1,720,953 research outputs found
Development of Dynamic Mass-Energy-Thermodynamics Constrained Hybrid Neural Network Models for Process Systems Applications
Get PDFFirst-principles models can provide very good predictions even for cases when there are no data at all, or data are limited in certain range of operating conditions, or for cases where data collection is infeasible. However, the development of accurate first-principles models for complex nonlinear dynamic systems can be time consuming, computationally expensive, and may be infeasible for certain systems due to lack of sufficient knowledge (information). It is also challenging to adapt first-principles models for time-varying systems. Furthermore, it can be difficult, if not impossible, to develop accurate models for some complex phenomena that are poorly understood. On the contrary, black-box or data-driven models are relatively easier to develop, simulate and adapt online. Over the last few decades, numerous advantages of using data science and artificial intelligence (AI) / machine learning (ML) approaches in the field of chemical engineering have been exploited and analyzed. Neural networks (NNs) have been identified as one of the many different artificial intelligence approaches that exhibit a strong potential to modeling highly complex nonlinear dynamic systems.
This work is primarily focused on the development, training and applications of novel deterministic and stochastic dynamic physics-constrained hybrid neural network structures and hybrid first principles-NN models for data-driven modeling of complex nonlinear dynamic chemical process systems. The NN models developed and proposed as part of this research can be either implemented independently in a fully data-driven approach or be synergistically hybridized with other first-principles (physics-based) models for exploiting their key strengths. Most of the AI/ML techniques, especially neural networks, have evolved since the last few decades for a diverse range of chemical engineering applications such as data classification, fault detection and diagnosis, chemical process design, monitoring, and control. Although a plethora of different types of deep and shallow NN models have been successfully implemented for modeling various complex nonlinear transient chemical processes, the successful development of such techniques require large data sets which may not often be available for modeling various chemical engineering systems. This work develops novel architectures and training algorithms for hybrid all-nonlinear series and parallel static-dynamic NN models for applications to chemical systems. The sequential decomposition-based training algorithms thus formulated exploit the model structure, leading to independent and separate training of static and dynamic networks by different parameter estimation algorithms while still achieving optimal convergence by solving an outer layer optimization. The all-nonlinear series and parallel networks have been shown to significantly outperform typical deep recurrent neural networks, with the proposed sequential training algorithms leading to 50-100 times faster computation than simultaneous training algorithms.
One of the more recent advancements in the field of process modeling using NNs points to the augmentation of various physics conservation laws pertaining to a system while constructing optimal network models during both training and simulation. The primary motivation of such approaches stems from the fact that the measurement / experimental data available for training may not necessarily satisfy mass and energy balance equations and/or other thermodynamics / physics based constraints. If such conservation laws are not considered during machine learning, model predictions can violate system physics and hence are not meaningful. However, in almost all existing literature on physics-informed neural networks (PINNs), the physics conservation equations have been augmented in the loss function as additional penalty terms, thus serving as soft constraints without ensuring an ‘exact’ satisfaction of the conservation laws. This work focuses on the development of algorithms for exactly satisfying physics conservation laws such as mass and energy balances as well as thermodynamics constraints during both training and forward problems using noisy transient data. The mass-energy-thermodynamics constrained neural network models developed in this research are found to be very accurate capturing the system truth with minimum bias for all examples that are evaluated, even when the training data are corrupted with uncertainties.
Another alternative approach that is commonly practiced for including mechanistic (first-principles) information along with data-driven models considers the synergistic integration of first-principles (FP) and AI models leading to the construction of hybrid first-principles artificial intelligence (FP+AI) models. This work also discusses novel approaches developed for coupling FP and AI models in series, parallel, and integrated configurations, along with algorithmic capabilities to ensure that the resulting optimal models do not violate system physics even after hybridization. Additionally, the proposed hybrid first-principles machine learning approaches, when applied to commercial power plant data, demonstrate \u3e15% improved predictive accuracy compared to the corresponding standalone FP models. For modeling uncertain systems, probabilistic neural networks can be highly useful. However, synthesis of optimal hybrid networks and parameter estimation for probabilistic neural network models of dynamic uncertain systems without suffering from the ‘curse of dimensionality’ is considerably challenging. It is also challenging to satisfy physics constraints when probabilistic NNs are used. The final set of model architectures and algorithms developed in this research are aimed at optimal integration of stochastic and conventional NNs as well as efficient formulation of training algorithms for hybrid series and parallel constrained stochastic-deterministic network models. The corresponding approaches proposed in this work for construction of optimal parsimonious hybrid stochastic networks exhibit considerably superior results compared to the state-of-the-art approaches when evaluated for large-scale complex nonlinear process systems. All proposed data-driven / hybrid models and algorithms are evaluated and validated for modeling various complex nonlinear transient noisy chemical process systems
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
Author Under Sail The Imagination of Jack London, 1893-1902
No full textIn Author Under Sail, Jay Williams offers the first complete literary biography of Jack London as a professional writer engaged in the labor of writing. It examines the authorial imagination in London's work, the use of imagination in both his fiction and nonfiction, and the ways he defined imagination in the creative process in his business dealings with his publishers, editors, and agents. In this first volume of a two-volume biography, Williams traverses the years 1893 to 1902, from London's "Story of a Typhoon" to The People of the Abyss. The Jack London who emerges in the pages of Author Under Sail is a writer whose partnership with publishers, most notably his productive alliance with George Brett of Macmillan, was one of the most formative in American literary history. London pioneered many author models during the heyday of realism and naturalism, blurring the boundaries of these popular genres by focusing on absorption and theatricality and the representation of the seen and unseen. London created an impassioned, sincere, and extremely personal realism unlike that of other American writers of the time. Author Under Sail is a literary tour de force that reveals the full range of London as writer, creative citizen, and entrepreneur at the same time it sheds light on the maverick side of machine-age literature.Intro -- Title Page -- Copyright Page -- Dedication -- Contents -- Acknowledgments -- Introduction -- 1. Spirit Truth -- 2. From Absorption to Theatricality and Back Again -- 3. "I Will Build a New Present" -- 4. Sons as Authors -- 5. Fathers as Publishers -- 6. The Daughter as Author -- 7. Lovers as Authors -- 8. At Sea with the Family -- 9. Yellow News, Yellow Stories -- 10. The Return Home -- Notes -- Bibliography -- Index -- About Jay WilliamsIn Author Under Sail, Jay Williams offers the first complete literary biography of Jack London as a professional writer engaged in the labor of writing. It examines the authorial imagination in London's work, the use of imagination in both his fiction and nonfiction, and the ways he defined imagination in the creative process in his business dealings with his publishers, editors, and agents. In this first volume of a two-volume biography, Williams traverses the years 1893 to 1902, from London's "Story of a Typhoon" to The People of the Abyss. The Jack London who emerges in the pages of Author Under Sail is a writer whose partnership with publishers, most notably his productive alliance with George Brett of Macmillan, was one of the most formative in American literary history. London pioneered many author models during the heyday of realism and naturalism, blurring the boundaries of these popular genres by focusing on absorption and theatricality and the representation of the seen and unseen. London created an impassioned, sincere, and extremely personal realism unlike that of other American writers of the time. Author Under Sail is a literary tour de force that reveals the full range of London as writer, creative citizen, and entrepreneur at the same time it sheds light on the maverick side of machine-age literature.Description based on publisher supplied metadata and other sources.Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, YYYY. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries
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