Search results for "LMA"
showing 10 items of 3320 documents
Experimental evidence of the long‐term effects of reindeer on Arctic vegetation greenness and species richness at a larger landscape scale
2019
1. Large herbivores influence plant community structure and ecosystem processes in many ecosystems. In large parts of the Arctic, reindeer (or caribou) are the only large herbivores present. Recent studies show that reindeer have the potential to mitigate recent warming-induced shrub encroachment in the Arctic and the associated greening of high-latitude ecosystems. This will potentially have large scale consequences for ecosystem productivity and carbon cycling. 2. To date, information on variation in the interactions between reindeer and plants across Arctic landscapes has been scarce. We utilized a network of experimental sites across a latitudinal gradient in the Scandinavian mountains …
Direct and transgenerational effects of an experimental heatwave on early life stages in a freshwater snail
2019
1. Global climate change imposes a serious threat to natural populations of many species. Estimates of the effects of climate change‐mediated environmental stresses are, however, often based only on their direct effects on organisms, and neglect the potential transgenerational (e.g. maternal) effects. 2. We tested whether high temperature (i.e. an experimental heatwave), which is known to reduce the performance of adult Lymnaea stagnalis snails, affects the produced offspring (eggs and hatchlings) through maternal effects, and how strong these effects are compared with the effects of direct exposure of offspring to high temperature. We examined the effect of maternal thermal environment (15…
Adaptive-gain extended Kalman filter: Extension to the continuous-discrete case
2009
In the present article we propose a nonlinear observer that merges the behaviors 1) of an extended Kalman filter, mainly designed to smooth off noise , and 2) of high-gain observers devoted to handle large perturbations in the state estimation. We specifically aim at continuous-discrete systems. The strategy consists in letting the high-gain self adapt according to the innovation. We define innovation computed over a time window and justify its usage via an important lemma. We prove the general convergence of the resulting observer.
Adaptive Consensus-Based Distributed Kalman Filter for WSNs with Random Link Failures
2016
Wireless Sensor Networks have emerged as a very powerful tool for the monitoring and control, over large areas, of diverse phenomena. One of the most appealing properties of these networks is their potentiality to perform complex tasks in a total distributed fashion, without requiring a central entity. In this scenario, where nodes are constrained to use only local information and communicate with one-hop neighbors, iterative consensus algorithms are extensively used due to their simplicity. In this work, we propose the design of a consensus-based distributed Kalman filter for state estimation, in a sensor network whose connections are subject to random failures. As a result of this unrelia…
Integrated GNSS/IMU Hub Motion Estimator for Offshore Wind Turbine Blade Installation
2019
Abstract Offshore wind turbines (OWTs) have become increasingly popular for their ability to harvest clean offshore wind energy. Bottom-fixed foundations are the most used foundation type. Because of its large diameter, the foundation is sensitive to wave loads. For typical manually assisted blade-mating operations, the decision to perform the mating operation is based on the relative distance and velocity between the blade root center and the hub, and in accordance with the weather window. Hence, monitoring the hub real-time position and velocity is necessary, whether the blade installation is conducted manually or automatically. In this study, we design a hub motion estimation algorithm f…
Ship-to-Ship State Observer Using Sensor Fusion and the Extended Kalman Filter
2019
In this paper, a solution for estimating the relative position and orientation between two ships in six degrees-of-freedom (6DOF) using sensor fusion and an extended Kalman filter (EKF) approach is presented. Two different sensor types, based on time-of-flight and inertial measurement principles, were combined to create a reliable and redundant estimate of the relative motion between the ships. An accurate and reliable relative motion estimate is expected to be a key enabler for future ship-to-ship operations, such as autonomous load transfer and handling. The proposed sensor fusion algorithm was tested with real sensors (two motion reference units (MRS) and a laser tracker) and an experime…
An adaptive multi-rate system for visual tracking in augmented reality applications
2016
The visual tracking of an object is a well-known problem, and it involves many fields of applications. Often a single sensor, the camera, could not provide enough information in order to track the whole object trajectory due to a low updating rate; therefore a multi-sensor system, based also on inertial measurements, could be necessary to improve the tracking accuracy. This leads to the fundamental question: how can information from different sensors be combined when they work at different rates? In this paper an approach based on recursive parameter estimation focusing on multi-rate situations is suggested. The problem is here formulated as the state-of-the-art problem of the visual tracki…
Automatic Take Off and Landing for UAS Flying in Turbulent Air - An EKF Based Procedure
2020
An innovative use of the Extended Kalman Filter (EKF) is proposed to perform automatic take off and landing by the rejection of disturbances due to turbulence. By using two simultaneously working Extended Kalman Filters, a procedure is implemented: the first filter, by using measurements gathered in turbulent air, estimates wind components; the second one, by using the estimated disturbances, obtains command laws that are able to reject disturbances. The fundamental innovation of such a procedure consists in the fact that the covariance matrices of process (Q) and measurement (R) noise are not treated as filter design parameters. In this way determined optimal values of the aforementioned m…
Using an Adaptive High-Gain Extended Kalman Filter With a Car Efficiency Model
2010
The authors apply the Adaptive High-Gain Extended Kalman Filter (AEKF) to the problem of estimating engine efficiency with data gathered from normal driving. The AEKF is an extension of the traditional Kalman Filter that allows the filter to be reactive to perturbations without sacrificing noise filtering. An observability normal form of the engine efficiency model is developed for the AEKF. The continuous-discrete AEKF is presented along with strategies for dealing with asynchronous data. Empiric test results are presented and contrasted with EKF-derived results.Copyright © 2010 by ASME
Online fitted policy iteration based on extreme learning machines
2016
Reinforcement learning (RL) is a learning paradigm that can be useful in a wide variety of real-world applications. However, its applicability to complex problems remains problematic due to different causes. Particularly important among these are the high quantity of data required by the agent to learn useful policies and the poor scalability to high-dimensional problems due to the use of local approximators. This paper presents a novel RL algorithm, called online fitted policy iteration (OFPI), that steps forward in both directions. OFPI is based on a semi-batch scheme that increases the convergence speed by reusing data and enables the use of global approximators by reformulating the valu…