Avancerad sökning
Visar resultat 1 - 5 av 45 avhandlingar som matchar ovanstående sökkriterier.
1. The Performance of a Multi Cylinder HCCI Engine using Variable Compression Ratio and Fast Thermal Management
Sammanfattning : The prime mover in the world today is the Internal Combustion (IC) engine. The development and improvement of the internal combustion engines since Nicolaus August Otto and Rudolf Diesel has continued until today and will continue long into the future. No major competitor to the IC engine has yet emerged. LÄS MER
2. Closed-Loop Combustion Control of a Multi Cylinder HCCI Engine using Variable Compression Ratio and Fast Thermal Management
Sammanfattning : The current Spark Ignited (SI) engine equipped with three-way catalyst offers low emissions, but has low efficiency at part load, which results in unnecessarily high CO2 emissions. The Compression Ignited (CI) engines have higher efficiency and hence lower CO2 emissions, but suffer from higher Nitrogen Oxide (NOx) and Particulate Matter (PM) emissions, and no three-way catalyst can be used. LÄS MER
3. Modelling for Fuel Optimal Control of a Variable Compression Engine
Sammanfattning : Variable compression engines are a mean to meet the demand on lower fuel consumption. A high compression ratio results in high engine efficiency, but also increases the knock tendency. On conventional engines with fixed compression ratio, knock is avoided by retarding the ignition angle. LÄS MER
4. Modeling of a Free Piston Energy Converter
Sammanfattning : ABSTRACTThe Free Piston Energy Converter (FPEC) is a new type of automotive prime mover, offering high efficiency in conjunction with low emissions. The FPEC essentially consists of two opposing cylinders with a linear alternator incorporated in-between that produces electric power directly. LÄS MER
5. Multi-LSTM Acceleration and CNN Fault Tolerance
Sammanfattning : This thesis addresses the following two problems related to the field of Machine Learning: the acceleration of multiple Long Short Term Memory (LSTM) models on FPGAs and the fault tolerance of compressed Convolutional Neural Networks (CNN). LSTMs represent an effective solution to capture long-term dependencies in sequential data, like sentences in Natural Language Processing applications, video frames in Scene Labeling tasks or temporal series in Time Series Forecasting. LÄS MER