Active Micro-Nano-Collaborative Bioelectronic Device for Advanced Electrophysiological Recording

The development of precise and sensitive electrophysiological recording platforms holds the utmost importance for research in the fields of cardiology and neuroscience.In recent years,active micro/nano-bioelectronic devices have undergone significant advancements,thereby facilitating the study of electrophysiology.The distinctive configuration and exceptional functionality of these active micro-nano-collaborative bioelectronic devices offer the potential for the recording of high-fidelity action potential signals on a large scale.In this paper,we review three-dimensional active nano-transistors and planar active micro-transistors in terms of their applications in electroexcitable cells,focusing on the evaluation of the effects of active micro/nano-bioelectronic devices on electrophysiological signals.Looking forward to the possibilities,challenges,and wide prospects of active micro-nano-devices,we expect to advance their progress to satisfy the demands of theoretical investigations and medical implementations within the domains of cardiology and neuroscience research.

A Device to Improve Collaborative Practices of Sanitation Department Agents of a Territory: The Case of the Lamkansa District in Casablanca

The aim of this article is to strengthen and improve the collaboration between professional agents of a service that manages one of the technical processes acting on a given territory by synchronizing the spatio-temporal dimensions including all agents assembled for the task. This proposal was tested in the Lamkansa neighborhood in Casablanca, Morocco. The employed approach is based GIS resources and on systemic analysis of communication present in a territory. We were inspired by several methodological developments that carried multi-actor processes in land use planning. We focused our work on strengthening the collaboration between professionals, field agents and office agents, in the process of design and monitoring the liquid sanitation system. The device is based on geolocation and synchronous feedback of topological, geographical, and multimedia data related to the liquid sanitation network. Thanks to a geo-collaborative, participative, and motivating logic, we reduced the management costs of the network and made it faster and more efficient by equally mobilizing another type of non-specialized actors (the inhabitants). This device uses spatial and temporal dimensions to consolidate collaborative work tools through ICT and GIS technologies that thematize and exchange information collected in the field. Furthermore, this device raises great interest as it entails the concept of integration of several actors in a geo-collaborative mode while combining geomatics with communication and information sciences.

A Multi-Layer Collaboration Framework for Industrial Parks with 5G Vehicle-to-Everything Networks

The fifth-generation(5G)wireless communication networks are expected to play an essential role in the transformation of vertical industries.Among many exciting applications to be enabled by 5G,logistics tasks in industry parks can be performed more efficiently via vehicle-to-everything(V2X)communications.In this paper,a multi-layer collaboration framework enabled by V2X is proposed for logistics management in industrial parks.The proposed framework includes three layers:a perception and execution layer,a logistics layer,and a configuration layer.In addition to the collaboration among these three layers,this study addresses the collaboration among devices,edge servers,and cloud services.For effective logistics in industrial parks,task collaboration is achieved through four functions:environmental perception and map construction,task allocation,path planning,and vehicle movement.To dynamically coordinate these functions,device–edge–cloud collaboration,which is supported by 5G slices and V2X communication technology,is applied.Then,the analytical target cascading method is adopted to configure and evaluate the collaboration schemes of industrial parks.Finally,a logistics analytical case study in industrial parks is employed to demonstrate the feasibility of the proposed collaboration framework.

A cloud-edge-device collaborative offloading scheme with heterogeneous tasks and its performance evaluation

How to collaboratively offload tasks between user devices,edge networks(ENs),and cloud data centers is an interesting and challenging research topic.In this paper,we investigate the offoading decision,analytical modeling,and system parameter optimization problem in a collaborative cloud-edge device environment,aiming to trade off different performance measures.According to the differentiated delay requirements of tasks,we classify the tasks into delay-sensitive and delay-tolerant tasks.To meet the delay requirements of delay-sensitive tasks and process as many delay-tolerant tasks as possible,we propose a cloud-edge device collaborative task offoading scheme,in which delay-sensitive and delay-tolerant tasks follow the access threshold policy and the loss policy,respectively.We establish a four-dimensional continuous-time Markov chain as the system model.By using the Gauss-Seidel method,we derive the stationary probability distribution of the system model.Accordingly,we present the blocking rate of delay-sensitive tasks and the average delay of these two types of tasks.Numerical experiments are conducted and analyzed to evaluate the system performance,and numerical simulations are presented to evaluate and validate the effectiveness of the proposed task offloading scheme.Finally,we optimize the access threshold in the EN buffer to obtain the minimum system cost with different proportions of delay-sensitive tasks.

A Weighted Mean Field Reinforcement Learning Algorithm for Large-Scale Multi-Agent Collaboration

Reinforcement learning has been proven to be an effective approach for solving multi-agent coordination problems in a dynamic open environment.For dealing with multi-agent cooper-ation issues,the mean field multi-agent reinforcement leaming method can better overcome the problems of slow learning speed,unstable convergent performance,and poor learning effect.However,the original mean field algorithm cannot extract features well when agents cooperate.In order to solve the large-scale multi-agent coordination problem,in this paper,the mean field multi-agent reinforcement learning algorithm is improved and optimized by combining the multi-head attention mechanism,and the attention-based mean field(MFA)structure is designed.The employment of a multi-head attention mechanism can optimize the interaction among agents,extract more effective cluster features and enable agents to learn more efficient strategies.This paper first introduces the framework structure of MFA and then expounds on the relevant theoretical basis based on the Q-Learning and Actor-Critic algorithms,and finally conducts large-scale multi-agent cooperative experiments on the MAgent platform.The ex-perimental results show that compared with the baseline algorithm,the attention-based mean field Q-learning(MFQA)and attention-based Actor-Critic(MFACA)algorithms can make large-scale multi-agent clusters converge to higher rewards,and perform better than the original mean field multi-agent algorithm.

面向云边端协同的多模态数据建模技术及其应用

云边端协同架构中数据类型多样,各级存储资源与计算资源存在差异,给数据管理带来新的挑战.现有数据模型或者数据模型的简单叠加,都难以同时满足云边端中多模态数据管理和协同管理需求.因此,研究面向云边端协同的多模态数据建模技术成为重要问题.其核心在于,如何高效地从云边端三层架构中得到满足应用所需的查询结果.从云边端三层数据的数据类型出发,提出了面向云边端协同的多模态数据建模技术,给出了基于元组的多模态数据模型定义,设计了6种基类,解决多模态数据统一表征困难的问题;提出了云边端协同查询的基本数据操作体系,以满足云边端业务场景的查询需求;给出了多模态数据模型的完整性约束,为查询优化奠定了理论基础.最后,给出了面向云边端协同多模态数据模型的示范应用,并从数据存储时间、存储空间和查询时间这3个方面对所提出的数据模型存储方法进行了验证.实验结果表明,所提方案能够有效地表示云边端协同架构中的多模态数据.

精细化管理在手术室骨科外来器械与植入物中的应用

目的探讨精细化管理在骨科外来器械与植入物管理中的应用效果。方法将2022年1月-3月进行的1275例骨科植入物手术设为对照组,将2022年4月-6月进行的骨科植入物手术1023例设为干预组,对照组采用常规的骨科手术配合管理模式,干预组采用精细化管理模式。结果干预组外来器械与植入物管理质量均明显高于对照组(均P<0.05),术中清点意外事件发生率显著低于对照组(P<0.05)。结论精细化管理能有效实施骨科外来器械与植入物的环节控制,显著提升外来器械与植入物管理质量,同时降低术中清点意外事件发生率。

经支气管诊疗机器人设计及其柔性末端执行器位姿研究

经呼吸道活检是肺结节诊断中较为常见的手术,但由于呼吸道疾病存在传染风险以及手动操作时关节受限等,医工结合的诊疗方式逐渐成为发展趋势。为实现柔性体在支气管腔道内复杂弯曲动态环境下灵活运动、精准定位与稳定介入,采用主从协同式远程控制机器人机构设计,模拟传统手术中医生的操作习惯,设计并搭建能够操控支气管镜、活检钳的集成机构原理样机,以实现经支气管进行微创诊疗的双器械协同操控。然后,基于Cosserat杆理论,利用MATLAB软件对机器人柔性末端执行器的力-位映射关系、位姿和工作空间进行仿真求解,并通过实验分析机器人柔性末端执行器在经支气管的远程微创活检手术中的真实位姿以及机器人的实际运行效果,验证了仿真结果的准确性。研究结果可为经自然腔道活检术的多器械协同控制提供理论基础。

端云协同智能计算的关键问题、方法和应用

端云协同智能计算是大数据、云计算、边缘计算发展的产物,可在保护用户隐私的前提下显著提升数据利用率,实现智能计算实时响应能力与服务鲁棒性的优势互补,而相应技术研发和实践应用具有复杂性。本文剖析了端云协同智能计算的应用价值,凝练了端学习效率优化、端少样本过拟合、端模型定制化、分布差异下虚假关联学习、通信开销与计算效率平衡等方面的技术难题;系统梳理了端云协同智能计算中主流方法研究进展,涉及作为应用基石的高效计算硬件、以端为中心的协同计算、以云为中心的协同计算、端云双向协同计算、可信端云协同智能计算等主要方向;总结了推荐系统、自动驾驶、安防系统、教育模式等端云协同智能计算的垂直领域应用情况。着眼端云协同智能计算的未来发展,需重点研究云资源在端模型个性化中的应用策略、端云协同多目标优化算法、端-端与云协同计算的优化策略。

HMFuzzer:一种基于人机协同的物联网设备固件漏洞挖掘方案

模糊测试是一种针对物联网设备固件漏洞挖掘的主流方法,能够先攻击者一步发现安全威胁,提升物联网设备的安全性.但是目前大部分的模糊测试技术关注于如何自动化地实现漏洞挖掘,忽略了专家经验对于设备固件漏洞挖掘工作的优势.本文提出一种基于人机协同的物联网设备固件漏洞挖掘方案HMFuzzer,设计了基于设备固件前后端交互的设备固件关键信息提取方法,通过模拟设备固件、设备管理界面以及用户三方交互模式获取固件潜在的关键信息,并通过二进制文件定位和函数分析技术解析出固件关键函数.此外,HMFuzzer通过在模糊测试的预处理、测试和结果分析阶段引入专家经验,利用上一阶段获取的关键信息,结合强化学习算法,优化种子变异和模糊测试流程,显著提升了模糊测试的覆盖率、效率以及漏洞挖掘能力.实验结果表明,相比于现有的固件漏洞挖掘方法,HMFuzzer的漏洞识别成功率能提高10%以上,具备更强的漏洞检测能力.特别是,针对真实厂商的物联网设备测试,HMFuzzer发现了多个0-day漏洞,其中已获得4个CVE/CNVD高危漏洞.

计及柔性限流装置与直流断路器协同动作的电弧抑制暂态特性研究

直流系统的故障隔离是保证直流系统稳定运行的重要技术。针对传统故障隔离策略对直流断路器(direct current circuit breaker, DCCB)的性能要求较高的问题,提出了一种利用柔性限流装置(flexible current limiting device,FCLD)与DCCB协同动作的故障隔离策略。首先,研究了直流系统永久性故障和瞬时性故障情况下FCLD与DCCB的协同作用机理。其次,分析考虑FCLD电流抑制作用下DCCB开断过程的电弧暂态特性。最后,在Matlab/Simulink平台中进行仿真,验证所提协同策略的可行性。结果表明:FCLD可有效抑制DCCB的开断电弧;基于所提故障隔离策略,直流系统可在瞬时故障情况下实现平稳穿越,永久故障情况下实现DCCB的无弧开断。该策略降低了直流系统故障隔离过程中对DCCB的开断要求,提升了直流系统的故障穿越能力。

以学习者为中心的智联学习环境:内涵、框架与实施路径

学习环境的构建是教育数字化转型、学与教方式变革的基础。推动学习环境的改造与智能升级,构建以学习者为中心的智联学习环境,实现精准推送学习服务,是发展数字教育、建设高质量教育体系的现实需求。围绕学习环境智联计算关键问题,从学习环境多模态感知与监测、多场景学习过程记录与分析、跨场域学习场景建模与推荐、人机协同学习社群建构与支持四方面界定智联学习环境的内涵。遵循“数据处理、模型训练、智能服务”的逻辑,设计出涵盖物理环境改造与数据汇聚、数据指标与算法模型构建、精准自适应支持与智能服务的整体框架。以场景化人工智能教育应用为抓手,提出跨场域学习环境设计与评测标准、智慧学习环境计算引擎及大规模智慧教室监测平台研发、循证导向的规模化示范应用的实施路径。最后,探讨了实现智联学习环境的关键挑战,包括云边端算力基础设施、智能模型的教育可解释性、人机协同与交互体验设计、数据安全与隐私保护。

基于UDI的医疗器械不良事件重点监测智慧管理平台的设计与应用

目的以医疗器械唯一标识(Unique Device Identification,UDI)体系为基础,设计基于UDI编码的医疗器械不良事件重点监测智慧管理平台。方法基于UDI编码和医疗设备全生命周期管理平台,融合移动互联网、工业互联网平台应用、大数据平台等先进架构理念和技术,采用Spring Cloud微服务架构和Kubernetes编排加Docker容器化技术,基于SpringCloud+Mybatis框架、使用Java语言、应用B/S架构,设计协同管理医疗器械不良事件重点监测智慧管理信息平台。平台支持多端多场景融合使用场景,医院、注册人和监测中心三方协同联动,实现不良事件重点监测信息化精准快捷上报。结果基于UDI的医疗器械不良事件重点监测智慧管理平台已收集不良事件及注册人采取的控制措施6例。该平台可实现不良事件高质量数据采集、多方信息精准推送、产品异常快速调查、企业控制措施快速反馈,有效提高医疗器械不良事件数据的准确性和风险发现、报告、处置、反馈的效率,实现医疗器械不良事件的全场景闭环智慧管理。结论该平台实现多家哨点单位由原始手工填报向信息化收集重点监测数据的转变,还可以推广到所有类别的医疗器械不良事件管理场景中,实现医疗器械不良事件的数据化、智能化管理。

Collaborative Planning of Distributed Wind Power Generation and Distribution Network with Large-scale Heat Pumps

With the advancement of clean heating projects and the integration of large-scale distributed heat pumps into rural distribution networks in northern China,power grid companies face tremendous pressure to invest in power grid upgrades,which bring opportunities for renewable power generation integration.The combination of heating by distributed renewable energy with the flexible operation of heat pumps is a feasible alternative for dealing with grid reinforcement challenges resulting from heating electrification.In this paper,a mathematical model of the collaborative planning of distributed wind power generation(DWPG)and distribution network with large-scale heat pumps is developed.In this model,the operational flexibility of the heat pump load is fully considered and the requirements of a comfortable indoor temperature are met.By applying this model,the goals of not only increasing the profit of DWPG but also reducing the cost of the power grid upgrade can be achieved.

Collaborative Interaction for Videos on Mobile Devices Based on Sketch Gestures

With the rapid progress of the network and mobile techniques, mobile devices such as mobile phones and portable devices, have become one of the most important parts in common life. Efficient techniques for watching, navigating and sharing videos on mobile devices collaboratively are appealing in mobile environment. In this paper, we propose a novel approach supporting efficiently collaborative operations on videos with sketch gestures. Furthermore, effective collaborative annotation and navigation operations are given to interact with videos on mobile devices for facilitating users＇ communication based on mobile devices＇ characteristics. Gesture operation and collaborative interaction architecture are given and improved during the interactive process. Finally, a user study is conducted showing that the effectivity and collaborative accessibility of video exploration is improved.

设施农业产业园智慧用能边缘控制装置研制及应用

针对目前乡村存在供电设施季节性过载问题突出、能源综合利用经济性差、光伏消纳能力弱和落地难等问题,研究设计了一种智慧用能边缘控制装置,构建云边协同系统控制架构,基于组态监控软件实现设施农业产业园中设施农业负荷的智能化控制,提高可再生能源利用率,提高经济效益。以某日光温室为研究对象,以CY-LFC121C设备为边缘控制核心进行了边缘控制装置的搭建,以日光温室日运行费用、光伏消纳率及CO_(2)减排率为指标,利用改进的蝗虫优化算法进行多目标寻优及实验室仿真试验,并于甘肃省某日光温室进行现场验证。结果表明,该装置可有效应对现代设施农业负荷综合能源利用率低、经济性差、落地难等问题,为边缘控制装置的优化设计提供参考。

基于序参量的云边端分布式体系协同安全评估

基于云边端的分布式计算体系成功服务于众多应用,已经成为一种主流,具有受众广、用户体验好、安全期望高等特点。然而,近年来针对云、边、端的攻击事件频发,给用户造成严重的安全隐患与巨大经济损失。当前云、边、端的防御机制各自为战,抵御风险的能力差异大,全域安全风险消除难。事前配置和事后“亡羊补牢”式的安全手段难以满足高负载、高实时的云边端协同网络的安全需求。其根源在于云、边、端分层架构各自为营,未形成有效的协同防御体系,导致产生跨域安全治理难度大、实时性差、协同一致性评估难等问题。提出了实时感知、动态决策、积极防御三位一体的云边端协同安全架构,深度融合云、边、端,形成安全协同能力,并进行严格理论一致性证明;提出了基于序参量的协同安全评估模型,实现云、边、端风险感知一致,策略决策一致,攻击防御一致,实行高效率、低成本的安全风险协同防护,最大化系统性安全收益;对一致性理论与评估模型进行仿真验证,结果证明了提出的协同安全体系以及一致性评价模型的正确性与有效性。

基于无线射频供能的端边协同智能任务推理机制

针对云侧进行智能任务推理时带宽需求高与实时性差、端侧设备计算能力与能量受限的问题,在端侧设备处引入无线射频供能技术以实现端侧设备独立供能,进而提出了基于无线射频供能的端边协同智能任务推理机制,以最大化端侧设备智能任务推理完成率。构建端侧设备能量收集和端边协同推理模型。考虑端侧设备射频(Radio Frequency,RF)能量收集时间与端边协同智能任务推理时间约束、端侧设备传输功率与可使用能量约束及边缘侧计算资源约束,最大化智能推理任务完成数量。提出了一种基于深度确定性策略梯度(Deep Deterministic Policy Gradient,DDPG)的深度神经网络(Deep Neural Network,DNN)模型分割与通信计算资源联合优化算法,以得到最优的端侧设备射频能量采集时间、端侧设备传输功率、DNN模型分割点和边缘计算资源分配。仿真结果表明,所提算法可有效提高智能任务推理完成率,且明显优于其他对比算法。

燃煤掺烧可再生燃料发电烟气多污染物协同控制研究进展

“双碳”背景下,煤电清洁低碳发展是必然趋势。通过煤与生物质、污泥、生活垃圾等可再生燃料耦合掺烧发电,能显著降低CO_(2)排放量,是燃煤电厂实现碳中和、碳达峰的路径之一。燃煤掺烧可再生燃料发电后,烟气中非常规污染物(痕量元素、VOCs等)种类及含量将增加,因此研究燃煤电厂现有的大气污染物控制装置(air pollution control devices,APCDs)的脱除能力,及未来可用于燃煤电厂的多污染物协同控制技术对于非常规污染物减排尤为必要。基于掺烧后烟气非常规污染物的排放特征,该文重点分析其在APCDs各环保设备中的迁移转化规律及净化效果,阐述为更高效控制非常规污染物的改进工艺的技术进展。进一步对污染物协同脱除资源化技术的发展现状、技术问题及工程应用进行探讨,其规模化发展仍要面临许多技术挑战。未来燃煤电厂可通过耦合可再生燃料发电、污染物的协同控制并联合CO_(2)捕集利用与封存技术,助力实现“减污降碳”目标。

基于边缘计算的输电线路监测物联网多路数据协同采集方法

传感终端实时在线监测输电线路运行状况,由于输电线路规模大,数据采集难度增加,数据采集存在冗余数据占比高等问题,提出基于边缘计算的输电线路监测物联网(IoT)多路数据协同采集方法。搭建物联网多路数据协同采集框架,设计输电线路监测智能传感终端,基于边缘计算功能,设计多路数据传输算法,制定协同采集程序,融合旋转门算法与数据平滑算法,自适应调整采集间隔,实现物联网多路数据协同采集。实验数据显示:提出方法多路数据传输时延最小为2 s,冗余数据占比最小为2.56%,丢失数据占比最小为0.23%,充分证实提出方法多路数据采集效率与质量较佳。