eUC-472 BIOMIMETIC REMOTE-CONTROLLED VEHICLE

The goal of this project is smoothly integrating instinctual concepts of control into devices beyond the body. It is essentially an attempt to extend the body without any complex prior training. To do this, we have developed a both a Bluetooth connection between hand movements and the motors of a multifaceted vehicle. Furthermore, the hand movements will be tracked using both an accelerometer and gyroscope found in the common hobbyist tool Arduino nano. Logging this data and processing it through the Bluetooth communication system, the intention is to provide real-time updates to the vehicle’s motors that ultimately sync the intentions of a user and its movement

Fostering Maritime Education Through Interdisciplinary Training

The nature and complexity of the challenges faced in today’s world are forcing a greater number of specialised individuals to collaborate together, in order to produce a joint effort combining their expertise. Based on this observation that the professional world is interdisciplinary, the learning and teaching provided in Higher Education should adapt and consider interdisciplinary approaches to subjects in order to help develop key employability skills for working in interdisciplinary teams. Building on the perceived benefits of interdisciplinary education, an academic exchange between boatbuilding and yacht design students has been conducted to investigate an interdisciplinary pedagogical model aimed at the maritime industry. The finding reveals clear learning outcomes, revolving around the learning experience, the reflection generated, and the enhanced capabilities; respectively supporting their studies, contributing to bridging the skills gap and enhancing employability, thereby offering a contribution to meeting the contemporary demands from both students and the maritime industry

Supervised machine learning for audio emotion recognition

The field of Music Emotion Recognition has become and established research sub-domain of Music Information Retrieval. Less attention has been directed towards the counterpart domain of Audio Emotion Recognition, which focuses upon detection of emotional stimuli resulting from non-musical sound. By better understanding how sounds provoke emotional responses in an audience, it may be possible to enhance the work of sound designers. The work in this paper uses the International Affective Digital Sounds set. A total of 76 features are extracted from the sounds, spanning the time and frequency domains. The features are then subjected to an initial analysis to determine what level of similarity exists between pairs of features measured using Pearson’s r correlation coefficient before being used as inputs to a multiple regression model to determine their weighting and relative importance. The features are then used as the input to two machine learning approaches: regression modelling and artificial neural networks in order to determine their ability to predict the emotional dimensions of arousal and valence. It was found that a small number of strong correlations exist between the features and that a greater number of features contribute significantly to the predictive power of emotional valence, rather than arousal. Shallow neural networks perform significantly better than a range of regression models and the best performing networks were able to account for 64.4% of the variance in prediction of arousal and 65.4% in the case of valence. These findings are a major improvement over those encountered in the literature. Several extensions of this research are discussed, including work related to improving data sets as well as the modelling processes

Modelling environmental life cycle performance of alternative marine power configurations with an integrated experimental assessment approach: A case study of an inland passenger barge

There is pressure on the global shipping industry to move towards greener propulsion and fuel technologies to reduce greenhouse gas emissions. Hydrogen and electricity are both recognised as pathways to achieve a net-zero. However, in the evaluation of the environmental performance of these alternative marine power configurations, conventional life cycle assessment (LCA) methods have limitations reflecting the varied nature of ship design and operational modes. The integration of LCA with experimental assessment could remedy the shortcoming of conventional approaches to data generation. The system energy demand data in this study was generated based on specific ship design and directly fed into life cycle assessment. To demonstrate the effectiveness and potential the approach was applied to a case study of inland waterway vessel. Suitable hybrid PV/electricity/diesel and hydrogen powered fuel cell systems for the case vessel were modelled; and hydrodynamic testing and dynamic system simulation was undertaken to provide ship performance data under various operational/environmental profiles. Lifecycle assessment (LCA) indicated hydrogen and electrical propulsion technologies have the potential for 85.7 % and 56.2 % emissions reduction against an MGO base case, respectively. The results highlight that implementation of both technologies is highly dependent on energy production pathways. Hydrogen systems reliant on fossil feedstocks risk an increase in emissions of up to 6.3 % against the MGO base case. Sensitivity analysis indicated an electrical system with electricity production from 79.5 % renewables could achieve savings of 82.2 % in GHG emissions compared to the MGO base case. Crucially, the results demonstrate a further development of the LCA approach which can enable a more accurate environmental performance evaluation of alternative marine power configurations considering specific ship design and operational characteristics. Ultimately this addition makes the results more meaningful for commercial operations and decision making in the selection of alternative marine power systems to support the transition to net-zero

Supervised machine learning for audio emotion recognition: Enhancing film sound design using audio features, regression models and artificial neural networks

This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s00779-020-01389-0The field of Music Emotion Recognition has become and established research sub-domain of Music Information Retrieval. Less attention has been directed towards the counterpart domain of Audio Emotion Recognition, which focuses upon detection of emotional stimuli resulting from non-musical sound. By better understanding how sounds provoke emotional responses in an audience, it may be possible to enhance the work of sound designers. The work in this paper uses the International Affective Digital Sounds set. A total of 76 features are extracted from the sounds, spanning the time and frequency domains. The features are then subjected to an initial analysis to determine what level of similarity exists between pairs of features measured using Pearson’s r correlation coefficient before being used as inputs to a multiple regression model to determine their weighting and relative importance. The features are then used as the input to two machine learning approaches: regression modelling and artificial neural networks in order to determine their ability to predict the emotional dimensions of arousal and valence. It was found that a small number of strong correlations exist between the features and that a greater number of features contribute significantly to the predictive power of emotional valence, rather than arousal. Shallow neural networks perform significantly better than a range of regression models and the best performing networks were able to account for 64.4% of the variance in prediction of arousal and 65.4% in the case of valence. These findings are a major improvement over those encountered in the literature. Several extensions of this research are discussed, including work related to improving data sets as well as the modelling processes

Thermospheric Weather as Observed by Ground‐Based FPIs and Modeled by GITM

The first long‐term comparison of day‐to‐day variability (i.e., weather) in the thermospheric winds between a first‐principles model and data is presented. The definition of weather adopted here is the difference between daily observations and long‐term averages at the same UT. A year‐long run of the Global Ionosphere Thermosphere Model is evaluated against a nighttime neutral wind data set compiled from six Fabry‐Perot interferometers at middle and low latitudes. First, the temporal persistence of quiet‐time fluctuations above the background climate is evaluated, and the decorrelation time (the time lag at which the autocorrelation function drops to e−1) is found to be in good agreement between the data (1.8 hr) and the model (1.9 hr). Next, comparisons between sites are made to determine the decorrelation distance (the distance at which the cross‐correlation drops to e−1). Larger Fabry‐Perot interferometer networks are needed to conclusively determine the decorrelation distance, but the current data set suggests that it is ∼1,000 km. In the model the decorrelation distance is much larger, indicating that the model results contain too little spatial structure. The measured decorrelation time and distance are useful to tune assimilative models and are notably shorter than the scales expected if tidal forcing were responsible for the variability, suggesting that some other source is dominating the weather. Finally, the model‐data correlation is poor (−0.07 < ρ < 0.36), and the magnitude of the weather is underestimated in the model by 65%.Plain Language SummaryMuch like in the lower atmosphere, weather in the upper atmosphere is harder to predict than climate. Physics‐based models are becoming sophisticated enough that they can in principle predict the weather, and we present the first long‐term evaluation of how well a particular model, Global Ionosphere Thermosphere Model, performs. To evaluate the model, we compare it with a year of data from six ground‐based sites that measure the thermospheric wind. First, we calculate statistics of the weather, such as the decorrelation time, which characterizes how long weather fluctuations persist (1.8 hr in the data and 1.9 hr in the model). We also characterize the spatial decorrelation by comparing weather at different sites. The model predicts that the weather is much more widespread than the data indicates; sites that are 790 km apart have a measured correlation of 0.4, while the modeled correlation is 0.8. In terms of being able to actually predict a weather fluctuation on a particular day, the model performs poorly, with a correlation that is near zero at the low latitude sites, but reaches an average of 0.19 at the midlatitude sites, which are closer to the source that most likely dominates the weather: heating in the auroral zone.Key PointsA long‐term data‐model comparison of day‐to‐day thermospheric variability finds that GITM represents the weather poorly (−0.07 < ρ < 0.36)The average measured decorrelation time of 1.8 hr agrees with the modeled time of 1.9 hrThe weather in GITM contains too little spatial structure, when compared with the measured ∼1,000‐km decorrelation distancePeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/148359/1/jgra54757_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/148359/2/jgra54757.pd

Mosaic Swarm Robotics: Emulating Natural Collective Behaviors for Efficient Task Execution with Custom Mobile Robots

Mosaics, as an artistic expression, involves the meticulous arrangement of diverse tiles to form a unified composition. Drawing inspiration from this concept, the field of swarm robotics seeks to emulate nature’s collective behaviors observed in ant colonies, fish schools, and bird flocks, employing multiple agents to accomplish tasks efficiently. Our research explores the concept of mosaic swarm robotics, where numerous nodes with specialized functions are deployed across various domains, including applications for outdoor data capture and environment mapping. We utilized custom mobile robots operated by Raspberry Pi microcontrollers. By establishing an elaborate web of client-to-client communications to enable true localized swarm interactions needed to procure a robust and adaptable system that can be operated through clustering techniques and wireless sensor networking. This research aims to localize swarm navigation through ArUco markers to accurately track the position of a robot in a collaborative environment. The foundational algorithms developed will not only serve the immediate purpose but also pave the way for future applications, extending to drone systems to facilitate seamless collaboration across multiple domains