Refine
Year of publication
- 2020 (139) (remove)
Document Type
- Conference Proceeding (47)
- Article (36)
- Report (14)
- Part of a Book (12)
- Book (6)
- Other Publications (6)
- Doctoral Thesis (4)
- Master's Thesis (4)
- Journal (Complete Issue of a Journal) (4)
- Bachelor Thesis (3)
Keywords
- 3D ship detection (1)
- Accelerometers (1)
- Accessible Tourism (1)
- Actions (1)
- Adaptive (1)
- Adivasi (1)
- Agiles Lehren (1)
- Agiles Management (1)
- Apnoe (1)
- Architektur (1)
- Assisted living (1)
- BCG (1)
- Ballistokardiographie (1)
- Bayesian convolutional neural networks (1)
- Bemusterung (1)
- Benchmark (1)
- Beobachterentwurf (1)
- Bernstein coefficient (1)
- Bernstein function (1)
- Bernstein polynomial (1)
- Bildungssprache (1)
- Binary codes (1)
- Biokybernetik (1)
- Biosignal analysis (1)
- Biosignal processing (1)
- Biovital signal (2)
- Birth Density (1)
- Blended values (1)
- Block codes (1)
- Breathing (1)
- Breathing rate (1)
- Brückenbau (1)
- COVID-19 financial crisis (1)
- Capability analysis (1)
- Carbon offset project (1)
- Cauchon algorithm (1)
- Central bank (1)
- Checkerboard partial order (1)
- Chemical quality criteria (1)
- Codes over Gaussian integers (1)
- Collaboration (1)
- Common Criteria (1)
- Complex Adaptive System (1)
- Compliance (7)
- Compliance Governance (1)
- Compliance im Mittelstand (1)
- Compliance-Anforderungen (1)
- Compliance-Management (6)
- Computational complexity (1)
- Conceptual framework (1)
- Conditionally negative semidefinite matrix (1)
- Convolution (1)
- Convolutional networks (1)
- Convolutional neural network (1)
- Cooperation (1)
- Corporate Development (1)
- Corporate Governance (1)
- Corporate accelerator (1)
- Corporate entrepreneurship (2)
- Corporate incubator (1)
- Corporate venturing (1)
- Correlation (1)
- Creative industries (1)
- Creative tourism (1)
- Cultural tourism (1)
- Damage Detection (1)
- Data Fusion (1)
- Data fusion (1)
- Deep Convolutional Neural Network (1)
- Deep Transformation Model (1)
- Deep learning (2)
- Deflation (1)
- Descending rank conditions (1)
- Design (1)
- Design-based research (1)
- Dieselskandal (2)
- Digital Change Management (1)
- Digital arithmetic (1)
- Digitalisierung (2)
- Digitalization (1)
- Digitally re-programmable space (1)
- Discontinuous innovation (1)
- Driving (1)
- Driving safety (1)
- Driving stress (1)
- ECG (3)
- Ecotourism (1)
- Effects of sanctions on trade (1)
- Electrocardiography (2)
- Electromyography (1)
- Elliptic curve cryptography (2)
- Elliptic curve point multiplication (1)
- Encoding (1)
- Energy transition (1)
- Entrepreneurship (2)
- Erdbeben (1)
- Ethnologie (1)
- Experteninterview (1)
- Fachsprachenunterricht (1)
- Familie (1)
- Feldforschung (1)
- Forest establishment (1)
- Framework for indigenous tourism (1)
- Freistellungssemesterbericht (14)
- Fruit drying (1)
- Fuel subsidy (1)
- GDPR (1)
- Gaussian integers (1)
- Gaussian processes (1)
- Generalized multi-stream spatial modulation (1)
- Geschichte (1)
- Glaubwürdigkeit (1)
- Good Corporate Governance (1)
- Hadamard inverse (1)
- Hadamard power (1)
- Hardware-in-the-loop (1)
- Heart rate (4)
- Heart rate variability (1)
- Herzfrequenz (1)
- Homeoffice (1)
- Hybrid organizations (1)
- IT-Compliance (1)
- IT-Integration (1)
- Index modulation (IM) (1)
- Indigenous tourism (1)
- Industrie 4.0 (1)
- Industry 4.0 (1)
- Industry 4.0 implementation (1)
- Infinitely divisible matrix (1)
- Inflation (1)
- Infrastructure (1)
- Innovation (1)
- Innovation management (1)
- Integrity (1)
- Integrity-Management (3)
- Integrität (3)
- Integritätsmanagement (2)
- Internet of Things (1)
- Interview (2)
- Inverse perspective (1)
- Ischemic stroke (1)
- KMU (1)
- Kerala (1)
- Knowledge Management (1)
- Knowledge exploration (1)
- Kommunikation im Raum (1)
- Kontaktloses Hardware-System (1)
- Korruptionsbekämpfung (1)
- Kraftfahrzeug (1)
- Kulturanthropologie (1)
- Laboratory experiments with Simulink and real hardware (1)
- Laborexperimente mit Simulink und echter Hardware (1)
- Lake Constance (1)
- Laser scanning (1)
- Lean Management (2)
- Lebenslanges Lernen (1)
- Lehrbuch (1)
- Lernen (1)
- Literature Review (1)
- Literaturrecherche (1)
- Low-complexity detection (1)
- Low-pass filters (1)
- MNE (1)
- Machine Learning (2)
- Machine learning (1)
- Machine-Learning (1)
- Magnetaktoren (1)
- Magnetic resonance imaging (1)
- Maschinelles Lernen (1)
- Mask R-CNN (1)
- Matrix interval (1)
- Maturity model (1)
- Maximum likelihood decoding (1)
- Media discourse analysis (1)
- Mediale Ausstellungsgestaltung (1)
- Mehrworteinheiten (1)
- Methodik des Fremdsprachenunterrichts (1)
- Mittelständische Unternehmen (1)
- Monitoring (1)
- Montgomery modular multiplication (1)
- Montgomery modular reduction (1)
- Multi Bernoulli Filter (1)
- Multi-spectral imaging systems (1)
- Multibeam echosounder (1)
- Multiple-input/multiple-output (MIMO) (1)
- Multistage detection (1)
- Multivariate polynomial (1)
- Myanmar (1)
- Nachhaltige Stadtentwicklung (1)
- Nachhaltigkeit (1)
- Network (1)
- Non-tariff barriers (1)
- Normalizing Flow (1)
- Object detection (1)
- One Mannheim error correcting codes (OMEC) (1)
- Organizational ambidexterity (1)
- PPG (1)
- PSG (1)
- PSQI (1)
- Pfadabhängigkeiten (1)
- Photoplethysmography (1)
- Platform (1)
- Point multiplication (1)
- Pressure sensors (2)
- Price level changes (1)
- Privacy by Design (1)
- Processor (1)
- Product Lifecycle Management (1)
- Product lifecycle management (1)
- Projektaufgaben (1)
- Projektkonzeption (1)
- Public key cryptography (2)
- Quality prediction (1)
- Quartiersentwicklung (1)
- Rauch-Tung-Striebel Recursion (1)
- Regression (1)
- Requirements Engineering (1)
- Residual Neural Network (1)
- Resource-constrained systems (1)
- Rezension (1)
- SME (1)
- Sampling (1)
- Sanction databases (1)
- Sanctions (1)
- Schlafstadien (1)
- Schlafstudie (1)
- Security by Design (1)
- Selbstevaluation (1)
- Sensorlose Folgeregelung (1)
- Sensors (1)
- Service Design (1)
- Servicegeschäftsmodelle (1)
- Ship dataset (1)
- Sign-regular matrix (1)
- Signal processing (1)
- Simulink (1)
- Skin (1)
- Sleep apnea (1)
- Sleep efficiency (1)
- Sleep medicine (1)
- Sleep quality (1)
- Sleep stages (1)
- Sleep study (2)
- Smart Building (1)
- Smart City (1)
- Smart Services (1)
- Smart home (1)
- Social impact (1)
- Solinas primes (1)
- South India (1)
- Spatial modulation (SM) (1)
- Spatial permutation modulation (SPM) (1)
- Spectral changes (1)
- Stress (1)
- Stress detection (2)
- Stress measurement (1)
- Strictly sign-regular matrix (1)
- Synergies (1)
- Szenografie (1)
- Tariffs (1)
- Technik-Compliance (1)
- Teleworking (1)
- Testverfahren (1)
- Text Mining (1)
- Totally nonnegative matrix (1)
- Trade policy (1)
- Tree seedlings (1)
- TwinCAT (1)
- Uncertainty (1)
- Unmanned aerial vehicles (1)
- Unternehmensethik (1)
- Unternehmensverantwortung (1)
- Venture creation (1)
- VerSanG (1)
- Verbandssanktionengesetz (1)
- Verbrennungsmotor (1)
- Vermittlung des wissenschaftlichen Schreibens (1)
- Voluntary carbon offset (1)
- Wertebasiertes Unternehmenshandeln (1)
- Wertemanagement (2)
- Werteorientierung (1)
- Wertstromanalyse (1)
- Wirksamkeit (1)
- Wirtschaftsethik (1)
- Wirtschaftskriminalität (1)
- Wissenschaftssprache (1)
- Wissensmanagement (1)
- Worms (1)
- Zeitreihenklassifikation (1)
Institute
- Fakultät Architektur und Gestaltung (4)
- Fakultät Bauingenieurwesen (9)
- Fakultät Elektrotechnik und Informationstechnik (1)
- Fakultät Informatik (20)
- Fakultät Maschinenbau (5)
- Fakultät Wirtschafts-, Kultur- und Rechtswissenschaften (13)
- Institut für Angewandte Forschung - IAF (4)
- Institut für Naturwissenschaften und Mathematik - INM (2)
- Institut für Optische Systeme - IOS (8)
- Institut für Strategische Innovation und Technologiemanagement - IST (10)
Totally nonnegative matrices, i.e., matrices having all their minors nonnegative, and matrix intervals with respect to the checkerboard partial order are considered. It is proven that if the two bound matrices of such a matrix interval are totally nonnegative and satisfy certain conditions, then all matrices from this interval are also totally nonnegative and satisfy the same conditions.
Let A = [a_ij] be a real symmetric matrix. If f:(0,oo)-->[0,oo) is a Bernstein function, a sufficient condition for the matrix [f(a_ij)] to have only one positive eigenvalue is presented. By using this result, new results for a symmetric matrix with exactly one positive eigenvalue, e.g., properties of its Hadamard powers, are derived.
In this thesis, the recognition problem and the properties of eigenvalues and eigenvectors of matrices which are strictly sign-regular of a given order, i.e., matrices whose minors of a given order have the same strict sign, are considered. The results are extended to matrices which are sign-regular of a given order, i.e., matrices whose minors of a given order have the same sign or are allowed to vanish. As a generalization, a new type of matrices called oscillatory of a specific order, are introduced. Furthermore, the properties for this type are investigated. Also, same applications to dynamic systems are given.
Forecasting is crucial for both system planning and operations in the energy sector. With increasing penetration of renewable energy sources, increasing fluctuations in the power generation need to be taken into account. Probabilistic load forecasting is a young, but emerging research topic focusing on the prediction of future uncertainties. However, the majority of publications so far focus on techniques like quantile regression, ensemble, or scenario-based methods, which generate discrete quantiles or sets of possible load curves. The conditioned probability distribution remains unknown and can only be estimated when the output is post-processed using a statistical method like kernel density estimation.
Instead, the proposed probabilistic deep learning model uses a cascade of transformation functions, known as normalizing flow, to model the conditioned density function from a smart meter dataset containing electricity demand information for over 4,000 buildings in Ireland. Since the whole probability density function is tractable, the parameters of the model can be obtained by minimizing the negative loglikelihood through the state of the art gradient descent. This leads to the model with the best representation of the data distribution.
Two different deep learning models have been compared, a simple three-layer fully connected neural network and a more advanced convolutional neural network for sequential data processing inspired by the WaveNet architecture. These models have been used to parametrize three different probabilistic models, a simple normal distribution, a Gaussian mixture model, and the normalizing flow model. The prediction horizon is set to one day with a resolution of 30 minutes, hence the models predict 48 conditioned probability distributions.
The normalizing flow model outperforms the two other variants for both architectures and proves its ability to capture the complex structures and dependencies causing the variations in the data. Understanding the stochastic nature of the task in such detail makes the methodology applicable for other use cases apart from forecasting. It is shown how it can be used to detect anomalies in the power grid or generate synthetic scenarios for grid planning.
Soft-input decoding of concatenated codes based on the Plotkin construction and BCH component codes
(2020)
Low latency communication requires soft-input decoding of binary block codes with small to medium block lengths.
In this work, we consider generalized multiple concatenated (GMC) codes based on the Plotkin construction. These codes are similar to Reed-Muller (RM) codes. In contrast to RM codes, BCH codes are employed as component codes. This leads to improved code parameters. Moreover, a decoding algorithm is proposed that exploits the recursive structure of the concatenation. This algorithm enables efficient soft-input decoding of binary block codes with small to medium lengths. The proposed codes and their decoding achieve significant performance gains compared with RM codes and recursive GMC decoding.
For a long time, the use of intermediate products in production has been growing more rapidly in most countries than domestic production. This is a strong indication of more interdependency in production. The main purpose of input-output analysis is to study the interdependency of industries in an economy. Often the term interindustry analysis is also used. Therefore, the exchange of intermediate products is a key issue of input-output analysis. We will use input–output data for this study that the author prepared for the new ‘Handbook on Supply, Use and Input–Output Tables with Extensions and Applications’ of the United Nations. The supply use and input–output tables contain separate valuation matrices for trade margins, transport margins, value added tax, other taxes on products and subsidies on products. For the study, two input–output models were developed to evaluate the impact of fuel subsidy and taxation reform on output, gross domestic product, inflation and trade. Six scenarios are discussed covering different aspects of the reform.
The evaluation of the effectiveness of different machine learning algorithms on a publicly available database of signals derived from wearable devices is presented with the goal of optimizing human activity recognition and classification. Among the wide number of body signals we choose a couple of signals, namely photoplethysmographic (optically detected subcutaneous blood volume) and tri-axis acceleration signals that are easy to be simultaneously acquired using commercial widespread devices (e.g. smartwatches) as well as custom wearable wireless devices designed for sport, healthcare, or clinical purposes. To this end, two widely used algorithms (decision tree and k-nearest neighbor) were tested, and their performance were compared to two new recent algorithms (particle Bernstein and a Monte Carlo-based regression) both in terms of accuracy and processing time. A data preprocessing phase was also considered to improve the performance of the machine learning procedures, in order to reduce the problem size and a detailed analysis of the compression strategy and results is also presented.
The transformation to an Industry 4.0, which is in general seen as a solution to increasing market challenges, is forcing companies to radically change their way of thinking and to be open to new forms of cooperation. In this context, the opening-up of the innovation process is widely seen as a necessity to meet these challenges, especially for small and medium enterprises (SMEs). The aim of the study therefore is to analyze how cooperation today can be characterized, how this character has changed since the establishment of the term Industry 4.0 at Hanover Fair in 2011 and which cooperation strategies have proven successful. The analysis consists of a quantitative, secondary data analysis that includes country-specific data from 35 European countries of 2010 and 2016 collected by the European Commission and the OECD. The research, focusing on the secondary sector, shows that multinational enterprises MNEs still tend to cooperate more than SMEs, with a slight overall trend towards protectionism. Nevertheless, there is a clear tendency towards the opening-up of SMEs. In this regard, especially universities, competitors and suppliers have become increasingly attractive as cooperation partners for SMEs.
Deep neural networks (DNNs) are known for their high prediction performance, especially in perceptual tasks such as object recognition or autonomous driving. Still, DNNs are prone to yield unreliable predictions when encountering completely new situations without indicating their uncertainty. Bayesian variants of DNNs (BDNNs), such as MC dropout BDNNs, do provide uncertainty measures. However, BDNNs are slow during test time because they rely on a sampling approach. Here we present a single shot MC dropout approximation that preserves the advantages of BDNNs without being slower than a DNN. Our approach is to analytically approximate for each layer in a fully connected network the expected value and the variance of the MC dropout signal. We evaluate our approach on different benchmark datasets and a simulated toy example. We demonstrate that our single shot MC dropout approximation resembles the point estimate and the uncertainty estimate of the predictive distribution that is achieved with an MC approach, while being fast enough for real-time deployments of BDNNs.
Die wenigen Literaturangaben zu Sorptionsisothermen von mineralischen Estrichen beziehen sich im Wesentlichen auf Calciumsulfatestriche und genormte Zementestriche, sowie i.d.R. nur auf eine festgesetzte Lufttemperatur (= 20 Grad C). Daher war es das Anliegen der im Beitrag beschriebenen Untersuchung, die Feuchtigkeitseigenschaften von Estrichen bei unterschiedlichen Klimaten mithilfe von Sorptionsisothermen zu charakterisieren. Ergänzend sollten die seit ca. 20 Jahren marktüblichen ternären Schnellzemente mit untersucht und die baupraktisch interessanten Temperaturen von 15 Grad C und 25 Grad C einbezogen werden. Ebenso wurden die Auswirkungen der Klimabedingungen auf der Baustelle (Jahreszeit, Luftfeuchtigkeit, Temperatur) auf den Hydratationsvorgang der Estriche untersucht. In Kombination mit den Ergebnissen der Gefügeuntersuchungen (u.a. Hg-Porosimetrie) wird belegt, weshalb sich die zement- und schnellzementgebundenen Estriche vollkommen anders verhalten als die calciumsulfatgebundenen Estriche. Dieses unterschiedliche Verhalten ist auch einer der Gründe, warum Estriche mit der KRL-Methode in Bezug auf ihren Feuchtegehalt nicht bewertet werden können. Deshalb folgt ein Vergleich der Materialfeuchtemessungen "KRL-Methode" mit der handwerksüblichen und seit Jahrzehnten in der Praxis bewährten "CM-Methode".
Ein Beitrag zum Beobachterentwurf und zur sensorlosen Folgeregelung translatorischer Magnetaktoren
(2020)
Botswana, a new construction project – the Maun Science Park - is to be built with a focus on sustainability and to create a new living space for the rapidly growing population in Africa. The project will be a blueprint for future projects in Africain terms of progress, technology and sustainability. This thesis will deal with its financial framework and will serve as a basis for the development of ways and means of financing such projects.
The ballistocardiography is a technique that measures the heart rate from the mechanical vibrations of the body due to the heart movement. In this work a novel noninvasive device placed under the mattress of a bed estimates the heart rate using the ballistocardiography. Different algorithms for heart rate estimation have been developed.
Globalization has increased the number of road trips and vehicles. The result has been an intensification of traffic accidents, which are becoming one of the most important causes of death worldwide. Traffic accidents are often due to human error, the probability of which increases when the cognitive ability of the driver decreases. Cognitive capacity is closely related to the driver’s mental state, as well as other external factors such as the CO2 concentration inside the vehicle. The objective of this work is to analyze how these elements affect driving. We have conducted an experiment with 50 drivers who have driven for 25 min using a driving simulator. These drivers completed a survey at the start and end of the experiment to obtain information about their mental state. In addition, during the test, their stress level was monitored using biometric sensors and the state of the environment (temperature, humidity and CO2 level) was recorded. The results of the experiment show that the initial level of stress and tiredness of the driver can have a strong impact on stress, driving behavior and fatigue produced by the driving test. Other elements such as sadness and the conditions of the interior of the vehicle also cause impaired driving and affect compliance with traffic regulations.
Kleine und mittelständische Unternehmen (KMU) sind bekannt für ihre Innovationskraft und bilden das Rückgrat der deutschen Wirtschaft. Wie Studien zeigen sind sie in Bezug auf Compliance-Maßnahmen im Vergleich zu
kapitalmarktorientierten Unternehmen jedoch im Rückstand. Eine gesonderte Betrachtung der IT-Compliance erfolgt dabei in den Studien in der Regel nicht. Auch wenn zu den Gründen und Motiven fehlender IT-Compliance-Strukturen in KMU kaum Forschungsergebnisse vorliegen, zeigen doch die vielen Publikationen, die sich mit Teilaspekten von Compliance und KMU beschäftigen, dass Handlungsbedarf besteht. Insbesondere die aktuellen Veränderungen unter dem Stichwort Digitalisierung deuten auf eine gesteigerte Bedeutung von IT-Compliance-Maßnahmen vor allem in mittelständischen Unternehmen. In dieser Arbeit sollen daher mithilfe einer Literaturrecherche die aktuell behandelten Themen in Bezug auf IT-Compliance und KMU analysiert sowie aktuelle Themenschwerpunkte herausgearbeitet werden.