Collaborating Authors


'A train wreck': what happens to workers and towns when the lights go out on coal power?

The Guardian > Energy

When Jacqui Coleman heard that Australia's largest coal-fired power station was to close seven years earlier than planned, she initially didn't believe it. Coleman is a retail worker in Dora Creek, the closest suburb to the Eraring power station on the shores of Lake Macquarie in New South Wales. For years, she has been selling pies, coffees and sandwiches to some of the hundreds of workers who pass through the News'n' More grocery store on either side of a shift. On Thursday morning, Origin Energy announced it was bringing forward the station's closure to 2025. Many workers at the site first learned their jobs were to be terminated seven years early when they heard it reported on the radio.

KnAC: an approach for enhancing cluster analysis with background knowledge and explanations Artificial Intelligence

Pattern discovery in multidimensional data sets has been a subject of research since decades. There exists a wide spectrum of clustering algorithms that can be used for that purpose. However, their practical applications share in common the post-clustering phase, which concerns expert-based interpretation and analysis of the obtained results. We argue that this can be a bottleneck of the process, especially in the cases where domain knowledge exists prior to clustering. Such a situation requires not only a proper analysis of automatically discovered clusters, but also a conformance checking with existing knowledge. In this work, we present Knowledge Augmented Clustering (KnAC), which main goal is to confront expert-based labelling with automated clustering for the sake of updating and refining the former. Our solution does not depend on any ready clustering algorithm, nor introduce one. Instead KnAC can serve as an augmentation of an arbitrary clustering algorithm, making the approach robust and model-agnostic. We demonstrate the feasibility of our method on artificially, reproducible examples and on a real life use case scenario.

Modelling the transition to a low-carbon energy supply Artificial Intelligence

A transition to a low-carbon electricity supply is crucial to limit the impacts of climate change. Reducing carbon emissions could help prevent the world from reaching a tipping point, where runaway emissions are likely. Runaway emissions could lead to extremes in weather conditions around the world -- especially in problematic regions unable to cope with these conditions. However, the movement to a low-carbon energy supply can not happen instantaneously due to the existing fossil-fuel infrastructure and the requirement to maintain a reliable energy supply. Therefore, a low-carbon transition is required, however, the decisions various stakeholders should make over the coming decades to reduce these carbon emissions are not obvious. This is due to many long-term uncertainties, such as electricity, fuel and generation costs, human behaviour and the size of electricity demand. A well choreographed low-carbon transition is, therefore, required between all of the heterogenous actors in the system, as opposed to changing the behaviour of a single, centralised actor. The objective of this thesis is to create a novel, open-source agent-based model to better understand the manner in which the whole electricity market reacts to different factors using state-of-the-art machine learning and artificial intelligence methods. In contrast to other works, this thesis looks at both the long-term and short-term impact that different behaviours have on the electricity market by using these state-of-the-art methods.

Coal country cleanup: U.S. plan sketches out possible future for former miners

The Japan Times

Now, with mining jobs hard to find, he's cleaning up the mess the industry left behind. The 68-year-old operates a bucket loader scraping away red, rocky waste dumped years ago by failed coal mine operators in a valley in the town of Clinchco, Virginia. The $17.50 an hour before overtime he makes cleaning up massive "gob piles," as the locals call them, is less than what he earned in decades as a miner. "If this work goes away, I don't know what I would do," Mullins said. Appalachia, long the heart of the U.S. coal-mining industry, may be set for a surge in jobs like Mullins' if President Joe Biden is successful in his ambitions to transition the United States to a cleaner energy economy to fight climate change.

Estimation of excess air coefficient on coal combustion processes via gauss model and artificial neural network Artificial Intelligence

It is no doubt that the most important contributing cause of global efficiency of coal fired thermal systems is combustion efficiency. In this study, the relationship between the flame image obtained by a CCD camera and the excess air coefficient ({\lambda}) has been modelled. The model has been obtained with a three-stage approach: 1) Data collection and synchronization: Obtaining the flame images by means of a CCD camera mounted on a 10 cm diameter observation port, {\lambda} data has been coordinately measured and recorded by the flue gas analyzer. 2) Feature extraction: Gridding the flame image, it is divided into small pieces. The uniformity of each piece to the optimal flame image has been calculated by means of modelling with single and multivariable Gaussian, calculating of color probabilities and Gauss mixture approach. 3) Matching and testing: A multilayer artificial neural network (ANN) has been used for the matching of feature-{\lambda}.

18 5G projects providing a vision for the future


The Internet of Things (IoT) – and what it will enable – has been a discussion point for well over a decade, but the speed, low latency and reliability of 5G promise to bring the concept to life. Network slicing will allow a wide range of product types, with distinct reliability and throughput requirements, to be run out of the same architecture, and edge computing will allow nodes to communicate directly with one another, bypassing the network's core and enhancing speed and reliability. These characteristics underpin some the most interesting projects currently making use of 5G, and have made a plethora of 5G use cases possible. Here are 18 of the best. Robots are already widely used in factories, particularly in the automotive industry.

Roof fall hazard detection with convolutional neural networks using transfer learning Artificial Intelligence

Roof falls due to geological conditions are major safety hazards in mining and tunneling industries, causing lost work times, injuries, and fatalities. Several large-opening limestone mines in the Eastern and Midwestern United States have roof fall problems caused by high horizontal stresses. The typical hazard management approach for this type of roof fall hazard relies heavily on visual inspections and expert knowledge. In this study, we propose an artificial intelligence (AI) based system for the detection roof fall hazards caused by high horizontal stresses. We use images depicting hazardous and non-hazardous roof conditions to develop a convolutional neural network for autonomous detection of hazardous roof conditions. To compensate for limited input data, we utilize a transfer learning approach. In transfer learning, an already-trained network is used as a starting point for classification in a similar domain. Results confirm that this approach works well for classifying roof conditions as hazardous or safe, achieving a statistical accuracy of 86%. However, accuracy alone is not enough to ensure a reliable hazard management system. System constraints and reliability are improved when the features being used by the network are understood. Therefore, we used a deep learning interpretation technique called integrated gradients to identify the important geologic features in each image for prediction. The analysis of integrated gradients shows that the system mimics expert judgment on roof fall hazard detection. The system developed in this paper demonstrates the potential of deep learning in geological hazard management to complement human experts, and likely to become an essential part of autonomous tunneling operations in those cases where hazard identification heavily depends on expert knowledge.

Graph-based Topic Extraction from Vector Embeddings of Text Documents: Application to a Corpus of News Articles Artificial Intelligence

Production of news content is growing at an astonishing rate. To help manage and monitor the sheer amount of text, there is an increasing need to develop efficient methods that can provide insights into emerging content areas, and stratify unstructured corpora of text into `topics' that stem intrinsically from content similarity. Here we present an unsupervised framework that brings together powerful vector embeddings from natural language processing with tools from multiscale graph partitioning that can reveal natural partitions at different resolutions without making a priori assumptions about the number of clusters in the corpus. We show the advantages of graph-based clustering through end-to-end comparisons with other popular clustering and topic modelling methods, and also evaluate different text vector embeddings, from classic Bag-of-Words to Doc2Vec to the recent transformers based model Bert. This comparative work is showcased through an analysis of a corpus of US news coverage during the presidential election year of 2016.

Up to 11,000 renewable energy jobs could be lost under Morrison government policies

The Guardian > Energy

Up to 11,000 renewable energy workers are expected to lose their jobs over the next two years under current government policies, according to a university analysis. If correct, the loss of jobs would be equivalent to the abolition of the domestic-focused coal industry, which employs a little more than 10,000 people in mining thermal coal for local use and running Australia's coal-fired power plants. Described as the first large-scale survey of renewable energy jobs in Australia, the research from the University of Technology Sydney found the industry would be a major source of jobs in the medium term, but its short-term future would depend on how Covid-19 stimulus packages were deployed. About 26,000 people are employed in renewable energy, but the study found this would fall to about 15,000 by 2022 under existing policies, including the Morrison government not replacing the national renewable energy target. The target, which requires energy companies to source about 23% of electricity from clean sources, was reached last year, triggering a 50% drop in large-scale renewable energy investment compared with 2018.

Polish firm's drones, from lifesaver to invisible model, take to the skies


The firm has also developed a drone able to fly around the underground corridors of coal mines to detect gas emissions and other potential threats. Marcin Dziekanski, coordinator of the drone project of the Silesian metropolis, an alliance of more than 40 cities in the coal-mining Katowice region, said they use drones to monitor the smoke produced by coal-heated individual houses. "They fly over Katowice, over the buildings, as well as over other cities, enabling us to intervene, in cooperation with the city police, showing that we are monitoring our space, our environment," he told AFP, adding that "we are creating a set of good practices that we are sharing with others." Spartaqs considers itself above all a research firm looking into new technologies, though it has already sold a dozen drones--at an average price of 50,000 euros ($55,000) a pop--in Poland and Georgia. But the company has realised that buyers like the Saudis and the Americans, who are very interested in certain models, want to see "the plant where they are produced." So they have begun looking for investors, including abroad, who would like to participate in the development of a serial production line.