The risk of missing data and subsequent incomplete data records at wind farms increases with the number of turbines and sensors. We propose here an imputation method that blends data-driven concepts with expert knowledge, by using the geometry of the wind farm in order to provide better estimates when performing Nearest Neighbor imputation. Our method relies on learning Laplacian eigenmaps out of the graph of the wind farm through spectral graph theory. These learned representations can be based on the wind farm layout only, or additionally account for information provided by collected data. The related weighted graph is allowed to change with time and can be tracked in an online fashion. Application to the Westermost Rough offshore wind farm shows significant improvement over approaches that do not account for the wind farm layout information.

Offshore wind farms represent a renewable energy source with a significant global growth trend, and their monitoring is strategic for territorial and environmental planning. This study's primary objective is to detect offshore wind plants at an instance level using semantic segmentation models and Sentinel-1 time series. The secondary objectives are: (a) to develop a database consisting of labeled data and S-1 time series; (b) to compare the performance of five deep semantic segmentation architectures (U-Net, U-Net++, Feature Pyramid Network - FPN, DeepLabv3+, and LinkNet); (c) develop a novel augmentation strategy that shuffles the positions of the images within the time series; (d) investigate different dimensions of time series intervals (1, 5, 10, and 15 images); and (e) evaluate the semantic-to-instance conversion procedure. LinkNet was the top-performing model, followed by U-Net++ and U-Net, while FPN and DeepLabv3+ presented the worst results. The evaluation of semantic segmentation models reveals enhanced Intersection over Union (IoU) (25%) and F-score metrics (18%) with the augmentation of time series images. The study showcases the augmentation strategy's capability to mitigate biases and precisely detect invariant targets. Furthermore, the conversion from semantic to instance segmentation demonstrates its efficacy in accurately isolating individual instances within classified regions - simplifying training data and reducing annotation effort and complexity.

In recent years, with the development of wind energy, the number and scale of wind farms have been developing rapidly. Since offshore wind farms have the advantages of stable wind speed, being clean renewable, non-polluting, and the non-occupation of cultivated land, they have gradually become a new trend in the wind power industry all over the world. The operation and maintenance of offshore wind powe has been developing in the direction of digitization and intelligence. It is of great significance to carry ou research on the monitoring, operation, and maintenance of offshore wind farms, which will be of benefit fo the reduction of the operation and maintenance costs, the improvement of the power generation efficiency improvement of the stability of offshore wind farm systems, and the building of smart offshore wind farms This paper will mainly summarize the monitoring, operation, and maintenance of offshore wind farms, with particular focus on the following points: monitoring of "offshore wind power engineering and biological and environment", the monitoring of power equipment, and the operation and maintenance of smart offshore wind farms. Finally, the future research challenges in relation to the monitoring, operation, and maintenance of smart offshore wind farms are proposed, and the future research directions in this field are explored especially in marine environment monitoring, weather and climate prediction, intelligent monitoring of powe equipment, and digital platforms.

DEME Offshore and Sabca have carried out a series of tests at the Rentel offshore wind farm with an aim to automate critical and ad hoc operations in the near future by using autonomous aerial vehicles (AAVs) and artificial intelligence (AI). The companies, which teamed up two years ago, have performed the first commercial, cross-border, "beyond visual line of sight" (BVLOS) drone operations at the wind farm 35 kilometres off the Belgian coast, where tests in Search & Rescue operations, environmental surveys, turbine and substation inspections, as well as parcel deliveries took place. During the tests, both a multicopter drone and a fixed-wing surveillance drone with a wing span of more than 3 metres were deployed in parallel. The long endurance surveillance drone took off from the Belgian coast and flew to the Rentel offshore wind farm. Meanwhile, an automated resident drone performed inspections and cargo flights from the substation and vessels.

"It's very common," says Jess Hanham casually, when asked how often he finds suspected unexploded bombs. Mr Hanham is a co-founder of Spectrum Offshore, a marine survey firm that does a lot of work in the Thames Estuary. His firm undertakes all sorts of marine surveying, but working on sites for new offshore wind farms has become a big business for him. Work in the Thames Estuary, and other areas that were the targets of bombing in World War 2, are likely to involve picking up signals of unexploded munitions. "You can find a significant amount of contacts that need further investigation and for a wind farm that will be established in the initial pre-engineering survey," he says.

With 2019 emerging as the warmest on record for the world's oceans, the call to climate action continues as the theme for the 50-year anniversary of Earth Day 2020, described as the world's largest environmental movement to drive transformative change for people and planet. Alongside the pandemic, the climate crisis presents an opportunity to use data and AI in ways never before considered. IBM itself began focusing on environmental sustainability before the first Earth Day was ever celebrated -- but its track record on greening its supply chain and driving innovative uses of tech has put it among the world's top eco-friendly Fortune 500 companies. Where IBM leads, customers reap benefits. Digital transformation efforts across industries has given the company a unique vantage point on critical challenges facing the world -- putting AI the work on a number of different issues, from drastically reducing energy consumption to lower C02 to optimizing large scale food production in the wake of climate chaos.

Fixed term contract until 1 March 2021 The Royal College of Art is the UK's only entirely postgraduate art and design university. In 2018/19 the College will have some 2,300 students registered for MA, MRes, MPhil and PhD degrees and over 450 permanent academic, technical and administrative staff, with more than 1,000 visiting lecturers and professors. The RCA Robotics Laboratory, recently established and directed by RCA's Academic Leader in Robotics, Dr Sina Sareh, develops new bioinspired technologies for robot mobility, manipulation and attachment in unstructured and extreme environments through funded projects by EPSRC, Innovate UK and industrial partners. Following the Royal College of Art's Strategic Plan 2016-2021, the lab is intended to create significant research and education capacity in robotics by 2020, to support the RCA's ambitious expansion plans in Battersea South including a new robotics facility and new research centres - the most radical transformation of the institution's campus in its 181-year history. Through the Innovate UK's "Robotics and AI: Inspect, Maintain and Repair in Extreme Environments" funding scheme, a research project grant entitled Multi-Platform Inspection, Maintenance & Repair in Extreme Environments (MIMRee) has been awarded to the RCA.

The two biggest societal challenges for the twenty-first century are also the biggest opportunities – automation and climate change. The epitaph of fossil fuels with its dark cloud burning a hole in the ozone layer is giving way to a rise of solar and wind farms worldwide. Servicing these plantations are fleets of robots and drones, providing greater possibilities of expanding CleanTech to the most remote regions of the planet. As 2017 comes to end, the solar industry for the first time in ten years has plateaued due to the proposed budget cuts by the Trump administration. Solar has had quite a run with an average annual growth rate of more than 65% for the past decade promoted largely by federal subsidies.

The two biggest societal challenges for the twenty-first century are also the biggest opportunities – automation and climate change. The epitaph of fossil fuels with its dark cloud burning a hole in the ozone layer is giving way to a rise of solar and wind farms worldwide. Servicing these plantations are fleets of robots and drones, providing greater possibilities of expanding CleanTech to the most remote regions of the planet. As 2017 comes to end, the solar industry for the first time in ten years has plateaued due to the proposed budget cuts by the Trump administration. Solar has had quite a run with an average annual growth rate of more than 65% for the past decade promoted largely by federal subsidies.

The same technology that lets soldiers see in the dark can also help protect birds and bats near offshore wind turbines. Night vision goggles use thermal imaging, which captures infrared light that's invisible to the human eye, and now, researchers are using thermal imaging to help birds and bats near offshore wind farms. The thermal tracking software automatically detects birds and bats, which is useful for night tracking they're hard to spot - and it could help inform policymakers about where new and existing offshore wind turbines should be placed. The thermal tracking software automatically detects birds and bats, which is useful for tracking them at night when they're hard to spot . The thermal imaging software, developed by researchers at the Department of Energy's Pacific Northwest National Laboratory (PNNL), is called ThermalTracker.