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    Une méthode de mesure du mouvement humain pour la programmation par démonstration

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    Programming by demonstration (PbD) is an intuitive approach to impart a task to a robot from one or several demonstrations by the human teacher. The acquisition of the demonstrations involves the solution of the correspondence problem when the teacher and the learner differ in sensing and actuation. Kinesthetic guidance is widely used to perform demonstrations. With such a method, the robot is manipulated by the teacher and the demonstrations are recorded by the robot's encoders. In this way, the correspondence problem is trivial but the teacher dexterity is afflicted which may impact the PbD process. Methods that are more practical for the teacher usually require the identification of some mappings to solve the correspondence problem. The demonstration acquisition method is based on a compromise between the difficulty of identifying these mappings, the level of accuracy of the recorded elements and the user-friendliness and convenience for the teacher. This thesis proposes an inertial human motion tracking method based on inertial measurement units (IMUs) for PbD for pick-and-place tasks. Compared to kinesthetic guidance, IMUs are convenient and easy to use but can present a limited accuracy. Their potential for PbD applications is investigated. To estimate the trajectory of the teacher's hand, 3 IMUs are placed on her/his arm segments (arm, forearm and hand) to estimate their orientations. A specific method is proposed to partially compensate the well-known drift of the sensor orientation estimation around the gravity direction by exploiting the particular configuration of the demonstration. This method, called heading reset, is based on the assumption that the sensor passes through its original heading with stationary phases several times during the demonstration. The heading reset is implemented in an integration and vector observation algorithm. Several experiments illustrate the advantages of this heading reset. A comprehensive inertial human hand motion tracking (IHMT) method for PbD is then developed. It includes an initialization procedure to estimate the orientation of each sensor with respect to the human arm segment and the initial orientation of the sensor with respect to the teacher attached frame. The procedure involves a rotation and a static position of the extended arm. The measurement system is thus robust with respect to the positioning of the sensors on the segments. A procedure for estimating the position of the human teacher relative to the robot and a calibration procedure for the parameters of the method are also proposed. At the end, the error of the human hand trajectory is measured experimentally and is found in an interval between 28.528.5 mm and 61.861.8 mm. The mappings to solve the correspondence problem are identified. Unfortunately, the observed level of accuracy of this IHMT method is not sufficient for a PbD process. In order to reach the necessary level of accuracy, a method is proposed to correct the hand trajectory obtained by IHMT using vision data. A vision system presents a certain complementarity with inertial sensors. For the sake of simplicity and robustness, the vision system only tracks the objects but not the teacher. The correction is based on so-called Positions Of Interest (POIs) and involves 3 steps: the identification of the POIs in the inertial and vision data, the pairing of the hand POIs to objects POIs that correspond to the same action in the task, and finally, the correction of the hand trajectory based on the pairs of POIs. The complete method for demonstration acquisition is experimentally evaluated in a full PbD process. This experiment reveals the advantages of the proposed method over kinesthesy in the context of this work.La programmation par démonstration est une approche intuitive permettant de transmettre une tâche à un robot à partir d'une ou plusieurs démonstrations faites par un enseignant humain. L'acquisition des démonstrations nécessite cependant la résolution d'un problème de correspondance quand les systèmes sensitifs et moteurs de l'enseignant et de l'apprenant diffèrent. De nombreux travaux utilisent des démonstrations faites par kinesthésie, i.e., l'enseignant manipule directement le robot pour lui faire faire la tâche. Ce dernier enregistre ses mouvements grâce à ses propres encodeurs. De cette façon, le problème de correspondance est trivial. Lors de telles démonstrations, la dextérité de l'enseignant peut être altérée et impacter tout le processus de programmation par démonstration. Les méthodes d'acquisition de démonstration moins invalidantes pour l'enseignant nécessitent souvent des procédures spécifiques pour résoudre le problème de correspondance. Ainsi l'acquisition des démonstrations se base sur un compromis entre complexité de ces procédures, le niveau de précision des éléments enregistrés et la commodité pour l'enseignant. Cette thèse propose ainsi une méthode de mesure du mouvement humain par capteurs inertiels pour la programmation par démonstration de tâches de ``pick-and-place''. Les capteurs inertiels sont en effet pratiques et faciles à utiliser, mais sont d'une précision limitée. Nous étudions leur potentiel pour la programmation par démonstration. Pour estimer la trajectoire de la main de l'enseignant, des capteurs inertiels sont placés sur son bras, son avant-bras et sa main afin d'estimer leurs orientations. Une méthode est proposée afin de compenser partiellement la dérive de l'estimation de l'orientation des capteurs autour de la direction de la gravité. Cette méthode, appelée ``heading reset'', est basée sur l'hypothèse que le capteur passe plusieurs fois par son azimut initial avec des phases stationnaires lors d'une démonstration. Cette méthode est implémentée dans un algorithme d'intégration et d'observation de vecteur. Des expériences illustrent les avantages du ``heading reset''. Cette thèse développe ensuite une méthode complète de mesure des mouvements de la main humaine par capteurs inertiels (IHMT). Elle comprend une première procédure d'initialisation pour estimer l'orientation des capteurs par rapport aux segments du bras humain ainsi que l'orientation initiale des capteurs par rapport au repère de référence de l'humain. Cette procédure, consistant en une rotation et une position statique du bras tendu, est robuste au positionnement des capteurs. Une seconde procédure est proposée pour estimer la position de l'humain par rapport au robot et pour calibrer les paramètres de la méthode. Finalement, l'erreur moyenne sur la trajectoire de la main humaine est mesurée expérimentalement entre 28.5 mm et 61.8 mm, ce qui n'est cependant pas suffisant pour la programmation par démonstration. Afin d'atteindre le niveau de précision nécessaire, une nouvelle méthode est développée afin de corriger la trajectoire de la main par IHMT à partir de données issues d'un système de vision, complémentaire des capteurs inertiels. Pour maintenir une certaine simplicité et robustesse, le système de vision ne suit que les objets et pas l'enseignant. La méthode de correction, basée sur des ``Positions Of Interest (POIs)'', est constituée de 3 étapes: l'identification des POIs dans les données issues des capteurs inertiels et du système de vision, puis l'association de POIs liées à la main et de POIs liées aux objets correspondant à la même action, et enfin, la correction de la trajectoire de la main à partir des paires de POIs. Finalement, la méthode IHMT corrigée est expérimentalement évaluée dans un processus complet de programmation par démonstration. Cette expérience montre l'avantage de la méthode proposée sur la kinesthésie dans le contexte de ce travail

    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 (Forlì Campus) in collaboration with the Romagna Chamber of Commerce (Forlì-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

    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

    Synthetic Aperture Radar (SAR) Meets Deep Learning

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    This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports

    Measuring the impact of COVID-19 on hospital care pathways

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    Care pathways in hospitals around the world reported significant disruption during the recent COVID-19 pandemic but measuring the actual impact is more problematic. Process mining can be useful for hospital management to measure the conformance of real-life care to what might be considered normal operations. In this study, we aim to demonstrate that process mining can be used to investigate process changes associated with complex disruptive events. We studied perturbations to accident and emergency (A &E) and maternity pathways in a UK public hospital during the COVID-19 pandemic. Co-incidentally the hospital had implemented a Command Centre approach for patient-flow management affording an opportunity to study both the planned improvement and the disruption due to the pandemic. Our study proposes and demonstrates a method for measuring and investigating the impact of such planned and unplanned disruptions affecting hospital care pathways. We found that during the pandemic, both A &E and maternity pathways had measurable reductions in the mean length of stay and a measurable drop in the percentage of pathways conforming to normative models. There were no distinctive patterns of monthly mean values of length of stay nor conformance throughout the phases of the installation of the hospital’s new Command Centre approach. Due to a deficit in the available A &E data, the findings for A &E pathways could not be interpreted

    Optimizing Flow Routing Using Network Performance Analysis

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    Relevant conferences were attended at which work was often presented and several papers were published in the course of this project. • Muna Al-Saadi, Bogdan V Ghita, Stavros Shiaeles, Panagiotis Sarigiannidis. A novel approach for performance-based clustering and management of network traffic flows, IWCMC, ©2019 IEEE. • M. Al-Saadi, A. Khan, V. Kelefouras, D. J. Walker, and B. Al-Saadi: Unsupervised Machine Learning-Based Elephant and Mice Flow Identification, Computing Conference 2021. • M. Al-Saadi, A. Khan, V. Kelefouras, D. J. Walker, and B. Al-Saadi: SDN-Based Routing Framework for Elephant and Mice Flows Using Unsupervised Machine Learning, Network, 3(1), pp.218-238, 2023.The main task of a network is to hold and transfer data between its nodes. To achieve this task, the network needs to find the optimal route for data to travel by employing a particular routing system. This system has a specific job that examines each possible path for data and chooses the suitable one and transmit the data packets where it needs to go as fast as possible. In addition, it contributes to enhance the performance of network as optimal routing algorithm helps to run network efficiently. The clear performance advantage that provides by routing procedures is the faster data access. For example, the routing algorithm take a decision that determine the best route based on the location where the data is stored and the destination device that is asking for it. On the other hand, a network can handle many types of traffic simultaneously, but it cannot exceed the bandwidth allowed as the maximum data rate that the network can transmit. However, the overloading problem are real and still exist. To avoid this problem, the network chooses the route based on the available bandwidth space. One serious problem in the network is network link congestion and disparate load caused by elephant flows. Through forwarding elephant flows, network links will be congested with data packets causing transmission collision, congestion network, and delay in transmission. Consequently, there is not enough bandwidth for mice flows, which causes the problem of transmission delay. Traffic engineering (TE) is a network application that concerns with measuring and managing network traffic and designing feasible routing mechanisms to guide the traffic of the network for improving the utilization of network resources. The main function of traffic engineering is finding an obvious route to achieve the bandwidth requirements of the network consequently optimizing the network performance [1]. Routing optimization has a key role in traffic engineering by finding efficient routes to achieve the desired performance of the network [2]. Furthermore, routing optimization can be considered as one of the primary goals in the field of networks. In particular, this goal is directly related to traffic engineering, as it is based on one particular idea: to achieve that traffic is routed according to accurate traffic requirements [3]. Therefore, we can say that traffic engineering is one of the applications of multiple improvements to routing; routing can also be optimized based on other factors (not just on traffic requirements). In addition, these traffic requirements are variable depending on analyzed dataset that considered if it is data or traffic control. In this regard, the logical central view of the Software Defined Network (SDN) controller facilitates many aspects compared to traditional routing. The main challenge in all network types is performance optimization, but the situation is different in SDN because the technique is changed from distributed approach to a centralized one. The characteristics of SDN such as centralized control and programmability make the possibility of performing not only routing in traditional distributed manner but also routing in centralized manner. The first advantage of centralized routing using SDN is the existence of a path to exchange information between the controller and infrastructure devices. Consequently, the controller has the information for the entire network, flexible routing can be achieved. The second advantage is related to dynamical control of routing due to the capability of each device to change its configuration based on the controller commands [4]. This thesis begins with a wide review of the importance of network performance analysis and its role for understanding network behavior, and how it contributes to improve the performance of the network. Furthermore, it clarifies the existing solutions of network performance optimization using machine learning (ML) techniques in traditional networks and SDN environment. In addition, it highlights recent and ongoing studies of the problem of unfair use of network resources by a particular flow (elephant flow) and the possible solutions to solve this problem. Existing solutions are predominantly, flow routing-based and do not consider the relationship between network performance analysis and flow characterization and how to take advantage of it to optimize flow routing by finding the convenient path for each type of flow. Therefore, attention is given to find a method that may describe the flow based on network performance analysis and how to utilize this method for managing network performance efficiently and find the possible integration for the traffic controlling in SDN. To this purpose, characteristics of network flows is identified as a mechanism which may give insight into the diversity in flow features based on performance metrics and provide the possibility of traffic engineering enhancement using SDN environment. Two different feature sets with respect to network performance metrics are employed to characterize network traffic. Applying unsupervised machine learning techniques including Principal Component Analysis (PCA) and k-means cluster analysis to derive a traffic performance-based clustering model. Afterward, thresholding-based flow identification paradigm has been built using pre-defined parameters and thresholds. Finally, the resulting data clusters are integrated within a unified SDN architectural solution, which improves network management by finding the best flow routing based on the type of flow, to be evaluated against a number of traffic data sources and different performance experiments. The validation process of the novel framework performance has been done by making a performance comparison between SDN-Ryu controller and the proposed SDN-external application based on three factors: throughput, bandwidth,and data transfer rate by conducting two experiments. Furthermore, the proposed method has been validated by using different Data Centre Network (DCN) topologies to demonstrate the effectiveness of the network traffic management solution. The overall validation metrics shows real gains, the results show that 70% of the time, it has high performance with different flows. The proposed routing SDN traffic-engineering paradigm for a particular flow therefore, dynamically provisions network resources among different flow types
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