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Suche nach „[Digital]“ hat 1166 Publikationen gefunden
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    DigitalF: Angewandte Informatik

    Beitrag (Sammelband oder Tagungsband)

    Monica I. Ciolacu, Leon Binder, P. M. Svasta, D. Stoichescu, I. Tache

    Education 4.0 - Jump to Innovation IoT in Higher Education

    Proceedings of the 2019 IEEE 25th International Symposium for Design and Technology in Electronic Packaging (SIITME) [Oct 23-26, 2019; Cluj-Napoca, Romania], New York, NY, USA

    2020

    DOI: 10.1109/SIITME47687.2019.8990825

    Abstract anzeigen

    Artificial Intelligence (AI) will play a key role in Higher Education. Our contribution leads the road to innovation within IoT for Education 4.0, using a system comprising smartwatch data, health data, learning analytics and Artificial Intelligence. By using embedded sensors from wearable devices, we can add value to Education 4.0. Smartwatches are IoT devices, equipped with a multitude of embedded sensors collecting distraction-free huge amounts of real-time data during students’ learning. In our paper we highlight advantages of wrist-based wearables like smartwatches for Education 4.0. We develop a User Experience Questionnaire for the measurement of acceptance of advanced electronic technology in Higher Education. We identify as experiment’s results the most important sensors and protocols for Education 4.0.

    DigitalF: Angewandte InformatikS: TC Grafenau

    Beitrag (Sammelband oder Tagungsband)

    Monica I. Ciolacu, Leon Binder, Heribert Popp

    Enabling IoT in Education 4.0 with Biosensors from Wearables and Artificial Intelligence

    Proceedings of the 2019 IEEE 25th International Symposium for Design and Technology in Electronic Packaging (SIITME) [Oct 23-26, 2019; Cluj-Napoca, Romania], New York, NY, USA

    2020

    DOI: 10.1109/SIITME47687.2019.8990763

    Abstract anzeigen

    A major challenge for Education 4.0 is to make use of wearable devices for helping students in monitoring their learning behavior and their activities (steps, heart rate variability, and heart rate) in real-time. The first aim of this paper is to present our implementation of adaptivity and Artificial Intelligence (AI) methods within the Education 4.0 process. In this work, we investigate embedded biosensors (noninvasive, low-cost, and distraction-free) used in smartphones and smartwatches. The next objective is to enable IoT for Higher Education, i.e. a novel system assisted by AI that takes embedded biosensor data and environmental data into account in order to estimate students’ wellbeing and health. In this regard, we propose a framework that uses wearable devices to collect data with biofeedback methods to support students’ academic success.

    DigitalF: Europan Campus Rottal-Inn

    Beitrag (Sammelband oder Tagungsband)

    T. Chondrogiannis, Mouzhi Ge

    Inferring ratings for custom trips from rich GPS traces

    LocalRec '19: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-based Recommendations

    2019

    DOI: 10.1145/3356994.3365502

    Abstract anzeigen

    Trip planning services are employed extensively by users to compute paths between locations and navigate within a road network. In some real-world scenarios such as planning for a hiking trip or running training, users usually require personalized trip planning. Although some existing systems can recommend trips that other users have posted, along with a set of ratings w.r.t. the difficulty of the route, conditions, or the enjoyment it provides. Very often though users want to define a custom trip that fits their personal needs, for which existing systems are unable to provide any rating. In this paper we therefore define the problem of inferring ratings for custom trips. We also outline a solution to infer ratings by utilizing the ratings of trips previously posted by users and their similarity with a given custom trip. Finally, we present the results of preliminary experiments were we evaluate the efficiency of our proposed approach on inferring ratings for trips related to hiking and other similar activities.

    DigitalF: Europan Campus Rottal-Inn

    Beitrag (Sammelband oder Tagungsband)

    L. Trang, H. Bangui, Mouzhi Ge, B. Buhnova

    Scaling Big Data Applications in Smart City with Coresets

    Proceedings of the 8th International Conference on Data Science, Technology and Applications, vol. Vol. 1: DATA

    2019

    DOI: 10.5220/0007958803570363

    Abstract anzeigen

    With the development of Big Data applications in Smart Cities, various Big Data applications are proposed within the domain. These are however hard to test and prototype, since such prototyping requires big computing resources. In order to save the effort in building Big Data prototypes for Smart Cities, this paper proposes an enhanced sampling technique to obtain a coreset from Big Data while keeping the features of the Big Data, such as clustering structure and distribution density. In the proposed sampling method, for a given dataset and an ε>0, the method computes an ε-coreset of the dataset. The ε-coreset is then modified to obtain a sample set while ensuring the separation and balance in the set. Furthermore, by considering the representativeness of each sample point, our method can helps to remove noises and outliers. We believe that the coreset-based technique can be used to efficiently prototype and evaluate Big Data applications in the Smart City.

    DigitalF: Europan Campus Rottal-Inn

    Beitrag (Sammelband oder Tagungsband)

    Mouzhi Ge, S. Chren, B. Rossi, T. Pitner

    Data Quality Management Framework for Smart Grid Systems

    Business Information Systems (BIS 2019), Cham, Switzerland, vol. 354

    2019

    DOI: 10.1007/978-3-030-20482-2_24

    DigitalF: Europan Campus Rottal-Inn

    Beitrag (Sammelband oder Tagungsband)

    B. Mbarek, Mouzhi Ge, T. Pitner

    Self-adaptive RFID Authentication for Internet of Things

    Advanced information networking and applications, vol. 926

    2019

    ISBN: 978-3-030-15031-0

    Abstract anzeigen

    With the development of wireless Internet of Things (IoT) devices, Radio frequency identification (RFID) has been used as a promising technology for the proliferation and communication in IoT networks. However, the RFID techniques are usually plagued with security and privacy issues due to the inherent weaknesses of underlying wireless radio communications. Although several RFID authentication mechanisms have been proposed to address the security concerns in RFID, most of them are still vulnerable to some active attacks, especially the jamming attack. This paper therefore proposes a novel self-adaptive authentication mechanism with a robust key updating, in order to tackle the security vulnerabilities of key updating algorithms and their inability to jamming attacks. Furthermore, we assess the performance and applicability of our solution with a thorough simulation by taking into account the energy consumption and authentication failure rate.

    DigitalF: Europan Campus Rottal-Inn

    Zeitschriftenartikel

    H. Bangui, Mouzhi Ge, B. Buhnova

    A Research Roadmap of Big Data Clustering Algorithms for Future Internet of Things

    International Journal of Organizational and Collective Intelligence, vol. 9, no. 2, pp. 16-30

    2019

    DOI: 10.4018/IJOCI.2019040102

    Abstract anzeigen

    Due to the massive data increase in different Internet of Things (IoT) domains such as healthcare IoT and Smart City IoT, Big Data technologies have been emerged as critical analytics tools for analyzing the IoT data. Among the Big Data technologies, data clustering is one of the essential approaches to process the IoT data. However, how to select a suitable clustering algorithm for IoT data is still unclear. Furthermore, since Big Data technology are still in its initial stage for different IoT domains, it is thus valuable to propose and structure the research challenges between Big Data and IoT. Therefore, this article starts by reviewing and comparing the data clustering algorithms that can be applied in IoT datasets, and then extends the discussions to a broader IoT context such as IoT dynamics and IoT mobile networks. Finally, this article identifies a set of research challenges that harvest a research roadmap for the Big Data research in IoT domains. The proposed research roadmap aims at bridging the research gaps between Big Data and various IoT contexts.

    DigitalF: Angewandte Wirtschaftswissenschaften

    Buch (Monographie)

    Christian Mandl, S. Nadarajah, S. Minner, S. Gavirneni

    Structured Data-Driven Operating Policies for Commodity Storage

    2019

    Abstract anzeigen

    Storage assets are critical for temporal trading of commodities under volatile prices. State-of-the-art methods for managing storage such as the reoptimization heuristic (RH), which are part of commercial software, approximate a Markov decision process (MDP) assuming full information regarding the state and the stochastic commodity price process and hence suffer from informational inconsistencies with observed price data and structural inconsistencies with the true optimal policy, which are both components of generalization error. Based on extensive backtests, we find that this error can lead to significantly suboptimal RH policies and qualitatively different performance compared to the known near-optimality and behavior of RH in the full-information setting. We develop a forward-looking data-driven approach (DDA) to learn policies and overcome generalization error. This approach extends standard (backward-looking) DDA in two ways: (i) it uses financial-market features and estimates of future prots as part of the training objective, which typically includes past prots alone; and (ii) it enforces structural properties of the optimal policy. To elaborate, DDA trains parameters of bang-bang and base-stock policies, respectively, by solving linear-and mixed-integer programs, thereby extending known DDAs that parameterize decisions as functions of features without enforcing policy structure. We backtest the performance of DDA and RH on six major commodities from 2000 to 2017 with features constructed using Thomson Reuters and Bloomberg data. DDA significantly improves RH on real data, with base-stock structure needed to realize this improvement. Our research advances the state-of-the-art for storage operations and suggests modifications to commercial software to handle generalization error.

    DigitalNachhaltigF: Europan Campus Rottal-Inn

    Beitrag (Sammelband oder Tagungsband)

    M. Küppers, M. Metzger, Matthias Huber, S. Paulus

    Archetypes of Country Energy Systems

    2019 IEEE Milan PowerTech Conference

    2019

    DOI: 10.1109/PTC.2019.8810765

    Abstract anzeigen

    Global challenges as decarbonization, the integration of renewables or an increasing electrification are confronting countries worldwide. Based on an analysis of different energy system models, archetypes of country energy systems are identified as an approach to simplify modeling the global challenges for most countries around the world. Applying a modified K-means algorithm to a broad and transparent data basis of socio-economic, geographic/climatic and energy-related data leads to the definition of the archetypes. An exemplary clustering of 140 countries generating 15 archetypes underlines the existence of patterns in energy systems, which can e.g. be characterized by the climatic circumstances or the energy mix. Overall the archetypes represent a possibility to summarize countries on a global level, leading to a simplified modeling process of countries in energy system models, providing a common data basis for models and identifying common challenges of different countries.

    DigitalNachhaltigF: Europan Campus Rottal-Inn

    Beitrag (Sammelband oder Tagungsband)

    O. Walter, Matthias Huber, Kueppers. M., A. Tremel, S. Becker

    Energy system design for deep decarbonization of a sunbelt city by using a hybrid storage approach

    Proceedings of the 13th International Renewable Energy Storage Conference 2019 (IRES 2019), vol. Vol. 4

    2019

    Abstract anzeigen

    With continuously falling cost of renewable power generation and ambitious decarbonization targets, renewable sources are about to rival fossil fuels for energy supply. For a high share of fluctuating renewable generation, large-scale energy storage is likely to be required. In addition to selling electricity, the reliable supply of heat and cold is a further interesting revenue pool, which makes hybrid storage technologies an interesting option. The main feature of hybrid energy storage – as defined here - is to offer charging and especially discharging in different forms of energy by combining different charging, discharging and storage devices. They can address various demands (e.g. electricity and cold) simultaneously. Two hybrid storages, pumped thermal energy storage (PTES) and power-to-heat-to-x (x: heat and/or electricity) energy storage (PHXES), are investigated based on a techno-economic analysis within this work. Both hybrid storage technologies are charged with electricity and can supply heat and electricity during discharging. They are implemented into a simplified energy system model of a prototype city in the earth’s sunbelt in the year 2030 to find a cost-optimal configuration. Different cases are evaluated: a power-to-power case (P2P), where only an electric demand must be addressed and a power-to-power-and-cooling (P2P&C) case, where the electric demand from the P2P case is divided into a residual electric demand and a cooling demand. For both cases, a natural gas-based benchmark scenario and a decarbonized, renewable-based scenario including the hybrid energy storage technologies are calculated. Both, total expenditures and CO2 emissions are lower in the P2P&C scenarios compared to P2P scenarios. PHXES plays a major role in both cases. PTES is part of the cost-optimal solution in the P2P&C decarb scenario, only if its specific cost are further decreased.

    DigitalNachhaltigF: Europan Campus Rottal-Inn

    Beitrag (Sammelband oder Tagungsband)

    D. Husarek, S. Paulus, Matthias Huber, M. Metzger, S. Niessen

    The Contribution of Carbon- Optimized Battery Electric Vehicle Charging to the Decarbonization of a Multi-Modal Energy System

    3rd E-Mobility Power System Integration Symposium

    2019

    DigitalNachhaltigF: Europan Campus Rottal-Inn

    Zeitschriftenartikel

    C. Müller, T. Falke, A. Hoffrichter, L. Wyrwoll, C. Schmitt, M. Trageser, A. Schnettler, M. Metzger, Matthias Huber, M. Küppers, D. Most, S. Paulus, H. Heger

    Integrated Planning and Evaluation of Multi-Modal Energy Systems for Decarbonization of Germany

    Energy Procedia, vol. 158, no. February, pp. 3482-3487

    2019

    DOI: 10.1016/j.egypro.2019.01.923

    Abstract anzeigen

    For a successful realization of the energy transition and a reduction of greenhouse gas emissions, an integrated view of multiple energy sectors (electricity, heat and mobility) is necessary. The coupling of different energy sectors is seen as an option to achieve the climate goals in a cost-effective way. In this paper, a methodical approach for multi-modal energy system planning and technology impact evaluation is presented. A key feature of the model is a coupled consideration of sectors electricity, heat and mobility. Energy demands, conversion and storage technologies in households, the Commerce, Trade and Services (CTS) area and the industry are modelled employing a bottom-up modelling approach. The model can be used for the calculation of a detailed transition pathway of energy systems taking into account politically defined climate goals. Based on these calculations, in-depth analyses of energy markets as well as transmission and distribution grids can be performed.

    DigitalNachhaltigF: Europan Campus Rottal-Inn

    Zeitschriftenartikel

    C. Müller, A. Hoffrichter, L. Wyrwoll, C. Schmitt, M. Trageser, T. Kulms, D. Beulertz, M. Metzger, M. Durckheim, Matthias Huber, M. Küppers, D. Most, S. Paulus, H. Heber, A. Schnettler

    Modeling framework for planning and operation of multi-modal energy systems in the case of Germany

    Applied Energy, vol. 250, no. 15 September 2019, pp. 1132-1146

    2019

    DOI: 10.1016/j.apenergy.2019.05.094

    Abstract anzeigen

    In order to reach the goals of the United Nations Framework Convention on Climate Change, a stepwise reduction of energy related greenhouse gas emissions as well as an increase in the share of renewable energies is necessary. For a successful realization of these changes in energy supply, an integrated view of multiple energy sectors is necessary. The coupling of different energy sectors is seen as an option to achieve the climate goals in a cost-effective way. In this paper, a methodical approach for multi-modal energy system planning and technology impact evaluation is presented. A key feature of the model is a coupled consideration of the sectors electricity, heat, fuel and mobility. The modeling framework enables system planners to optimally plan future investments in a detailed transition pathway of the energy system of a country, considering politically defined climate goals. Based on these calculations, in-depth analyses of energy markets as well as electrical transmission and distribution grids can be performed using the presented optimization models. Energy demands, conversion and storage technologies in households, the Commerce, Trade and Services (CTS) area and the industry are modeled employing a bottom-up modeling approach. The results for the optimal planning of the German energy system until 2050 show that the combination of an increased share of renewable energies and the direct electrification of heat and mobility sectors together with the use of synthetic fuels are the main drivers to achieve the climate goals in a cost-efficient way.

    DigitalI: Zentrum für Akademische WeiterbildungP: DEG-DLM2

    Buch (Monographie)

    Christian Ebner

    Bericht Begleitforschung Hochschulzertifikat „Data Analytics“

    2019

    DigitalNachhaltigF: Angewandte WirtschaftswissenschaftenS: TC Grafenau

    Beitrag (Sammelband oder Tagungsband)

    S. Goisser, J. Krause, Michael Fernandes, H. Mempel

    Determination of tomato quality attributes using portable NIR-sensors

    Proceedings of OCM 2019: 4th International Conference on Optical Characterization of Materials (13-14 March, 2019; Karlsruhe), Karlsruhe

    2019

    ISBN: 978-3-7315-0864-9

    DOI: 10.5445/KSP/1000087509

    Abstract anzeigen

    As part of a research project a multidisciplinary approach of different research institutes is followed to investigate the possibility of using a commercially available miniaturized NIR-sensor for the determination of tomato fruit quality parameters in postharvest. Correlation of spectra and tomato reference values of firmness, dry matter and total soluble solids showed good prediction accuracy. Additionally the decline of firmness over storage time with respect to storage temperature of tomatoes could be modelled. Therefore, the decline of firmness as an indicator for shelf-life can be predicted using this portable NIR-Sensor.

    DigitalNachhaltigExtern

    Beitrag (Sammelband oder Tagungsband)

    Anton Schmailzl, S. Hüntelmann, T. Loose, Johannes Käsbauer, F. Maiwald, S. Hierl

    Potentials of the ALE-Method for Modeling Plastics Welding Processes, in Particular for the Quasi-Simultaneous Laser Transmission Welding

    Mathematical Modelling of Weld Phenomena 12, Graz, Österreich, vol. 12

    2019

    ISBN: 978-3-85125-615-4

    DOI: 10.3217/978-3-85125-615-4-51

    Abstract anzeigen

    The Arbitrary-Lagrangian-Eulerian-Method (ALE-Method) offers the possibility to model the quasi-simultaneous laser transmission welding of plastics, in which a squeeze-flow of molten plastic occurs. It is of great interest to get a deeper understanding of the fluid-structure-interactions in the welding zone, since the occurring squeeze-flow transports heated material out of the joining zone, causing a temperature decrease inside. In addition, the numerical modelling offers the possibility to investigate the flow conditions in the joining zone. The aim of this article is to show the potentials of the ALE-Method to simulate the quasi-simultaneous laser transmission welding with the commercially available software LS-DYNA. The central challenge is to realize a bi-directional thermo-mechanically coupled simulation, which considers the comparatively high thermal expansion and calculates the interactions of solid and melted plastic correctly. Finally, the potentials of the ALE element formulations for the mathematical description of welding processes are shown, especially for those with a squeeze-flow.

    DigitalNachhaltigExtern

    Beitrag (Sammelband oder Tagungsband)

    T. Heberholz, Andrey Prihodovsky, M. Nowottnick

    Influence of a New Abnormal (CuNi)6Sn5 / (NiCu)3Sn4 Layer Growth at Temperatures Above 175°C in Tin Silver Based Lead-Free Solder Joints

    Proceedings of the SMTA International Conference 2019 (22-26 September 2019; Rosemont, IL, USA)

    2019

    DigitalS: TC Parsberg/Lupburg

    Vortrag

    Anton Schmailzl, Korbinian Schröcker, Ludwig Gansauge, Andrey Prihodovsky

    Potenziale von hybriden Fertigungsprozessketten

    1. TRIOKON 2019 – Die ostbayerische Transferkonferenz für Wirtschaft, Wissenschaft und Gesellschaft, Regensburg

    2019

    DigitalNachhaltigExtern

    Beitrag (Sammelband oder Tagungsband)

    B. Quandt, Korbinian Schröcker, S. Hierl

    Prozessüberwachung beim quasi-simultanen Laser-Durchstrahlschweißen glasfaserverstärkter Thermoplaste

    9. Landshuter Leichtbau-Colloquium (2019), Landshut, vol. 9

    2019

    ISBN: 978-3-9818439-2-7

    DigitalNachhaltigExtern

    Beitrag (Sammelband oder Tagungsband)

    Anton Schmailzl, B. Quandt, S. Hierl, M. Schmidt

    Correlation between Joint Strength and Process Temperature in Quasi-Simultaneous Laser Transmission Welding of Polyamide 6

    Proceedings of LiM2019 - Lasers in Manufacturing (23 June 2019, Munich)

    2019