88 research outputs found
A Novel Uncalibrated Visual Servoing Controller Baesd on Model-Free Adaptive Control Method with Neural Network
Nowadays, with the continuous expansion of application scenarios of robotic
arms, there are more and more scenarios where nonspecialist come into contact
with robotic arms. However, in terms of robotic arm visual servoing,
traditional Position-based Visual Servoing (PBVS) requires a lot of calibration
work, which is challenging for the nonspecialist to cope with. To cope with
this situation, Uncalibrated Image-Based Visual Servoing (UIBVS) frees people
from tedious calibration work. This work applied a model-free adaptive control
(MFAC) method which means that the parameters of controller are updated in real
time, bringing better ability of suppression changes of system and environment.
An artificial intelligent neural network is applied in designs of controller
and estimator for hand-eye relationship. The neural network is updated with the
knowledge of the system input and output information in MFAC method. Inspired
by "predictive model" and "receding-horizon" in Model Predictive Control (MPC)
method and introducing similar structures into our algorithm, we realizes the
uncalibrated visual servoing for both stationary targets and moving
trajectories. Simulated experiments with a robotic manipulator will be carried
out to validate the proposed algorithm.Comment: 16 pages, 8 figure
Adaptive Finite-Time Model Estimation and Control for Manipulator Visual Servoing using Sliding Mode Control and Neural Networks
The image-based visual servoing without models of system is challenging since
it is hard to fetch an accurate estimation of hand-eye relationship via merely
visual measurement. Whereas, the accuracy of estimated hand-eye relationship
expressed in local linear format with Jacobian matrix is important to whole
system's performance. In this article, we proposed a finite-time controller as
well as a Jacobian matrix estimator in a combination of online and offline way.
The local linear formulation is formulated first. Then, we use a combination of
online and offline method to boost the estimation of the highly coupled and
nonlinear hand-eye relationship with data collected via depth camera. A neural
network (NN) is pre-trained to give a relative reasonable initial estimation of
Jacobian matrix. Then, an online updating method is carried out to modify the
offline trained NN for a more accurate estimation. Moreover, sliding mode
control algorithm is introduced to realize a finite-time controller. Compared
with previous methods, our algorithm possesses better convergence speed. The
proposed estimator possesses excellent performance in the accuracy of initial
estimation and powerful tracking capabilities for time-varying estimation for
Jacobian matrix compared with other data-driven estimators. The proposed scheme
acquires the combination of neural network and finite-time control effect which
drives a faster convergence speed compared with the exponentially converge
ones. Another main feature of our algorithm is that the state signals in system
is proved to be semi-global practical finite-time stable. Several experiments
are carried out to validate proposed algorithm's performance.Comment: 24 pages, 10 figure
Robustness of Deep Equilibrium Architectures to Changes in the Measurement Model
Deep model-based architectures (DMBAs) are widely used in imaging inverse
problems to integrate physical measurement models and learned image priors.
Plug-and-play priors (PnP) and deep equilibrium models (DEQ) are two DMBA
frameworks that have received significant attention. The key difference between
the two is that the image prior in DEQ is trained by using a specific
measurement model, while that in PnP is trained as a general image denoiser.
This difference is behind a common assumption that PnP is more robust to
changes in the measurement models compared to DEQ. This paper investigates the
robustness of DEQ priors to changes in the measurement models. Our results on
two imaging inverse problems suggest that DEQ priors trained under mismatched
measurement models outperform image denoisers
FLAIR: A Conditional Diffusion Framework with Applications to Face Video Restoration
Face video restoration (FVR) is a challenging but important problem where one
seeks to recover a perceptually realistic face videos from a low-quality input.
While diffusion probabilistic models (DPMs) have been shown to achieve
remarkable performance for face image restoration, they often fail to preserve
temporally coherent, high-quality videos, compromising the fidelity of
reconstructed faces. We present a new conditional diffusion framework called
FLAIR for FVR. FLAIR ensures temporal consistency across frames in a
computationally efficient fashion by converting a traditional image DPM into a
video DPM. The proposed conversion uses a recurrent video refinement layer and
a temporal self-attention at different scales. FLAIR also uses a conditional
iterative refinement process to balance the perceptual and distortion quality
during inference. This process consists of two key components: a
data-consistency module that analytically ensures that the generated video
precisely matches its degraded observation and a coarse-to-fine image
enhancement module specifically for facial regions. Our extensive experiments
show superiority of FLAIR over the current state-of-the-art (SOTA) for video
super-resolution, deblurring, JPEG restoration, and space-time frame
interpolation on two high-quality face video datasets.Comment: 32 pages, 27 figure
Cassava genome from a wild ancestor to cultivated varieties
Cassava is a major tropical food crop in the Euphorbiaceae family that has high carbohydrate production potential and adaptability to diverse environments. Here we present the draft genome sequences of a wild ancestor and a domesticated variety of cassava and comparative analyses with a partial inbred line. We identify 1,584 and 1,678 gene models specific to the wild and domesticated varieties, respectively, and discover high heterozygosity and millions of single-nucleotide variations. Our analyses reveal that genes involved in photosynthesis, starch accumulation and abiotic stresses have been positively selected, whereas those involved in cell wall biosynthesis and secondary metabolism, including cyanogenic glucoside formation, have been negatively selected in the cultivated varieties, reflecting the result of natural selection and domestication. Differences in microRNA genes and retrotransposon regulation could partly explain an increased carbon flux towards starch accumulation and reduced cyanogenic glucoside accumulation in domesticated cassava. These results may contribute to genetic improvement of cassava through better understanding of its biology
The United States COVID-19 Forecast Hub dataset
Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages
Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States
Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks
Analytical Research on the Impact Test of Light Steel Keel and Lightweight Concrete of Composite Wall
In order to study the impact resistance of light steel keel and lightweight concrete of composite walls (LSKLCW) under low-velocity impact, four composite wall specimens were designed to conduct dynamic simulation impact tests, and the failure mode, time-history curves of strain and displacement were analyzed and studied using test equipment and a loading system. The results show that the failure characteristics of the composite wall sample were elastic–plastic. Moreover, the vertical displacement and strain at the most unfavorable collision point were linearly related to the impingement height. Furthermore, the capacity of the composite wall (such as crack resistance, elastic–plastic deformation and energy dissipation) was affected by the concrete strength and the arrangement of the light steel netting. In addition, the impact resistance of the wall was significantly improved when the concrete strength was enhanced and the light steel netting was installed. Lastly, the test results were fitted and verified through the impact force calculation model of the composite wall, and then the accuracy of the test model was analyzed. The certain experimental basis and theoretical analysis basis for the impact resistance research of the composite wall can be provided by these research results
Analytical Research on the Impact Test of Light Steel Keel and Lightweight Concrete of Composite Wall
In order to study the impact resistance of light steel keel and lightweight concrete of composite walls (LSKLCW) under low-velocity impact, four composite wall specimens were designed to conduct dynamic simulation impact tests, and the failure mode, time-history curves of strain and displacement were analyzed and studied using test equipment and a loading system. The results show that the failure characteristics of the composite wall sample were elastic–plastic. Moreover, the vertical displacement and strain at the most unfavorable collision point were linearly related to the impingement height. Furthermore, the capacity of the composite wall (such as crack resistance, elastic–plastic deformation and energy dissipation) was affected by the concrete strength and the arrangement of the light steel netting. In addition, the impact resistance of the wall was significantly improved when the concrete strength was enhanced and the light steel netting was installed. Lastly, the test results were fitted and verified through the impact force calculation model of the composite wall, and then the accuracy of the test model was analyzed. The certain experimental basis and theoretical analysis basis for the impact resistance research of the composite wall can be provided by these research results
- …