基于分布式知识推理的语义认知网络

6G无线网络“服务随心所想、网络随需而变、资源随愿共享”的全新愿景与需求,激发了一种新的通信范式——语义通信和语义认知网络的发展。语义通信通过传输信息的真实含义而非传输和复现完整的原始消息来提升通信效率和可靠性。要在6G网络中部署和充分发挥语义通信的潜力,需要一种能够有效处理和利用语义信息的新技术。提出了一种基于图推理和联邦学习的6G网络语义通信新框架,框架结合了图推理技术,例如图神经网络和知识图嵌入,以实现对大规模和复杂语义知识库的高效且可扩展的推理;框架集成了联邦学习技术,可以跨多个边缘服务器进行协作和隐私保护推理,同时将敏感数据和个人数据保留在边缘服务器上。进行广泛的实验,以评估所提框架在推理准确性、效率和可扩展性方面的性能,并证明其相对于现有方法的优越性。框架在语义通信、图推理和联合边缘计算领域开辟了新的研究方向,对实现6G智能内生的通信网络的愿景至关重要。

面向6G智能内生的隐性语义认知通信

语义通信的目标在于识别信息蕴藏的含义并传递给目标用户。这种基于语义理解的新型通信范式可以实现信息的高效传输、精准表达与智能处理,进而使得网络本身能够理解用户的意图和需求、提供更加个性化和智能化服务,符合人们对未来智能内生通信网络的需求。在目前语义通信领域中,大部分工作着重于传输直接从源信号中识别的显性语义(如标签以及信号特征)。针对隐性语义通信问题展开研究,提出了一种面向智能内生的隐性语义认知通信架构,即通过将信源信号无法识别的隐性关系与其关联的语义信息传递给目标用户,实现了隐性语义的传输与恢复。与传统通信致力于追求通信总量最大化不同,本架构致力于帮助接收端学习一个基于有限信息自动生成隐性语义推理规则。在此架构下,接收端用户始终可以学习与信源用户相匹配的推理规则。实验结果表明,所提出的架构在目标用户的符号误码率方面取得了较大改进,优于现有的非推理通信解决方案。

吉西他滨、顺铂联合5S健康管理治疗晚期肺癌的临床效果观察

目的:分析吉西他滨、顺铂联合5S健康管理治疗晚期肺癌的临床效果。方法:选取2022年6月—2023年6月萍乡市上栗县人民医院收治的晚期肺癌患者100例作为研究对象,双盲选法分为对照组和观察组,各50例。对照组给予吉西他滨、顺铂治疗,观察组在对照组基础上联合5S健康管理法。比较两组患者临床疗效、并发症发生情况、炎性因子水平、免疫功能指标、负性情绪[焦虑自评量表(SAS)和抑郁自评量表(SDS)]评分。结果:观察组治疗总有效率高于对照组,差异有统计学意义(P<0.05);观察组并发症发生率低于对照组,差异有统计学意义(P=0.0001);治疗后,观察组基质金属蛋白酶抑制剂-1、C反应蛋白、肿瘤坏死因子水平低于对照组,差异有统计学意义(P<0.05);治疗后,观察组CD3^(+)、CD4^(+)、CD8^(+)水平高于对照组,差异有统计学意义(P<0.05);治疗后,观察组SAS与SDS评分低于对照组,差异有统计学意义(P<0.05)。结论:吉西他滨、顺铂联合5S健康管理治疗晚期肺癌,可改善患者的负性情绪、免疫功能、炎性反应,继而保证治疗效果,并发症发生率较低。

基于胶囊网络的群智感知数据分类模型

针对群智感知平台中感知数据分类的准确度问题,提出了一种基于胶囊网络的感知数据分类模型。首先,构建了群智感知系统模型,结合感知数据构建了胶囊网络结构和样本数据,通过胶囊网络实现了样本数据和预测值之间的映射。然后,胶囊网络对样本数据的像素点特征矩阵进行拟合学习,使用动态路由算法更新胶囊网络的耦合系数。最后,结合间隔损失函数和重构损失函数,实现了对基于胶囊网络的感知数据分类模型参数的优化。仿真结果表明相对于残差网络的数据分类模型,所提模型有效提高了数据分类的准确度。

Effects of RapidEye Imagery's Red-edge Band and Vegetation Indices on Land Cover Classification in an Arid Region

Land cover classification(LCC) in arid regions is of great significance to the assessment, prediction, and management of land desertification. Some studies have shown that the red-edge band of RapidE ye images was effective for vegetation identification and could improve LCC accuracy. However, there has been no investigation of the effects of RapidE ye images' red-edge band and vegetation indices on LCC in arid regions where there are spectrally similar land covers mixed with very high or low vegetation coverage information and bare land. This study focused on a typical inland arid desert region located in Dunhuang Basin of northwestern China. First, five feature sets including or excluding the red-edge band and vegetation indices were constructed. Then, a land cover classification system involving plant communities was developed. Finally, random forest algorithm-based models with different feature sets were utilized for LCC. The conclusions drawn were as follows: 1) the red-edge band showed slight contribution to LCC accuracy; 2) vegetation indices had a significant positive effect on LCC; 3) simultaneous addition of the red-edge band and vegetation indices achieved a significant overall accuracy improvement(3.46% from 86.67%). In general, vegetation indices had larger effect than the red-edge band, and simultaneous addition of them significantly increased the accuracy of LCC in arid regions.

Modeling and analysis of cloud computing system survivability based on Bio-PEPA

For the cloud computing system,combined wth the memory function and incomplete matching of the biological immune system,a formal modeling and analysis method of the cloud computing system survivability is proposed by analyzing the survival situation of critical cloud services.First,on the basis of the SAIR(susceptible,active,infected,recovered)model,the SEIRS(susceptible,exposed,infected,recovered,susceptible)model and the vulnerability diffusion model of the distributed virtual system,the evolution state of the virus is divided into six types,and then the diffusion rules of the virus in the service domain of the cloud computing system and the propagation rules between service domains are analyzee.Finally,on the basis of Bio-PEPA(biological-performance evaluation process algebra),the formalized modeling of the survivability evolution of critical cloud services is made,and the SLIRAS(susceptible,latent,infected,recovered,antidotal,susceptible)model is obtained.Based on the stochastic simulation and the ODEs(ordinary differential equations)simulation of the Bio-PEPA model,the sensitivity parameters of the model are analyzed from three aspects,namely,the virus propagation speed of inter-domain,recovery ability and memory ability.The results showthat the proposed model has high approximate fitting degree to the actual cloud computing system,and it can well reflect the survivable change of the system.