130 research outputs found

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

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    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

    Deep Learning for Time-Series Analysis of Optical Satellite Imagery

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    In this cumulative thesis, I cover four papers on time-series analysis of optical satellite imagery. The contribution is split into two parts. The first one introduces DENETHOR and DynamicEarthNet, two landmark datasets with high-quality ground truth data for agricultural monitoring and change detection. Second, I introduce SiROC and SemiSiROC, two methodological contributions to label-efficient change detection

    AdaBrowse: Adaptive Video Browser for Efficient Continuous Sign Language Recognition

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    Raw videos have been proven to own considerable feature redundancy where in many cases only a portion of frames can already meet the requirements for accurate recognition. In this paper, we are interested in whether such redundancy can be effectively leveraged to facilitate efficient inference in continuous sign language recognition (CSLR). We propose a novel adaptive model (AdaBrowse) to dynamically select a most informative subsequence from input video sequences by modelling this problem as a sequential decision task. In specific, we first utilize a lightweight network to quickly scan input videos to extract coarse features. Then these features are fed into a policy network to intelligently select a subsequence to process. The corresponding subsequence is finally inferred by a normal CSLR model for sentence prediction. As only a portion of frames are processed in this procedure, the total computations can be considerably saved. Besides temporal redundancy, we are also interested in whether the inherent spatial redundancy can be seamlessly integrated together to achieve further efficiency, i.e., dynamically selecting a lowest input resolution for each sample, whose model is referred to as AdaBrowse+. Extensive experimental results on four large-scale CSLR datasets, i.e., PHOENIX14, PHOENIX14-T, CSL-Daily and CSL, demonstrate the effectiveness of AdaBrowse and AdaBrowse+ by achieving comparable accuracy with state-of-the-art methods with 1.44Ă—\times throughput and 2.12Ă—\times fewer FLOPs. Comparisons with other commonly-used 2D CNNs and adaptive efficient methods verify the effectiveness of AdaBrowse. Code is available at \url{https://github.com/hulianyuyy/AdaBrowse}.Comment: ACMMM202

    Modelling, Dimensioning and Optimization of 5G Communication Networks, Resources and Services

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    This reprint aims to collect state-of-the-art research contributions that address challenges in the emerging 5G networks design, dimensioning and optimization. Designing, dimensioning and optimization of communication networks resources and services have been an inseparable part of telecom network development. The latter must convey a large volume of traffic, providing service to traffic streams with highly differentiated requirements in terms of bit-rate and service time, required quality of service and quality of experience parameters. Such a communication infrastructure presents many important challenges, such as the study of necessary multi-layer cooperation, new protocols, performance evaluation of different network parts, low layer network design, network management and security issues, and new technologies in general, which will be discussed in this book

    Impacts of DEM Type and Resolution on Deep Learning-Based Flood Inundation Mapping

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    This paper presents a comprehensive study focusing on the influence of DEM type and spatial resolution on the accuracy of flood inundation prediction. The research employs a state-of-the-art deep learning method using a 1D convolutional neural network (CNN). The CNN-based method employs training input data in the form of synthetic hydrographs, along with target data represented by water depth obtained utilizing a 2D hydrodynamic model, LISFLOOD-FP. The performance of the trained CNN models is then evaluated and compared with the observed flood event. This study examines the use of digital surface models (DSMs) and digital terrain models (DTMs) derived from a LIDAR-based 1m DTM, with resolutions ranging from 15 to 30 meters. The proposed methodology is implemented and evaluated in a well-established benchmark location in Carlisle, UK. The paper also discusses the applicability of the methodology to address the challenges encountered in a data-scarce flood-prone region, exemplified by Pakistan. The study found that DTM performs better than DSM at lower resolutions. Using a 30m DTM improved flood depth prediction accuracy by about 21% during the peak stage. Increasing the resolution to 15m increased RMSE and overlap index by at least 50% and 20% across all flood phases. The study demonstrates that while coarser resolution may impact the accuracy of the CNN model, it remains a viable option for rapid flood prediction compared to hydrodynamic modeling approaches

    Leveraging Graph-based Cross-modal Information Fusion for Neural Sign Language Translation

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    Sign Language (SL), as the mother tongue of the deaf community, is a special visual language that most hearing people cannot understand. In recent years, neural Sign Language Translation (SLT), as a possible way for bridging communication gap between the deaf and the hearing people, has attracted widespread academic attention. We found that the current mainstream end-to-end neural SLT models, which tries to learning language knowledge in a weakly supervised manner, could not mine enough semantic information under the condition of low data resources. Therefore, we propose to introduce additional word-level semantic knowledge of sign language linguistics to assist in improving current end-to-end neural SLT models. Concretely, we propose a novel neural SLT model with multi-modal feature fusion based on the dynamic graph, in which the cross-modal information, i.e. text and video, is first assembled as a dynamic graph according to their correlation, and then the graph is processed by a multi-modal graph encoder to generate the multi-modal embeddings for further usage in the subsequent neural translation models. To the best of our knowledge, we are the first to introduce graph neural networks, for fusing multi-modal information, into neural sign language translation models. Moreover, we conducted experiments on a publicly available popular SLT dataset RWTH-PHOENIX-Weather-2014T. and the quantitative experiments show that our method can improve the model

    Sign and human action detection using deep learning

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    Human beings usually rely on communication to express their feeling, and ideas and solve disputes among them. A major component required for effective communication is language. Language can occur in different forms, including written symbols, gestures, or even vocals. It is usually essential for all the communicating parties to be fully conversant with a common language that they are using. However, this hasn’t been the case between speech impaired people who use sign language and the regular people in the society who use spoken languages. Different studies have pointed out a significant gap between these people and the regular people, limiting the ease of communication. Therefore, this study aims to develop an efficient deep learning model that can be used to predict British sign language. This is in an attempt to narrow this communication gap between the speech-impaired people and the regular people in the community. Two models were developed in the research, which includes CNN and LSTM, and their performance was evaluated using a multi-class confusion matrix. The CNN model emerged with the highest performance, attaining training, and testing accuracies of 98.8% and 97.4%, respectively. The model also achieved average weighted precession, and recall was also 97% and 96%, respectively. On the other hand, the LSTM model’s performance was quite poor, with maximum training and testing, the achieved performance is 49.4% and 48.7% respectively. The research concluded that the CNN model was the best for recognizing and determining British sign language

    Recent Trends in Artificial Intelligence-Assisted Coronary Atherosclerotic Plaque Characterization

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    Coronary artery disease is a major cause of morbidity and mortality worldwide. Its underlying histopathology is the atherosclerotic plaque, which comprises lipid, fibrous and—when chronic—calcium components. Intravascular ultrasound (IVUS) and intravascular optical coherence tomography (IVOCT) performed during invasive coronary angiography are reference standards for characterizing the atherosclerotic plaque. Fine image spatial resolution attainable with contemporary coronary computed tomographic angiography (CCTA) has enabled noninvasive plaque assessment, including identifying features associated with vulnerable plaques known to presage acute coronary events. Manual interpretation of IVUS, IVOCT and CCTA images demands scarce physician expertise and high time cost. This has motivated recent research into and development of artificial intelligence (AI)-assisted methods for image processing, feature extraction, plaque identification and characterization. We performed parallel searches of the medical and technical literature from 1995 to 2021 focusing respectively on human plaque characterization using various imaging modalities and the use of AI-assisted computer aided diagnosis (CAD) to detect and classify atherosclerotic plaques, including their composition and the presence of high-risk features denoting vulnerable plaques. A total of 122 publications were selected for evaluation and the analysis was summarized in terms of data sources, methods—machine versus deep learning—and performance metrics. Trends in AI-assisted plaque characterization are detailed and prospective research challenges discussed. Future directions for the development of accurate and efficient CAD systems to characterize plaque noninvasively using CCTA are proposed.</jats:p

    Automated Detection of Electric Energy Consumption Load Profile Patterns

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    [EN] Load profiles of energy consumption from smart meters are becoming more and more available, and the amount of data to analyse is huge. In order to automate this analysis, the application of state-of-the-art data mining techniques for time series analysis is reviewed. In particular, the use of dynamic clustering techniques to obtain and visualise temporal patterns characterising the users of electrical energy is deeply studied. The performed review can be used as a guide for those interested in the automatic analysis and groups of behaviour detection within load profile databases. Additionally, a selection of dynamic clustering algorithms have been implemented and the performances compared using an available electric energy consumption load profile database. The results allow experts to easily evaluate how users consume energy, to assess trends and to predict future scenarios.The data analysed has been facilitated by the Spanish Distributor Iberdrola Electrical Distribution S.A. as part of the research project GAD (Active Management of the Demand), national project by DEVISE 2010 funded by the INGENIIO 2010 program and the CDTI (Centre for Industrial Technology Development), Business Public Entity dependent of the Ministry of Economy and Competitiveness of the Government of Spain.BenĂ­tez, I.; Diez, J. (2022). Automated Detection of Electric Energy Consumption Load Profile Patterns. Energies. 15(6):1-26. https://doi.org/10.3390/en1506217612615

    Latent gaze information in highly dynamic decision-tasks

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    Die Digitalisierung durchdringt immer mehr Lebensbereiche. Aufgaben werden zunehmend digital erledigt und damit schneller, effizienter, aber auch zielorientierter und erfolgreicher erfüllt. Die rasante Entwicklung im Bereich der künstlichen Intelligenz in den letzten Jahren hat dabei eine große Rolle gespielt, denn sie hat viele hilfreiche Ansätze hervorgebracht, auf die immer weiter aufgebaut werden kann. Gleichzeitig werden die Augen, ihre Bewegungen und die Bedeutung dieser Bewegungen immer weiter erforscht. Die Verknüpfung dieser Entwicklungen hat zu spannenden Ansätzen in der Wissenschaft geführt. In dieser Dissertation stelle ich einige der Ansätze vor, an denen ich während meiner Promotion gearbeitet habe. Zunächst gebe ich einen Einblick in die Entwicklung von Modellen, die mit Hilfe künstlicher Intelligenz Verbindungen zwischen Augenbewegungsdaten und visueller Expertise herstellen. Dies wird anhand zwei verschiedener Bereiche, genauer gesagt zwei verschiedener Personengruppen, demonstriert: Sportler bei Entscheidungsfindungen und Chirurgen bei arthroskopischen Eingriffen. Die daraus resultierenden Modelle können als digitale Diagnosemodelle für die automatische Erkennung von visueller Expertise betrachtet werden. Darüber hinaus stelle ich Ansätze vor, die die Übertragbarkeit von Augenbewegungsmustern auf verschiedene Kompetenzbereiche untersuchen sowie wichtige Aspekte von Techniken zur Generalisierung. Schließlich befasse ich mich mit der zeitlichen Erkennung von Verwirrung auf der Grundlage von Augenbewegungsdaten. Die Ergebnisse legen eine Nutzung der Modelle als Zeitgeber für mögliche digitale Assistenzoptionen in der Ausbildung von Berufsanfängern nahe. Eine Besonderheit meiner Untersuchungen besteht darin, dass ich auf sehr wervolle Daten von DFB-Jugendkaderathleten sowie von langjährigen Experten in der Arthroskopie zurückgreifen konnte. Insbesondere die Arbeit mit den DFB-Daten stieß auf das Interesse von Radiound Printmedien, genauer, DeutschlandFunk Nova und SWR DasDing. Alle hier vorgestellten Beiträge wurden in international renommierten Fachzeitschriften oder auf Konferenzen veröffentlicht.Digitization is penetrating more and more areas of life. Tasks are increasingly being completed digitally, and are therefore not only fulfilled faster, more efficiently but also more purposefully and successfully. The rapid developments in the field of artificial intelligence in recent years have played a major role in this, as they brought up many helpful approaches to build on. At the same time, the eyes, their movements, and the meaning of these movements are being progressively researched. The combination of these developments has led to exciting approaches. In this dissertation, I present some of these approaches which I worked on during my Ph.D. First, I provide insight into the development of models that use artificial intelligence to connect eye movements with visual expertise. This is demonstrated for two domains or rather groups of people: athletes in decision-making actions and surgeons in arthroscopic procedures. The resulting models can be considered as digital diagnostic models for automatic expertise recognition. Furthermore, I show approaches that investigate the transferability of eye movement patterns to different expertise domains and subsequently, important aspects of techniques for generalization. Finally, I address the temporal detection of confusion based on eye movement data. The results suggest the use of the resulting model as a clock signal for possible digital assistance options in the training of young professionals. An interesting aspect of my research is that I was able to draw on very valuable data from DFB youth elite athletes as well as on long-standing experts in arthroscopy. In particular, the work with the DFB data attracted the interest of radio and print media, namely DeutschlandFunk Nova and SWR DasDing. All resulting articles presented here have been published in internationally renowned journals or at conferences
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