Forcasting EarthQuake by usining ANN

 Theory of the gravitational interaction between the solar system bodies and our planet earthquake system.
By M. Franzini 


a connection between the moviments of some stars and planets and sun and moon, and finally with vulcans eruptions, and in special mode earthquakes.
so...is there really a scentific connection  between the body of our solar system and the earthquakes on our planet
report and examples

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At the beginning we used an open source software called Fanntool, under Linux platform. Very well known and stable project.
Then we felt the needs to use a muti-Processing Neural-network tool. We developed a personal tool that we built for our purpose, that can use multiple CPU on the same time.

https://sites.google.com/site/earthquakewatchdog/home

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Artificial Neural Network Approach to Mobile Robot Localization

Artificial Neural Network Approach to Mobile Robot Localization
David Swords
Aalto University / School of Electrical Engineering
Department of Automation and Systems Technology
Thesis submitted in partial fulfillment of the requirements for the degree of
Master of Science in Technology

In the robotics community, localization is considered a solved problem, however, the topic is still open to investigation. Mobile robot localization has been focused on developing low-cost approaches and there has been great success using probabilistic methods. Parallel to this, and to a much lesser extent, artificial neural networks (ANNs) have been applied to the problem area with varying success.

A system is proposed in this thesis where the typical probabilistic approach is replaced with one based purely on ANNs. This type of localization attempts to harness the simplicity, scalability and adaptability that ANNs are known for. The ANN approach allows for the encapsulation of a number of steps and elements well known in a probabilistic approach, resulting in the elimination of an internal explicit map, providing pose estimate on network output and network update at runtime.

First, a coordinate-based approach to localization is explored: 1D and 2D trained maps with pose estimates. Second, the coordinate-based approach is eliminated in an effort to replicate a more biologically inspired localization. Finally, a path-finding algorithm applying the new localizaiton approach is presented
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The FANN library (FANN) provides tools implementing, designing, visualizing
and simulating ANNs.
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FANN provides many commandline functions and visualization tools for creating,
training and simulating ANNs. With the availablity of visualization tools,
like FANNTool, make it much easier to deveop ANNs and applying them to
common tasks like curve fitting, pattern recognition, and clustering.
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FANNTool is a GUI to the FANN library which allows ease of use without the
need to program the function calls necessary to perform simple tasks. FANNTool
is an open source project, supported by a community of FANN users.

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Competing visions? Simulating alternative coastal futures using a GISANN web application

Paulo Morgado*, Eduardo Gomes, Nuno Costa
Centre of Geographic Studies, Institute of Geography and Spatial Planning of the University of Lisbon, 1600 Lisboa, Portugal

 Competing visions? Simulating alternative coastal futures using a GISANN web application

In this paper, we demonstrate the use of scenario building in the context of contested land use visions.
We examine a small coastal community located 20 kms south of Lisbon. In Almada e Trafaria/Costa da
Caparica, competing stakeholders such as central government, local government, environmental NGO's
and private companies each have competing development visions for the area. These include the
development of recreation and leisure facilities, a container terminal and the re-naturalization of unused
land. We illustrate the added value of the GIS-ANN tool in steering negotiations between these different
visions and the potential of a scenario building web application as a tool for problem solving.

The emergence of user-created GIS-based web content in Planning has transformed passive users and
consumers of geospatial information into active contributors to the development of spatial visions of the
future. It allows stakeholders to gauge alternative future land uses thus making planning and decisionmaking
processes potentially more transparent and democratic. In this paper, we detail a new method
that enhances GIS-web-based public participation. We build on a combination of GIS basic capabilities
and the data mining methods of Artificial Neural Networks (ANN), namely Multilayer Perceptron (MLP)
packaged in a friendly (GUI) user interface that runs on the Google Earth platform. Users will be able to
articulate different spatial development scenarios for a specific area, to conduct sensitivity analyses for various competing scenarios and to explore causal connections between them

To implement a GIS-ANN-MLP model that runs through a Web application we opted for a free open source neural network library, named Fast Artificial Neural Network (FANN). Before implementation, FANN was tested through the FANNTool GUI This enables us to prepare the data in FANN library standard, design, train, test and run the ANN. This enables us to prepare the data in FANN library standard, design, train, test and run the ANN.

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First Tests on Near Real Time Ice Type Classification in Antartica

In this paper, we explore the capabilities of an algorithm for ice type classification. Our main motivation and exemplary application was the recent incident of the research vessel Akademik Shokalskiy, which was trapped in pack ice for about two weeks. Strong winds had driven ice floes into a bay, forming an area of pack ice, blocking the ship's advancement. High-resolution satellite images helped to assess the ice conditions at the location. To extract relevant information automatically from the images, we apply an algorithm that is aimed to generate an ice chart, outlining the different ice type zones such as pack ice, fast ice, open water.

The algorithm is based on texture analysis. Textures are selected that allow recognition of different structures in ice. Subsequently, a neural network performs the classification. Since results are output in near real time, the algorithm offers new opportunities for ship routing in ice infested areas.
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For the artificial neural network, we rely on the popular and well-tested FANN library [6]. We use 10 input neurons, two hidden layers with 8 and 9 neurons, respectively. Training procedure is RPROP. The number of output neurons depends on the number of classes. For the scene presented in this paper, we use neurons for each of the following ice types: fast ice, highly deformed pack ice, little deformed pack ice, and open water with high as well as low ice concentration. Land ice was visible in the scene yet was not used in the classification, since it is irrelevant once land masking is applied. FANN output is shown in Figure 6.

As can be seen, it coincides well with the real ice conditions noted in Figure 2. Only small parts of the images are misclassified: The left margin of the open water zone is classified as fast ice. The fast ice zone is speckled with “ponds” of open water and pack ice in FANN output.

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