2,618 research outputs found

    Active and assisted living ecosystem for the elderly

    Get PDF
    A novel ecosystem to promote the physical, emotional and psychic health and well-being of the elderly is presented. Our proposal was designed to add several services developed to meet the needs of the senior population, namely services to improve social inclusion and increase contribution to society. Moreover, the solution monitors the vital signs of elderly individuals, as well as environmental parameters and behavior patterns, in order to seek eminent danger situations and predict potential hazardous issues, acting in accordance with the various alert levels specified for each individual. The platform was tested by seniors in a real scenario. The experimental results demonstrated that the proposed ecosystem was well accepted and is easy to use by seniors

    Bayesian learning for the robust verification of autonomous robots

    Get PDF
    Autonomous robots used in infrastructure inspection, space exploration and other critical missions operate in highly dynamic environments. As such, they must continually verify their ability to complete the tasks associated with these missions safely and effectively. Here we present a Bayesian learning framework that enables this runtime verification of autonomous robots. The framework uses prior knowledge and observations of the verified robot to learn expected ranges for the occurrence rates of regular and singular (e.g., catastrophic failure) events. Interval continuous-time Markov models defined using these ranges are then analysed to obtain expected intervals of variation for system properties such as mission duration and success probability. We apply the framework to an autonomous robotic mission for underwater infrastructure inspection and repair. The formal proofs and experiments presented in the paper show that our framework produces results that reflect the uncertainty intrinsic to many real-world systems, enabling the robust verification of their quantitative properties under parametric uncertainty

    Precision Surface Processing and Software Modelling Using Shear-Thickening Polishing Slurries

    Get PDF
    Mid-spatial frequency surface error is a known manufacturing defect for aspherical and freeform precision surfaces. These surface ripples decrease imaging contrast and system signal-to-noise ratio. Existing sub-aperture polishing techniques are limited in their abilities to smooth mid-spatial frequency errors. Shear-thickening slurries have been hypothesised to reduce mid-spatial frequency errors on precision optical surfaces by increasing the viscosity at the tool-part interface. Currently, controlling the generation and mitigating existing mid-spatial frequency surface errors for aspherical and freeform surfaces requires extensive setup and the experience of seasoned workers. This thesis reports on the experimental trials of shear-thickening polishing slurries on glass surfaces. By incorporating shear-thickening slurries with the precessed bonnet technology, the aim is to enhance the ability of the precessions technology in mitigating mid-spatial frequency errors. The findings could facilitate a more streamlined manufacturing chain for precision optics for the versatile precessions technology from form correction and texture improvement, to MSF mitigation, without needing to rely on other polishing technologies. Such improvement on the existing bonnet polishing would provide a vital steppingstone towards building a fully autonomous manufacturing cell in a market of continual economic growth. The experiments in this thesis analysed the capabilities of two shear-thickening slurry systems: (1) polyethylene glycol with silica nanoparticle suspension, and (2) water and cornstarch suspension. Both slurry systems demonstrated the ability at mitigating existing surface ripples. Looking at power spectral density graphs, polyethylene glycol slurries reduced the power of the mid-spatial frequencies by ~50% and cornstarch suspension slurries by 60-90%. Experiments of a novel polishing approach are also reported in this thesis to rotate a precessed bonnet at a predetermined working distance above the workpiece surface. The rapidly rotating tool draws in the shear-thickening slurry through the gap to stiffen the fluid for polishing. This technique demonstrated material removal capabilities using cornstarch suspension slurries at a working distance of 1.0-1.5mm. The volumetric removal rate from this process is ~5% of that of contact bonnet polishing, so this aligns more as a finishing process. This polishing technique was given the term rheological bonnet finishing. The rheological properties of cornstarch suspension slurries were tested using a rheometer and modelled through CFD simulation. Using the empirical rheological data, polishing simulations of the rheological bonnet finishing process were modelled in Ansys to analyse the effects of various input parameters such as working distance, tool headspeed, precess angle, and slurry viscosity

    GPT models in construction industry: Opportunities, limitations, and a use case validation

    Get PDF
    Large Language Models (LLMs) trained on large data sets came into prominence in 2018 after Google introduced BERT. Subsequently, different LLMs such as GPT models from OpenAI have been released. These models perform well on diverse tasks and have been gaining widespread applications in fields such as business and education. However, little is known about the opportunities and challenges of using LLMs in the construction industry. Thus, this study aims to assess GPT models in the construction industry. A critical review, expert discussion and case study validation are employed to achieve the study's objectives. The findings revealed opportunities for GPT models throughout the project lifecycle. The challenges of leveraging GPT models are highlighted and a use case prototype is developed for materials selection and optimization. The findings of the study would be of benefit to researchers, practitioners and stakeholders, as it presents research vistas for LLMs in the construction industry

    Language integrated relational lenses

    Get PDF
    Relational databases are ubiquitous. Such monolithic databases accumulate large amounts of data, yet applications typically only work on small portions of the data at a time. A subset of the database defined as a computation on the underlying tables is called a view. Querying views is helpful, but it is also desirable to update them and have these changes be applied to the underlying database. This view update problem has been the subject of much previous work before, but support by database servers is limited and only rarely available. Lenses are a popular approach to bidirectional transformations, a generalization of the view update problem in databases to arbitrary data. However, perhaps surprisingly, lenses have seldom actually been used to implement updatable views in databases. Bohannon, Pierce and Vaughan propose an approach to updatable views called relational lenses. However, to the best of our knowledge this proposal has not been implemented or evaluated prior to the work reported in this thesis. This thesis proposes programming language support for relational lenses. Language integrated relational lenses support expressive and efficient view updates, without relying on updatable view support from the database server. By integrating relational lenses into the programming language, application development becomes easier and less error-prone, avoiding the impedance mismatch of having two programming languages. Integrating relational lenses into the language poses additional challenges. As defined by Bohannon et al. relational lenses completely recompute the database, making them inefficient as the database scales. The other challenge is that some parts of the well-formedness conditions are too general for implementation. Bohannon et al. specify predicates using possibly infinite abstract sets and define the type checking rules using relational algebra. Incremental relational lenses equip relational lenses with change-propagating semantics that map small changes to the view into (potentially) small changes to the source tables. We prove that our incremental semantics are functionally equivalent to the non-incremental semantics, and our experimental results show orders of magnitude improvement over the non-incremental approach. This thesis introduces a concrete predicate syntax and shows how the required checks are performed on these predicates and show that they satisfy the abstract predicate specifications. We discuss trade-offs between static predicates that are fully known at compile time vs dynamic predicates that are only known during execution and introduce hybrid predicates taking inspiration from both approaches. This thesis adapts the typing rules for relational lenses from sequential composition to a functional style of sub-expressions. We prove that any well-typed functional relational lens expression can derive a well-typed sequential lens. We use these additions to relational lenses as the foundation for two practical implementations: an extension of the Links functional language and a library written in Haskell. The second implementation demonstrates how type-level computation can be used to implement relational lenses without changes to the compiler. These two implementations attest to the possibility of turning relational lenses into a practical language feature

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

    Get PDF
    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    The Application of Data Analytics Technologies for the Predictive Maintenance of Industrial Facilities in Internet of Things (IoT) Environments

    Get PDF
    In industrial production environments, the maintenance of equipment has a decisive influence on costs and on the plannability of production capacities. In particular, unplanned failures during production times cause high costs, unplanned downtimes and possibly additional collateral damage. Predictive Maintenance starts here and tries to predict a possible failure and its cause so early that its prevention can be prepared and carried out in time. In order to be able to predict malfunctions and failures, the industrial plant with its characteristics, as well as wear and ageing processes, must be modelled. Such modelling can be done by replicating its physical properties. However, this is very complex and requires enormous expert knowledge about the plant and about wear and ageing processes of each individual component. Neural networks and machine learning make it possible to train such models using data and offer an alternative, especially when very complex and non-linear behaviour is evident. In order for models to make predictions, as much data as possible about the condition of a plant and its environment and production planning data is needed. In Industrial Internet of Things (IIoT) environments, the amount of available data is constantly increasing. Intelligent sensors and highly interconnected production facilities produce a steady stream of data. The sheer volume of data, but also the steady stream in which data is transmitted, place high demands on the data processing systems. If a participating system wants to perform live analyses on the incoming data streams, it must be able to process the incoming data at least as fast as the continuous data stream delivers it. If this is not the case, the system falls further and further behind in processing and thus in its analyses. This also applies to Predictive Maintenance systems, especially if they use complex and computationally intensive machine learning models. If sufficiently scalable hardware resources are available, this may not be a problem at first. However, if this is not the case or if the processing takes place on decentralised units with limited hardware resources (e.g. edge devices), the runtime behaviour and resource requirements of the type of neural network used can become an important criterion. This thesis addresses Predictive Maintenance systems in IIoT environments using neural networks and Deep Learning, where the runtime behaviour and the resource requirements are relevant. The question is whether it is possible to achieve better runtimes with similarly result quality using a new type of neural network. The focus is on reducing the complexity of the network and improving its parallelisability. Inspired by projects in which complexity was distributed to less complex neural subnetworks by upstream measures, two hypotheses presented in this thesis emerged: a) the distribution of complexity into simpler subnetworks leads to faster processing overall, despite the overhead this creates, and b) if a neural cell has a deeper internal structure, this leads to a less complex network. Within the framework of a qualitative study, an overall impression of Predictive Maintenance applications in IIoT environments using neural networks was developed. Based on the findings, a novel model layout was developed named Sliced Long Short-Term Memory Neural Network (SlicedLSTM). The SlicedLSTM implements the assumptions made in the aforementioned hypotheses in its inner model architecture. Within the framework of a quantitative study, the runtime behaviour of the SlicedLSTM was compared with that of a reference model in the form of laboratory tests. The study uses synthetically generated data from a NASA project to predict failures of modules of aircraft gas turbines. The dataset contains 1,414 multivariate time series with 104,897 samples of test data and 160,360 samples of training data. As a result, it could be proven for the specific application and the data used that the SlicedLSTM delivers faster processing times with similar result accuracy and thus clearly outperforms the reference model in this respect. The hypotheses about the influence of complexity in the internal structure of the neuronal cells were confirmed by the study carried out in the context of this thesis

    A BIM - GIS Integrated Information Model Using Semantic Web and RDF Graph Databases

    Get PDF
    In recent years, 3D virtual indoor and outdoor urban modelling has become an essential geospatial information framework for civil and engineering applications such as emergency response, evacuation planning, and facility management. Building multi-sourced and multi-scale 3D urban models are in high demand among architects, engineers, and construction professionals to achieve these tasks and provide relevant information to decision support systems. Spatial modelling technologies such as Building Information Modelling (BIM) and Geographical Information Systems (GIS) are frequently used to meet such high demands. However, sharing data and information between these two domains is still challenging. At the same time, the semantic or syntactic strategies for inter-communication between BIM and GIS do not fully provide rich semantic and geometric information exchange of BIM into GIS or vice-versa. This research study proposes a novel approach for integrating BIM and GIS using semantic web technologies and Resources Description Framework (RDF) graph databases. The suggested solution's originality and novelty come from combining the advantages of integrating BIM and GIS models into a semantically unified data model using a semantic framework and ontology engineering approaches. The new model will be named Integrated Geospatial Information Model (IGIM). It is constructed through three stages. The first stage requires BIMRDF and GISRDF graphs generation from BIM and GIS datasets. Then graph integration from BIM and GIS semantic models creates IGIMRDF. Lastly, the information from IGIMRDF unified graph is filtered using a graph query language and graph data analytics tools. The linkage between BIMRDF and GISRDF is completed through SPARQL endpoints defined by queries using elements and entity classes with similar or complementary information from properties, relationships, and geometries from an ontology-matching process during model construction. The resulting model (or sub-model) can be managed in a graph database system and used in the backend as a data-tier serving web services feeding a front-tier domain-oriented application. A case study was designed, developed, and tested using the semantic integrated information model for validating the newly proposed solution, architecture, and performance

    Microcredentials to support PBL

    Get PDF

    Aktivitätstracker im Alltag: Charakteristika von Motivation und User Diversity zur Erklärung individueller Nutzungstrajektorien

    Get PDF
    Die fortlaufend stärkere Durchdringung unseres Alltags mit digitalen Technologien wird besonders deutlich durch tragbare Geräte wie Smartphones, auf die jederzeit zugegriffen werden kann. Noch einen Schritt weiter gehen körpernah getragene, vernetzte Self-Tracking-Systeme wie Aktivitätstracker, welche kontinuierlich Bewegungsdaten und physiologische Parameter erfassen, algorithmisch aufbereiten und an die Nutzer*innen als quantifiziertes Feedback, oft zur Verhaltensmodifikation, zurückmelden. Diese spezifische Form der Interaktion zwischen Mensch und Technologie – körpernah, kontinuierlich, quantifiziert, vernetzt und persuasiv – ist für die Ingenieurpsychologie besonders relevant, da sie eine sehr enge Verbindung von Körper und Technik erfordert und spezifische Herausforderungen für die Stärkung der Selbstbestimmung ihrer Nutzer*innen bereithält. Einerseits dienen Aktivitätstracker der erleichterten Selbstreflexion durch Sichtbarmachung von Zusammenhängen, die zuvor verborgen blieben, wie etwa zwischen sportlicher Aktivität und Ruheherzfrequenz. Andererseits sollen Aktivitätstracker die Motivation für körperliche Verhaltensänderungen steigern. Die Nutzung von Aktivitätstrackern bewegt sich also potenziell in einem Spannungsfeld zwischen der Steigerung von Selbstbestimmung durch erweitertes Wissen sowie Aufzeigen von Handlungsoptionen und der Einschränkung der Selbstbestimmung durch persuasive Strategien zur Motivationssteigerung. Dieses Spannungsfeld bedingt neue Ansätze zur Beziehungsgestaltung zwischen Mensch und Trackingsystem. In der empirischen Forschung zur Nutzung von Aktivitätstrackern wird häufig darauf hingewiesen, dass ein Großteil der Nutzenden nach wenigen Wochen oder Monaten den kontinuierlichen Gebrauch beendet. Dieser Befund deutet daraufhin, dass Barrieren existieren, die die Langzeitnutzung unwahrscheinlicher machen. Des Weiteren wird immer wieder über negative Effekte der Trackernutzung berichtet, beispielsweise Stress. Allerdings ist auch bekannt, dass zahlreiche andere Personen ihr Trackingsystem über Jahre hinweg intensiv und erfolgreich gebrauchen. Es lässt sich also in Bezug auf die Nutzungstrajektorien eine bedeutsame Varianz feststellen, die es zu erklären gilt, um Self-Tracking-Anwendungen für diverse Nutzende gewinnbringend zu gestalten. Um diesem Vorhaben gerecht zu werden, ist es unabdingbar zu verstehen, welche individuellen Differenzen in der Gruppe der Nutzer*innen die Interaktion mit dem Aktivitätstracker, insbesondere in Bezug auf motivationale Aspekte, prägen. Dieser Herausforderung stellt sich die vorliegende Dissertation und greift dazu auf etablierte Theorien und Konzepte der Persönlichkeits- und Sozialpsychologie zurück. Da der theoriegeleitete Einbezug von Personenmerkmalen in die ingenieurpsychologische Forschung noch wenig vorangetrieben war, bestand zu Beginn des Promotionsvorhabens die Notwendigkeit, ein Konstrukt zu konzeptualisieren, welches zum einen auf einem stabilen psychologischen Theoriefundament steht und zum anderen spezifisch auf den Kontext der Mensch-Technik-Interaktion zugeschnitten ist. Im Rahmen der vorliegenden Dissertation wurde aus diesem Grund an der Herleitung der interaktionsbezogenen Technikaffinität (ATI) als kontextspezifische Variante der Denkfreude und ihrer Messbarmachung gearbei-tet. Insgesamt umfassten die Datenerhebungen zur Bestimmung der Gütekriterien der ATI-Skala fünf Datensätze mit über 1500 Teilnehmenden. Das Resultat der Skalenentwicklung ist ein unidimensionales, ökonomisches, reliables und valides Erhebungsinstrument der interaktionsbezogenen Technikaffinität (Artikel 1). Als relativ stabiles Persönlichkeitsmerkmal, das die Motivation zur Auseinandersetzung mit Technik grundlegend beeinflusst, wurde ATI in die folgenden Studien zur Interaktion zwischen Mensch und Aktivitätstracker miteinbezogen. Um die alltägliche, individuelle Mensch-Tracker-Interaktion umfassend zu verstehen und erklären zu können, wie es zu den unterschiedlichen Nutzungsverläufen kommt, müssen verschiedene Phasen der Nutzung untersucht werden. Zunächst ist zu klären, welche Motivatoren Menschen eigentlich dazu veranlassen, mit der Trackernutzung zu beginnen. Weiterhin ist die Nutzungsphase selbst zu beleuchten, um zu beschreiben, wie sich die oben beschriebene, spezifische Form der Trackerinteraktion auf die Nutzungserfahrung und anhaltende Motivation auswirkt und wie sich negative Nutzungskonsequenzen bemerkbar machen. Schließlich sind zum Verständnis der Nutzungstrajektorien die Gründe für den Abbruch zu berücksichtigen, sodass auch die Phase nach der Nutzung relevant ist. Da sich diese Dissertation dezidiert damit beschäftigt, wie sich die Interaktion mit Aktivitätstrackern im Alltag gestaltet, ist die Untersuchung der Nutzung in Stichproben von tatsächlichen bzw. ehemaligen Aktivitätstracker-Nutzer*innen angezeigt. Aus diesem Grund wurden zwei Online-Erhebungen durchgeführt, um ebendiese Stichproben zu erreichen. Das Ziel der ersten Studie (N = 210) war die quantitative Analyse von Nutzungsmotivationen sowie unintendierten, negativen Effekten der Trackernutzung im Alltagsgebrauch. Es zeigte sich, dass das Tracken sowohl zum Selbstzweck (intrinsische Motivation) als auch zur Erreichung eines externen Ziels (extrinsische Motivation) durchgeführt wird und diese Motivationstypen oft gleichzeitig auftreten. Darüber hinaus konnte gezeigt werden, dass negative Effekte in Form von Motivationsverlusten in Bezug auf die Trackernutzung und die körperliche Aktivität eine Rolle im Alltag vieler Nutzer*innen spielen. Die Wahrscheinlichkeit des Auftretens dieser Effekte wird teilweise von Personenmerkmalen wie ATI und der Nutzungsmotivation bestimmt (Artikel 2). Die zweite Studie nahm ehemalige Nutzer*innen (N = 159) in den Blick und fokussierte auf die Erfassung der Gründe für den Nutzungsabbruch sowie die Stabilität der Abbruchentscheidung. Die Ergebnisse machten deutlich, dass zahlreiche Nutzungsbarrieren für die Entscheidung, den Tracker abzulegen, ausschlaggebend sind. Außerdem sind die Abbruchentscheidungen oft nicht permanent, was auf eine episodische Trackernutzung hindeutet (Artikel 3). Schließlich wurden wiederum Personenmerkmale und außerdem Interaktionscharakteristika in Betracht gezogen, um die große Varianz hinsichtlich Abbruchgründen und -permanenz zu erklären. Die Analysen offenbarten unter anderem, dass eine episodische Nutzung (d. h. nicht endgültige Beendigung) wahrscheinlicher ist, wenn sich die Nutzungsmotivation durch einen hohen Grad an Selbstbestimmung auszeichnet (Artikel 4). Abschließend betonen die Befunde der Dissertation die zentrale Rolle der wahrgenommenen Selbstbestimmung im Kontext der Mensch-Tracker-Interaktion und geben Anlass für Designrichtlinien, die die Beziehung zwischen Trackingsystem und Nutzer*in mit all ihren gegenseitigen Abhängigkeiten und individuellen Merkmalen berücksichtigen, um so die Selbstbestimmung zu erhalten oder sogar durch vertieftes Selbstwissen zu stärken.The ongoing permeation of our daily life with digital technologies is particularly evident in wearable devices such as smartphones, which can be accessed at any time. Wearable, connected self-tracking systems such as activity trackers go even a step further. They continuously record movement data and physiological parameters, process them algorithmically and provide quantified feedback to the user, often for behavioral modification. This specific form of interaction between humans and technology – close to the body, continuous, quantified, connected, and persuasive – is particularly relevant for engineering psychology, as it requires a very close connection between body and technology and poses specific challenges for strengthening the self-determination of its users. That is, on the one hand, activity trackers serve to facilitate self-reflection by revealing relationships which were previously hidden, such as the relationship between physical activity and resting heart rate. On the other hand, activity trackers are intended to enhance motivation for physical behavioral changes. The use of activity trackers thus potentially moves in a field of tension between the increase of self-determination through expanded knowledge as well as the identification of behavioral options and the restriction of self-determination through persuasive strategies to increase motivation. This tension requires new approaches to the design of relationships between people and tracking systems. Empirical research on activity tracker usage often highlights that a large proportion of users stop continuous use after a few weeks or months. This finding suggests the existence of barriers that make long-term use less likely. Furthermore, negative effects of tracker use, such as stress, are repeatedly reported. However, it is also known that many other users have enjoyed intensive and successful use of their tracking system for many years. Thus, a significant variance in usage trajectories can be observed, which needs to be explained in order to make self-tracking applications beneficial for diverse users. To meet this goal, it is essential to understand which individual differences in the group of users shape the interaction with their activity tracker, especially with respect to motivational aspects. This dissertation addresses this challenge by drawing on established theories and concepts of personality and social psychology. At the beginning of the dissertation project, the theory-based inclusion of personal characteristics in engineering psychology had not yet been sufficiently advanced. Thus, there was a need to conceptualize a construct which, on the one hand, stands on a stable psychological theoretical foundation and, on the other hand, is specifically tailored to the context of human-technology interaction. For this reason, the conceptualization of affinity for technology interaction (ATI) as a context-specific variant of need for cognition and its measurability took place within the context of the dissertation. In total, the data collection to determine the quality criteria of the ATI scale comprised five data sets with over 1500 participants. The result of the scale development is a unidimensional, economical, reliable, and valid survey instrument of ATI (Article 1). As a relatively stable personality trait that fundamentally influences motivation to engage with technology, ATI was included in subsequent studies of human-activity tracker interaction. In order to comprehensively understand the everyday, individual human-tracker interaction and to be able to explain how the various usage patterns occur, different phases of usage must be examined. First, it must be clarified which motivators actually cause a person to start using a tracker. Furthermore, the usage phase itself must be examined to describe how the specific form of tracker interaction described above affects the usage experience and ongoing motivation, and how negative usage consequences become apparent. Finally, to understand usage trajectories, the reasons for discontinuation need to be considered, hence the post-usage phase is also relevant. Since this dissertation decidedly focuses on the interaction with activity trackers in everyday life, the investigation of actual or former activity tracker users is indicated. For this reason, two online surveys were conducted to assess these actual (former) users. The aim of the first study (N = 210) was to quantitatively analyze motivations for usage as well as unintended, negative effects of tracker usage in daily use. It was shown that tracking is performed both for an end in itself (intrinsic motivation) and to achieve an external goal (extrinsic motivation), and that these motivation types often occur simultaneously. Furthermore, it was shown that negative effects in terms of motivation losses with respect to tracker use as well as physical activity play a role in many users' daily lives. The likelihood of these effects occurring is partly determined by personal characteristics such as ATI and motivation for usage (Article 2). The second study examined former users (N = 159) and focused on the reasons for discontinuing use and the stability of abandonment. The results indicated that numerous barriers to use are decisive for the decision to discontinue tracking. In addition, abandonment decisions are often not permanent, suggesting episodic tracker use (Article 3). Finally, person and interaction characteristics were considered to explain the large variance in abandonment reasons and permanence. The analyses revealed, among other things, that episodic use (i.e., not definitive termination) is more likely when the motivation for usage is characterized by a high degree of self-determination (Article 4). In conclusion, the findings of the dissertation emphasize the central role of perceived self-determination in the context of human-tracker interaction and give rise to design guidelines that take into account the relationship between the tracking system and the user with all its interdependencies and individual characteristics in order to preserve or even strengthen self-determination through deeper self-knowledge
    corecore