199 research outputs found
Processing of epoxy/ graphene nanocomposites
Graphene can significantly improve physical properties of epoxy at extremely low loading when incorporated appropriately. However, in practical terms, graphene is not suitable to disperse in epoxy just by simple mixing, which is due to graphene’s pronounced tendency to reaggregate in the matrix due to the strong van der Waals force between separately dispersed graphene sheets. In order to substitute traditional epoxy reinforcements with graphene, there are still some issues like dispersion, homogenization, and reaggregation. Therefore, the preparation of epoxy/graphene nanocomposites affects strongly to the final properties of nanocomposites. In this work, graphene bundles have been dispersed in two-component epoxy system by bath sonication. Dispersion state and reaggregation behaviour of graphene in this system have been studied. Light transmittance in ultraviolet-visible spectroscopy has been used to quantify the reaggregation by a series of controlled experiments
N,N-Dimethylformamide (DMF) usage in epoxy/graphene nanocomposites: problems associated with reaggregation
DMF is one the most commonly-used solvents for preparing graphene nanocomposites. Various processing variables for DMF are being used for the preparation of epoxy/graphene nanocomposites. Whilst the emphasis of all of these reported studies are on the improvements in mechanical, and other properties, of the epoxy/graphene nanocomposites, there is no study investigating how DMF affects the processing and how it is associated with the final properties of the nanocomposites. In this work, different dosages of DMF have been used to prepare nanocomposites. Mechanical testing, X-ray diffraction (XRD), dynamic mechanical analysis (DMA), thermogravimetric analysis (TGA), and scanning electron microscopy (SEM) have been used to analyze the effectiveness of DMF dosage on the properties of processed nanocomposites. Larger dosages of DMF are not always ideal for dispersing graphene as it promotes reaggregation of graphene during the processing
Text-guided Eyeglasses Manipulation with Spatial Constraints
Virtual try-on of eyeglasses involves placing eyeglasses of different shapes
and styles onto a face image without physically trying them on. While existing
methods have shown impressive results, the variety of eyeglasses styles is
limited and the interactions are not always intuitive or efficient. To address
these limitations, we propose a Text-guided Eyeglasses Manipulation method that
allows for control of the eyeglasses shape and style based on a binary mask and
text, respectively. Specifically, we introduce a mask encoder to extract mask
conditions and a modulation module that enables simultaneous injection of text
and mask conditions. This design allows for fine-grained control of the
eyeglasses' appearance based on both textual descriptions and spatial
constraints. Our approach includes a disentangled mapper and a decoupling
strategy that preserves irrelevant areas, resulting in better local editing. We
employ a two-stage training scheme to handle the different convergence speeds
of the various modality conditions, successfully controlling both the shape and
style of eyeglasses. Extensive comparison experiments and ablation analyses
demonstrate the effectiveness of our approach in achieving diverse eyeglasses
styles while preserving irrelevant areas.Comment: Revised version: add some experiment
A Universal Framework of Superimposed RIS-Phase Modulation for MISO Communication
To fully exploit the additional dimension brought by reconfigurable
intelligent surface (RIS), it is recently suggested by information theory that
modulating information upon RIS phases is able to send extra information with
increased communication rate. In this paper, we propose a novel superimposed
RIS-phase modulation (SRPM) scheme to transfer extra messages by superimposing
information-bearing phase offsets to conventionally optimized RIS phases. The
proposed SRPM is interpreted as a universal framework for RIS phase modulation.
Theoretical union bound of the average bit error rate (ABER) of the proposed
SRPM is also derived with the maximum likelihood (ML) detection. The diversity
order is characterized as 0.5 for all parameter settings, which is useful for
determining the optimal choice of the phase modulation parameters. Furthermore,
we discover that doubling the number of either RIS reflecting elements or the
transmit antennas is equivalent to a 3 dB increment in the transmit power for
SRPM. Numerical results demonstrate the effectiveness of SRPM and reveal that
it achieves reliable communication of more bits than existing schemes.Comment: Accepted by IEEE Transactions on Vehicular Technolog
Design and analysis of driving motor system for hybrid electric vehicle
In order to improve the reliability and stability of hybrid electric vehicle driving motor system, according to the performance parameters of the hybrid electric vehicle, the driving motor system is designed and analyzed for the hybrid electric vehicle. Based on the performance parameters of the hybrid electric vehicle, the power parameters of the permanent magnet synchronous motor (PMSM) are calculated and determined, then the parameters of the stator core, the permanent magnet and the rotor core are designed and calculated, as well as other main characteristic parameters of the driving motor system are calculated. The model of a PMSM is established and simulated by ANSOFT Maxwell according to the obtained motor parameters, and then the steady state and transient state of the driving motor are simulated in different working points, and the electromagnetic and performance curves are combined to determine the overall performance requirements of the driving motor, which can be used to match the hybrid electric vehicle. The simulation results show that the designed PMSM can be used to match the hybrid electric vehicle and meet the performance requirements of the vehicle. The final simulation analysis results are in good agreement with the theoretical calculation results, which indicates that this method can be used to afford a theoretical basis to reduce the cogging torque and optimize the in-wheel motor of electric vehicle in the future
Superimposed RIS-phase Modulation for MIMO Communications: A Novel Paradigm of Information Transfer
Reconfigurable intelligent surface (RIS) is regarded as an important enabling
technology for the sixth-generation (6G) network. Recently, modulating
information in reflection patterns of RIS, referred to as reflection modulation
(RM), has been proven in theory to have the potential of achieving higher
transmission rate than existing passive beamforming (PBF) schemes of RIS. To
fully unlock this potential of RM, we propose a novel superimposed RIS-phase
modulation (SRPM) scheme for multiple-input multiple-output (MIMO) systems,
where tunable phase offsets are superimposed onto predetermined RIS phases to
bear extra information messages. The proposed SRPM establishes a universal
framework for RM, which retrieves various existing RM-based schemes as special
cases. Moreover, the advantages and applicability of the SRPM in practice is
also validated in theory by analytical characterization of its performance in
terms of average bit error rate (ABER) and ergodic capacity. To maximize the
performance gain, we formulate a general precoding optimization at the base
station (BS) for a single-stream case with uncorrelated channels and obtain the
optimal SRPM design via the semidefinite relaxation (SDR) technique.
Furthermore, to avoid extremely high complexity in maximum likelihood (ML)
detection for the SRPM, we propose a sphere decoding (SD)-based layered
detection method with near-ML performance and much lower complexity. Numerical
results demonstrate the effectiveness of SRPM, precoding optimization, and
detection design. It is verified that the proposed SRPM achieves a higher
diversity order than that of existing RM-based schemes and outperforms PBF
significantly especially when the transmitter is equipped with limited
radio-frequency (RF) chains.Comment: Submitted to IEEE for possible publicatio
Collaborative Propagation on Multiple Instance Graphs for 3D Instance Segmentation with Single-point Supervision
Instance segmentation on 3D point clouds has been attracting increasing
attention due to its wide applications, especially in scene understanding
areas. However, most existing methods operate on fully annotated data while
manually preparing ground-truth labels at point-level is very cumbersome and
labor-intensive. To address this issue, we propose a novel weakly supervised
method RWSeg that only requires labeling one object with one point. With these
sparse weak labels, we introduce a unified framework with two branches to
propagate semantic and instance information respectively to unknown regions
using self-attention and a cross-graph random walk method. Specifically, we
propose a Cross-graph Competing Random Walks (CRW) algorithm that encourages
competition among different instance graphs to resolve ambiguities in closely
placed objects, improving instance assignment accuracy. RWSeg generates
high-quality instance-level pseudo labels. Experimental results on ScanNet-v2
and S3DIS datasets show that our approach achieves comparable performance with
fully-supervised methods and outperforms previous weakly-supervised methods by
a substantial margin
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