203 research outputs found
Research on Methods for Discovering and Selecting Cloud Infrastructure Services Based on Feature Modeling
Nowadays more and more cloud infrastructure service providers are providing large numbers of service instances which are a combination of diversified resources, such as computing, storage, and network. However, for cloud infrastructure services, the lack of a description standard and the inadequate research of systematic discovery and selection methods have exposed difficulties in discovering and choosing services for users. First, considering the highly configurable properties of a cloud infrastructure service, the feature model method is used to describe such a service. Second, based on the description of the cloud infrastructure service, a systematic discovery and selection method for cloud infrastructure services are proposed. The automatic analysis techniques of the feature model are introduced to verify the model’s validity and to perform the matching of the service and demand models. Finally, we determine the critical decision metrics and their corresponding measurement methods for cloud infrastructure services, where the subjective and objective weighting results are combined to determine the weights of the decision metrics. The best matching instances from various providers are then ranked by their comprehensive evaluations. Experimental results show that the proposed methods can effectively improve the accuracy and efficiency of cloud infrastructure service discovery and selection
Fast GPU-Based Two-Way Continuous Collision Handling
Step-and-project is a popular way to simulate non-penetrated deformable
bodies in physically-based animation. First integrating the system in time
regardless of contacts and post resolving potential intersections practically
strike a good balance between plausibility and efficiency. However, existing
methods could be defective and unsafe when the time step is large, taking risks
of failures or demands of repetitive collision testing and resolving that
severely degrade performance. In this paper, we propose a novel two-way method
for fast and reliable continuous collision handling. Our method launches the
optimization at both ends of the intermediate time-integrated state and the
previous intersection-free state, progressively generating a piecewise-linear
path and finally reaching a feasible solution for the next time step.
Technically, our method interleaves between a forward step and a backward step
at a low cost, until the result is conditionally converged. Due to a set of
unified volume-based contact constraints, our method can flexibly and reliably
handle a variety of codimensional deformable bodies, including volumetric
bodies, cloth, hair and sand. The experiments show that our method is safe,
robust, physically faithful and numerically efficient, especially suitable for
large deformations or large time steps
Reversible Watermarking in Deep Convolutional Neural Networks for Integrity Authentication
Deep convolutional neural networks have made outstanding contributions in
many fields such as computer vision in the past few years and many researchers
published well-trained network for downloading. But recent studies have shown
serious concerns about integrity due to model-reuse attacks and backdoor
attacks. In order to protect these open-source networks, many algorithms have
been proposed such as watermarking. However, these existing algorithms modify
the contents of the network permanently and are not suitable for integrity
authentication. In this paper, we propose a reversible watermarking algorithm
for integrity authentication. Specifically, we present the reversible
watermarking problem of deep convolutional neural networks and utilize the
pruning theory of model compression technology to construct a host sequence
used for embedding watermarking information by histogram shift. As shown in the
experiments, the influence of embedding reversible watermarking on the
classification performance is less than 0.5% and the parameters of the model
can be fully recovered after extracting the watermarking. At the same time, the
integrity of the model can be verified by applying the reversible watermarking:
if the model is modified illegally, the authentication information generated by
original model will be absolutely different from the extracted watermarking
information.Comment: Accepted to ACM MM 202
AmbiguityVis: Visualization of Ambiguity in Graph Layouts
Node-link diagrams provide an intuitive way to explore networks and have inspired a large number of automated graphlayout strategies that optimize aesthetic criteria. However, any particular drawing approach cannot fully satisfy all these criteriasimultaneously, producing drawings with visual ambiguities that can impede the understanding of network structure. To bring attentionto these potentially problematic areas present in the drawing, this paper presents a technique that highlights common types of visualambiguities: ambiguous spatial relationships between nodes and edges, visual overlap between community structures, and ambiguityin edge bundling and metanodes. Metrics, including newly proposed metrics for abnormal edge lengths, visual overlap in communitystructures and node/edge aggregation, are proposed to quantify areas of ambiguity in the drawing. These metrics and others arethen displayed using a heatmap-based visualization that provides visual feedback to developers of graph drawing and visualizationapproaches, allowing them to quickly identify misleading areas. The novel metrics and the heatmap-based visualization allow a userto explore ambiguities in graph layouts from multiple perspectives in order to make reasonable graph layout choices. The effectivenessof the technique is demonstrated through case studies and expert reviews
Decoupled measurement and modeling of interface reaction kinetics of ion-intercalation battery electrodes
Ultrahigh rate performance of active particles used in lithium-ion battery
electrodes has been revealed by single-particle measurements, which indicates a
huge potential for developing high-power batteries. However, the
charging/discharging behaviors of single particles at ultrahigh C-rates can no
longer be described by the traditional electrochemical kinetics in such
ion-intercalation active materials. In the meantime, regular kinetic measuring
methods meet a challenge due to the coupling of interface reaction and
solid-state diffusion processes of active particles. Here, we decouple the
reaction and diffusion kinetics via time-resolved potential measurements with
an interval of 1 ms, revealing that the classical Butler-Volmer equation
deviates from the actual relation between current density, overpotential, and
Li+ concentration. An interface ion-intercalation model is developed which
considers the excess driving force of Li+ (de)intercalation in the charge
transfer reaction for ion-intercalation materials. Simulations demonstrate that
the proposed model enables accurate prediction of charging/discharging at both
single-particle and electrode scales for various active materials. The kinetic
limitation processes from single particles to composite electrodes are
systematically revealed, promoting rational designs of high-power batteries
Surface Model Based Modeling and Simulation of Filling Process in Gas-Assisted Injection Molding
Overpotential decomposition enabled decoupling of complex kinetic processes in battery electrodes
Identifying overpotential components of electrochemical systems enables
quantitative analysis of polarization contributions of kinetic processes under
practical operating conditions. However, the inherently coupled kinetic
processes lead to an enormous challenge in measuring individual overpotentials,
particularly in composite electrodes of lithium-ion batteries. Herein, the full
decomposition of electrode overpotential is realized by the collaboration of
single-layer structured particle electrode (SLPE) constructions and
time-resolved potential measurements, explicitly revealing the evolution of
kinetic processes. Perfect prediction of the discharging profiles is achieved
via potential measurements on SLPEs, even in extreme polarization conditions.
By decoupling overpotentials in different electrode/cell structures and
material systems, the dominant limiting processes of battery rate performance
are uncovered, based on which the optimization of electrochemical kinetics can
be conducted. Our study not only shades light on decoupling complex kinetics in
electrochemical systems, but also provides vitally significant guidance for the
rational design of high-performance batteries
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