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Most real-world vehicle nodes can be structured into an interconnected network of vehicles. Through structuring these services and vehicle device interactions into multiple types, such internet of vehicles becomes multidimensional heterogeneous overlay networks. The heterogeneousness of the overlays makes it difficult for the overlay networks to coordinate with each other to improve their performance. Therefore, it poses an interesting but critical challenge to the effective analysis of heterogeneous virtual vehicular networks. A variety of virtual vehicular networks can be easily deployed onto the native network by applying the concept of SDN (Software Defined Networking). These virtual networks reflect their heterogeneousness due to their different performance goals, and they compete for the same physical resources of the underlying network, so that a sub-optimal performance of the virtual networks may be achieved. Therefore, we propose a Deep Reinforcement Learning (DRL) approach to make the virtual networks cooperate with each other through the SDN controller. A cooperative solution based on the asymmetric Nash bargaining is proposed for co-existing virtual networks to improve their performance. Moreover, the Markov Chain model and DRL resolution are introduced to leverage the heterogeneous performance goals of virtual networks. The implementation of the approach is introduced, and simulation results confirm the performance improvement of the latency sensitive, loss-rate sensitive and throughput sensitive heterogeneous vehicular networks using our cooperative solution. 相似文献
3.
本文以云杉八齿小蠹Ips typographus Linnaeus为例,经标本选取,观察虫体整体形态,绘制整体结构草图,电子显微镜观察局部、得到局部数字图像,分别建立虫体每一部分的高精度细节。把模型每部分拼装在一起组成整体模型,构建出了云杉八齿小蠹三维虚拟昆虫数字化模型。 相似文献
4.
目前管道泄漏检测方法可有效检测突发泄漏,对于缓慢泄漏则存在检测灵敏度低、定位不准确等问题。基于此,提出了一种基于信号增强的缓慢泄漏检测方法。通过信号压缩(抽取及移位)克服缓慢泄漏压力信号下降平缓的缺点;根据声波信号具有波形尖锐突出、对突发泄漏敏感的优点,通过建立以压力为输入、虚拟声波为输出的声波信号变送器模型,将压力信号转换为声波信号,克服了泄漏压力信号容易被淹没在管道压力波动及背景噪声中的缺点,实现了缓慢泄漏信号的增强;利用临近插值方法重构虚拟声波信号,基于延时互相关分析实现了缓慢泄漏的准确定位。实验结果表明,该方法具有显著的信号增强效果和定位精度,实现了缓慢泄漏的准确检测。 相似文献
5.
AbstractIndustry 4.0 aims at providing a digital representation of a production landscape, but the challenges in building, maintaining, optimizing, and evolving digital models in inter-organizational production chains have not been identified yet in a systematic manner. In this paper, various Industry 4.0 research and technical challenges are addressed, and their present scenario is discussed. Moreover, in this article, the novel concept of developing experience-based virtual models of engineering entities, process, and the factory is presented. These models of production units, processes, and procedures are accomplished by virtual engineering object (VEO), virtual engineering process (VEP), and virtual engineering factory (VEF), using the knowledge representation technique of Decisional DNA. This blend of the virtual and physical domains permits monitoring of systems and analysis of data to foresee problems before they occur, develop new opportunities, prevent downtime, and even plan for the future by using simulations. Furthermore, the proposed virtual model concept not only has the capability of Query Processing and Data Integration for Industrial Data but also real-time visualization of data stream processing. 相似文献
6.
As a symbol of sharing economy, ride-hailing services have spread to Asia, where various forms of services have been established according to the existing socio-technical regimes, including legal, policy, and environmental concerns. China, in particular, has accepted ride-hailing services by revising existing institutions. This study analyzes China’s acceptance of ride-hailing services in the socio-technical system context set up for legacy services. Simulation modeling, combined with transition theory and an agent-based model, is used to analyze the data. This study calculates consumer disutility based on mobility market share, reflecting consumer preference, and predicts the sustainability of ride-hailing services. The simulation results conclude that legalization and socio-technical context are of importance for sustainable mobility. 相似文献
7.
It is expected that demand response might provide soon ancillary services to the power system. This could be done, for example, by managing the use of Electric Vehicles (EV) batteries, or the production of flexible energy commodities such as hydrogen (H2), that can be used for fuel cell vehicles (H2EV) or in industrial processes. This paper analyses the impact of a transition to H2EV as an alternative to EV for passengers’ cars on a Spanish-like power sector. A simple H2 demand estimation is developed and provided to CEVESA, an operation and expansion model for the Iberian Power System Electricity Market (MIBEL). For this study, CEVESA was extended to include the investments and operation decisions of H2 production. Simulations were performed to determine the optimal evolution of the H2 production capacity and of the electricity generation mix, considering scenarios with different shares of EV and H2EV. The impact of H2EV vs EV mobility is assessed based on the recent Spanish National Plan for Energy and Climate (NECP) as the base case scenario. Results show that, even if H2EV mobility alternative is still more costly than EV, H2 production could provide a significant flexibility to the system that should also be appraised. Indeed, H2EV mobility could become a feasible and complementary alternative to decarbonize mobility by powering H2 production with the renewable generation surplus. This, together with the on-going learning process of this technology that will decrease its production costs and increase its efficiency in the coming years, could boost, even more, the development of the H2 economy. 相似文献
8.
ABSTRACT The concept of digital game-based learning (DGBL) evolves rapidly together with technological enhancements of virtual reality (VR) and smart phones. However, the mental workload (MWL) that VR-training applications demand and motivational qualities originating from user experience (UX) should be identified in order to create effective and enjoyable training/learning challenges that fit with individual users’ capabilities. This study examined the effects of reality-based interaction (RBI) and VR on measures of student motivation and MWL, in a mental arithmetic game for secondary school pupils. In a randomised controlled trial with sixty school children, a mental arithmetic game was tested with three different interaction and two different presentation methods – VR RBI, VR head-mounted-display tapping and tablet flick-gesture. Results found a significant effect of RBI on MWL but no differences in enjoyment of training were found between VR-experience and tablet training-experience. In fact, adding the gaming-context to the mental arithmetic task created an enjoyable, motivating experience regardless of presentation or interaction-style. 相似文献
9.
Australia's electricity market is rapidly adding renewable energy generation. Utility-scale batteries could have a major role in facilitating these transitions; however, their deployment is still largely state-subsidized. We summarize the current and future roles for batteries from a legal-economic perspective in the context of Australia's electricity market framework. We find that the future of batteries in Australia is not only a function of the large-scale deployment of renewables, their cost development and the comparative future cost of competing gas turbines but also of national electricity market and state policy reforms focusing on reliability. 相似文献
10.
Insider trading is a kind of criminal behavior in stock market by using nonpublic information. In recent years, it has become the major illegal activity in China’s stock market. In this study, a combination approach of GBDT (Gradient Boosting Decision Tree) and DE (Differential Evolution) is proposed to identify insider trading activities by using data of relevant indicators. First, insider trading samples occurred from year 2007 to 2017 and corresponding non-insider trading samples are collected. Next, the proposed method is trained by the GBDT, and initial parameters of the GBDT are optimized by the DE. Finally, out-of-samples are classified by the trained GBDT–DE model and its performances are evaluated. The experiment results show that our proposed method performed the best for insider trading identification under time window length of ninety days, indicating the relevant indicators under 90-days time window length are relatively more useful. Additionally, under all three time window lengths, relative importance result shows that several indicators are consistently crucial for insider trading identification. Furthermore, the proposed approach significantly outperforms other benchmark methods, demonstrating that it could be applied as an intelligent system to improve identification accuracy and efficiency for insider trading regulation in China stock market. 相似文献