Venue-specific training has, over the years, proven to be a key asset for sailing teams performing at major championships and at the Olympics. A comprehensive understanding of the environmental features and possible weather scenarios in an outdoor sport like sailing can provide athletes with a significant strategic advantage. At the same time, GPS tracking is becoming a readily available technology that athletes use both in training and during races to analyse their own and their competitor’s performance. This work couples environmental and meteorological data with GPS tracks for Olympic sailing classes, linking weather features with strategic decisions on the race track. We propose a greedy algorithm to search for an optimal route based on weather forecasts to present the best strategy prior to the race. Our results show the potential of this approach to provide valuable decision support for athletes in Olympic sailing competitions, demonstrated for the 470 Olympic class.

Accurate energy consumption prediction is crucial for optimising the operation of electric commercial heavy-duty vehicles, particularly for efficient route planning, refining charging strategies, and ensuring optimal truck configuration for specific tasks. This study investigates the application of multi-task curriculum learning to enhance machine learning models for forecasting the energy consumption of various onboard systems in electric vehicles. Multi-task learning, unlike traditional training approaches, leverages auxiliary tasks to provide additional training signals, which has been shown to enhance predictive performance in many domains. By further incorporating curriculum learning, where simpler tasks are learned before progressing to more complex ones, neural network training becomes more efficient and effective. We evaluate the suitability of these methodologies in the context of electric vehicle energy forecasting, examining whether the combination of multi-task learning and curriculum learning enhances algorithm generalisation, even with limited training data. We primarily focus on understanding the efficacy of different curriculum learning strategies, including sequential learning and progressive continual learning, using complex, real-world industrial data. Our research further explores a set of auxiliary tasks designed to facilitate the learning process by targeting key consumption characteristics projected into future time frames. The findings illustrate the potential of multi-task curriculum learning to advance energy consumption forecasting, significantly contributing to the optimisation of electric heavy-duty vehicle operations. This work offers a novel perspective on integrating advanced machine learning techniques to enhance energy efficiency in the exciting field of electromobility. 

Batteries are a safety-critical and the most expensive component for electric buses (EBs). Monitoring their condition, or the state of health (SoH), is crucial for ensuring the reliability of EB operation. However, EBs come in many models and variants, including different mechanical configurations, and deploy to operate under various conditions. Developing new degradation models for each combination of settings and faults quickly becomes challenging due to the unavailability of data for novel conditions and the low evidence for less popular vehicle populations. Therefore, building machine learning models that can generalize to new and unseen settings becomes a vital challenge for practical deployment. This study aims to develop and evaluate feature selection methods for robust machine learning models that allow estimating the SoH of batteries across various settings of EB configuration and usage. Building on our previous work, we propose two approaches, a genetic algorithm for domain invariant features (GADIF) and causal discovery for selecting invariant features (CDIF). Both aim to select features that are invariant across multiple domains. While GADIF utilizes a specific fitness function encompassing both task performance and domain shift, the CDIF identifies pairwise causal relations between features and selects the common causes of the target variable across domains. Experimental results confirm that selecting only invariant features leads to a better generalization of machine learning models to unseen domains. The contribution of this work comprises the two novel invariant feature selection methods, their evaluation on real-world EBs data, and a comparison against state-of-the-art invariant feature selection methods. Moreover, we analyze how the selected features vary under different settings. 

The Concordance Index (C-index) is a commonly used metric in Survival Analysis for evaluating the performance of a prediction model. In this paper, we propose a decomposition of the C-index into a weighted harmonic mean of two quantities: one for ranking observed events versus other observed events, and the other for ranking observed events versus censored cases. This decomposition enables a finer-grained analysis of the relative strengths and weaknesses between different survival prediction methods. The usefulness of this decomposition is demonstrated through benchmark comparisons against classical models and state-of-the-art methods, together with the new variational generative neural-network-based method (SurVED) proposed in this paper. The performance of the models is assessed using four publicly available datasets with varying levels of censoring. Using the C-index decomposition and synthetic censoring, the analysis shows that deep learning models utilize the observed events more effectively than other models. This allows them to keep a stable C-index in different censoring levels. In contrast to such deep learning methods, classical machine learning models deteriorate when the censoring level decreases due to their inability to improve on ranking the events versus other events. 

Linear potential flow (LPF) models remain the tools-of-the trade in marine and ocean engineering despite their well-known assumptions of small amplitude waves and motions. As of now, nonlinear simulation tools are still too computationally demanding to be used in the entire design loop, especially when it comes to the evaluation of numerous irregular sea states. In this paper we aim to enhance the performance of the LPF models by introducing a hybrid LPF-ML (machine learning) approach, based on identification of nonlinear force corrections. The corrections are defined as the difference in hydrodynamic force (vis- cous and pressure-based) between high-fidelity CFD and LPF models. Using prescribed chirp motions with different amplitudes, we train a long short-term memory (LSTM) network to predict the corrections. The LSTM network is then linked to the MoodyMarine LPF model to provide the nonlinear correction force at every time-step, based on the dynamic state of the body and the corresponding forces from the LPF model. The method is illustrated for the case of a heaving sphere in decay, regular and irregular waves – including passive control. The hybrid LPF-model is shown to give significant improvements compared to the baseline LPF model, even though the training is quite generic.

Dance improvisation is an active research topic in the arts. Motion analysis of improvised dance can be challenging due to its unique dynamics. Data-driven dance motion analysis, including recognition and generation, is often limited to skeletal data. However, data of other modalities, such as audio, can be recorded and benefit downstream tasks. This paper explores the application and performance of multimodal fusion methods for human motion recognition in the context of dance improvisation. We propose an attention-based model, component attention network (CANet), for multimodal fusion on three levels: 1) feature fusion with CANet, 2) model fusion with CANet and graph convolutional network (GCN), and 3) late fusion with a voting strategy. We conduct thorough experiments to analyze the impact of each modality in different fusion methods and distinguish critical temporal or component features. We show that our proposed model outperforms the two baseline methods, demonstrating its potential for analyzing improvisation in dance

Numerical models used in the design of floating bodies routinely rely on linear hydrodynamics. Extensions for hydrodynamic nonlinearities can be approximated using e.g. Morison type drag and nonlinear Froude-Krylov forces. This paper aims to improve the approximation of nonlinear forces acting on floating bodies by using machine learning (ML). Many ML models are general function approximators and therefore suitable for representing such nonlinear correction terms. A hierarchical modelling approach is used to build mappings between higher-fidelity simulations and the linear method. The ML corrections are built up for FNPF, Euler and RANS simulations. Results for decay tests of a sphere in model scale using recurrent neural networks (RNN) are presented. The RNN algorithm is shown to satisfactory predict the correction terms if the most nonlinear case is used as training data. No difference in the performance of the RNN model is seen for the different hydrodynamic models.

We present a hybrid linear potential flow - machine learning (LPF-ML) model for simulating weakly nonlinear wave-body interaction problems. In this paper we focus on using hierarchical modelling for generating training data to be used with recurrent neural networks (RNNs) in order to derive nonlinear correction forces. Three different approaches are investigated: (i) a baseline method where data from a Reynolds averaged Navier Stokes (RANS) model is directly linked to data from a LPF model to generate nonlinear corrections; (ii) an approach in which we start from high-fidelity RANS simulations and build the nonlinear corrections by stepping down in the fidelity hierarchy; and (iii) a method starting from low-fidelity, successively moving up the fidelity staircase. The three approaches are evaluated for the simple test case of a heaving sphere. The results show that the baseline model performs best, as expected for this simple test case. Stepping up in the fidelity hierarchy very easily introduce errors that propagate through the hierarchical modelling via the correction forces. The baseline method was found to accurately predict the motion of the heaving sphere. The hierarchical approaches struggled with the task, with the approach that steps down in fidelity performing somewhat better of the two.

This study applies a data-driven anomaly detection framework based on a Long Short-Term Memory (LSTM) autoencoder network for several subsystems of a public transport bus. The proposed framework efficiently detects abnormal data, significantly reducing the false alarm rate compared to available alternatives. Using historical repair records, we demonstrate how detection of abnormal sequences in the signals can be used for predicting equipment failures. The deviations from normal operation patterns are detected by analysing the data collected from several on-board sensors (e.g., wet tank air pressure, engine speed, engine load) installed on the bus. The performance of LSTM autoencoder (LSTM-AE) is compared against the multi-layer autoencoder (mlAE) network in the same anomaly detection framework. The experimental results show that the performance indicators of the LSTM-AE network, in terms of F1 Score, Recall, and Precision, are better than those of the mlAE network. © 2022, The Author(s)