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Research on resource allocation methods for traditional Chinese medicine services based on deep reinforcement learning

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Neural Computing and Applications 2025年第3期37卷 1601-1616页

作者： Ma, Yuntao Fang, Xiaolin Qi, Jin Sun, Yanfei School of Computer Science and Engineering Southeast University Jiangsu Nanjing China School of Internet of Things Nanjing University of Posts and Telecommunications Jiangsu Nanjing China

Chinese medicine resources are the crystallization of traditional Chinese culture, and more and more people are choosing Chinese medicine services for their health. To address the problems of pluralistic heterogeneity, waste of service resources, and lagging demand response in the resource allocation model for traditional Chinese medicine (TCM) services, a deep reinforcement learning-based resource allocation method for TCM services is proposed. To address the fragmentation of TCM service resources, this paper presents a TCM service resource association method based on improved spectral clustering and establishes a good resource-demand matching model. For the problem of TCM service resource allocation after resource association, we establish a TCM service resource allocation model and collaboratively solve the TCM service resource allocation problem via the deep reinforcement learning method. The results show that the proposed solution can accelerate the demand response of TCM service resources, effectively reduce the cost of TCM services for patients, improve the quality of TCM services, and satisfy the demand for TCM services for patients. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Deep reinforcement learning

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Target Detection-Oriented RGCN Inference Enhancement Method

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computers, Materials & Continua 2025年第4期83卷 1219-1237页

作者： Lijuan Zhang Xiaoyu Wang Songtao Zhang Yutong Jiang Dongming Li Weichen Sun School of Internet of Things Engineering Wuxi UniversityWuxi214105China College of Computer Science and Engineering Changchun University of TechnologyChangchun130012China Computer Science and Technology at Guohao Academy Tongji UniversityShanghai200092China China North Vehicle Research Institute Beijing100072China

In this paper,a reasoning enhancement method based on RGCN(Relational Graph Convolutional Network)is proposed to improve the detection capability of UAV(Unmanned Aerial Vehicle)on fast-moving military targets in urban battlefield *** combining military images with the publicly available VisDrone2019 dataset,a new dataset called VisMilitary was built and multiple YOLO(You Only Look Once)models were tested on *** to the low confidence problem caused by fuzzy targets,the performance of traditional YOLO models on real battlefield images decreases ***,we propose an improved RGCN inference model,which improves the performance of the model in complex environments by optimizing the data processing and graph network *** results show that the proposed method achieves an improvement of 0.4%to 1.7%on mAP@0.50,which proves the effectiveness of the model in military target *** research of this paper provides a new technical path for UAV target detection in urban battlefield,and provides important enlightenment for the application of deep learning in military field.

关键词： RGCN target detection urban battlefield YOLO visual reasoning

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Optimizing System Latency for Blockchain-Encrypted Edge Computing in internet of Vehicles

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computers, Materials & Continua 2025年第5期83卷 3519-3536页

作者： Cui Zhang Maoxin Ji Qiong Wu Pingyi Fan Qiang Fan School of Internet of Things Engineering Wuxi Institute of TechnologyWuxi214121China School of Internet of Things Engineering Jiangnan UniversityWuxi214122China Department of Electronic Engineering Beijing National Research Center for Information Science and TechnologyTsinghua UniversityBeijing100084China Qualcomm San JoseCA 95110USA

As internet of Vehicles(IoV)technology continues to advance,edge computing has become an important tool for assisting vehicles in handling complex ***,the process of offloading tasks to edge servers may expose vehicles to malicious external attacks,resulting in information loss or even tampering,thereby creating serious security *** technology can maintain a shared ledger among *** the Raft consensus mechanism,as long as more than half of the nodes remain operational,the system will not collapse,effectively maintaining the system’s robustness and *** protect vehicle information,we propose a security framework that integrates the Raft consensus mechanism from blockchain technology with edge *** address the additional latency introduced by blockchain,we derived a theoretical formula for system delay and proposed a convex optimization solution to minimize the system latency,ensuring that the system meets the requirements for low latency and high *** results demonstrate that the optimized data extraction rate significantly reduces systemdelay,with relatively stable variations in ***,the proposed optimization solution based on this model can provide valuable insights for enhancing security and efficiency in future network environments,such as 5G and next-generation smart city systems.

关键词： Blockchain edge computing internet of vehicles latency optimization

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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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Reflective clothing detection based on YOLOv5s fused with SE attention mechanism

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International Journal of Reasoning-based Intelligent Systems 2025年第1期17卷 9-16页

作者： Wang, Yuanyuan Gu, Dingyun Zhu, Yemeng Zhang, Zhihan Cao, Jiahui Zhang, Haiyan School of Computer and Software Engineering Huaiyin Institute of Technology Huaian223001 China Engineering Laboratory of Mobile Interconnection Technology for Internet of Things Jiangsu Province Huai’an Huaian City223001 China

This paper proposes a YOLOv5s deep learning algorithm incorporating the SE attention mechanism to address the issue of workers failing to wear reflective clothing on duty, which has resulted in casualties from time to time. The YOLOv5s model is used to train the reflective clothing dataset obtained by collecting images of construction sites, and the SE attention mechanism module is added to the network structure of YOLOv5 for improved performance. The reflective clothing detection algorithm is then deployed to the web for real-time detection. The experimental results show that the algorithm achieves mAP value of 96.70% with the SE attention module, resulting in an accuracy improvement of 1.09% compared to the model without the SE module. This enables practical applications to reduce the incidence of construction accidents at construction sites. Coрyright © 2025 Inderscience Enterрrises Ltd.

关键词： attention mechanism flask reflective clothing detection vue YOLOv5

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E-Waste Management using Deep Learning 6

E-Waste Management using Deep Learning

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Kumar, Boddu Manoj Sujitha, Kaduru Abinaya, K. School of Computer Science Engineering with Blockchain Technology Sathyabama Institute of Science and Technology Tamil Nadu Chennai600119 India School of Computer Science Engineering with Internet of Things Sathyabama Institute of Science and Technology Tamil Nadu Chennai600119 India School of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai600119 India

ISBN: (纸本)9798331522667

Using the YOLO v8 model and deep learning approaches, this study explores the field of e-waste management and provides effective item detection. Our research attempts to increase the accuracy and scalability of e-waste detection, simplifying its incorporation into current waste management systems. Through utilizing deep learning, our goal is to transform the way that electronic waste is categorized, from tiny parts to larger appliances, allowing for more efficient as well as environmentally friendly management techniques. When it comes to aiming for higher performance, privacy comes first. Methods of preservation guarantee the accuracy of sensitive data correlated with pictures of e-waste. By means of execution utilizing data anonymization and encryption methods, we ensure compliance with legal requirements while safeguarding against potential privacy threats and ethical issues. The YOLO v8 model achieved an average precision (Map) of 97.1% in detecting e-waste items, demonstrating its effectiveness and scalability for real-world applications. © 2025 IEEE.

关键词： Differential privacy

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Temporal Logic-Guided DQN for Object Delivery and Transportation Using Magnetic Microrobots With Local Control

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IEEE Robotics and Automation Letters 2025年第6期10卷 5441-5448页

作者： Hou, Zhe Liu, Yueyue Fan, Qigao Jiangnan University School of Internet of Things Engineering Wuxi214122 China Guangxi University of Science and Technology Guangxi key laboratory of Automobile Components and Vehicle Technology Liuzhou545006 China

Microrobots driven by magnetic fields hold significant promise in medical applications, particularly in drug delivery–a key area in the biomedical field. Traditional approaches primarily rely on global magnetic fields to control single microrobots, which makes it challenging to independently control multiple microrobots. To address this limitation, this letter introduces a local magnetic field generation system that enables independent control of multiple microrobots. The proposed system employs a printed circuit board (PCB) array-based magnetic microrobot system, utilizing a microcoil array for precise and localized control. To enhance task execution, we integrate Deep Reinforcement Learning (DRL) with Linear Temporal Logic (LTL) to generate obstacle-avoiding paths for single and dual magnetic microrobots. The system is validated through magnetic droplet transport experiments. Experimental results demonstrate the effectiveness of the proposed system in achieving autonomous multi-task drug delivery and droplet fusion. This work underscores the potential of magnetic field-driven microcoil array systems in advancing transportation and drug delivery technologies in biomedical engineering. © 2016 IEEE.

关键词： Deep reinforcement learning

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Multi-Orbit Circumnavigation Control of UAVs With Arbitrary Angular Spacing in Three-Dimensional Space

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IEEE Transactions on Network science and engineering 2025年第3期12卷 1540-1551页

作者： Luo, Yanhong Wang, Zhen Zhang, Huaguang Xie, Xiangpeng Northeastern University State Key Laboratory of Synthetical Automation for Process Industries School of Information Science and Engineering Shenyang110004 China Nanjing University of Posts and Telecommunications School of Internet of Things Nanjing21003 China

In this paper, a novel multi-orbit circumnavigation control law is proposed for a group of UAVs with arbitrary angular spacing based on local information. By constructing the actual relative velocity and the ideal relative velocity in three dimensional space, the circumnavigation control problem of moving target is first transformed into a relative velocity tracking problem. Further, to remove the limitation that the UAVs require the global information of the moving target, the corresponding fixed-time convergent estimators with the cyclic directed graph are developed. Based on these distributed estimators and relative kinematics equations, the dynamic linear feedback is introduced to derive the distributed control law. At the same time, the angular spacing update law and the circumnavigation radius coefficient are designed, so that the UAVs can circumnavigate on multi-orbit with any angular spacing. Finally, the exponential stability of the control law is proved by Lyapunov method, and the effectiveness of the control law is verified by five examples. © 2013 IEEE.

关键词： Lyapunov methods

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MS-Loc: Toward Pervasive Indoor Localization Utilizing Mobile Single-Site

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IEEE internet of things Journal 2025年第10期12卷 14188-14201页

作者： Nie, Wendi Wang, Xiaoyang Liu, Zexing Duan, Yaoxin Lam, Kam-Yiu Liu, Kai Ng, Joseph K. Y. Xue, Chun Jason Chongqing University of Posts and Telecommunications Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education China China Institute of Industrial Internet Chongqing401120 China Chongqing University of Posts and Telecommunications School of Automation/School of Industrial Internet Chongqing40065 China City University of Hong Kong Computer Science Department Hong Kong Chongqing University College of Computer Science Chongqing400030 China Hong Kong Baptist University Computer Science Department Hong Kong Mohamed Bin Zayed University of Artificial Intelligence Department of Computer Science Abu Dhabi United Arab Emirates School of Automation School of Industrial Internet Chongqing40065 China

Leveraging the widespread deployment of existing WiFi sites, WiFi-based techniques offer substantial potential for achieving pervasive indoor localization among various indoor localization techniques. Conventional WiFi-based indoor localization techniques primarily focus on providing fine-grained accuracy. However, previous techniques are not pervasive due to the following constraints: 1) they can hardly be implemented in environments with limited resources of WiFi sites;and 2) they are constrained by high hardware requirements, such as the need for multiple antennas. In this paper, we propose a novel technique called Mobile Single-site Localization (MS-Loc), which leverages a mobile single-site to perform indoor localization. Specifically, MS-Loc utilizes existing hardware at off-the-shelf mobile WiFi sites to achieve pervasive localization rather than relying on multiple sites or multiple antennas. Moreover, in MS-Loc, a tailor-designed path planning algorithm guides the movement of the mobile single-site to locate targets quickly and accurately. We conducted extensive experiments using a real-world testbed. The experimental results demonstrate that MS-Loc presents a competitive localization accuracy compared to previous techniques but is pervasive. © 2014 IEEE.

关键词： Wi-Fi

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An integrated strategy of AEF attribute evaluation for reliable thunderstorm detection

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Digital Communications and Networks 2025年第1期11卷 234-245页

作者： Xu Yang Hongyan Xing Xinyuan Ji Xin Su Witold Pedrycz School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing 210044China Department of Electrical and Computer Engineering University of AlbertaEdmonton AB T6G 2R3Canada College of Internet of Things Engineering Hohai UniversityChangzhou 213022China Systems Research Institute Polish Academy of Sciences00-901 WarsawPoland Department of Computer Engineering Faculty of Engineering and Natural SciencesIstinye UniversitySariyer/IstanbulTurkey

Thunderstorm detection based on the Atmospheric Electric Field(AEF)has evolved from time-domain models to space-domain *** is especially important to evaluate and determine the particularly Weather Attribute(WA),which is directly related to the detection reliability and *** this paper,a strategy is proposed to integrate three currently competitive WA's evaluation ***,a conventional evaluation method based on AEF statistical indicators is *** evaluation approaches include competing AEF-based predicted value intervals,and AEF classification based on fuzzy *** AEF attributes contribute to a more accurate AEF classification to different *** resulting dynamic weighting applied to these attributes improves the classification *** evaluation method is applied to evaluate the WA of a particular AEF,to obtain the corresponding evaluation *** integration in the proposed strategy takes the form of a score *** cumulative score levels correspond to different final WA *** imaging is performed to visualize thunderstorm activities using those AEFs already evaluated to exhibit thunderstorm *** results confirm that the proposed strategy effectively and reliably images thunderstorms,with a 100%accuracy of WA *** is the first study to design an integrated thunderstorm detection strategy from a new perspective of WA evaluation,which provides promising solutions for a more reliable and flexible thunderstorm detection.

关键词： Atmospheric electric field(AEF) Thunderstorm Attribute Fuzzy c-means Imaging

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