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El sistema utiliza el propio móvil del alumno (con un Smartphone básico sirve) y las gafas cardboard de Realidad Aumentada, para permitir al usuario ver la misma realidad que vería sin las gafas, pero interactuando con los elementos de un escenario de riesgo que queramos configurar en el ejercicio mediante la disposición de simples pegatinas con imágenes o códigos QR, que el móvil sustituirá por el elemento oportuno (una extintor, chorro de espuma o fuego por ejemplo), al ver la pegatina, permitiendo la simulación casi real el riesgo y su entorno totalmente personalizado. El uso de la Realidad Aumentada en el campo de la Prevención de Riesgos Laborales y en concreto en la extinción de incendios o evacuación de edificios, locales, almacenes, etc… posibilita la incorporación de elementos de inmersión e interacción con los elementos de riesgo (fuego, salidas bloqueadas, humo, etc…); lo cual genera una experiencia práctica del usuario con la posible situación real del riesgo.
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Increased aeration of the soil; Stabilized soil structure; Higher and more diversified crop production; Better workability of the land; Earlier planting dates; Reduction of peak discharges by an increased temporary storage of water in the soil decomposition of organic matter; soil subsidence; reduced irrigation efficiency; increased risk of drought. excessive leaching of valuable nutrients from the soil; downstream environmental damage by salty or otherwise polluted drainage water; the presence of ditches, canals, and structures impending accessibility and interfering with other infrastructural elements of the land.
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Content; 1. Top spherical dome. 2. Top ring beam. 3. Cylindrical wall. 4. Bottom ring beam. 5. Conical dome. 6. Circular ring beam. The basics of enticing water tank design and the related components are broadly calculated in this document. The next few documents will demonstrate the design of Intze tank members like column, bracing and foundation. Keep following the updates.....
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LIST OF EXPERIMENTS: 1. Implement simple vector addition in Tensor Flow. 2. Implement a regression model in Keras. 3. Implement a perception in TensorFlow/Keras Environment. 4. Implement a Feed Forward Network in TensorFlow/Keras. 5. Implement an image classifier using CNN in TensorFlow/Keras. 6. Improve the deep Learning model by fine tuning hyper parameters. 7. Implement a Transfer Learning concept in image classification. 8. Using a pre trained model on Keras for transfer learning. 9. Perform Sentimental Analysis using RNN. 10. Implement an LSTM based Auto encoding inTensorflow/Keras. 11. Image generation using GAN. ADDITIONAL EXPERIMENTS 12. Train a deep Learning model to classify a given image using pre trained model. 13. Recommendation system from sales data using Deep Learning. 14. Implement Object detection using CNN. 15. Implement any simple Reinforcement Algorithm for an NLP problem.
CCS335 _ Neural Networks and Deep Learning Laboratory_Lab Complete Record
CCS335 _ Neural Networks and Deep Learning Laboratory_Lab Complete Record
Asst.prof M.Gokilavani
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NFPA 5000 2024 standard .
NFPA 5000 2024 standard .
CCS335 _ Neural Networks and Deep Learning Laboratory_Lab Complete Record
CCS335 _ Neural Networks and Deep Learning Laboratory_Lab Complete Record
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