4,775 research outputs found
Olga Koubrak: Protecting the Caribbean Sawfish
No full textStudent editor Patrick Sheppard sits down with Professor Olga Koubrak of the Schulich School of Law to discuss her work on the legal frameworks to protect sawfish in the Caribbean. Olga is the author of a 2018 paper titled “A Future for a Forgotten Predator: An Assessment of International Legal Frameworks for Protection and Recovery of the Caribbean Sawfishes,” and co-author of the more recent 2022 article titled “Strengthening Marine Species Protections in Cuba: A Case Study on the Critically Endangered Smalltooth Sawfish.” Patrick and Olga discuss the sawfish, means of protecting the animal domestically and internationally, problems in enforcement and international cooperation, and how the public perception of an animal affects how it is protected by authorities. To learn more about Olga and her work, check out her website at www.sealifelaw.org
The metaphysics of death in prose of Olga Tokarczuk
No full textThe article presents and analyses the motive of death in the works of Olga Tokarczuk. The author focuses on anthropological and philosophic grasp of that category in her narrative prose. The text included here is a fragment of one of the chapters of author’s doctoral thesis entitled: The metaphysics of death, time and love in the works of Olga Tokarczuk
The metaphysics of death in prose of Olga Tokarczuk
No full textThe article presents and analyses the motive of death in the works of Olga Tokarczuk. The
author focuses on anthropological and philosophic grasp of that category in her narrative prose.
The text included here is a fragment of one of the chapters of author’s doctoral thesis entitled: The metaphysics of death, time and love in the works of Olga Tokarczuk.Zadanie pt. „Digitalizacja i udostępnienie w Cyfrowym Repozytorium Uniwersytetu Łódzkiego kolekcji czasopism naukowych wydawanych przez Uniwersytet Łódzki” nr 885/P-DUN/2014 zostało dofinansowane ze środków MNiSW w ramach działalności upowszechniającej nauk
Ageing-aware battery discharge prediction with deep learning
No full textISSN:0306-2619ISSN:1872-9118ISSN:1872-911
The space in the literary work of Olga Tokarczuk
No full textThe article presents and analyses types of space existing in the literary output of Olga Tokarczuk. The author focuses on exploring two triple divisions of this phenomenon. First division deals with an area understood as both open and closed sites, and objects. The second division distinguishes realistic space (specific events and places), internal (a hero’s psychology and a relationship between a human being and a place) and mythical (placing reality in myth)
Canonical Polyadic Decomposition and Deep Learning for Machine Fault Detection
Full text linkAcoustic monitoring for machine fault detection is a recent and expanding
research path that has already provided promising results for industries.
However, it is impossible to collect enough data to learn all types of faults
from a machine. Thus, new algorithms, trained using data from healthy
conditions only, were developed to perform unsupervised anomaly detection. A
key issue in the development of these algorithms is the noise in the signals,
as it impacts the anomaly detection performance. In this work, we propose a
powerful data-driven and quasi non-parametric denoising strategy for spectral
data based on a tensor decomposition: the Non-negative Canonical Polyadic (CP)
decomposition. This method is particularly adapted for machine emitting
stationary sound. We demonstrate in a case study, the Malfunctioning Industrial
Machine Investigation and Inspection (MIMII) baseline, how the use of our
denoising strategy leads to a sensible improvement of the unsupervised anomaly
detection. Such approaches are capable to make sound-based monitoring of
industrial processes more reliable.Comment: 9 pages, 5 figures, conference paper from PHM Society European
Conference 2021 (Vol. 6, No. 1
Olga Stychin and Nancy Appleby
No full textPhotograph - Olga Stychin and Nancy Appleby on a berry picking trip, Athabasca, Albert
Olga Stychin and Alice B. Donahue
No full textPhotograph - Olga Stychin and Alice B. Donahue on a berry picking trip, Athabasca, Albert
Exploiting Explanations to Detect Misclassifications of Deep Learning Models in Power Grid Visual Inspection
Full text linkUncertainty-Aware Prognosis via Deep Gaussian Process
No full textThe task of predicting how long a certain industrial asset will be able to operate within its nominal specifications is called Remaining Useful Life (RUL) estimation. Efficient methods of performing this task promise to drastically transform the world of industrial maintenance, paving the way for the so-called Industry 4.0 revolution. Given the abundance of data resulting from the advent of the digitalization era, Machine Learning (ML) models are the ideal candidates for tackling the RUL estimation problem in a fully data-driven fashion. However, given the safety-critical nature of maintenance operations on industrial assets, it’s crucial that such ML-based methods be designed such that their levels of transparency and reliability are maximized. Modern ML algorithms, however, are often employed as black-box methods, which do not provide any clue regarding the confidence level associated with their output. In this paper, we address this limitation by investigating the performance of a recently proposed class of algorithms, Deep Gaussian Processes, which provide uncertainty estimates associated with their RUL prediction, yet retain the expressive power of modern ML techniques. Contrary to standard approaches to uncertainty quantification, such methods scale favourably with the size of the available datasets, allowing their usage in the “big data” setting. We perform a thorough evaluation and comparison of several variants of DGPs applied to RUL predictions. The performance of the algorithms is evaluated on the NASA N-CMAPSS (New Commercial Modular Aero-Propulsion System Simulation) dataset for aircraft engines. The results show that the proposed methods are able to yield very accurate RUL predictions along with sensible uncertainty estimates, providing more reliable solutions for (safety-critical) real-life industrial applications
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