Effect of external and internal cues on core muscle activation during the Sahrmann five-level core stability test

BACKGROUND Pain in the back or pelvis or fear of back pain may affect the timing or cocontraction of the core muscles.In both static and dynamic movements,the Sahrmann core stability test provides an assessment of core muscle activation and a person's ability to stabilize the lumbopelvic complex.Preparatory cues and images can be used to increase the activation of these muscles.To attain optimal movement patterns,it will be necessary to determine what cueing will give the most effective results for core stability.AIM To investigate the effects of external and internal cues on core muscle activation during the Sahrmann five-level core stability test.METHODS Total 68 participants(21.83±3.47 years)were randomly allocated to an external(n=35)or internal cue group(n=33).Participants performed the Sahrmann fivelevel core stability test without a cue as baseline and the five-level stability exercises with an internal or external cue.External cue group received a pressure biofeedback unit(PBU),and the internal cue group received an audio cue.A Delsys Trigno^(TM)surface electromyography unit was used for muscle activation from the rectus abdominis,external oblique,and transverse abdominis/internal oblique muscles.RESULTS Linear mixed effects model analysis showed that cueing had a significant effect on core muscle activation(P=0.001);however,there was no significant difference between cue types(internal or external)(P=0.130).CONCLUSION Both external and internal cueing have significant effects on core muscle activation during the Sahrmann five-level core stability test and the PBU does not create higher muscle activation than internal cueing.

基于Multi-agents的智能变电站警报处理及故障诊断系统

针对传统变电站故障诊断的不足,在智能变电站架构的基础上,提出了基于multi-agents的智能变电站警报处理及故障诊断系统。根据智能变电站的体系结构、信息流和数据流特点,设计了警报处理、输变电设备诊断等主要功能模块,以此满足变电站事故分析各层次的功能需求。就警报处理和输变电设备故障诊断系统中各个agent及agent之间的协作机制等方面做了详细论述,实际变电站故障案例证明了该警报处理和输变电诊断模型的可行性和有效性。

基于multi-agents的网络防卫体系中预警定位系统的研究与实现

传统的网络安全措施,如加密认证、防火墙和入侵检测系统等,虽然在保护信息的保密性、完整性、可用性和控制访问方面有一定的效果,但在协同和预警方面依然存在不足。文章提出了1个基于multi-agents的网络安全防卫系统,该系统由协同预警定位系统、协同审计系统、安全隔离系统、事故恢复系统等多个模块构成,模块之间由多个多级分层agents来负责通信任务。系统控制中心的agent server负责控制和协调整个安全体系,制定全网统一的安全控制策略。在该系统中,整个网络被划分成不同级别的分区,建立不同级别的协同预警定位系统,各分区既相互协作,又能够独立自治,通过协作的方式共同维护着整个网络的安全。在IPv6环境下测试的结果表明,该系统可以高效进行预警,IDS的捕获率约为95%、漏报率小于6%、误报率小于7%。

基于Multi-Agents分布式医学诊断系统研究

医学诊断系统是一个新兴的复杂的应用系统,人工智能技术,计算机协作支持技术及高速通信网络体系结构的发展促进了计算机支持的诊断系统的发展.当前医学诊断系统的难点在于如何利用网络这个资源分布平台来获取所需要的数据及在数据不完整状态进行推理求解,而这些问题的解决在于能够有一种机制使得能在一个标准的应用系统结构中准确的表示并获取信息及集成各种医学资源使之相互协作.本文描述了一种利用多智能体(Multi-agents system,MAS)体系结构和中间件(middleware)技术如公共请求代理结构(Common Object Request Broker Architecture,CORBA)进行设计的分布式医学诊断系统.该系统能集成多种医学资源和医学应用实体并且能实现参与诊断的医学实体之间的协作,以减少由于信息缺乏而带来的诊断偏差.另外本文还将一种实验室开发的模糊最小最大神经网络(Fuzzy Min—Max Neural Network,FMMNN)的模糊规则提取方法应用于该系统以证实该分布式诊断系统的优越性.

基于Multi-Agents的多媒体信息检索引擎探讨

在介绍了数字图书馆等概念的基础上 ,简要地介绍了基于Multi Agents (MAS)的多媒体信息检索引擎在数字图书馆中的应用 ,并结合当前的研究状况 ,描述了基于MAS的多媒体信息检索引擎应用的光明未来。

基于Multi-Agent的无人机集群体系自主作战系统设计

针对无人集群自主作战体系设计中的关键问题,提出基于Multi-Agent的无人集群自主作战系统设计方法。建立无人集群各节点的Agent模型及其推演规则;对于仿真系统模块化和通用化的需求,设计系统互操作式接口和无人集群自主作战的交互关系;开展无人集群系统仿真推演验证。仿真结果表明,所提设计方案不仅能够有效开展并完成自主作战网络生成-集群演化-效能评估的全过程动态演示验证,而且能够通过重复随机试验进一步评估无人集群的协同作战效能,最后总结了集群协同作战的策略和经验。

基于Multi-agents系统的黑启动决策方法

在黑启动过程中,建立相应的发电机、母线及线路开关等分层主体,进行相互通信与协调控制,实时监测电力系统的状态变化,并采用Petri net算法进行优化建模,从而提出相应的故障恢复方案或是大停电状态下的黑启动方案。

基于Multi-Agent的水电站变压器故障诊断系统

为了精准、快速完成水电站变压器的故障诊断,设计基于Multi-Agent的水电站变压器故障诊断系统。变压器状态监控agent将检测到的变压器故障信息发送给系统管理agent,系统管理agent通过通信agent将变压器故障信息发送给变压器故障诊断agent,变压器故障诊断agent利用小波变换方法提取变压器故障特征,并将其作为IFOA-SVM模型输入,完成变压器故障分类后,获取变压器故障诊断结果,该结果通过通信agent显示给用户。实验表明,该系统可有效诊断变压器故障诊断,诊断成功率受系统故障信息丢失率的影响较小,诊断耗时、耗能小,并具有较高故障诊断成功率。

Consensus control for multi-agents in a non-rectangular bounded space: algorithmand experiments

Aiming for the coordinated motion and cooperative control of multi-agents in a non-rectangular bounded space, a velocity consensus algorithm for the agents with double- integrator dynamics is presented. The traditional consensus algorithm for bounded space is only applicable to rectangular bouncing boundaries, not suitable for non-rectangular space. In order to extend the previous consensus algorithm to the non- rectangular space, the concept of mirrored velocity is introduced, which can convert the discontinuous real velocity to continuous mirrored velocity, and expand a bounded space into an infinite space. Using the consensus algorithm, it is found that the mirrored velocities of multi-agents asymptotically converge to the same values. Because each mirrored velocity points to a unique velocity in real space, it can be concluded that the real velocities of multi-agents also asymptotically converge. Finally, the effectiveness of the proposed consensus algorithm is examined by theoretical proof and numerical simulations. Moreover, an experiment is performed with the algorithm in a real multi-robot system successfully.

Intelligent Control and Maintenance Man-agement Integrated System Based on Multi-Agents for Coal-Preparation Plant

This paper discusses the progress of computer integrated processing （CIPS） of coal-preparation and then preserits an intelligence controlled production-process, device-maintenance and production-management system of coal- preparation based on multi-agents （IICMMS-CP）. The construction of the IICMMS-CP, the distributed network control system based on live intelligence control stations and the strategy of implementing distributed intelligence control system are studied in order to overcome the disadvantages brought about by the wide use of the PLC system by coaipreparation plants. The software frame, based on a Multi-Agent Intelligence Control and Maintenance Management integrated system, is studied and the implemention methods of IICMMS-CP are discussed. The characteristics of distributed architecture, cooperation and parallel computing meet the needs of integrated control of coal-preparation plants with large-scale spatial production distribution, densely-related processes and complex systems. Its application further improves the reliability and precision of process control, accuracy of fault identification and intelligence of production adjustment, establishes a technical basis for system integration and flexible production. The main function of the system has been tested in a coal-preparation plant to good effect in stabilizing product quality, improving efficiency and reducing consumption.

研究数据存储库获得CoreTrustSeal认证策略研究——以高校图书馆为例

研究数据存储库定期评估认证,可以保证研究数据存储库的可信赖度,提高研究数据质量。文章对比分析CoreTrustSeal认证、WDS认证等多种研究数据存储库的认证方式,调研认证种类及方式并针对认证数量最多的CoreTrustSeal认证,分析16项评估要求,总结对比国外高校图书馆获得CoreTrustSeal认证的案例经验,在此基础上提出促进我国高校图书馆建立值得信赖研究数据存储库的优势与策略。

基于DWC PCIE Core的数据传输系统设计

在铁路信号系统中,为有效减少铁路信号系统设备CPU资源占用率,设计一种基于DWC PCIE Core的数据发送与接收系统。该系统采用内嵌ARM Cortex-A9双核的FPGA开发板套件,利用FPGA的灵活性和可扩展性,采用可配置的PCIE硬核IP模块以及以太网硬核IP模块。该系统主要介绍Host PC与FPGA之间基于PCIE 2.0的DMA数据传输以及FPGA之间基于GMAC的以太网数据传输,通过PCIe总线、以太网基于DMA模块实现数据高速可靠传输。

UAV-Assisted Dynamic Avatar Task Migration for Vehicular Metaverse Services: A Multi-Agent Deep Reinforcement Learning Approach

Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metaverses. However, avatar tasks include a multitude of human-to-avatar and avatar-to-avatar interactive applications, e.g., augmented reality navigation,which consumes intensive computing resources. It is inefficient and impractical for vehicles to process avatar tasks locally. Fortunately, migrating avatar tasks to the nearest roadside units(RSU)or unmanned aerial vehicles(UAV) for execution is a promising solution to decrease computation overhead and reduce task processing latency, while the high mobility of vehicles brings challenges for vehicles to independently perform avatar migration decisions depending on current and future vehicle status. To address these challenges, in this paper, we propose a novel avatar task migration system based on multi-agent deep reinforcement learning(MADRL) to execute immersive vehicular avatar tasks dynamically. Specifically, we first formulate the problem of avatar task migration from vehicles to RSUs/UAVs as a partially observable Markov decision process that can be solved by MADRL algorithms. We then design the multi-agent proximal policy optimization(MAPPO) approach as the MADRL algorithm for the avatar task migration problem. To overcome slow convergence resulting from the curse of dimensionality and non-stationary issues caused by shared parameters in MAPPO, we further propose a transformer-based MAPPO approach via sequential decision-making models for the efficient representation of relationships among agents. Finally, to motivate terrestrial or non-terrestrial edge servers(e.g., RSUs or UAVs) to share computation resources and ensure traceability of the sharing records, we apply smart contracts and blockchain technologies to achieve secure sharing management. Numerical results demonstrate that the proposed approach outperforms the MAPPO approach by around 2% and effectively reduces approximately 20% of the latency of avatar task execution in UAV-assisted vehicular Metaverses.

Maintaining Complex Formations and Avoiding Obstacles for Multi-Agents

This paper researches some problems in complex formation for multi-agents,in which two matrices are proposed to record the formation.The pattern matrix is used to describe the pattern of the formation;meanwhile,the location matrix is used to record the location of each agent.Thus,all desired positions of each agent will be obtained by geometrical relationship on the basis of two matrices above.In addition a self-adaptation flocking algorithm is proposed to control all agents to form a desired formation and avoid obstacles.The main idea is as follows:agents will form a desired formation through the method of formation control when far away from obstacles;otherwise,agents will freely fly to pass through the area of obstacles.In the simulation,three scenarios are designed to verify the effectiveness of our method.The results show that our method also can be applied in three dimensions.All agents will form a stable formation and keep the same velocity at last.

Finite-time Prescribed Performance Time-Varying Formation Control for Second-Order Multi-Agent Systems With Non-Strict Feedback Based on a Neural Network Observer

This paper studies the problem of time-varying formation control with finite-time prescribed performance for nonstrict feedback second-order multi-agent systems with unmeasured states and unknown nonlinearities.To eliminate nonlinearities,neural networks are applied to approximate the inherent dynamics of the system.In addition,due to the limitations of the actual working conditions,each follower agent can only obtain the locally measurable partial state information of the leader agent.To address this problem,a neural network state observer based on the leader state information is designed.Then,a finite-time prescribed performance adaptive output feedback control strategy is proposed by restricting the sliding mode surface to a prescribed region,which ensures that the closed-loop system has practical finite-time stability and that formation errors of the multi-agent systems converge to the prescribed performance bound in finite time.Finally,a numerical simulation is provided to demonstrate the practicality and effectiveness of the developed algorithm.

Robust adaptive leaderless consensus of unknown non-minimum phase linear multi-agent systems subject to disturbances and/or unmodeled dynamics

This article investigates the problem of robust adaptive leaderless consensus for heterogeneous uncertain nonminimumphase linear multi-agent systems over directed communication graphs. Each agent is assumed tobe of unknown nominal dynamics and also subject to external disturbances and/or unmodeled dynamics. Anovel distributed robust adaptive control strategy is proposed. It is shown that the robust adaptive leaderlessconsensus problem is solved with the proposed control strategy under some sufficient conditions. Two examplesare provided to demonstrate the efficacy of the proposed control strategy.

Enhancing electromagnetic wave absorption with core‐shell structured SiO_(2)@MXene@MoS_(2) nanospheres

Material composition and structural design are important factors influencing the electromagnetic wave(EMW)absorption performance of materials.To alleviate the impedance mismatch attributed to the high dielectric constant of Ti_(3)C_(2)T_(x)MXene,we have successfully synthesized core‐shell structured SiO_(2)@MXene@MoS_(2)nanospheres.This architecture,comprising SiO_(2) as the core,MXene as the intermediate layer,and MoS_(2) as the outer shell,is achieved through an electrostatic self‐assembly method combined with a hydrothermal process.This complex core‐shell structure not only provides a variety of loss mechanisms that effectively dissipate electromagnetic energy but also prevents self‐aggregation of MXene and MoS_(2) nanosheets.Notably,the synergistic combination of SiO_(2) and MoS_(2) with highly conductive MXene enables the suitable dielectric constant of the composites,ensuring optimal impedance matching.Therefore,the core‐shell structured SiO_(2)@MXene@MoS_(2) nanospheres exhibit excellent EMW absorption performance,featuring a remarkable minimum reflection loss(RL_(min))of−52.11 dB(2.4 mm).It is noteworthy that these nanospheres achieve an ultra‐wide effective absorption bandwidth(EAB)of 6.72 GHz.This work provides a novel approach for designing and synthesizing high‐performance EMW absorbers characterized by“wide bandwidth and strong reflection loss.”

Distributed Consensus of High-Order Multi-Agents with Nonlinear Dynamics

This paper deals with the distributed consensus problem of high-order multi-agent systems with nonlinear dynamics subject to external disturbances. The network topology is assumed to be a fixed undirected graph. Some sufficient conditions are derived, under which the consensus can be achieved with a prescribed norm bound. It is shown that the parameter matrix in the consensus algorithm can be designed by solving two linear matrix inequalities (LMIs). In particular, if the nonzero eigenvalues of the laplacian matrix ac-cording to the network topology are identical, the parameter matrix in the consensus algorithm can be de-signed by solving one LMI. A numerical example is given to illustrate the proposed results.

Methodological Approaches to Development and Implementation of Multi-agents Investment DSS Using JADE Platform

In these latter days software agents are used for the development and implementation of intellectual decision support systems. In order to implement intelligence in a system some or several dozen of software agents are used and the made system becomes multi-agent. For the development of these systems a set of methodologies, i.e., the sequence of consequent steps of analysis, designing and implementation, is offered. The carried out analysis of the methodologies showed that as a rule they are limited by the spectrum of their pending problem （within the pales of the requirements of specific applied task, within the pales of the possibilities of technical implementation） or within the pales of amount of detail. The variety of methodologies is influenced by the fact that for the development of these systems the requirements and attitudes are offered by the specialists of related spheres such as software, numeral intellect engineers. In the course of the development of hardware and software appeared possibilities to implement mobile multi-agents systems, however, there is no one united mobile multi-agent systems design methodology, whereas existing systems are underdeveloped and their number is small. In this article we introduce the course of the designing of an intellectual real time multi-agent investment management decision support information system adapting and combining some methodologies where the choice to use either communicating or mobile agents is the question of rather technical implementation than methodological. In the article we introduce two ways of system implementation by JADE platform： the first one-using communicating agents, and the second one-using mobile agents.

Discovering Latent Variables for the Tasks With Confounders in Multi-Agent Reinforcement Learning

Efficient exploration in complex coordination tasks has been considered a challenging problem in multi-agent reinforcement learning(MARL). It is significantly more difficult for those tasks with latent variables that agents cannot directly observe. However, most of the existing latent variable discovery methods lack a clear representation of latent variables and an effective evaluation of the influence of latent variables on the agent. In this paper, we propose a new MARL algorithm based on the soft actor-critic method for complex continuous control tasks with confounders. It is called the multi-agent soft actor-critic with latent variable(MASAC-LV) algorithm, which uses variational inference theory to infer the compact latent variables representation space from a large amount of offline experience.Besides, we derive the counterfactual policy whose input has no latent variables and quantify the difference between the actual policy and the counterfactual policy via a distance function. This quantified difference is considered an intrinsic motivation that gives additional rewards based on how much the latent variable affects each agent. The proposed algorithm is evaluated on two collaboration tasks with confounders, and the experimental results demonstrate the effectiveness of MASAC-LV compared to other baseline algorithms.