Restoring data is the main purpose of data backup in storage systems. The fragmentation issue, caused by physically scattering logically continuous data across a variety of disk locations, poses a negative impact on t...
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Edge computing is a novel computing paradigm that offers kinds of resources at the network edge. In edge computing, terminal users are connected to edge servers via the wireless network and there are various channels ...
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Requirements documentation is an important activity in requirements development. Embedded systems consists of software and hardware, as well as multiple stakeholders. As systems grow in size and complexity, writing re...
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ISBN:
(数字)9798350395518
ISBN:
(纸本)9798350395525
Requirements documentation is an important activity in requirements development. Embedded systems consists of software and hardware, as well as multiple stakeholders. As systems grow in size and complexity, writing requirements documents becomes more time-consuming and laborious. Based on the strong correlation between embedded system and physical device, this paper proposes to use device knowledge base to support requirements documentation. Stakeholders only need to fill in the intention and device selection, and then the device and system requirements are supplemented by device knowledge. Templates and environment modeling and analysis methods are proposed to further guide requirements acquisition. This leads to considerably simplify and accelerate the requirements document development for embedded systems.
The complexity and diversity of high-performancecomputer architectures have brought great challenges to parallel application development. Using DSL (Domain Specific Language) to achieve multi-platform automatic paral...
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The sixth generation(6G)mobile networks will reshape the world by offering instant,efficient,and intelligent hyper-connectivity,as envisioned by the previously proposed Ubiquitous-X 6G *** hyper-massive and global con...
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The sixth generation(6G)mobile networks will reshape the world by offering instant,efficient,and intelligent hyper-connectivity,as envisioned by the previously proposed Ubiquitous-X 6G *** hyper-massive and global connectivity will introduce tremendous challenges into the operation and management of 6G networks,calling for revolutionary theories and technological *** this end,we propose a new route to boost network capabilities toward a wisdom-evolutionary and primitive-concise network(WePCN)vision for the Ubiquitous-X 6G *** particular,we aim to concretize the evolution path toward the WePCN by first conceiving a new semantic representation framework,namely semantic base,and then establishing an intelligent and efficient semantic communication(IE-SC)network *** the IE-SC architecture,a semantic intelligence plane is employed to interconnect the semantic-empowered physical-bearing layer,network protocol layer,and application-intent layer via semantic information *** proposed architecture integrates artificial intelligence and network technologies to enable intelligent interactions among various communication objects in *** features a lower bandwidth requirement,less redundancy,and more accurate intent *** also present a brief review of recent advances in semantic communications and highlight potential use cases,complemented by a range of open challenges for 6G.
Active power dispatch of wind farms plays an important role in power grid scheduling. In this paper, a data-driven active power dispatch strategy for wind farms is proposed, which uses the key point of minimizing the ...
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ISBN:
(数字)9798350361674
ISBN:
(纸本)9798350361681
Active power dispatch of wind farms plays an important role in power grid scheduling. In this paper, a data-driven active power dispatch strategy for wind farms is proposed, which uses the key point of minimizing the standard deviation of wind turbines' output power series to reduce their fluctuations. First, to build the optimization problem, the historical active power output data of each wind turbine is collected from the Supervisory Control And Data Acquisition (SCADA) system. Then, the objective function is developed based on the idea of minimizing the standard deviation of the output power time series. Meanwhile, considering the constraints of wind turbines' output power and active power balance of the grid, the feasible domain of the cost function is constructed. Further, to solve the proposed nonlinear optimization problem with equality and inequality constraints, the trust region interior point method (TRIPM) is employed to explore optimal solution within the feasible domain. Finally, actual data collected from wind farms is used to test effectiveness of the designed approach and experimental results show that our method can achieve better output power smoothing performance than conventional Proportional Dispatch (PD) method.
Unmanned aerial vehicles(UAVs) have been attracting more and more attention in the research and industry field. Aerial search is a common mission and is intrinsically fit for UAVs, e.g. disaster rescue, remote sensing...
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Unmanned aerial vehicles(UAVs) have been attracting more and more attention in the research and industry field. Aerial search is a common mission and is intrinsically fit for UAVs, e.g. disaster rescue, remote sensing and environmental monitoring. With the improvement of UAV hardware and software, UAVs tend to achieve better autonomy and accomplish more complex tasks. However,current UAV aerial search is usually hardcoded, which limits their adaptability, autonomy and robustness in realistic scenarios. In this paper, we propose to address this problem by leveraging reinforcement learning(RL) and a recent control architecture, behavior trees(BTs). We develop robust and adaptive UAV systems that can automatically conduct multi-phase complex aerial search, including search, communication and refueling. Experimental results in a3 D robot simulator verify the effectiveness and robustness of the proposed approach, which achieves better performance than the baseline.
Vision Transformers (ViTs) that leverage self-attention mechanism have shown superior performance on many classical vision tasks compared to convolutional neural networks (CNNs) and gain increasing popularity recently...
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Autonomous driving systems (ADSs) have undergone remarkable development and are increasingly employed in safety-critical applications. However, recently reported data on fatal accidents involving ADSs suggests that th...
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Deep Learning (DL) technologies have been widely adopted to tackle various tasks. In this process, through software dependencies, a multi-layer DL supply chain (SC) is formed, with DL frameworks acting as the root, DL...
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ISBN:
(数字)9798350330663
ISBN:
(纸本)9798350330670
Deep Learning (DL) technologies have been widely adopted to tackle various tasks. In this process, through software dependencies, a multi-layer DL supply chain (SC) is formed, with DL frameworks acting as the root, DL packages acting as the bridge nodes, and downstream DL projects acting as the periphery. However, most Open Source Software (OSS) projects may fail. Considering the crucial position of DL packages in the DL SC, to foster the sustainable development of DL SCs and DL packages, we aim to forecast the long-term sustainability of DL packages. Here, sustained activity is adopted as the main proxy of sustainability, and the sustainability status is classified as “sus-tainable” or “dormant”. Relatedly, a DL package is considered as “sustainable” if it has sustained activity in its last 12 months. Otherwise, it is deemed as “dormant”. To this end, we propose an approach that begins with obtaining longitudinal features for each DL package in each month. Then, we develop a model to forecast the sustainability of DL packages by incorporating the longitudinal features, which can aptly predict sustainability with an accuracy of up to 0.81. Subsequently, an interpretable module is developed to interpret the determinants (i.e., important features) that impact the sustainability of DL packages. Finally, we generate sustainability trajectories for each DL package to better understand the monthly changes of their sustainability status. Our findings uncover that for most DL packages, fewer but more centralized developers and a balanced collaboration are more likely to help sustain the DL packages. Furthermore, although some DL packages are sustainable, their sustainability trajectories present statistically decreasing trends over time. Based on the findings, we shed light on the dynamic sustainability of DL packages, highlight future research directions, and provide practical suggestions to DL package maintainers, developers, users, and software engineering researchers.
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