Collaboratively editing game worlds and the underlying asset version management techniques present us with many challenges when using a traditional Version Control System (VCS). This paper suggests a new method that will aid in the tracking, branching, and selection of asset versions within a game development pipeline. A prototype has been developed which demonstrated im-provements in both productivity and usability providing greater visibility of incremental asset changes and alterations to their relationships which equated to approximately a 30% better user experience when compared to an existing industry VCS.
DOCUMENT
Data-driven modeling is an imperative tool in various industrial applications, including many applications in the sectors of aeronautics and commercial aviation. These models are in charge of providing key insights, such as which parameters are important on a specific measured outcome or which parameter values we should expect to observe given a set of input parameters. At the same time, however, these models rely heavily on assumptions (e.g., stationarity) or are “black box” (e.g., deep neural networks), meaning that they lack interpretability of their internal working and can be viewed only in terms of their inputs and outputs. An interpretable alternative to the black box models and with considerably less assumptions is symbolic regression (SR). SR searches for the optimal model structure while simultaneously optimizing the model’s parameters without relying on an a priori model structure. In this work, we apply SR on real-life exhaust gas temperature (EGT) data, collected at high frequencies through the entire flight, in order to uncover meaningful algebraic relationships between the EGT and other measurable engine parameters. The experimental results exhibit promising model accuracy, as well as explainability returning an absolute difference of 3°C compared to the ground truth and demonstrating consistency from an engineering perspective.
DOCUMENT
Estimating the remaining useful life (RUL) of an asset lies at the heart of prognostics and health management (PHM) of many operations-critical industries such as aviation. Mod- ern methods of RUL estimation adopt techniques from deep learning (DL). However, most of these contemporary tech- niques deliver only single-point estimates for the RUL without reporting on the confidence of the prediction. This practice usually provides overly confident predictions that can have severe consequences in operational disruptions or even safety. To address this issue, we propose a technique for uncertainty quantification (UQ) based on Bayesian deep learning (BDL). The hyperparameters of the framework are tuned using a novel bi-objective Bayesian optimization method with objectives the predictive performance and predictive uncertainty. The method also integrates the data pre-processing steps into the hyperparameter optimization (HPO) stage, models the RUL as a Weibull distribution, and returns the survival curves of the monitored assets to allow informed decision-making. We vali- date this method on the widely used C-MAPSS dataset against a single-objective HPO baseline that aggregates the two ob- jectives through the harmonic mean (HM). We demonstrate the existence of trade-offs between the predictive performance and the predictive uncertainty and observe that the bi-objective HPO returns a larger number of hyperparameter configurations compared to the single-objective baseline. Furthermore, we see that with the proposed approach, it is possible to configure models for RUL estimation that exhibit better or comparable performance to the single-objective baseline when validated on the test sets.
DOCUMENT
The research, supported by our partners, sets out to understand the drivers and barriers to sustainable logistics in port operations using a case study of drone package delivery at Rotterdam Port. Beyond the technical challenges of drone technology as an upcoming technology, it needs to be clarified how drones can operate within a port ecosystem and how they could contribute to sustainable logistics. KRVE (boatmen association), supported by other stakeholders of Rotterdam port, approached our school to conduct exploratory research. Rotterdam Port is the busiest port in Europe in terms of container volume. Thirty thousand vessels enter the port yearly, all needing various services, including deliveries. Around 120 packages/day are delivered to ships/offices onshore using small boats, cars, or trucks. Deliveries can take hours, although the distance to the receiver is close via the air. Around 80% of the packages are up to 20kg, with a maximum of 50kg. Typical content includes documents, spare parts, and samples for chemical analysis. Delivery of packages using drones has advantages compared with traditional transport methods: 1. It can save time, which is critical to port operators and ship owners trying to reduce mooring costs. 2. It can increase logistic efficiency by streamlining operations. 3. It can reduce carbon emissions by limiting the use of diesel engines, boats, cars, and trucks. 4. It can reduce potential accidents involving people in dangerous environments. The research will highlight whether drones can create value (economic, environmental, social) for logistics in port operations. The research output links to key national logistic agenda topics such as a circular economy with the development of innovative logistic ecosystems, energy transition with the reduction of carbon emissions, societal earning potential where new technology can stimulate the economy, digitalization, key enabling technology for lean operations, and opportunities for innovative business models.
The scientific publishing industry is rapidly transitioning towards information analytics. This shift is disproportionately benefiting large companies. These can afford to deploy digital technologies like knowledge graphs that can index their contents and create advanced search engines. Small and medium publishing enterprises, instead, often lack the resources to fully embrace such digital transformations. This divide is acutely felt in the arts, humanities and social sciences. Scholars from these disciplines are largely unable to benefit from modern scientific search engines, because their publishing ecosystem is made of many specialized businesses which cannot, individually, develop comparable services. We propose to start bridging this gap by democratizing access to knowledge graphs – the technology underpinning modern scientific search engines – for small and medium publishers in the arts, humanities and social sciences. Their contents, largely made of books, already contain rich, structured information – such as references and indexes – which can be automatically mined and interlinked. We plan to develop a framework for extracting structured information and create knowledge graphs from it. We will as much as possible consolidate existing proven technologies into a single codebase, instead of reinventing the wheel. Our consortium is a collaboration of researchers in scientific information mining, Odoma, an AI consulting company, and the publisher Brill, sharing its data and expertise. Brill will be able to immediately put to use the project results to improve its internal processes and services. Furthermore, our results will be published in open source with a commercial-friendly license, in order to foster the adoption and future development of the framework by other publishers. Ultimately, our proposal is an example of industry innovation where, instead of scaling-up, we scale wide by creating a common resource which many small players can then use and expand upon.
The drive to reduce the carbon intensity of the energy system has generated much interest in applying carbon-free fuels such as ammonia (NH3) in combustion systems. The high hydrogen density and well-established production processes make NH3 a valuable chemical energy carrier to address and sustain the energy shift toward renewable energy source integration. However, some difficulties can be highlighted in the NH3 practical application. The combustion of NH3 is prone to producing harmful nitric oxides. In addition, NH3 has lower reactivity than most hydrocarbon fuels, which makes ignition challenging. Also, admixing NH3 with highly reactive fuels such as DME will facilitate ignition. The partnerships of this proposal are very interested in applying renewable NH3 as fuel in combined heat and power engines, and this research proposal suggests simulating a dual-fuel engine with NH3 as its primary fuel. The results of this research will help determine the optimum operating conditions for performing an experimental study.
