128 research outputs found
Damage characteristics of Zr-based metallic glasses under helium ions irradiation
No full textMetallic glasses (MGs) exhibit extremely high strength and superior resistance to corrosion. They are also supposed to be resistant against displacive irradiation due to their inherent disordered structure, and thereby are viewed as potential candidates for applications in irradiation environments. However, the structures and properties evolution of metallic glasses, especially bulk metallic glasses (BMGs), under irradiation has not been fully understood up to now. In this work, the structural stability and damage characteristics of a Zr-based BMG under helium ions irradiation environment were investigated. Meanwhile, the effect of structural relaxation and crystallization on the irradiation response of the BMG was also studied. Results show that the BMG reserves the amorphous structure within the studied range of fluence, and exhibits better irradiation resistance compared to that of the crystalline alloys. In our opinion, the initial free volume concentration affects the damage morphology of the BMG, while partial crystallization will lead to significantly embrittlement under irradiation. © 2016 Trans Tech Publications, Switzerland
Hierarchical P2P based RFID code resolution network: structure, tools and application
No full textIn order to build open-loop large scale RFID applications, it is necessary to establish public RFID service infrastructure, the key issue is to build efficient and robust RFID code resolution network. But there exist some problems, such as load balancing, single node failure, etc. On the basis of recent research, according to features of governmental administration and enterprise applications, a hierarchical P2P based RFID code resolution network structure is proposed and implemented. The core components are specified formally by TIOA. Meanwhile, a management toolset is developed, including SNMP based traffic monitor, register, authorization etc. The resolution network is verified by a practical application "Volunteer Beijing".http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000288110500030&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Computer Science, Hardware & ArchitectureEngineering, Electrical & ElectronicTelecommunicationsEICPCI-S(ISTP)
Modeling and Optimization of Bilayered TaOx RRAM Based on Defect Evolution and Phase Transition Effects
No full textA comprehensive physical model on the resistive switching (RS) behaviors of bilayered TaOx-based RS access memory [resistive random access memory (RRAM)] is presented. In the model, the effects of the generation and recombination (G-R) of oxygen vacancies (V-O), phase transition (P-T) between Ta2O5 and TaO2, and the interaction (I-A) between Ta2O5 and TaOx layers are involved to explain the RS behaviors based on ab initio calculations. An atomistic Monte Carlo simulation method based on the model is developed to investigate the dynamic physical processes and reproduce the experimental phenomena. The impacts of G-R and P-T as well as the I-A effects on the RS behaviors of a bilayered Ta2O5/TaOx structure and the device performances are identified. This paper indicates that the G-R effect dominates the RS behaviors, and self-compliance is due to the I-A effect. Based on the simulations, the optimization guidance of a bilayered TaOx-based RRAM is presented.National Natural Science Foundation of China [61334007, 61421005]SCI(E)[email protected]; [email protected]; [email protected]; [email protected]; [email protected]; [email protected]; [email protected]; [email protected]; [email protected]; [email protected]; [email protected]
Incremental Generalized Category Discovery
No full textWe explore the problem of Incremental Generalized Category Discovery (IGCD).
This is a challenging category incremental learning setting where the goal is
to develop models that can correctly categorize images from previously seen
categories, in addition to discovering novel ones. Learning is performed over a
series of time steps where the model obtains new labeled and unlabeled data,
and discards old data, at each iteration. The difficulty of the problem is
compounded in our generalized setting as the unlabeled data can contain images
from categories that may or may not have been observed before. We present a new
method for IGCD which combines non-parametric categorization with efficient
image sampling to mitigate catastrophic forgetting. To quantify performance, we
propose a new benchmark dataset named iNatIGCD that is motivated by a
real-world fine-grained visual categorization task. In our experiments we
outperform existing related methodsComment: This paper is accepted at ICCV 202
Incremental Generalized Category Discovery
No full textWe explore the problem of Incremental Generalized Category Discovery (IGCD). This is a challenging category incremental learning setting where the goal is to develop models that can correctly categorize images from previously seen categories, in addition to discovering novel ones. Learning is performed over a series of time steps where the model obtains new labeled and unlabeled data, and discards old data, at each iteration. The difficulty of the problem is compounded in our generalized setting as the unlabeled data can contain images from categories that may or may not have been observed before. We present a new method for IGCD which combines non-parametric categorization with efficient image sampling to mitigate catastrophic forgetting. To quantify performance, we propose a new benchmark dataset named iNatIGCD that is motivated by a real-world fine-grained visual categorization task. In our experiments we outperform existing related methods
Dependence of Shape Parameter of Weibull Distribution on Wave Spectral Width and Nonlinearity
No full textExploring How Rivals and Complementors Affect Evolutionary Rate of B2C Apps: An Empirical Study
No full textThe hyper competition among rivals and enveloping threats from complementors are crucial external sources that influence app update strategies of B2C platforms. However, prior app-related literature largely focuses on factors affecting app performance, with scant attention on external drivers of the continuous app evolution, that is app updates. Besides, the results of app updates on market performance are mixed in extant literature. Therefore, this study is motivated to explore how competitive pressures from rivals and enveloping threats from complementors affect evolutionary rate of B2C apps and its subsequent effects on market performance. Our empirical study demonstrates that quick evolution of rival and complementor apps increases evolutionary rate of B2C apps. In contrast, a greater number of better performed rival and complementor apps decreases the evolutionary rate. Furthermore, we unveiled an inverted U-shaped relationship between evolutionary rate of B2C apps and market performance. The theoretical implications are also discussed
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