Q3176693 (Q3176693): Difference between revisions
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(Created claim: summary (P836): The overall objective of the project is to develop new technologies in the form of Artificial Intelligence (AI) algorithms using neural networks (NN) that, once trained, are able to offer a layer of knowledge that allows to make better decisions and, ultimately, fish better._x000D_ _x000D_ The specific objectives to be achieved are the following:_x000D_ _x000D_ interpretation of hydroacoustic probe data from fishing satellite buoys with FADs (Fi...) |
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The overall objective of the project is to develop new technologies in the form of Artificial Intelligence (AI) algorithms using neural networks (NN) that, once trained, are able to offer a layer of knowledge that allows to make better decisions and, ultimately, fish better._x000D_ _x000D_ The specific objectives to be achieved are the following:_x000D_ _x000D_ interpretation of hydroacoustic probe data from fishing satellite buoys with FADs (Fish Aggregating Device), through neural network architectures_x000D_ The objectives are to develop an architecture of different neural networks that feeds data from probes, oceanographic data, time of year, etc., and once trained they can automatically give recommendations to a pattern depending on the data sent from FADs, (if there is something interesting, if it is noise, if it is the seabed, size of the fish, presence plankton, etc.) to make the best decision on which satellite buoy to go fishing. In short, the aim is to improve the understanding of the information that buoys send to ships through learning systems based on Artificial Intelligence (IA)._x000D_ _x000D_ Development of a prototype user interface for the assimilation of fishing recommendations_x000D_ The objective is the development of a prototype software to present the recommendations generated by neural networks. A data presentation module will be developed, so that the pattern can assimilate data intuitively and easily._x000D_ _x000D_ The project is divided into three work packages, PT1 Definition of requirements, PT2 Development a neural network architecture and PT3 Testing and validation, runs into two milestones, in 24 months. (English) | |||||||||||||||
| Property / summary: The overall objective of the project is to develop new technologies in the form of Artificial Intelligence (AI) algorithms using neural networks (NN) that, once trained, are able to offer a layer of knowledge that allows to make better decisions and, ultimately, fish better._x000D_ _x000D_ The specific objectives to be achieved are the following:_x000D_ _x000D_ interpretation of hydroacoustic probe data from fishing satellite buoys with FADs (Fish Aggregating Device), through neural network architectures_x000D_ The objectives are to develop an architecture of different neural networks that feeds data from probes, oceanographic data, time of year, etc., and once trained they can automatically give recommendations to a pattern depending on the data sent from FADs, (if there is something interesting, if it is noise, if it is the seabed, size of the fish, presence plankton, etc.) to make the best decision on which satellite buoy to go fishing. In short, the aim is to improve the understanding of the information that buoys send to ships through learning systems based on Artificial Intelligence (IA)._x000D_ _x000D_ Development of a prototype user interface for the assimilation of fishing recommendations_x000D_ The objective is the development of a prototype software to present the recommendations generated by neural networks. A data presentation module will be developed, so that the pattern can assimilate data intuitively and easily._x000D_ _x000D_ The project is divided into three work packages, PT1 Definition of requirements, PT2 Development a neural network architecture and PT3 Testing and validation, runs into two milestones, in 24 months. (English) / rank | |||||||||||||||
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| Property / summary: The overall objective of the project is to develop new technologies in the form of Artificial Intelligence (AI) algorithms using neural networks (NN) that, once trained, are able to offer a layer of knowledge that allows to make better decisions and, ultimately, fish better._x000D_ _x000D_ The specific objectives to be achieved are the following:_x000D_ _x000D_ interpretation of hydroacoustic probe data from fishing satellite buoys with FADs (Fish Aggregating Device), through neural network architectures_x000D_ The objectives are to develop an architecture of different neural networks that feeds data from probes, oceanographic data, time of year, etc., and once trained they can automatically give recommendations to a pattern depending on the data sent from FADs, (if there is something interesting, if it is noise, if it is the seabed, size of the fish, presence plankton, etc.) to make the best decision on which satellite buoy to go fishing. In short, the aim is to improve the understanding of the information that buoys send to ships through learning systems based on Artificial Intelligence (IA)._x000D_ _x000D_ Development of a prototype user interface for the assimilation of fishing recommendations_x000D_ The objective is the development of a prototype software to present the recommendations generated by neural networks. A data presentation module will be developed, so that the pattern can assimilate data intuitively and easily._x000D_ _x000D_ The project is divided into three work packages, PT1 Definition of requirements, PT2 Development a neural network architecture and PT3 Testing and validation, runs into two milestones, in 24 months. (English) / qualifier | |||||||||||||||
point in time: 12 October 2021
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Revision as of 18:35, 12 October 2021
Project Q3176693 in Spain
| Language | Label | Description | Also known as |
|---|---|---|---|
| English | No label defined |
Project Q3176693 in Spain |
Statements
292,232.0 Euro
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365,290.0 Euro
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80.0 percent
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1 July 2018
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30 June 2020
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MARINE INSTRUMENTS SA
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42°8'25.94"N, 8°48'25.49"W
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36035
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El objetivo global del proyecto es desarrollar nuevas tecnologías en forma de algoritmos de Inteligencia Artificial (IA) usando redes neuronales (NN) que, una vez entrenadas, sean capaces de ofrecer una capa de conocimiento que permita tomar mejores decisiones y, en definitiva, pescar mejor._x000D_ _x000D_ Los objetivos específicos que se pretenden conseguir son los que a continuación se relacionan:_x000D_ _x000D_ Desarrollo de algoritmos de interpretación de los datos de sondas hidroacústicas de las boyas satelitarias de pesca con FADs (Fish Aggregating Device), a través de arquitecturas de redes neuronales_x000D_ El objetivos es desarrollar una arquitectura de diferentes redes neuronales que se alimente de datos de sondas, datos oceanográficos, época del año, etc., y una vez entrenadas puedan de forma automática dar recomendaciones a un patrón dependiendo de los datos que se envían procedentes de los FADs, (si hay algo interesante, si es ruido, si es el fondo marino, tamaño del pescado, presencia plancton, etc.) para que tome la mejor decisión sobre qué boya satelitaria ir a pescar. En definitiva, se trata de mejorar el entendimiento de la información que las boyas envían a los barcos por medio de sistemas de aprendizaje basados en Inteligencia Artificial (IA)._x000D_ _x000D_ Desarrollo de un interfaz de usuario prototipo para la asimilación de recomendaciones de pesca_x000D_ El objetivo es el desarrollo de un software prototipo de presentación de las recomendaciones generadas por las redes neuronales. Se desarrollará un módulo de presentación de los datos, de manera que el patrón pueda asimilar los datos de forma intuitiva y sencilla._x000D_ _x000D_ El proyecto se divide en tres paquetes de trabajo, PT1 Definición de requisitos, PT2 Desarrollo una arquitectura de redes neuronales y PT3 Realización de pruebas y validación, se ejecuta en dos hitos, en 24 meses. (Spanish)
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The overall objective of the project is to develop new technologies in the form of Artificial Intelligence (AI) algorithms using neural networks (NN) that, once trained, are able to offer a layer of knowledge that allows to make better decisions and, ultimately, fish better._x000D_ _x000D_ The specific objectives to be achieved are the following:_x000D_ _x000D_ interpretation of hydroacoustic probe data from fishing satellite buoys with FADs (Fish Aggregating Device), through neural network architectures_x000D_ The objectives are to develop an architecture of different neural networks that feeds data from probes, oceanographic data, time of year, etc., and once trained they can automatically give recommendations to a pattern depending on the data sent from FADs, (if there is something interesting, if it is noise, if it is the seabed, size of the fish, presence plankton, etc.) to make the best decision on which satellite buoy to go fishing. In short, the aim is to improve the understanding of the information that buoys send to ships through learning systems based on Artificial Intelligence (IA)._x000D_ _x000D_ Development of a prototype user interface for the assimilation of fishing recommendations_x000D_ The objective is the development of a prototype software to present the recommendations generated by neural networks. A data presentation module will be developed, so that the pattern can assimilate data intuitively and easily._x000D_ _x000D_ The project is divided into three work packages, PT1 Definition of requirements, PT2 Development a neural network architecture and PT3 Testing and validation, runs into two milestones, in 24 months. (English)
12 October 2021
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Nigrán
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Identifiers
IDI-20180923
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