96 research outputs found
Adaptive Sampling for Learning Gaussian Processes Using Mobile Sensor Networks
This paper presents a novel class of self-organizing sensing agents that adaptively learn an anisotropic, spatio-temporal Gaussian process using noisy measurements and move in order to improve the quality of the estimated covariance function. This approach is based on a class of anisotropic covariance functions of Gaussian processes introduced to model a broad range of spatio-temporal physical phenomena. The covariance function is assumed to be unknown a priori. Hence, it is estimated by the maximum a posteriori probability (MAP) estimator. The prediction of the field of interest is then obtained based on the MAP estimate of the covariance function. An optimal sampling strategy is proposed to minimize the information-theoretic cost function of the Fisher Information Matrix. Simulation results demonstrate the effectiveness and the adaptability of the proposed scheme
Layered Cost-Map-Based Traffic Management for Multiple Automated Mobile Robots via a Data Distribution Service
This letter proposes traffic management for multiple automated mobile robots
(AMRs) based on a layered cost map. Multiple AMRs communicate via a data
distribution service (DDS), which is shared by topics in the same DDS domain.
The cost of each layer is manipulated by topics. The traffic management server
in the domain sends or receives topics to each of AMRs. Using the layered cost
map, the new concept of prohibition filter, lane filter, fleet layer, and
region filter are proposed and implemented. The prohibition filter can help a
user set an area that would prohibit an AMR from trespassing. The lane filter
can help set one-way directions based on an angle image. The fleet layer can
help AMRs share their locations via the traffic management server. The region
filter requests for or receives an exclusive area, which can be occupied by
only one AMR, from the traffic management server. All the layers are
experimentally validated with real-world AMRs. Each area can be configured with
user-defined images or text-based parameter files.Comment: 8 pages, 13 figure
Clustering Techniques for Stable Linear Dynamical Systems with applications to Hard Disk Drives
In Robust Control and Data Driven Robust Control design methodologies,
multiple plant transfer functions or a family of transfer functions are
considered and a common controller is designed such that all the plants that
fall into this family are stabilized. Though the plants are stabilized, the
controller might be sub-optimal for each of the plants when the variations in
the plants are large. This paper presents a way of clustering stable linear
dynamical systems for the design of robust controllers within each of the
clusters such that the controllers are optimal for each of the clusters. First
a k-medoids algorithm for hard clustering will be presented for stable Linear
Time Invariant (LTI) systems and then a Gaussian Mixture Models (GMM)
clustering for a special class of LTI systems, common for Hard Disk Drive
plants, will be presented.Comment: 6 pages, 4 figure
Spatial Regression With Multiplicative Errors, and Its Application With Lidar Measurements
Multiplicative errors in addition to spatially referenced observations often
arise in geodetic applications, particularly in surface estimation with light
detection and ranging (LiDAR) measurements. However, spatial regression
involving multiplicative errors remains relatively unexplored in such
applications. In this regard, we present a penalized modified least squares
estimator to handle the complexities of a multiplicative error structure while
identifying significant variables in spatially dependent observations for
surface estimation. The proposed estimator can be also applied to classical
additive error spatial regression. By establishing asymptotic properties of the
proposed estimator under increasing domain asymptotics with stochastic sampling
design, we provide a rigorous foundation for its effectiveness. A comprehensive
simulation study confirms the superior performance of our proposed estimator in
accurately estimating and selecting parameters, outperforming existing
approaches. To demonstrate its real-world applicability, we employ our proposed
method, along with other alternative techniques, to estimate a rotational
landslide surface using LiDAR measurements. The results highlight the efficacy
and potential of our approach in tackling complex spatial regression problems
involving multiplicative errors
Robot Manipulation Task Learning by Leveraging SE(3) Group Invariance and Equivariance
This paper presents a differential geometric control approach that leverages
SE(3) group invariance and equivariance to increase transferability in learning
robot manipulation tasks that involve interaction with the environment.
Specifically, we employ a control law and a learning representation framework
that remain invariant under arbitrary SE(3) transformations of the manipulation
task definition. Furthermore, the control law and learning representation
framework are shown to be SE(3) equivariant when represented relative to the
spatial frame. The proposed approach is based on utilizing a recently presented
geometric impedance control (GIC) combined with a learning variable impedance
control framework, where the gain scheduling policy is trained in a supervised
learning fashion from expert demonstrations. A geometrically consistent error
vector (GCEV) is fed to a neural network to achieve a gain scheduling policy
that remains invariant to arbitrary translation and rotations. A comparison of
our proposed control and learning framework with a well-known Cartesian space
learning impedance control, equipped with a Cartesian error vector-based gain
scheduling policy, confirms the significantly superior learning transferability
of our proposed approach. A hardware implementation on a peg-in-hole task is
conducted to validate the learning transferability and feasibility of the
proposed approach
Feasibility of using red cell distribution width for prediction of postoperative mortality in severe burn patients: an association with acute kidney injury after surgery
Background Severe burns cause pathophysiological processes that result in mortality. A laboratory biomarker, red cell distribution width (RDW), is known as a predictor of mortality in critically-ill patients. We examined the association between RDW and postoperative mortality in severe burn patients. Methods We retrospectively analyzed medical data of 731 severely burned patients who underwent surgery under general anesthesia. We evaluated whether preoperative RDW value can predict 3-month mortality after burn surgery using receiver operating characteristic (ROC) curve analysis, logistic regression, and Cox proportional-hazards regression analysis. Mortality was also analyzed according to preoperative RDW values and incidence of postoperative acute kidney injury (AKI). Results The 3-month mortality rate after burn surgery was 27.1% (198/731). The area under the ROC curve of preoperative RDW to predict mortality after burn surgery was 0.701 (95% confidence interval [CI], 0.667–0.734; P 12.9 was 1.238 (95% CI, 1.138–1.347; P 12.9. Preoperative RDW was considered an independent risk factor for mortality (odds ratio, 1.679; 95% CI, 1.378–2.046; P 12.9 and postoperative AKI may further increase mortality after burn surgery
Diffusion-EDFs: Bi-equivariant Denoising Generative Modeling on SE(3) for Visual Robotic Manipulation
Diffusion generative modeling has become a promising approach for learning
robotic manipulation tasks from stochastic human demonstrations. In this paper,
we present Diffusion-EDFs, a novel SE(3)-equivariant diffusion-based approach
for visual robotic manipulation tasks. We show that our proposed method
achieves remarkable data efficiency, requiring only 5 to 10 human
demonstrations for effective end-to-end training in less than an hour.
Furthermore, our benchmark experiments demonstrate that our approach has
superior generalizability and robustness compared to state-of-the-art methods.
Lastly, we validate our methods with real hardware experiments. Project
Website: https://sites.google.com/view/diffusion-edfs/homeComment: 31 pages, 13 figure
Teatro e ensino da matemática: atividade desenvolvida num curso de formação docente
Anais do II Seminário Seminário Estadual PIBID do Paraná: tecendo saberes / organizado por Dulcyene Maria Ribeiro e Catarina Costa Fernandes — Foz do Iguaçu: Unioeste; Unila, 2014Este trabalho relata uma aula desenvolvida pelas alunas do Curso de Formação de Docentes
do Instituto Estadual de Educação de Londrina com a colaboração dos Bolsistas do Programa
Institucional de Bolsas de Iniciação à Docência – PIBID – Subprojeto de Matemática, para alunos de
primeiro ano do Ensino Fundamental utilizando o teatro como forma de apresentar conteúdos
matemáticos como números, sequência de números, operações básicas como adição, subtração e
conteúdos de língua portuguesa como leitura e escrita de número
OPTIMAL COORDINATION OF MOBILE SENSOR NETWORKS USING GAUSSIAN PROCESSES
ABSTRACT In this paper, we introduce a family of spatio-tempora
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