Wyszukiwarka
Nie znaleźliśmy wyników w zadanych kryteriach!
Ale mamy wyniki w innych katalogach.Filtry
wszystkich: 9875
-
Katalog
- Publikacje 7864 wyników po odfiltrowaniu
- Czasopisma 439 wyników po odfiltrowaniu
- Konferencje 31 wyników po odfiltrowaniu
- Osoby 258 wyników po odfiltrowaniu
- Projekty 35 wyników po odfiltrowaniu
- Zespoły Badawcze 1 wyników po odfiltrowaniu
- Kursy Online 305 wyników po odfiltrowaniu
- Wydarzenia 20 wyników po odfiltrowaniu
- Dane Badawcze 922 wyników po odfiltrowaniu
wyświetlamy 1000 najlepszych wyników Pomoc
Wyniki wyszukiwania dla: DEEP LEARNING RENEWABLE ENERGY SOURCES PHOTOVOLTAICS BUILDINGS LONG SHORT-TERM MEMORY MICRO-GRIDS
-
Deep learning-enabled integration of renewable energy sources through photovoltaics in buildings
PublikacjaInstalling photovoltaic (PV) systems in buildings is one of the most effective strategies for achieving sustainable energy goals and reducing carbon emissions. However, the requirement for efficient energy management, the fluctuating energy demands, and the intermittent nature of solar power are a few of the obstacles to the seamless integration of PV systems into buildings. These complexities surpass the capabilities of rule-based...
-
Prediction of Sorption Processes Using the Deep Learning Methods (Long Short-Term Memory)
Publikacja -
Neural networks and deep learning
PublikacjaIn this chapter we will provide the general and fundamental background related to Neural Networks and Deep Learning techniques. Specifically, we divide the fundamentals of deep learning in three parts, the first one introduces Deep Feed Forward Networks and the main training algorithms in the context of optimization. The second part covers Convolutional Neural Networks (CNN) and discusses their main advantages and shortcomings...
-
An automated learning model for twitter sentiment analysis using Ranger AdaBelief optimizer based Bidirectional Long Short Term Memory
PublikacjaSentiment analysis is an automated approach which is utilized in process of analysing textual data to describe public opinion. The sentiment analysis has major role in creating impact in the day-to-day life of individuals. However, a precise interpretation of text still relies as a major concern in classifying sentiment. So, this research introduced Bidirectional Long Short Term Memory with Ranger AdaBelief Optimizer (Bi-LSTM RAO)...
-
Multiplicative Long Short-Term Memory with Improved Mayfly Optimization for LULC Classification
PublikacjaLand Use and Land Cover (LULC) monitoring is crucial for global transformation, sustainable land control, urban planning, urban growth prediction, and the establishment of climate regulations for long-term development. Remote sensing images have become increasingly important in many environmental planning and land use surveys in recent times. LULC is evaluated in this research using the Sat 4, Sat 6, and Eurosat datasets. Various...
-
An Intelligent Approach to Short-Term Wind Power Prediction Using Deep Neural Networks
PublikacjaIn this paper, an intelligent approach to the Short-Term Wind Power Prediction (STWPP) problem is considered, with the use of various types of Deep Neural Networks (DNNs). The impact of the prediction time horizon length on accuracy, and the influence of temperature on prediction effectiveness have been analyzed. Three types of DNNs have been implemented and tested, including: CNN (Convolutional Neural Networks), GRU (Gated Recurrent...
-
Feature Weighted Attention-Bidirectional Long Short Term Memory Model for Change Detection in Remote Sensing Images
PublikacjaIn remote sensing images, change detection (CD) is required in many applications, such as: resource management, urban expansion research, land management, and disaster assessment. Various deep learning-based methods were applied to satellite image analysis for change detection, yet many of them have limitations, including the overfitting problem. This research proposes the Feature Weighted Attention (FWA) in Bidirectional Long...
-
Using Long-Short term Memory networks with Genetic Algorithm to predict engine condition
PublikacjaPredictive maintenance (PdM) is a type of approach for maintenance processes, allowing maintenance actions to be managed depending on the machine's current condition. Maintenance is therefore carried out before failures occur. The approach doesn’t only help avoid abrupt failures but also helps lower maintenance cost and provides possibilities to manufacturers to manage maintenance budgets in a more efficient way. A new deep neural...
-
Sathwik Prathapagiri
OsobySathwik was born in 2000. In 2022, he completed his Master’s of Science in Biological Sciences and Bachelor’s of Engineering in Chemical Engineering in an integrated dual degree program from Birla Institute Of Technology And Science, Pilani, India. During his final year, he worked as a research intern under Dr Giri P Krishnan at Bazhenov lab, University of California San Diego school of medicine to pursue his Master’s Thesis on...
-
Experience-Based Cognition for Driving Behavioral Fingerprint Extraction
PublikacjaABSTRACT With the rapid progress of information technologies, cars have been made increasingly intelligent. This allows cars to act as cognitive agents, i.e., to acquire knowledge and understanding of the driving habits and behavioral characteristics of drivers (i.e., driving behavioral fingerprint) through experience. Such knowledge can be then reused to facilitate the interaction between a car and its driver, and to develop better and...