aware Moment Graph (CAMG) network, to jointly model the temporal context and semantic context. Also, we design a multi-step fusion scheme to aggregate object, motion and textual features. A Query-Gated Integration Module is further designed to select queried objects and filter out noisy ones. Our model achieves superior performance to state-of-the-art methods on two widely-used benchmark datasets. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Semantics

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ANALYSIS OF TRIPARTITE GAME IN POWER SYSTEMS BASED ON UNCERTAIN NASH EQUILIBRIUM THEORY

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2025年第1期87卷 117-128页

作者： Wu, Liang Yan, Xiaolin Yao, Zhangsong Zhao, Teng School of Management and Ningxia Key Laboratory of intelligent information and Big Data Processing North Minzu University Ningxia Yinchuan750021 China School of Mathematical Sciences Tiangong University Tianjin300387 China School of Information Engineering Nanjing Xiaozhuang University Nanjing211171 China School of Mathematics and Information Science North Minzu University Ningxia Yinchuan750021 China

Power systems serve as the fundamental infrastructure for the socioeconomic development of modern societies. Researching power systems can stimulate the growth of the power industry and contribute to the sustainable development of society. This paper aims to address uncertainties prevalent in power systems, including extreme weather events, disruptions in renewable energy supply, and adjustments in economic policies. To achieve this objective, an uncertain Nash equilibrium model is established. Subsequently, a game theoretic analysis is conducted to maximize the interests of users, grid companies, and proxy purchasers. Under the condition of not necessarily continuous differentiability, the Riemann-Stieltjes discrete approximation method has been proposed. The difficulty of calculating the expectation of the optimal revenue function has been resolved. Weak first-order equilibrium condition based on Clarke’s generalized gradient, the convergence and convergence rate of uncertain Nash equilibrium solutions are proved. © 2025, Politechnica University of Bucharest. All rights reserved.

关键词： power system tripartite game uncertain Nash equilibrium uncertainty theory

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MARL-DCF: A Dual-Channel Fusion Routing Algorithm Based on Multi-agent Reinforcement Learning 24th

MARL-DCF: A Dual-Channel Fusion Routing Algorithm Based on ...

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24th International Conference on Algorithms and Architectures for Parallel processing, ICA3PP 2024

作者： Li, Zhenyu Sun, Runyuan Liang, Zhifeng Liu, Bo Sun, Zhenxiang School of Information Science and Engineering University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan250022 China

ISBN: (纸本)9789819615414

Emerging applications have imposed stringent demands on Internet quality of service (QoS). To ensure that various data streams within different Internet services receive the appropriate QoS, advancements in flow classification technology, software-defined networking (SDN), and programmable network devices have enabled the network to identify user demands and precisely control fine-grained traffic routing quickly. Although significant progress has been made with these technologies, optimizing the transmission paths of business flows to meet diverse QoS requirements in real-time remains a challenge. To address this issue, this paper proposes a dual-channel fusion routing algorithm based on multi-agent reinforcement learning (MARL-DCF). Implemented within an SDN architecture, the algorithm was experimentally validated in a Mininet environment using real network topologies and traffic trajectories. The results demonstrate that MARL-DCF can optimize routing paths according to the network environment, meet various QoS demands, and exhibit strong adaptability and reliability in complex scenarios such as link failures and traffic fluctuations. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Quality of service

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Efficient Homomorphic Approximation of Max Pooling for Privacy-Preserving Deep Learning 6th

Efficient Homomorphic Approximation of Max Pooling for Pri...

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6th International Conference on Machine Learning for Cyber Security, ML4CS 2024

作者： Zhang, Peng Qiu, Dongyan Duan, Ao Liu, Hongwei The Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen University Guangdong Shenzhen518060 China College of Big Data and Internet Shenzhen Technology University Guangdong Shenzhen518118 China

ISBN: (纸本)9789819645657

Privacy-Preserving Deep Learning (PPDL) using Fully Homomorphic Encryption (FHE) addresses potential data privacy exposure risks associated with deploying deep learning models in untrusted cloud environments. FHE-based PPDL enables users to encrypt their data locally, allowing cloud service providers to perform computations directly on the encrypted data without ever accessing it. However, FHE faces challenges in efficiently handling nonlinear computations, which are essential to deep learning frameworks, particularly in implementing activation functions and max pooling layers. To address this challenge, we improve the homomorphic max pooling approximation scheme HMaxPool, and propose an efficient homomorphic max pooling algorithm, denoted as HMaxPool+. Specifically, we first redefine the approximation of the maximum function, and then design sub-algorithm to address the potential output expansion issue. The experimental results validate that both the homomorphic max pooling algorithm itself and the corresponding homomorphic SqueezeNet neural network demonstrate enhancements in computational efficiency and accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Differential privacy

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Bearing fault signal detection based on whale algorithm to optimize stochastic resonance parameters 34

Bearing fault signal detection based on whale algorithm to o...

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第34届中国控制与决策会议

作者： Huang Weichao Zhang Ganggang Faculty of automation and Information Engineering Xi'an University of Technology Shannxi Key Laboratory of Complex System Control and Intelligent Information Processing

ISBN: (纸本)9781665478977

Aiming at the problem that the traditional stochastic resonance system cannot adaptively adjust the structural parameters in the bearing fault signal detection,this paper proposes a parameter adaptive stochastic resonance bearing fault signal detection method based on the whale optimization *** method is based on the bistable stochastic resonance system model,and the structural parameters of the bistable stochastic resonance are optimized through the adaptive whale algorithm,so that the fault signal is enhanced,and the bearing fault signal detection is *** method was verified by actual bearing fault signals,and a series of comparative experiments were carried *** results show that the method in this paper has a simple model,few algorithm parameters,fast convergence speed,and a large output signal-to-noise ratio of the stochastic resonance system,which can accurately and efficiently detect bearing fault signals.

关键词： Stochastic Resonance Whale Optimization Algorithm Bearing Failure Signal

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Improved beluga whale optimization algorithm based cluster routing in wireless sensor networks

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Mathematical Biosciences and Engineering 2024年第3期21卷 4587-4625页

作者： Yuan, Hao Chen, Qiang Li, Hongbing Zeng, Die Wu, Tianwen Wang, Yuning Zhang, Wei Chongqing Key Laboratory of Geological Environmental Monitoring and Disaster Early Warning in the Three Gorges Reservoir Area Chongqing Three Gorges University Chongqing404120 China Internet of Things and Intelligent Control Technology Chongqing Engineering Research Center Chongqing Three Gorges University Chongqing404120 China Chongqing Municipal Key Laboratory of Intelligent Information Processing and Control Chongqing Three Gorges University Chongqing404120 China

Cluster routing is a critical routing approach in wireless sensor networks (WSNs). However, the uneven distribution of selected cluster head nodes and impractical data transmission paths can result in uneven depletion of network energy. For this purpose, we introduce a new routing strategy for clustered wireless sensor networks that utilizes an improved beluga whale optimization algorithm, called tCBWO-DPR. In the selection process of cluster heads, we introduce a new excitation function to evaluate and select more suitable candidate cluster heads by establishing the correlation between the energy of node and the positional relationship of nodes. In addition, the beluga whale optimization (BWO) algorithm has been improved by incorporating the cosine factor and t-distribution to enhance its local and global search capabilities, as well as to improve its convergence speed and ability. For the data transmission path, we use Prim’s algorithm to construct a spanning tree and introduce DPR for determining the optimal route between cluster heads based on the correlation distances of cluster heads. This effectively shortens the data transmission path and enhances network stability. Simulation results show that the improved beluga whale optimization based algorithm can effectively improve the survival cycle and reduce the average energy consumption of the network. © 2024 the Author(s).

关键词： Heuristic algorithms

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GOAL: Generalized Jointly Sparse Linear Discriminant Regression for Feature Extraction

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第10期5卷 4959-4971页

作者： Lu, Haoquan Lai, Zhihui Zhang, Junhong Yu, Zhuozhen Wen, Jiajun Shenzhen University Computer Vision Institute College of Computer Science and Software Engineering Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen518060 China Peking University Shenzhen Graduate School School of Electronic and Computer Engineering Shenzhen518055 China

Ridge regression (RR)-based methods aim to obtain a low-dimensional subspace for feature extraction. However, the subspace's dimensionality does not exceed the number of data categories, hence compromising its capability of feature representation. Moreover, these methods with L2-norm metric and regularization cannot extract highly robust features from data with corruption. To address these problems, in this article, we propose generalized jointly sparse linear discriminant regression (GOAL), a novel regression method based on joint L2,1-norm and capped-L2-norm, which can integrate sparsity, locality, and discriminability into one model to learn a full-rank robust feature extractor. The sparsely selected discriminative features are robust enough to characterize the decision boundary between classes. Locality is related to manifold structure and Laplacian smoothing, which can enhance the robustness of the model. By using the multinorm metric and regularization regression framework, the proposed method obtains the projection with joint sparsity and guarantees that the rank of the projection matrix will not be limited by the number of classes. An iterative algorithm is proposed to compute the optimal solution. Complexity analysis and proofs of convergence are also given in the article. Experiments on well-known datasets demonstrate our model's superiority and generalization ability. © 2020 IEEE.

关键词： Data mining

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DisenGCD: a meta multigraph-assisted disentangled graph learning framework for cognitive diagnosis 24

DisenGCD: a meta multigraph-assisted disentangled graph lear...

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Proceedings of the 38th International Conference on Neural information processing Systems

作者： Shangshang Yang Mingyang Chen Ziwen Wang Xiaoshan Yu Panpan Zhang Haiping Ma Xingyi Zhang Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei Anhui P. R. China and Anhui Province Key Laboratory of Intelligent Computing and Applications Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei Anhui P. R. China and Department of Information Materials and Intelligent Sensing Laboratory of Anhui Province Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei Anhui P. R. China

ISBN: (纸本)9798331314385

Existing graph learning-based cognitive diagnosis (CD) methods have made relatively good results, but their student, exercise, and concept representations are learned and exchanged in an implicit unified graph, which makes the interaction-agnostic exercise and concept representations be learned poorly, failing to provide high robustness against noise in students' interactions. Besides, lower-order exercise latent representations obtained in shallow layers are not well explored when learning the student representation. To tackle the issues, this paper suggests a meta multigraph-assisted disentangled graph learning framework for CD (DisenGCD), which learns three types of representations on three disentangled graphs: student-exercise-concept interaction, exercise-concept relation, and concept dependency graphs, respectively. Specifically, the latter two graphs are first disentangled from the interaction graph. Then, the student representation is learned from the interaction graph by a devised meta multigraph learning module; multiple learnable propagation paths in this module enable current student latent representation to access lower-order exercise latent representations, which can lead to more effective nad robust student representations learned; the exercise and concept representations are learned on the relation and dependency graphs by graph attention modules. Finally, a novel diagnostic function is devised to handle three disentangled representations for prediction. Experiments show better performance and robustness of DisenGCD than state-of-the-art CD methods and demonstrate the effectiveness of the disentangled learning framework and meta multigraph module. The source code is available at https://***/BIMK/intelligent-Education/tree/main/DisenGCD.

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AIoT for Aircraft Final Assembly:An intelligent and Collaborative Framework

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Journal of Communications and information Networks 2024年第3期9卷 262-276页

作者： Cheng Ren Xiaojing Wen Xinping Guan Cailian Chen Yehan Ma Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of System Control and Information Processing Ministry of Education of ChinaShanghai 200240China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong UniversityShanghai 200240China

Aircraft final assembly line(AFAL)involves thousands of processes that must be completed before ***,the heavy reliance on manual labor in most assembly processes affects the quality and prolongs the delivery *** the advent of artificial intelligence of things(AIoT)technologies has introduced advancements in certain AFAL scenarios,systematically enhancing the intelligence level of the AFAL and promoting the widespread deployment of artificial intelligence(AI)technologies remain significant *** address these challenges,we propose the intelligent and collaborative aircraft assembly(ICAA)framework,which integrates AI technologies within a cloud-edge-terminal *** ICAA framework is designed to support AI-enabled applications in the AFAL,with the goal of improving assembly efficiency at both individual and multiple process *** analyze specific demands across various assembly scenarios and introduce corresponding AI technologies to meet these *** three-tier ICAA framework consists of the assembly field,edge data platform,and assembly cloud platform,facilitating the collection of heterogeneous terminal data and the deployment of AI *** framework enhances assembly efficiency by reducing reliance on manual labor for individual processes and fostering collaboration across multiple *** provide detailed descriptions of how AI functions at each level of the ***,we apply the ICAA framework to a real AFAL,focusing explicitly on the flight control system testing *** practical implementation demonstrates the effectiveness of the framework in improving assembly efficiency and promoting the adoption of AIoT technologies.

关键词： intelligent aircraft final assembly line AIoT cloud-edge-terminal framework design

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