2,714 research outputs found

    Volumetric Techniques for Product Routing and Loading Optimisation in Industry 4.0: A Review

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    Industry 4.0 has become a crucial part in the majority of processes, components, and related modelling, as well as predictive tools that allow a more efficient, automated and sustainable approach to industry. The availability of large quantities of data, and the advances in IoT, AI, and data-driven frameworks, have led to an enhanced data gathering, assessment, and extraction of actionable information, resulting in a better decision-making process. Product picking and its subsequent packing is an important area, and has drawn increasing attention for the research community. However, depending of the context, some of the related approaches tend to be either highly mathematical, or applied to a specific context. This article aims to provide a survey on the main methods, techniques, and frameworks relevant to product packing and to highlight the main properties and features that should be further investigated to ensure a more efficient and optimised approach

    Self-supervised learning for transferable representations

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    Machine learning has undeniably achieved remarkable advances thanks to large labelled datasets and supervised learning. However, this progress is constrained by the labour-intensive annotation process. It is not feasible to generate extensive labelled datasets for every problem we aim to address. Consequently, there has been a notable shift in recent times toward approaches that solely leverage raw data. Among these, self-supervised learning has emerged as a particularly powerful approach, offering scalability to massive datasets and showcasing considerable potential for effective knowledge transfer. This thesis investigates self-supervised representation learning with a strong focus on computer vision applications. We provide a comprehensive survey of self-supervised methods across various modalities, introducing a taxonomy that categorises them into four distinct families while also highlighting practical considerations for real-world implementation. Our focus thenceforth is on the computer vision modality, where we perform a comprehensive benchmark evaluation of state-of-the-art self supervised models against many diverse downstream transfer tasks. Our findings reveal that self-supervised models often outperform supervised learning across a spectrum of tasks, albeit with correlations weakening as tasks transition beyond classification, particularly for datasets with distribution shifts. Digging deeper, we investigate the influence of data augmentation on the transferability of contrastive learners, uncovering a trade-off between spatial and appearance-based invariances that generalise to real-world transformations. This begins to explain the differing empirical performances achieved by self-supervised learners on different downstream tasks, and it showcases the advantages of specialised representations produced with tailored augmentation. Finally, we introduce a novel self-supervised pre-training algorithm for object detection, aligning pre-training with downstream architecture and objectives, leading to reduced localisation errors and improved label efficiency. In conclusion, this thesis contributes a comprehensive understanding of self-supervised representation learning and its role in enabling effective transfer across computer vision tasks

    Neural Architecture Search for Image Segmentation and Classification

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    Deep learning (DL) is a class of machine learning algorithms that relies on deep neural networks (DNNs) for computations. Unlike traditional machine learning algorithms, DL can learn from raw data directly and effectively. Hence, DL has been successfully applied to tackle many real-world problems. When applying DL to a given problem, the primary task is designing the optimum DNN. This task relies heavily on human expertise, is time-consuming, and requires many trial-and-error experiments. This thesis aims to automate the laborious task of designing the optimum DNN by exploring the neural architecture search (NAS) approach. Here, we propose two new NAS algorithms for two real-world problems: pedestrian lane detection for assistive navigation and hyperspectral image segmentation for biosecurity scanning. Additionally, we also introduce a new dataset-agnostic predictor of neural network performance, which can be used to speed-up NAS algorithms that require the evaluation of candidate DNNs

    La traduzione specializzata all’opera per una piccola impresa in espansione: la mia esperienza di internazionalizzazione in cinese di Bioretics© S.r.l.

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    Global markets are currently immersed in two all-encompassing and unstoppable processes: internationalization and globalization. While the former pushes companies to look beyond the borders of their country of origin to forge relationships with foreign trading partners, the latter fosters the standardization in all countries, by reducing spatiotemporal distances and breaking down geographical, political, economic and socio-cultural barriers. In recent decades, another domain has appeared to propel these unifying drives: Artificial Intelligence, together with its high technologies aiming to implement human cognitive abilities in machinery. The “Language Toolkit – Le lingue straniere al servizio dell’internazionalizzazione dell’impresa” project, promoted by the Department of Interpreting and Translation (ForlĂŹ Campus) in collaboration with the Romagna Chamber of Commerce (ForlĂŹ-Cesena and Rimini), seeks to help Italian SMEs make their way into the global market. It is precisely within this project that this dissertation has been conceived. Indeed, its purpose is to present the translation and localization project from English into Chinese of a series of texts produced by Bioretics© S.r.l.: an investor deck, the company website and part of the installation and use manual of the Aliquis© framework software, its flagship product. This dissertation is structured as follows: Chapter 1 presents the project and the company in detail; Chapter 2 outlines the internationalization and globalization processes and the Artificial Intelligence market both in Italy and in China; Chapter 3 provides the theoretical foundations for every aspect related to Specialized Translation, including website localization; Chapter 4 describes the resources and tools used to perform the translations; Chapter 5 proposes an analysis of the source texts; Chapter 6 is a commentary on translation strategies and choices

    DRAGON: A Dialogue-Based Robot for Assistive Navigation with Visual Language Grounding

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    Persons with visual impairments (PwVI) have difficulties understanding and navigating spaces around them. Current wayfinding technologies either focus solely on navigation or provide limited communication about the environment. Motivated by recent advances in visual-language grounding and semantic navigation, we propose DRAGON, a guiding robot powered by a dialogue system and the ability to associate the environment with natural language. By understanding the commands from the user, DRAGON is able to guide the user to the desired landmarks on the map, describe the environment, and answer questions from visual observations. Through effective utilization of dialogue, the robot can ground the user's free-form descriptions to landmarks in the environment, and give the user semantic information through spoken language. We conduct a user study with blindfolded participants in an everyday indoor environment. Our results demonstrate that DRAGON is able to communicate with the user smoothly, provide a good guiding experience, and connect users with their surrounding environment in an intuitive manner.Comment: Webpage and videos are at https://sites.google.com/view/dragon-wayfinding/hom

    DoReMi: Grounding Language Model by Detecting and Recovering from Plan-Execution Misalignment

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    Large language models encode a vast amount of semantic knowledge and possess remarkable understanding and reasoning capabilities. Previous research has explored how to ground language models in robotic tasks to ensure that the sequences generated by the language model are both logically correct and practically executable. However, low-level execution may deviate from the high-level plan due to environmental perturbations or imperfect controller design. In this paper, we propose DoReMi, a novel language model grounding framework that enables immediate Detection and Recovery from Misalignments between plan and execution. Specifically, LLMs are leveraged for both planning and generating constraints for planned steps. These constraints can indicate plan-execution misalignments and we use a vision question answering (VQA) model to check constraints during low-level skill execution. If certain misalignment occurs, our method will call the language model to re-plan in order to recover from misalignments. Experiments on various complex tasks including robot arms and humanoid robots demonstrate that our method can lead to higher task success rates and shorter task completion times. Videos of DoReMi are available at https://sites.google.com/view/doremi-paper.Comment: 21 pages, 13 figure

    Algorithms for light applications: from theoretical simulations to prototyping

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    [eng] Although the first LED dates to the middle of the 20th century, it has not been until the last decade that the market has been flooded with high efficiency and high durability LED solutions compared to previous technologies. In addition, luminaires that include types of LEDs differentiated in hue or color have already appeared. These luminaires offer new possibilities to reach colorimetric or non-visual capabilities not seen to date. Due to the enormous number of LEDs on the market, with very different spectral characteristics, the use of the spectrometer as a measuring device for determining LEDs properties has become popular. Obtaining colorimetric information from a luminaire is a necessary step to commercialize it, so it is a tool commonly used by many LED manufacturers. This doctoral thesis advances the state-of-the-art and knowledge of LED technology at the level of combined spectral emission, as well as applying innovative spectral reconstruction techniques to a commercial multichannel colorimetric sensor. On the one hand, new spectral simulation algorithms that allow obtaining a very high number of results have been developed, being able to obtain optimized values of colorimetric and non-visual parameters in multichannel light sources. MareNostrum supercomputer has been used and new relationships between colorimetric and non-visual parameters in commercial white LED datasets have been found through data analysis. Moreover, the functional improvement of a multichannel colorimetric sensor has been explored by providing it with a neural network for spectral reconstruction. A large amount of data has been generated, which has allowed simulations and statistical studies on the error committed in the spectral reconstruction process using different techniques. This improvement has led to an increase in the spectral resolution measured by the sensor, allowing better accuracy in the calculation of colorimetric parameters. Prototypes of the light sources and the colorimetric sensor have been developed in order to experimentally demonstrate the theoretical framework generated. All the prototypes have been characterized and the errors generated with respect to the theoretical models have been evaluated. The results obtained have been validated through the application of different industry standards by comparison with calibrated commercial devices.[cat] Aquesta tesi doctoral realitza un avançament en l’estat de l’art i en el coneixement sobre la tecnologia LED a nivell d’emissiĂł espectral combinada, a mĂ©s d’aplicar tĂšcniques innovadores de reconstrucciĂł espectral a un sensor colorimĂštric multicanal comercial. Per una banda, s’han desenvolupat nous algoritmes de simulaciĂł espectral que permeten obtenir un nombre molt elevat de resultats, sent capaços d’obtenir valors optimitzats de parĂ metres colorimĂštrics i no-visuals en fonts de llum multicanal. S’ha fet Ășs del supercomputador MareNostrum i s’han trobat noves relacions entre parĂ metres colorimĂštrics i no visuals en conjunts de LEDs blancs comercials a travĂ©s de l’anĂ lisi de dades. Per altra banda, s’ha explorat la millora funcional d’un sensor colorimĂštric multicanal, dotant-lo d’una xarxa neuronal per a la reconstrucciĂł espectral. S’han generat una gran quantitat de dades que han permĂšs realitzar simulacions i estudis estadĂ­stics sobre l’error comĂšs en el procĂ©s de reconstrucciĂł espectral utilitzant diferents tĂšcniques. Aquesta millora ha implicat un augment de la resoluciĂł espectral mesurada pel sensor, permetent obtenir una millor precisiĂł en el cĂ lcul de parĂ metres colorimĂštrics. S’han desenvolupat prototips de les fonts de llum i del sensor colorimĂštric amb l’objectiu de demostrar experimentalment el marc teĂČric generat. Tots els prototips han estat caracteritzats i s’han avaluat els errors generats respecte els models teĂČrics. Els resultats obtinguts s’han validat a travĂ©s de l’aplicaciĂł de diferents estĂ ndards de la indĂșstria o a travĂ©s de la comparativa amb dispositius comercials calibrats

    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

    Physical sketching tools and techniques for customized sensate surfaces

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    Sensate surfaces are a promising avenue for enhancing human interaction with digital systems due to their inherent intuitiveness and natural user interface. Recent technological advancements have enabled sensate surfaces to surpass the constraints of conventional touchscreens by integrating them into everyday objects, creating interactive interfaces that can detect various inputs such as touch, pressure, and gestures. This allows for more natural and intuitive control of digital systems. However, prototyping interactive surfaces that are customized to users' requirements using conventional techniques remains technically challenging due to limitations in accommodating complex geometric shapes and varying sizes. Furthermore, it is crucial to consider the context in which customized surfaces are utilized, as relocating them to fabrication labs may lead to the loss of their original design context. Additionally, prototyping high-resolution sensate surfaces presents challenges due to the complex signal processing requirements involved. This thesis investigates the design and fabrication of customized sensate surfaces that meet the diverse requirements of different users and contexts. The research aims to develop novel tools and techniques that overcome the technical limitations of current methods and enable the creation of sensate surfaces that enhance human interaction with digital systems.Sensorische OberflĂ€chen sind aufgrund ihrer inhĂ€renten IntuitivitĂ€t und natĂŒrlichen BenutzeroberflĂ€che ein vielversprechender Ansatz, um die menschliche Interaktionmit digitalen Systemen zu verbessern. Die jĂŒngsten technologischen Fortschritte haben es ermöglicht, dass sensorische OberflĂ€chen die BeschrĂ€nkungen herkömmlicher Touchscreens ĂŒberwinden, indem sie in AlltagsgegenstĂ€nde integriert werden und interaktive Schnittstellen schaffen, die diverse Eingaben wie BerĂŒhrung, Druck, oder Gesten erkennen können. Dies ermöglicht eine natĂŒrlichere und intuitivere Steuerung von digitalen Systemen. Das Prototyping interaktiver OberflĂ€chen, die mit herkömmlichen Techniken an die BedĂŒrfnisse der Nutzer angepasst werden, bleibt jedoch eine technische Herausforderung, da komplexe geometrische Formen und variierende GrĂ¶ĂŸen nur begrenzt berĂŒcksichtigt werden können. DarĂŒber hinaus ist es von entscheidender Bedeutung, den Kontext, in dem diese individuell angepassten OberflĂ€chen verwendet werden, zu berĂŒcksichtigen, da eine Verlagerung in Fabrikations-Laboratorien zum Verlust ihres ursprĂŒnglichen Designkontextes fĂŒhren kann. Zudem stellt das Prototyping hochauflösender sensorischer OberflĂ€chen aufgrund der komplexen Anforderungen an die Signalverarbeitung eine Herausforderung dar. Diese Arbeit erforscht dasDesign und die Fabrikation individuell angepasster sensorischer OberflĂ€chen, die den diversen Anforderungen unterschiedlicher Nutzer und Kontexte gerecht werden. Die Forschung zielt darauf ab, neuartigeWerkzeuge und Techniken zu entwickeln, die die technischen BeschrĂ€nkungen derzeitigerMethoden ĂŒberwinden und die Erstellung von sensorischen OberflĂ€chen ermöglichen, die die menschliche Interaktion mit digitalen Systemen verbessern
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