252 research outputs found
Thermal analysis of dual-phase-lag model in a two-dimensional plate subjected to a heat source moving along elliptical trajectories
In this paper, we focus on the study of heat transfer behavior for the
dual-phase-lag heat conduction model, which describes the evolution of
temperature in a two-dimensional rectangular plate caused by the activity of a
point heat source moving along elliptical trajectories. At first, Green's
function approach is applied to derive the analytical solution of temperature
for the given model. Based on the series representation of this analytical
solution, the thermal responses for the underlying heat transfer problem,
including the relations between the moving heat source and the concomitant
temperature peak, the influences of the pair of phase lags and the angular
velocity of heat source on temperature, are then investigated, analyzed and
discussed in detail for three different movement trajectories. Compared with
the results revealed for the common situation that the heat source moves in a
straight line with a constant speed, the present results show quite distinctive
thermal behaviors for all cases, which subsequently can help us to better
understand the internal mechanism of the dual-phase-lag heat transfer subjected
to a moving heat source with curved trajectory.Comment: 15 pages, 41 figure
Interdependent Self-Construal as a Moderator in the Relationship Between Extrinsic Aspiration for Children and Parental Psychological Well-Being
Research on Self-Determination Theory suggests that the pursuit of extrinsic aspirations (e.g. to be rich) can have negative consequences on well-being. Yet little research has examined whether holding extrinsic aspiration for other people evidences similar relationships. The current research examined how holding extrinsic aspirations for one’s children (AFC) is related to parents’ own psychological well-being. I expected endorsing extrinsic AFC might also bear negatively predict parental psychological well-being. However, I contended that the strength of relationship might vary according to interdependent self-construal. To the extent people’s self-construal is interdependent, they might attach different meaning to extrinsic AFC (e.g. as a way to be responsible and competent parents). These additional meaning could neutralize or even reverse its negative implication over parental psychological well-being.
Two studies (one within-culture and one cross-cultural) were designed to test these hypotheses. Parent participants completed individual difference measure of self-construal, extrinsic AFC and parental psychological well-being. The results generally confirmed the hypotheses
I-f Control with Zero D-axis Current Operation for Surface Mounted Permanent Magnet Synchronous Machine Drives
A Threshold Based Handover Triggering Scheme in Heterogeneous Wireless Networks
The widespread popularity of Wireless Local Area Network (WLAN) is recognized as an effective approach to complementing cellular networks for the high data rate and cost effective connectivity delivered to mobile users. Efficient handover and offloading schemes for integrated WLAN and cellular networks, referred to as Heterogeneous Wireless Networks, have thus attracted lots of attentions from both academia and industry. This paper proposes a novel Multiple-Threshold based Triggering (MTT) scheme for Cellular-to-WLAN handover control. Aiming at minimizing the probability of handover failures and unnecessary handovers, three thresholds are calculated based on a variety of network parameters such as system performance requirements, radius of the WLAN coverage, user mobility and handover delays. The thresholds are then compared against the predicted user residence time and estimated channel holding time inside WLAN to make vertical handover decisions (VHDs). Simulations were carried out to evaluate the effectiveness of MTT and results show that MTT minimizes handover failures and avoids unnecessary handovers in integrated cellular and WLAN networks, thus providing satisfactory Quality of Service (QoS) to users and improving system resource utilization
Strengthening mechanisms of highly textured Cu/Co and Ag/Al nanolayers with high density twins and stacking faults
Metallic nanolayers have attracted increasing attention as they provide unique opportunity to investigate the influence of layer interfaces on mechanical properties of metallic nanocomposites. High strength is often achieved at small (several nm) individual layer thickness (h). Recently, we discovered high-density stacking faults in FCC Co in highly (100) textured Cu/Co multilayers. In contrast in (111) textured Cu/Co nanolayers, Co remained its stable HCP structure at large h. The two Cu/Co systems have very different size dependent strengthening behavior. HCP Cu/Co has much greater peak strength than FCC Cu/Co. The large discrepancy in their strengthening mechanisms is discussed and compared to those of highly textured Cu/Ni multilayer systems. In another highly textured nanolayers system, Ag/Al, epitaxial interfaces were observed across various h (1‑200 nm). High-density nanotwins and stacking faults appear in both Ag and Al layers, and stacking fault density in Al increases sharply with decreasing h. At smaller h, hardness of Ag/Al nanolayers increases monotonically and no softening was observed. These studies allow us to investigate the influence of layer interfaces, stacking faults and nanotwins on strengthening mechanisms of metallic nanolayers. This research is funded by DOE–OBES
Excitation and voltage-gated modulation of single-mode dynamics in a planar nano-gap spin Hall nano-oscillator
We experimentally study the dynamical modes excited by current-induced
spin-orbit torque and its electrostatic gating effect in a 3-terminal planar
nano-gap spin Hall nano-oscillator (SHNO) with a moderate interfacial
perpendicular magnetic anisotropy (IPMA). Both quasilinear propagating
spin-wave and localized "bullet" modes are achieved and controlled by varying
the applied in-plane magnetic field and driving current. The minimum linewidth
shows a linear dependence on the actual temperature of the active area,
confirming single-mode dynamics based on the nonlinear theory of spin-torque
nano-oscillation with a single mode. The observed electrostatic gating tuning
oscillation frequency arises from voltage-controlled magnetic anisotropy and
threshold current of SHNO via modification of the nonlinear damping and/or the
interfacial spin-orbit coupling of the magnetic multilayer. In contrast to
previously observed two-mode coexistence degrading the spectral purity in
Py/Pt-based SHNOs with a negligible IPMA, a single coherent spin-wave mode with
a low driven current can be achieved by selecting the ferromagnet layer with a
suitable IPMA because the nonlinear mode coupling can be diminished by bringing
in the PMA field to compensate the easy-plane shape anisotropy. Moreover, the
simulations demonstrate that the experimentally observed current and
gate-voltage modulation of auto-oscillation modes are also closely associated
with the nonlinear damping and mode coupling, which are determined by the
ellipticity of magnetization precession. The demonstrated nonlinear mode
coupling mechanism and electrical control approach of spin-wave modes could
provide the clue to facilitate the implementation of the mutual synchronization
map for neuromorphic computing applications in SHNO array networks.Comment: 11 pages, 10 figure
PIM-QAT: Neural Network Quantization for Processing-In-Memory (PIM) Systems
Processing-in-memory (PIM), an increasingly studied neuromorphic hardware,
promises orders of energy and throughput improvements for deep learning
inference. Leveraging the massively parallel and efficient analog computing
inside memories, PIM circumvents the bottlenecks of data movements in
conventional digital hardware. However, an extra quantization step (i.e. PIM
quantization), typically with limited resolution due to hardware constraints,
is required to convert the analog computing results into digital domain.
Meanwhile, non-ideal effects extensively exist in PIM quantization because of
the imperfect analog-to-digital interface, which further compromises the
inference accuracy.
In this paper, we propose a method for training quantized networks to
incorporate PIM quantization, which is ubiquitous to all PIM systems.
Specifically, we propose a PIM quantization aware training (PIM-QAT) algorithm,
and introduce rescaling techniques during backward and forward propagation by
analyzing the training dynamics to facilitate training convergence. We also
propose two techniques, namely batch normalization (BN) calibration and
adjusted precision training, to suppress the adverse effects of non-ideal
linearity and stochastic thermal noise involved in real PIM chips. Our method
is validated on three mainstream PIM decomposition schemes, and physically on a
prototype chip. Comparing with directly deploying conventionally trained
quantized model on PIM systems, which does not take into account this extra
quantization step and thus fails, our method provides significant improvement.
It also achieves comparable inference accuracy on PIM systems as that of
conventionally quantized models on digital hardware, across CIFAR10 and
CIFAR100 datasets using various network depths for the most popular network
topology.Comment: 25 pages, 12 figures, 8 table
Understanding User Behavior in Volumetric Video Watching: Dataset, Analysis and Prediction
Volumetric video emerges as a new attractive video paradigm in recent years
since it provides an immersive and interactive 3D viewing experience with six
degree-of-freedom (DoF). Unlike traditional 2D or panoramic videos, volumetric
videos require dense point clouds, voxels, meshes, or huge neural models to
depict volumetric scenes, which results in a prohibitively high bandwidth
burden for video delivery. Users' behavior analysis, especially the viewport
and gaze analysis, then plays a significant role in prioritizing the content
streaming within users' viewport and degrading the remaining content to
maximize user QoE with limited bandwidth. Although understanding user behavior
is crucial, to the best of our best knowledge, there are no available 3D
volumetric video viewing datasets containing fine-grained user interactivity
features, not to mention further analysis and behavior prediction. In this
paper, we for the first time release a volumetric video viewing behavior
dataset, with a large scale, multiple dimensions, and diverse conditions. We
conduct an in-depth analysis to understand user behaviors when viewing
volumetric videos. Interesting findings on user viewport, gaze, and motion
preference related to different videos and users are revealed. We finally
design a transformer-based viewport prediction model that fuses the features of
both gaze and motion, which is able to achieve high accuracy at various
conditions. Our prediction model is expected to further benefit volumetric
video streaming optimization. Our dataset, along with the corresponding
visualization tools is accessible at
https://cuhksz-inml.github.io/user-behavior-in-vv-watching/Comment: Accepted by ACM MM'2
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