5G核心网业务模型的智能化预测研究

摘 要：核心网业务模型的建立是5G网络容量规划和网络建设的基础，通过现有方法得到的理论业务模型是静态不可变的且与实际网络存在偏离。为了克服现有5G核心网业务模型与现网模型适配性较差以及规划设备无法满足用户实际业务需求的问题，提出了一种长短期记忆（long short-term memory，LSTM）网络与卷积LSTM （convolution LSTM，ConvLSTM）网络双通道融合的 5G 核心网业务模型预测方法。该方法基于人工智能（artificial intelligence，AI）技术以实现高质量的核心网业务模型的智能预测，形成数据反馈闭环，实现网络自优化调整，助力网络智能化建设。     

基于改进灰狼优化算法的服务功能链映射算法

随着工业互联网、车联网、元宇宙等新型互联网应用的兴起，网络的低时延、可靠性、安全性、确定性等方面的需求正面临严峻挑战。采用网络功能虚拟化技术在虚拟网络部署过程中，存在服务功能链映射效率低与部署资源开销大等问题，联合考虑节点激活成本、实例化开销，以最小化平均部署网络成本为优化目标建立了整数线性规划模型，提出基于改进灰狼优化算法的服务功能链映射（improved grey wolf optimization based service function chain mapping，IMGWO-SFCM）算法。该算法在标准灰狼优化算法基础上添加了基于无环K最短路径（K shortest path，KSP）问题算法的映射方案搜索、映射方案编码以及基于反向学习与非线性收敛改进三大策略，较好地平衡了其全局搜索及局部搜索能力，实现服务功能链映射方案的快速确定。仿真结果显示，该算法在保证更高的服务功能链请求接受率下，相较于对比算法降低了11.86%的平均部署网络成本。     

Automated prediction system of household energy consumption in cities using web crawler and optimized artificial intelligence

The supply of electrical energy is critical to convenient and comfortable living. However, people consume a large amount of energy, contributing to an energy crisis and global warming, and damaging some ecological cycles. Residential electricity consumption has greater elasticity than industrial and business consumption; it therefore has high energy-saving potential. This work establishes an automated platform, which provides information about residential electricity consumption in each city in Taiwan. Machine learning was used to forecast future residential electricity demand. A nature-inspired optimization method was applied to enhance the accuracy of the best machine learner, yielding an even better hybrid ensemble model. Performance measures indicate that the resulting model is accurate and provides effective information for reference. An automatic web-based system based on the model was combined with a web crawler and scheduled to run automatically to provide information on monthly residential electricity consumption in each county and city. By providing energy consumption information across the country, power providers and government can discuss policy and set different goals for energy use. The results of this study can facilitate the early implementation of energy-saving and carbon emission-reducing in cities and aid utility companies in establishing energy conservation guidelines.     

Additional deepwater sampling of prey fish in Lake Michigan annual bottom trawl survey reveals new insights for depth distribution dynamics

Beginning in 2013, sites at the 128-m bottom depth contour were added to the sampling design of the annual Lake Michigan bottom trawl survey for prey fish, which has been conducted by the U.S. Geological Survey Great Lakes Science Center (GLSC) each fall since 1973, to better assess fish depth distributions in a changing ecosystem. The standard sampling design included bottom depths from 9 to 110 m, although the GLSC also sporadically conducted bottom trawl tows at the 128-m bottom depth contour during 1973–1988. Enactment of this new sampling design in 2013 revealed that mean biomass density of deepwater sculpins (Myoxocephalus thompsonii) at the 128-m depth exceeded the sum of mean biomass densities at shallower depths, indicating that the bulk of the deepwater sculpin population is residing in waters deeper than 110 m. Thus, our findings supported the hypothesis that the depth distribution of the deepwater sculpin population had shifted to deeper waters beginning in 2007, thereby explaining, at least in part, the marked decline in deepwater sculpin abundance since 2006 based on the standard sampling design. In contrast, our results did not support the hypothesis that the slimy sculpin (Cottus cognatus) population had shifted to deeper waters sometime after 2000. A portion of the burbot (Lota lota) population may have also shifted in depth distribution to waters deeper than 110 m after 2007, based on our results. Our findings have served as an impetus to further expand the range of depths sampled in our bottom trawl survey.     

Efficiency evaluation of healthcare services in China based on stochastic multicriteria acceptability analysis and directional distance function

Evaluating the efficiency of healthcare services accurately can help in analyzing the rationality of inputs and outputs in such services. Considering the consistency and equity of assessment criteria, this study conducts the stochastic multicriteria acceptability analysis (SMAA-2) with a directional distance function to evaluate the efficiency of healthcare services in 31 provincial administrative regions of mainland China, as observed in 2018. We use SMAA-DDF to explore all the projection directions to the efficient frontier instead of a certain projection direction. We measure the maximum and average efficiencies for each of the 31 provincial healthcare services. Our empirical findings show that only seven provinces achieve optimal healthcare service efficiency; the eastern area performed the best, followed by the central, western, and northeast areas. Furthermore, the path along the projection directions is provided to help inefficient provinces improve their efficiency and obtain the best possible positions.     

Design of frameworks for self-adaptive service-oriented applications: A systematic analysis

Self-adaptive service-oriented Applications (Self-Apps) must be able to understand themselves or the environment in which they are executed, and propose solutions to meet changing conditions. The development of these applications is not a trivial task, since it encompasses issues from different research areas. Despite the importance of frameworks for Self-Apps, there is a lack of comprehensive analysis of how the design of such applications is performed, and regarding the standardization of concepts and coverage of minimum requirements for Self-Apps. The main contribution of this article is to present this comprehensive analysis, providing the state of the art for this subject. This analysis was built through a Systematic Mapping Study, based on a total of 65 studies, from which we identify the main attributes for Quality of Service (QoS), search strategies, and service management strategies employed in the design of frameworks for Self-Apps. The main aspects of requirements involved in the design of Self-Apps were pointed out to stakeholders. For example, these applications must implement a method for evaluation of QoS based on metrics. We also put forward the S-Frame, a modular solution that brings together the main features for the design of Self-Apps, and describe the main challenges concerning these applications.     

大数据环境下基于K中心点优化算法的Web服务组合

在当前Web服务海量增加、现有Web服务选择算法低效、用户匹配度差的基础上，针对K中心点算法存在的质点偏移、准确率低和容易发生畸变等问题，提出一种大数据环境下基于K中心点优化算法的Web服务组合方法。该方法是在大数据环境下，根据不同用户需求满意度及Web服务QoS参数，对基于优化初始聚类中心的K中心点算法的Web服务选择及最优Web服务组合进行研究。同时针对不同的选择方法对服务动态选择及组合的准确度、迭代更新次数、候选集选择时间及选择总时间进行实验分析，验证了本文研究方法的有效性和可靠性。     

面向云计算应用层演化的隐私保护方法研究

云计算应用层中的组合服务具有演化属性,因此,隐私数据在服务组合过程中,用户的隐私数据可能会因为服务或服务流程的演化而暴露。根据服务演化的特征,以描述逻辑为基础,提出了一种面向云计算应用层演化的隐私保护方法。对隐私协议进行形式化描述;根据服务的演化特征,对服务的演化进行监控,保证满足用户的隐私需求;利用实例研究证明该方法的正确性与可行性。     

基于卷积神经网络的单目深度估计

针对利用深度学习方法对街道图像进行深度估计，提出采用语义分割的方法解决深度图出现边界模糊等问题；估计深度通过左右视角图生成视差图进行无监督的训练。在网络模型中添加语义分割层，采取多个空洞卷积并行的结构增加感受野，同时减少了图像下采样的次数，降低了由于下采样带来的信息损失，使得的结果更加准确。这也是在深度估计中首次与空洞卷积相结合增加准确率。通过对KITTI街道数据集进行训练，与现有结果相比，除了增加检测准确性，降低错误率之外，使得效果图中的物体更加清晰，并且在效果图中还保留了一些原模型中被忽视掉的细节信息，将原始图像更加完整的表现出来。     

面向连续查询的敏感语义位置隐私保护方案

针对连续查询位置服务中构造匿名区域未考虑语义位置信息导致敏感隐私泄露问题，通过设计[(K,θ)]-隐私模型，提出一种路网环境下面向连续查询的敏感语义位置隐私保护方案。该方案利用Voronoi图将城市路网预先划分为独立的Voronoi单元，依据用户的移动路径和移动速度，选择具有相似特性的其他[K-1]个用户，构建匿名用户集；利用匿名用户集用户设定的敏感语义位置类型和语义安全阈值，以及用户所处语义位置的Voronoi单元，构建满足[(K,θ)]-隐私模型的语义安全匿名区域，可以同时防止连续查询追踪攻击和语义推断攻击。实验结果表明，与SCPA算法相比，该方案在隐私保护程度上提升约15%，系统开销上降低约20%。