Article
Refine
Year of publication
- 2020 (102) (remove)
Institute
- Fachbereich Medizintechnik und Technomathematik (44)
- IfB - Institut für Bioengineering (26)
- Fachbereich Luft- und Raumfahrttechnik (17)
- Fachbereich Chemie und Biotechnologie (10)
- INB - Institut für Nano- und Biotechnologien (9)
- Fachbereich Energietechnik (7)
- Fachbereich Wirtschaftswissenschaften (7)
- Fachbereich Elektrotechnik und Informationstechnik (6)
- Fachbereich Maschinenbau und Mechatronik (6)
- ECSM European Center for Sustainable Mobility (5)
Language
- English (102) (remove)
Document Type
- Article (102) (remove)
Keywords
- rebound-effect (2)
- sustainability (2)
- Adaptive control (1)
- Biofuel (1)
- Biorefinery (1)
- Brownian Pillow (1)
- Butanol (1)
- Clostridium acetobutylicum (1)
- Conservation laws (1)
- Crámer–von-Mises distance (1)
- Dimensional splitting (1)
- Entropy solution (1)
- Experimental validation (1)
- Exponential time differencing (1)
- Finite difference methods (1)
- Higher-order codes (1)
- Hoeffding-Blum-Kiefer-Rosenblatt independence test (1)
- Human Development Index (1)
- Implicit methods (1)
- Incomplete data (1)
- Interior transmission eigenvalues (1)
- Iterative learning control (1)
- Lignocellulose (1)
- MILP (1)
- MINLP (1)
- Marginal homogeneity test (1)
- Matrix exponential (1)
- Monotone methods (1)
- Multi-dimensional partial differential equations (1)
- Nonlinear PDEs (1)
- Paired sample (1)
- Pumping systems (1)
- Reaction-diffusion systems (1)
- Real distinct pole (1)
- Resampling test (1)
- Robotic rehabilitation (1)
- SSE) JEL : O33 (1)
- Source term (1)
- Technical Operations Research (1)
- agility (1)
- architectural design (1)
- architectural layered representation (1)
- automotive (1)
- community dwelling (1)
- corporate sustainability (1)
- critical (1)
- digital twin (1)
- efficiency side-effects (1)
- elastic scattering (1)
- grey energy (1)
- healthy aging (1)
- industry 4.0 (1)
- jevons paradox (1)
- literature (1)
- marketing (1)
- method of fundamental solutions (1)
- not identically distributed (1)
- performativity in personal knowledge (1)
- personal way of designing (1)
- prevention (1)
- psychosocial (1)
- random effects meta-regression model (1)
- resource abundance (1)
- review (1)
- socio-economic welfare (1)
- systematic (1)
Is part of the Bibliography
- no (102)
To meet the challenges of manufacturing smart products, the manufacturing plants have been radically changed to become smart factories underpinned by industry 4.0 technologies. The transformation is assisted by employment of machine learning techniques that can deal with modeling both big or limited data. This manuscript reviews these concepts and present a case study that demonstrates the use of a novel intelligent hybrid algorithms for Industry 4.0 applications with limited data. In particular, an intelligent algorithm is proposed for robust data modeling of nonlinear systems based on input-output data. In our approach, a novel hybrid data-driven combining the Group-Method of Data-Handling and Singular-Value Decomposition is adapted to find an offline deterministic model combined with Pareto multi-objective optimization to overcome the overfitting issue. An Unscented-Kalman-Filter is also incorporated to update the coefficient of the deterministic model and increase its robustness against data uncertainties. The effectiveness of the proposed method is examined on a set of real industrial measurements.
To meet the challenges of manufacturing smart products, the manufacturing plants have been radically changed to become smart factories underpinned by industry 4.0 technologies. The transformation is assisted by employment of machine learning techniques that can deal with modeling both big or limited data. This manuscript reviews these concepts and present a case study that demonstrates the use of a novel intelligent hybrid algorithms for Industry 4.0 applications with limited data. In particular, an intelligent algorithm is proposed for robust data modeling of nonlinear systems based on input-output data. In our approach, a novel hybrid data-driven combining the Group-Method of Data-Handling and Singular-Value Decomposition is adapted to find an offline deterministic model combined with Pareto multi-objective optimization to overcome the overfitting issue. An Unscented-Kalman-Filter is also incorporated to update the coefficient of the deterministic model and increase its robustness against data uncertainties. The effectiveness of the proposed method is examined on a set of real industrial measurements.
Game-based learning is a promising approach to anti-phishing education, as it fosters motivation and can help reduce the perceived difficulty of the educational material. Over the years, several prototypes for game-based applications have been proposed, that follow different approaches in content selection, presentation, and game mechanics. In this paper, a literature and product review of existing learning games is presented. Based on research papers and accessible applications, an in-depth analysis was conducted, encompassing target groups, educational contexts, learning goals based on Bloom’s Revised Taxonomy, and learning content. As a result of this review, we created the publications on games (POG) data set for the domain of anti-phishing education. While there are games that can convey factual and conceptual knowledge, we find that most games are either unavailable, fail to convey procedural knowledge or lack technical depth. Thus, we identify potential areas of improvement for games suitable for end-users in informal learning contexts.
A second-order L-stable exponential time-differencing (ETD) method is developed by combining an ETD scheme with approximating the matrix exponentials by rational functions having real distinct poles (RDP), together with a dimensional splitting integrating factor technique. A variety of non-linear reaction-diffusion equations in two and three dimensions with either Dirichlet, Neumann, or periodic boundary conditions are solved with this scheme and shown to outperform a variety of other second-order implicit-explicit schemes. An additional performance boost is gained through further use of basic parallelization techniques.
To prevent the reduction of muscle mass and loss of strength coming along with the human aging process, regular training with e.g. a leg press is suitable. However, the risk of training-induced injuries requires the continuous monitoring and controlling of the forces applied to the musculoskeletal system as well as the velocity along the motion trajectory and the range of motion. In this paper, an adaptive norm-optimal iterative learning control algorithm to minimize the knee joint loadings during the leg extension training with an industrial robot is proposed. The response of the algorithm is tested in simulation for patients with varus, normal and valgus alignment of the knee and compared to the results of a higher-order iterative learning control algorithm, a robust iterative learning control and a recently proposed conventional norm-optimal iterative learning control algorithm. Although significant improvements in performance are made compared to the conventional norm-optimal iterative learning control algorithm with a small learning factor, for the developed approach as well as the robust iterative learning control algorithm small steady state errors occur.
Exercise training effectively mitigates aging-induced health and fitness impairments. Traditional training recommendations for the elderly focus separately on relevant physiological fitness domains, such as balance, flexibility, strength and endurance. Thus, a more holistic and functional training framework is needed. The proposed agility training concept integratively tackles spatial orientation, stop and go, balance and strength. The presented protocol aims at introducing a two-armed, one-year randomized controlled trial, evaluating the effects of this concept on neuromuscular, cardiovascular, cognitive and psychosocial health outcomes in healthy older adults. Eighty-five participants were enrolled in this ongoing trial. Seventy-nine participants completed baseline testing and were block-randomized to the agility training group or the inactive control group. All participants undergo pre- and post-testing with interim assessment after six months. The intervention group currently receives supervised, group-based agility training twice a week over one year, with progressively demanding perceptual, cognitive and physical exercises. Knee extension strength, reactive balance, dual task gait speed and the Agility Challenge for the Elderly (ACE) serve as primary endpoints and neuromuscular, cognitive, cardiovascular, and psychosocial meassures serve as surrogate secondary outcomes. Our protocol promotes a comprehensive exercise training concept for older adults, that might facilitate stakeholders in health and exercise to stimulate relevant health outcomes without relying on excessively time-consuming physical activity recommendations.
This paper presents a novel method for airfoil drag estimation at Reynolds numbers between 4×10⁵ and 4×10⁶. The novel method is based on a systematic study of 40 airfoils applying over 600 numerical simulations and considering natural transition. The influence of the airfoil thickness-to-chord ratio, camber, and freestream Reynolds number on both friction and pressure drag is analyzed in detail. Natural transition significantly affects drag characteristics and leads to distinct drag minima for different Reynolds numbers and thickness-to-chord ratios. The results of the systematic study are used to develop empirical correlations that can accurately predict an airfoil drag at low-lift conditions. The new approach estimates a transition location based on airfoil thickness-to-chord ratio, camber, and Reynolds number. It uses the transition location in a mixed laminar–turbulent skin-friction calculation, and corrects the skin-friction coefficient for separation effects. Pressure drag is estimated separately based on correlations of thickness-to-chord ratio, camber, and Reynolds number. The novel method shows excellent accuracy when compared with wind-tunnel measurements of multiple airfoils. It is easily integrable into existing aircraft design environments and is highly beneficial in the conceptual design stage.
The successful implementation and continuous development of sustainable corporate-level solutions is a challenge. These are endeavours in which social, environmental, and financial aspects must be weighed against each other. They can prove difficult to handle and, in some cases, almost unrealistic. Concepts such as green controlling, IT, and manufacturing look promising and are constantly evolving. This paper aims to achieve a better understanding of the field of corporate sustainability (CS). It will evaluate the hypothesis by which Corporate Sustainability thrives, via being efficient, increasing the performance, and raising the value of the input of the enterprises to the resources used. In fact, Corporate Sustainability on the surface could seem to contradict the idea, which supports the understanding that it encourages the reduction of the heavy reliance on the use of natural resources, the overall environmental impact, and above all, their protection. To understand how the contradictory notion of CS came about, in this part of the paper, the emphasis is placed on providing useful insight to this regard. The first part of this paper summarizes various definitions, organizational theories, and measures used for CS and its derivatives like green controlling, IT, and manufacturing. Second, a case study is given that combines the aforementioned sustainability models. In addition to evaluating the hypothesis, the overarching objective of this paper is to demonstrate the use of green controlling, IT, and manufacturing in the corporate sector. Furthermore, this paper outlines the current challenges and possible directions for CS in the future.