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Most things that you buy online have been on a shit at one time or another. Ships are able to carry an incredible amount of cargo relatively cheaply, which is why various companies are offering free shipping so commonly. However, this kind of convenience has its price – shipping industry’s greenhouse gas emissions are increasing.

Shipping industry is still polluting air more than people know

Ships have huge engines, burning dirty fuel oil. This substance is basically a byproduct of production of our normal fuels. Image credit: Pjotr Mahhonin via Wikimedia (CC BY-SA 4.0)

The International Maritime Organisation (IMO) conducted a study, which showed that greenhouse gas emissions are increasing in the shipping industry. They hit an all time high in 2017, but they are actually expected to increase even further due to insufficient regulation. This is a bit worrying, because as cars are becoming more and more eco-friendly, the shipping industry is improving very slowly. Ships are used for decades and, although they are becoming better, they are still huge polluters. Furthermore, the shipping industry is growing and there are more ships in the sea now.

Shipping industry should cut emissions by at least 50% by 2050 compared to 2008 levels – this goal was set by the IMO. And the situation is improving – in 2018 industry’s emissions were 0.3% lower than emissions levels a decade earlier. Experts are looking into emissions of ships via satellite data, which allowed them to estimate that 30% of total shipping emissions fall directly within national government responsibility. This is huge, because it means that goals set by IMO are not going to be as effective as previously thought, because IMO’s responsibility ends where national governments begin. Not great.

But wait – why do we say that the situation is getting worse if overall emissions are going down, even if it is slower than would be desired? Well, methane is a very potent greenhouse gas and emissions of it have increased by 150 % over the study period, because many ships still have old worn out engines. Dr Tristan Smith, co-author of the study, said: “Poor accountancy creates persistent underestimation of the magnitude of responsibility and role that should be taken nationally to decarbonise shipping. Hopefully this study will encourage countries to look again and bring shipping firmly into their national GHG policy and action.” If anything, this study shows that the magnitude of air pollution of ships is largely unknown.

How ships are going to become cleaner in the coming decades? Well, better engines, better exhaust systems, maybe even hybrid technology. However, you should not have any illusions. Ships could be much greener now, but they are burning very dirty fuel oil, which is basically a byproduct of production of petrol and diesel.

 

Source: UCL




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Adaptive Meta-Learning for Identification of Rover-Terrain Dynamics

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The dynamics of extraterrestrial rovers is dependent on the terrain. The high-level terrain classification used in most current rovers is often not enough to ensure safe path selection, as the experience with NASA’s Curiosity and Spirit shows.

Adaptive Meta Learning for Identification of Rover Terrain Dynamics

Credits: NASA/JPL-Caltech/MSSS

A recent paper suggests a model of the terrain parameters that govern wheel-terrain interaction. Knowing the terrain may help to predict whether the neighboring regions are traversable, plan the safest route, and prevent damage.

A linear model, which relates terrain parameters (namely cohesion and internal friction angle) and rover dynamics is supplemented by a meta-learned neural network. The interpretability of the model is enhanced by the orthogonality of nominal and meta-learned features. The model is capable of rapid adaptation and provides low estimation errors (the largest error is less than 5%).

Rovers require knowledge of terrain to plan trajectories that maximize safety and efficiency. Terrain type classification relies on input from human operators or machine learning-based image classification algorithms. However, high level terrain classification is typically not sufficient to prevent incidents such as rovers becoming unexpectedly stuck in a sand trap; in these situations, online rover-terrain interaction data can be leveraged to accurately predict future dynamics and prevent further damage to the rover. This paper presents a meta-learning-based approach to adapt probabilistic predictions of rover dynamics by augmenting a nominal model affine in parameters with a Bayesian regression algorithm (P-ALPaCA). A regularization scheme is introduced to encourage orthogonality of nominal and learned features, leading to interpretable probabilistic estimates of terrain parameters in varying terrain conditions.

Link: https://arxiv.org/abs/2009.10191