Industrial structural geology : principles, techniques and integration : an introduction

The authors wish to acknowledge the generous financial support provided in association with this volume to the Geological Society and the Petroleum Group by Badley Geoscience Ltd, BP, CGG Robertson, Dana Petroleum Ltd, Getech Group plc, Maersk Oil North Sea UK Ltd, Midland Valley Exploration Ltd, Rock Deformation Research (Schlumberger) and Borehole Image & Core Specialists (Wildcat Geoscience, Walker Geoscience and Prolog Geoscience). We would like to thank the fine team at the Geological Society’s Publishing House for the excellent support and encouragement that they have provided to the editors and authors of this Special Publication.Peer reviewedPublisher PD

Image Segmentation Using Ant System-based Clustering Algorithm

Industrial applications of computer vision sometimes require detection of atypical objects that occur as small groups of pixels in digital images. These objects are difficult to single out because they are small and randomly distributed. In this work we propose an image segmentation method using the novel Ant System-based Clustering Algorithm (ASCA). ASCA models the foraging behaviour of ants, which move through the data space searching for high data-density regions, and leave pheromone trails on their path. The pheromone map is used to identify the exact number of clusters, and assign the pixels to these clusters using the pheromone gradient. We applied ASCA to detection of microcalcifications in digital mammograms and compared its performance with state-of-the-art clustering algorithms such as 1D Self-Organizing Map, k-Means, Fuzzy c-Means and Possibilistic Fuzzy c-Means. The main advantage of ASCA is that the number of clusters needs not to be known a priori. The experimental results show that ASCA is more efficient than the other algorithms in detecting small clusters of atypical data

Single-pulse classifier for the LOFAR Tied-Array All-sky Survey

Searches for millisecond-duration, dispersed single pulses have become a standard tool used during radio pulsar surveys in the last decade. They have enabled the discovery of two new classes of sources: rotating radio transients and fast radio bursts. However, we are now in a regime where the sensitivity to single pulses in radio surveys is often limited more by the strong background of radio frequency interference (RFI, which can greatly increase the false-positive rate) than by the sensitivity of the telescope itself. To mitigate this problem, we introduce the Single-pulse Searcher (SPS). This is a new machine-learning classifier designed to identify astrophysical signals in a strong RFI environment, and optimized to process the large data volumes produced by the new generation of aperture array telescopes. It has been specifically developed for the LOFAR Tied-Array All-Sky Survey (LOTAAS), an ongoing survey for pulsars and fast radio transients in the northern hemisphere. During its development, SPS discovered seven new pulsars and blindly identified ˜80 known sources. The modular design of the software offers the possibility to easily adapt it to other studies with different instruments and characteristics. Indeed, SPS has already been used in other projects, e.g. to identify pulses from the fast radio burst source FRB 121102. The software development is complete and SPS is now being used to re-process all LOTAAS data collected to date

Analysis of Parkinson\u27s Disease Data

In this paper, we investigate the diagnostic data from patients suffering with Parkinson\u27s disease (PD) and design classification/prediction model to simplify the diagnosis. The main aim of this research is to open possibilities to be able to apply deep learning algorithms to help better understand and diagnose the disease. To our knowledge, the capabilities of deep learning algorithms have not yet been completely utilized in the field of Parkinson\u27s research and we believe that by having an in-depth understanding of data, we can create a platform to apply different algorithms to automate the Parkinson\u27s Disease diagnosis to certain extent. We use Parkinson\u27s Progression Markers Initiative (PPMI) dataset provided by Michael J. Fox Foundation to perform our analysis

Recent Advances in Anomaly Detection Methods Applied to Aviation

International audienceAnomaly detection is an active area of research with numerous methods and applications. This survey reviews the state-of-the-art of data-driven anomaly detection techniques and their application to the aviation domain. After a brief introduction to the main traditional data-driven methods for anomaly detection, we review the recent advances in the area of neural networks, deep learning and temporal-logic based learning. In particular, we cover unsupervised techniques applicable to time series data because of their relevance to the aviation domain, where the lack of labeled data is the most usual case, and the nature of flight trajectories and sensor data is sequential, or temporal. The advantages and disadvantages of each method are presented in terms of computational efficiency and detection efficacy. The second part of the survey explores the application of anomaly detection techniques to aviation and their contributions to the improvement of the safety and performance of flight operations and aviation systems. As far as we know, some of the presented methods have not yet found an application in the aviation domain. We review applications ranging from the identification of significant operational events in air traffic operations to the prediction of potential aviation system failures for predictive maintenance

Towards an Effective Imaging-Based Decision Support System for Skin Cancer

The usage of expert systems to aid in medical decisions has been employed since 1980s in distinct ap plications. With the high demands of medical care and limited human resources, these technologies are required more than ever. Skin cancer has been one of the pathologies with higher growth, which suf fers from lack of dermatology experts in most of the affected geographical areas. A permanent record of examination that can be further analyzed are medical imaging modalities. Most of these modalities were also assessed along with machine learning classification methods. It is the aim of this research to provide background information about skin cancer types, medical imaging modalities, data mining and machine learning methods, and their application on skin cancer imaging, as well as the disclosure of a proposal of a multi-imaging modality decision support system for skin cancer diagnosis and treatment assessment based in the most recent available technology. This is expected to be a reference for further implementation of imaging-based clinical support systems.info:eu-repo/semantics/publishedVersio

Prediction of Traffic Flow via Connected Vehicles

We propose a Short-term Traffic flow Prediction (STP) framework so that transportation authorities take early actions to control flow and prevent congestion. We anticipate flow at future time frames on a target road segment based on historical flow data and innovative features such as real time feeds and trajectory data provided by Connected Vehicles (CV) technology. To cope with the fact that existing approaches do not adapt to variation in traffic, we show how this novel approach allows advanced modelling by integrating into the forecasting of flow, the impact of the various events that CV realistically encountered on segments along their trajectory. We solve the STP problem with a Deep Neural Networks (DNN) in a multitask learning setting augmented by input from CV. Results show that our approach, namely MTL-CV, with an average Root-Mean-Square Error (RMSE) of 0.052, outperforms state-of-the-art ARIMA time series (RMSE of 0.255) and baseline classifiers (RMSE of 0.122). Compared to single task learning with Artificial Neural Network (ANN), ANN had a lower performance, 0.113 for RMSE, than MTL-CV. MTL-CV learned historical similarities between segments, in contrast to using direct historical trends in the measure, because trends may not exist in the measure but do in the similarities

The fight to breathe - an analysis of the anti-smog-organization Polski Alarm Smogowy in Poland

The objective of this thesis is to show how the organization Polski Alarm Smogowy (Polish Smog Alert) has organized themselves and their ways of fighting against high levels of air pollution in Poland. By basing their actions on scientific evidence of detrimental somatic effects of low-stack emissions of coal in private households, the organization has revealed widespread and harmful cultural practices of burning low quality coal and toxic waste. The organization now consists of about 30 local alerts, spread all over Poland. At the inception of the organization in Kraków they started advocating for a ‘coal ban’. Seven years later, this law from November 2019. I have identified six approaches employed by seven local smog alerts in Poland that demonstrate a pragmatic and professional way of conducting environmentalism. I also looked at how joined organizational approaches and profile and analyzed how they relate to different ideology among members. A complex compound of few but very resourceful members from professional NGOs and civil activists in combination with their rational approaches has enabled the organization to gain trust and support from an increasing number of civilians, politicians and members of the authorities, set air pollution on the political agenda and changed laws all the way up to constitutional level