5,823 research outputs found
Predicting Alder shrub expansion in Sub-Arctic Alaska using machine learning, satellite data, and environmental variables
The wider Fairbanks area, a sub-Arctic region of Alaska, USA, is home to a variety of alpine, oroarctic tundra that is being impacted by climate warming. This has resulted in an infilling and expansion of shrubs across the tundra and an elevational increase in the range limits of tall shrubs. Expansion of Alder (a key pioneer tall shrub) is thought to result from Arctic warming and shifts in its spread are likely to be a result of such warming.
Alder can fix atmospheric nitrogen by virtue of a mutualistic association with soil bacteria, which subsequently becomes available to other shrubs, potentially relieving local soil nitrogen limitations and promoting a positive growth response to climate warming. This potential landscape-scale change requires information of change at a suitable scale. However, Alder and other tall shrubs have been hard to measure using existing remote sensing approaches alone. This is mainly due to issues surrounding data availability and suitable spatial resolution of imagery.
Satellite remote sensing and environmental data are combined to create a map of Alder expansion across the wider Fairbanks area. A methodology is presented where ecological variables are integrated into prediction maps using a combination of regression and machine learning to estimate spatial extents. A baseline for a minimum number of high resolution training polygons is found to understand minimum required inputs. Field-based validation data were collected using a random sampling design across four different locations within the Yukon-Koyukuk area, Alaska. The combination of satellite data and environmental variables yields the best results for predicting Alder locations across the study area with a model accuracy of 0.99 and User’s accuracy of 43.66%. Orthomosaics as validation data are found to be very useful, enabling better quantification of smaller plant functional types for more accurate error matrix class assignment increasing overall model accuracy
Computational techniques to interpret the neural code underlying complex cognitive processes
Advances in large-scale neural recording technology have significantly improved the
capacity to further elucidate the neural code underlying complex cognitive processes.
This thesis aimed to investigate two research questions in rodent models. First, what
is the role of the hippocampus in memory and specifically what is the underlying
neural code that contributes to spatial memory and navigational decision-making.
Second, how is social cognition represented in the medial prefrontal cortex at the
level of individual neurons. To start, the thesis begins by investigating memory and
social cognition in the context of healthy and diseased states that use non-invasive
methods (i.e. fMRI and animal behavioural studies). The main body of the thesis
then shifts to developing our fundamental understanding of the neural mechanisms
underpinning these cognitive processes by applying computational techniques to ana lyse stable large-scale neural recordings. To achieve this, tailored calcium imaging
and behaviour preprocessing computational pipelines were developed and optimised
for use in social interaction and spatial navigation experimental analysis. In parallel,
a review was conducted on methods for multivariate/neural population analysis. A
comparison of multiple neural manifold learning (NML) algorithms identified that non linear algorithms such as UMAP are more adaptable across datasets of varying noise
and behavioural complexity. Furthermore, the review visualises how NML can be
applied to disease states in the brain and introduces the secondary analyses that
can be used to enhance or characterise a neural manifold. Lastly, the preprocessing
and analytical pipelines were combined to investigate the neural mechanisms in volved in social cognition and spatial memory. The social cognition study explored
how neural firing in the medial Prefrontal cortex changed as a function of the social
dominance paradigm, the "Tube Test". The univariate analysis identified an ensemble
of behavioural-tuned neurons that fire preferentially during specific behaviours such
as "pushing" or "retreating" for the animal’s own behaviour and/or the competitor’s
behaviour. Furthermore, in dominant animals, the neural population exhibited greater
average firing than that of subordinate animals. Next, to investigate spatial memory,
a spatial recency task was used, where rats learnt to navigate towards one of three
reward locations and then recall the rewarded location of the session. During the
task, over 1000 neurons were recorded from the hippocampal CA1 region for five rats
over multiple sessions. Multivariate analysis revealed that the sequence of neurons encoding an animal’s spatial position leading up to a rewarded location was also active
in the decision period before the animal navigates to the rewarded location. The result
posits that prospective replay of neural sequences in the hippocampal CA1 region
could provide a mechanism by which decision-making is supported
Improving the Effect of Electric Vehicle Charging on Imbalance Index in the Unbalanced Distribution Network Using Demand Response Considering Data Mining Techniques
With the development of electrical network infrastructure and the emergence of concepts such as demand response and using electric vehicles for purposes other than transportation, knowing the behavioral patterns of network technical specifications to manage electrical systems has become very important optimally. One of the critical parameters in the electrical system management is the distribution network imbalance. There are several ways to improve and control network imbalances. One of these ways is to detect the behavior of bus imbalance profiles in the network using data analysis. In the past, data analysis was performed for large environments such as states and countries. However, after the emergence of smart grids, behavioral study and recognition of these patterns in small-scale environments has found a fundamental and essential role in the deep management of these networks. One of the appropriate methods in identifying behavioral patterns is data mining. This paper uses the concepts of hierarchical and k-means clustering methods to identify the behavioral pattern of the imbalance index in an unbalanced distribution network. For this purpose, first, in an unbalanced network without the electric vehicle parking, the imbalance profile for all busses is estimated. Then, by applying the penetration coefficient of 25 and 75 for electric vehicles in the network, charging/discharging effects on the imbalance profile is determined. Then, by determining the target cluster and using demand response, the imbalance index is improved. This method reduces the number of busses competing in demand response programs. Next, using the concept of classification, a decision tree is constructed to minimize metering time
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
RGB-Only Reconstruction of Tabletop Scenes for Collision-Free Manipulator Control
We present a system for collision-free control of a robot manipulator that
uses only RGB views of the world. Perceptual input of a tabletop scene is
provided by multiple images of an RGB camera (without depth) that is either
handheld or mounted on the robot end effector. A NeRF-like process is used to
reconstruct the 3D geometry of the scene, from which the Euclidean full signed
distance function (ESDF) is computed. A model predictive control algorithm is
then used to control the manipulator to reach a desired pose while avoiding
obstacles in the ESDF. We show results on a real dataset collected and
annotated in our lab.Comment: ICRA 2023. Project page at https://ngp-mpc.github.io
Adaptive Robotic Information Gathering via Non-Stationary Gaussian Processes
Robotic Information Gathering (RIG) is a foundational research topic that
answers how a robot (team) collects informative data to efficiently build an
accurate model of an unknown target function under robot embodiment
constraints. RIG has many applications, including but not limited to autonomous
exploration and mapping, 3D reconstruction or inspection, search and rescue,
and environmental monitoring. A RIG system relies on a probabilistic model's
prediction uncertainty to identify critical areas for informative data
collection. Gaussian Processes (GPs) with stationary kernels have been widely
adopted for spatial modeling. However, real-world spatial data is typically
non-stationary -- different locations do not have the same degree of
variability. As a result, the prediction uncertainty does not accurately reveal
prediction error, limiting the success of RIG algorithms. We propose a family
of non-stationary kernels named Attentive Kernel (AK), which is simple, robust,
and can extend any existing kernel to a non-stationary one. We evaluate the new
kernel in elevation mapping tasks, where AK provides better accuracy and
uncertainty quantification over the commonly used stationary kernels and the
leading non-stationary kernels. The improved uncertainty quantification guides
the downstream informative planner to collect more valuable data around the
high-error area, further increasing prediction accuracy. A field experiment
demonstrates that the proposed method can guide an Autonomous Surface Vehicle
(ASV) to prioritize data collection in locations with significant spatial
variations, enabling the model to characterize salient environmental features.Comment: International Journal of Robotics Research (IJRR). arXiv admin note:
text overlap with arXiv:2205.0642
Exploring the effects of robotic design on learning and neural control
The ongoing deep learning revolution has allowed computers to outclass humans
in various games and perceive features imperceptible to humans during
classification tasks. Current machine learning techniques have clearly
distinguished themselves in specialized tasks. However, we have yet to see
robots capable of performing multiple tasks at an expert level. Most work in
this field is focused on the development of more sophisticated learning
algorithms for a robot's controller given a largely static and presupposed
robotic design. By focusing on the development of robotic bodies, rather than
neural controllers, I have discovered that robots can be designed such that
they overcome many of the current pitfalls encountered by neural controllers in
multitask settings. Through this discovery, I also present novel metrics to
explicitly measure the learning ability of a robotic design and its resistance
to common problems such as catastrophic interference.
Traditionally, the physical robot design requires human engineers to plan
every aspect of the system, which is expensive and often relies on human
intuition. In contrast, within the field of evolutionary robotics, evolutionary
algorithms are used to automatically create optimized designs, however, such
designs are often still limited in their ability to perform in a multitask
setting. The metrics created and presented here give a novel path to automated
design that allow evolved robots to synergize with their controller to improve
the computational efficiency of their learning while overcoming catastrophic
interference.
Overall, this dissertation intimates the ability to automatically design
robots that are more general purpose than current robots and that can perform
various tasks while requiring less computation.Comment: arXiv admin note: text overlap with arXiv:2008.0639
Behavior of Hot Rolled Asymmetric Steel I-Beams: Concept to Construction
Steel composite floor systems have been commonly used in commercial and residential floor systems. As the push to reduce building heights and increase building construction speed, concrete floor systems with a shallow depth to span ratio have surged ahead of steel composite floor systems. To make steel more competitive in shallow floor systems, multiple unique asymmetric built-up sections have been produced. These built-up sections are labor-intensive and require long lead times to produce. To make steel more competitive in residential floor systems, a hot-roll asymmetric steel I-beam (termed A-shape) was devised.
The overarching goal of this research was to create new knowledge toward the behavior of A-shapes throughout their life, which includes manufacturing, construction, and operation. The research presented herein is divided into five phases. Phase 1 was thermal-mechanical finite element modeling. The modeling approach used nonlinear steel properties and was experimentally validated. The purpose of the modeling was to capture the residual stresses due to the manufacturing cooling process. A parametric study was executed and the controlling flange width-to-thickness ratios were identified for A-shapes. The modeling was also used to study global deformations due to the cooling process, which was found to be manageable for realistic A-shape geometry.
Phase 2 was the development and production of proof-of-concept (POC) beams. The top flange of W-sections was reduced, and the beams were reheated to simulate the cooling of future hot-rolled asymmetric beams. The POC beams were used for further validation of the simulated global deformations and provided specimens for a full-scale experiment. Phase 2 also incorporated expert feedback from steel mills, which provided guidelines for A-shape dimensions.
Phase 3 was the experimental testing of a full-scale floor system incorporating the POC beams. During all stages of construction and actuator loading, A-shape beam strains and deflections were monitored. The data collected was used to understand the structural behavior of A-shapes as well as validate the theoretical calculations to be used in the next phase.
Phase 4 was a comprehensive analytical study to understand the controlling limits for A-shapes during construction and in-service. The study that was devised evaluated four unique loading scenarios, under 18 limit states, to fully understand the behavior of A-shapes. Phase 5 utilized the understanding of A-shape manufacturing, construction, and in-service behavior to develop recommended A-shape dimensions
Analytical validation of innovative magneto-inertial outcomes: a controlled environment study.
peer reviewe
- …