Research of North Pole amphibious vehicle's technologies and Powertrain simulation

The aim of this master’s thesis is to provide a useful framework for upcoming researches about amphibious vehicles designed for the North Pole. This thesis is a brief introduction to the North Pole Amphibious Vehicles, which consists in studying the main requirements of this kind of vehicle. Thus, the research about amphibious vehicle technologies is done on the state-of-art and different powertrain systems and configurations are studied. In this project, a 3D design of an amphibious vehicle and its flow simulation were created with Solidworks in order to calculate the longitudinal parameters of the vehicle. The powertrain system has been chosen following an analysis of conventional, electric and hybrid powertrains, so as to determine which system has a better performance at the North Pole. Different powertrain configurations have been modelled in Matlab Simulink following the mathematical equations that describe the components or using the Powertrain Blockset offered by Matlab. The software simulates the whole powertrain dynamics in conjunction. The results obtained by the simulation were used to compare the 4WD and 6WD powertrains in order to find out which are the most suitable.Outgoin

Network On Network for Tabular Data Classification in Real-world Applications

Tabular data is the most common data format adopted by our customers ranging from retail, finance to E-commerce, and tabular data classification plays an essential role to their businesses. In this paper, we present Network On Network (NON), a practical tabular data classification model based on deep neural network to provide accurate predictions. Various deep methods have been proposed and promising progress has been made. However, most of them use operations like neural network and factorization machines to fuse the embeddings of different features directly, and linearly combine the outputs of those operations to get the final prediction. As a result, the intra-field information and the non-linear interactions between those operations (e.g. neural network and factorization machines) are ignored. Intra-field information is the information that features inside each field belong to the same field. NON is proposed to take full advantage of intra-field information and non-linear interactions. It consists of three components: field-wise network at the bottom to capture the intra-field information, across field network in the middle to choose suitable operations data-drivenly, and operation fusion network on the top to fuse outputs of the chosen operations deeply. Extensive experiments on six real-world datasets demonstrate NON can outperform the state-of-the-art models significantly. Furthermore, both qualitative and quantitative study of the features in the embedding space show NON can capture intra-field information effectively