7,506 research outputs found
Supporting Information
No full textIntermediate process data for calculation of multi-scale pore size distribution curves
The utlized data in article "Assessment of multi-scale pore structures and pore connectivity of marine shales based on fractal dimensions and connectivity probability of digital cores "
No full textIt includes the original data counted by CTSTA program and produced data for the computation of pore size distribution curve
Data for multi-scale PSD curve calculation
No full textThe produced and utilized data in artical "Assessment of multi-scale pore structures and pore connectivity of marine shales based on fractal dimensions and connectivity probability of digital cores
Supporting Information
No full textAnalysis data ana intermediate process data for computing multi-scale pore size distribution (PSD) curves for Sample W23 and J24
The utlized data in article "Assessment of multi-scale pore structures and pore connectivity of marine shales based on fractal dimensions and connectivity probability of digital cores ".
No full textThe dataset includes the orignal data counted by CTSTA program and produced data for the calculation of pore size distribution curves
Using performance assessment in secondary school mathematics: an empirical study in a Singapore classroom
No full textThis article reports an exploratory study on using performance assessment in mathematics instruction in a high-performing secondary school in Singapore. An intact mathematics class participated in the study, and received chapter-based performance tasks as intervention during regular mathematics lessons for about one and a half school years. The performance tasks used included authentic and/or open-ended tasks. The students’ academic achievements and attitudes in mathematics were compared with a comparison class that did not receive the intervention. Both quantitative and qualitative data were collected, mainly through questionnaire surveys, performance task tests, conventional school exams, and interviews with students and teachers. The results suggest that the students receiving the intervention performed significantly better than their counterparts in solving conventional exam problems, and in general they also showed more positive changes in attitudes towards mathematics and mathematics learning. The students from the experimental class also expressed positive views about the benefits of using performance tasks in promoting their ability in higher order thinking, though no statistically significant difference was detected between the two classes of students in solving unconventional tasks before and after intervention. Overall, the results appear to support teachers’ using contextualised problems in real life situations and open-ended investigations in students’ learning of mathematic
Richardson, Barbauld, and the construction of an early modern fan club
No full textMPhilMuch has been written about the life and long works of the eighteenth century epistolary novelist, Samuel Richardson, but the prospect of his position as the first celebrity novelist – responsible for courting his own fame as well as initiating his own fan club – has largely been ignored. The body of manuscripts housed at the National Art Library in the Victoria and Albert Museum in London provides the modern scholar with evidence of the skeletal beginnings of an early fan club. This thesis aims to show how these manuscripts were turned into a saleable commodity by the publisher and entrepreneur Richard Phillips, while under the guiding hand of another, slightly later, literary celebrity, Anna Laetitia Barbauld. In order to restore Richardson’s reputation amongst a new nineteenth century audience, Barbauld was required to construct her own idea of him as an eighteenth century celebrity author, and in doing so the insecurities of a self-professed, apparently diffident man, are revealed. Barbauld’s capacious, but heavily edited selection of letters is analyzed in this thesis, providing ample evidence that Richardson’s correspondents were more than just eager letter writers. By using Barbauld’s biography of Richardson this thesis aims to show how she manipulates the genre of life writing in her construction of him.
This thesis offers an alternative reading of how the Richardson manuscripts are viewed, redefining them as not simply a collection of letters, but as a collective entity, deliberately selected and archived as evidence of an early modern fan club, and its celebrity managing director
Sparse representation in deep vision models
No full textSubmission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-12-01The student, Yuchen Fan, accepted the attached license on 2021-12-03 at 15:51.The student, Yuchen Fan, submitted this Dissertation for approval on 2021-12-03 at 15:58.This Dissertation was approved for publication on 2021-12-03 at 16:06.DSpace SAF Submission Ingestion Package generated from Vireo submission #17388 on 2022-04-06 at 17:17:54Made available in DSpace on 2022-04-29T21:46:19Z (GMT). No. of bitstreams: 2
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Previous issue date: 2021-12-03Embargo set by: Seth Robbins for item 123372
Lift date: 2024-04-29T21:46:25Z
Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemEmbargo set by: Seth Robbins for item 123372
Lift date: 2024-04-29T21:47:53Z
Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemAuthor requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemU of I OnlySparse representation plays a critical role in vision problems, including generation and understanding. Image generation tasks are inherently ill-posed, where the input signal usually has insufficient information while the output has infinitely many solutions w.r.t. the same input. Thus, it is commonly believed that sparse representation is more robust to handle the considerable diversity of solutions. Image understanding also depends on invariant and robust sparse representation for various transformations, e.g., color, lighting, viewpoint, etc.
Deep neural networks extend the sparse coding-based methods from linear structure to cascaded linear and non-linear structures. However, sparsity of hidden representation in deep neural networks cannot be solved by iterative optimization as sparse coding, since deep networks are feed-forward during inference. I invented a method that can structurally enforce sparsity constraints upon hidden neurons in deep networks but also keep representation in high dimensionality. Given high-dimensional neurons, I divide them into groups along channels and allow only one group of neurons to be non-zero each time. The adaptive selection of the non-sparse group is modeled by tiny side networks upon context features. And computation is also saved when only performed on the non-zero group.
I further extended the sparse constraints to an attention mechanism. Attention mechanism is built upon paired correlation between any two pixels and needs quadratic computation cost respecting to the input size. This mutual correlation is inherently sparse, since pixels in a single image are not necessary highly correlated to most of other pixels. I proposed a method to achieve more efficient computation of attention mechanism given the sparse prior of correlation matrix.
I also investigated the sparse scene representation modeled with deep neural networks. With sparsely rendered views of a 3D scene, the proposed deep neural network approach performs spatiotemporal reconstruction of high-definition images from a novel viewpoint efficiently
A computer vision approach to improve maintenance automation for thermal power plants lubrication systems
No full textPurpose – Lubricating oil leakage is a common issue in thermal power plant operation sites, requiring prompt
equipment maintenance. The real-time detection of leakage occurrences could avoid disruptive consequences
caused by the lack of timely maintenance. Currently, inspection operations are mostly carried out manually,
resulting in time-consuming processes prone to health and safety hazards. To overcome such issues, this paper
proposes a machine vision-based inspection system aimed at automating the oil leakage detection for
improving the maintenance procedures.
Design/methodology/approach – The approach aims at developing a novel modular-structured automatic
inspection system. The image acquisition module collects digital images along a predefined inspection path
using a dual-light (i.e. ultraviolet and blue light) illumination system, deploying the fluorescence of the
lubricating oil while suppressing unwanted background noise. The image processing module is designed to
detect the oil leakage within the digital images minimizing detection errors. A case study is reported to validate
the industrial suitability of the proposed inspection system.
Findings – On-site experimental results demonstrate the capabilities to complete the automatic inspection
procedures of the tested industrial equipment by achieving an oil leakage detection accuracy up to 99.13%.
Practical implications – The proposed inspection system can be adopted in industrial context to detect
lubricant leakage ensuring the equipment and the operators safety.
Originality/value – The proposed inspection system adopts a computer vision approach, which deploys the
combination of two separate sources of light, to boost the detection capabilities, enabling the application for a
variety of particularly hard-to-inspect industrial contexts
Intelligent robot assistants for the integration of neurodiverse operators in manufacturing industry
No full textThe integration of neurodiverse operators in industry poses interesting challenges highlighting the potential for human-robot collaboration(HRC) to provide the necessary support and assistance to complete manufacturing tasks. This study proposes a reciprocal learning-based framework to support the operator to carry out assembly tasks via collaborative robot (Cobot) assistance. Such framework includes image acquisition and processing, machine learning (ML)-based classification of workpieces and Cobot operational tasks. An experimental case study is proposed to verify the framework applicability to a number of likely scenarios. Preliminary results show the suitability of HRC systems to effectively aid neurodiverse operator's with memory issues in performing assembly tasks correctly, while at the same time improving the operator's ability to learn the assembly sequence and iteratively improving ML classification accuracy. The potential for intelligent robotics-based increased inclusiveness in manufacturing industry is discussed in terms of benefits to support individuals with cognitive disabilities
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