3,175 research outputs found
Digital technologies for behavioral change in sustainability domains: a systematic mapping review
Sustainability research has emerged as an interdisciplinary area of knowledge about how to achieve sustainable development, while political actions toward the goal are still in their infancy. A sustainable world is mirrored by a healthy environment in which humans can live without jeopardizing the survival of future generations. The main aim of this contribution was to carry out a systematic mapping (SM) of the applications of digital technologies in promoting environmental sustainability. From a rigorous search of different databases, a set of more than 1000 studies was initially retrieved and then, following screening criteria based on the ROSES (RepOrting standards for Systematic Evidence Syntheses) procedure, a total of N = 37 studies that met the eligibility criteria were selected. The studies were coded according to different descriptive variables, such as digital technology used for the intervention, type of sustainable behavior promoted, research design, and population for whom the intervention was applied. Results showed the emergence of three main clusters of Digital Technologies (i.e., virtual/immersive/augmented reality, gamification, and power-metering systems) and two main Sustainable Behaviors (SBs) (i.e., energy and water-saving, and pollution reduction). The need for a clearer knowledge of which digital interventions work and the reasons why they work (or do not work) does not emerge from the outcomes of this set of studies. Future studies on digital interventions should better detail intervention design characteristics, alongside the reasons underlying design choices, both behaviourally and technologically. This should increase the likelihood of the successful adoption of digital interventions promoting behavioral changes in a more sustainable direction
Digitalization and Development
This book examines the diffusion of digitalization and Industry 4.0 technologies in Malaysia by focusing on the ecosystem critical for its expansion. The chapters examine the digital proliferation in major sectors of agriculture, manufacturing, e-commerce and services, as well as the intermediary organizations essential for the orderly performance of socioeconomic agents.
The book incisively reviews policy instruments critical for the effective and orderly development of the embedding organizations, and the regulatory framework needed to quicken the appropriation of socioeconomic synergies from digitalization and Industry 4.0 technologies. It highlights the importance of collaboration between government, academic and industry partners, as well as makes key recommendations on how to encourage adoption of IR4.0 technologies in the short- and long-term.
This book bridges the concepts and applications of digitalization and Industry 4.0 and will be a must-read for policy makers seeking to quicken the adoption of its technologies
Reliable Sensor Intelligence in Resource Constrained and Unreliable Environment
The objective of this research is to design a sensor intelligence that is reliable in a resource constrained, unreliable environment. There are various sources of variations and uncertainty involved in intelligent sensor system, so it is critical to build reliable sensor intelligence. Many prior works seek to design reliable sensor intelligence by developing robust and reliable task. This thesis suggests that along with improving task itself, task reliability quantification based early warning can further improve sensor intelligence. DNN based early warning generator quantifies task reliability based on spatiotemporal characteristics of input, and the early warning controls sensor parameters and avoids system failure. This thesis presents an early warning generator that predicts task failure due to sensor hardware induced input corruption and controls the sensor operation. Moreover, lightweight uncertainty estimator is presented to take account of DNN model uncertainty in task reliability quantification without prohibitive computation from stochastic DNN. Cross-layer uncertainty estimation is also discussed to consider the effect of PIM variations.Ph.D
Natural and Technological Hazards in Urban Areas
Natural hazard events and technological accidents are separate causes of environmental impacts. Natural hazards are physical phenomena active in geological times, whereas technological hazards result from actions or facilities created by humans. In our time, combined natural and man-made hazards have been induced. Overpopulation and urban development in areas prone to natural hazards increase the impact of natural disasters worldwide. Additionally, urban areas are frequently characterized by intense industrial activity and rapid, poorly planned growth that threatens the environment and degrades the quality of life. Therefore, proper urban planning is crucial to minimize fatalities and reduce the environmental and economic impacts that accompany both natural and technological hazardous events
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Automated identification and behaviour classification for modelling social dynamics in group-housed mice
Mice are often used in biology as exploratory models of human conditions, due to their similar genetics and physiology. Unfortunately, research on behaviour has traditionally been limited to studying individuals in isolated environments and over short periods of time. This can miss critical time-effects, and, since mice are social creatures, bias results.
This work addresses this gap in research by developing tools to analyse the individual behaviour of group-housed mice in the home-cage over several days and with minimal disruption. Using data provided by the Mary Lyon Centre at MRC Harwell we designed an end-to-end system that (a) tracks and identifies mice in a cage, (b) infers their behaviour, and subsequently (c) models the group dynamics as functions of individual activities. In support of the above, we also curated and made available a large dataset of mouse localisation and behaviour classifications (IMADGE), as well as two smaller annotated datasets for training/evaluating the identification (TIDe) and behaviour inference (ABODe) systems. This research constitutes the first of its kind in terms of the scale and challenges addressed. The data source (side-view single-channel video with clutter and no identification markers for mice) presents challenging conditions for analysis, but has the potential to give richer information while using industry standard housing.
A Tracking and Identification module was developed to automatically detect, track and identify the (visually similar) mice in the cluttered home-cage using only single-channel IR video and coarse position from RFID readings. Existing detectors and trackers were combined with a novel Integer Linear Programming formulation to assign anonymous tracks to mouse identities. This utilised a probabilistic weight model of affinity between detections and RFID pickups.
The next task necessitated the implementation of the Activity Labelling module that classifies the behaviour of each mouse, handling occlusion to avoid giving unreliable classifications when the mice cannot be observed. Two key aspects of this were (a) careful feature-selection, and (b) judicious balancing of the errors of the system in line with the repercussions for our setup.
Given these sequences of individual behaviours, we analysed the interaction dynamics between mice in the same cage by collapsing the group behaviour into a sequence of interpretable latent regimes using both static and temporal (Markov) models. Using a permutation matrix, we were able to automatically assign mice to roles in the HMM, fit a global model to a group of cages and analyse abnormalities in data from a different demographic
Exploration autonome et efficiente de chantiers miniers souterrains inconnus avec un drone filaire
Abstract: Underground mining stopes are often mapped using a sensor located at the end of a pole that the operator introduces into the stope from a secure area. The sensor emits laser beams that provide the distance to a detected wall, thus creating a 3D map. This produces shadow zones and a low point density on the distant walls. To address these challenges, a research team from the Université de Sherbrooke is designing a tethered drone equipped with a rotating LiDAR for this mission, thus benefiting from several points of view. The wired transmission allows for unlimited flight time, shared computing, and real-time communication. For compatibility with the movement of the drone after tether entanglements, the excess length is integrated into an onboard spool, contributing to the drone payload. During manual piloting, the human factor causes problems in the perception and comprehension of a virtual 3D environment, as well as the execution of an optimal mission. This thesis focuses on autonomous navigation in two aspects: path planning and exploration. The system must compute a trajectory that maps the entire environment, minimizing the mission time and respecting the maximum onboard tether length. Path planning using a Rapidly-exploring Random Tree (RRT) quickly finds a feasible path, but the optimization is computationally expensive and the performance is variable and unpredictable. Exploration by the frontier method is representative of the space to be explored and the path can be optimized by solving a Traveling Salesman Problem (TSP) but existing techniques for a tethered drone only consider the 2D case and do not optimize the global path. To meet these challenges, this thesis presents two new algorithms. The first one, RRT-Rope, produces an equal or shorter path than existing algorithms in a significantly shorter computation time, up to 70% faster than the next best algorithm in a representative environment. A modified version of RRT-connect computes a feasible path, shortened with a deterministic technique that takes advantage of previously added intermediate nodes. The second algorithm, TAPE, is the first 3D cavity exploration method that focuses on minimizing mission time and unwound tether length. On average, the overall path is 4% longer than the method that solves the TSP, but the tether remains under the allowed length in 100% of the simulated cases, compared to 53% with the initial method. The approach uses a 2-level hierarchical architecture: global planning solves a TSP after frontier extraction, and local planning minimizes the path cost and tether length via a decision function. The integration of these two tools in the NetherDrone produces an intelligent system for autonomous exploration, with semi-autonomous features for operator interaction. This work opens the door to new navigation approaches in the field of inspection, mapping, and Search and Rescue missions.La cartographie des chantiers miniers souterrains est souvent réalisée à l’aide d’un capteur situé au bout d’une perche que l’opérateur introduit dans le chantier, depuis une zone sécurisée. Le capteur émet des faisceaux laser qui fournissent la distance à un mur détecté, créant ainsi une carte en 3D. Ceci produit des zones d’ombres et une faible densité de points sur les parois éloignées. Pour relever ces défis, une équipe de recherche de l’Université de Sherbrooke conçoit un drone filaire équipé d’un LiDAR rotatif pour cette mission, bénéficiant ainsi de plusieurs points de vue. La transmission filaire permet un temps de vol illimité, un partage de calcul et une communication en temps réel. Pour une compatibilité avec le mouvement du drone lors des coincements du fil, la longueur excédante est intégrée dans une bobine embarquée, qui contribue à la charge utile du drone. Lors d’un pilotage manuel, le facteur humain entraîne des problèmes de perception et compréhension d’un environnement 3D virtuel, et d’exécution d’une mission optimale. Cette thèse se concentre sur la navigation autonome sous deux aspects : la planification de trajectoire et l’exploration. Le système doit calculer une trajectoire qui cartographie l’environnement complet, en minimisant le temps de mission et en respectant la longueur maximale de fil embarquée. La planification de trajectoire à l’aide d’un Rapidly-exploring Random Tree (RRT) trouve rapidement un chemin réalisable, mais l’optimisation est coûteuse en calcul et la performance est variable et imprévisible. L’exploration par la méthode des frontières est représentative de l’espace à explorer et le chemin peut être optimisé en résolvant un Traveling Salesman Problem (TSP), mais les techniques existantes pour un drone filaire ne considèrent que le cas 2D et n’optimisent pas le chemin global. Pour relever ces défis, cette thèse présente deux nouveaux algorithmes. Le premier, RRT-Rope, produit un chemin égal ou plus court que les algorithmes existants en un temps de calcul jusqu’à 70% plus court que le deuxième meilleur algorithme dans un environnement représentatif. Une version modifiée de RRT-connect calcule un chemin réalisable, raccourci avec une technique déterministe qui tire profit des noeuds intermédiaires préalablement ajoutés. Le deuxième algorithme, TAPE, est la première méthode d’exploration de cavités en 3D qui minimise le temps de mission et la longueur du fil déroulé. En moyenne, le trajet global est 4% plus long que la méthode qui résout le TSP, mais le fil reste sous la longueur autorisée dans 100% des cas simulés, contre 53% avec la méthode initiale. L’approche utilise une architecture hiérarchique à 2 niveaux : la planification globale résout un TSP après extraction des frontières, et la planification locale minimise le coût du chemin et la longueur de fil via une fonction de décision. L’intégration de ces deux outils dans le NetherDrone produit un système intelligent pour l’exploration autonome, doté de fonctionnalités semi-autonomes pour une interaction avec l’opérateur. Les travaux réalisés ouvrent la porte à de nouvelles approches de navigation dans le domaine des missions d’inspection, de cartographie et de recherche et sauvetage
Exploring the transition from techno centric industry 4.0 towards value centric industry 5.0: a systematic literature review
This systematic literature review synthesises the literature on human centric IN 4.0 and IN 5.0 while exploring driving forces behind the transition from technocentric IN 4.0 to value centric IN 5.0 using the principles of the multiple level perspective (MLP). Works that discuss contextual, regime and niche level factors which impact on the transition were explored. The Covid- 19 pandemic and Climate change are identified as key contextual, ‘Landscape’, factors impacting the transition while Trust, Mass personalisation and Autonomy are highlighted as key Regime factors. In terms of Niche innovations, Advanced Extended reality technologies, Cobots/ Advanced Robotics, and Advanced AI are often connected with landscape or regime issues. Drawing on MLP theory, the study demonstrates that the transition from IN 4.0 towards IN 5.0 is occurring through a reconfiguration pattern. The paper further emphasises aspects that both practitioners and academics need to be cognisant of in order to affect a transition from IN 4.0 to IN 5.0
Mechanical characterization, constitutive modeling and applications of ultra-soft magnetorheological elastomers
Mención Internacional en el título de doctorSmart materials are bringing sweeping changes in the way humans interact with engineering devices. A myriad of state-of-the-art applications are based on novel ways to actuate on structures that respond under different types of stimuli. Among them, materials that respond to magnetic fields allow to remotely modify their mechanical properties and macroscopic
shape. Ultra-soft magnetorheological elastomers (MREs) are composed of a highly stretchable soft elastomeric matrix in the order of 1 kPa and magnetic particles embedded in it. This combination allows large deformations with small external actuations.
The type of the magnetic particles plays a crucial role as it defines the reversibility or remanence of the material magnetization. According to the fillers used, MREs are referred to as soft-magnetic magnetorheological elastomers (sMREs) and hard-magnetic magnetorheological elastomers (hMREs). sMREs exhibit strong changes in their mechanical properties
when an external magnetic field is applied, whereas hMREs allow sustained magnetic effects along time and complex shape-morphing capabilities. In this regard, end-of-pipe applications of MREs in the literature are based on two major characteristics: the modification of their mechanical properties and macrostructural shape changes. For instance, smart actuators,
sensors and soft robots for bioengineering applications are remotely actuated to perform functional deformations and autonomous locomotion. In addition, hMREs have been used for industrial applications, such as damping systems and electrical machines.
From the analysis of the current state of the art, we identified some impediments to advance in certain research fields that may be overcome with new solutions based on ultrasoft MREs. On the mechanobiology area, we found no available experimental methodologies to transmit complex and dynamic heterogeneous strain patterns to biological systems in a reversible manner. To remedy this shortcoming, this doctoral research proposes
a new mechanobiology experimental setup based on responsive ultra-soft MRE biological substrates. Such an endeavor requires deeper insights into the magneto-viscoelastic and microstructural mechanisms of ultra-soft MREs. In addition, there is still a lack of guidance for the selection of the magnetic fillers to be used for MREs and the final properties provided
to the structure. Eventually, the great advances on both sMREs and hMREs to date pose a timely question on whether the combination of both types of particles in a hybrid MRE may optimize the multifunctional response of these active structures.
To overcome these roadblocks, this thesis provides an extensive and comprehensive experimental characterization of ultra-soft sMREs, hMREs and hybrid MREs. The experimental methodology uncovers magneto-mechanical rate dependences under numerous loading and manufacturing conditions. Then, a set of modeling frameworks allows to delve into such
mechanisms and develop three ground-breaking applications. Therefore, the thesis has lead to three main contributions. First and motivated on mechanobiology research, a computational framework guides a sMRE substrate to transmit complex strain patterns in vitro to biological systems. Second, we demonstrate the ability of remanent magnetic fields in hMREs to arrest cracks propagations and improve fracture toughness. Finally, the combination of soft- and hard-magnetic particles is proved to enhance the magnetorheological and magnetostrictive effects, providing promising results for soft robotics.Los materiales inteligentes están generando cambios radicales en la forma que los humanos interactúan con dispositivos ingenieriles. Distintas aplicaciones punteras se basan en formas novedosas de actuar sobre materiales que responden a diferentes estímulos. Entre ellos, las estructuras que responden a campos magnéticos permiten la modificación de manera remota tanto de sus propiedades mecánicas como de su forma. Los elastómeros magnetorreológicos (MREs) ultra blandos están compuestos por una matriz elastomérica con gran ductilidad y una rigidez en torno a 1 kPa, reforzada con partículas magnéticas. Esta combinación permite
inducir grandes deformaciones en el material mediante la aplicación de campos magnéticos pequeños.
La naturaleza de las partículas magnéticas define la reversibilidad o remanencia de la magnetización del material compuesto. De esta manera, según el tipo de partículas que contengan, los MREs pueden presentar magnetización débil (sMRE) o magnetización fuerte (hMRE). Los sMREs experimentan grandes cambios en sus propiedades mecánicas al aplicar
un campo magnético externo, mientras que los hMREs permiten efectos magneto-mecánicos sostenidos a lo largo del tiempo, así como programar cambios de forma complejos. En este sentido, las aplicaciones de los MREs se basan en dos características principales: la modificación de sus propiedades mecánicas y los cambios de forma macroestructurales. Por
ejemplo, los campos magnéticos pueden emplearse para inducir deformaciones funcionales en actuadores y sensores inteligentes, o en robótica blanda para bioingeniería. Los hMREs también se han aplicado en el ámbito industrial en sistemas de amortiguación y máquinas eléctricas.
A partir del análisis del estado del arte, se identifican algunas limitaciones que impiden el avance en ciertos campos de investigación y que podrían resolverse con nuevas soluciones basadas en MREs ultra blandos. En el área de la mecanobiología, no existen metodologías experimentales para transmitir patrones de deformación complejos y dinámicos a sistemas
biológicos de manera reversible. En esta investigación doctoral se propone una configuración experimental novedosa basada en sustratos biológicos fabricados con MREs ultra blandos. Dicha solución requiere la identificación de los mecanismos magneto-viscoelásticos y microestructurales de estos materiales, según el tipo de partículas magnéticas, y las consiguientes
propiedades macroscópicas del material. Además, investigaciones recientes en sMREs y hMREs plantean la pregunta sobre si la combinación de distintos tipos de partículas magnéticas en un MRE híbrido puede optimizar su respuesta multifuncional.
Para superar estos obstáculos, la presente tesis proporciona una caracterización experimental completa de sMREs, hMREs y MREs híbridos ultra blandos. Estos resultados muestran las dependencias del comportamiento multifuncional del material con la velocidad de aplicación de cargas magneto-mecánicas. El desarrollo de un conjunto de modelos
teórico-computacionales permite profundizar en dichos mecanismos y desarrollar aplicaciones innovadoras. De este modo, la tesis doctoral ha dado lugar a tres bloques de aportaciones principales. En primer lugar, este trabajo proporciona un marco computacional para guiar el diseño de sustratos basados en sMREs para transmitir patrones de deformación complejos in vitro a sistemas biológicos. En segundo lugar, se demuestra la capacidad de los campos magnéticos remanentes en los hMRE para detener la propagación de grietas y mejorar la tenacidad a la fractura. Finalmente, se establece que la combinación de partículas magnéticas de magnetización débil y fuerte mejora el efecto magnetorreológico y magnetoestrictivo, abriendo nuevas posibilidades para el diseño de robots blandos.I want to acknowledge the support from the Ministerio de Ciencia, Innovación y Universidades, Spain (FPU19/03874), and the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 947723, project: 4D-BIOMAP).Programa de Doctorado en Ingeniería Mecánica y de Organización Industrial por la Universidad Carlos III de MadridPresidente: Ramón Eulalio Zaera Polo.- Secretario: Abdón Pena Francesch.- Vocal: Laura de Lorenzi
Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5
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
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