Thermal and air quality modeling of an electric vehicle cabin with low-cost sensors and reinforcement learning

The design process of any electric vehicle system has to be oriented towards the best energy efficiency, together with the constraint of maintaining comfort in the vehicle cabin. Main aim of this study is to research the best thermal management solution in terms of HVAC efficiency without compromising occupant’s comfort and internal air quality. An Arduino controlled Low Cost System of Sensors was developed and compared against reference instrumentation (average R-squared of 0.92) and then used to characterise the vehicle cabin in real parking and driving conditions trials. Data on the energy use of the HVAC was retrieved from the car On-Board Diagnostic port. Energy savings using recirculation can reach 30 %, but pollutants concentration in the cabin builds up in this operating mode. Moreover, the temperature profile appeared strongly nonuniform with air temperature differences up to 10° C. Optimisation methods often require a high number of runs to find the optimal configuration of the system. Fast models proved to be beneficial for these task, while CFD-1D model are usually slower despite the higher level of detail provided. In this work, the collected dataset was used to train a fast ML model of both cabin and HVAC using linear regression. Average scaled RMSE over all trials is 0.4 %, while computation time is 0.0077 ms for each second of simulated time on a laptop computer. Finally, a reinforcement learning environment was built in OpenAI and Stable-Baselines3 using the built-in Proximal Policy Optimisation algorithm to update the policy and seek for the best compromise between comfort, air quality and energy reward terms. The learning curves show an oscillating behaviour overall, with only 2 experiments behaving as expected even if too slow. This result leaves large room for improvement, ranging from the reward function engineering to the expansion of the ML model.

A corpus-assisted approach to apology-like expressions in Japanese computer-mediated discourse

This thesis provides a corpus-assisted pragmatic investigation of three Japanese expressions commonly signalled as apologetic, namely gomen, su(m)imasen and mōshiwake arimasen, which can be roughly translated in English with ‘(I’m) sorry’. The analysis is based on a web corpus of 306,670 tokens collected from the Q&A website Yahoo! Chiebukuro, which is examined combining quantitative (statistical) and qualitative (traditional close reading) methods. By adopting a form-to-function approach, the aim of the study is to shed light on three main topics of interest: the pragmatic functions of apology-like expressions, the discursive strategies they co-occur with, and the behaviours that warrant them. The overall findings reveal that apology-like expressions are multifunctional devices whose meanings extend well beyond ‘apology’ alone. These meanings are affected by a number of discursive strategies that can either increase or decrease the perceived (im)politeness level of the speech act to serve interactants’ face needs and communicative goals. The study also identifies a variety of behaviours that people frame as violations, not necessarily because they are actually face-threatening to the receiver, but because doing so is functional to the projection of the apologiser as a moral persona. An additional finding that emerged from the analysis is the pervasiveness of reflexive usages of apology-like expressions, which are often employed metadiscursively to convey, negotiate and challenge opinions on how language should be used. To conclude, the study provides a unique insight into the use of three expressions whose pragmatic meanings are more varied than anticipated. The findings reflect the use of (im)politeness in an online and non-Western context and, hopefully, represent a step towards a more inclusive notion of ‘apologies’ and related speech acts.