scholarly journals The importance of time of day for magnetic body alignment in songbirds

2022 ◽  
Author(s):  
Giuseppe Bianco ◽  
Robin Clemens Köhler ◽  
Mihaela Ilieva ◽  
Susanne Åkesson
Keyword(s):  
Geomagnetic Field ◽  
Ad Hoc ◽  
Migratory Birds ◽  
Learning Algorithm ◽  
Underlying Mechanism ◽  
Time Of Day ◽  
Model Organisms ◽  
Magnetic Alignment ◽  
Acrocephalus Scirpaceus ◽  
Compass Calibration

AbstractSpontaneous magnetic alignment is the simplest known directional response to the geomagnetic field that animals perform. Magnetic alignment is not a goal directed response and its relevance in the context of orientation and navigation has received little attention. Migratory songbirds, long-standing model organisms for studying magnetosensation, have recently been reported to align their body with the geomagnetic field. To explore whether the magnetic alignment behaviour in songbirds is involved in the underlying mechanism for compass calibration, which have been suggested to occur near to sunset, we studied juvenile Eurasian reed warblers (Acrocephalus scirpaceus) captured at stopover during their first autumn migration. We kept one group of birds in local daylight conditions and an experimental group under a 2 h delayed sunset. We used an ad hoc machine learning algorithm to track the birds’ body alignment over a 2-week period. Our results show that magnetic body alignment occurs prior to sunset, but shifts to a more northeast–southwest alignment afterwards. Our findings support the hypothesis that body alignment could be associated with how directional celestial and magnetic cues are integrated in the compass of migratory birds.

scholarly journals Theoretically possible spatial accuracy of geomagnetic maps used by migrating animals

2017 ◽  
Vol 14 (128) ◽  
pp. 20161002 ◽  
Author(s):  
Andrei V. Komolkin ◽  
Pavel Kupriyanov ◽  
Andrei Chudin ◽  
Julia Bojarinova ◽  
Kirill Kavokin ◽  
...  
Keyword(s):  
Field Intensity ◽  
Geomagnetic Field ◽  
Site Fidelity ◽  
Large Scale ◽  
Migratory Birds ◽  
Time Of Day ◽  
Northern Europe ◽  
Spatial Accuracy ◽  
Total Field ◽  
Migrating Birds

Many migrating animals, belonging to different taxa, annually move across the globe and cover hundreds and thousands of kilometres. Many of them are able to show site fidelity, i.e. to return to relatively small migratory targets, from distant areas located beyond the possible range of direct sensory perception. One widely debated possibility of how they do it is the use of a magnetic map, based on the dependence of parameters of the geomagnetic field (total field intensity and inclination) on geographical coordinates. We analysed temporal fluctuations of the geomagnetic field intensity as recorded by three geomagnetic observatories located in Europe within the route of many avian migrants, to study the highest theoretically possible spatial resolution of the putative map. If migratory birds measure total field intensity perfectly and take the time of day into account, in northern Europe 81% of them may return to a strip of land of 43 km in width along one of coordinates, whereas in more southern areas such a strip may be narrower than 10 km. However, if measurements are performed with an error of 0.1%, the strip width is increased by approximately 40 km, so that in spring migrating birds are able to return to within 90 km of their intended goal. In this case, migrating birds would probably need another navigation system, e.g. an olfactory map, intermediate between the large-scale geomagnetic map and the local landscape cues, to locate their goal to within several kilometres.

Kalmag: a high spatio temporal model of the Geomagnetic field

2021 ◽  
Author(s):  
Julien Baerenzung ◽  
Matthias Holschneider
Keyword(s):  
Geomagnetic Field ◽  
Learning Algorithm ◽  
Dynamical Evolution ◽  
Small Scale ◽  
Large Set ◽  
The Core ◽  
Resolution Model ◽  
Auto Regressive ◽  
Spatio Temporal ◽  
Marine Vessels

<p>We present a new high resolution model of the Geomagnetic field spanning the last 121 years. The model derives from a large set of data taken by low orbiting satellites, ground based observatories, marine vessels, airplane and during land surveys. It is obtained by combining a Kalman filter to a smoothing algorithm. Seven different magnetic sources are taken into account. Three of them are of internal origin. These are the core, the lithospheric  and the induced / residual ionospheric fields. The other four sources are of external origin. They are composed by a close, a remote and a fluctuating magnetospheric fields as well as a source associated with field aligned currents. The dynamical evolution of each source is prescribed by an auto regressive process of either first or second order, except for the lithospheric field which is assumed to be static. The parameters of the processes were estimated through a machine learning algorithm with a sample of data taken by the low orbiting satellites of the CHAMP and Swarm missions. In this presentation we will mostly focus on the rapid variations of the core field, and the small scale lithospheric field.  We will also discuss the nature of model uncertainties and the limitiations they imply.</p>

HYSTERESIS CHARACTERIZATION BY A FUZZY LEARNING ALGORITHM

1994 ◽  
Vol 02 (03) ◽  
pp. 351-360
Author(s):  
EDGE C. YEH ◽  
SHAO HOW LU
Keyword(s):  
Fuzzy Sets ◽  
Prediction Accuracy ◽  
Learning Algorithm ◽  
Numerical Data ◽  
Underlying Mechanism ◽  
Fuzzy Rules ◽  
Hysteresis Phenomenon ◽  
Input Output ◽  
Fuzzy Learning

In this paper, the hysteresis characterization in fuzzy spaces is presented by utilizing a fuzzy learning algorithm to generate fuzzy rules automatically from numerical data. The hysteresis phenomenon is first described to analyze its underlying mechanism. Then a fuzzy learning algorithm is presented to learn the hysteresis phenomenon and is used for predicting a simple hysteresis phenomenon. The results of learning are illustrated by mesh plots and input-output relation plots. Furthermore, the dependency of prediction accuracy on the number of fuzzy sets is studied. The method provides a useful tool to model the hysteresis phenomenon in fuzzy spaces.

A Statistical Design of Experiments Approach to Machine Learning Model Selection in Engineering Applications

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
T. Munger ◽  
S. Desa
Keyword(s):  
Machine Learning ◽  
Model Selection ◽  
Real World ◽  
Ad Hoc ◽  
Learning Algorithm ◽  
Random Search ◽  
Statistical Design ◽  
Orthogonal Arrays ◽  
Statistical Design Of Experiments ◽  
Engineering Applications

Abstract An important but insufficiently addressed issue for machine learning in engineering applications is the task of model selection for new problems. Existing approaches to model selection generally focus on optimizing the learning algorithm and associated hyperparameters. However, in real-world engineering applications, the parameters that are external to the learning algorithm, such as feature engineering, can also have a significant impact on the performance of the model. These external parameters do not fit into most existing approaches for model selection and are therefore often studied ad hoc or not at all. In this article, we develop a statistical design of experiment (DOEs) approach to model selection based on the use of the Taguchi method. The key idea is that we use orthogonal arrays to plan a set of build-and-test experiments to study the external parameters in combination with the learning algorithm. The use of orthogonal arrays maximizes the information learned from each experiment and, therefore, enables the experimental space to be explored extremely efficiently in comparison with grid or random search methods. We demonstrated the application of the statistical DOE approach to a real-world model selection problem involving predicting service request escalation. Statistical DOE significantly reduced the number of experiments necessary to fully explore the external parameters for this problem and was able to successfully optimize the model with respect to the objective function of minimizing total cost in addition to the standard evaluation metrics such as accuracy, f-measure, and g-mean.

Magnetic Sensors for Navigation Applications: An Overview

2013 ◽  
Vol 67 (2) ◽  
pp. 263-275 ◽  
Author(s):  
Wei Li ◽  
Jinling Wang
Keyword(s):  
Geomagnetic Field ◽  
Magnetic Sensor ◽  
Magnetic Sensors ◽  
Global Navigation Satellite Systems ◽  
Satellite Systems ◽  
Current Trends ◽  
Compass Calibration ◽  
Global Navigation Satellite ◽  
Available Information ◽  
Electronic Compass

This paper reviews currently existing electronic magnetic sensor technologies for navigation applications. Magnetic compasses have been used in navigation for centuries. The Earth's geomagnetic field is considered to provide accurate, reliable and economically available information for orientation. Meanwhile, modern magnetometers and compass calibration technologies have allowed the electronic compass to become a crucial navigation tool, even in times of modern satellite navigation using Global Navigation Satellite Systems (GNSS). Magnetic sensor technologies, error modelling and compensating approaches have been reviewed in this paper. Current trends and the outlook for future development of the electronic compass are analysed.

scholarly journals Urban Crowd Detection Using SOM, DBSCAN and LBSN Data Entropy: A Twitter Experiment in New York and Madrid

Electronics ◽  
2019 ◽  
Vol 8 (6) ◽  
pp. 692
Author(s):  
Mohamed Sakkari ◽  
Abeer D. Algarni ◽  
Mourad Zaied
Keyword(s):  
New York ◽  
Social Network ◽  
Reference Model ◽  
Learning Algorithm ◽  
Detection System ◽  
Time Of Day ◽  
Time Interval ◽  
Entropy Model ◽  
The City ◽  
Abnormal Events

The surfer and the physical location are two important concepts associated with each other in the social network-based localization service. This work consists of studying urban behavior based on location-based social networks (LBSN) data; we focus especially on the detection of abnormal events. The proposed crowd detection system uses the geolocated social network provided by the Twitter application programming interface (API) to automatically detect the abnormal events. The methodology we propose consists of using an unsupervised competitive learning algorithm (self-organizing map (SOM)) and a density-based clustering method (density-based spatial clustering of applications with noise (DBCSAN)) to identify and detect crowds. The second stage is to build the entropy model to determine whether the detected crowds fit into the daily pattern with reference to a spatio-temporal entropy model, or whether they should be considered as evidence that something unusual occurs in the city because of their number, size, location and time of day. To detect an abnormal event in the city, it is sufficient to determine the real entropy model and to compare it with the reference model. For the normal day, the reference model is constructed offline for each time interval. The obtained results confirm the effectiveness of our method used in the first stage (SOM and DBSCAN stage) to detect and identify clusters dynamically, and imitating human activity. These findings also clearly confirm the detection of special days in New York City (NYC), which proves the performance of our proposed model.

scholarly journals Spatio-Temporal Analysis of Wetland Changes Using a Kernel Extreme Learning Machine Approach

2018 ◽  
Vol 10 (7) ◽  
pp. 1129 ◽  
Author(s):  
Yi Lin ◽  
Jie Yu ◽  
Jianqing Cai ◽  
Nico Sneeuw ◽  
Fengting Li
Keyword(s):  
Extreme Learning Machine ◽  
Human Activities ◽  
Migratory Birds ◽  
Learning Algorithm ◽  
Great Influence ◽  
Temporal Analysis ◽  
Scientific Basis ◽  
Kernel Extreme Learning Machine ◽  
Spatio Temporal ◽  
Learning Machine

Natural wetland ecosystems provide not only important habitats for many wildlife species, but also food for migratory and resident animals. In Shanghai, the Chongming Dongtan International Wetland, located at the mouth of the Yangtze River, plays an important role in maintaining both ecosystem health and ecological security of the island. Meanwhile it provides an especially important stopover and overwintering site for migratory birds, being located in the middle of the East Asian-Australasian Flyway. However, with the increase in development intensity and human activities, this wetland suffers from increasing environmental pressure. On the other hand, biological succession in the mudflat wetland makes Chongming Dongtan a rapidly developing and rare ecosystem in the world. Therefore, studying the wetland spatio-temporal change is an important precondition for analyzing the relationship between wetland evolution processes and human activities. This paper presents a novel method for analyzing land-use/cover changes (LUCC) on Chongming Dongtan wetland using multispectral satellite images. Our method mainly takes advantages of a machine learning algorithm, named the Kernel Extreme Learning Machine (K-ELM), which is applied to distinguish between different objects and extract their information from images. In the K-ELM, the kernel trick makes it more stable and accurate. The comparison between K-ELM and three other conventional classification methods indicates that the proposed K-ELM has the highest overall accuracy, especially for distinguishing between Spartina alternflora, Scirpus mariqueter, and Phragmites australis. Meanwhile, its efficiency is remarkable as well. Then a total of eight Landsat TM series images acquired from 1986 to 2013 were used for the LUCC analysis with K-ELM. According to the classification result, the change detection and spatio-temporal quantitative analysis were performed. The specific analysis of different objects are significant for learning about the historical changes to Chongming Dongtan and obtaining the evaluation rules. Generally, the rapid speed of Chongming Dongtan’s urbanization brought about great influence with respect to natural resources and the environment. Integrating the results into the ecological analysis and ecological regional planning of Dongtan could provide a reliable scientific basis for rational planning, development, and the ecological balance and regional sustainability of the wetland area.

scholarly journals The magnetic compass mechanisms of birds and rodents are based on different physical principles

2006 ◽  
Vol 3 (9) ◽  
pp. 583-587 ◽  
Author(s):  
Peter Thalau ◽  
Thorsten Ritz ◽  
Hynek Burda ◽  
Regina E Wegner ◽  
Roswitha Wiltschko
Keyword(s):  
Geomagnetic Field ◽  
Migratory Birds ◽  
Radical Pair ◽  
Broad Band ◽  
Static Field ◽  
Magnetic Compass ◽  
Radical Pair Mechanism ◽  
Zeeman Frequency ◽  
Compass Mechanisms ◽  
Physical Principles

Recently, oscillating magnetic fields in the MHz-range were introduced as a useful diagnostic tool to identify the mechanism underlying magnetoreception. The effect of very weak high-frequency fields on the orientation of migratory birds indicates that the avian magnetic compass is based on a radical pair mechanism. To analyse the nature of the magnetic compass of mammals, we tested rodents, Ansell's mole-rats, using their tendency to build their nests in the southern part of the arena as a criterion whether or not they could orient. In contrast to birds, their orientation was not disrupted when a broad-band field of 0.1–10 MHz of 85 nT or a 1.315 MHz field of 480 nT was added to the static geomagnetic field of 46 000 nT. Even increasing the intensity of the 1.315 MHz field (Zeeman frequency in the local geomagnetic field) to 4800 nT, more than a tenth of the static field, the mole-rats remained unaffected and continued to build their nests in the south. These results indicate that in contrast to that of birds, their magnetic compass does not involve radical pair processes; it seems to be based on a fundamentally different principle, which probably involves magnetite.

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