The blessings of explainable AI in operations & maintenance of wind turbines

Wind turbines play an integral role in generating clean energy, but regularly suffer from operational inconsistencies and failures leading to unexpected downtimes and significant Operations & Maintenance (O&M) costs. Condition-Based Monitoring (CBM) has been utilised in the past to monitor operational inconsistencies in turbines by applying signal processing techniques to vibration data. The last decade has witnessed growing interest in leveraging Supervisory Control & Acquisition (SCADA) data from turbine sensors towards CBM. Machine Learning (ML) techniques have been utilised to predict incipient faults in turbines and forecast vital operational parameters with high accuracy by leveraging SCADA data and alarm logs. More recently, Deep Learning (DL) methods have outperformed conventional ML techniques, particularly for anomaly prediction. Despite demonstrating immense promise in transitioning to Artificial Intelligence (AI), such models are generally black-boxes that cannot provide rationales behind their predictions, hampering the ability of turbine operators to rely on automated decision making. We aim to help combat this challenge by providing a novel perspective on Explainable AI (XAI) for trustworthy decision support.This thesis revolves around three key strands of XAI – DL, Natural Language Generation (NLG) and Knowledge Graphs (KGs), which are investigated by utilising data from an operational turbine. We leverage DL and NLG to predict incipient faults and alarm events in the turbine in natural language as well as generate human-intelligible O&M strategies to assist engineers in fixing/averting the faults. We also propose specialised DL models which can predict causal relationships in SCADA features as well as quantify the importance of vital parameters leading to failures. The thesis finally culminates with an interactive Question- Answering (QA) system for automated reasoning that leverages multimodal domain-specific information from a KG, facilitating engineers to retrieve O&M strategies with natural language questions. By helping make turbines more reliable, we envisage wider adoption of wind energy sources towards tackling climate change

Towards Personalized and Human-in-the-Loop Document Summarization

The ubiquitous availability of computing devices and the widespread use of the internet have generated a large amount of data continuously. Therefore, the amount of available information on any given topic is far beyond humans' processing capacity to properly process, causing what is known as information overload. To efficiently cope with large amounts of information and generate content with significant value to users, we require identifying, merging and summarising information. Data summaries can help gather related information and collect it into a shorter format that enables answering complicated questions, gaining new insight and discovering conceptual boundaries. This thesis focuses on three main challenges to alleviate information overload using novel summarisation techniques. It further intends to facilitate the analysis of documents to support personalised information extraction. This thesis separates the research issues into four areas, covering (i) feature engineering in document summarisation, (ii) traditional static and inflexible summaries, (iii) traditional generic summarisation approaches, and (iv) the need for reference summaries. We propose novel approaches to tackle these challenges, by: i)enabling automatic intelligent feature engineering, ii) enabling flexible and interactive summarisation, iii) utilising intelligent and personalised summarisation approaches. The experimental results prove the efficiency of the proposed approaches compared to other state-of-the-art models. We further propose solutions to the information overload problem in different domains through summarisation, covering network traffic data, health data and business process data.Comment: PhD thesi

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Connecting Art and Science: An Artist’s Perspective on Environmental Sustainability

In the current climate of environmental precarity, the need to prompt ecological change becomes more than just a job for the scientific community. Looking towards culture and the arts has proven an effective way to begin addressing current environmental issues. Artists use their work to shine light on issues of environmental justice, raise awareness to environmental insecurities and risks, and imagine more sustainable futures. By combining the arts with environmental science, we are able to inspire transdisciplinary learning, thus sparking new ways of imagining and envisioning how we might live in the future. The purpose of this study was to understand the relationship between the artist, their art and the environment. Throughout the course of this work, I interviewed a variety of artists around Vermont, gathering their personal experiences creating art, and what messages they are trying to compel in regards to the environment. This project ultimately serves to provide brief artist profiles, along with a look into the way climate action can be addressed in ways other than traditionally thought

Inventing Eden: primitivism, millennialism, and the making of New England

Seventeenth-century exegetes described Eden as a three-fold paradise because they believed that Adam and Eve lived in an external garden of delight, possessed incorrupt physiologies, and enjoyed intellectual, spiritual, and social perfections before the Fall. Accordingly, the dissertation is organized thematically, treating the ways in which New England colonists sought to mold their lands, bodies, minds, language, souls, and social spheres after the pattern provided in Eden. Chapter one traces the transition of terms used to describe the New England landscape from the present paradise of John Smith to the hideous and desolate wilderness of William Bradford and the prospective Paradise of Cotton Mather. Chapter two outlines programs of physiological reform, as colonists like Anne Bradstreet disciplined their physical bodies and ministers like Edward Taylor regulated the ecclesiastical body's consumption of communion in order to achieve humoral temperance--the somatic and spiritual state of Adam and Eve in Eden. Chapters three and four document Francis Bacon's influence on educational and linguistic aspirations in New England. I argue that because the encyclopedic knowledge and divinely denotative language of Adam were believed to be inseparably linked, Leonard Hoar's plans to turn Harvard into the world's first experimental laboratory in chemistry situated at a university and John Cotton's attempt to model the language of the Bay Psalm Book after the lingua humana of Eden should be understood as related endeavors, companion contributions from New England to the Baconian project for the instauration of prelapsarian intellectual perfections. Chapter five examines the ways in which ministers of the Great Awakening presented Adam and Eve to their congregants as types of Christian conversion, and chapter six details the process by which theories of natural law distilled from Genesis became the basis for colonial rebellion and republican government through the influence of Oceana, James Harrington's vision of an idealized, edenic republic. Spanning two centuries and surveying the works of major British and American authors from George Herbert and John Milton to Jonathan Edwards and Benjamin Franklin, Inventing Eden is the history of an idea that irrevocably altered the theology, literature, and culture of early modern New England

graduate catalog 1996-1998

https://kb.gcsu.edu/catalogs/1089/thumbnail.jp

The semantic transparency of English compound nouns

What is semantic transparency, why is it important, and which factors play a role in its assessment? This work approaches these questions by investigating English compound nouns. The first part of the book gives an overview of semantic transparency in the analysis of compound nouns, discussing its role in models of morphological processing and differentiating it from related notions. After a chapter on the semantic analysis of complex nominals, it closes with a chapter on previous attempts to model semantic transparency. The second part introduces new empirical work on semantic transparency, introducing two different sets of statistical models for compound transparency. In particular, two semantic factors were explored: the semantic relations holding between compound constituents and the role of different readings of the constituents and the whole compound, operationalized in terms of meaning shifts and in terms of the distribution of specifc readings across constituent families

The semantic transparency of English compound nouns

What is semantic transparency, why is it important, and which factors play a role in its assessment? This work approaches these questions by investigating English compound nouns. The first part of the book gives an overview of semantic transparency in the analysis of compound nouns, discussing its role in models of morphological processing and differentiating it from related notions. After a chapter on the semantic analysis of complex nominals, it closes with a chapter on previous attempts to model semantic transparency. The second part introduces new empirical work on semantic transparency, introducing two different sets of statistical models for compound transparency. In particular, two semantic factors were explored: the semantic relations holding between compound constituents and the role of different readings of the constituents and the whole compound, operationalized in terms of meaning shifts and in terms of the distribution of specifc readings across constituent families. All semantic annotations used in the book are freely available