Essays about: "improving student behavior"

Showing result 16 - 20 of 41 essays containing the words improving student behavior.

1. 16. Low Power Pre-Distorter Design For 5G Radio Using Machine Learning

   University essay from Lunds universitet/Institutionen för elektro- och informationsteknik

   Author : Sumeeth Diddigi Kulkarni; Di Wang; [2020]
   Keywords : Technology and Engineering;

   Abstract : A Power Amplifier (PA) is an essential electronic component in all microwave and millimeter-wave applications and, more specifically, in any transmitting system where the level of input power signal needs amplification to the desired level.Linearity and high efficiency are of utmost importance in PAs. READ MORE

3. 17. Efficient Discovery Of Binary Stars

   University essay from Lunds universitet/Astronomi - Genomgår omorganisation; Lunds universitet/Institutionen för astronomi och teoretisk fysik - Genomgår omorganisation

   Author : Pablo Navarro Barrachina; [2020]
   Keywords : Binary Stars; Spectroscopic binary stars; SB2; Machine Learning; Data Mining; Physics and Astronomy;

   Abstract : Purpose: even in the era of exponential increase in the amount of stellar data gathered, binaries are still often overlooked in observational data due to the special handling they require. The goal of this work is to develop a method capable of automatically and efficiently identifying and extract double-lined spectroscopic binaries (SB2) from a spectroscopic survey, while being scalable and technically successful, and to identify and optimize the parameters that influence their detection. READ MORE

4. 18. Multitask Convolutional Neural Network Emulators for Global Crop Models - Supervised Deep Learning in Large Hypercubes of Non-IID Data

   University essay from Lunds universitet/Matematisk statistik

   Author : Amanda Nilsson; [2020]
   Keywords : Multitask Learning; Convolutional Neural Network CNN ; Branched Neural Network; Dynamic Global Vegetation Models DGVM ; Automated Feature Extraction; Feature Importance; Supervised Machine Learning; Emulator; Surrogate Model; Response Surface Model; Approximation Model; Metamodeling; Model Composition; Regularization; Robustness; Hyperparameter Optimization; Mathematics and Statistics;

   Abstract : The aim of this thesis is to establish whether a neural network (NN) can be used for emulation of simulated global crop production - retrieved from the computationally demanding dynamic global vegetation model (DGVM) Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS). It has been devoted to elaboration with various types of neural network architectures: Branched NNs capable of processing inputs of mixed data types; Convolutional Neural Network (CNN) architectures able to perform automated temporal feature extraction of the given weather time series; simpler fully connected (FC) structures as well as Multitask NNs. READ MORE

5. 19. Human Factors in Deepwater Drilling - A New Approach to Safety and Operational Excellence

   University essay from Lunds universitet/Avdelningen för Riskhantering och Samhällssäkerhet

   Author : José Carlos Silveira Bruno; [2020]
   Keywords : Deepwater; Drilling; Human Factors; Oil Gas; Performance; Safety; FLMU06; Technology and Engineering;

   Abstract : Deepwater drilling operates high hazardous and complex systems. Technological and operational complexities, harsh environmental conditions, geological uncertainties, and high-pressure flammable fluids are some of the critical factors that pose clear threats towards safe operations and may result in high consequence events, as the Macondo blowout in 2010. READ MORE

6. 20. On the statistics and practical application of the reassignment method for Gabor spectrograms

   University essay from Lunds universitet/Matematisk statistik

   Author : Erik. M Månsson; [2019]
   Keywords : Mathematics and Statistics;

   Abstract : The reassignment method is a technique for improving the concentration of signals in spectrograms and other time-frequency representations (TFR). It achieves this by displacing the points in a TFR according to the reassignment vector for every point. READ MORE