465 research outputs found
Tests of an ensemble Kalman filter for mesoscale and regional-scale data assimilation
This dissertation examines the performance of an ensemble Kalman filter (EnKF)
implemented in a mesoscale model in increasingly realistic contexts from under a perfect
model assumption and in the presence of significant model error with synthetic
observations to real-world data assimilation in comparison to the three-dimensional
variational (3DVar) method via both case study and month-long experiments. The EnKF
is shown to be promising for future application in operational data assimilation practice.
The EnKF with synthetic observations, which is implemented in the mesoscale
model MM5, is very effective in keeping the analysis close to the truth under the perfect
model assumption. The EnKF is most effective in reducing larger-scale errors but less
effective in reducing errors at smaller, marginally resolvable scales. In the presence of
significant model errors from physical parameterization schemes, the EnKF performs
reasonably well though sometimes it can be significantly degraded compared to its
performance under the perfect model assumption. Using a combination of different
physical parameterization schemes in the ensemble (the so-called âÂÂmulti-schemeâ ensemble) can significantly improve filter performance due to the resulting better
background error covariance and a smaller ensemble bias. The EnKF performs
differently for different flow regimes possibly due to scale- and flow-dependent error
growth dynamics and predictability.
Real-data (including soundings, profilers and surface observations) are assimilated
by directly comparing the EnKF and 3DVar and both are implemented in the Weather
Research and Forecasting model. A case study and month-long experiments show that
the EnKF is efficient in tracking observations in terms of both prior forecast and
posterior analysis. The EnKF performs consistently better than 3DVar for the time
period of interest due to the benefit of the EnKF from both using ensemble mean for
state estimation and using a flow-dependent background error covariance. Proper
covariance inflation and using a multi-scheme ensemble can significantly improve the
EnKF performance. Using a multi-scheme ensemble results in larger improvement in
thermodynamic variables than in other variables. The 3DVar system can benefit
substantially from using a short-term ensemble mean for state estimate. Noticeable
improvement is also achieved in 3DVar by including some flow dependence in its
background error covariance
SememeASR: Boosting Performance of End-to-End Speech Recognition against Domain and Long-Tailed Data Shift with Sememe Semantic Knowledge
Recently, excellent progress has been made in speech recognition. However,
pure data-driven approaches have struggled to solve the problem in
domain-mismatch and long-tailed data. Considering that knowledge-driven
approaches can help data-driven approaches alleviate their flaws, we introduce
sememe-based semantic knowledge information to speech recognition (SememeASR).
Sememe, according to the linguistic definition, is the minimum semantic unit in
a language and is able to represent the implicit semantic information behind
each word very well. Our experiments show that the introduction of sememe
information can improve the effectiveness of speech recognition. In addition,
our further experiments show that sememe knowledge can improve the model's
recognition of long-tailed data and enhance the model's domain generalization
ability.Comment: Accepted by INTERSPEECH 202
Modeling and Design of the Communication Sensing and Control Coupled Closed-Loop Industrial System
With the advent of 5G era, factories are transitioning towards wireless
networks to break free from the limitations of wired networks. In 5G-enabled
factories, unmanned automatic devices such as automated guided vehicles and
robotic arms complete production tasks cooperatively through the periodic
control loops. In such loops, the sensing data is generated by sensors, and
transmitted to the control center through uplink wireless communications. The
corresponding control commands are generated and sent back to the devices
through downlink wireless communications. Since wireless communications,
sensing and control are tightly coupled, there are big challenges on the
modeling and design of such closed-loop systems. In particular, existing
theoretical tools of these functionalities have different modelings and
underlying assumptions, which make it difficult for them to collaborate with
each other. Therefore, in this paper, an analytical closed-loop model is
proposed, where the performances and resources of communication, sensing and
control are deeply related. To achieve the optimal control performance, a
co-design of communication resource allocation and control method is proposed,
inspired by the model predictive control algorithm. Numerical results are
provided to demonstrate the relationships between the resources and control
performances.Comment: 6 pages, 3 figures, received by GlobeCom 202
Enhancing the vocal range of single-speaker singing voice synthesis with melody-unsupervised pre-training
The single-speaker singing voice synthesis (SVS) usually underperforms at
pitch values that are out of the singer's vocal range or associated with
limited training samples. Based on our previous work, this work proposes a
melody-unsupervised multi-speaker pre-training method conducted on a
multi-singer dataset to enhance the vocal range of the single-speaker, while
not degrading the timbre similarity. This pre-training method can be deployed
to a large-scale multi-singer dataset, which only contains audio-and-lyrics
pairs without phonemic timing information and pitch annotation. Specifically,
in the pre-training step, we design a phoneme predictor to produce the
frame-level phoneme probability vectors as the phonemic timing information and
a speaker encoder to model the timbre variations of different singers, and
directly estimate the frame-level f0 values from the audio to provide the pitch
information. These pre-trained model parameters are delivered into the
fine-tuning step as prior knowledge to enhance the single speaker's vocal
range. Moreover, this work also contributes to improving the sound quality and
rhythm naturalness of the synthesized singing voices. It is the first to
introduce a differentiable duration regulator to improve the rhythm naturalness
of the synthesized voice, and a bi-directional flow model to improve the sound
quality. Experimental results verify that the proposed SVS system outperforms
the baseline on both sound quality and naturalness
Direct observation of phase transition dynamics in suspensions of soft colloidal hydrogel particles
Due to the tunability of their softness and volume as a function of temperature, poly(N-isopropylacrylamide) (pNIPAm) hydrogel particles have emerged as a model system for soft colloidal spheres. By introducing AAc as comonomer, one can also tune the particle volume via pH. We report on the phase behavior of these stimuli-responsive colloids as measured with a microdialysis cell. This device, which integrates microfluidics with Particle Tracking Video-microscopy allows for simple and quick investigation of the phase behavior of suspensions the soft colloidal hydrogel as a function of pH as well as its packing density. In particular, we demonstrate the existence of an unusually broad liquid/crystal coexistence region as a function of effective particle volume fraction. Additionally, we reveal that nonequilibrium jammed states can be created in the coexistence region upon sudden large changes of pH. The phase diagram is indicative of complex interparticle interactions with weakly attractive components.https://digitalcommons.chapman.edu/sees_books/1002/thumbnail.jp
Fast Neighbor Discovery for Wireless Ad Hoc Network with Successive Interference Cancellation
Neighbor discovery (ND) is a key step in wireless ad hoc network, which
directly affects the efficiency of wireless networking. Improving the speed of
ND has always been the goal of ND algorithms. The classical ND algorithms lose
packets due to the collision of multiple packets, which greatly affects the
speed of the ND algorithms. Traditional methods detect packet collision and
implement retransmission when encountering packet loss. However, they does not
solve the packet collision problem and the performance improvement of ND
algorithms is limited. In this paper, the successive interference cancellation
(SIC) technology is introduced into the ND algorithms to unpack multiple
collision packets by distinguishing multiple packets in the power domain.
Besides, the multi-packet reception (MPR) is further applied to reduce the
probability of packet collision by distinguishing multiple received packets,
thus further improving the speed of ND algorithms. Six ND algorithms, namely
completely random algorithm (CRA), CRA based on SIC (CRA-SIC), CRA based on SIC
and MPR (CRA-SIC-MPR), scan-based algorithm (SBA), SBA based on SIC (SBA-SIC),
and SBA based on SIC and MPR (SBA-SIC-MPR), are theoretically analyzed and
verified by simulation. The simulation results show that SIC and MPR reduce the
ND time of SBA by 69.02% and CRA by 66.03% averagely.Comment: 16 pages, 16 figure
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