scholarly journals Integrating telemetry data into spatial capture–recapture modifies inferences on multi‐scale resource selection

Ecosphere ◽  
2018 ◽  
Vol 9 (4) ◽  
Author(s):  
Daniel W. Linden ◽  
Alexej P. K. Sirén ◽  
Peter J. Pekins
Keyword(s):  
Resource Selection ◽  
Telemetry Data ◽  
Multi Scale ◽  
Capture Recapture

scholarly journals Integrating telemetry data into spatial capture-recapture modifies inferences on multi-scale resource selection

2017 ◽  
Author(s):  
Daniel W. Linden ◽  
Alexej P. K. Sirén ◽  
Peter J. Pekins
Keyword(s):  
Resource Selection ◽  
Wildlife Conservation ◽  
Coniferous Forest ◽  
Space Use ◽  
Camera Traps ◽  
Telemetry Data ◽  
Multi Scale ◽  
Management Objectives ◽  
Capture Recapture ◽  
Conservation And Management

AbstractEstimating population size and resource selection functions (RSFs) are common approaches in applied ecology for addressing wildlife conservation and management objectives. Traditionally such approaches have been undertaken separately with different sources of data. Spatial capture-recapture (SCR) provides a framework for jointly estimating density and multi-scale resource selection, and data integration techniques provide opportunities for improving inferences from SCR models. Here we illustrate an application of integrated SCR-RSF modeling to a population of American marten (Martes americana) in alpine forests of northern New England. Spatial encounter data from camera traps were combined with telemetry locations from radio-collared individuals to examine how density and space use varied with spatial environmental features. We compared multi-model inferences between the integrated SCR-RSF model with telemetry and a standard SCR model with no telemetry. The integrated SCR-RSF model supported more complex relationships with spatial variation in third-order resource selection (i.e., individual space use), including selection for areas with shorter distances to mixed coniferous forest and rugged terrain. Both models indicated increased second-order selection (i.e., density) for areas close to mixed coniferous forest, while the integrated SCR-RSF model had a lower effect size due to modulation from spatial variability in space use. Our application of the integrated SCR-RSF model illustrates the improved inferences from spatial encounter data that can be achieved from integrating auxiliary telemetry data. Integrated modeling allows ecologists to join empirical data to ecological theory using a robust quantitative framework to better address conservation and management objectives.

scholarly journals A Novel Framework for Anomaly Detection for Satellite Momentum Wheel Based on Optimized SVM and Huffman-Multi-Scale Entropy

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 1062
Author(s):  
Yuqing Li ◽  
Mingjia Lei ◽  
Pengpeng Liu ◽  
Rixin Wang ◽  
Minqiang Xu
Keyword(s):  
Anomaly Detection ◽  
Detection Rate ◽  
Recognition Accuracy ◽  
Simulation Models ◽  
Physical Models ◽  
Telemetry Data ◽  
Data Types ◽  
Multi Scale ◽  
Svm Model ◽  
The Time Domain

The health status of the momentum wheel is vital for a satellite. Recently, research on anomaly detection for satellites has become more and more extensive. Previous research mostly required simulation models for key components. However, the physical models are difficult to construct, and the simulation data does not match the telemetry data in engineering applications. To overcome the above problem, this paper proposes a new anomaly detection framework based on real telemetry data. First, the time-domain and frequency-domain features of the preprocessed telemetry signal are calculated, and the effective features are selected through evaluation. Second, a new Huffman-multi-scale entropy (HMSE) system is proposed, which can effectively improve the discrimination between different data types. Third, this paper adopts a multi-class SVM model based on the directed acyclic graph (DAG) principle and proposes an improved adaptive particle swarm optimization (APSO) method to train the SVM model. The proposed method is applied to anomaly detection for satellite momentum wheel voltage telemetry data. The recognition accuracy and detection rate of the method proposed in this paper can reach 99.60% and 99.87%. Compared with other methods, the proposed method can effectively improve the recognition accuracy and detection rate, and it can also effectively reduce the false alarm rate and the missed alarm rate.

Effects of sagebrush treatments on multi-scale resource selection by pygmy rabbits

2011 ◽  
Vol 75 (2) ◽  
pp. 393-398 ◽  
Author(s):  
Tammy L. Wilson ◽  
Frank P. Howe ◽  
Thomas C. Edwards
Keyword(s):  
Resource Selection ◽  
Multi Scale ◽  
Pygmy Rabbits

scholarly journals Estimating animal resource selection from telemetry data using point process models

2013 ◽  
Vol 82 (6) ◽  
pp. 1155-1164 ◽  
Author(s):  
Devin S. Johnson ◽  
Mevin B. Hooten ◽  
Carey E. Kuhn
Keyword(s):  
Point Process ◽  
Resource Selection ◽  
Process Models ◽  
Telemetry Data ◽  
Point Process Models ◽  
Animal Resource

scholarly journals Spacecraft Anomaly Recognition Based on Morphological Variational Mode Decomposition and JRD

2018 ◽  
Vol 36 (1) ◽  
pp. 20-27
Author(s):  
Haixu Jiang ◽  
Ke Zhang ◽  
Jingyu Wang ◽  
Meibo Lü
Keyword(s):  
Measured Data ◽  
Variational Mode Decomposition ◽  
Telemetry Data ◽  
Recognition Method ◽  
State Recognition ◽  
Multi Scale ◽  
Mode Decomposition ◽  
Sample Data ◽  
Orbital Spacecraft ◽  
Weak Anomaly

Considering the difficulty in identifying the in-orbital spacecraft weak anomaly, a spacecraft anomaly state recognition method based on Morphological variational mode decomposition and JRD distance is proposed. First of all, the telemetry data of the spacecraft is decomposed into multi-scale modal functions with different frequencies via morphological variational modal decomposition. Then the Rényi entropy of each modal function is extracted, which is regarded as the feature of telemetry data. Finally, the recognition of spacecraft anomaly state is realized by comparing the JRD distance between the sample data and the measured data. The proposed method is verified by means of the telemetry data of the weak anomaly speed of a satellite reaction wheel. The simulation results demonstrate that the proposed method can effectively identify the anomaly of the spacecraft and has obvious advantage in recognition speed.

scholarly journals Movement-assisted localization from acoustic telemetry data

2019 ◽  
Author(s):  
Nathan J. Hostetter ◽  
J. Andrew Royle
Keyword(s):  
Acoustic Telemetry ◽  
Resource Selection ◽  
Marine Mammals ◽  
Sensor Arrays ◽  
Telemetry Data ◽  
Movement Model ◽  
History Data ◽  
Signal Rate ◽  
Localization Model ◽  
History Of

AbstractBackgroundAcoustic telemetry technologies are being rapidly deployed to study a variety of aquatic taxa including fishes, reptiles, and marine mammals. Large cooperative telemetry networks produce vast quantities of data useful in the study of movement, resource selection and species distribution. Efficient use of acoustic telemetry data requires estimation of acoustic source locations from detections at sensors (i.e. localization). Multiple processes provide information for localization estimation including detection/non-detection data at sensors, information on signal rate, and an underlying movement model describing how individuals move and utilize space. Frequently, however, localization methods only integrate a subset of these processes and do not utilize the full spatial encounter history information available from sensor arrays.MethodsIn this paper we draw analogies between the challenges of acoustic telemetry localization and newly developed methods of spatial capture-recapture (SCR). We develop a framework for localization that integrates explicit sub-models for movement, signal (or cue) rate, and detection probability, based on acoustic telemetry spatial encounter history data. This method, which we call movement-assisted localization, makes efficient use of the full encounter history data available from acoustic sensor arrays, provides localizations with fewer than three detections, and even allows for predictions to be made of the position of an individual when it was not detected at all. We demonstrate these concepts by developing generalizable Bayesian formulations of the SCR movement-assisted localization model to address study-specific challenges common in acoustic telemetry studies.ResultsSimulation studies show that movement-assisted localization models improve point-wise RMSE of localization estimates by > 50% and greatly increased the precision of estimated trajectories compared to localization using only the detection history of a given signal. Additionally, integrating a signal rate sub-model reduced biases in the estimation of movement, signal rate, and detection parameters observed in independent localization models.ConclusionsMovement-assisted localization provides a flexible framework to maximize the use of acoustic telemetry data. Conceptualizing localization within an SCR framework allows extensions to a variety of data collection protocols, improves the efficiency of studies interested in movement, resource selection, and space-use, and provides a unifying framework for modeling acoustic data.

scholarly journals Predicting queue wait time probabilities for multi-scale computing

2019 ◽  
Vol 377 (2142) ◽  
pp. 20180151 ◽  
Author(s):  
Vytautas Jancauskas ◽  
Tomasz Piontek ◽  
Piotr Kopta ◽  
Bartosz Bosak
Keyword(s):  
Resource Selection ◽  
High Performance ◽  
Wait Time ◽  
Regression Methods ◽  
Multi Scale ◽  
Time To Completion ◽  
Probability Estimates ◽  
Single Number ◽  
Rest Api ◽  
Performance Computing

We describe a method for queue wait time prediction in supercomputing clusters. It was designed for use as a part of multi-criteria brokering mechanisms for resource selection in a multi-site High Performance Computing environment. The aim is to incorporate the time jobs stay queued in the scheduling system into the selection criteria. Our method can also be used by the end users to estimate the time to completion of their computing jobs. It uses historical data about the particular system to make predictions. It returns a list of probability estimates of the form ( t i ,  p i ), where p i is the probability that the job will start before time t i . Times t i can be chosen more or less freely when deploying the system. Compared to regression methods that only return a single number as a queue wait time estimate (usually without error bars) our prediction system provides more useful information. The probability estimates are calculated using the Bayes theorem with the naive assumption that the attributes describing the jobs are independent. They are further calibrated to make sure they are as accurate as possible, given available data. We describe our service and its REST API and the underlying methods in detail and provide empirical evidence in support of the method's efficacy. This article is part of the theme issue ‘Multiscale modelling, simulation and computing: from the desktop to the exascale’.

Integrating Resource Selection with Spatial Capture-Recapture Models

2014 ◽  
pp. 349-363 ◽  
Author(s):  
J.Andrew Royle ◽  
Richard B. Chandler ◽  
Rahel Sollmann ◽  
Beth Gardner
Keyword(s):  
Resource Selection ◽  
Capture Recapture

scholarly journals A General Framework for the Analysis of Animal Resource Selection from Telemetry Data

Biometrics ◽  
2007 ◽  
Vol 64 (3) ◽  
pp. 968-976 ◽  
Author(s):  
Devin S. Johnson ◽  
Dana L. Thomas ◽  
Jay M. Ver Hoef ◽  
Aaron Christ
Keyword(s):  
Resource Selection ◽  
General Framework ◽  
Telemetry Data ◽  
Animal Resource

scholarly journals Integrating resource selection information with spatial capture-recapture

2013 ◽  
Vol 4 (6) ◽  
pp. 520-530 ◽  
Author(s):  
J. Andrew Royle ◽  
Richard B. Chandler ◽  
Catherine C. Sun ◽  
Angela K. Fuller
Keyword(s):  
Resource Selection ◽  
Capture Recapture
Sign in / Sign up

Export Citation Format

Share Document