Risk management in solitary agricultural work: new technologies for handling emergency and falls from great heights (SHADE)

Solitary work and agricultural activities are the scenarios of a large number of severe injuries and deaths, also because first aid may be difficult to achieve in isolated locations. This work proposes a technology available on smartphones that allows triggering an emergency call when a fall from height or an unconsciousness state is detected. The results of several tests, which include different detection algorithms and scenarios, are reported in this work. Tests performed with the aid of a dummy have allowed developing a reliable algorithm for the detection of dangerous situations. This system is available as an Android application

Nonlinear control of multibody flexible mechanisms: A model-free approach

In this paper a novel nonlinear controller for position and vibration control of flexible-link mechanisms is introduced. The proposed control strategy is model-free and does not require the measurement of the elastic deformation of the mechanism, since the control relies only on the knowledge of the angular position of the actuator and on its time derivative, which can be measured simply with a quadrature encoder. The conditions for the closed-loop stability are evaluated using Lyapunov theory. The performance of the proposed technique is evaluated on a four-bar flexible-link mechanism. Superior vibration damping and more accurate trajectory tracking is obtained in comparison with a PD controller and a fractional order controller, which relies on the same set of measurement as the proposed nonlinear controller

Unsupervised Semantic Discovery Through Visual Patterns Detection

We propose a new fast fully unsupervised method to discover semantic patterns. Our algorithm is able to hierarchically find visual categories and produce a segmentation mask where previous methods fail. Through the modeling of what is a visual pattern in an image, we introduce the notion of “semantic levels" and devise a conceptual framework along with measures and a dedicated benchmark dataset for future comparisons. Our algorithm is composed by two phases. A filtering phase, which selects semantical hotsposts by means of an accumulator space, then a clustering phase which propagates the semantic properties of the hotspots on a superpixels basis. We provide both qualitative and quantitative experimental validation, achieving optimal results in terms of robustness to noise and semantic consistency. We also made code and dataset publicly available

Robotics and Vibration Mechanics

Robotics and vibration mechanics are among the main research areas in mechanical engineering [...

Recommendation Systems: An Insight Into Current Development and Future Research Challenges

Research on recommendation systems is swiftly producing an abundance of novel methods, constantly challenging the current state-of-the-art. Inspired by advancements in many related fields, like Natural Language Processing and Computer Vision, many hybrid approaches based on deep learning are being proposed, making solid improvements over traditional methods. On the downside, this flurry of research activity, often focused on improving over a small number of baselines, makes it hard to identify reference methods and standardized evaluation protocols. Furthermore, the traditional categorization of recommendation systems into content-based, collaborative filtering and hybrid systems lacks the informativeness it once had. With this work, we provide a gentle introduction to recommendation systems, describing the task they are designed to solve and the challenges faced in research. Building on previous work, an extension to the standard taxonomy is presented, to better reflect the latest research trends, including the diverse use of content and temporal information. To ease the approach toward the technical methodologies recently proposed in this field, we review several representative methods selected primarily from top conferences and systematically describe their goals and novelty. We formalize the main evaluation metrics adopted by researchers and identify the most commonly used benchmarks. Lastly, we discuss issues in current research practices by analyzing experimental results reported on three popular datasets

A MULTIDISCIPLINARY DOCUMENTAL REPRESENTATION METHOD FOR KINETIC AND ENVIRONMENTAL ART

Abstract. The contribution addresses the definition of a new collaborative documental method for designing and managing the different phases of conservation of kinetic-programmed art. Our approach consists of developing a new representative model that includes both mechanical parts and spatial characteristics. The research stems from a specific case-study, Ambiente – Strutturazione a parametri virtuali (1969) by Gabriele Devecchi, permanently displayed at Museo del '900 in Milan since 2010. Starting from the dimensional and technical data, we obtained a graphical model of lamps 2D and 3D. They were enriched by a detailed abacus describing all the elements and by specific maps capturing all the phases of regular and extraordinary maintenance underwent by the environment. The second part was carried out with a report about physical motion. The goal has been representing speed and geometry of the movement inside the space. By merging the first part of the documenting process and the second one we've got a graphic digital model including information about the individual parts of the installation and their mechanical interaction. The third and last step is ongoing and tackles the challenge of using virtual technologies for the description of the whole environment. Thanks to a collaboration between technicians and theoretic scholars, we attempted to match the study of the physical motion and all data about the structural parts with the careful consideration of historical-artistic and perception-related features. The work led to the conclusion that a virtual, immersive reproduction of the environment is not enough for deeply understanding the experience enjoyed by users inside it, because it misses the embodied perception activated by the artwork. For this reason, this study may be considered as a step in a broader research path about documentation of complex environmental, immersive, kinetic works of art.</p

A Survey on Text Classification Algorithms: From Text to Predictions

In recent years, the exponential growth of digital documents has been met by rapid progress in text classification techniques. Newly proposed machine learning algorithms leverage the latest advancements in deep learning methods, allowing for the automatic extraction of expressive features. The swift development of these methods has led to a plethora of strategies to encode natural language into machine-interpretable data. The latest language modelling algorithms are used in conjunction with ad hoc preprocessing procedures, of which the description is often omitted in favour of a more detailed explanation of the classification step. This paper offers a concise review of recent text classification models, with emphasis on the flow of data, from raw text to output labels. We highlight the differences between earlier methods and more recent, deep learning-based methods in both their functioning and in how they transform input data. To give a better perspective on the text classification landscape, we provide an overview of datasets for the English language, as well as supplying instructions for the synthesis of two new multilabel datasets, which we found to be particularly scarce in this setting. Finally, we provide an outline of new experimental results and discuss the open research challenges posed by deep learning-based language models

Enhancing fluency and productivity in human-robot collaboration through online scaling of dynamic safety zones

Industrial collaborative robotics is promising for manufacturing activities where the presence of a robot alongside a human operator can improve operator’s working conditions, flexibility, and productivity. A collaborative robotic application has to guarantee not only safety of the human operator, but also fluency in the collaboration, as well as performance in terms of productivity and task time. In this paper, we present an approach to enhance fluency and productivity in human-robot collaboration through online scaling of dynamic safety zones. A supervisory controller runs online safety checks between bounding volumes enclosing robot and human to identify possible collision dangers. To optimize the sizes of safety zones enclosing the manipulator, the method minimizes the time of potential stop trajectories considering the robot dynamics and its torque constraints, and leverages the directed speed of the robot parts with respect to the human. Simulations and experimental tests on a seven-degree-of-freedom robotic arm verify the effectiveness of the proposed approach, and collaborative fluency metrics show the benefits of the method with respect to existing approaches

Performance Investigation and Repeatability Assessment of a Mobile Robotic System for 3D Mapping

In this paper, we present a quantitative performance investigation and repeatability assessment of a mobile robotic system for 3D mapping. With the aim of a more efficient and automatic data acquisition process with respect to well-established manual topographic operations, a 3D laser scanner coupled with an inertial measurement unit is installed on a mobile platform and used to perform a high-resolution mapping of the surrounding environment. Point clouds obtained with the use of a mobile robot are compared with those acquired with the device carried manually as well as with a terrestrial laser scanner survey that serves as a ground truth. Experimental results show that both mapping modes provide similar accuracy and repeatability, whereas the robotic system compares favorably with respect to the handheld modality in terms of noise level and point distribution. The outcomes demonstrate the feasibility of the mobile robotic platform as a promising technology for automatic and accurate 3D mapping