A Game-Theoretic Perspective on Resource Management for Large-Scale UAV Communication Networks

As a result of rapid development in electronics and communication technology,large-scale unmanned aerial vehicles(UAVs)are harnessed for various promising applications in a coordinated manner.Although it poses numerous advantages,resource management among various domains in large-scale UAV communication networks is the key challenge to be solved urgently.Specifically,due to the inherent requirements and future development trend,distributed resource management is suitable.In this article,we investigate the resource management problem for large-scale UAV communication networks from game-theoretic perspective which are exactly coincident with the distributed and autonomous manner.By exploring the inherent features,the distinctive challenges are discussed.Then,we explore several gametheoretic models that not only combat the challenges but also have broad application prospects.We provide the basics of each game-theoretic model and discuss the potential applications for resource management in large-scale UAV communication networks.Specifically,mean-field game,graphical game,Stackelberg game,coalition game and potential game are included.After that,we propose two innovative case studies to highlight the feasibility of such novel game-theoretic models.Finally,we give some future research directions to shed light on future opportunities and applications.

Quantum Communication Networks and Trust Management: A Survey

This paper summarizes the state of art in quantum communication networks and trust management in recent years.As in the classical networks,trust management is the premise and foundation of quantum secure communication and cannot simply be attributed to security issues,therefore the basic and importance of trust management in quantum communication networks should be taken more seriously.Compared with other theories and techniques in quantum communication,the trust of quantum communication and trust management model in quantum communication network environment is still in its initial stage.In this paper,the core technologies of establishing secure and reliable quantum communication networks are categorized and summarized,and the trends of each direction in trust management of quantum communication network are discussed in depth.

Effective Energy Management Scheme by IMPC

The primary purpose of the Energy Management Scheme(EMS)is to monitor the energy fluctuations present in the load profile.In this paper,the improved model predictive controller is adopted for the EMS in the power system.Emperor Penguin Optimization(EPO)algorithm optimized Artificial Neural Network(ANN)with Model Predictive Control(MPC)scheme for accurate prediction of load and power forecasting at the time of preoptimizing EMS is presented.For the power generation,Renewable Energy Sources(RES)such as photo voltaic(PV)and wind turbine(WT)are utilized along with that the fuel cell is also presented in case of failure by the RES.Such a setup is connected with the grid and applies to the household appliances.In improved model predictive control(IMPC),the set of constraints for the powerflow in the system is optimized by the ANN,which is trained by EPO.Such a tuning based prediction model is presented in the IMPC technique.The proposed work is implemented in the MATLAB/Simulink platform.The energy management capability of the proposed system is analyzed for different atmospheric conditions.The total system cost,life cycle cost and annualized cost for IMPC are 48%,45%and 15%,respectively.From the performance analysis,the cost obtained by the proposed method is very low compared to that obtained by the existing techniques.

Development of Crisis Management Models Combined with Cloud Computing

Three monitor models of enterprise crisis were introduced,i.e.,the monitoring model of enterprise crisis based on intelligent Meta search,the enterprise crisis management model based on artificial neural network and the combined early-warning model.Combined with the advantages of cloud computing,the prominent crisis management models are improved and more efficient,comprehensive and accurate in enterprise crisis management.Through the empirical study of the models,cloud computing makes the early warning structures of enterprise crisis tend to be more simple and efficient,cloud computing can effectively enhance the recognition ability and learning ability of the crisis management,and cloud computing can keep data information updating and realize the dynamic management of enterprise joint early-warning.At the same time,according to the comparative analysis and the experimental result,the crisis management models based on cloud computing also need some improvements.

Fuzzy-Neuro Model for Intelligent Credit Risk Management

This paper presents hybrid fuzzy logic and neural network algorithm to solve credit risk management problem. Credit risk is the risk of loss due to a debtor’s non-payment of a loan or other line of credit. A method of evaluating the credit worthiness of a customer is complex and non-linear due to the diverse combinations of risk involve. To address this problem a credit scoring method is proposed in this paper using hybrid fuzzy logic-neural network (HFNN) model. The model will be implemented, tested, and validated for individual auto loans using real life bank data. The neural network is used as the learner and the fuzzy logic is used as the implementer. The neural network will fine tune the fuzzy sets, remove redundant input variables, and extract fuzzy rules. The extracted fuzzy rules are evaluated to retain the best k number of rules that will give final and intelligent decisions. The experiment results show that the perform-ance of the proposed HFNN model is very accurate, robust, and reliable. Comparison of these results to other previous published works is also presented in this paper.

Analytical Modeling of a Multi-queue Nodes Network Router

This paper presents the derivation of an analytical model for a multi-queue nodes network router, which is referred to as the multi-queue nodes （mQN） model. In this model, expressions are derived to calculate two performance metrics, namely, the queue node and system utilization factors. In order to demonstrate the flexibility and effectiveness of the mQN model in analyzing the performance of an mQN network router, two scenarios are performed. These scenarios investigated the variation of queue nodes and system utilization factors against queue nodes dropping probability for various system sizes and packets arrival routing probabilities. The performed scenarios demonstrated that the mQN analytical model is more flexible and effective when compared with experimental tests and computer simulations in assessing the performance of an mQN network router.

市场渗透率约束下的拼车奖励方案优化模型

目前,我国拼车出行交通模式的市场份额相对较低,在缓解交通拥堵、节能减排方面仍有巨大潜力未被充分挖掘。基于奖励的交通需求管理策略可以提高民众拼车出行意愿,但奖励方案的设置与拼车出行的市场渗透率高度相关,不合理的奖励方案容易导致成本增加,甚至项目破产。为进一步激发拼车需求,合理利用交通资源,文中提出一种基于路段的拼车奖励方案优化模型,以拼车为支点,以奖励为杠杆,实现社会出行总成本的降低。其中,上层模型旨在寻找最优拼车奖励方案以最小化社会出行总成本,下层模型为相应奖励方案下的拼车出行车辆和单人驾驶车辆用户均衡流量分配模型。采用嵌套Frank-Wolfe算法的遗传算法求解该模型,并以Sioux Falls路网和Nguyen Dupuis路网为算例对模型的可行性及有效性进行了验证。结果表明:不符合市场渗透率的预算投入会导致社会总成本大幅上涨;在最优拼车奖励方案下,社会出行总成本降低约24.53%,50%的拥堵路段的拥堵得到缓解,出行公平性问题得到缓和。文中提出的模型可为道路管理者设置科学、合理的拼车奖励方案提供理论基础。

数字化转型背景下的人力资源管理者胜任特征模型建构

数字经济时代,人工智能、区块链、云计算、大数据等数字化技术的应用对人力资源管理者的任职条件提出了新的要求。文章基于网络文本分析法和典型案例分析法,构建了包含专业知识、职业技能、通用能力和个人特质4个因子27个条目的胜任特征模型。其中,数据分析能力、云平台及大数据操作管理技能、快速学习能力、场景迁移能力等胜任特征体现了数字化转型背景下的人力资源管理者胜任特征新要求。企业要进行数字化转型,应重视数字化实践中人力资源管理者的配置与培训,通过新的胜任特征模型建立人才画像和人才评估标准,运用大数据技术提升人力资源管理部门工作效率。

基于隐含空间模型降维和LDA模型的学科主题识别研究

【目的/意义】现有学科研究主题的梳理多为领域专家的定性分析和学科学者的文献梳理,一定程度会由于研究思维的局限性和获取知识的片面性造成学科研究主题误判,为有效避免漏判误判现象的发生,提出识别模型。【方法/过程】首先,运用传统LDA模型分析主题特征词;其次,结合上下文语义信息进行中文分词,形成学科主题词库;最后,结合隐含位置聚类算法发现潜在社区,提高主题识别效果。【结果/结论】提出的方法一定程度上优化了主题挖掘算法在识别短文本主题的效果,消除主观意愿。由计算机自行分类并实现科学研究前沿主题的预测,揭示前沿领域的研究热点,为致力于研究前沿学科的新兴学者提供参考价值。

面向算力网络的算力建模与度量技术研究

算力度量与建模技术作为算力网络发展的重要基础,通过统一建模算力需求和资源,结合网络性能指标,形成算网能力模板,为算力路由、管理和计费提供统一度量标准。深入探讨了该技术的发展趋势与挑战,分析了算力网络发展中的算力度量需求。在此基础上对算力网络各层级进行抽象建模,设计了一种基于任务基本处理单元的算力度量和管理方法,并提出了一套完善的算力度量与建模体系,为算力网络的进一步发展提供了有力支撑和新的思路。

Intensive management enhances mycorrhizal respiration but decreases free-living microbial respiration by affecting microbial abundance and community structure in Moso bamboo forest soils

Intensive management is known to markedly alter soil carbon(C)storage and turnover in Moso bamboo forests compared with extensive management.However,the effects of intensive management on soil respiration(RS)components remain unclear.This study aimed to evaluate the changes in different RScomponents(root,mycorrhizal,and free-living microorganism respiration)in Moso bamboo forests under extensive and intensive management practices.A1-year in-situ microcosm experiment was conducted to quantify the RScomponents in Moso bamboo forests under the two management practices using mesh screens of varying sizes.The results showed that the total RSand its components exhibited similar seasonal variability between the two management practices.Compared with extensive management,intensive management significantly increased cumulative respiration from mycorrhizal fungi by 36.73%,while decreased cumulative respiration from free-living soil microorganisms by 8.97%.Moreover,the abundance of arbuscular mycorrhizal fungi(AMF)increased by 43.38%,but bacterial and fungal abundances decreased by 21.65%and 33.30%,respectively,under intensive management.Both management practices significantly changed the bacterial community composition,which could be mainly explained by soil pH and available potassium.Mycorrhizal fungi and intensive management affected the interrelationships between bacterial members.Structural equation modeling indicated that intensive management changed the cumulative RSby elevating AMF abundance and lowering bacterial abundance.We concluded that intensive management reduced the microbial respiration-derived C loss,but increased mycorrhizal respiration-derived C loss.

基于需求功率预测的电动拖拉机能量管理策略

针对电动拖拉机在犁耕工况下电机需求电流波动比较大的特点,为了改善动力电池的输出电流过高或过低及电动拖拉机犁耕持续作业时间短的现象,利用超级电容高功率密度的特点,设计了一种锂电池+超级电容结构的双电源电动拖拉机,并建立了Amesim/Simulink联合仿真模型。以模型预测控制作为双电源系统的能量管理方法,基于长短期记忆神经网络建立电动拖拉机犁耕工况下的需求功率预测模型,使用动态规划算法求解最佳的锂电池输出电流。仿真结果表明:相比于模糊控制策略,基于模型预测控制策略有效降低了锂电池大电流放电的频率且峰值电流降低了40%,有效提高了锂电池的使用寿命;超级电容的SOC保持在比较高的范围内,且电动拖拉机在犁耕工况下的单位里程能量消耗降低了2.17%,实现了双电源电流分配最优,提高了电动拖拉机的动力性和经济性。

量子模糊信息管理数学模型研究

为了高效处理大数据所具有的复杂性和不确定问题,将“不确定性问题+直觉模糊集理论+量子计算”交叉融合,构建基于直觉模糊集理论的量子模糊信息管理数学模型。为了验证该模型的可行性、合理性和有效性,设计了不确定性环境下基于参数化量子线路的量子模糊神经网络仿真实验。实验结果表明,基于该模型的量子模糊神经网络模型能更客观、准确、全面地反映不确定性问题中各对象所蕴含的知识信息,从而提高算法处理大数据的准确性。

基于粒子群优化算法的急诊科心电监护设备风险管理模式研究

目的:基于粒子群优化(PSO)算法构建设备风险管理模型,探讨其在急诊科心电监护设备管理中的应用价值。方法:采用PSO算法优化神经网络模型构建心电监护设备风险管理模型,收集北京市普仁医院心电监护设备运行风险数据进行归一化处理,并将2021年11月至2023年10月北京市普仁医院急诊科在用的30台心电监护设备,按照设备管理模式不同对其分别采用反向传播(BP)神经网络模型(简称传统BP模式,设备运行时段为2021年11月至2022年10月)和PSO算法的设备风险管理模型(简称PSO算法模式,设备运行时段为2022年11月至2023年10月)进行管理,比较两种管理模型设备风险故障识别效果(测试集与训练集)、警报风险控制效果和设备故障维修诊断用时。结果:采用PSO算法的测试集风险故障数据识别受试者工作特征(ROC)曲线下面积(AUC)值、准确率、灵敏度和特异度分别为0.869、93.6%、92.8%和95.1%,训练集风险故障数据识别AUC值、准确率、灵敏度和特异度分别为0.839、95.6%、97.9%和96.7%,均高于传统BP模式,差异有统计学意义(x_(测试)^(2)=3.691、4.023、3.557、3.409,x_(训练)^(2)=6.884、5.962、5.334、3.215;P<0.05)。采用PSO算法的心电监护设备警报阈值合格率和设备维护平均合格率分别为(98.61±3.07)%和(98.79±3.11)%,均高于传统BP模式,警报静音率为(1.14±0.27)%,低于传统BP模式,差异均有统计学意义(Z=11.831、10.020、21.141,P<0.05)。采用PSO算法的心电监护设备内部报修用时、外部报修用时、故障诊断用时和报修总用时分别为(1.21±0.96)、(3.18±1.09)、(5.08±1.93)和(10.95±2.81)min,均少于传统BP模式,差异有统计学意义(t=15.404、19.020、16.694、25.511,P<0.05)。结论:基于PSO算法构建的心电监护设备风险管理模型应用,能够提高心电监护设备风险故障数据识别灵敏度、特异度和准确性,提升警报阈值合格率和设备维护合格率,降低警报静音率,缩短故障诊断报修用时。

Cyber Resilience through Real-Time Threat Analysis in Information Security

This paper examines how cybersecurity is developing and how it relates to more conventional information security. Although information security and cyber security are sometimes used synonymously, this study contends that they are not the same. The concept of cyber security is explored, which goes beyond protecting information resources to include a wider variety of assets, including people [1]. Protecting information assets is the main goal of traditional information security, with consideration to the human element and how people fit into the security process. On the other hand, cyber security adds a new level of complexity, as people might unintentionally contribute to or become targets of cyberattacks. This aspect presents moral questions since it is becoming more widely accepted that society has a duty to protect weaker members of society, including children [1]. The study emphasizes how important cyber security is on a larger scale, with many countries creating plans and laws to counteract cyberattacks. Nevertheless, a lot of these sources frequently neglect to define the differences or the relationship between information security and cyber security [1]. The paper focus on differentiating between cybersecurity and information security on a larger scale. The study also highlights other areas of cybersecurity which includes defending people, social norms, and vital infrastructure from threats that arise from online in addition to information and technology protection. It contends that ethical issues and the human factor are becoming more and more important in protecting assets in the digital age, and that cyber security is a paradigm shift in this regard [1].

总体国家安全观下网络舆情信息传播特征

在新时代总体国家安全观视角下,基于新浪微博舆情信息大数据,探究基于不同类型意见领袖(官媒、自媒体与大V)的网络舆情信息传播机制。通过构建独立级联模型与社会网络分析模型,对3类意见领袖传播舆情信息特征予以评价。结果表明,官媒对舆情信息传播的主导作用最强,具有相当规模用户群的大V和自媒体,以不同方式影响舆情信息演化。研究结果为相关突发事件中的用户分析和舆情信息传播研究与网络信息内容生态治理提供了新的理论视角。

新型电力负荷管理系统的多模态通信组网研究

针对新型电力系统背景下电力负荷管理通信终端海量接入带来的通信可靠性问题,提出了一种基于多模态智慧网络的通信组网模型及其应用部署方案。首先在分析新型电力负荷管理系统业务通信可靠性需求基础上,构建了基于多模态智慧网络的层次化通信组网模型与部署架构;然后在所提组网模型框架下,针对现阶段新型电力负荷管理系统通信接入网层部署可靠性,设计了适应负控业务场景包含有线专网、无线专网和无线虚拟专网的通信接入网层工程应用部署方案;最后分析了新型电力负荷管理系统通信组网应用过程中尚待解决的关键技术问题,为新型电力负荷管理系统通信通道建设提供技术理论参考。

一种基于轻量化神经网络的电磁信号识别方法

针对电磁频谱管控领域中基于神经网络模型的电磁信号识别方法,占用内存、计算时间和传输资源消耗较大,导致难以边缘部署应用的问题,提出了一种基于轻量化神经网络的电磁信号识别方法。首先,对电磁I/Q路数据进行空白值去除、划窗切片、归一化、频域特征提取四个信号预处理步骤,接着训练残差神经网络模型对其进行分类识别,最后通过参数剪枝和聚类量化两个步骤完成网络轻量化。所提方法在实测信号判识准确率较之前变化较小的前提下,内存压缩率为4.65,相较于深度残差网络(Deep Residual Network,ResNet)计算时间加快61.15 s,表明该方法能够在达到高识别率的同时有效降低模型存储规模,在计算时间、计算量方面也具有优势。

基于四网融合的既有铁路市域化运营改造与发展研究——以西户铁路为例

针对四网融合背景下既有铁路市域化运营改造的相关问题,以西户铁路为例,研究既有铁路市域化运营改造的新思路与新模式。首先结合市域铁路的功能定位,在梳理既有铁路开行市域列车建设模式和运营特点的基础上,提出现阶段利用既有铁路开行市域列车存在技术标准不完善、线网融合程度低、客流强度不高、运营亏损严重等问题;然后以西户铁路开行市域列车为例进行研究,分析运输组织模式、客流特征、交通接驳方式等内容,深入探讨单线市域铁路客货混跑模式的适用性;最后论述市域铁路高质量发展的优化策略,提出统筹前期规划、优化交通衔接、提升运营效率、加快用地开发、打破管理壁垒等建议方案,为后续既有铁路改造提升提供参考。