537 research outputs found

    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

    Graphonomics and your Brain on Art, Creativity and Innovation : Proceedings of the 19th International Graphonomics Conference (IGS 2019 – Your Brain on Art)

    Get PDF
    [Italiano]: “Grafonomia e cervello su arte, creatività e innovazione”. Un forum internazionale per discutere sui recenti progressi nell'interazione tra arti creative, neuroscienze, ingegneria, comunicazione, tecnologia, industria, istruzione, design, applicazioni forensi e mediche. I contributi hanno esaminato lo stato dell'arte, identificando sfide e opportunità, e hanno delineato le possibili linee di sviluppo di questo settore di ricerca. I temi affrontati includono: strategie integrate per la comprensione dei sistemi neurali, affettivi e cognitivi in ambienti realistici e complessi; individualità e differenziazione dal punto di vista neurale e comportamentale; neuroaesthetics (uso delle neuroscienze per spiegare e comprendere le esperienze estetiche a livello neurologico); creatività e innovazione; neuro-ingegneria e arte ispirata dal cervello, creatività e uso di dispositivi di mobile brain-body imaging (MoBI) indossabili; terapia basata su arte creativa; apprendimento informale; formazione; applicazioni forensi. / [English]: “Graphonomics and your brain on art, creativity and innovation”. A single track, international forum for discussion on recent advances at the intersection of the creative arts, neuroscience, engineering, media, technology, industry, education, design, forensics, and medicine. The contributions reviewed the state of the art, identified challenges and opportunities and created a roadmap for the field of graphonomics and your brain on art. The topics addressed include: integrative strategies for understanding neural, affective and cognitive systems in realistic, complex environments; neural and behavioral individuality and variation; neuroaesthetics (the use of neuroscience to explain and understand the aesthetic experiences at the neurological level); creativity and innovation; neuroengineering and brain-inspired art, creative concepts and wearable mobile brain-body imaging (MoBI) designs; creative art therapy; informal learning; education; forensics

    Imagining & Sensing: Understanding and Extending the Vocalist-Voice Relationship Through Biosignal Feedback

    Get PDF
    The voice is body and instrument. Third-person interpretation of the voice by listeners, vocal teachers, and digital agents is centred largely around audio feedback. For a vocalist, physical feedback from within the body provides an additional interaction. The vocalist’s understanding of their multi-sensory experiences is through tacit knowledge of the body. This knowledge is difficult to articulate, yet awareness and control of the body are innate. In the ever-increasing emergence of technology which quantifies or interprets physiological processes, we must remain conscious also of embodiment and human perception of these processes. Focusing on the vocalist-voice relationship, this thesis expands knowledge of human interaction and how technology influences our perception of our bodies. To unite these different perspectives in the vocal context, I draw on mixed methods from cog- nitive science, psychology, music information retrieval, and interactive system design. Objective methods such as vocal audio analysis provide a third-person observation. Subjective practices such as micro-phenomenology capture the experiential, first-person perspectives of the vocalists them- selves. Quantitative-qualitative blend provides details not only on novel interaction, but also an understanding of how technology influences existing understanding of the body. I worked with vocalists to understand how they use their voice through abstract representations, use mental imagery to adapt to altered auditory feedback, and teach fundamental practice to others. Vocalists use multi-modal imagery, for instance understanding physical sensations through auditory sensations. The understanding of the voice exists in a pre-linguistic representation which draws on embodied knowledge and lived experience from outside contexts. I developed a novel vocal interaction method which uses measurement of laryngeal muscular activations through surface electromyography. Biofeedback was presented to vocalists through soni- fication. Acting as an indicator of vocal activity for both conscious and unconscious gestures, this feedback allowed vocalists to explore their movement through sound. This formed new perceptions but also questioned existing understanding of the body. The thesis also uncovers ways in which vocalists are in control and controlled by, work with and against their bodies, and feel as a single entity at times and totally separate entities at others. I conclude this thesis by demonstrating a nuanced account of human interaction and perception of the body through vocal practice, as an example of how technological intervention enables exploration and influence over embodied understanding. This further highlights the need for understanding of the human experience in embodied interaction, rather than solely on digital interpretation, when introducing technology into these relationships

    Behavior quantification as the missing link between fields: Tools for digital psychiatry and their role in the future of neurobiology

    Full text link
    The great behavioral heterogeneity observed between individuals with the same psychiatric disorder and even within one individual over time complicates both clinical practice and biomedical research. However, modern technologies are an exciting opportunity to improve behavioral characterization. Existing psychiatry methods that are qualitative or unscalable, such as patient surveys or clinical interviews, can now be collected at a greater capacity and analyzed to produce new quantitative measures. Furthermore, recent capabilities for continuous collection of passive sensor streams, such as phone GPS or smartwatch accelerometer, open avenues of novel questioning that were previously entirely unrealistic. Their temporally dense nature enables a cohesive study of real-time neural and behavioral signals. To develop comprehensive neurobiological models of psychiatric disease, it will be critical to first develop strong methods for behavioral quantification. There is huge potential in what can theoretically be captured by current technologies, but this in itself presents a large computational challenge -- one that will necessitate new data processing tools, new machine learning techniques, and ultimately a shift in how interdisciplinary work is conducted. In my thesis, I detail research projects that take different perspectives on digital psychiatry, subsequently tying ideas together with a concluding discussion on the future of the field. I also provide software infrastructure where relevant, with extensive documentation. Major contributions include scientific arguments and proof of concept results for daily free-form audio journals as an underappreciated psychiatry research datatype, as well as novel stability theorems and pilot empirical success for a proposed multi-area recurrent neural network architecture.Comment: PhD thesis cop

    Machine Learning Algorithm for the Scansion of Old Saxon Poetry

    Get PDF
    Several scholars designed tools to perform the automatic scansion of poetry in many languages, but none of these tools deal with Old Saxon or Old English. This project aims to be a first attempt to create a tool for these languages. We implemented a Bidirectional Long Short-Term Memory (BiLSTM) model to perform the automatic scansion of Old Saxon and Old English poems. Since this model uses supervised learning, we manually annotated the Heliand manuscript, and we used the resulting corpus as labeled dataset to train the model. The evaluation of the performance of the algorithm reached a 97% for the accuracy and a 99% of weighted average for precision, recall and F1 Score. In addition, we tested the model with some verses from the Old Saxon Genesis and some from The Battle of Brunanburh, and we observed that the model predicted almost all Old Saxon metrical patterns correctly misclassified the majority of the Old English input verses

    Dialogue without barriers. A comprehensive approach to dealing with stuttering

    Get PDF
    publishedVersio

    K prozodii mluvené češtiny metodami korpusové lingvistiky

    Get PDF
    Prosody is a key aspect of spoken language, yet it is currently underrepresented in the spoken Czech corpora on offer at the Czech National Corpus. This is mainly because spoken corpora are very expensive and manual work intensive as it is, and adding more annotation manually is infeasible. The present dissertation thus charts a way to provide an automatic prosodic annotation for the spoken corpora of the CNC using the Prosogram framework, in combination with other tools and various custom postprocessing strategies and heuristics. Acaseisalsomadeinfavoroftheory-light,predominantlydescriptiveapproaches when preparing general-purpose spoken corpus annotations for the consumption of the linguistics research community at large, in a variety of contexts and research tasks. This case is philosophically anchored in a discriminative approach to meaning, which is shown to be the correct, paradox-free alternative to the currently more dominant paradigm of compositionality. Finally, a selection of results based on the Prosogram-generated annotation is presented. A particular focus is given to pitch range, which is characteristically restricted in Czech compared to other languages like English, but other features such as glissandos are also considered. Keywords: Czech, speech, prosody, corpus linguistics,...Prozodie je klíčovým aspektem mluveného jazyka, nicméně v korpusech mluvené češtiny, které jsou aktuálně v nabídce Českého národního korpusu, je reprezen- tována jen okrajově. Primární důvod je ten, že mluvené korpusy jsou už tak velmi náročné co se nákladů a manuální práce týče, takže přidávat další manuálně an- otované prvky není schůdné. Předkládaná práce tak nabízí cestu, jak prozodickou anotaci doplnit do těchto korpusů automaticky, pomocí systému Prosogram v kom- binaci s dalšími nástroji a vlastními postprocessingovými postupy a heuristikami. Součástí teoretického zdůvodnění volby Prosogramu jako anotačního nástroje je i analýza toho, jak funguje v jazyce význam. Filozoficky je ukotvená v diskrimina- tivním pojetí významu, které na rozdíl od aktuálně dominantního kompozičního pojetí neskýtá při důsledné aplikaci žádné paradoxy. Vyplývá z ní, že anotaci obec- ných mluvených korpusů, která cílí na užití širokou lingvistickou komunitou v různýchkontextecha při různých výzkumných úkolech,je vhodné cílit deskriptivně, s minimální poplatností konkrétním teoriím. Prezentované výsledky, získané pomocí zpracování Prosogramem, se soustředí zejména na intonační rozpětí, protože omezené intonační rozpětí je poměrně ná- padným rysem češtiny ve srovnání s jinými jazyky, např. angličtinou. Věnujeme se...Ústav germánských studiíInstitute of Germanic StudiesFaculty of ArtsFilozofická fakult

    Dialogue without barriers. A comprehensive approach to dealing with stuttering

    Get PDF
    The book Dialogue without barriers: A comprehensive approach to dealing with stuttering is the result of Norwegian-Polish cooperation undertaken in the project LOGOLab – Dialogue without barriers. Three partners have been involved in the production of this book, namely, the University of Silesia in Katowice, Poland, the UiT Arctic University of Norway in Tromsø, and the Agere Aude Foundation for Knowledge and Social Dialogue. The project was implemented under the Education Program financed by the EEA Grants (EEA / 19 / K1 / D1 / W / 0031). The EEA Grants represent the contribution of Iceland, Liechtenstein, and Norway towards a green, competitive, and inclusive Europe. The most important goal of the LOGOLab project was to raise the standards of speech-language therapy in stuttering by incorporating the principles of Evidence-based practice, taking into account the assumptions of inclusive education and community-based model of intervention. An essential strategy for achieving this goal has become the dissemination of reliable and up-to-date knowledge about stuttering, and the development of appropriate social attitudes towards stuttering. The improvement of the quality of academic education for speech-language therapy students and of vocational training for certified speech-language therapists should also be mentioned. An additional aim was to provide reliable information for leaders of the self-help movement, who support people with stuttering non-institutionally

    Brain Computations and Connectivity [2nd edition]

    Get PDF
    This is an open access title available under the terms of a CC BY-NC-ND 4.0 International licence. It is free to read on the Oxford Academic platform and offered as a free PDF download from OUP and selected open access locations. Brain Computations and Connectivity is about how the brain works. In order to understand this, it is essential to know what is computed by different brain systems; and how the computations are performed. The aim of this book is to elucidate what is computed in different brain systems; and to describe current biologically plausible computational approaches and models of how each of these brain systems computes. Understanding the brain in this way has enormous potential for understanding ourselves better in health and in disease. Potential applications of this understanding are to the treatment of the brain in disease; and to artificial intelligence which will benefit from knowledge of how the brain performs many of its extraordinarily impressive functions. This book is pioneering in taking this approach to brain function: to consider what is computed by many of our brain systems; and how it is computed, and updates by much new evidence including the connectivity of the human brain the earlier book: Rolls (2021) Brain Computations: What and How, Oxford University Press. Brain Computations and Connectivity will be of interest to all scientists interested in brain function and how the brain works, whether they are from neuroscience, or from medical sciences including neurology and psychiatry, or from the area of computational science including machine learning and artificial intelligence, or from areas such as theoretical physics
    • …
    corecore