Dynamic performance management system

An integrated, efficient and effective performance management system, ＂dynamic performance management system＂, is presented, which covers the entire performance management process including measures design, analysis, and dynamic update. The analysis of performance measures using causal loop diagrams, qualitative inference and analytic network process is mainly discussed. A real world case study is carried out throughout the paper to explain how the framework works. A software tool for DPMS, Performance Analyzer, is also introduced.

Assessing Air Traffic Management Performance Interdependencies through Bayesian Networks： Preliminary Applications and Results

The performance model proposed by this study represents an innovative approach to deal with performance assessment in ATM （air traffic management）. It is based on Bayesian networks methodology, which presents several advantages but also some drawbacks as highlighted along the paper. We illustrate the main steps required for building the model and present a number of interesting results. The contribution of the paper is two-fold： （1） It presents a new methodological approach to deal with a problem which is of strategic importance for ANSPs （air navigation service providers）; （2） It provides insights on the interdependencies between factors influencing performance. Both results are considered particularly important nowadays, due to the SES （Single European Sky） performance scheme and its related target setting process.

Traffic Characteristics Based Dynamic Radio Resource Management in Heterogeneous Wireless Networks

The traffic with tidal phenomenon in Heterogeneous Wireless Networks(HWNs)has radically increased the complexity of radio resource management and its performance analysis.In this paper,a Simplified Dynamic Hierarchy Resource Management(SDHRM)algorithm exploiting the resources dynamically and intelligently is proposed with the consideration of tidal traffic.In network-level resource allocation,the proposed algorithm first adopts wavelet neural network to forecast the traffic of each sub-area and then allocates the resources to those sub-areas to maximise the network utility.In connection-level network selection,based on the above resource allocation and the pre-defined QoS requirement,three typical network selection policies are provided to assign traffic flow to the most appropriate network.Furthermore,based on multidimensional Markov model,we analyse the performance of SDHRM in HWNs with heavy tailed traffic.Numerical results show that our theoretical values coincide with the simulation results and the SDHRM can improve the resource utilization.

A Novel Outage Capacity Objective Function for Optimal Performance Monitoring and Predictive Fault Detection in Hybrid Free-Space Optical and RF Wireless Networks

This study develops an optimal performance monitoring metric for a hybrid free space optical and radio wireless network, the Outage Capacity Objective Function. The objective function—the dependence of hybrid channel outage capacity upon the error rate, jointly quantifies the effects of atmospheric optical impairments on the performance of the free space optical segment as well as the effect of RF channel impairments on the radio frequency segment. The objective function is developed from the basic information-theoretic capacity of the optical and radio channels using the gamma-gamma model for optical fading and Ricean statistics for the radio channel fading. A simulation is performed by using the hybrid network. The objective function is shown to provide significantly improved sensitivity to degrading performance trends and supports of proactive link failure prediction and mitigation when compared to current thresholding techniques for signal quality metrics.

帮信罪与拒履网安义务罪适用差异研究——以刑法网络治理模式为视角

帮助信息网络犯罪活动罪的司法案件数量和学术讨论热度远超拒不履行信息网络安全管理义务罪,教义学分析无法充分解释二罪差异。实证分析二罪理论文献及司法解释文件,发现国家对拒不履行信息网络安全管理义务罪的网络治理效能抱有期待。适当转变司法治理思维,减少直接性控制,承认社会个体治理能力,建立“共建共享公治”的多元网络治理格局,既符合我国的社会发展趋势也具备政策基础。多元治理利于调动网络安全管理义务人网络专长,及时处理信息。即使无法完全采取多元治理模式,也应对帮助信息网络犯罪活动罪的体系突破进行限缩,防止其持续膨胀。

大数据时代人工智能在计算机网络技术中的应用

人工智能是计算机科学的一个分支,该领域的研究包括机器人、语言识别、图像识别、自然语言处理和专家系统等。在大数据时代的背景下,人工智能技术与计算机网络的融合发展成为信息技术领域一个新的研究热点。探讨人工智能在计算机网络技术中的应用及其带来的影响,深入分析人工智能在网络技术中的应用场景,以及下一代网络技术的发展方向,对人工智能时代计算机网络技术的发展和应用具有理论指导和实践意义。

SONPMSA系统通信平台关键技术的研究

面向业务的网络性能管理系统是以网络中运行的业务为被管理对象的,而传统网络中间节点的封闭性限制了系统的实现。SONPMSA系统通信平台的设计与实现是通过引入主动代码技术、定义基于应用层实现的主动信包的格式、利用Java的平台无关性解决了主动代码分发机制中关键主动代码的解析和加载两大功能的设计与实现问题。从而解决了面向业务的网络性能管理系统通信平台动态地执行网络业务数据的管理问题。

城市配电网工程建设实施全过程安全管理研究

针对配电网建设中存在的问题,探讨了全过程管理的优势,并分析了标准化管理过程中的常见问题,提出了配网工程建设施工全过程的安全管理措施,包括选址路径安全管理、施工阶段安全管理以及流程正常运行的绩效考核与控制。

数据通信网络中的智能网络技术实现与优化

随着信息技术的快速发展,数据通信网络已成为现代社会的基础设施之一。智能网络技术作为提升网络性能、增强服务能力的关键技术,其研究和应用引起广泛关注。主要探讨智能网络技术在数据通信网络中的应用现状,分析其在网络管理、服务质量保证、资源分配等方面的优势。同时,针对智能网络技术在实际应用中遇到的问题,提出一系列优化策略。

基于全光网络的光纤通信传送网性能监测技术

文章针对全光网络中光纤通信传送网的性能监测问题,提出一种基于光电检测、信号处理、大数据分析以及机器学习等技术的智能监测系统。该系统采用模块化设计,可实现光信号质量监测、网络性能分析、故障诊断与定位等功能。通过实验验证,该系统在实时性、准确性、可靠性等方面均达到预期目标,为全光网络的智能化管理和优化控制提供了有力支撑。

协同创新网络视角下装备制造企业创新绩效影响研究

随着技术复杂性的日益提升,装备制造业产业链不断优化升级,仅依靠企业单打独斗难以实现创新,必须将企业置于协同创新网络中,与协作企业、大学和科研院所、政府、金融机构等多主体一起协同发展。因此,以协同创新网络为视角,探究装备制造企业创新绩效的影响因素。将知识管理作为中介,探究协同创新、知识管理以及创新绩效之间的关系,参考相关文献,构建三者之间的假设关系。运用SPSS26.0及AMOS23.0软件进行信效度分析,运用结构方程模型验证关系假设。结果表明,协同创新网络规模、协同创新网络关系强度、网络开放性、知识管理均对创新绩效有显著正向影响,同时知识管理也可作为中介效应,协同创新通过知识管理对创新绩效产生正向影响。

基于物联网技术的智能家居系统质量管理与性能评估

本文深入探讨了基于物联网技术的智能家居系统质量管理与性能评估的关键问题。通过对智能家居设备的数据采集、传输、处理和控制等环节进行全面分析,建立了一套系统性的质量管理框架。在此框架下,针对传感器网络、通信协议和数据处理算法等关键技术,提出了相应的性能评估方法,以确保系统在实际应用中具备高度的可靠性和稳定性。同时,本文还探讨了智能家居系统在复杂环境中的性能优化策略,包括能耗优化、响应速度提升等方面的关键技术。通过系统的实验验证和性能评估,本研究不仅为智能家居系统的质量管理提供了有效的方法,也为推动物联网技术在智能家居领域的进一步应用奠定了技术基础。

网络中介服务的刑事归责:边界划定与罪名诠义

网络中介服务提供者不履行安全管理义务导致严重后果的,应在不作为共犯的路径上探寻承担刑事责任的可行性。网络中介服务者在客观上违反法律、法规或行业准则为他人的犯罪行为提供了帮助,主观上具有帮助他人实施犯罪的目的,或是直接针对具有犯罪性质的软件、网页或功能提供技术服务,可判定逾越了中立帮助的界限;若客观上无明显异常,则要看主观上与正犯之间有无“通谋”。网络中介服务者对网络空间犯罪活动的不作为参与,未经责令改正而拒不改正的过程,通常不能以犯罪论处,但与正犯就不履行安全管理义务具有提供服务前的明知或提供服务后的通谋的,可直接成立相关犯罪的不作为犯。网络中介服务者积极参与的帮助,看其是否与正犯具有指向一致的犯罪故意内容,再决定是以帮助信息网络犯罪活动罪还是正犯所为之罪的共犯论处。

Reinforcement Learning-Based Optimization for Drone Mobility in 5G and Beyond Ultra-Dense Networks

Drone applications in 5th generation(5G)networks mainly focus on services and use cases such as providing connectivity during crowded events,human-instigated disasters,unmanned aerial vehicle traffic management,internet of things in the sky,and situation awareness.4G and 5G cellular networks face various challenges to ensure dynamic control and safe mobility of the drone when it is tasked with delivering these services.The drone can fly in three-dimensional space.The drone connectivity can suffer from increased handover cost due to several reasons,including variations in the received signal strength indicator,co-channel interference offered to the drone by neighboring cells,and abrupt drop in lobe edge signals due to antenna nulls.The baseline greedy handover algorithm only ensures the strongest connection between the drone and small cells so that the drone may experience several handovers.Intended for fast environment learning,machine learning techniques such as Q-learning help the drone fly with minimum handover cost along with robust connectivity.In this study,we propose a Q-learning-based approach evaluated in three different scenarios.The handover decision is optimized gradually using Q-learning to provide efficient mobility support with high data rate in time-sensitive applications,tactile internet,and haptics communication.Simulation results demonstrate that the proposed algorithm can effectively minimize the handover cost in a learning environment.This work presents a notable contribution to determine the optimal route of drones for researchers who are exploring UAV use cases in cellular networks where a large testing site comprised of several cells with multiple UAVs is under consideration.

Multi-indicator Active Queue Management Method

A considerable number of applications are running over IP networks.This increased the contention on the network resource,which ultimately results in congestion.Active queue management(AQM)aims to reduce the serious consequences of network congestion in the router buffer and its negative effects on network performance.AQM methods implement different techniques in accordance with congestion indicators,such as queue length and average queue length.The performance of the network is evaluated using delay,loss,and throughput.The gap between congestion indicators and network performance measurements leads to the decline in network performance.In this study,delay and loss predictions are used as congestion indicators in a novel stochastic approach for AQM.The proposed method estimates the congestion in the router buffer and then uses the indicators to calculate the dropping probability,which is responsible for managing the router buffer.The experimental results,based on two sets of experiments,have shown that the proposed method outperformed the existing benchmark algorithms including RED,ERED and BLUE algorithms.For instance,in the first experiment,the proposed method resides in the third-place in terms of delay when compared to the benchmark algorithms.In addition,the proposed method outperformed the benchmark algorithms in terms of packet loss,packet dropping,and packet retransmission.Overall,the proposed method outperformed the benchmark algorithms because it preserves packet loss while maintaining reasonable queuing delay.

Managing and Measuring Business Networks in Russia

The research topic of this article ＂Managing and measuring business networks in Russia＂ has been under consideration in three successive research projects oriented to Russian business to business （B2B） networks. The projects have been accomplished by HAMK, VTT Technical Research Centre of Finland, and Tampere University of Technology （TUT）. Russian experts from The Higher School of Economics in Moscow and the Graduate School of Management in St. Petersburg were involved in these projects. We asked how managing and measuring B2B networks should be developed, when a company becomes established to Russian market. Both qualitative and quantitative analytical methods were used. Results concerning performance measurement presented in this article are based on data which was collected using survey and a questionnaire. Methodical triangulation was carried out as both research instruments produced 17 full answers for questions concerning performance measurement. Wider results are based on about 200 face-to-face interviews and answers for questionnaires. Results indicate that in Russia the management and leadership models as well as the company performance measurement should be adjusted to Russian traditions. Networked companies have difficulties in applying matrix type organizations in Russia, as management practices are based traditionally on authoritarian leadership. However, clear and specific targets should be set for projects and strategic business areas. Targets and bonuses should be agreed on with managers and staff throughout the company and the business network. Personal networks, within project stake- holders, have more emphasis in Russia than in Finland, because personal relations to authorities＇, and organizations are needed at every stage of the project. Therefore, managing SMEs working in B2B networks is challenging in Russia. In this article, a method for setting and implementing strategic targets for a business networked company is presented. Understanding networked performance measurement indicators will enable successful business for all networked partners in the long tenn.

基于信息化平台中医住院医师规范化培训绩效考评体系研究

随着中医住院医师规范化培训工作的全面实施和深入推进,提升培训质量成为核心问题。绩效考评是一种重要管理手段,在信息化管理平台的基础上,成都中医药大学附属医院从确立绩效考评管理构架、设立绩效考评评价细则、构建绩效考评模式3方面健全“临床科室-带教老师-住培学员”的多元多维绩效考评体系,将临床科室、带教老师、住培学员的住培工作纳入绩效考评,提升住培考评在绩效考评中的比重,加强住培师资队伍的教学责任感与积极性,激励住培学员的培训热情,达到提升成都中医药大学附属医院中医住院医师规范化培训质量的目标。

面向6G的用户为中心网络研究综述

与第5代移动通信网络(5G)相比,第6代移动通信网络(6G)有望引入新的性能指标和应用方案,如全球覆盖、更高的频谱/能源/成本效率、更高的智能和安全水平。用户为中心网络(UCN)将成为实现6G的关键推动者,因为其突破了传统基站为中心网络并与信息产业的新兴技术形成了有效融合。该文将从物理层的信道估计和预测、网络上层的性能分析以及链路层的无线资源管理等方面综述这一新型网络的研究现状。首先,讨论和分析UCN的概念和总体网络架构;其次,总结UCN网络中信道估计预测方法、性能分析策略和无线资源管理(RRM)方案;最后,在广泛的调研基础上,探讨UCN网络中的开放性问题,为今后的研究方向提供思路。此综述旨在使读者快速而全面地了解UCN的当前技术状况,从而吸引更多的研究人员进入这一领域。

基于改进LSTM的多源绩效数据综合评估与预测模型设计

传统绩效的评估与预测方法采取指标单一且覆盖面较窄,无法对医院绩效情况进行准确估计。针对上述不足,文中依托深度学习技术构建了多源绩效数据评估预测模型。根据DRG模型选取各项指标对传统LSTM网络进行改进,并采用双向网络提高了参数迭代的准确度。同时通过引入注意力机制有效提升了权重因子的计算精确度,再建立三重残差网络实现了对数据的训练与预测。数据实验结果表明,所提算法在预测拟合度方面与实际结果最为接近,且其误差指标在对比算法中均为最优,从而证明了该算法具有较好的综合性能。

基于Telemetry的OTN设备性能采集系统研究

【目的】软件定义网络(SDN)设备规模日益增大,承载的业务越来越多。针对传统光传送网(OTN)设备使用简单网络管理协议(SNMP)采集性能数据不能解决大规模和高性能网络管理的问题,文章提出了基于Telemetry架构的OTN设备性能采集系统。【方法】通过设计分布式数据模型转换和数据编码架构,提高数据转换和编码效率以及采集系统的规模,同时均衡设备算力,设备以秒级周期定时采集并上报性能数据到采集器。【结果】文章对使用SNMP和部署Telemetry系统设备的性能采集能力进行了实验对比分析。【结论】结果表明,系统在数据模型转换和编码、数据传输效率、采集数据的时效性以及数据获取的灵活性方面更有优势,满足SDN对智能运维的要求。