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4th International Conference on Electronic information engineering and Computer Communication, EIECC 2024

作者： Kong, Mengfan Luo, Aimin National University of Defense Technology National Key Laboratory of Information Systems Engineering Changsha China

ISBN: (纸本)9798331534622

Recommendation systems play an increasingly important role in social platforms, providing users with personalized content services. With the explosive growth of multi-modal contents, traditional recommendation systems based solely on interaction data can no longer meet user needs. In recent years, many researchers have attempted to integrate multi-modal features into recommendation system. Meanwhile, user intent determines whether users will interact with items or not. Intent modeling is crucial for capturing user preferences and explaining recommendation results. Recommendation system based on interaction data model user intents at a coarse-grained level, because the information contained in user IDs and item IDs is too limited. In contrast, multi-modal features can reflect finer-grained user preferences from a semantic perspective. To address this issue, we propose a Multi-Intent Learning method for Multi-Modal Recommendation (MIMM). Specifically, we design a Modality-Specific Intent Graph Convolution Network to disentangle user and item intents from different modalities and filter out self-supervision signals at the semantic level. Finally, a cross-modality intent contrastive learning task is introduced to align user intents across different modality. Experiments on real-world dataset demonstrate the effectiveness of our MIMM. © 2024 IEEE.

关键词： Self-supervised learning

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An AD-SPSO Algorithm for Solving the Measurement Resource Scheduling Problem with Satellite Cataloging Requirements

An AD-SPSO Algorithm for Solving the Measurement Resource Sc...

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2024 IEEE International Conference on Control science and systems engineering, ICCSSE 2024

作者： Niu, Ge Jiao, Yuanyuan Pan, Xiaogang National University of Defense Technology National Key Laboratory of Information Systems Engineering Changsha China

ISBN: (纸本)9798331517199

Under the condition of limited ground measurement resources, the increasing scale of satellites brings more satellite cataloging requirements and aggravates the measured pressure. How to better utilize limited measurement resources is a crucial issue. In this paper, the measurement resource scheduling problem with satellite cataloging requirements is examined, a measurement resource scheduling model is established with the goal of maximizing profits from satellite cataloging task completion, and a switching particle swarm optimization algorithm based on adaptive large neighborhood search and dynamic neighborhood (AD-SPSO) is proposed. The AD-SPSO algorithm uses the time greedy strategy and chaos mapping to obtain an initial population with better quality. Then, the algorithm designs the dynamic neighborhood of the particle, after which the particle's velocity is updated by selecting the optimal position from the dynamic neighborhood of the particle, so as to enhance the inter-particle information interaction and utilize the population information. In addition, the AD-SPSO algorithm joins the adaptive large neighborhood search algorithm, which selects different particles for perturbation according to the population state. This effectively prevents its early convergence. To evaluate the performance of the AD-SPSO algorithm, this paper performs experiments in four scenarios, and in different scenarios, compared with other improved algorithms, the AD-SPSO algorithm can obtain higher and more stable overall profit for scheduling plans, which verifies its effectiveness in solving the measurement resource scheduling problem with satellite cataloging requirements. © 2024 IEEE.

关键词： Profitability

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BEDiff: denoising diffusion probabilistic models for building extraction

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Optoelectronics Letters 2025年第5期21卷 298-305页

作者： LEI Yanjing WANG Yuan CHAN Sixian HU Jie ZHOU Xiaolong ZHANG Hongkai College of Computer Science and Technology Zhejiang University of Technology Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering College of Computer and Information China Three Gorges University Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province Wenzhou University College of Electrical and Information Engineering Quzhou University School of Electronic and Information Engineering Anhui Jianzhu University

Accurately identifying building distribution from remote sensing images with complex background information is challenging. The emergence of diffusion models has prompted the innovative idea of employing the reverse denoising process to distill building distribution from these complex backgrounds. Building on this concept, we propose a novel framework, building extraction diffusion model(BEDiff), which meticulously refines the extraction of building footprints from remote sensing images in a stepwise fashion. Our approach begins with the design of booster guidance, a mechanism that extracts structural and semantic features from remote sensing images to serve as priors, thereby providing targeted guidance for the diffusion process. Additionally, we introduce a cross-feature fusion module(CFM) that bridges the semantic gap between different types of features, facilitating the integration of the attributes extracted by booster guidance into the diffusion process more effectively. Our proposed BEDiff marks the first application of diffusion models to the task of building extraction. Empirical evidence from extensive experiments on the Beijing building dataset demonstrates the superior performance of BEDiff, affirming its effectiveness and potential for enhancing the accuracy of building extraction in complex urban landscapes.

关键词： A

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A Time-tightly Coupled UAV Control Scheduling Scheme 9

A Time-tightly Coupled UAV Control Scheduling Scheme

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9th IEEE International Conference on Data science in Cyberspace, DSC 2024

作者： Guo, Yuanyuan Zhu, Xianqiang Zhang, Qianzhen Liu, Bin National University of Defense Technology National Key Laboratory of Information Systems Engineering Changsha China

ISBN: (纸本)9798350391367

Aiming at the problem of unbalanced task scheduling and distribution of single-thread control task of control station under the background of multi-UAV collaboration, based on the planned UAV flight track and task execution time window, the whole UAV track is divided into two types of control periods: patrol time window and task execution time window, and a multi-objective optimization model with the minimum maximum task completion time, the minimum maximum load of the control station and the minimum load of the computer in the control station is constructed. By drawing a Gantt chart to visualize the allocation scheme of the simulation data set, the results show that the proposed algorithm can provide a more balanced effective control and scheduling scheme for UAV. © 2024 IEEE.

关键词： Multiobjective optimization

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Weapon Target Assignment Based on Deep Q-learning 9

Weapon Target Assignment Based on Deep Q-learning

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9th IEEE International Conference on Data science in Cyberspace, DSC 2024

作者： Qin, Jianbo Wang, Yi Ding, Zhaoyun National University of Defense Technology National Key Laboratory of Information Systems Engineering ChangSha China

ISBN: (纸本)9798350391367

This paper introduces an innovative approach to addressing the Weapon Target Assignment (WTA) problem, utilizing the Deep Q-Learning (DQN) algorithm. The goal is to optimize the allocation between weapons and targets to achieve the most effective combat outcome. Initially proposed by Manne, the WTA problem is a multi-objective, multi-constraint NP-complete problem, the complexity of which increases with the number and variety of weapons and targets. The DQN model presented in this paper transforms the WTA problem into a reinforcement learning issue, effectively addressing the issues of low efficiency and the propensity to fall into local optima associated with traditional heuristic algorithms in large-scale scenarios. © 2024 IEEE.

关键词： Deep reinforcement learning

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Effects of various information scenarios on layer-wise relevance propagation-based interpretable convolutional neural networks for air handling unit fault diagnosis

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Building Simulation 2024年第10期17卷 1709-1730页

作者： Chenglong Xiong Guannan Li Ying Yan Hanyuan Zhang Chengliang Xu Liang Chen School of Urban Construction Wuhan University of Science and TechnologyWuhan 430065China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science and TechnologyNanjing 210044China Shandong Key Laboratory of Intelligent Buildings Technology School of Information and Electrical EngineeringShandong Jianzhu UniversityJinan 250101China Key Laboratory of Low-grade Energy Utilization Technologies and Systems(Chongqing University) Ministry of Education of ChinaChongqing UniversityChongqing 400044 China State Key Laboratory of Green Building in Western China Xi’an University of Architecture&TechnologyXi’an 710055China

Deep learning(DL),especially convolutional neural networks(CNNs),has been widely applied in air handling unit(AHU)fault diagnosis(FD).However,its application faces two major ***,the accessibility of operational state variables for AHU systems is limited in practical,and the effectiveness and applicability of existing DL methods for diagnosis require further ***,the interpretability performance of DL models under various information scenarios needs further *** address these challenges,this study utilized publicly available ASHRAE RP-1312 AHU fault data and employed CNNs to construct three FD models under three various information ***,the layer-wise relevance propagation(LRP)method was used to interpret and explain the effects of these three various information scenarios on the CNN *** R-threshold was proposed to systematically differentiate diagnostic criteria,which further elucidates the intrinsic reasons behind correct and incorrect decisions made by the *** results showed that the CNN-based diagnostic models demonstrated good applicability under the three various information scenarios,with an average diagnostic accuracy of 98.55%.The LRP method provided good interpretation and explanation for understanding the decision mechanism of CNN models for the unlimited information *** the very limited information scenario,since the variables are restricted,although LRP can reveal key variables in the model’s decision-making process,these key variables have certain limitations in terms of data and physical explanations for further improving the model’s ***,an in-depth analysis of model parameters—such as the number of convolutional layers,learning rate,βparameters,and training set size—was conducted to examine their impact on the interpretative *** study contributes to clarifying the effects of various information scenarios on the diagnostic performance and interpr

关键词： air handling unit(AHU) fault diagnosis convolutional neural network(CNN) layer-wise relevance propagation(LRP) interpretation and explanation various information scenarios

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Silicon dioxide-protection boosting the peroxidase-like activity of Fe single-atom catalyst for combining chemo-photothermal therapy

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Nano Research 2024年第6期17卷 4924-4933页

作者： Yu Fan Yu Yi Hongpan Rong Jiatao Zhang Beijing Key Laboratory of Construction-Tailorable Advanced Functional Materials and Green Applications School of Materials Science&EngineeringBeijing Institute of TechnologyBeijing 100081China CAS Center for Excellence in Nanoscience CAS KeyLaboratory for Biomedical Effects of Nanomaterials and NanosafetyNational Center for Nanoscience and Technology(NCNST)Beijing 100190China Key Laboratory of Medical Molecule Science and Pharmaceutics Engineering Ministry of Industry and Information TechnologySchool of Chemistry&Chemical EngineeringBeijing Key Laboratory of Construction-Tailorable Advanced Functional Materials and Green ApplicationsSchool of Materials Science and EngineeringBeijing Institute of TechnologyBeijing 100081China

Carbon-based single-atom catalysts(SACs)have been widely studied in the field of biomedicine due to their excellent catalytic ***,carbon-based SACs usually aggregate during pyrolysis,which leads to the reduction of catalytic ***,we describe a method to improve the monodispersion of SACs using silicon dioxide as a protective *** decoration of silicon dioxide serves as a buffer layer for individual nanoparticles,which is not destroyed during the pyrolysis process,ensuring the single-particle dispersion of the nanoparticles after *** approach increased the hydroxyl groups on the surface of Fe-SAC(Fe-SAC-SE)and improved its water solubility,resulting in a four times enhancement of the peroxidase(POD)-like activity of Fe-SAC-SE(58.4 U/mg)than that of non-protected SACs(13.9 U/mg).The SiO_(2)-protection approach could also improve the catalytic activities of SACs with other metals such as Mn,Co,Ni,and Cu,indicating its generality for SACs *** advantage of the high POD-like activity,photothermal properties,and large specific surface area of Fe-SAC-SE,we constructed a synergistic therapeutic system(Fe-SAC-SE@DOX@PEG)for combining the photothermal therapy,catalytic therapy,and *** was verified that the photothermal properties of Fe-SAC-SE@DOX@PEG could effectively improve its POD-like activity,exhibiting excellent tumor-killing performance at the cellular *** work may provide a general approach to improve the performances of SACs for disease therapy and diagnosis.

关键词： single-atom catalysts peroxidase-like activity SiO2-protection photothermal therapy catalytic therapy chemotherapy

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A comprehensive survey of visible infrared person re-identification from an application perspective

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Multimedia Tools and Applications 2024年第42期83卷 90243-90270页

作者： Chang, Hua Xu, Xin Liu, Wei Lu, Lingyi Li, Weigang School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan430065 China School of Information Science and Engineering Wuhan University of Science and Technology Wuhan China

Person re-identification (ReID) is a significant issue in computer vision, aiming to match the same pedestrian across various cameras. Recent research on this issue has successfully reached a satisfactory performance level in the daytime scenario. However, the performance of ReID in low illumination scenarios suffers a dramatic degradation due to the insufficient personal information captured by visible cameras in low or no light scenarios. To address this challenge, the adoption of novel camera technologies for more effective pedestrian data is imperative. Owing to the constraints of distance and usage scenarios in deploying depth cameras, practical infrared cameras are frequently utilized, spurring research in visible infrared person re-identification (VI-ReID). Significant progress has been made in the field of VI-ReID in recent years, but only a few reviews have categorized this problem based on methodology. This article offers a novel perspective by classifying VI-ReID according to application scenarios. Following our categorization scheme, the main advancements in deep learning for VI-ReID over the past five years have been grouped into three categories as follows: retrieval of infrared images using visible images (V→IR), retrieval of visible images using infrared images (IR→V), and joint retrieval of infrared and visible images (V→IR and IR→V). Next, this survey outlines the available datasets and evaluation metrics. After that, we categorize diverse methodologies under the proposed classification and assess their performance metrics across common datasets. Finally, we present two prospective avenues for future research in this domain, informed by the current state of advancement. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Infrared imaging

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Adaptive dynamic surface control for quadrotor-slung load transportation system with uncertainties

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science China(Technological sciences) 2023年第10期66卷 2917-2929页

作者： DING Feng HUANG Jian SUN Chong AI Yong XU WenXia YANG ChenGuang School of Computer Science South-Central Minzu UniversityWuhan 430074China Key Laboratory of Ministryof EducationforImage Processing and Intelligent ControlHubei Key Laboratoryof Brain-inspired Intelligent Systems School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China School of Computer Science and Engineering Wuhan Institute of TechnologyWuhan 430205China Key Laboratory of Ministry of Education for Autonomous Systems and Networked Control Guangdong Engineering Technology Research Center of Control of Intelligent SystemsCollege of Automation Science and EngineeringSouth China University of TechnologyGuangzhou 510640China

Quadrotors play a significant role in our lives and are transforming our *** cable-suspended loads is an unavoidable quadrotor application trend and a hot research topic in the control ***,the load swing and unpredictability pose significant challenges to the quadrotor's *** this paper,an anti-swing controller with an inner-outer control strategy for the quadrotor-slung load transportation system is *** facilitate the controller design,the outer position dynamics are restructured in the form of ***,a virtual controller is created to force the underactuated states to the dynamic surface to ensure the position subsystem's *** improve robustness,an adaptive law is used to eliminate the effects of uncertain cable ***,a dynamic surface controller for the inner attitude subsystem is presented to drive the actual force to the virtual *** is demonstrated that the control strategy can stabilize the quadrotor despite mass and cable length *** results are provided to demonstrate the efficacy and durability of the proposed method.

关键词： quadrotor-slung load transportation system dynamic surface control adaptive law cascade system model uncertainties

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Safe motion planning and formation control of quadruped robots

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Autonomous Intelligent systems 2024年第1期4卷 38-49页

作者： Zongrui Ji Yi Dong College of Electronic and Information Engineering National Key Laboratory of Autonomous Intelligent Unmanned SystemsFrontiers Science Center for Intelligent Autonomous SystemsMinistry of EducationTongji UniversityShanghai 200092China

This paper introduces a motion planning and cooperative formation control approach for quadruped robots and multi-agent ***,in order to improve the efficiency and safety of quadruped robots navigating in complex environments,this paper proposes a new planning method that combines the dynamic model of quadruped robots and a gradient-optimized obstacle avoidance strategy without Euclidean Signed Distance *** framework is suitable for both static and slow dynamic obstacle environments,aiming to achieve multiple goals of obstacle avoidance,minimizing energy consumption,reducing impact,satisfying dynamic constraints,and ensuring trajectory *** approach differs in that it reduces energy consumption throughout the movement from a new ***,this method effectively reduces the impact of the ground on the robot,thus mitigating the damage to its ***,we combine the dynamic control barrier function and the virtual leader-follower model to achieve efficient and safe formation control through model predictive ***,the proposed algorithm is validated through both simulations and real-world scenarios testing.

关键词： Efficient motion planning Dynamic model Safety-critical Model predictive control

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