<p>Domain adaptation is one of the hottest directions in solving annotation insufficiency problem of deep learning. General domain adaptation is not consistent with the practical scenarios in the industry. In this thesis, we focus on two concerns as below.</p> <p>  </p> <p>  First is that labeled data are generally collected from multiple domains. In other words, multi-source adaptation is a more common situation. Simply extending these single-source approaches to the multi-source cases could cause sub-optimal inference, so specialized multi-source adaptation methods are essential. The main challenge in the multi-source scenario is a more complex divergence situation. Not only the divergence between target and each source plays a role, but the divergences among distinct sources matter as well. However, the significance of maintaining consistency among multiple sources didn't gain enough attention in previous work. In this thesis, we propose an Enhanced Consistency Multi-Source Adaptation (EC-MSA) framework to address it from three perspectives. First, we mitigate feature-level discrepancy by cross-domain conditional alignment, narrowing the divergence between each source and target domain class-wisely. Second, we enhance multi-source consistency via dual mix-up, diminishing the disagreements among different sources. Third, we deploy a target distilling mechanism to handle the uncertainty of target prediction, aiming to provide high-quality pseudo-labeled target samples to benefit the previous two aspects. Extensive experiments are conducted on several common benchmark datasets and demonstrate that our model outperforms the state-of-the-art methods.</p> <p>  </p> <p>  Second is that data privacy and security is necessary in practice. That is, we hope to keep the raw data stored locally while can still obtain a satisfied model. In such a case, the risk of data leakage greatly decreases. Therefore, it is natural for us to combine the federated learning paradigm with domain adaptation. Under the source-private setting, the main challenge for us is to expose information from the source domain to the target domain while make sure that the communication process is safe enough. In this thesis, we propose a method named Fourier Transform-Assisted Federated Domain Adaptation (FTA-FDA) to alleviate the difficulties in two ways. We apply Fast Fourier Transform to the raw data and transfer only the amplitude spectra during the communication. Then frequency space interpolations between these two domains are conducted, minimizing the discrepancies while ensuring the contact of them and keeping raw data safe. What's more, we make prototype alignments by using the model weights together with target features, trying to reduce the discrepancy in the class level. Experiments on Office-31 demonstrate the effectiveness and competitiveness of our approach, and further analyses prove that our algorithm can help protect privacy and security.</p>

The continuous monitoring of the state-of-health (SOH) of electric vehicles (EV) represents a problem with great research relevance due to the time-consuming battery cycling and capacity measurements that are usually required to create a SOH estimation model. Instead of the widely used approach of modelling the battery’s degradation behaviour with as little cycling effort as possible, the applied SOH monitoring approach is the first of its kind that is solely based on commonly logged battery management system (BMS) signals and does not rely on tedious capacity measurements. These are used to train the digital battery twins, which are subsequently subjected to virtual capacity tests to estimate the SOH. In this work, transfer-learning is applied to increase the data and computational efficiency of the digital battery twins training process to facilitate a real-world application as it enables SOH estimation for unknown ageing states due to the selective parameter initialisation at less than a tenth of the common training time. However, the successful SOH estimation with a mean SOH deviation of 0.05% using transfer-learning still requires the presence of pauses in the dataset. Meta-learning extends the idea of transfer-learning as the baseline model simultaneously takes several ageing states into account. Learning the basic battery-electric behaviour it is forced to preserve a certain level of uncertainty at the same time, which seems crucial for the successful fine-tuning of the model parameters based on three pause-free load profiles resulting in a mean SOH deviation of 0.85%. This optimised virtual SOH experiment framework provides the cornerstone for a scalable and robust estimation of the remaining battery capacity on a pure data basis.