1,555 research outputs found

    Explainable AI models for predicting drop coalescence in microfluidics device

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    In the field of chemical engineering, understanding the dynamics and probability of drop coalescence is not just an academic pursuit, but a critical requirement for advancing process design by applying energy only where it is needed to build necessary interfacial structures, increasing efficiency towards Net Zero manufacture. This research applies machine learning predictive models to unravel the sophisticated relationships embedded in the experimental data on drop coalescence in a microfluidics device. Through the deployment of SHapley Additive exPlanations values, critical features relevant to coalescence processes are consistently identified. Comprehensive feature ablation tests further delineate the robustness and susceptibility of each model. Furthermore, the incorporation of Local Interpretable Model-agnostic Explanations for local interpretability offers an elucidative perspective, clarifying the intricate decision-making mechanisms inherent to each model’s predictions. As a result, this research provides the relative importance of the features for the outcome of drop interactions. It also underscores the pivotal role of model interpretability in reinforcing confidence in machine learning predictions of complex physical phenomena that are central to chemical engineering applications

    Climate Change and Critical Agrarian Studies

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    Climate change is perhaps the greatest threat to humanity today and plays out as a cruel engine of myriad forms of injustice, violence and destruction. The effects of climate change from human-made emissions of greenhouse gases are devastating and accelerating; yet are uncertain and uneven both in terms of geography and socio-economic impacts. Emerging from the dynamics of capitalism since the industrial revolution — as well as industrialisation under state-led socialism — the consequences of climate change are especially profound for the countryside and its inhabitants. The book interrogates the narratives and strategies that frame climate change and examines the institutionalised responses in agrarian settings, highlighting what exclusions and inclusions result. It explores how different people — in relation to class and other co-constituted axes of social difference such as gender, race, ethnicity, age and occupation — are affected by climate change, as well as the climate adaptation and mitigation responses being implemented in rural areas. The book in turn explores how climate change – and the responses to it - affect processes of social differentiation, trajectories of accumulation and in turn agrarian politics. Finally, the book examines what strategies are required to confront climate change, and the underlying political-economic dynamics that cause it, reflecting on what this means for agrarian struggles across the world. The 26 chapters in this volume explore how the relationship between capitalism and climate change plays out in the rural world and, in particular, the way agrarian struggles connect with the huge challenge of climate change. Through a huge variety of case studies alongside more conceptual chapters, the book makes the often-missing connection between climate change and critical agrarian studies. The book argues that making the connection between climate and agrarian justice is crucial

    Pre-processing training data improves accuracy and generalisability of convolutional neural network based landscape semantic segmentation

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    In this paper, we trialled different methods of data preparation for Convolutional Neural Network (CNN) training and semantic segmentation of land use land cover (LULC) features within aerial photography over the Wet Tropics and Atherton Tablelands, Queensland, Australia. This was conducted through trialling and ranking various training patch selection sampling strategies, patch and batch sizes and data augmentations and scaling. We also compared model accuracy through producing the LULC classification using a single pass of a grid of patches and averaging multiple grid passes and three rotated version of each patch. Our results showed: a stratified random sampling approach for producing training patches improved the accuracy of classes with a smaller area while having minimal effect on larger classes; a smaller number of larger patches compared to a larger number of smaller patches improves model accuracy; applying data augmentations and scaling are imperative in creating a generalised model able to accurately classify LULC features in imagery from a different date and sensor; and producing the output classification by averaging multiple grids of patches and three rotated versions of each patch produced and more accurate and aesthetic result. Combining the findings from the trials, we fully trained five models on the 2018 training image and applied the model to the 2015 test image with the output LULC classifications achieving an average kappa of 0.84 user accuracy of 0.81 and producer accuracy of 0.87. This study has demonstrated the importance of data pre-processing for developing a generalised deep-learning model for LULC classification which can be applied to a different date and sensor. Future research using CNN and earth observation data should implement the findings of this study to increase LULC model accuracy and transferability

    Image-based Decision Support Systems: Technical Concepts, Design Knowledge, and Applications for Sustainability

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    Unstructured data accounts for 80-90% of all data generated, with image data contributing its largest portion. In recent years, the field of computer vision, fueled by deep learning techniques, has made significant advances in exploiting this data to generate value. However, often computer vision models are not sufficient for value creation. In these cases, image-based decision support systems (IB-DSSs), i.e., decision support systems that rely on images and computer vision, can be used to create value by combining human and artificial intelligence. Despite its potential, there is only little work on IB-DSSs so far. In this thesis, we develop technical foundations and design knowledge for IBDSSs and demonstrate the possible positive effect of IB-DSSs on environmental sustainability. The theoretical contributions of this work are based on and evaluated in a series of artifacts in practical use cases: First, we use technical experiments to demonstrate the feasibility of innovative approaches to exploit images for IBDSSs. We show the feasibility of deep-learning-based computer vision and identify future research opportunities based on one of our practical use cases. Building on this, we develop and evaluate a novel approach for combining human and artificial intelligence for value creation from image data. Second, we develop design knowledge that can serve as a blueprint for future IB-DSSs. We perform two design science research studies to formulate generalizable principles for purposeful design — one for IB-DSSs and one for the subclass of image-mining-based decision support systems (IM-DSSs). While IB-DSSs can provide decision support based on single images, IM-DSSs are suitable when large amounts of image data are available and required for decision-making. Third, we demonstrate the viability of applying IBDSSs to enhance environmental sustainability by performing life cycle assessments for two practical use cases — one in which the IB-DSS enables a prolonged product lifetime and one in which the IB-DSS facilitates an improvement of manufacturing processes. We hope this thesis will contribute to expand the use and effectiveness of imagebased decision support systems in practice and will provide directions for future research

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well

    Blending the Material and Digital World for Hybrid Interfaces

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    The development of digital technologies in the 21st century is progressing continuously and new device classes such as tablets, smartphones or smartwatches are finding their way into our everyday lives. However, this development also poses problems, as these prevailing touch and gestural interfaces often lack tangibility, take little account of haptic qualities and therefore require full attention from their users. Compared to traditional tools and analog interfaces, the human skills to experience and manipulate material in its natural environment and context remain unexploited. To combine the best of both, a key question is how it is possible to blend the material world and digital world to design and realize novel hybrid interfaces in a meaningful way. Research on Tangible User Interfaces (TUIs) investigates the coupling between physical objects and virtual data. In contrast, hybrid interfaces, which specifically aim to digitally enrich analog artifacts of everyday work, have not yet been sufficiently researched and systematically discussed. Therefore, this doctoral thesis rethinks how user interfaces can provide useful digital functionality while maintaining their physical properties and familiar patterns of use in the real world. However, the development of such hybrid interfaces raises overarching research questions about the design: Which kind of physical interfaces are worth exploring? What type of digital enhancement will improve existing interfaces? How can hybrid interfaces retain their physical properties while enabling new digital functions? What are suitable methods to explore different design? And how to support technology-enthusiast users in prototyping? For a systematic investigation, the thesis builds on a design-oriented, exploratory and iterative development process using digital fabrication methods and novel materials. As a main contribution, four specific research projects are presented that apply and discuss different visual and interactive augmentation principles along real-world applications. The applications range from digitally-enhanced paper, interactive cords over visual watch strap extensions to novel prototyping tools for smart garments. While almost all of them integrate visual feedback and haptic input, none of them are built on rigid, rectangular pixel screens or use standard input modalities, as they all aim to reveal new design approaches. The dissertation shows how valuable it can be to rethink familiar, analog applications while thoughtfully extending them digitally. Finally, this thesis’ extensive work of engineering versatile research platforms is accompanied by overarching conceptual work, user evaluations and technical experiments, as well as literature reviews.Die Durchdringung digitaler Technologien im 21. Jahrhundert schreitet stetig voran und neue Geräteklassen wie Tablets, Smartphones oder Smartwatches erobern unseren Alltag. Diese Entwicklung birgt aber auch Probleme, denn die vorherrschenden berührungsempfindlichen Oberflächen berücksichtigen kaum haptische Qualitäten und erfordern daher die volle Aufmerksamkeit ihrer Nutzer:innen. Im Vergleich zu traditionellen Werkzeugen und analogen Schnittstellen bleiben die menschlichen Fähigkeiten ungenutzt, die Umwelt mit allen Sinnen zu begreifen und wahrzunehmen. Um das Beste aus beiden Welten zu vereinen, stellt sich daher die Frage, wie neuartige hybride Schnittstellen sinnvoll gestaltet und realisiert werden können, um die materielle und die digitale Welt zu verschmelzen. In der Forschung zu Tangible User Interfaces (TUIs) wird die Verbindung zwischen physischen Objekten und virtuellen Daten untersucht. Noch nicht ausreichend erforscht wurden hingegen hybride Schnittstellen, die speziell darauf abzielen, physische Gegenstände des Alltags digital zu erweitern und anhand geeigneter Designparameter und Entwurfsräume systematisch zu untersuchen. In dieser Dissertation wird daher untersucht, wie Materialität und Digitalität nahtlos ineinander übergehen können. Es soll erforscht werden, wie künftige Benutzungsschnittstellen nützliche digitale Funktionen bereitstellen können, ohne ihre physischen Eigenschaften und vertrauten Nutzungsmuster in der realen Welt zu verlieren. Die Entwicklung solcher hybriden Ansätze wirft jedoch übergreifende Forschungsfragen zum Design auf: Welche Arten von physischen Schnittstellen sind es wert, betrachtet zu werden? Welche Art von digitaler Erweiterung verbessert das Bestehende? Wie können hybride Konzepte ihre physischen Eigenschaften beibehalten und gleichzeitig neue digitale Funktionen ermöglichen? Was sind geeignete Methoden, um verschiedene Designs zu erforschen? Wie kann man Technologiebegeisterte bei der Erstellung von Prototypen unterstützen? Für eine systematische Untersuchung stützt sich die Arbeit auf einen designorientierten, explorativen und iterativen Entwicklungsprozess unter Verwendung digitaler Fabrikationsmethoden und neuartiger Materialien. Im Hauptteil werden vier Forschungsprojekte vorgestellt, die verschiedene visuelle und interaktive Prinzipien entlang realer Anwendungen diskutieren. Die Szenarien reichen von digital angereichertem Papier, interaktiven Kordeln über visuelle Erweiterungen von Uhrarmbändern bis hin zu neuartigen Prototyping-Tools für intelligente Kleidungsstücke. Um neue Designansätze aufzuzeigen, integrieren nahezu alle visuelles Feedback und haptische Eingaben, um Alternativen zu Standard-Eingabemodalitäten auf starren Pixelbildschirmen zu schaffen. Die Dissertation hat gezeigt, wie wertvoll es sein kann, bekannte, analoge Anwendungen zu überdenken und sie dabei gleichzeitig mit Bedacht digital zu erweitern. Dabei umfasst die vorliegende Arbeit sowohl realisierte technische Forschungsplattformen als auch übergreifende konzeptionelle Arbeiten, Nutzerstudien und technische Experimente sowie die Analyse existierender Forschungsarbeiten

    Sensing Collectives: Aesthetic and Political Practices Intertwined

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    Are aesthetics and politics really two different things? The book takes a new look at how they intertwine, by turning from theory to practice. Case studies trace how sensory experiences are created and how collective interests are shaped. They investigate how aesthetics and politics are entangled, both in building and disrupting collective orders, in governance and innovation. This ranges from populist rallies and artistic activism over alternative lifestyles and consumer culture to corporate PR and governmental policies. Authors are academics and artists. The result is a new mapping of the intermingling and co-constitution of aesthetics and politics in engagements with collective orders

    Digital agriculture: research, development and innovation in production chains.

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    Digital transformation in the field towards sustainable and smart agriculture. Digital agriculture: definitions and technologies. Agroenvironmental modeling and the digital transformation of agriculture. Geotechnologies in digital agriculture. Scientific computing in agriculture. Computer vision applied to agriculture. Technologies developed in precision agriculture. Information engineering: contributions to digital agriculture. DIPN: a dictionary of the internal proteins nanoenvironments and their potential for transformation into agricultural assets. Applications of bioinformatics in agriculture. Genomics applied to climate change: biotechnology for digital agriculture. Innovation ecosystem in agriculture: Embrapa?s evolution and contributions. The law related to the digitization of agriculture. Innovating communication in the age of digital agriculture. Driving forces for Brazilian agriculture in the next decade: implications for digital agriculture. Challenges, trends and opportunities in digital agriculture in Brazil

    Revealing More Details: Image Super-Resolution for Real-World Applications

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