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。

Multi-Agent模式下的城市暴雨内涝应急决策方法研究

为厘清应对暴雨内涝灾害动态决策过程中决策主体、决策、决策方案等决策要素间的不确定关系,提出1种多主体(Multi-Agent)和贝叶斯决策网络(BDN)相结合的应急决策方法。首先分阶段构建“主体-任务”可视化网络,分析暴雨内涝灾害各应急阶段的主要任务和参与的决策主体;在考虑到决策要素间的动态不确定性可能造成决策风险的前提下,运用Multi-Agent和BDN方法探究各决策要素间的影响关系,以便进行方案集优选。研究结果表明:该方法具有实用性和现实意义,研究结果可为城市暴雨内涝灾害的应急决策提供理论参考。

Multi-Agent Deep Reinforcement Learning for Cross-Layer Scheduling in Mobile Ad-Hoc Networks

Due to the fading characteristics of wireless channels and the burstiness of data traffic,how to deal with congestion in Ad-hoc networks with effective algorithms is still open and challenging.In this paper,we focus on enabling congestion control to minimize network transmission delays through flexible power control.To effectively solve the congestion problem,we propose a distributed cross-layer scheduling algorithm,which is empowered by graph-based multi-agent deep reinforcement learning.The transmit power is adaptively adjusted in real-time by our algorithm based only on local information(i.e.,channel state information and queue length)and local communication(i.e.,information exchanged with neighbors).Moreover,the training complexity of the algorithm is low due to the regional cooperation based on the graph attention network.In the evaluation,we show that our algorithm can reduce the transmission delay of data flow under severe signal interference and drastically changing channel states,and demonstrate the adaptability and stability in different topologies.The method is general and can be extended to various types of topologies.

Multi-scale data joint inversion of minerals and porosity in altered igneous reservoirs—A case study in the South China Sea

There are abundant igneous gas reservoirs in the South China Sea with significant value of research,and lithology classification,mineral analysis and porosity inversion are important links in reservoir evaluation.However,affected by the diverse lithology,complicated mineral and widespread alteration,conventional logging lithology classification and mineral inversion become considerably difficult.At the same time,owing to the limitation of the wireline log response equation,the quantity and accuracy of minerals can hardly meet the exploration requirements of igneous formations.To overcome those issues,this study takes the South China Sea as an example,and combines multi-scale data such as micro rock slices,petrophysical experiments,wireline log and element cutting log to establish a set of joint inversion methods for minerals and porosity of altered igneous rocks.Specifically,we define the lithology and mineral characteristics through core slices and mineral data,and establish an igneous multi-mineral volumetric model.Then we determine element cutting log correction method based on core element data,and combine wireline log and corrected element cutting log to perform the lithology classification and joint inversion of minerals and porosity.However,it is always difficult to determine the elemental eigenvalues of different minerals in inversion.This paper uses multiple linear regression methods to solve this problem.Finally,an integrated inversion technique for altered igneous formations was developed.The results show that the corrected element cutting log are in good agreement with the core element data,and the mineral and porosity results obtained from the joint inversion based on the wireline log and corrected element cutting log are also in good agreement with the core data from X-ray diffraction.The results demonstrate that the inversion technique is applicable and this study provides a new direction for the mineral inversion research of altered igneous formations.

Improved Scatter Search Algorithm for Multi-skilled Personnel Scheduling of Ship Block Painting

This paper focuses on the optimization method for multi-skilled painting personnel scheduling.The budget working time analysis is carried out considering the influence of operating area,difficulty of spraying area,multi-skilled workers,and worker’s efficiency,then a mathematical model is established to minimize the completion time. The constraints of task priority,paint preparation,pump management,and neighbor avoidance in the ship block painting production are considered. Based on this model,an improved scatter search(ISS)algorithm is designed,and the hybrid approximate dynamic programming(ADP)algorithm is used to improve search efficiency. In addition,the two solution combination methods of path-relinking and task sequence combination are used to enhance the search breadth and depth. The numerical experimental results show that ISS has a significant advantage in solving efficiency compared with the solver in small scale instances;Compared with the scatter search algorithm and genetic algorithm,ISS can stably improve the solution quality. Verified by the production example,ISS effectively shortens the total completion time of the production,which is suitable for scheduling problems in the actual painting production of the shipyard.

Integration of Multiple Spectral Data via a Logistic Regression Algorithm for Detection of Crop Residue Burned Areas:A Case Study of Songnen Plain,Northeast China

The burning of crop residues in fields is a significant global biomass burning activity which is a key element of the terrestrial carbon cycle,and an important source of atmospheric trace gasses and aerosols.Accurate estimation of cropland burned area is both crucial and challenging,especially for the small and fragmented burned scars in China.Here we developed an automated burned area mapping algorithm that was implemented using Sentinel-2 Multi Spectral Instrument(MSI)data and its effectiveness was tested taking Songnen Plain,Northeast China as a case using satellite image of 2020.We employed a logistic regression method for integrating multiple spectral data into a synthetic indicator,and compared the results with manually interpreted burned area reference maps and the Moderate-Resolution Imaging Spectroradiometer(MODIS)MCD64A1 burned area product.The overall accuracy of the single variable logistic regression was 77.38%to 86.90%and 73.47%to 97.14%for the 52TCQ and 51TYM cases,respectively.In comparison,the accuracy of the burned area map was improved to 87.14%and 98.33%for the 52TCQ and 51TYM cases,respectively by multiple variable logistic regression of Sentind-2 images.The balance of omission error and commission error was also improved.The integration of multiple spectral data combined with a logistic regression method proves to be effective for burned area detection,offering a highly automated process with an automatic threshold determination mechanism.This method exhibits excellent extensibility and flexibility taking the image tile as the operating unit.It is suitable for burned area detection at a regional scale and can also be implemented with other satellite data.

Incidence, risk factors and clinical outcome of multidrug-resistant organisms after heart transplantation

BACKGROUND Transplant recipients commonly harbor multidrug-resistant organisms(MDROs),as a result of frequent hospital admissions and increased exposure to antimi-crobials and invasive procedures.AIM To investigate the impact of patient demographic and clinical characteristics on MDRO acquisition,as well as the impact of MDRO acquisition on intensive care unit(ICU)and hospital length of stay,and on ICU mortality and 1-year mortality post heart transplantation.METHODS This retrospective cohort study analyzed 98 consecutive heart transplant patients over a ten-year period(2013-2022)in a single transplantation center.Data was collected regarding MDROs commonly encountered in critical care.RESULTS Among the 98 transplanted patients(70%male),about a third(32%)acquired or already harbored MDROs upon transplantation(MDRO group),while two thirds did not(MDRO-free group).The prevalent MDROs were Acinetobacter baumannii(14%),Pseudomonas aeruginosa(12%)and Klebsiella pneumoniae(11%).Compared to MDRO-free patients,the MDRO group was characterized by higher body mass index(P=0.002),higher rates of renal failure(P=0.017),primary graft dysfunction(10%vs 4.5%,P=0.001),surgical re-exploration(34%vs 14%,P=0.017),mechanical circulatory support(47%vs 26%P=0.037)and renal replacement therapy(28%vs 9%,P=0.014),as well as longer extracorporeal circulation time(median 210 vs 161 min,P=0.003).The median length of stay was longer in the MDRO group,namely ICU stay was 16 vs 9 d in the MDRO-free group(P=0.001),and hospital stay was 38 vs 28 d(P=0.006),while 1-year mortality was higher(28%vs 7.6%,log-rank-χ2:7.34).CONCLUSION Following heart transplantation,a predominance of Gram-negative MDROs was noted.MDRO acquisition was associated with higher complication rates,prolonged ICU and total hospital stay,and higher post-transplantation mortality.

GIS-Based Multi-Criteria Decision Analysis (MCDA) and Analytical Hierarchy Process (AHP) Techniques to Derive Flood Risks Management on Rice Productivity in Gishari Marshland

Floods are phenomenon with significant socio-economic implications mainly for human loss, agriculture, livestock, soil loss and land degradation, for which many researchers try to identify the most appropriate methodologies by analyzing their temporal and spatial development. This study therefore attempts to employ the GIS-based multi-criteria decision analysis and analytical hierarchy process techniques to derive the flood risks management on rice productivity in the Gishari Agricultural Marshland in Rwamagana district, Rwanda. Here, six influencing potential factors to flooding, including river slope, soil texture, Land Use Land Cover through Land Sat 8, rainfall, river distance and Digital Elevation Model are considered for the delineation of flood risk zones. Data acquisition like Landsat 8 images, DEM, land use land cover, slope, and soil class in the study area were considered. Results showed that if the DEM is outdated or inaccurate due to changes in the terrain, such as construction, excavation, or erosion, the predicted flood patterns might not reflect the actual water flow. This could result unexpected flood extents and depths, potentially inundating rice fields that were not previously at risk and this, expectedly explained that the increase 1 m in elevation would reduce the rice productivity by 0.17% due to unplanned flood risks in marshland. It was found that the change in rainfall distribution in Gishari agricultural marshland would also decrease the rice productivity by 0.0018%, which is a sign that rainfall is a major factor of flooding in rice scheme. Rainfall distribution plays a crucial role in flooding analysis and can directly impact rice productivity. Oppositely, another causal factor was Land Use Land Cover (LULC), where the Multivariate Logistic Regression Model Analysis findings showed that the increase of one unit in Land Use Land Cover would increase rice productivity by 0.17% of the total rice productivity from the Gishari Agricultural Marshland. Based on findings from these techniques, the Gishari Agricultural Marshlands having steeped land with grassland is classified into five classes of flooding namely very low, low, moderate, high, and very high which include 430%, 361%, 292%, 223%, and 154%. Government of Rwanda and other implementing agencies and major key actors have to contribute on soil and water conservation strategies to reduce the runoff and soil erosion as major contributors of flooding.

基于Multi-WHFPN与SimAM注意力机制的版面分割

作为OCR的预处理工作,版面分割技术越来越受到学术界和工业界重视。针对版面分割中遇到的检测速度慢、目标区域边界不准确以及细小目标易遗漏等问题,提出了YOLOv7-MSY模型。此模型首先借鉴残差连接思想,提出了Multi-WHFPN网络结构。它采用可训练的权重参数,突出特征融合过程中特征重要性,并添加了小目标检测头,从而提升对小目标的检测性能;其次,引入SimAM注意力机制,可以在不增加额外参数的基础上在3D维度评估特征权重,以增强重要特征,抑制无效特征;最后,使用YEIOU来代替原模型中的定位损失函数,提升了模型的收敛速度与回归精度。在江苏省档案馆提供的数据集上进行实验对比,YOLOv7-MSY对目标区域边界检测更加敏感,对细小目标的检测效果更好。YOLOv7-MSY的mAP@.5达到了0.871,相较于原YOLOv7模型提高了7.84%。该模型的版面分割的效果优于其他类型的版面分割算法,具有良好的泛化性能,并且版面分割速度处于较高水平。

COMSOL Multiphysics在锂离子电池中的应用

作为一种具有前景的能量存储系统,锂离子电池需要进一步提高能量密度、功率密度、可靠性和循环稳定性,以满足不断增长的大型能源存储、电动汽车和便携式电子设备需求。当前对锂离子电池的实验研究仍然面临多个挑战,这些挑战包括电解液的导电性和安全性、高能量负极的沉积-剥离机制的优化、高能量正极的循环电压和容量维持、高电流条件下的界面极化和容量释放,以及在极端电流-温度-针刺条件下的热失控管理等问题。这些问题涉及到电-化-力-热等多个场的耦合作用,需要进行协同优化处理。COMSOL Multiphysics提供了一种可行的工具,通过求解多物理场耦合的连续方程,能够同时考虑载流子浓度、电流密度、电-化学势、温度、应力/应变和几何形态等综合信息的演化。本文概述了该工具在锂离子电池的电解液、负极和正极设计等方面的研究,并聚焦于多场耦合对电池性能的综合影响、多场耦合模拟方法以及理论模拟与实验表征的结合。最后,本文对理论与实验联合研究中的多场和多尺度问题进行了展望。

基于MAS(Multi-AgentSystem)的多机器人系统:协作多机器人学发展的一个重要方向

机器人的应用方式正在由部件式单元应用向系统式应用方向发展 .这是实际应用的需要 ,也是技术发展的必然趋势 ;相关技术如计算机网络技术的发展也为它的实现提供了相应支持 .多机器人协作理论问题必然也已经成为机器人学研究的一个热点 ,其中 ,分布式人工智能 ( DAI)中的多智能体 (代理 )系统 ( MAS:Multi-agentSystem)理论已引起多机器人协作理论研究者的关注 .本文即在揭示协作多机器人系统与 MAS的内在联系的基础上 ,指出基于 MAS的协作多机器人系统是协作多机器人学发展的一个重要方向 .

基于Multi-Agent系统的分布式协调紧急控制

提出一种基于Multi-Agent系统的分布式协调紧急控制策略。系统由一个服务器Agent和多个发电机Agent组成。服务器Agent负责对电力系统运行状态进行实时检测,当检测到电力系统将失稳时便启动发电机Agent,各个发电机Agent则随即判断自己的运行状态是否失稳,若失稳则给出控制方案。采用该方法对美国新英格兰39节点电力系统进行了测试,取得了较好的控制效果。

基于Multi-agent技术的Internet信息挖掘研究

介绍了Multi-Agent系统的概念，提出了具有Control-agent的Multi-agent系统，在此基础上分析了一种基于Multi-agent技术进行Internet信息下载和信息挖掘的模型及具体实现方法，并结合实际应用，介绍了可实现比较购物功能的WebDruid系统。

基于Multi-Agent系统的多飞行器协同路径规划方法的研究

无人多飞行器(UAV)协同技术是当前分布式人工智能的一个热点领域,其中一个关键技术在于如何实现多UAV集群根据复杂环境中目标、威胁、地形变化以及各UAV之间的性能约束动态进行实时性航路规划。提出一种基于Multi-agent系统的多UAV对实时动态多目标进行路径规划的方法。其核心是基于Multi-agent系统的decen-tralized控制方案。在Multi-agent平台上,实现了agent对于环境、目标、任务等路劲规划约束条件的建模,同时提出了多agent动态路径规划方法的实现方案。方案使用DisCSP模型框架,将基于真实复杂战场环境的实时路径规划问题所涉及的多复杂限制条件,抽象成Multi-agent系统中的各个约束条件,通过多agent间Dynamic Programming过程求解多UAV实时动态多目标的路径规划和协同任务分配的ABT算法,并实现在动态威胁和地形以及动态目标下具备集群协同能力的多UAV实时仿真系统。

基于信用和关系网的Multi-agent System信任体系

提出了一种MAS环境下基于信用和关系网的Agent信任体系,给出了模型的构建方法及相关的量化公式。该信任体系可以使系统中的推荐信任和直接信任等同看待,能抑制恶意推荐、协同作弊这些干扰现象,能促进关系网的凝聚性,简化Agent合作伙伴的选取,从而促进系统中Agent间的合作。

用于函数优化的正交Multi-Agent遗传算法

将Multi Agent系统、遗传算法和正交试验设计方法相结合,提出了一种混合进化算法———正交Multi Agent遗传算法。它以Multi Agent系统为基础,通过Agent间的相互作用与每个Agent所具有的知识和自学习功能来提高算法的全局优化能力和收敛速度;同时利用正交试验设计方法产生较好的初始种群和设计正交交叉算子以获得更好的后代;针对正交试验设计产生初始化种群在函数维数很高时需很大存贮空间的缺点,提出了子空间分割法来产生所需的初始化种群,它只需要原来存贮空间的十分之一。首先,对维数为30或100的12个标准测试函数进行仿真试验,结果表明正交Multi Agent遗传算法具有很强的全局优化能力和较快的收敛速度;其次,算法对这些标准测试函数进行高维优化(高达200维),实验结果表明正交Multi Agent遗传算法具有较好的高维搜索能力。

正交Multi-agent遗传算法及其性能分析

将Multi-agent系统、遗传算法与正交试验设计方法相结合,提出一种新的遗传算法——正交Multi-agent遗传算法.其主要思想是:利用正交设计的方法产生初始化种群;用正交交叉算子代替传统的算术交叉算子;利用agent间的竞争作用与每个agent所具有的知识和自学习能力进行启发式搜索,以提高进化的速度.仿真试验和性能分析表明,正交Multi-agent遗传算法不但具有很强的全局优化能力和较快的收敛速度,而且具有很强的鲁棒性.

基于Multi-Agent的高速公路集成交通控制系统

高速公路集成交通控制系统是一个非线性分布的动态复杂巨型系统。在智能控制系统分级递阶结构的基础上,提出了高速公路集成交通控制系统的集散递阶体系结构,并以Agent 作为各级智能控制器,建立了高速公路集成交通控制系统MAS模型,探讨了MAS中主体的结构模型、构造方法、通信机制和协商机制,为基于Multi Agent的高速公路集成交通控制系统的最终实现提供了理论指导和方法依据。

基于multi-Agent和Petri网的分布式风光互补系统

近年来随着"大机组、高电压、大电网"发供电模式的弊端日益显露,分散式供电系统成为21世纪电力工业的发展方向.此外,随着风力、太阳能等可再生能源的飞速发展及其发电效率的不断提高,使研究风光互补系统的分布式供电有着重要意义.文中提出一种基于multi-Agent的分布式风光互补混合供电系统,将分散的风力、太阳能光伏发电装置组成分布式系统,并采用有色Petri网建立了multi-Agent的协作模型.通过模拟风力–太阳能的运行情况,证明了分散控制策略的有效性.

基于Multi-agent的矿井通风仿真系统研究

针对实时性有一定要求的通风仿真系统中必须满足的时间和空间复杂度要求 ,提出了一种基于多A gent系统的分析和解决方案。介绍了基于多Agent系统仿真的特点和功能 ,给出了通风仿真系统中Agent的结构。最后 ,采用了一种“投票”式的协同策略 ,从一个新角度来解决通风中的风网解算存在问题 ,并构筑了一个通风仿真系统。