485 research outputs found
Lamb wave mode conversion-based crack detection for plate-like structures without baseline information
Traditional structural health monitoring (SHM) techniques are vulnerable to factors such as temperature change and vibration noise that are not relevant to structural damages. To overcome these drawbacks, this paper develops a reference-free crack detection method based on Lamb wave mode conversion. Neither baseline data nor damage threshold is required in this method. According to PZT polarization characteristics, feature signals which contained crack-lead Lamb wave converted modes are obtained from PZT components of two sets of side-by-side, and their amplitudes are obtained in the frequency domain to represent signal energy. Whether there being a crack is judged by comparing energy of each feature signal. Simulation and experiments show that the proposed method is not sensitive to optimal excitation frequency and sampling time, therefore it has strong robustness and applicability
A constructive characterization of total domination vertex critical graphs
AbstractLet G be a graph of order n and maximum degree Δ(G) and let γt(G) denote the minimum cardinality of a total dominating set of a graph G. A graph G with no isolated vertex is the total domination vertex critical if for any vertex v of G that is not adjacent to a vertex of degree one, the total domination number of G−v is less than the total domination number of G. We call these graphs γt-critical. For any γt-critical graph G, it can be shown that n≤Δ(G)(γt(G)−1)+1. In this paper, we prove that: Let G be a connected γt-critical graph of order n (n≥3), then n=Δ(G)(γt(G)−1)+1 if and only if G is regular and, for each v∈V(G), there is an A⊆V(G)−{v} such that N(v)∩A=0̸, the subgraph induced by A is 1-regular, and every vertex in V(G)−A−{v} has exactly one neighbor in A
A Normalization Model for Analyzing Multi-Tier Millimeter Wave Cellular Networks
Based on the distinguishing features of multi-tier millimeter wave (mmWave)
networks such as different transmit powers, different directivity gains from
directional beamforming alignment and path loss laws for line-of-sight (LOS)
and non-line-of-sight (NLOS) links, we introduce a normalization model to
simplify the analysis of multi-tier mmWave cellular networks. The highlight of
the model is that we convert a multi-tier mmWave cellular network into a
single-tier mmWave network, where all the base stations (BSs) have the same
normalized transmit power 1 and the densities of BSs scaled by LOS or NLOS
scaling factors respectively follow piecewise constant function which has
multiple demarcation points. On this basis, expressions for computing the
coverage probability are obtained in general case with beamforming alignment
errors and the special case with perfect beamforming alignment in the
communication. According to corresponding numerical exploration, we conclude
that the normalization model for multi-tier mmWave cellular networks fully
meets requirements of network performance analysis, and it is simpler and
clearer than the untransformed model. Besides, an unexpected but sensible
finding is that there is an optimal beam width that maximizes coverage
probability in the case with beamforming alignment errors.Comment: 7 pages, 4 figure
Robust Unsupervised Cross-Lingual Word Embedding using Domain Flow Interpolation
This paper investigates an unsupervised approach towards deriving a
universal, cross-lingual word embedding space, where words with similar
semantics from different languages are close to one another. Previous
adversarial approaches have shown promising results in inducing cross-lingual
word embedding without parallel data. However, the training stage shows
instability for distant language pairs. Instead of mapping the source language
space directly to the target language space, we propose to make use of a
sequence of intermediate spaces for smooth bridging. Each intermediate space
may be conceived as a pseudo-language space and is introduced via simple linear
interpolation. This approach is modeled after domain flow in computer vision,
but with a modified objective function. Experiments on intrinsic Bilingual
Dictionary Induction tasks show that the proposed approach can improve the
robustness of adversarial models with comparable and even better precision.
Further experiments on the downstream task of Cross-Lingual Natural Language
Inference show that the proposed model achieves significant performance
improvement for distant language pairs in downstream tasks compared to
state-of-the-art adversarial and non-adversarial models
Shared memory parallel computing procedures for nonlinear dynamic analysis of super high rise buildings
The proposed parallel state transformation procedures (PSTP) of fiber beam-column elements and multi-layered shell elements, combined with the parallel factorization of Jacobian (PF), are incorporated into a finite element program. In PSTP, elements are classified into different levels of workload prior to state determination in order to balance workload among different threads. In PF, the multi-threaded version of OpenBLAS is adopted to compute super-nodes. A case study on four super high-rise buildings, i.e. S1~S4, has demonstrated that the combination of PSTP and PF does not have any observable influence on computational accuracy. As number of elements and DOFs increases, the ratio of time consumed in the formation of the Jacobian to that consumed in the solution of algebraic equations tends to decrease. The introduction of parallel solver can yield a substantial reduction in computational cost. Combination of PSTP and PF can give rise to a further significant reduction. The acceleration ratios associated with PSTP do not exhibit a significant decrease as PGA level increases. Even PGA level is equal to 2.0g, PSTP still can result in acceleration ratios of 2.56 and 1.92 for S1 and S4, respectively. It is verified that it is more effective to accelerate analysis by reducing the time spent in solving algebraic equations rather than reducing that spent in the formation of the Jacobian for super high-rise buildings
s-LWSR: Super Lightweight Super-Resolution Network
Deep learning (DL) architectures for superresolution (SR) normally contain
tremendous parameters, which has been regarded as the crucial advantage for
obtaining satisfying performance. However, with the widespread use of mobile
phones for taking and retouching photos, this character greatly hampers the
deployment of DL-SR models on the mobile devices. To address this problem, in
this paper, we propose a super lightweight SR network: s-LWSR. There are mainly
three contributions in our work. Firstly, in order to efficiently abstract
features from the low resolution image, we build an information pool to mix
multi-level information from the first half part of the pipeline. Accordingly,
the information pool feeds the second half part with the combination of
hierarchical features from the previous layers. Secondly, we employ a
compression module to further decrease the size of parameters. Intensive
analysis confirms its capacity of trade-off between model complexity and
accuracy. Thirdly, by revealing the specific role of activation in deep models,
we remove several activation layers in our SR model to retain more information
for performance improvement. Extensive experiments show that our s-LWSR, with
limited parameters and operations, can achieve similar performance to other
cumbersome DL-SR methods
Contrastive Bayesian Analysis for Deep Metric Learning
Recent methods for deep metric learning have been focusing on designing
different contrastive loss functions between positive and negative pairs of
samples so that the learned feature embedding is able to pull positive samples
of the same class closer and push negative samples from different classes away
from each other. In this work, we recognize that there is a significant
semantic gap between features at the intermediate feature layer and class
labels at the final output layer. To bridge this gap, we develop a contrastive
Bayesian analysis to characterize and model the posterior probabilities of
image labels conditioned by their features similarity in a contrastive learning
setting. This contrastive Bayesian analysis leads to a new loss function for
deep metric learning. To improve the generalization capability of the proposed
method onto new classes, we further extend the contrastive Bayesian loss with a
metric variance constraint. Our experimental results and ablation studies
demonstrate that the proposed contrastive Bayesian metric learning method
significantly improves the performance of deep metric learning in both
supervised and pseudo-supervised scenarios, outperforming existing methods by a
large margin.Comment: Accepted by IEEE Transactions on Pattern Analysis and Machine
Intelligenc
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