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6G Technology in Intelligent Healthcare: Smart Health and Its Security and Privacy Perspectives

ieee WIRELESS COMMUNICATIONS 2025年第1期32卷 116-121页

作者： Arastouei, Narges Khan, Md Arman Goethe Univ Frankfurt Frankfurt Germany

This article explores the integration of 6G technology in intelligent healthcare, focusing on crucial security and privacy concerns amidst the growing demand for advanced solutions driven by aging populations. Recognizing the potential of 6G's high bandwidth, low latency, and enhanced security features, this article proposes a multi-layered security and privacy framework incorporating artificial intelligence, machine learning, and distributed ledger technology to safeguard sensitive patient data within the 6G ecosystem. This holistic framework addresses potential vulnerabilities in emerging 6G healthcare applications, such as holographic telepresence and digital twin technology, by implementing robust encryption, authentication, and access control mechanisms. Furthermore, the article examines the transformative impact of 6G on these applications, enabling real-time remote diagnosis and personalized treatment plans with enhanced security and privacy. By addressing the unique security challenges and unlocking novel use cases, this article provides a multi-layer framework for realizing the full potential of 6G in revolutionizing healthcare delivery.

关键词： 6G mobile communication Government policies Privacy Telepresence Smart healthcare Medical services Machine learning Real-time systems Security Low latency communication

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A Novel Intelligent Online Fire Monitoring Approach for Underground Pipe Galleries 13

A Novel Intelligent Online Fire Monitoring Approach for Unde...

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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Wang, Yongjing Wen, Chenying Zhu, Zheren Zhang, Xinming Song, Zhihuan Zhu, Zugang Fan, Xueer Zhejiang University The State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Hangzhou310027 China Hangzhou Underground Pipeline Development Co. Ltd Hangzhou310064 China

ISBN: (纸本)9798350361674

Underground pipe galleries are constructed beneath urban areas, providing pathways for the installation of various municipal pipelines. These galleries help protect pipelines and efficiently utilize underground space, serving as a significant facilitator for the construction of smart cities. Due to the confined space inside the galleries and the simultaneous presence of multiple pipelines, there is a potential safety hazard of fire incidents. To ensure the safe operation of pipelines, online monitoring and timely warning of fire incidents are valuable tasks. Current traditional computer vision and machine learning approaches for object detection often experience slow response time. Therefore the You Only Look Once (YOLO) series due to its fast response time, has become a popular method in real-time detection. Nonetheless, YOLOv8 still suffer from information fusion problems. In response to this issue, this paper proposed YOLOH algorithm, which improves YOLOv8 by employing the Gather-and-Distribute (GD) mechanism for intelligent online monitoring of fire incidents in underground pipe galleries. Finally, the effectiveness of YOLOH was validated using a combination of publicly available fire datasets and simulated fire datasets in underground pipe galleries. The YOLOH attains a 63.2 mAP, surpassing the YOLOv8-N baseline by +2.15 mAP. © 2024 ieee.

关键词： Fires

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A data-enabled Dual learning based Online Receding Horizon Safe-Critical control for Nonlinear systems under Uncertainty 76

A Data-enabled Dual Learning based Online Receding Horizon S...

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76th Annual ieee National Aerospace and Electronics conference (NAECON)

作者： Dey, Shawon Xu, Hao Univ Nevada Dept Elect & Biomed Engn Reno NV 89557 USA

ISBN: (纸本)9798350367638;9798350367621

In this paper, a real-time reliable receding horizon control (RHC) with a guaranteed safe adaptation mechanism is developed for uncertain complex nonlinear systems. Ensuring receding horizon optimality and safety, particularly in the presence of uncertain nonlinear system dynamics, poses a significant challenge in both control and learning societies. To tackle this challenge, a novel safe-critical RHC framework has been developed to enhance classical RHC with the capability of prioritizing system safety by timely recognizing and adapting environmental uncertainties. Specifically, the developed frame-work utilizes a novel dual-learning approach with slow learning to recognize environmental uncertainties and further refine RHC along with a situation-aware physics-informed neural network (SA-PINN), and fast learning to ensure system safety by using a safe-critical control with fast learned adaptive control barrier (FA-CBF) function. Therefore, slow learning in the developed dual-learning approach can provide optimal RHC albeit with longer computation time, while the fast learning component provides safe control effectively adapting to the uncertain environment in real-time.

关键词： Contrastive learning

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Safety-Certified Self-Triggered Cooperative Path Following control via data-driven learning and Neurodynamic Optimization

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ieee TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 2595-2606页

作者： Cong, Siming Wang, Dan Gu, Nan Li, Tieshan Peng, Zhouhua Dalian Maritime Univ Sch Marine Elect Engn Dalian 116026 Peoples R China Dalian Key Lab Swarm Control & Elect Technol Inte Dalian 116026 Peoples R China State Key Lab Maritime Technol & Safety Dalian 116026 Peoples R China Univ Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Peoples R China

This paper investigates the safety-certified cooperative path following problem for second-order nonlinear systems with multiple-input multiple-output strict-feedback form subject to safety, state, and input constraints. A safety-certified learning control approach is proposed to achieve collision-free cooperative path following based on command optimization, online learning, and self-triggered communication. Specifically, an extended-state-observer-aided learning neural predictor is developed to simultaneously identify nonlinear functions and unknown input gains without measuring state derivatives. control barrier functions are then employed to ensure safety through forward invariant sets. Next, command optimization is utilized to generate the optimal virtual control signals that satisfy safety constraints, state constraints, and input constraints. A neurodynamic optimization technique is employed to solve the quadratic optimization problem in real time. Additionally, a self-triggered mechanism is introduced in path variable coordination to reduce the listening and triggering times. By using the proposed safety-certified cooperative path following approach, a safe formation is guaranteed for input-to-state safety. Simulation results are provided to illustrate the effectiveness of the proposed method. Note to Practitioners-This paper addresses the safety-certified cooperative path following problem of multi-agent systems, which has practical implications in various applications. These applications include formation patrol, cargo transportation, search and rescue missions, swarm robotics, and agricultural tasks. By coordinating the movements of multiple agents, cooperative path following enhances efficiency in these scenarios. The challenges posed by safety, state, and input constraints are commonly encountered in practical. To address these challenges, the command optimization approach is proposed in this paper. The optimization problem is efficiently solved in real

关键词： Cooperative path following command optimization online learning safety-certified control self-triggered communication

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Transferability-Oriented Adversarial Robust Security-Constrained Optimal Power Flow

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ieee TRANSACTIONS ON SMART GRID 2024年第5期15卷 5169-5181页

作者： Zuo, Ke Sun, Mingyang Zhang, Zhenyong Cheng, Peng Strbac, Goran Kang, Chongqing Zhejiang Univ Control Sci & Engn Hangzhou 310058 Peoples R China Peking Univ Coll Engn Dept Ind Engn & Management Beijing 100091 Peoples R China Imperial Coll London Dept Elect & Elect Engn London SW7 2AZ England Guizhou Univ Dept Comp Sci & Technol Guiyang 550025 Peoples R China Imperial Coll London Dept Elect & Elect Engn London SW7 2AZ England Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

Security-constrained optimal power flow (SCOPF) aims to achieve an economical operation while considering the security issues during contingencies. data-driven security assessment methods can provide security constraints for SCOPF and have been widely developed in the smart grid community to deal with the challenges arising from the increased penetration of renewable energy resources and the deployment of power electronic devices. Nevertheless, it has been recognized that the machine learning model is vulnerable to adversarial examples, and the power system communication network is prone to cyber attacks, which affects the decision-making of security assessment and SCOPF. To this end, a transferability-oriented adversarial robust security-constrained optimal power flow (TOAR-SCOPF) is proposed to diminish the potential security risks from attacks, formulated as an attacker-defender problem. To alleviate the conservatism of TOAR-SCOPF, a more realistic scenario with unknown features and unknown selected machine learning models is considered in this work by developing a novel transferability-oriented adversarial attack (TOA) method for data-driven security assessment while considering the power system's physical constraints to bypass the bad data detection mechanism. Case studies are conducted based on the ieee 39-bus and ieee 68-bus systems, respectively, to explore the vulnerability and demonstrate the effectiveness of the proposed TOAR-SCOPF.

关键词： Power system stability Transient analysis Security Robustness Load flow Stability analysis Closed box Machine learning robustness cyber security transferability-oriented adversarial attack security-constrained optimal power flow

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data-driven stabilization of non-zero equilibrium for polynomial systems 63

Data-driven stabilization of non-zero equilibrium for polyno...

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63rd ieee conference on Decision and control, CDC 2024

作者： Liu, Yixuan Guo, Meichen Delft University of Technology Delft Center for Systems and Control Delft2628 CD Netherlands

ISBN: (纸本)9798350316339

Most existing work on direct data-driven stabilization considers the equilibrium at the origin. When the desired equilibrium is not the origin, existing data-driven approaches often require performing coordinate transformation, or adding integrator action to the controller. As an alternative, this work addresses data-driven state feedback stabilization of any given assignable equilibrium via dissipativity theory. We show that for a polynomial system, if a data-driven stabilizer can be designed to render the origin globally asymptotically stable, then by modifying the stabilizer, we obtain a stabilizer for any given assignable equilibrium. © 2024 ieee.

关键词： State feedback

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Full Wavelength Selection and Deep Spectra Feature Extraction Network for Near-Infrared Spectroscopy Quantitative Analysis 13

Full Wavelength Selection and Deep Spectra Feature Extractio...

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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Liu, Jinlong Luan, Xiaoli Liu, Fei Jiangnan University Key Laboratory for Advanced Process Control of Light Industry of the Ministry of Education Jiangsu Wuxi214122 China

ISBN: (纸本)9798350361674

Near-infrared spectroscopy (NIRS) has been widely used in the chemical industry and has attracted much attention because of its fast and non-destructive characteristics. However, spectra are susceptible to interference from external environmental factors, leading to spectral overlap and noise problems. To solve these problems, spectral wavelength selection can be performed. Traditional selection methods require manually choosing the appropriate method based on spectral data and combining it with appropriate regression modeling, which is complicated and time-consuming. In contrast, this study proposes a novel spectral wavelength selection method that intelligently filters the entire wavelength based on the correlation between spectra and output variables. Simultaneously, it utilizes a deep spectra feature extraction module to obtain a more abstract spectral representation applied in regression prediction. Evaluation results on the training and test sets demonstrate the outstanding performance of this model: RMSE of 0.007796 and 0.01036 for the training and test sets, respectively, R2 of 0.9918 and 0.9842, with an impressive RPD of 7.9703. These metrics indicate that this model meets the rigorous requirements for high-performance models in the chemical engineering domain. © 2024 ieee.

关键词： Near infrared spectroscopy

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ROAR: Routing Packets in P4 Switches With Multi-Agent Decisions Logic 1

ROAR: Routing Packets in P4 Switches With Multi-Agent Decisi...

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1st ieee International conference on Machine learning for Communication and Networking (ieee ICMLCN)

作者： Angi, Antonino Sacco, Alessio Esposito, Flavio Marchetto, Guido Politecn Torino Dept Control & Comp Engn Turin Italy St Louis Univ Dept Comp Sci St Louis MO 63103 USA

ISBN: (纸本)9798350343205;9798350343199

The soaring complexity of networks has led to more complex methods to efficiently manage and orchestrate the multitude of network environments. Recent advances in machine learning (ML) have opened new opportunities for network management automation, exploiting existing advances in software-defined infrastructures. Advanced routing strategies have been proposed to accommodate the traffic demand of interactive systems, where the common architecture is composed of a data-driven network management schema collecting network data that feed a reinforcement learning (RL) algorithm. However, the overhead introduced by the SDN controller and its operations can be mitigated if the networking architecture is redesigned. In this paper, we propose ROAR, a novel architectural solution that implements Deep Reinforcement learning (DRL) inside P4 programmable switches to perform adaptive routing policies based on network conditions and traffic patterns. The network devices act independently in a multi-agent reinforcement learning (MARL) framework but are able to learn cooperative behaviors to reduce the queuing time of transmitting packets. Experimental results show that for an increasing amount of traffic in the network, there is both a throughput and delay improvement in the transmission compared to traditional approaches.

关键词： deep reinforcement learning P4 routing

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data-driven ROBUST OPTIMAL ITERATIVE learning control OF LINEAR systems WITH STRONG CROSS-AXIS COUPLING

DATA-DRIVEN ROBUST OPTIMAL ITERATIVE LEARNING CONTROL OF LIN...

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American control conference (ACC)

作者： Zhang, Zezhou Zou, Qingze Rutgers State Univ Dept Mech & Aerosp Engn Piscataway NJ 08854 USA

ISBN: (纸本)9798350328066

In this paper, a data-driven iterative learning control approach to multi-input-multi-output (MIMO) systems with strong cross-axis coupling is proposed. Iterative learning control (ILC) of MIMO systems with strong cross-axis coupling effect is challenging as model-based ILC to MIMO systems is complicated by the modeling process of MIMO systems being involved and time-consuming, the trade-off between the model accuracy and the performance, and the limitation to systems with weak-cross-coupling. Contrarily, constant gain ILC methods are mainly effective for tracking at low-speed, and suffer from slow convergence, particularly in the presence of the random disturbances. Thus, the aim of this paper is to develop a data-driven robust optimal ILC (DDRO-ILC) approach to MIMO systems of strong cross-axis coupling under random output disturbance. The iteration gain is constructed and updated by using past input and output data to capture the dynamics of the system via the singular value decomposition (SVD) technique. It is shown that monotonic convergence in the presence of random disturbance is guaranteed, and an optimal gain can be obtained to maximize the convergence rate and minimize the residual error. The proposed DDRO-ILC technique is illustrated through a numerical simulation on a three-input three-output linear time invariant system model, and compared to the multi-axis inversion-based iterative control (MAIIC) technique. The simulation shows that the proposed DDRO-ILC outperformed the MAIIC method when the cross-axis coupling is strong, and achieved precision tacking with rapid convergence in the presence of random disturbance.

关键词： Linear time-invariant system

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Detection of Melanoma Based on Improved YOLOv8 13

Detection of Melanoma Based on Improved YOLOv8

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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Ji, Honghai Zheng, Xuechen Xiong, Shuangshuang Fan, Lingling Wang, Li School of Electronics & Control Engineering North China University of Technology Beijing100144 China School of Automation Beijing Information Science and Technology University Beijing100096 China

ISBN: (纸本)9798350361674

The incidence of skin cancer is increasing globally, with melanoma being the most formidable form of skin cancer. Melanoma originates from melanocytes, which exhibit a high degree of visual similarity. Moreover, dermoscopy image data often suffer from hair occlusion and the lesion area appears relatively small in the images, making it challenging for traditional methods to achieve accurate recognition and detection results. In this regard, we propose a YOLOv8 framework based on multi-scale sequence fusion(MSSF) for melanoma detection. This approach enhances the Neck structure of YOLOv8 and improves the network's ability to extract multi-scale information by introducing the Multi-scale Sequence Fusion module. In addition, we implemented a morphological black hat based hair processing method to reduce hair occlusion. In the ISIC datasets, compared with the unimproved algorithm, the mean average precision(Map) at 50 IoU is increased by 5.7%, Map between 50 and 95 IoU is increased by 7.5%, and the number of parameters is decreased by 12.5%. © 2024 ieee.

关键词： Lung cancer

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