AIMC Topic: Environment

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Novel hybrid adaptive controller for manipulation in complex perturbation environments.

PloS one
In this paper we present a hybrid control scheme, combining the advantages of task-space and joint-space control. The controller is based on a human-like adaptive design, which minimises both control effort and tracking error. Our novel hybrid adapti...

A BHR Composite Network-Based Visualization Method for Deformation Risk Level of Underground Space.

PloS one
This study proposes a visualization processing method for the deformation risk level of underground space. The proposed method is based on a BP-Hopfield-RGB (BHR) composite network. Complex environmental factors are integrated in the BP neural networ...

Morbidity Rate Prediction of Dengue Hemorrhagic Fever (DHF) Using the Support Vector Machine and the Aedes aegypti Infection Rate in Similar Climates and Geographical Areas.

PloS one
BACKGROUND: In the past few decades, several researchers have proposed highly accurate prediction models that have typically relied on climate parameters. However, climate factors can be unreliable and can lower the effectiveness of prediction when t...

Application of time dependent probabilistic collision state checkers in highly dynamic environments.

PloS one
When computing the trajectory of an autonomous vehicle, inevitable collision states must be avoided at all costs, so no harm comes to the device or pedestrians around it. In dynamic environments, considering collisions as binary events is risky and i...

Adaptive Robust Output Feedback Control for a Marine Dynamic Positioning System Based on a High-Gain Observer.

IEEE transactions on neural networks and learning systems
This paper develops an adaptive robust output feedback control scheme for dynamically positioned ships with unavailable velocities and unknown dynamic parameters in an unknown time-variant disturbance environment. The controller is designed by incorp...

Opportunistic Behavior in Motivated Learning Agents.

IEEE transactions on neural networks and learning systems
This paper focuses on the novel motivated learning (ML) scheme and opportunistic behavior of an intelligent agent. It extends previously developed ML to opportunistic behavior in a multitask situation. Our paper describes the virtual world implementa...

Adaptive Neural Control of Nonaffine Systems With Unknown Control Coefficient and Nonsmooth Actuator Nonlinearities.

IEEE transactions on neural networks and learning systems
This brief considers the asymptotic tracking problem for a class of high-order nonaffine nonlinear dynamical systems with nonsmooth actuator nonlinearities. A novel transformation approach is proposed, which is able to systematically transfer the ori...

A hierarchical model of goal directed navigation selects trajectories in a visual environment.

Neurobiology of learning and memory
We have developed a Hierarchical Look-Ahead Trajectory Model (HiLAM) that incorporates the firing pattern of medial entorhinal grid cells in a planning circuit that includes interactions with hippocampus and prefrontal cortex. We show the model's fle...

Innovations in Quail Welfare: Integrating Environmental Enrichment, Nutrition and Genetic Advances for Improved Health and Productivity.

Veterinary medicine and science
The demand for ethical and sustainable poultry production is driving up the importance of quail welfare. Because quail meat and eggs are in high demand, quails are frequently kept in harsh production environments that may harm their health and well-b...

Improvement in genomic prediction of maize with prior gene ontology information depends on traits and environmental conditions.

The plant genome
Classical genomic prediction approaches rely on statistical associations between traits and markers rather than their biological significance. Biologically informed selection of genomic regions can help prioritize polymorphisms by considering underly...