508 research outputs found
Tribological performances of fabric self-lubricating liner with different weft densities under severe working conditions
Several woven fabric self-lubricating liners with weft densities of 200-450 root/10cm in a spacing of 50 root/10cm have been prepared to investigate the tribological performances of the liner under severe working conditions, such as low velocity and heavy load (110, 179 and 248 MPa) and high velocity and light load (9, 18 and 27 m/min) by utilizing the self-lubricating liner performance assessment tester, and MMU-5G friction and wear tester respectively. The worn surface is characterized using confocal laser scanning microscopy. The tribological results show that the fabric self-lubricating liners with different weft densities share almost the same tribological property variation tendency. Fabric tightness affects the wear rate and the stability of wear resistance of liners under severe working conditions. The overall level of friction coefficient and the wear rate of liners with different weft densities are influenced by the cold flow degree of the polymer. In addition, proper weft density improves the tribological properties of liner and a preferred weft density for the liner under severe working conditions is found to be 300-350 root/10cm
Network Algebraization and Port Relationship for Power-Electronic-Dominated Power Systems
Different from the quasi-static network in the traditional power system, the
dynamic network in the power-electronic-dominated power system should be
considered due to rapid response of converters' controls. In this paper, a
nonlinear differential-algebraic model framework is established with algebraic
equations for dynamic electrical networks and differential equations for the
(source) nodes, by generalizing the Kron reduction. The internal and terminal
voltages of source nodes including converters are chosen as ports of nodes and
networks. Correspondingly, the impact of dynamic network becomes clear, namely,
it serves as a voltage divider and generates the terminal voltage based on the
internal voltage of the sources instantaneously, even when the dynamics of
inductance are included. With this simplest model, the roles of both nodes and
the network become apparent.Simulations verify the proposed model framework in
the modified 9-bus system.Comment: 4 pages, 6 figure
Learn from Unpaired Data for Image Restoration: A Variational Bayes Approach
Collecting paired training data is difficult in practice, but the unpaired
samples broadly exist. Current approaches aim at generating synthesized
training data from the unpaired samples by exploring the relationship between
the corrupted and clean data. This work proposes LUD-VAE, a deep generative
method to learn the joint probability density function from data sampled from
marginal distributions. Our approach is based on a carefully designed
probabilistic graphical model in which the clean and corrupted data domains are
conditionally independent. Using variational inference, we maximize the
evidence lower bound (ELBO) to estimate the joint probability density function.
Furthermore, we show that the ELBO is computable without paired samples under
the inference invariant assumption. This property provides the mathematical
rationale of our approach in the unpaired setting. Finally, we apply our method
to real-world image denoising and super-resolution tasks and train the models
using the synthetic data generated by the LUD-VAE. Experimental results
validate the advantages of our method over other learnable approaches
LoG-CAN: local-global Class-aware Network for semantic segmentation of remote sensing images
Remote sensing images are known of having complex backgrounds, high
intra-class variance and large variation of scales, which bring challenge to
semantic segmentation. We present LoG-CAN, a multi-scale semantic segmentation
network with a global class-aware (GCA) module and local class-aware (LCA)
modules to remote sensing images. Specifically, the GCA module captures the
global representations of class-wise context modeling to circumvent background
interference; the LCA modules generate local class representations as
intermediate aware elements, indirectly associating pixels with global class
representations to reduce variance within a class; and a multi-scale
architecture with GCA and LCA modules yields effective segmentation of objects
at different scales via cascaded refinement and fusion of features. Through the
evaluation on the ISPRS Vaihingen dataset and the ISPRS Potsdam dataset,
experimental results indicate that LoG-CAN outperforms the state-of-the-art
methods for general semantic segmentation, while significantly reducing network
parameters and computation. Code is available
at~\href{https://github.com/xwmaxwma/rssegmentation}{https://github.com/xwmaxwma/rssegmentation}.Comment: Accepted at ICASSP 202
De novo transcriptome sequencing and comprehensive analysis of the drought-responsive genes in the desert plant Cynanchum komarovii
The figure of an agarose gel with PCR products. (TIFF 11899 kb
Development of an improved competitive ELISA based on a monoclonal antibody against lipopolysaccharide for the detection of bovine brucellosis
Thaw Settlement Monitoring and Active Layer Thickness Retrieval Using Time Series COSMO-SkyMed Imagery in Iqaluit Airport
Thaw consolidation of degrading permafrost is a serious hazard to the safety and operation of infrastructure. Monitoring thermal changes in the active layer (AL), the proportion of the soil above permafrost that thaws and freezes periodically, is critical to understanding the conditions of the top layer above the permafrost and regulating the construction, operation, and maintenance of facilities. However, this is a very challenging task using ground-based methods such as ground-penetrating radar (GPR) or temperature sensors. This study explores the integration of interferometric measurements from high-resolution X-band Synthetic Aperture Radar (SAR) images and volumetric water content (VWC) data from SoilGrids to quantify detailed spatial variations in active layer thickness (ALT) in Iqaluit, the territorial capital of Nunavut in Canada. A total of 21 SAR images from COSMO Sky-Med (CSK) were first analyzed using the freely connected network interferometric synthetic aperture radar (FCNInSAR) method to map spatial and temporal variations in ground surface subsidence in the study area. Subsequently, we built an ALT retrieval model by introducing the thaw settlement coefficient, which takes soil properties and saturation state into account. The subsidence measurements from InSAR were then integrated with VWC extracted from the SoilGrids database to estimate changes in ALT. For validation, we conducted a comparison between estimated ALTs and in situ measurements in the airport sector. The InSAR survey identifies several sites of ground deformation at Iqaluit, subsiding at rates exceeding 80 mm/year. The subsidence rate changes along the runway coincide with frost cracks and ice-wedge furrows. The obtained ALTs, ranging from 0 to 5 m, vary significantly in different sediments. Maximum ALTs are found for rock areas, while shallow ALTs are distributed in the till blanket (Tb), the intertidal (Mi) sediments, and the alluvial flood plain (Afp) sediment units. The intersection of taxiway and runway has an AL thicker than other parts in the glaciomarine deltaic (GMd) sediments. Our study suggests that combining high-resolution SAR imagery with VWC data can provide more comprehensive ALT knowledge for hazard prevention and infrastructure operation in the permafrost zone
An On-demand Photonic Ising Machine with Simplified Hamiltonian Calculation by Phase-encoding and Intensity Detection
Photonic Ising machine is a new paradigm of optical computing, which is based
on the characteristics of light wave propagation, parallel processing and low
loss transmission. Thus, the process of solving the combinatorial optimization
problems can be accelerated through photonic/optoelectronic devices. In this
work, we have proposed and demonstrated the so-called Phase-Encoding and
Intensity Detection Ising Annealer (PEIDIA) to solve arbitrary Ising problems
on demand. The PEIDIA is based on the simulated annealing algorithm and
requires only one step of optical linear transformation with simplified
Hamiltonian calculation. With PEIDIA, the Ising spins are encoded on the phase
term of the optical field and only intensity detection is required during the
solving process. As a proof of principle, several 20 and 30-dimensional Ising
problems have been solved with high ground state probability
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