移动边缘计算环境下的动态资源分配策略

在通讯设备爆炸式增长的时代,移动边缘计算作为5G通讯技术的核心技术之一,对其进行合理的资源分配显得尤为重要。移动边缘计算的思想是把云计算中心下沉到基站部署(边缘云),使云计算中心更加靠近用户,以快速解决计算资源分配问题。但是,相对于大型的云计算中心,边缘云的计算资源有限,传统的虚拟机分配方式不足以灵活应对边缘云的计算资源分配问题。为解决此问题,提出一种根据用户综合需求变化的动态计算资源和频谱分配算法(DRFAA),采用"分治"策略,并将资源模拟成"流体"资源进行分配,以寻求较大的吞吐量和较低的传输时延。实验仿真结果显示,动态计算资源和频谱分配算法可以有效地降低用户与边缘云之间的传输时延,也可以提高边缘云的吞吐量。     

A survey of multiscale modeling: Foundations,historical milestones,current status,and future prospects

Research problems in the domains of physical, engineering, biological sciences often span multiple time and length scales, owing to the complexity of information transfer underlying mechanisms. Multiscale modeling (MSM) and high-performance computing (HPC) have emerged as indispensable tools for tackling such complex problems. We review the foundations, historical developments, and current paradigms in MSM. A paradigm shift in MSM implementations is being fueled by the rapid advances and emerging paradigms in HPC at the dawn of exascale computing. Moreover, amidst the explosion of data science, engineering, and medicine, machine learning (ML) integrated with MSM is poised to enhance the capabilities of standard MSM approaches significantly, particularly in the face of increasing problem complexity. The potential to blend MSM, HPC, and ML presents opportunities for unbound innovation and promises to represent the future of MSM and explainable ML that will likely define the fields in the 21st century.     

Model-Based Design Space Exploration for FPGA-based Image Processing Applications Employing Parameterizable Approximations

The increasing demand for low power consumption and high computational performance is outpacing available technological improvements in embedded systems. Approximate computing is a novel design paradigm trying to bridge this gap by leveraging the inherent error resilience of certain applications and trading in quality to achieve reductions in resource usage. Numerous approximation methods have emerged in this research field. While these methods are commonly demonstrated in isolation, their combination can increase the achieved benefits in complex systems. However, the propagation of errors throughout the system necessitates a global optimization of parameters, leading to an exponentially growing design space. Additionally, the parameterization of approximated components must consider potential cross-dependencies between them. This work proposes a systematic approach to integrate and optimally configure parameterizable approximate components in FPGA-based applications, focusing on low-level but high-bandwidth image processing pipelines. The design space is explored by a multi-objective genetic algorithm which takes parameter dependencies between different components into account. During the exploration, appropriate models are used to estimate the quality-resource trade-off for probed solutions without the need for time-consuming synthesis. We demonstrate and evaluate the effectiveness of our approach on two image processing applications that employ multiple approximations. The experimental results show that the proposed methods are able to produce a wide range of Pareto-optimal solutions, offering various choices regarding the desired quality-resource trade-off.     

Emotion recognition at the edge with AI specific low power architectures

Nowadays Deep Learning is applied in almost every research field and helps getting amazing results in a great number of challenging tasks. The main problem is that this kind of learning and consequently Neural Networks that can be defined deep, are resource intensive. They need specialized hardware to perform computation in a reasonable time. Many tasks are mandatory to be as much real-time as possible . It is needed to optimize many components such as code, algorithms, numeric accuracy and hardware, to make them “efficient and usable”. All these optimizations can help us to produce incredibly accurate and fast learning models. The paper reports a study in this direction for the challenging face detection and emotion recognition tasks.     

基于Prophet人工智能算法的网络潮汐效应预测研究

本文使用Prophet人工智能算法研究与预测移动通信网络“潮汐效应”现象，探索网络“潮汐效应”在优化网络资源配置实现网络降本增效的作用。Prophet人工智能算法是一种简单、有效，且易于实现的人工智能算法。通过facebook的人工智能开源框架fbprophet，研究4G网络PRB利用率等网络资源指标的“潮汐效应”，并预测这些网络资源指标在未来的变化趋势，用来指导当前4G网络减容、扩容和4/5G节电节能等，实现优化网络资源配置达到降本增效的目的。     

平板显示基板玻璃窑炉烟气脱硝技术探究

随着生态环境意识的加强和环保治理力度的提升,平板玻璃窑炉烟气氮氧化物治理已经全面落实。而由于平板显示基板玻璃窑炉烟气性质的独特性,其脱硝技术路线的选择也取决于不同地区氮氧化物排放浓度的限值差异。详细介绍了选择性催化还原法(SCR)、选择性非催化还原法(SNCR)和逆向流选择性催化还原法(CSCR)三种脱硝技术并对比了三者之间在脱硝效率、反应温度、初始投资、全生命周期成本的差异。同时,对未来的发展应用进行了展望。     

Remaining useful life prediction of PEMFC based on cycle reservoir with jump model

Accurate prognosis of limited durability is one of the key factors for the commercialization of proton exchange membrane fuel cell (PEMFC) on a large scale. Thanks to ignoring the structure of the PEMFC and simplifying the prognostic process, the data-driven prognostic approaches was the commonly used for predicting remaining useful life (RUL) at present. In this paper, the proposed cycle reservoir with jump (CRJ) model improves the ESN model, changes the connection mode of neurons in the reservoir and speeds up the linear fitting process. The experiment will verify the performance of CRJ model to predict stacks voltage under static current and quasi-dynamic current conditions. In addition, the reliability of the CRJ model is verified with different amount of data as the training and test sets. The experimental results demonstrate that the CRJ model can achieve better effect in the remaining useful life prognosis of fuel cells.     

An Advanced Analysis of Cloud Computing Concepts Based on the Computer Science Ontology

Our primary research hypothesis stands on a simple idea: The evolution of top-rated publications on a particular theme depends heavily on the progress and maturity of related topics. And this even when there are no clear relations or some concepts appear to cease to exist and leave place for newer ones starting many years ago. We implemented our model based on Computer Science Ontology (CSO) and analyzed 44 years of publications. Then we derived the most important concepts related to Cloud Computing (CC) from the scientific collection offered by Clarivate Analytics. Our methodology includes data extraction using advanced web crawling techniques, data preparation, statistical data analysis, and graphical representations. We obtained related concepts after aggregating the scores using the Jaccard coefficient and CSO Ontology. Our article reveals the contribution of Cloud Computing topics in research papers in leading scientific journals and the relationships between the field of Cloud Computing and the interdependent subdivisions identified in the broader framework of Computer Science.     

Exergoeconomic analysis of hydrogen production using a standalone high-temperature electrolyzer

The conventional hydrogen production methods, primarily steam methane reforming and coal gasification, produce massive amounts of greenhouse gas emissions which significantly cause impacts on the environment. An alternative hydrogen production method is high-temperature electrolysis using Solid Oxide Electrolyzer that combines both high conversion efficiency and saleable high purity hydrogen production. The produced hydrogen can feed the various industrial processes at different scales in addition to offering an environmentally friendly storage option. The scope of this paper is to examine the economic feasibility of this technology through the utilization of the exergoeconomic concept, which traces the flow of exergy through the system and price both waste and products. Therefore, a standalone solid oxide electrolyzer of a 1MWe is considered for hydrogen production using renewably generated electricity. Having the detailed exergy analysis conducted in earlier studies, the focus of this article is on the costing of each exergy stream to determine the exergy cost and the potential changes outcomes as a result of the system operating or design parameters optimization. It is found that the cost of hydrogen production through the modular high-temperature electrolyzer varies between $3-$9/kg with an average of about $5.7/kg, respectively.     

Comparative techno-economic assessment of a large-scale hydrogen transport via liquid transport media

A large-scale point to point hydrogen transport is one strategy for a prospective energy import scenario for certain countries. The case for a hydrogen transport from Australia to Japan has been addressed in several studies. However, most studies lack transparency and detailed insights into the made assumptions thus a fair evaluation of different transport pathways is challenging. To address this issue, we developed a model where a large-scale point to point hydrogen transport of liquid hydrogen is compared with the transport via liquid organic hydrogen carrier (LOHC), namely via methyl cyclohexane and hydrogenated dibenzyl toluene. We analyzed, where energy is required along the different pathways, where hydrogen losses do occur and how the costs are put together. Furthermore, the influence of hydrogen feed costs is also considered. For hydrogen production costs of 5 €₂₀₁₈/kg_H2 the total delivery costs are in the range of 6.40– 8.10 €₂₀₁₈/kg_H2.